Climate, Habitat and Land-use Effects on California Vernal Pool Communities

Climate, Habitat and Land-use Effects on California Vernal Pool Communities | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Climate, Habitat and Land-use Effects on California Vernal Pool Communities Hannah Savage, Jennifer Buck-Diaz, Anna Klimaszewski-Patterson, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9097717/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Biodiversity at different spatial scales is affected by many processes, including habitat size, climate, and human activities. Vernal pools are seasonal freshwater wetlands found throughout California’s Central Valley. They support high levels of endemism and only 10% of this habitat remains in California. This study used two previously collected vernal pool plant and invertebrate datasets to understand how land-use, climate, and pool size influence diversity of vernal pool taxonomic and functional groups at local, regional, and biogeographic scales. Plant richness generally increased with latitude and decreased with measures of pool size while invertebrate richness generally increased with measures of pool size. Native plant gamma diversity was the only factor with a negative association with land use. Latitude, climate, pool characteristics, and land-use all contributed to richness in at least one of the functional groups. This study highlights how diversity of plant and invertebrate functional groups responds to factors at different spatial scales, which should also be considered in restoration and management. Functional groups Invertebrates Latitude Plants Seasonal wetlands Species diversity Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 INTRODUCTION Climate is one of the primary factors that determine the abundance and distribution of organisms (Baselga et al. 2012 , Thomas 2010 ) that has changed in recent decades (IPCC 2023 ). Changes in temperature and precipitation can cascade to the community level and influence species diversity in communities (Coehlo et al. 2023, Epele et al . 2023, 2024). This is often examined across spatial gradients as in the latitudinal diversity gradient pattern that predicts increasing diversity with decreasing latitude (Hillebrand 2004 , Mittlebach et al . 2007). Ecological explanations have been ascribed to this increase based on a climate gradient that coincides with latitude, including increasing temperature, precipitation, and stabilitywith decreasing latitude (Mittlebach et al . 2007). However, different taxa and functional groups can often respond differently to this gradient (Hillebrand 2004 , Kinlock et al. 2018 ). Moreover, different ecosystems may exhibit different relationships with latitude (Hillebrand 2004 , Hof et al. 2008 ). This includes freshwater ecosystems that are closely connected to variation in temperature and rainfall (e.g., Hof et al. 2008 ). Climate is important for wetlands because it largely determines their size (i.e. depth and surface area). This is especially true for seasonal wetlands which are laregely driven by precipitation and evaporation dynamics (Boix et al. 2016 ) and other anthropogenic factors (Bouahim et al. 2014 ). This in turn can determine both invertebrate and plant diversity patterns (Oertli et al. 2002 , Junk et al. 2013 , Epele et al. 2022 ), which can be explained by the species-area relationship (MacArthur and Wilson 1967 , Kohn and Walsh 1994 ) and changes in heterogeneity (Stein et al. 2014 ). The size-diversity pattern is not always found (see Batzer 2013 for examples), and there is usually substantial unexplained variation (Batzer 2013 , Rendl et al . 2023). Wetland biodiversity is especially vulnerable to changes in climate because temperature and precipitation can affect other characteristics of wetlands, such as invasive species success (Gerhardt and Collinge 2007 , Rahel and Olden 2008), temperature, water chemistry (Reddy et al. 2010 , Bird and Day 2014 ), and anthropogenic changes. Wetlands are vulnerable to anthropogenic land-use changes because they are often located in or near agriculture (MEA 2005, Zedler and Kercher 2005 , Asselen et al. 2013 , Reis et al. 2017 , Wilson et al. 2022 ). Most research of anthropogenic land-use effects and climate on wetlands has been conducted on permanent freshwater wetlands. Temporary wetlands are often overlooked, although some types of land-use, such as grazing effects on plants, are well studied (Marty 2005 , Bouahim et al. 2010 , Silver and Vamosi 2012 , Michaels et al. 2022 ). Generally, land use (grazing and agriculture) effects have mixed effects on plant (e.g., Bouahim et al. 2010 , Michaels et al. 2022 ) and crustacean species composition (e.g., Steinman et al. 2003 , Tangen et al. 2003, Van den Broeck et al. 2019 ). However, a combination of factors (e.g., climate, pool characteristics) can interact with anthropogenic activities to affect plant and invertebrate diversity in temporary ponds (El Madihi et al. 2017 ). Vernal pools are temporary freshwater wetlands that occur in Mediterranean climates that fill with the winter rains and dry out by summer (Holland 2009 , Boix et al. 2016 ). Vernal pools in Mediterranean-climate California are found mostly in the Central Valley and are one of the most threatened ecosystems worldwide (Holland and Jain 1981, Keeler-Wolf et al. 1998 ). They are habitat to many rare and endemic species (King et al. 1996 , Barbour et al. 2007 ) adapted to living in temporary waters (Zacharias and Zamparas 2010 ). The primary threat to these ecosystems is habitat loss from urban and agricultural development (Underwood et al. 2009 , Cooper et al. 2013 , Wilson et al. 2022 ). Further, there has been a historical lack of understanding of the contribution of vernal pools to biodiversity due to their temporary nature (Beja and Alcazar 2003 , Zacharias and Zamparas 2010 , Boix et al. 2016 ). Freshwater wetlands, including temporary ones, provide a wide array of ecosystem services including maintenance of water quality, sequestering carbon, recreation, flood water storage, groundwater recharge, habitat for many species listed as endangered or threatened, and are often culturally significant to indigenous peoples (Gibbs 2000 , Duffy and Kahara 2011 , Hankins 2013 ). Many of these ecosystem services help to mitigate the impacts of climate change through climate regulation and carbon sequestration (Millenium Ecosystem Assessment 2005). Thus, understanding how, and if, anthropogenic land-use affects these habitats is critical for conservation. In this study, we explored how climate (including latitude), pool characteristics, and land-use are associated with vernal pool community diversity (Appendix Table 1) using broad scale previously published datasets (Barbour et al. 2007 , Kneitel 2016 , 2018 ). The data was collected over 8–10 years and across approximately 10 degrees of latitude. The purpose was not to identify how the independent variables affected the communities of a particular year but to identify general spatial patterns. Previous studies have identified correlations among latitude, climate, and wetland area and these can also be associated with plant and invertebrate species richness (e.g., Houlahan et al. 2006 , Kneitel 2016 , El Madihi et al. 2017 ). In addition to latitude and climate-driven pool size, climate often influences what human land-use occurs in a particular area. Feld et al. ( 2016 ) found that geo-climatic variables, such as latitude and precipitation, were strong determinants of human land-use patterns in European freshwater systems. Their study also found that the geoclimatic variables explained more of the variation in patterns of biodiversity in wetlands than land-use. Thus, we hypothesized that the land-use variables (i.e. developed land, agricultural land) would predict some of the variation in species diversity, but climate (i.e. temperature, precipitation, latitude) and pool characteristics (i.e. depth and surface area) would predict the most variation. Table 1 Explanatory variables included in the final models Dependent variables Predictor variables Plants Alpha diversity, gamma diversity, beta diversity, native alpha diversity, exotic alpha diversity, native gamma diversity, exotic gamma diversity, native beta diversity, exotic beta diversity, forb alpha diversity, graminoid alpha diversity Climate: latitude, average yearly temperature, average yearly precipitation Land-use: percent cover developed land, percent cover agricultural land Pool characteristics: average pool depth (cm), average pool surface area (km 2 ), number of pools sampled at a site Invertebrate Alpha diversity, gamma diversity, beta diversity, passive disperser alpha diversity, active disperser alpha diversity, passive disperser gamma diversity, active disperser gamma diversity, passive disperser beta diversity, active disperser beta diversity, prey species alpha diversity, predator species alpha diversity. Climate 1 : latitude Land-use: percent cover developed land, percent cover agricultural land Pool characteristics 2 : average pool depth (cm), number of pools sampled at a site 1. 1 Average yearly temperature and precipitation were excluded from the invertebrate models due to overfit issues 2. 2 Average pool surface area was excluded from the invertebrate models due to missing measurements. Diversity was categorized based on plant and invertebrate functional groups (see below) to assess their responses to the independent variables. We predicted that for some taxa (native and exotic plant species), land-use variables would predict the greatest amount of species diversity given the magnitude of disturbance caused by land-use changes. Land-use that causes disturbance can also make it easier for exotic species to be introduced and become established (Hansen and Clevenger 2005 ). Other land-uses, such as grazing, can reduce the presence of exotic species and increase native species cover (Marty 2015 ). Our aim was to identify which factors best predict diversity to better understand broad scale patterns of plant and invertebrate richness to determine the implications for management and restoration. METHODS Plant and Invertebrate Data We compiled existing vernal pool data sets, including plants (Barbour et al. 2007 ) and aquatic invertebrates (insects and crustaceans) (Kneitel 2016 , 2018 ) as our response variables. The data sets were collected over the course of many years (2001–2009 for plants and 2002–2012 for invertebrates), and individual pool sampling occurred over 1–2 days (plants) or the accumulation of several samples (approximately five times) over the hydroperiod (invertebrates). Details of sampling can be found in Barbour et al. ( 2003 , 2007 ) for the plants and Kneitel ( 2016 ) for aquatic invertebrates (insects and crustaceans). Our objective was to address general patterns of spatial diversity, as they relate to long-term average and variation of climate and general land use datasets (see below). The plant data set was collected from pools in 15 of the 17 vernal pool regions in California as defined by Keller-Wolf et al. (1998) and included northwest Nevada and southern Oregon. In this present study, only data from California pools was used, which included a total of 367 samples from 52 sites across 19 counties in California (Appendix Table 1, Appendix Fig. 1). Pool variables such as surface area and volume were also recorded (Appendix Table 2). Vernal pool plants can be categorized in a variety of functional groups based on their morphology and invasion status. Ecological functional groups are typically used to better understand and predict responses to the environment (Wiggins et al. 1980 , Welborn et al. 1996). Most vernal pool plants are herbaceous, including Lasthenia fremontii and Navarretia leucocephala (Barbour et al. 2007 ). Many of the species are also annuals, but perennial herbs (e.g., Eryngium vaseyi, Isoetes orcuttii ) are also present. In addition, many exotic plants can be found in vernal pools (e.g., Bromus hordeaceus , Erodium botrys ). The plants vary in the timing of their germination, growth, and flowering phenology during the inundation-desiccation period (Bliss and Zedler 1998 , Barbour et al 2007 , Collinge 2011). The diversity of plant species found in vernal pools were categorized by the following functional types: 1) invasive status (exotic or native) and 2) herbaceous groups (forb or graminoid) based on data from calflora.org and from the USDA Plants Database (USDA, NRCS 2024). Forbs are defined as vascular plants without significant woody tissue while graminoids are defined as grasses or grass-like plants, including sedges and rushes (USDA, NRCS 2024). These categories were used to better understand how different taxonomic groups respond to the environment, and whether invasive species respond differently to the environment than native species (Collinge et al. 2011 , Marty 2015 ). Table 2 Generalized Linear Models for overall measures of plant diversity. Bold p-values indicate significance at p < 0.05. The models for alpha diversity and beta diversity models were best fitted using the gamma family which is an inverse link. The signs should be interpreted in the opposite of typical linear models. The model for gamma diversity was best fit using the gaussian family. Response variable Explanatory variable Chi-square value Partial R 2 P-value Coefficient estimate Standard error Plant alpha diversity Percent developed land 0.285 0.017 0.072 6.43 X 10 − 5 7.66 X 10 − 5 Percent agricultural land 0.002 -0.003 0.868 7.79 X 10 − 6 5.74 X 10 − 5 Latitude 2.053 0.278 < 0.001 -0.003 7.54 X 10 − 4 Average yearly temperature 0.113 0.012 0.257 -0.001 0.001 Average yearly precipitation 0.002 0.001 0.872 -8.91 X 10 − 6 9.46 X 10 − 5 Surface area (km 2 ) 0.823 0.057 0.002 2.131 0.795 Pool depth (cm) 0.015 0.082 0.677 -8.76 X 10 − 5 2.23 X 10 − 4 Number of pools sampled 0.010 0.006 0.742 7.57 X 10 − 5 2.31X 10 − 4 Plant gamma diversity Percent developed land 1010 0.019 0.084 -0.114 0.127 Percent agricultural land 2.200 0.034 0.936 -0.146 0.121 Latitude 4561.20 0.181 < 0.001 3.951 1.297 Average yearly temperature 531.20 0.038 0.210 2.099 1.619 Average yearly precipitation 148.20 0.001 0.508 0.044 0.198 Surface area (km 2 ) 6979.70 0.189 < 0.001 -0.004 -0.001 Pool depth (cm) 74.300 0.199 0.639 0.428 0.449 Number of pools sampled 8290.70 0.369 < 0.001 2.409 0.486 Plant beta diversity Percent developed land 0.035 0.009 0.270 0.001 0.003 Percent agricultural land 0.006 -0.015 0.644 0.001 0.002 Latitude 0.011 0.008 0.543 0.015 0.027 Average yearly temperature 0.010 0.001 0.561 0.012 0.035 Average yearly precipitation 0.054 0.003 0.171 -5.45 X 10 − 4 0.004 Surface area (km 2 ) 0.150 0.067 0.022 53.618 42.504 Pool depth (cm) 0.000 0.086 0.920 -0.008 0.009 Number of pools sampled 0.603 0.372 < 0.001 -0.042 0.009 The aquatic invertebrates were primarily insects and crustaceans that were compiled by Kneitel ( 2016 , 2018 ) from vernal pool survey reports submitted to United States Fish and Wildlife Service and from collected data spanning 2005–2012. A total of 886 samples from 29 sites across 16 counties are represented (Appendix Table 1, Appendix Fig. 1). Invertebrate taxa (identified from the species to family level) presence/absence was recorded, and pool data included depth for all pools and surface area for some pools (Appendix Table 2). Invertebrate species were categorized by the following functional types: 1) predator or prey and 2) active or passive dispersers. Active dispersers are species adapted to move between habitats (e.g., species with wings). Examples of active disperses include the orders Trichoptera (caddisflies) and Plecoptera (stoneflies), families Corixidae (water boatmen) and Hydrophillidae (water scavenger beetles), and some of the top predators are in this category (e.g., Odonata [dragonflies]). Passive dispersers rely on vectors such as wind, water, or other invertebrates to move them between habitats (Kneitel 2018 ) and many of these species have life history strategies that include egg cysts to survive periods of drought (Colburn et al. 2007 ). Examples of passive dispersers include order Cladocera (water fleas) and Acariformes (water mites) and many are threatened or endangered species (e.g., Lepidurus packardi ). Total diversity of both plants (species) and invertebrate (taxa) was measured at three spatial scales: alpha (local), beta (turnover), and gamma (regional or site) diversity. These diversity measures provide three glimpses into how diversity is distributed in space (e.g., Leibold et al. 2004 , Heino 2013 ). Alpha diversity (α), or local diversity, was calculated as the average richness of all pools at one site. Alpha diversity of the different functional groups of plants and invertebrates was also calculated. Gamma diversity (γ), or regional diversity, was calculated as the total number of species in all pools across a site. Beta diversity (β), or dissimilarity across local communities, was calculated as dissimilarity: 1 - (α/γ), which ranged from 0 (low dissimilarity among communities) to 1 (high dissimilarity among communities). Both data sets were collected across various years, and the purpose of the analyses was not to specifically identify the direct effects of the independent variables for a given year but to identify general relationships with spatial diversity patterns. Climate Data Historical climate data from the years 1970 to 2000 were obtained from Worldclim 2.1 at a geographic scale of 10’ minutes (Fick and Hijmans 2017 ). The monthly maximum and minimum temperatures and precipitation were used to calculate yearly average maximum and minimum temperatures and precipitation and the yearly standard deviation of temperature and precipitation maximums and minimums. The goal with these calculations was to quantify the average and variation in climate at each site. Land-use/Land-cover Data Land cover data was obtained from the Multi-resolution Land Characteristics Consortium NLCD land cover data for the year 2016 (Dewitz 2019 , Homer et al. 2020 ). This data is in a raster format with a 30 m spatial resolution. Multiple land-use and land cover (LULC) types are represented in this data (Appendix Table 3). A circle of 1 km radius around the center of each vernal pool site was created using ArcGIS Pro. The percent cover of each land cover type within each circle surrounding pool sites was calculated using the “Tabulate Area” tool. All natural land cover types (open water, deciduous forest, evergreen forest, mixed forest, shrub/scrub, grassland/herbaceous, woody wetlands, and emergent herbaceous wetlands) were then removed to better understand the impacts of anthropogenic LULC. Due to high correlation among the variables, the percent cover of the four categories of human developed land (open space, low intensity, medium intensity, and high intensity) were combined into a single variable called “developed”. The percent cover of the “hay/pasture” and “cultivated crops” land-cover types were combined into a single variable called “agriculture”. Table 3 Generalized Linear Models for graminoid diversity and forb diversity. The forb diversity model was best fit using the gamma family which is an inverse link while the graminoid diversity model was best fit using the gaussian family. Results should be interpreted as in Table 2 . Response variable Explanatory variable Chi-square value Partial R 2 P-value Coefficient estimate Standard error Graminoids Percent developed land 21.06 0.023 0.112 -0.020 0.020 Percent agricultural land 3.27 0.009 0.531 0.012 0.019 Latitude 23.09 0.037 0.096 0.259 0.204 Average yearly temperature 0.00 0.002 0.994 0.078 0.254 Average yearly precipitation 7.06 0.018 0.357 0.028 0.031 Surface area (km 2 ) 19.37 0.044 0.127 -246.363 176.746 Pool depth (cm) 0.72 0.029 0.769 0.023 0.071 Number of pools sampled 0.88 0.003 0.746 0.025 0.076 Forbs Percent developed land 0.277 0.023 0.130 8.54 X 10 − 5 1.28 X 10 − 4 Percent agricultural land 0.002 -0.002 0.890 1.76 X 10 − 5 9.41 X 10 − 5 Latitude 3.39 0.305 < 0.001 -0.006 0.001 Average yearly temperature 0.254 0.010 0.147 -0.003 0.002 Average yearly precipitation 0.002 < 0.001 0.909 -3.73 X 10 − 6 1.54 X 10 − 4 Surface area (km 2 ) 1.22 0.018 0.001 3.852 1.43 Pool depth (cm) 0.008 0.060 0.794 -1.02 X 10 − 4 3.88 X 10 − 4 Number of pools sampled 0.019 0.007 0.694 1.21 X 10 − 4 3.80 X 10 − 4 Data Analysis Generalized linear models (GLM) were used to determine which of the land-use/land-cover, pool, and climate variables were best able to predict the measures of species diversity. Before building the models, some explanatory variables were removed based on various thresholds. Correlations and variance inflation factor (VIF) analysis were used to determine if there was multicollinearity among the explanatory variables. Based on those results, temperature standard deviation and precipitation standard deviation were removed. Due to the smaller sample size of the invertebrate dataset (Appendix Table 1 ), average temperature and average precipitation were excluded from the invertebrate models to avoid overfitting. Surface area was also excluded from the invertebrate models due to the number of missing measurements (Appendix Table 2). The number of pools in each site was also included in both plant and invertebrate models as a variable because it likely influenced some measures of diversity (such as gamma and beta). The final explanatory variables used in the plant GLM models were: percent cover developed land, percent cover agriculture land, latitude, average yearly temperature, average yearly precipitation, average pool surface area (km 2 ), average pool depth (cm), and the number of pools sampled at site (Table 1 ). The explanatory variables used in the invertebrate GLM models were: percent cover developed land, percent cover agricultural land, latitude, average pool average depth (cm), and the number of pools sampled at a site (Table 1 ). Separate GLM models were created for each response variable. Residual plots were used to assess model fit of various families (Gaussian, Gamma, Tweedie). Note that the gamma family is an inverse link so models that were best fit with the gamma family need to be interpreted with this in mind. The family used to fit each model is included in the table captions. Because we could not calculate beta diversity at sites where only one pool was sampled, those sites were excluded from analysis of beta diversity. A total of 22 models were created, 11 models for the plant data and 11 models for the invertebrate data. Statistical analyses were performed using R Statistical Software Version 4.2.1 (R Core Team 2022) in RStudio software v.2022.7.1.554 (R Studio Team 2022), using DHARMa (Hartig 2022), tweedie (Dunn and Smyth 2005 ), glmmTMB (Brooks et al. 2017 ), rsq (Zhang 2024 ), and car (Fox and Weisberg 2019 ) packages. RESULTS Plant alpha, beta (dissimilarity), and gamma diversity were explained by multiple variables. The significant predictor variables included latitude, pool surface area, and the number of pools within the site (Table 2 ). Alpha and gamma diversity had positive relationships with latitude. (Fig. 1 ). Increased number of pools sampled at a site increased beta and gamma diversity. Alpha, gamma, and beta diversity all had negative relationships with pool surface area (Fig. 2 ). Latitude, percent developed land, pool surface area, and the number of pools sampled were significant predictors of plant functional groups (Tables 3 and 4 ). Forb alpha diversity had a positive relationship with latitude and a negative relationship with pool surface area. Graminoid alpha diversity did not have any significant relationships with the explanatory variables. Native species alpha diversity had a positive relationship with latitude and a negative relationship with pool surface area (Fig. 3 ). Native species gamma diversity had a positive relationship with the number of pools sampled and with latitude, and a negative relationship with pool surface area and with percent cover of developed land. Native species beta diversity had a positive relationship with the number of pools sampled and a negative relationship with pool surface area. Exotic species alpha diversity had a negative relationship with pool surface area (Fig. 3 ) while exotic species gamma and beta diversity had positive relationships with the number of pools sampled. Table 4 Generalized Linear Models for measures of native and exotic plant species diversity. Native beta diversity and exotic beta diversity models were best fitted using the gamma family which is an inverse link. The rest of the models in the table were best fit using the gaussian family. Results should be interpreted as in Table 2 . Response variable Explanatory variable Chi-square value Partial R 2 P-value Coefficient estimate Standard error Native alpha Percent developed land 219.86 0.015 0.055 -0.043 0.053 Percent agricultural land 0.000 < 0.001 1.000 4.65 X 10 − 4 0.051 Latitude 1373.21 0.331 < 0.001 2.479 0.545 Average yearly temperature 92.59 0.049 0.212 1.0 0.680 Average yearly precipitation 0.640 0.001 0.917 0.020 0.083 Surface area (km 2 ) 435.36 0.141 0.007 -1241.0 472.400 Pool depth (cm) 0.310 0.181 0.942 -0.039 0.189 Number of pools sampled 108.54 0.042 0.177 -0.276 0.204 Native Gamma Percent developed land 862.5 0.031 0.036 -0.114 0.097 Percent agricultural land 31.6 0.057 0.689 -0.146 0.092 Latitude 3600.1 0.252 < 0.001 3.728 0.991 Average yearly temperature 279.6 0.033 0.234 1.475 1.237 Average yearly precipitation 10.6 < 0.001 0.817 -0.019 0.151 Surface area (km 2 ) 3669.7 0.177 < 0.001 -2579.0 859.5 Pool depth (cm) 9.900 0.198 0.823 0.215 0.343 Number of pools sampled 3335.8 0.287 < 0.001 1.528 0.371 Native beta Percent developed land 0.041 0.009 0.308 0.001 0.003 Percent agricultural land 0.000 -0.006 0.948 0.003 0.003 Latitude 0.019 0.005 0.490 0.014 0.033 Average yearly temperature 0.021 0.006 0.460 0.025 0.041 Average yearly precipitation 0.091 0.015 0.127 -0.002 0.005 Surface area (km 2 ) 0.185 0.080 0.029 73.997 52.760 Pool depth (cm) 0.017 0.109 0.507 -0.015 0.010 Number of pools sampled 0.660 0.310 < 0.001 -0.045 0.010 Exotic alpha Percent developed land 0.185 < 0.001 0.883 0.003 0.020 Percent agricultural land 1.011 < 0.001 0.730 0.001 0.019 Latitude 9.209 0.012 0.298 0.147 0.206 Average yearly temperature 0.531 0.003 0.803 0.088 0.257 Average yearly precipitation 10.67 0.021 0.263 0.030 0.031 Surface area (km 2 ) 41.35 0.107 0.027 -400.600 178.50 Pool depth (cm) 17.97 0.125 0.146 0.114 0.071 Number of pools sampled 18.74 0.050 0.138 0.115 0.077 Exotic gamma Percent developed land 0.900 < 0.001 0.876 0.003 0.042 Percent agricultural land 38.36 < 0.001 0.309 -0.004 0.040 Latitude 21.05 0.002 0.451 0.116 0.429 Average yearly temperature 29.40 0.018 0.373 0.475 0.536 Average yearly precipitation 33.64 0.006 0.340 0.033 0.065 Surface area (km 2 ) 409.10 0.109 < 0.001 0.084 0.073 Pool depth (cm) 29.13 0.083 0.375 0.202 0.149 Number of pools sampled 860.92 0.356 < 0.001 0.776 0.161 Exotic beta Percent developed land 0.000 0.003 0.943 -0.001 0.003 Percent agricultural land 0.063 0.021 0.195 -0.001 0.002 Latitude 0.001 0.022 0.875 0.021 0.030 Average yearly temperature 0.002 0.004 0.839 -0.014 0.043 Average yearly precipitation 0.000 0.017 0.976 0.005 0.005 Surface area (km 2 ) 0.120 0.016 0.074 26.209 44.37 Pool depth (cm) 0.051 0.020 0.243 0.006 0.010 Number of pools sampled 0.494 0.284 < 0.001 -0.038 0.010 For aquatic invertebrates, the significant predictor variables of beta (dissimilarity) diversity included pool depth (negative relationship) (Fig. 4 ), and number of pools sampled (positive relationship; Table 5 ). Gamma diversity also had a positive relationship with the number of pools sampled while alpha diversity did not have significant relationships with any of the variables. Table 5 Generalized Linear Models for overall measures of invertebrate diversity. Alpha diversity and gamma diversity models were best fitted using the gamma family which is an inverse link. The beta diversity model was best fit using the gaussian family. Results should be interpreted as in Table 2 . Response variable Explanatory variable Chi-square value Partial R 2 P-value Coefficient estimate Standard error Alpha Percent developed land 0.248 0.051 0.147 -6.17 X 10 − 4 5.27 X 10 − 4 Percent agricultural land 0.019 -0.022 0.689 -1.46 X 10 − 4 4.66 X 10 − 4 Latitude 0.008 -0.031 0.792 -0.003 0.009 Pool depth (cm) 0.525 0.103 0.034 -0.005 0.003 Number of pools sampled 0.039 0.049 0.562 2.89 X 10 − 4 4.21 X 10 − 4 Gamma Percent developed land 0.039 0.021 0.375 -1.21 X 10 − 4 1.66 X 10 − 4 Percent agricultural land 0.066 0.079 0.249 -1.47 X 10 − 4 1.12 X 10 − 4 Latitude 0.007 0.013 0.705 -9.74 X 10 − 4 0.003 Pool depth (cm) 0.047 -0.016 0.329 -4.80 X 10 − 4 6.47 X 10 − 4 Number of pools sampled 0.318 0.244 0.011 -2.12 X 10 − 4 8.57 X 10 − 5 Beta Percent developed land 0.018 0.032 0.250 -0.002 0.001 Percent agricultural land 1.16 X 10 − 4 < 0.001 0.927 -3.77 X 10 − 5 8.22 X 10 − 4 Latitude 0.002 0.008 0.676 -0.007 0.016 Pool depth (cm) 0.053 0.099 0.051 -0.007 0.005 Number of pools sampled 0.081 0.202 0.016 0.002 7.00 X 10 − 4 Significant predictors of invertebrate functional groups (i.e. dispersal and trophic level categories) were pool depth and number of pools sampled (Tables 6 and 7 ). Passive disperser alpha diversity had a positive relationship with pool depth (Fig. 5 ), while beta diversity of passive dispersers had a negative relationship with depth (Fig. 4 ). Gamma diversity of passive dispersers did not have a significant relationship with any of the variables. Active disperser gamma and beta diversity had positive relationships with the number of pools sampled. Active disperser alpha diversity did not have significant relationships with any of the variables. Prey alpha diversity had a positive relationship with pool depth (Fig. 5 ) while predator alpha diversity did not have significant relationships with any of the predictor variables. Table 6 Generalized Linear Model models for measures of diversity of active and passive dispersing invertebrates. All models except for passive beta were best fitted using the gamma family which is an inverse link. The passive beta model was best fit using the gaussian family. Results should be interpreted as in Table 2 . Response variable Explanatory variable Chi-square value Partial R 2 P-value Coefficient estimate Standard error passive alpha Percent developed land 0.099 0.032 0.394 -8.48 X 10 − 4 8.89 X 10 − 4 Percent agricultural land 0.034 0.025 0.615 2.29 X 10 − 4 8.25 X 10 − 4 Latitude 0.148 < 0.001 0.298 -0.018 0.017 Pool depth (cm) 0.783 0.097 0.016 -0.008 0.004 Number of pools sampled 4.4 X 10 − 4 0.015 0.955 1.23 X 10 − 4 6.41 X 10 − 4 passive gamma Percent developed land 0.003 < 0.001 0.849 -9.25 X 10 − 5 3.55 X 10 − 4 Percent agricultural land 0.001 < 0.001 0.896 -1.90 X 10 − 5 2.56 X 10 − 4 Latitude 0.028 0.015 0.537 -0.003 0.005 Pool depth (cm) 0.066 0.013 0.346 -8.47 X 10 − 4 0.001 Number of pools sampled 0.173 0.071 0.126 -2.68 X 10 − 4 1.86 X 10 − 4 passive beta Percent developed land 0.010 0.029 0.386 -9.39 X 10 − 4 0.001 Percent agricultural land 4.81 X 10 − 4 < 0.001 0.848 -9.80 X 10 − 5 7.98 X 10 − 4 Latitude 0.019 0.060 0.227 -0.019 0.015 Pool depth (cm) 0.077 0.109 0.016 -0.008 0.005 Number of pools sampled 0.029 0.090 0.134 0.001 6.80 X 10 − 4 Active alpha Percent developed land 0.210 0.022 0.383 -7.97 X 10 − 4 0.002 Percent agricultural land 0.004 0.010 0.907 3.64 X 10 − 4 0.002 Latitude 0.123 0.151 0.505 0.025 0.036 Pool depth (cm) 0.313 0.130 0.287 -0.015 0.012 Number of pools sampled 0.259 0.121 0.333 0.002 0.002 Active gamma Percent developed land 0.004 < 0.001 0.854 -4.58 X 10 − 5 7.03 X 10 − 4 Percent agricultural land 0.035 0.025 0.607 -2.58 X 10 − 4 4.47 X 10 − 4 Latitude 0.001 0.004 0.929 -9.17 X 10 − 4 0.010 Pool depth (cm) 0.041 -0.043 0.579 -0.001 0.003 Number of pools sampled 0.655 0.169 0.026 -6.58 X 10 − 4 3.12 X 10 − 4 Active beta Percent developed land 0.005 0.003 0.730 5.91 X 10 − 4 0.003 Percent agricultural land 0.008 0.012 0.665 -6.57 X 10 − 4 0.002 Latitude 2.70 X 10 − 4 0.002 0.938 0.003 0.044 Pool depth (cm) 0.053 0.046 0.274 0.012 0.012 Number of pools sampled 0.265 0.200 0.014 -0.004 0.002 Table 7 Generalized Linear Model models for measures of diversity for invertebrate functional groups. Both models were best fitted using the gamma family which is an inverse link. Results should be interpreted as in Table 2 . Response variable Explanatory variable Chi-square value Partial R 2 P-value Coefficient estimate Standard error Predator alpha Percent developed land 0.098 -0.021 0.541 -0.002 0.002 Percent agricultural land 1.00 X 10 − 5 -0.006 0.995 -5.61 X 10 − 4 0.002 Latitude 0.277 < 0.001 0.304 -0.043 0.044 Pool depth (cm) 0.031 < 0.001 0.733 9.15 X 10 − 4 0.012 Number of pools sampled 0.202 0.019 0.380 0.001 0.002 Prey alpha Percent developed land 0.148 0.012 0.291 -3.70 X 10 − 4 9.07 X 10 − 4 Percent agricultural land 0.028 0.078 0.646 6.51 X 10 − 4 8.03 X 10 − 4 Latitude 0.038 0.060 0.594 0.008 0.015 Pool depth (cm) 1.086 0.313 0.004 -0.012 0.004 Number of pools sampled 0.004 0.123 0.866 4.91 X 10 − 4 6.15 X 10 − 4 DISCUSSION The hypothesis that spatial diversity patterns would be explained by average climate and pool variables and LULC was partially supported. Both average climate and pool variables explained many measures of diversity for both plants and invertebrates, but the percent developed land cover had a significant relationship only with plant gamma diversity. The taxonomic resolution of both plants (species level) and invertebrates (species to family level) differed, which could have influenced the observed patterns of diversity responses to the independent variables. Nevertheless, there were also consistencies across the two taxonomic groups. Most measures of invertebrate diversity did not have significant relationships with the independent variables. Invertebrate beta diversity (total and passive), passive alpha, and prey alpha had significant relationships with the pool characteristics. In contrast, plant species diversity had significant relationships with most of the variables. One explanation for the prevalent plant patterns might be the presence of invasive species. Vernal pools in California usually exist in a grassland landscape dominated by invasive plant species (Keeley and Zedler 1998 , Collinge et al. 2011 , Tang et al. 2023 ). While this current study did not directly look at the correlation between invasive and native plant species, previous studies have found that exotic grasses outcompete native plant species in vernal pools (Boughton et al. 2011 , Bando et al. 2023 ), and their thatch can suppress both plant germination and crustacean emergence (Kneitel et al. 2017). Another possible explanation is that plants and invertebrates interact with the environment differently. Vernal pool plants do not need very much water to germinate and grow (Bliss and Zedler 1998 , Casanova and Brock 2000 ) whereas most of the invertebrates in this study require certain aquatic conditions before they hatch and complete their life cycle (Brendonck 1996 , Colburn et al. 2007 ). Many measures of plant diversity had positive relationships with latitude. This relationship is uncommon, and generally species diversity decreases as latitude increases because of changes in climate (Willig et al. 2003 , Currie et al. 2004 , Hillebrand 2004 ). While this reverse latitudinal diversity gradient is unusual, several previous studies have found this pattern in temporary pools with both invertebrate and plant species (Brendonck et al. 2014 , Kneitel 2016 , El Madihi et al. 2017 ). The reverse latitudinal diversity gradient found in this present study is likely because in California temperature decreases and precipitation increases with increasing latitude. These conditions create larger sized pools and longer hydroperiods, which can result in higher diversity (Kneitel 2016 ). In this study, there were no significant relationships between latitude and any measure of invertebrate diversity. This differs from Kneitel ( 2016 ) who found latitude positively related to alpha diversity and negatively related to beta diversity of invertebrates. Differences may be the result of different statistical approaches, spatial scale of gamma diversity, and newer sites added. However, pool sizes (depth) have also been associated with latitude (Kneitel 2016 ). In our study, pool sizes explained patterns in vernal pool invertebrate communities (total and passive beta, passive and prey alpha), which is consistent with the previous results (Kneitel 2016 , O’Brien et al. 2023 ). Pool Variables and Diversity Measures of diversity had variable relationships with pool-size characteristics (depth and surface area). Generally, larger pools tended to support higher species diversity, which was found with invertebrates. This result is expected from the species-area relationship that predicts larger habitats (pools) should support greater diversity (MacArthur and Wilson 1967 , Kohn and Walsh 1994 ). This has been found in vernal pools for a variety of taxonomic groups including plants (Holland and Jain 1981, Ebert and Balko 1987 ), crustaceans (King et al. 1996 , Ripley and Simovich 2009 ), and invertebrates broadly (Kneitel 2016 ). In a vernal pool ecosystem, larger pools tend to have a longer hydroperiod, meaning they are inundated for a longer period. This can allow both short- and long-lived species to complete their life cycles and allow species that occupy similar niches to coexist by allowing for difference in the timing of hatching or development of the species, thus reducing overlap in the adults of the species that occupy similar niches (King et al. 1996 , Waterkeyn et al. 2008 ). These effects of large pools are likely to benefit passive dispersers more than active dispersers, and likely explains why, in our study, only the passive dispersers and the prey species had positive relationships with pool size. The negative relationships between invertebrate beta diversity and pool size have been previously demonstrated (Kneitel 2016 ) and is likely because larger pools are more likely to be connected to other pools, thus making it easier for passive dispersers to move between the pools making pools more similar and beta diversity lower (Carl and Blumenshine 2005 ). Smaller pools tend to be more temporary and less connected which decreases dispersal opportunities. However, this is not always the case for many freshwater ecosystems and taxonomic groups worldwide; indeed, many other measured and unmeasured factors can contribute to diversity (e.g., Oertli et al. 2002 , Batzer 2013 , Reindl et al. 2023 ), including habitat structure and soil composition. In contrast to invertebrates, we found the opposite relationship between pool size and alpha diversity with several plant groups (forbs, natives, and exotics). There may be several explanations for this seldom seen pattern. First, habitat heterogeneity is generally an explanation for increases in diversity with habitat size, but this is not always the case (Stein et al. 2014 , Schuler et al. 2017 ). There was no data to assess this, but the decrease of heterogeneity as pool size increases could result in decreased diversity. Habitat heterogeneity can change across spatial and temporal scales, and different taxonomic groups can respond to different types of heterogeneity (Gonzalez-Megias et al. 2007). Second, larger habitat sizes (and increased hydroperiod) may create abiotic conditions that result in increased competitive dominance, species exclusions, or predator effects (Welborn et al. 1996, Winikoff and Finlay 2023 ). Alpha diversity of exotic plant species had a negative relationship with pool size. This is likely explained by the fact that, unlike the native plant species, exotic species are not adapted to inundation (Gerhardt and Collinge 2007 , Faist and Beals 2018 ). We expect pools with a smaller surface area and shallower depth (representing a shorter inundation period) to have more exotic species, as demonstrated here in the negative relationship between exotic plant species diversity and pool surface area. A shortcoming in our methods included not having the pairing of hydro-regime and diversity of specific pools in a given year. Future studies could examine the relationship between vernal pool size and heterogeneity to better understand the manifold effects on species diversity. Land-cover and Diversity The percent cover of developed land had a significant but weak negative relationship with only plant native gamma diversity, a similar pattern found in many previous studies (Gibbs 2000 , Galatowitsch et al. 2000 , Houlahan et al. 2006 , Tran et al. 2010 ). There are many potential factors that could explain this, including contamination from stormwater runoff (Tran et al. 2010 ), road density altering propagule movement and hydrology (Houlahan et al. 2006 ), soil nutrients associated with increased urbanization such as heavy metals (King and Hovick 2020 ), increased isolation of populations (Ramírez-Delgado et al. 2022 ), and other habitat matrix changes (Ramírez-Delgado et al. 2022 ). Additionally, human development that leads to habitat loss and fragmentation can decrease plant species diversity (Gerstner et al. 2014, Zambrano et al. 2019 ) but this can be influenced by dispersal traits (Schopke et al. 2019). While increased invasive plants are also associated with land use (e.g., Lázaro-Lobo and Ervin 2021 , Boscutti et al. 2022 ), we did not find an association between land use and invasive plant diversity. Conclusions We examined land-use/land-cover, pool characteristics, and climate to determine their associations with plant and invertebrate species diversity and functional groups in California vernal pools. Invertebrates had significant relationships with local pool characteristics (pool depth), while plants had significant relationships with local pool characteristics, climate, and land use. This study is an important step in understanding how local and regional environmental conditions, along with human activities, influence species composition in vernal pools. Vernal pool species composition is also known to be affected by a variety of factors, including water chemistry (Rhazi et al. 2001 , Kido and Kneitel 2019 ) and restoration (O’Brien et al. 2023 ). Moreover, we did not specifically test a specific year’s weather pattern, hydro-regime, and emergent patterns of diversity within pools, which could be included in future studies. Many of the relationships in this study appear to be driven by functional groups which highlight their importance when planning restoration or conservation activities. The results of this study highlight the manifold factors influencing the various taxonomic groups found in an ecosystem. While the interactions among the explanatory variables were not assessed, this likely has important effects on diversity too. For example, pool size was an important variable, and any factor that influences this, whether that be surrounding land-use encroaching on the pool or changes to hydro-regime due to a changing climate, will affect vernal pool diversity. Thus, by reducing the impacts of climate change and other human activities, we might better manage the imperiled vernal pool ecosystem and associated species. Declarations ACKNOWLEDGEMENTS Vernal pools were sampled by JMK habitat under USFWS Permit (#TE192702). The CSUS Provost Research Fellowship (2013-14) and the National Science Foundation (DEB 1354724) provided initial support for collection of the invertebrate data set. CSUS students Vincent Chen and Ryan Castle assisted with the initial organization of these data sets. The plant dataset was collected by Michael Barbour, Ayzik Solomeshch, Jennifer Buck-Diaz, Robert Holland, Carol Witham, Roderick Macdonald, Sandra Starr, and Kristi Lazar. We are grateful for the constructive comments provided by the three reviewers. Author contributions HS and JMK contributed to the study’s conception and design. HS, JBD, AKP, TMD and JMK managed data and conducted statistics. HS wrote the first draft of the manuscript, and JBD, AKP, and TMD reviewed and edited early versions. All authors read and approved the final manuscript. Conflict of interest The authors declare no conflict of interest . Ethical approval We confirm that the manuscript has been read and approved by all named authors and that there are no other persons who satisfied the criteria for authorship but are not listed. We further confirm that all have approved the order of authors listed in the manuscript. Data statement Data will be made available upon request Funding statement National Science Foundation (DEB 1354724) CSUS Provost Research Fellowship (2013-14) and the References Asselen, Sv., Vermaat, J.E., Janse, J.H. 2013. Drivers of wetland conversion: a global meta-analysis. PLoS ONE 8: e81292. doi:10.1371/journal.pone.0081292. Bando, F.M., B.R.S. Figueiredo, D.A. Moi, S.M. Thomaz, T.S. Michelan, J. Garcia-Giron, J. Heino, J. Alahuhta, G.Q. Romero, R.P. Mormul. 2023. Invasion by an exotic grass species homogenizes native freshwater plant communities. Journal of Ecology 00:1-15. DOI: 10.1111/1365-2745.14061 Baselga, A., J.M. Lobo, J. Svenning, and M.A. Araujo. 2012. Global patterns in the shape of species geographical ranges reveal range determinants. Journal of Biogeography 39: 760-771. Beja, P. and R. Alcazar. 2003. Conservation of Mediterranean temporary ponds under agricultural intensification: and evaluation using amphibians. Biological Conservation 114: 317-326. Barbour, M., A. Solomeshch, C. Witham, R. Holland, R. MacDonald, S. Cilliers, J.A. Molina, J. Buck, and J. Hillman. 2003. Vernal pool vegetation of California: variation within pools. Madrono 50: 129-146. Barbour, M.G., A.I. Solomeshch, J.J. Buck, R.F. Holland, C.W. Witham, R.L. Macdonald, S.L. Starr, and K.A. Lazar. 2007. Classification, ecological characterization, and presence of listed plant taxa of vernal pool associations in California. U.S. Fish and Wildlife Service, Sacramento, CA. Batzer D.P. 2013. The seemingly intractable ecological responses of invertebrates in North American wetlands: a review. Wetlands 33:1–15. Batzer, D.P., R. Cooper, and S.A. Wissinger. 2019. Wetland invertebrate ecology. Pages 151-184 in: Ecology of Freshwater and Estuarine Wetlands. University of California Press. https://doi.org/10.1525/9780520959118-008 Beever, E.A., L.E. Hall, J. Varner, A.E. Loosen, J.B. Dunham, M.K. Gahl, F.A. Smith, and J.J. Lawler. 2017. Behavioral flexibility as a mechanism for coping with climate change. Frontiers in Ecology and the Environment 15: 299-308. Bird, M. S., & J. A. Day. 2014. Wetlands in changed landscapes: the influence of habitat transformation on the physico-chemistry of temporary depression wetlands. Plos one, 9(2), e88935 Bliss, S.A. and P.H. Zedler. 1998. The germination process in vernal pools: sensitivity to environmental conditions and effects on community structure. Oecologia 113: 67-73. Brendonck, L. 1996. Diapause, quiescence, hatching requirements: what we can learn from large freshwater branchiopods (Crustacea: Branchiopoda: Anostraca, Notostraca, Conchostraca). Hydrobiologia 320: 85-97. Brendonck, L., M. Jocque, K. Tuytens, B.V. Timms, and B. Vanschoenwinkel. 2014. Hydrologic stability drives both local and regional diversity patterns in rock pool metacommunities. Oikos 124: 741-749. Brooks, M.E., K. Kristensen, K.J. Van Benthem, A. Magnusson, C.W. Berg, A. Nielsen, H.J. Skaug, M. Maechler, and B.M. Bolker. 2017. glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. The R Journal 9: 378-400. Boix, D., J. Kneitel, B.J. Robson, C. Duchet, L. Zuniga, J. Day, S. Gascon, J. Sala, X.D. Quintana, and L. Blaustein. 2016. Invertebrates of freshwater temporary ponds in Mediterranean climates. Pages 141-189 in : Invertebrates in Freshwater Wetlands. Springer International Publishing. Boscutti, F., F. Lami, E. Pellegrini, M. Buccheri, F. Busato, F. Martini, R. Sibella, M. Sigura and L. Marini. 2022. Urban sprawl facilitates invasions of exotic plants across multiple spatial scales. Biological Invasions 24: 1497-1510. Bouahim, S., L. Rhazi, B. Amami, N. Sahib, M. Rhazi, A. Waterkeyn, A. Zouahri, F. Mesleard, S.D. Muller, and P. Grillas. 2010. Impact of grazing on species richness of plant communities in Mediterranean temporary pools (western Morocco). Comptes Rendus Biologies 333: 670-679. Bouahim, S., L. Rhazi, B. Amami, A. Waterkeyn, A., M. Rhazi, E.R. Saber, Zouahri, A., M. Van den Broeck, S.D. Muller, L. Brendonck, and P. Grillas. 2014. Unravelling the impact of anthropogenic pressure on plant communities in Mediterranean temporary ponds. Marine and Freshwater Research 65: 918-929. Boughton, E.H., P.F. Quintana-Ascencio, D. Nickerson, and P.J. Bohlen. 2011. Management intensity affects the relationship between non-native and native species in subtropical wetlands. Applied Vegetation Science 14: 210-220. Carl, T. and S. Blumenshine. 2005. Relationships among vernal pool invertebrate assemblages with habitat morphology and distribution. Bios 76: 145-152. Casanova, M.T. and M.A. Brock. 2000. How do depth, duration and frequency of flooding influence the establishment of wetland plant communities? Plant Ecology 147: 237-250. Coelho, M.T.P., Barreto, E., Rangel, T.F. et al. 2023. The geography of climate and the global patterns of species diversity. Nature 622, 537–544. https://doi.org/10.1038/s41586-023-06577-5 Colburn, E.A., S.C. Weeks, and S.K. Reed. 2007. Diversity and ecology of vernal pool invertebrates. Science and conservation of vernal pools in northeastern North America. CRC Press, Boca Raton, pp. 105-126. Collinge, S.K., C. Ray, and F. Gerhardt. 2011. Long-term dynamics of biotic and abiotic resistance to exotic species invasion in restored vernal pool plant communities. Ecological Applications 21: 2105-2118. Cooper, S.D., P.S. Lake, S. Sabater, J.M. Melack, and J.L. Sabo. 2013. The effects of land use changes on streams and rivers in mediterranean climates. Hydrobiologia 719: 383-425. Currie, D.J., G.M. Mittelbach, H.V. Cornell, R. Field, J. Guegan, B.A. Hawkins, D.M. Kaufman, J.T. Kerr, T. Oberdorff, E.O’Brien, and J.R.G. Turner. 2004. Predictions and tests of climate-based hypotheses of broad-scale variation in taxonomic richness. Ecology Letters 7: 1121-1134. Dewitz, J., 2019, National Land Cover Database (NLCD) 2016 Products: U.S. Geological Survey data release, https://doi.org/10.5066/P96HHBIE. Dickman, C.R. 1987. Habitat fragmentation and vertebrate species richness in an urban environment. Journal of Applied Ecology 24: 337-351. Dudgeon, D., A.H. Arthington, M.O. Gessner, Z. Kawabata, D.J. Knowler, C. Leveque, R.J. Naiman, A. Prieur-Richard, D. Soto, M.L.J. Stiassny, and C.A. Sullivan. 2006. Freshwater biodiversity: importance, threats, status and conservation challenges. Biological Reviews 81: 163-182. Dunn, P.K. and G.K. Smyth. 2005. Series evaluation of Tweedie exponential dispersion models. Statistics and Computing 15: 267-280. Duffy, W.G. and S.N. Kahara. 2011. Wetland ecosystem services in California’s Central Valley and implications for the Wetland Reserve Program. Ecological Applications 21: 18-30. Ebert, T.A. and M.L. Balko. 1987. Temporary pools as islands in space and in time: the biota of vernal pools in San Diego, Southern California, USA. Hydrobiologia 110: 101-123. El Madihi, M., L. Rhazi, M. V. den Broeck, M. Rhazi, A. Waterkeyn, E. Saber, S. Bouahim, M. Arahou, A. Zouahri, A. Guelmami, S.D. Muller, L. Brendonck, and P. Grillas. 2017. Plant community patterns in Moroccan temporary ponds along latitudinal and anthropogenic disturbance gradients. Plant Ecology and Diversity 10:197-215. Epele, L.B., M.G. Grech, E.A. Williams-Subiza, C. Sternert, K. McLean, H.S. Greig, L. Maltchik, M.M. Pires, M.S. Bird, A. Boissezon, D. Boix, E. Demierre, P.E. Garcia, S. Gascon, M. Jeffries, J.M. Kneitel, O. Loskutova, L.M. Manzo, G> Mataloni, M.C. Mlambo, B. Oertli, J. Sala, E.E. Scheibler, H. Wu, S.A. Wissinger, and D.P. Batzer. 2022. Perils of life on the edge: climatic threats to global patterns of wetland macroinvertebrates. Science of the Total Environment 820: 153052. Epele, L.B. Grech. M.G., Williams-Subiza, E.A., Stenert C., McLean, K., Greig, H.S., Maltchik L., Marques Pires, M., Bird, M.S., Boissezon A., Boix, D., Demierre E., Garcia, P.E., Gascon, S., Jeffries, M., Kneitel, J.M., Loskutova, O., Manzo, L.M., Mataloni, G.M., Mlambo, M.C., Oertli, B., Sala, J., Scheibler, E. E., Wu, H., Wissinger, S.A. & D. P. Batzer. 2024. A global assessment of environmental and climatic controls on wetland macroinvertebrate structure and function. Global Change Biology: https://doi.org/10.1111/gcb.17173. Faist, A.M. and S.C. Beals. 2018. Invasive plant feedbacks promote alternative states in California vernal pools. Restoration Ecology 26: 255-263. Feld, C.K., S. Birk, D. Eme, M. Gerisch, D. Hering, M. Kernan, K. Maileht, U. Mischke, I. Ott, F. Pletterbauer, S. Poikane, J. Salgado, C.D. Sayer, J. van Wichelen, and F. Malard. 2016. Disentangling the effects of land use and geo-climatic factors on diversity in European freshwater ecosystems. Ecological Indicators 60: 71-83. Fick, S.E. and R.J. Hijmans, 2017. WorldClim 2: new 1km spatial resolution climate surfaces for global land areas. International Journal of Climatology 37 (12): 4302-4315. Foley, J.A., R. DeFries, G.A. Asner, C. Barford, G. Bonan, S.R. Carpenter, F.S. Chapin, M.T. Coe, G.C. Daily, H.K. Gibbs, J.H. Helkowski, T. Holloway, E.A. Howard, C.J. Kucharik, C. Monfreda, J.A. Patz, I.C. Prentice, N. Ramankutty, and P.K. Snyder. 2005. Global consequences of land use. Science 309: 570-574. Fox, J. and S. Weisberg. 2019. An {R} companion to applied regression, Third Edition. Thousand Oaks, California. Sage. Gerhardt, F., & Collinge, S. K. 2007. Abiotic constraints eclipse biotic resistance in determining invasibility along experimental vernal pool gradients. Ecological Applications 17 (3): 922-933.Gerstner, K., C.F. Dormann, A. Stein, A.M. Manceur and R. Seppelt. 2014. Effects of land use on plant diversity–A global meta‐analysis. Journal of Applied Ecology 51 : 1690-1700. Gibbs, J.P. 2000. Wetland loss and biodiversity conservation. Conservation Biology 14: 314-317. Galatowitsch, S.M., D.C. Whited, R. Lehtinen, J. Husveth, and K. Schik. 2000. The vegetation of wet meadows in relation to their land-use. Environmental Monitoring and Assessment 60: 121-144. Gerhardt, F. and S.K. Collinge. 2007. Abiotic constraints eclipse biotic resistance in determining invasability along experimental vernal pool gradients. Ecological Applications 17: 922-933. Gonzalez, P., R.P. Neilson, J.M. Lenihan, and R.J. Drapek. 2010. Global patterns in the vulnerability of ecosystems to vegetation shifts due to climate change. Global Ecology and Biogeography 19: 755-768. González‐Megías, A., J. María Gómez and F. Sánchez‐Piñero. 2007. Diversity‐habitat heterogeneity relationship at different spatial and temporal scales. Ecography 30 : 31-41. Guerin, G.R., H. Wen, and A.J. Lowe. 2012. Leaf morphology shift linked to climate change. Biology Letters 8: 882-886. Hankins, D.L. 2013. The effects of indigenous prescribed fire on riparian vegetation in central California. Ecological Processes 2:24. Hansen, M.J. and A.P. Clevenger. 2005. The influence of disturbance and habitat on the presence of non-native plant species along transport corridors. Biological Conservation 125: 249-259. Heino, J. 2013. The importance of metacommunity ecology for environmental assessment research in the freshwater realm. Biological Reviews 88: 166-178. Hillebrand, H. 2004. On the generality of the latitudinal diversity gradient. The American Naturalist 163: 192-211. Hof, C., M. Brändle, and R. Brandl. 2008. Latitudinal variation of diversity in European freshwater invertebrates is not concordant across habitat types. Global Ecology and Biogeography DOI: 10.1111/j.1466-8238.2008.00394.x Holland, R.F. Talk titled Glimmerings from the Hogwallows . Presented at the International Conference on Mediterranean Temporary Ponds, Menorca, 5-8 May 2009. Holland, R.F. and S.K. Jain. 2018. Insular biogeography of vernal pools in the central valley of California. The American Naturalist 117: 24-37. Homer, C., J. Dewitz, S. Jin, G. Xian, C. Costello, P. Danielson, L. Gass, M. Funk, J. Wickham, S. Stehman, R. Auch, and K. Riitters. 2020. Conterminous United States land cover change patterns 2001–2016 from the 2016 National Land Cover Database: ISPRS Journal of Photogrammetry and Remote Sensing, v. 162, p. 184–199, at https://doi.org/10.1016/j.isprsjprs.2020.02.019 Houlahan, J.E., P.A. Keddy, K. Makkay, and C.S. Findlay. 2006. Effects of adjacent land use on wetland species richness and community composition. Society of Wetland Scientists 26: 79-96. IPCC, 2023. Climate Change 2023: Synthesis Report. Contribution of Working Groups I, II and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team, H. Lee and J. Romero (eds.)]. IPCC, Geneva, Switzerland, pp. 35-115, doi: 10.59327/IPCC/AR6-9789291691647 Junk, W.J., S. An, C.M. Finlayson, B. Gopal, J. Kvet, S.A. Mitchell, W.J. Mitsch, and R.D. Roberts, 2013. Current state of knowledge regarding the world’s wetlands and their future under global climate change: a synthesis. Aquatic Sciences 75: 151-167. Keeler-Wolf, T., D.R. Elam, K. Lewis, and S.A. Flint. 1998. California vernal pool assessment: a preliminary report. Resources Agency, California Department of Fish and Game, Sacramento, CA. Keeley, J.E. and P.H. Zedler. 1998. Characterization and global distribution of vernal pools. Ecology, conservation, and management of vernal pool ecosystems- proceedings from a 1996 conference, California Native Plant Society, Sacramento, CA. Kido, R.R. and J.M. Kneitel. 2019. Eutrophication effects differ among functional groups in vernal pool invertebrate communities. Hydrobiologia 848: 1659-1673. King, C.M. and S.M. Hovick. 2020. Wetland plant community variation across replicate urban to rural gradients: non-native species as both drivers and passengers in systems impacted by anthropogenic land-use. Urban Ecosystems https://doi.org/10.1007/s11252-020-01012-3. King, J.L., M.A. Simovich, and R.C. Brusca. 1996. Species richness, endemism and ecology of crustacean assemblages in northern California vernal pools. Hydrobiologia 328: 85-116. Kinlock, N.L., L. Prowant, E.M. Herstoff et al. 2018. Explaining global variation in the latitudinal diversity gradient: meta-analysis confirms known patterns and uncovers new ones. Global Ecology and Biogeography 27: 125-141. Kneitel, J.M. 2016. Climate-driven habitat size determines the latitudinal diversity gradient in temporary ponds. Ecology 97: 961-968. Kneitel, J.M. 2018. Occupancy and environmental responses of habitat specialists and generalists depend on dispersal traits. Ecosphere 9:e02143. Kohn, D.D. and D.M. Walsh. 1994. Plant species richness- The effect of island size and habitat diversity. Journal of Ecology 82: 367-377. Lázaro-Lobo, A. and G.N. Ervin, 2021. Native and exotic plant species respond differently to ecosystem characteristics at both local and landscape scales. Biological Invasions 23: 143-156. Leibold, M.A., M. Holyoak, N. Mouquet, P. Amarasekare, J.M. Chase, M.F. Hoopes, R.D. Holt, R.D., J. B. Shurin, R. Law, D. Tilman, and M. Loreau, 2004. The metacommunity concept: a framework for multi‐scale community ecology. Ecology Letters 7: 601-613. MacArthur, R.H. and E.O. Wilson. 1967. The theory of island biogeography. Princeton University Press, Princeton, New Jersey. Marty, J.T. 2005. Effects of cattle grazing on diversity in ephemeral wetlands. Conservation Biology 19: 1626-1632. Marty, J.T. 2015. Loss of biodiversity and hydrologic function in seasonal wetlands persists over 10 years of livestock grazing removal. Restoration Ecology 23: 548-554. Michaels, J.S., K.W. Tate, and V.T. Eviner. 2022. Vernal pool wetlands respond to livestock grazing, exclusion and reintroduction. Journal of Applied Ecology 59: 67-78. Millennium Ecosystem Assessment, 2005. Ecosystems and Human Well-Being: Wetlands and Water Synthesis. World Resources Institute, Washington, DC. Mittelbach, G.G., D.W. Schemske, H.V. Cornell et al. 2007. Evolution and the latitudinal diversity gradient: speciation, extinction and biogeography. Ecology Letters 10: 315-331. Moomaw, W.R., G.L. Chmura, G.T. Davies, C.M. Finlayson, B.A. Middleton, S.M. Natali, J.E. Perry, N. Roulet, A.E. Sutton-Grier. 2018. Wetlands in a changing climate: science, policy and management. Wetlands 38: 183-205. O’Brien, S.M., B.P. Helm, T.M. Davidson, and J.M. Kneitel. 2023. Invertebrate community composition differs between restored and natural vernal pools. Society for Ecological Restoration doi: 10.1111/rec.14080 Oertli, B., Joye, D.A., Castella, E., Juge, R., Cambin, D. and Lachavanne, J.B., 2002. Does size matter? The relationship between pond area and biodiversity. Biological conservation , 104 (1), pp.59-70. Pardini, R., E. Nichols, and T. Puttker. 2017. Biodiversity response to habitat loss and fragmentation. Encyclopedia of the Anthropocene , 3, pp. 229-239. Elsevier. Pires, M.M., P.E. Garcia, L. Maltchik, C. Stenert, L.B. Epele, K.I. McLean, J.M. Kneitel, S. Racey, D.P. Batzer. 2024. Searching for the Achilles heel(s) for maintaining invertebrate biodiversity across complexes of depressional wetlands. Journal of Nature Conservation: https://doi.org/10.1016/j.jnc.2023.126332 Rahel, F.J. and J.D. Olden. 2007. Assessing the effects of climate change on aquatic invasive species. Conservation Biology 22: 521-533. Reddy, K.R., R. DeLaune, and C.B. Craft. 2010. Nutrients in wetlands: implications to water quality under changing climatic conditions. Final Report submitted to U.S. Environmental Protection Agency. EPA Contract No. EP-C-09-001. Reindl, S., K.I. McLean, J.M. Kneitel, D.A. Bell, and D.P. Batzer. 2023. Doing the same thing over and over again and getting the same results: assessing variance in wetland invertebrate assemblages. Wetlands 43: 84 https://doi.org/10.1007/s13157-023-01734-y Reis, V., V. Hermoso, S.K. Hamilton, D. Ward, E. Fluet-Chouinard, B. Lehner, and S. Linke. 2017. A global assessment of inland wetland conservation status. BioScience 67: 523-533. Renaud, S., J. Michauz, D.N. Schmidt, J. Aguilar, P. Mein, and J. Auffray. 2005. Morphological evolution, ecological diversification and climate change in rodents. Proceedings of Biological Science 272: 609-617. Rhazi, L., P. Grillas, A.M. Toure, L.T. Ham. 2001. Impact of land use in catchment and human activities on water, sediment and vegetation of Mediterranean temporary pools. Life Sciences 324: 165-177. Ripley, B.J. and M.A. Simovich. 2009. Species richness on islands in time: variation in ephemeral pond crustacean communities in relation to habitat duration and size. Hydrobiologia 617: 181-196. Ramírez-Delgado, J.P., Di Marco, M., Watson, J.E., Johnson, C.J., Rondinini, C., Corredor Llano, X., Arias, M. and Venter, O. 2022. Matrix condition mediates the effects of habitat fragmentation on species extinction risk. Nature Communications 13: 595. Schöpke, B., Heinze, J., Pätzig, M. and Heinken, T., 2019. Do dispersal traits of wetland plant species explain tolerance against isolation effects in naturally fragmented habitats?. Plant Ecology , 220 (9), pp.801-815. Schuler, M.S., J.M. Chase, and T.M. Knight. 2017. Habitat size modulates the influence of heterogeneity on species richness patterns in a model zooplankton community. Ecology 98: 1651-1659. Sharitz, R.R. and S.C. Pennings. 2019. Development of wetland plant communities. Pages 133-150 in: Ecology of Freshwater and Estuarine Wetlands. University of California Press. https://doi.org/10.1525/9780520959118-007 Silver, C.A. and S.M. Vamosi. 2012. Macroinvertebrate community composition of temporary prairie wetlands: a preliminary test of the effect of rotational grazing. Wetlands 32: 185-197. Smith, V.H., G.D. Tilman, and J.C. Nekola. 1999. Eutrophication: impacts of excess nutrient inputs on freshwater, marine, and terrestrial ecosystems. Environmental Pollution 100: 179-196. Spear, S.F. and A. Storfer. 2010. Anthropogenic and natural disturbance lead to differing patterns of gene flow in the Rocky Mountain tailed frog, Ascaphus montanus . Biological Conservation 143: 778-786. Spencer, M., L. Blaustein, S.S. Schwartz, and J.E. Cohen. 1999. Species richness and the proportion of predatory invertebrate species in temporary freshwater pools: relationships with habitat size and permanence. Ecology Letters 2: 157-166. Stein, A., K. Gerstner, and H. Kreft. 2014. Environmental heterogeneity as a universal driver of species richness across taxa, biomes and spatial scales. Ecology Letters doi: 10.1111/ele.12277 Steinman, A.D., J. Conklin, P.J. Bohlen, and D.G. Uzarski. 2003. Influence of cattle grazing and pasture land use on macroinvertebrate communities in freshwater wetlands. Wetlands 23 : 877-889. Tang, J., M. Nolan, C. D’Antonio, S.D. Cooper, and L. Stratton. 2023. Reinvasion of restored California vernal pools reveals the importance of long-term restoration planning. Restoration Ecology 31: e13991.Tangen, B.A., M.G. Butler, and M.J. Ell. 2003. Weak correspondence between macroinvertebrate assemblages and land use in prairie pothole region wetlands, USA. Wetlands 23:104–115 Thomas, C.D. 2010. Climate, climate change and range boundaries. Diversity and Distributions 16: 488-495. Tornwall, B.M., A.L. Pitt, B.L. Brown, J. Hawley-Howard. And R.F. Baldwin. 2020. Diversity patterns associated with varying dispersal capabilities as a function of spatial and local environmental variables in small wetlands in forested ecosystems. Forests 11: 1146. Tran, C.P., R.W. Bode, A.J. Smith, and G.S. Kleppel. 2010. Land-use proximity as a basis for assessing stream water quality in New York State (USA). Ecological Indicators 10: 727-733. Underwood, E.C., J.H. Viers, K.R. Klausmeyer, R.L. Cox, and M.R. Shaw. 2009. Threats and biodiversity in the mediterranean biome. Diversity and Distributions 15: 188-197. USDA, NRCS. 2023. The PLANTS database (http://plants.usda.gov). National Plant Data Team, Greensboro, NC USA. U.S. Geological Survey Gap Analysis Program, 20160513, GAP/LANDFIRE National Terrestrial Ecosystems 2011: U.S. Geological Survey, https://doi.org/10.5066/F7ZS2TM0 Van den Broeck, M., L. Rhazi, A. Waterkeyn, M. El Madihi, P. Grillas, J.M. Kneitel, and L. Brendonck. 2019. Livestock disturbances in Mediterranean temporary ponds: a mesocosm experiment with sheep manure and simulated trampling. Freshwater Biology 64: 856-869. Vanschoenwinkel, B., A. Hulsmans, E. De Roeck, C. De Vries, M. Seaman, and L. Brendonck. 2009. Community structure in temporary freshwater pools: disentangling the effects of habitat size and hydroregime. Freshwater Biology 54: 1487-1500. Warren, R., J. VanDerWal, J. Price, J.A. Welbergen, I. Atkinson, J. Ramirez-Villegas, T.J. Osborn, A. Jarvis, L.P. Shoo, S.E. Williams, and J. Lowe. 2013. Quantifying the benefit of early climate change mitigation in avoiding biodiversity loss. Nature Climate Change DOI: 10.1038/NCLIMATE1887 Waterkeyn, A., P. Grillas, B. Vanschoenwinkel, and L. Brendonck. 2008.. Invertebrate community patterns in Mediterranean temporary wetlands along hydroperiod and salinity gradients. Freshwater Biology 53: 1808-1822. Weiskopf, S.R., M.A. Rubenstein, L.G. Corzier, S. Gaichas, R. Griffis, J.E. Halofsky, K.J.W. Hyde, T.L. Morelli, J.T. Morisette, R.C. Munoz, A.J. Pershing, D.L. Peterson, R. Poudel, M.D. Staudinger, A.E. Sutton-Grier, L. Thompson, J. Vose, J.F. Weltzin, and K.P. Whyte. 2020. Climate change effects on biodiversity, ecosystems, ecosystem services, and natural resource management in the United States. Science of the Total Environment 733: 137782. Wellborn, G.A., D.K. Skelly, and E.E. Werner. 1996. Mechanisms creating community structure across a freshwater habitat gradient. Annual Review of Ecology and Systematics 27: 337-363. Wiggins, G. B., R. J. Mackay, and I. M. Smith. 1980. Evolutionary and ecological strategies of invertebrates in annual temporary pools. Archiv fur Hydrobiologie 58(Suppl.): 97–206. Willig, M.R., D.M. Kaufman, R.D. Stevens. 2003. Latitudinal gradients of biodiversity: pattern, process, scale, and synthesis. Annual Review of Ecology, Evolution, and Systematics 34: 273-309. Wilson, T.S., Matchett, E., Byrd, K.B., Conlisk, E., Reiter, M.E., Wallace, C., Flint, L.E., Flint, A.L., Joyce, B. and Moritsch, M.M., 2022. Climate and land change impacts on future managed wetland habitat: a case study from California’s Central Valley. Landscape Ecology 37: 1-21. Winikoff, S. G., and J. C. Finlay. 2023. Water-quality outcomes of wetland restoration depend on hydroperiod rather than restoration strategy. Freshwater Science 42 (1): 70-87. Zacharias, I. and M. Zamparas. 2010. Mediterranean temporary ponds. A disappearing ecosystem. Biodiversity Conservation 19: 3827-3834. Zambrano, J., Garzon-Lopez, C.X., Yeager, L., Fortunel, C., Cordeiro, N.J. and Beckman, N.G., 2019. The effects of habitat loss and fragmentation on plant functional traits and functional diversity: what do we know so far?. Oecologia , 191 (3), pp.505-518. Zedler, J.B. and S. Kercher. 2004. Causes and consequences of invasive plants in wetlands: opportunities, opportunities, and outcomes. Critical Reviews in Plant Sciences 23: 431-452. Zedler, J.B. and S. Kercher. 2005. Wetland resources: status, trends, ecosystem services, and restorability. Annual Review of Environmental Resources 30: 39-74. Zhang, D. 2024. _rsq: R-squared and related measures_. R package version 2.7. https://CRAN.R-project.org/package=rsq Zimova, M., L.S. Mills, and J.J. Nowak. 2016. High fitness costs of climate change-induced camouflage mismatch. Ecology Letters 19: 299-307. Additional Declarations No competing interests reported. Supplementary Files SavageetalAppendix.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 08 May, 2026 Reviewers agreed at journal 17 Apr, 2026 Reviewers invited by journal 17 Mar, 2026 Editor assigned by journal 14 Mar, 2026 Submission checks completed at journal 13 Mar, 2026 First submitted to journal 11 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9097717","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":607599783,"identity":"bd51202f-4a0d-438e-b5dc-8933b0bd5cb5","order_by":0,"name":"Hannah Savage","email":"","orcid":"","institution":"California State University, Sacramento","correspondingAuthor":false,"prefix":"","firstName":"Hannah","middleName":"","lastName":"Savage","suffix":""},{"id":607599785,"identity":"0674fc0f-f328-4993-91dd-ef84cb62ccf9","order_by":1,"name":"Jennifer Buck-Diaz","email":"","orcid":"","institution":"California Native Plant Society","correspondingAuthor":false,"prefix":"","firstName":"Jennifer","middleName":"","lastName":"Buck-Diaz","suffix":""},{"id":607599788,"identity":"cfbd23c6-84f1-4e3d-8496-536cf3602a30","order_by":2,"name":"Anna Klimaszewski-Patterson","email":"","orcid":"","institution":"California State University, Sacramento","correspondingAuthor":false,"prefix":"","firstName":"Anna","middleName":"","lastName":"Klimaszewski-Patterson","suffix":""},{"id":607599791,"identity":"040bf6b2-6839-45dd-a3fc-e606a72df8a4","order_by":3,"name":"Timothy M. Davidson","email":"","orcid":"","institution":"California State University, Sacramento","correspondingAuthor":false,"prefix":"","firstName":"Timothy","middleName":"M.","lastName":"Davidson","suffix":""},{"id":607599794,"identity":"cf72bf2d-7478-42b4-8eba-0d4d4304ef65","order_by":4,"name":"Jamie M. Kneitel","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAw0lEQVRIiWNgGAWjYBACCSA+wMBgIQflMxOp5QCDhDFpWoDWSCQ2EK1FcnbzwcMfKiTS+/vPGH9gqLCG6cUNpGWOJRw4cEYid8aNHDMJhjPphLXISeQYHDjYJpG7QYLHjIGx7TAxWvI/HDj4TyLdgB/oMMZ/RGiRlshhOHCwQSLBgCHHQIKxgQgtkjPSDA6cOSZhOONGWplEwrF0Y4JaJG4kP/5QUWMjz99/ePOHDzXWsgS1oIIE0pSPglEwCkbBKMAFANJVP1Z0bC+7AAAAAElFTkSuQmCC","orcid":"","institution":"California State University, Sacramento","correspondingAuthor":true,"prefix":"","firstName":"Jamie","middleName":"M.","lastName":"Kneitel","suffix":""}],"badges":[],"createdAt":"2026-03-11 19:38:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9097717/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9097717/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104981218,"identity":"8817d592-7d1d-4b90-88de-af38e52451fe","added_by":"auto","created_at":"2026-03-19 13:24:09","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":87360,"visible":true,"origin":"","legend":"\u003cp\u003eScatterplots of the relationships between plant alpha diversity and latitude (a) (p\u0026lt;0.001, partial R\u003csup\u003e2\u003c/sup\u003e=0.278) and plant gamma diversity and latitude (b) (p\u0026lt;0.001, partial R\u003csup\u003e2\u003c/sup\u003e=0.181).\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-9097717/v1/cea90fc0f78585d590e4e91b.png"},{"id":104981160,"identity":"4abf35f1-a811-4d7c-8017-9ed9005df60e","added_by":"auto","created_at":"2026-03-19 13:23:58","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":102900,"visible":true,"origin":"","legend":"\u003cp\u003eScatterplots of the relationships between plant alpha diversity and surface area (a) (p=0.002, partial R\u003csup\u003e2\u003c/sup\u003e=0.057), plant gamma diversity and surface area (b) (p\u0026lt;0.001, partial R\u003csup\u003e2\u003c/sup\u003e=0.189), and plant beta diversity and surface area (c) (p=0.022, partial R\u003csup\u003e2\u003c/sup\u003e=0.067).\u0026nbsp;\u0026nbsp;\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-9097717/v1/4231bb94746b1caed27e2e94.png"},{"id":104981692,"identity":"97b16aea-bb16-4c7d-887f-d7856e2789ed","added_by":"auto","created_at":"2026-03-19 13:26:27","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":62683,"visible":true,"origin":"","legend":"\u003cp\u003eScatterplot of the relationships between pool surface area and alpha diversity of exotic and native plant species. Both native and exotic plant species had significant relationships with pool surface area (p=0.007, partial R\u003csup\u003e2\u003c/sup\u003e=0.141 and p=0.027, partial R\u003csup\u003e2\u003c/sup\u003e=0.107, respectively)\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-9097717/v1/3227b475ffcda5c03273cd85.png"},{"id":104981781,"identity":"2a99cd48-21dc-4a62-be7c-fb533460722b","added_by":"auto","created_at":"2026-03-19 13:26:41","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":5716,"visible":true,"origin":"","legend":"\u003cp\u003eScatterplots of the relationships between invertebrate beta diversity and pool depth (a) (p=0.051, partial R\u003csup\u003e2\u003c/sup\u003e=0.202) and passive beta diversity and pool depth (b) (p=0.016, partial R\u003csup\u003e2\u003c/sup\u003e=0.109)\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-9097717/v1/03cb0bf679fae8ba0b5b22f7.png"},{"id":104981133,"identity":"5d6a6430-dc3e-4d31-a78b-73b1ca5885b5","added_by":"auto","created_at":"2026-03-19 13:23:35","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":82659,"visible":true,"origin":"","legend":"\u003cp\u003eScatterplots of the relationships between passive disperser alpha diversity and pool depth (a) (p=0.016, partial R\u003csup\u003e2\u003c/sup\u003e=0.097) and prey species alpha diversity and pool depth (b) (p=0.004, partial R\u003csup\u003e2\u003c/sup\u003e=0.313).\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-9097717/v1/c974040606807fa0253d4e45.png"},{"id":104981909,"identity":"fc5d4217-12e6-4528-b569-eb906f1dbc61","added_by":"auto","created_at":"2026-03-19 13:27:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2310015,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9097717/v1/0cbe57e3-46b4-4829-b699-e71e665c327f.pdf"},{"id":104981140,"identity":"0e9a199a-f227-421d-815f-db0a9453fd7b","added_by":"auto","created_at":"2026-03-19 13:23:44","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":229874,"visible":true,"origin":"","legend":"","description":"","filename":"SavageetalAppendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-9097717/v1/74ebc219861e01d9d7e8fb09.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eClimate, Habitat and Land-use Effects on California Vernal Pool Communities\u003c/p\u003e","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eClimate is one of the primary factors that determine the abundance and distribution of organisms (Baselga et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2012\u003c/span\u003e, Thomas \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) that has changed in recent decades (IPCC \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Changes in temperature and precipitation can cascade to the community level and influence species diversity in communities (Coehlo et al. 2023, Epele \u003cem\u003eet al\u003c/em\u003e. 2023, 2024). This is often examined across spatial gradients as in the latitudinal diversity gradient pattern that predicts increasing diversity with decreasing latitude (Hillebrand \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2004\u003c/span\u003e, Mittlebach \u003cem\u003eet al\u003c/em\u003e. 2007). Ecological explanations have been ascribed to this increase based on a climate gradient that coincides with latitude, including increasing temperature, precipitation, and stabilitywith decreasing latitude (Mittlebach \u003cem\u003eet al\u003c/em\u003e. 2007). However, different taxa and functional groups can often respond differently to this gradient (Hillebrand \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2004\u003c/span\u003e, Kinlock et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Moreover, different ecosystems may exhibit different relationships with latitude (Hillebrand \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2004\u003c/span\u003e, Hof et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). This includes freshwater ecosystems that are closely connected to variation in temperature and rainfall (e.g., Hof et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eClimate is important for wetlands because it largely determines their size (i.e. depth and surface area). This is especially true for seasonal wetlands which are laregely driven by precipitation and evaporation dynamics (Boix et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and other anthropogenic factors (Bouahim et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). This in turn can determine both invertebrate and plant diversity patterns (Oertli et al. \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2002\u003c/span\u003e, Junk et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2013\u003c/span\u003e, Epele et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), which can be explained by the species-area relationship (MacArthur and Wilson \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e1967\u003c/span\u003e, Kohn and Walsh \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e1994\u003c/span\u003e) and changes in heterogeneity (Stein et al. \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The size-diversity pattern is not always found (see Batzer \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2013\u003c/span\u003e for examples), and there is usually substantial unexplained variation (Batzer \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2013\u003c/span\u003e, Rendl \u003cem\u003eet al\u003c/em\u003e. 2023). Wetland biodiversity is especially vulnerable to changes in climate because temperature and precipitation can affect other characteristics of wetlands, such as invasive species success (Gerhardt and Collinge \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2007\u003c/span\u003e, Rahel and Olden 2008), temperature, water chemistry (Reddy et al. \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2010\u003c/span\u003e, Bird and Day \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), and anthropogenic changes.\u003c/p\u003e \u003cp\u003eWetlands are vulnerable to anthropogenic land-use changes because they are often located in or near agriculture (MEA 2005, Zedler and Kercher \u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e2005\u003c/span\u003e, Asselen et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2013\u003c/span\u003e, Reis et al. \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, Wilson et al. \u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Most research of anthropogenic land-use effects and climate on wetlands has been conducted on permanent freshwater wetlands. Temporary wetlands are often overlooked, although some types of land-use, such as grazing effects on plants, are well studied (Marty \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2005\u003c/span\u003e, Bouahim et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2010\u003c/span\u003e, Silver and Vamosi \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2012\u003c/span\u003e, Michaels et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Generally, land use (grazing and agriculture) effects have mixed effects on plant (e.g., Bouahim et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2010\u003c/span\u003e, Michaels et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and crustacean species composition (e.g., Steinman et al. \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2003\u003c/span\u003e, Tangen et al. 2003, Van den Broeck et al. \u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). However, a combination of factors (e.g., climate, pool characteristics) can interact with anthropogenic activities to affect plant and invertebrate diversity in temporary ponds (El Madihi et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eVernal pools are temporary freshwater wetlands that occur in Mediterranean climates that fill with the winter rains and dry out by summer (Holland \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2009\u003c/span\u003e, Boix et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Vernal pools in Mediterranean-climate California are found mostly in the Central Valley and are one of the most threatened ecosystems worldwide (Holland and Jain 1981, Keeler-Wolf et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). They are habitat to many rare and endemic species (King et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e1996\u003c/span\u003e, Barbour et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) adapted to living in temporary waters (Zacharias and Zamparas \u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The primary threat to these ecosystems is habitat loss from urban and agricultural development (Underwood et al. \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2009\u003c/span\u003e, Cooper et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2013\u003c/span\u003e, Wilson et al. \u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Further, there has been a historical lack of understanding of the contribution of vernal pools to biodiversity due to their temporary nature (Beja and Alcazar \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2003\u003c/span\u003e, Zacharias and Zamparas \u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e2010\u003c/span\u003e, Boix et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Freshwater wetlands, including temporary ones, provide a wide array of ecosystem services including maintenance of water quality, sequestering carbon, recreation, flood water storage, groundwater recharge, habitat for many species listed as endangered or threatened, and are often culturally significant to indigenous peoples (Gibbs \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2000\u003c/span\u003e, Duffy and Kahara \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2011\u003c/span\u003e, Hankins \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Many of these ecosystem services help to mitigate the impacts of climate change through climate regulation and carbon sequestration (Millenium Ecosystem Assessment 2005). Thus, understanding how, and if, anthropogenic land-use affects these habitats is critical for conservation.\u003c/p\u003e \u003cp\u003eIn this study, we explored how climate (including latitude), pool characteristics, and land-use are associated with vernal pool community diversity (Appendix Table\u0026nbsp;1) using broad scale previously published datasets (Barbour et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2007\u003c/span\u003e, Kneitel \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The data was collected over 8\u0026ndash;10 years and across approximately 10 degrees of latitude. The purpose was not to identify how the independent variables affected the communities of a particular year but to identify general spatial patterns. Previous studies have identified correlations among latitude, climate, and wetland area and these can also be associated with plant and invertebrate species richness (e.g., Houlahan et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2006\u003c/span\u003e, Kneitel \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, El Madihi et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In addition to latitude and climate-driven pool size, climate often influences what human land-use occurs in a particular area. Feld et al. (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) found that geo-climatic variables, such as latitude and precipitation, were strong determinants of human land-use patterns in European freshwater systems. Their study also found that the geoclimatic variables explained more of the variation in patterns of biodiversity in wetlands than land-use. Thus, we hypothesized that the land-use variables (i.e. developed land, agricultural land) would predict some of the variation in species diversity, but climate (i.e. temperature, precipitation, latitude) and pool characteristics (i.e. depth and surface area) would predict the most variation.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eExplanatory variables included in the final models\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDependent variables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePredictor variables\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePlants\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlpha diversity, gamma diversity, beta diversity, native alpha diversity, exotic alpha diversity, native gamma diversity, exotic gamma diversity, native beta diversity, exotic beta diversity, forb alpha diversity, graminoid alpha diversity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClimate: latitude, average yearly temperature, average yearly precipitation\u003c/p\u003e \u003cp\u003eLand-use: percent cover developed land, percent cover agricultural land\u003c/p\u003e \u003cp\u003ePool characteristics: average pool depth (cm), average pool surface area (km\u003csup\u003e2\u003c/sup\u003e), number of pools sampled at a site\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eInvertebrate\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlpha diversity, gamma diversity, beta diversity, passive disperser alpha diversity, active disperser alpha diversity, passive disperser gamma diversity, active disperser gamma diversity, passive disperser beta diversity, active disperser beta diversity, prey species alpha diversity, predator species alpha diversity.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClimate\u003csup\u003e1\u003c/sup\u003e: latitude\u003c/p\u003e \u003cp\u003eLand-use: percent cover developed land, percent cover agricultural land\u003c/p\u003e \u003cp\u003ePool characteristics\u003csup\u003e2\u003c/sup\u003e: average pool depth (cm), number of pools sampled at a site\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003e1. \u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e\u003cem\u003eAverage yearly temperature and precipitation were excluded from the invertebrate models due to overfit issues\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003e2. \u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003eAverage pool surface area was excluded from the invertebrate models due to missing measurements.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eDiversity was categorized based on plant and invertebrate functional groups (see below) to assess their responses to the independent variables. We predicted that for some taxa (native and exotic plant species), land-use variables would predict the greatest amount of species diversity given the magnitude of disturbance caused by land-use changes. Land-use that causes disturbance can also make it easier for exotic species to be introduced and become established (Hansen and Clevenger \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Other land-uses, such as grazing, can reduce the presence of exotic species and increase native species cover (Marty \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Our aim was to identify which factors best predict diversity to better understand broad scale patterns of plant and invertebrate richness to determine the implications for management and restoration.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePlant and Invertebrate Data\u003c/h2\u003e \u003cp\u003eWe compiled existing vernal pool data sets, including plants (Barbour et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) and aquatic invertebrates (insects and crustaceans) (Kneitel \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) as our response variables. The data sets were collected over the course of many years (2001\u0026ndash;2009 for plants and 2002\u0026ndash;2012 for invertebrates), and individual pool sampling occurred over 1\u0026ndash;2 days (plants) or the accumulation of several samples (approximately five times) over the hydroperiod (invertebrates). Details of sampling can be found in Barbour et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2003\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) for the plants and Kneitel (\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) for aquatic invertebrates (insects and crustaceans). Our objective was to address general patterns of spatial diversity, as they relate to long-term average and variation of climate and general land use datasets (see below).\u003c/p\u003e \u003cp\u003eThe plant data set was collected from pools in 15 of the 17 vernal pool regions in California as defined by Keller-Wolf \u003cem\u003eet al.\u003c/em\u003e (1998) and included northwest Nevada and southern Oregon. In this present study, only data from California pools was used, which included a total of 367 samples from 52 sites across 19 counties in California (Appendix Table\u0026nbsp;1, Appendix Fig.\u0026nbsp;1). Pool variables such as surface area and volume were also recorded (Appendix Table\u0026nbsp;2). Vernal pool plants can be categorized in a variety of functional groups based on their morphology and invasion status. Ecological functional groups are typically used to better understand and predict responses to the environment (Wiggins et al. \u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e1980\u003c/span\u003e, Welborn et al. 1996). Most vernal pool plants are herbaceous, including \u003cem\u003eLasthenia fremontii\u003c/em\u003e and \u003cem\u003eNavarretia leucocephala\u003c/em\u003e (Barbour et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Many of the species are also annuals, but perennial herbs (e.g., \u003cem\u003eEryngium vaseyi, Isoetes orcuttii\u003c/em\u003e) are also present. In addition, many exotic plants can be found in vernal pools (e.g., \u003cem\u003eBromus hordeaceus\u003c/em\u003e, \u003cem\u003eErodium botrys\u003c/em\u003e). The plants vary in the timing of their germination, growth, and flowering phenology during the inundation-desiccation period (Bliss and Zedler \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1998\u003c/span\u003e, Barbour et al \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2007\u003c/span\u003e, Collinge 2011). The diversity of plant species found in vernal pools were categorized by the following functional types: 1) invasive status (exotic or native) and 2) herbaceous groups (forb or graminoid) based on data from calflora.org and from the USDA Plants Database (USDA, NRCS 2024). Forbs are defined as vascular plants without significant woody tissue while graminoids are defined as grasses or grass-like plants, including sedges and rushes (USDA, NRCS 2024). These categories were used to better understand how different taxonomic groups respond to the environment, and whether invasive species respond differently to the environment than native species (Collinge et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2011\u003c/span\u003e, Marty \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGeneralized Linear Models for overall measures of plant diversity. Bold p-values indicate significance at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. The models for alpha diversity and beta diversity models were best fitted using the gamma family which is an inverse link. The signs should be interpreted in the opposite of typical linear models. The model for gamma diversity was best fit using the gaussian family.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResponse variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExplanatory variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChi-square value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePartial R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCoefficient estimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eStandard error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlant alpha diversity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePercent developed land\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.285\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.43 X 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.66 X 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePercent agricultural land\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.868\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.79 X 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.74 X 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eLatitude\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.54 X 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAverage yearly temperature\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAverage yearly precipitation\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.872\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-8.91 X 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.46 X 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eSurface area (km\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.823\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.795\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePool depth (cm)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.677\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-8.76 X 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.23 X 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eNumber of pools sampled\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.742\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.57 X 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.31X 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlant gamma diversity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePercent developed land\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePercent agricultural land\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.936\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eLatitude\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4561.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.951\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.297\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAverage yearly temperature\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e531.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.619\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAverage yearly precipitation\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e148.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.508\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eSurface area (km\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6979.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePool depth (cm)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74.300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.639\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.428\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.449\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eNumber of pools sampled\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8290.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.369\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.409\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.486\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlant beta diversity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePercent developed land\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.270\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePercent agricultural land\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.644\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eLatitude\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.543\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAverage yearly temperature\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.561\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAverage yearly precipitation\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-5.45 X 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eSurface area (km\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.022\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e53.618\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e42.504\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePool depth (cm)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.920\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eNumber of pools sampled\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.603\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.372\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe aquatic invertebrates were primarily insects and crustaceans that were compiled by Kneitel (\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) from vernal pool survey reports submitted to United States Fish and Wildlife Service and from collected data spanning 2005\u0026ndash;2012. A total of 886 samples from 29 sites across 16 counties are represented (Appendix Table\u0026nbsp;1, Appendix Fig.\u0026nbsp;1). Invertebrate taxa (identified from the species to family level) presence/absence was recorded, and pool data included depth for all pools and surface area for some pools (Appendix Table\u0026nbsp;2). Invertebrate species were categorized by the following functional types: 1) predator or prey and 2) active or passive dispersers. Active dispersers are species adapted to move between habitats (e.g., species with wings). Examples of active disperses include the orders Trichoptera (caddisflies) and Plecoptera (stoneflies), families Corixidae (water boatmen) and Hydrophillidae (water scavenger beetles), and some of the top predators are in this category (e.g., Odonata [dragonflies]). Passive dispersers rely on vectors such as wind, water, or other invertebrates to move them between habitats (Kneitel \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and many of these species have life history strategies that include egg cysts to survive periods of drought (Colburn et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Examples of passive dispersers include order Cladocera (water fleas) and Acariformes (water mites) and many are threatened or endangered species (e.g., \u003cem\u003eLepidurus packardi\u003c/em\u003e).\u003c/p\u003e \u003cp\u003eTotal diversity of both plants (species) and invertebrate (taxa) was measured at three spatial scales: alpha (local), beta (turnover), and gamma (regional or site) diversity. These diversity measures provide three glimpses into how diversity is distributed in space (e.g., Leibold et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2004\u003c/span\u003e, Heino \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Alpha diversity (α), or local diversity, was calculated as the average richness of all pools at one site. Alpha diversity of the different functional groups of plants and invertebrates was also calculated. Gamma diversity (γ), or regional diversity, was calculated as the total number of species in all pools across a site. Beta diversity (β), or dissimilarity across local communities, was calculated as dissimilarity: 1 - (α/γ), which ranged from 0 (low dissimilarity among communities) to 1 (high dissimilarity among communities). Both data sets were collected across various years, and the purpose of the analyses was not to specifically identify the direct effects of the independent variables for a given year but to identify general relationships with spatial diversity patterns.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eClimate Data\u003c/h3\u003e\n\u003cp\u003eHistorical climate data from the years 1970 to 2000 were obtained from Worldclim 2.1 at a geographic scale of 10\u0026rsquo; minutes (Fick and Hijmans \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The monthly maximum and minimum temperatures and precipitation were used to calculate yearly average maximum and minimum temperatures and precipitation and the yearly standard deviation of temperature and precipitation maximums and minimums. The goal with these calculations was to quantify the average and variation in climate at each site.\u003c/p\u003e\n\u003ch3\u003eLand-use/Land-cover Data\u003c/h3\u003e\n\u003cp\u003eLand cover data was obtained from the Multi-resolution Land Characteristics Consortium NLCD land cover data for the year 2016 (Dewitz \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, Homer et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This data is in a raster format with a 30 m spatial resolution. Multiple land-use and land cover (LULC) types are represented in this data (Appendix Table\u0026nbsp;3). A circle of 1 km radius around the center of each vernal pool site was created using ArcGIS Pro. The percent cover of each land cover type within each circle surrounding pool sites was calculated using the \u0026ldquo;Tabulate Area\u0026rdquo; tool. All natural land cover types (open water, deciduous forest, evergreen forest, mixed forest, shrub/scrub, grassland/herbaceous, woody wetlands, and emergent herbaceous wetlands) were then removed to better understand the impacts of anthropogenic LULC. Due to high correlation among the variables, the percent cover of the four categories of human developed land (open space, low intensity, medium intensity, and high intensity) were combined into a single variable called \u0026ldquo;developed\u0026rdquo;. The percent cover of the \u0026ldquo;hay/pasture\u0026rdquo; and \u0026ldquo;cultivated crops\u0026rdquo; land-cover types were combined into a single variable called \u0026ldquo;agriculture\u0026rdquo;.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGeneralized Linear Models for graminoid diversity and forb diversity. The forb diversity model was best fit using the gamma family which is an inverse link while the graminoid diversity model was best fit using the gaussian family. Results should be interpreted as in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResponse variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExplanatory variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChi-square value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePartial R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCoefficient estimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eStandard error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGraminoids\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePercent developed land\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePercent agricultural land\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.531\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eLatitude\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAverage yearly temperature\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAverage yearly precipitation\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eSurface area (km\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-246.363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e176.746\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePool depth (cm)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.769\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eNumber of pools sampled\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.746\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eForbs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePercent developed land\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.54 X 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.28 X 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePercent agricultural land\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.890\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.76 X 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.41 X 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eLatitude\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAverage yearly temperature\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAverage yearly precipitation\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-3.73 X 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.54 X 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eSurface area (km\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.852\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePool depth (cm)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.794\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.02 X 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.88 X 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eNumber of pools sampled\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.694\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.21 X 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.80 X 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eData Analysis\u003c/h2\u003e \u003cp\u003eGeneralized linear models (GLM) were used to determine which of the land-use/land-cover, pool, and climate variables were best able to predict the measures of species diversity. Before building the models, some explanatory variables were removed based on various thresholds. Correlations and variance inflation factor (VIF) analysis were used to determine if there was multicollinearity among the explanatory variables. Based on those results, temperature standard deviation and precipitation standard deviation were removed. Due to the smaller sample size of the invertebrate dataset (Appendix Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), average temperature and average precipitation were excluded from the invertebrate models to avoid overfitting. Surface area was also excluded from the invertebrate models due to the number of missing measurements (Appendix Table\u0026nbsp;2). The number of pools in each site was also included in both plant and invertebrate models as a variable because it likely influenced some measures of diversity (such as gamma and beta). The final explanatory variables used in the plant GLM models were: percent cover developed land, percent cover agriculture land, latitude, average yearly temperature, average yearly precipitation, average pool surface area (km\u003csup\u003e2\u003c/sup\u003e), average pool depth (cm), and the number of pools sampled at site (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The explanatory variables used in the invertebrate GLM models were: percent cover developed land, percent cover agricultural land, latitude, average pool average depth (cm), and the number of pools sampled at a site (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Separate GLM models were created for each response variable. Residual plots were used to assess model fit of various families (Gaussian, Gamma, Tweedie). Note that the gamma family is an inverse link so models that were best fit with the gamma family need to be interpreted with this in mind. The family used to fit each model is included in the table captions. Because we could not calculate beta diversity at sites where only one pool was sampled, those sites were excluded from analysis of beta diversity. A total of 22 models were created, 11 models for the plant data and 11 models for the invertebrate data. Statistical analyses were performed using R Statistical Software Version 4.2.1 (R Core Team 2022) in RStudio software v.2022.7.1.554 (R Studio Team 2022), using DHARMa (Hartig 2022), tweedie (Dunn and Smyth \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), glmmTMB (Brooks et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), rsq (Zhang \u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), and car (Fox and Weisberg \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) packages.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003ePlant alpha, beta (dissimilarity), and gamma diversity were explained by multiple variables. The significant predictor variables included latitude, pool surface area, and the number of pools within the site (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Alpha and gamma diversity had positive relationships with latitude. (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Increased number of pools sampled at a site increased beta and gamma diversity. Alpha, gamma, and beta diversity all had negative relationships with pool surface area (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eLatitude, percent developed land, pool surface area, and the number of pools sampled were significant predictors of plant functional groups (Tables\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Forb alpha diversity had a positive relationship with latitude and a negative relationship with pool surface area. Graminoid alpha diversity did not have any significant relationships with the explanatory variables. Native species alpha diversity had a positive relationship with latitude and a negative relationship with pool surface area (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Native species gamma diversity had a positive relationship with the number of pools sampled and with latitude, and a negative relationship with pool surface area and with percent cover of developed land. Native species beta diversity had a positive relationship with the number of pools sampled and a negative relationship with pool surface area. Exotic species alpha diversity had a negative relationship with pool surface area (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) while exotic species gamma and beta diversity had positive relationships with the number of pools sampled.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGeneralized Linear Models for measures of native and exotic plant species diversity. Native beta diversity and exotic beta diversity models were best fitted using the gamma family which is an inverse link. The rest of the models in the table were best fit using the gaussian family. Results should be interpreted as in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResponse variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExplanatory variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChi-square value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePartial R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCoefficient estimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eStandard error\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNative alpha\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePercent developed land\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e219.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePercent agricultural land\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.65 X 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eLatitude\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1373.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.479\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.545\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAverage yearly temperature\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e92.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.680\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAverage yearly precipitation\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.640\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.917\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.083\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eSurface area (km\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e435.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.007\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1241.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e472.400\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePool depth (cm)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.189\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eNumber of pools sampled\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e108.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.276\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.204\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNative Gamma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePercent developed land\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e862.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.036\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.097\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePercent agricultural land\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.689\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.092\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eLatitude\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3600.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.728\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.991\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAverage yearly temperature\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e279.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.475\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.237\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAverage yearly precipitation\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.817\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.151\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eSurface area (km\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3669.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2579.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e859.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePool depth (cm)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.823\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.343\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eNumber of pools sampled\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3335.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.287\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.528\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.371\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNative beta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePercent developed land\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePercent agricultural land\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.948\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eLatitude\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.490\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAverage yearly temperature\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.460\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAverage yearly precipitation\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eSurface area (km\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.029\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e73.997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e52.760\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePool depth (cm)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.507\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eNumber of pools sampled\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.660\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExotic alpha\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePercent developed land\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.883\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePercent agricultural land\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.730\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eLatitude\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.206\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAverage yearly temperature\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.531\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.803\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.257\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAverage yearly precipitation\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eSurface area (km\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.027\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-400.600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e178.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePool depth (cm)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eNumber of pools sampled\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.077\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExotic gamma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePercent developed land\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.876\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePercent agricultural land\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.309\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eLatitude\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.451\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.429\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAverage yearly temperature\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.373\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.475\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.536\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAverage yearly precipitation\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eSurface area (km\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e409.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.073\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePool depth (cm)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.149\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eNumber of pools sampled\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e860.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.356\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.776\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.161\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExotic beta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePercent developed land\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePercent agricultural land\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eLatitude\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAverage yearly temperature\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.839\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAverage yearly precipitation\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.976\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eSurface area (km\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26.209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e44.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePool depth (cm)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eNumber of pools sampled\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.494\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.284\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor aquatic invertebrates, the significant predictor variables of beta (dissimilarity) diversity included pool depth (negative relationship) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), and number of pools sampled (positive relationship; Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Gamma diversity also had a positive relationship with the number of pools sampled while alpha diversity did not have significant relationships with any of the variables.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGeneralized Linear Models for overall measures of invertebrate diversity. Alpha diversity and gamma diversity models were best fitted using the gamma family which is an inverse link. The beta diversity model was best fit using the gaussian family. Results should be interpreted as in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResponse variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExplanatory variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChi-square value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePartial R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCoefficient estimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eStandard error\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlpha\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePercent developed land\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-6.17 X 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.27 X 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePercent agricultural land\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.689\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.46 X 10\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;4\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.66 X 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eLatitude\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.792\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePool depth (cm)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.525\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eNumber of pools sampled\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.562\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.89 X 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.21 X 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGamma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePercent developed land\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.21 X 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.66 X 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePercent agricultural land\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.47 X 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.12 X 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eLatitude\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.705\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-9.74 X 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePool depth (cm)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.329\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-4.80 X 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.47 X 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eNumber of pools sampled\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.011\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2.12 X 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.57 X 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBeta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePercent developed land\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePercent agricultural land\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.16 X 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.927\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-3.77 X 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.22 X 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eLatitude\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.676\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePool depth (cm)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.051\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eNumber of pools sampled\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.016\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.00 X 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSignificant predictors of invertebrate functional groups (i.e. dispersal and trophic level categories) were pool depth and number of pools sampled (Tables\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e and \u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Passive disperser alpha diversity had a positive relationship with pool depth (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), while beta diversity of passive dispersers had a negative relationship with depth (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Gamma diversity of passive dispersers did not have a significant relationship with any of the variables. Active disperser gamma and beta diversity had positive relationships with the number of pools sampled. Active disperser alpha diversity did not have significant relationships with any of the variables. Prey alpha diversity had a positive relationship with pool depth (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) while predator alpha diversity did not have significant relationships with any of the predictor variables.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGeneralized Linear Model models for measures of diversity of active and passive dispersing invertebrates. All models except for passive beta were best fitted using the gamma family which is an inverse link. The passive beta model was best fit using the gaussian family. Results should be interpreted as in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResponse variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExplanatory variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChi-square value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePartial R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCoefficient estimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eStandard error\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epassive alpha\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePercent developed land\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.394\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-8.48 X 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.89 X 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePercent agricultural land\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.615\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.29 X 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.25 X 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eLatitude\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePool depth (cm)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.783\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.016\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eNumber of pools sampled\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.4 X 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.955\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.23 X 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.41 X 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epassive gamma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePercent developed land\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.849\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-9.25 X 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.55 X 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePercent agricultural land\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.90 X 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.56 X 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eLatitude\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.537\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePool depth (cm)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.346\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-8.47 X 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eNumber of pools sampled\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2.68 X 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.86 X 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epassive beta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePercent developed land\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.386\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-9.39 X 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePercent agricultural land\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.81 X 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.848\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-9.80 X 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.98 X 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eLatitude\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePool depth (cm)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.016\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eNumber of pools sampled\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.80 X 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eActive alpha\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePercent developed land\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.383\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-7.97 X 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePercent agricultural land\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.907\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.64 X 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eLatitude\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.505\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePool depth (cm)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.313\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.287\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eNumber of pools sampled\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eActive gamma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePercent developed land\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.854\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-4.58 X 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.03 X 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePercent agricultural land\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.607\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2.58 X 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.47 X 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eLatitude\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.929\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-9.17 X 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePool depth (cm)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.579\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eNumber of pools sampled\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.655\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.026\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-6.58 X 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.12 X 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eActive beta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePercent developed land\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.730\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.91 X 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePercent agricultural land\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.665\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-6.57 X 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eLatitude\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.70 X 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.938\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePool depth (cm)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eNumber of pools sampled\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.014\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGeneralized Linear Model models for measures of diversity for invertebrate functional groups. Both models were best fitted using the gamma family which is an inverse link. Results should be interpreted as in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResponse variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExplanatory variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChi-square value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePartial R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCoefficient estimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eStandard error\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredator alpha\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePercent developed land\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.541\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePercent agricultural land\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 X 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-5.61 X 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eLatitude\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePool depth (cm)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.733\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.15 X 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eNumber of pools sampled\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.380\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrey alpha\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePercent developed land\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-3.70 X 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.07 X 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePercent agricultural land\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.646\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.51 X 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.03 X 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eLatitude\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.594\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePool depth (cm)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.313\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eNumber of pools sampled\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.866\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.91 X 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.15 X 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThe hypothesis that spatial diversity patterns would be explained by average climate and pool variables and LULC was partially supported. Both average climate and pool variables explained many measures of diversity for both plants and invertebrates, but the percent developed land cover had a significant relationship only with plant gamma diversity. The taxonomic resolution of both plants (species level) and invertebrates (species to family level) differed, which could have influenced the observed patterns of diversity responses to the independent variables. Nevertheless, there were also consistencies across the two taxonomic groups.\u003c/p\u003e \u003cp\u003eMost measures of invertebrate diversity did not have significant relationships with the independent variables. Invertebrate beta diversity (total and passive), passive alpha, and prey alpha had significant relationships with the pool characteristics. In contrast, plant species diversity had significant relationships with most of the variables. One explanation for the prevalent plant patterns might be the presence of invasive species. Vernal pools in California usually exist in a grassland landscape dominated by invasive plant species (Keeley and Zedler \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e1998\u003c/span\u003e, Collinge et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2011\u003c/span\u003e, Tang et al. \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). While this current study did not directly look at the correlation between invasive and native plant species, previous studies have found that exotic grasses outcompete native plant species in vernal pools (Boughton et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2011\u003c/span\u003e, Bando et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and their thatch can suppress both plant germination and crustacean emergence (Kneitel et al. 2017). Another possible explanation is that plants and invertebrates interact with the environment differently. Vernal pool plants do not need very much water to germinate and grow (Bliss and Zedler \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1998\u003c/span\u003e, Casanova and Brock \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) whereas most of the invertebrates in this study require certain aquatic conditions before they hatch and complete their life cycle (Brendonck \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1996\u003c/span\u003e, Colburn et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMany measures of plant diversity had positive relationships with latitude. This relationship is uncommon, and generally species diversity decreases as latitude increases because of changes in climate (Willig et al. \u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e2003\u003c/span\u003e, Currie et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2004\u003c/span\u003e, Hillebrand \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). While this reverse latitudinal diversity gradient is unusual, several previous studies have found this pattern in temporary pools with both invertebrate and plant species (Brendonck et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2014\u003c/span\u003e, Kneitel \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, El Madihi et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The reverse latitudinal diversity gradient found in this present study is likely because in California temperature decreases and precipitation increases with increasing latitude. These conditions create larger sized pools and longer hydroperiods, which can result in higher diversity (Kneitel \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this study, there were no significant relationships between latitude and any measure of invertebrate diversity. This differs from Kneitel (\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) who found latitude positively related to alpha diversity and negatively related to beta diversity of invertebrates. Differences may be the result of different statistical approaches, spatial scale of gamma diversity, and newer sites added. However, pool sizes (depth) have also been associated with latitude (Kneitel \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In our study, pool sizes explained patterns in vernal pool invertebrate communities (total and passive beta, passive and prey alpha), which is consistent with the previous results (Kneitel \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, O\u0026rsquo;Brien et al. \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003ePool Variables and Diversity\u003c/h3\u003e\n\u003cp\u003eMeasures of diversity had variable relationships with pool-size characteristics (depth and surface area). Generally, larger pools tended to support higher species diversity, which was found with invertebrates. This result is expected from the species-area relationship that predicts larger habitats (pools) should support greater diversity (MacArthur and Wilson \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e1967\u003c/span\u003e, Kohn and Walsh \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e1994\u003c/span\u003e). This has been found in vernal pools for a variety of taxonomic groups including plants (Holland and Jain 1981, Ebert and Balko \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1987\u003c/span\u003e), crustaceans (King et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e1996\u003c/span\u003e, Ripley and Simovich \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), and invertebrates broadly (Kneitel \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In a vernal pool ecosystem, larger pools tend to have a longer hydroperiod, meaning they are inundated for a longer period. This can allow both short- and long-lived species to complete their life cycles and allow species that occupy similar niches to coexist by allowing for difference in the timing of hatching or development of the species, thus reducing overlap in the adults of the species that occupy similar niches (King et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e1996\u003c/span\u003e, Waterkeyn et al. \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). These effects of large pools are likely to benefit passive dispersers more than active dispersers, and likely explains why, in our study, only the passive dispersers and the prey species had positive relationships with pool size. The negative relationships between invertebrate beta diversity and pool size have been previously demonstrated (Kneitel \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and is likely because larger pools are more likely to be connected to other pools, thus making it easier for passive dispersers to move between the pools making pools more similar and beta diversity lower (Carl and Blumenshine \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Smaller pools tend to be more temporary and less connected which decreases dispersal opportunities. However, this is not always the case for many freshwater ecosystems and taxonomic groups worldwide; indeed, many other measured and unmeasured factors can contribute to diversity (e.g., Oertli et al. \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2002\u003c/span\u003e, Batzer \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2013\u003c/span\u003e, Reindl et al. \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), including habitat structure and soil composition.\u003c/p\u003e \u003cp\u003eIn contrast to invertebrates, we found the opposite relationship between pool size and alpha diversity with several plant groups (forbs, natives, and exotics). There may be several explanations for this seldom seen pattern. First, habitat heterogeneity is generally an explanation for increases in diversity with habitat size, but this is not always the case (Stein et al. \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2014\u003c/span\u003e, Schuler et al. \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). There was no data to assess this, but the decrease of heterogeneity as pool size increases could result in decreased diversity. Habitat heterogeneity can change across spatial and temporal scales, and different taxonomic groups can respond to different types of heterogeneity (Gonzalez-Megias et al. 2007). Second, larger habitat sizes (and increased hydroperiod) may create abiotic conditions that result in increased competitive dominance, species exclusions, or predator effects (Welborn et al. 1996, Winikoff and Finlay \u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Alpha diversity of exotic plant species had a negative relationship with pool size. This is likely explained by the fact that, unlike the native plant species, exotic species are not adapted to inundation (Gerhardt and Collinge \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2007\u003c/span\u003e, Faist and Beals \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). We expect pools with a smaller surface area and shallower depth (representing a shorter inundation period) to have more exotic species, as demonstrated here in the negative relationship between exotic plant species diversity and pool surface area. A shortcoming in our methods included not having the pairing of hydro-regime and diversity of specific pools in a given year. Future studies could examine the relationship between vernal pool size and heterogeneity to better understand the manifold effects on species diversity.\u003c/p\u003e\n\u003ch3\u003eLand-cover and Diversity\u003c/h3\u003e\n\u003cp\u003eThe percent cover of developed land had a significant but weak negative relationship with only plant native gamma diversity, a similar pattern found in many previous studies (Gibbs \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2000\u003c/span\u003e, Galatowitsch et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2000\u003c/span\u003e, Houlahan et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2006\u003c/span\u003e, Tran et al. \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). There are many potential factors that could explain this, including contamination from stormwater runoff (Tran et al. \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), road density altering propagule movement and hydrology (Houlahan et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), soil nutrients associated with increased urbanization such as heavy metals (King and Hovick \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), increased isolation of populations (Ram\u0026iacute;rez-Delgado et al. \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and other habitat matrix changes (Ram\u0026iacute;rez-Delgado et al. \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Additionally, human development that leads to habitat loss and fragmentation can decrease plant species diversity (Gerstner et al. 2014, Zambrano et al. \u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) but this can be influenced by dispersal traits (Schopke et al. 2019). While increased invasive plants are also associated with land use (e.g., L\u0026aacute;zaro-Lobo and Ervin \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Boscutti et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), we did not find an association between land use and invasive plant diversity.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eWe examined land-use/land-cover, pool characteristics, and climate to determine their associations with plant and invertebrate species diversity and functional groups in California vernal pools. Invertebrates had significant relationships with local pool characteristics (pool depth), while plants had significant relationships with local pool characteristics, climate, and land use. This study is an important step in understanding how local and regional environmental conditions, along with human activities, influence species composition in vernal pools. Vernal pool species composition is also known to be affected by a variety of factors, including water chemistry (Rhazi et al. \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2001\u003c/span\u003e, Kido and Kneitel \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and restoration (O\u0026rsquo;Brien et al. \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Moreover, we did not specifically test a specific year\u0026rsquo;s weather pattern, hydro-regime, and emergent patterns of diversity within pools, which could be included in future studies. Many of the relationships in this study appear to be driven by functional groups which highlight their importance when planning restoration or conservation activities.\u003c/p\u003e \u003cp\u003eThe results of this study highlight the manifold factors influencing the various taxonomic groups found in an ecosystem. While the interactions among the explanatory variables were not assessed, this likely has important effects on diversity too. For example, pool size was an important variable, and any factor that influences this, whether that be surrounding land-use encroaching on the pool or changes to hydro-regime due to a changing climate, will affect vernal pool diversity. Thus, by reducing the impacts of climate change and other human activities, we might better manage the imperiled vernal pool ecosystem and associated species.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eACKNOWLEDGEMENTS\u003c/p\u003e\n\u003cp\u003eVernal pools were sampled by JMK habitat under USFWS Permit (#TE192702). The\u003c/p\u003e\n\u003cp\u003eCSUS Provost Research Fellowship (2013-14) and the National Science Foundation (DEB 1354724) provided initial support for collection of the invertebrate data set. CSUS students Vincent Chen and Ryan Castle assisted with the initial organization of these data sets. The plant dataset was collected by Michael Barbour, Ayzik Solomeshch, Jennifer Buck-Diaz, Robert Holland, Carol Witham, Roderick Macdonald, Sandra Starr, and Kristi Lazar. We are grateful for the constructive comments provided by the three reviewers.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eHS and JMK contributed to the study\u0026rsquo;s conception and design. HS, JBD, AKP, TMD and JMK managed data and conducted statistics. HS wrote the first draft of the\u003c/p\u003e\n\u003cp\u003emanuscript, and JBD, AKP, and TMD reviewed and edited early versions. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConflict of interest\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest\u003cem\u003e.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eEthical approval\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe confirm that the manuscript has been read and approved by all named authors and that there are no other persons who satisfied the criteria for authorship but are not listed. We further confirm that all have approved the order of authors listed in the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eData statement\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eData will be made available upon request\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFunding statement\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNational Science Foundation (DEB 1354724)\u003c/p\u003e\n\u003cp\u003eCSUS Provost Research Fellowship (2013-14) and the\u0026nbsp;\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAsselen, Sv., Vermaat, J.E., Janse, J.H. 2013. Drivers of wetland conversion: a global meta-analysis. PLoS ONE 8: e81292. doi:10.1371/journal.pone.0081292.\u003c/li\u003e\n\u003cli\u003eBando, F.M., B.R.S. Figueiredo, D.A. Moi, S.M. Thomaz, T.S. Michelan, J. Garcia-Giron, J. Heino, J. Alahuhta, G.Q. Romero, R.P. Mormul. 2023. Invasion by an exotic grass species homogenizes native freshwater plant communities. Journal of Ecology 00:1-15. DOI: 10.1111/1365-2745.14061\u003c/li\u003e\n\u003cli\u003eBaselga, A., J.M. Lobo, J. Svenning, and M.A. Araujo. 2012. Global patterns in the shape of species geographical ranges reveal range determinants. Journal of Biogeography 39: 760-771.\u003c/li\u003e\n\u003cli\u003eBeja, P. and R. Alcazar. 2003. Conservation of Mediterranean temporary ponds under agricultural intensification: and evaluation using amphibians. Biological Conservation 114: 317-326.\u003c/li\u003e\n\u003cli\u003eBarbour, M., A. Solomeshch, C. Witham, R. Holland, R. MacDonald, S. Cilliers, J.A. Molina, J. Buck, and J. Hillman. 2003. Vernal pool vegetation of California: variation within pools. Madrono 50: 129-146.\u003c/li\u003e\n\u003cli\u003eBarbour, M.G., A.I. Solomeshch, J.J. Buck, R.F. Holland, C.W. Witham, R.L. Macdonald, S.L. Starr, and K.A. Lazar. 2007. Classification, ecological characterization, and presence of listed plant taxa of vernal pool associations in California. U.S. Fish and Wildlife Service, Sacramento, CA. \u003c/li\u003e\n\u003cli\u003eBatzer D.P. 2013. The seemingly intractable ecological responses of invertebrates in North American wetlands: a review. Wetlands 33:1\u0026ndash;15. \u003c/li\u003e\n\u003cli\u003eBatzer, D.P., R. Cooper, and S.A. Wissinger. 2019. Wetland invertebrate ecology. Pages 151-184 \u003cem\u003ein: \u003c/em\u003eEcology of Freshwater and Estuarine Wetlands. University of California Press. https://doi.org/10.1525/9780520959118-008\u003c/li\u003e\n\u003cli\u003eBeever, E.A., L.E. Hall, J. Varner, A.E. Loosen, J.B. Dunham, M.K. Gahl, F.A. Smith, and J.J. Lawler. 2017. Behavioral flexibility as a mechanism for coping with climate change. Frontiers in Ecology and the Environment 15: 299-308.\u003c/li\u003e\n\u003cli\u003eBird, M. S., \u0026amp; J. A. Day. 2014. Wetlands in changed landscapes: the influence of habitat transformation on the physico-chemistry of temporary depression wetlands. Plos one, 9(2), e88935 \u003c/li\u003e\n\u003cli\u003eBliss, S.A. and P.H. Zedler. 1998. The germination process in vernal pools: sensitivity to environmental conditions and effects on community structure. Oecologia 113: 67-73.\u003c/li\u003e\n\u003cli\u003eBrendonck, L. 1996. Diapause, quiescence, hatching requirements: what we can learn from large freshwater branchiopods (Crustacea: Branchiopoda: Anostraca, Notostraca, Conchostraca). Hydrobiologia 320: 85-97.\u003c/li\u003e\n\u003cli\u003eBrendonck, L., M. Jocque, K. Tuytens, B.V. Timms, and B. Vanschoenwinkel. 2014. Hydrologic stability drives both local and regional diversity patterns in rock pool metacommunities. Oikos 124: 741-749.\u003c/li\u003e\n\u003cli\u003eBrooks, M.E., K. Kristensen, K.J. Van Benthem, A. Magnusson, C.W. Berg, A. Nielsen, H.J. Skaug, M. Maechler, and B.M. Bolker. 2017. glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. The R Journal 9: 378-400.\u003c/li\u003e\n\u003cli\u003eBoix, D., J. Kneitel, B.J. Robson, C. Duchet, L. Zuniga, J. Day, S. Gascon, J. Sala, X.D. Quintana, and L. Blaustein. 2016. Invertebrates of freshwater temporary ponds in Mediterranean climates. Pages 141-189 \u003cem\u003ein\u003c/em\u003e: Invertebrates in Freshwater Wetlands. Springer International Publishing. \u003c/li\u003e\n\u003cli\u003eBoscutti, F., F. Lami, E. Pellegrini, M. Buccheri, F. Busato, F. Martini, R. Sibella, M. Sigura and L. Marini. 2022. Urban sprawl facilitates invasions of exotic plants across multiple spatial scales. Biological Invasions 24: 1497-1510.\u003c/li\u003e\n\u003cli\u003eBouahim, S., L. Rhazi, B. Amami, N. Sahib, M. Rhazi, A. Waterkeyn, A. Zouahri, F. Mesleard, S.D. Muller, and P. Grillas. 2010. Impact of grazing on species richness of plant communities in Mediterranean temporary pools (western Morocco). Comptes Rendus Biologies 333: 670-679.\u003c/li\u003e\n\u003cli\u003eBouahim, S., L. Rhazi, B. Amami, A. Waterkeyn, A., M. Rhazi, E.R. Saber, Zouahri, A., M. Van den Broeck, S.D. Muller, L. Brendonck, and P. Grillas. 2014. Unravelling the impact of anthropogenic pressure on plant communities in Mediterranean temporary ponds. Marine and Freshwater Research 65: 918-929.\u003c/li\u003e\n\u003cli\u003eBoughton, E.H., P.F. Quintana-Ascencio, D. Nickerson, and P.J. Bohlen. 2011. Management intensity affects the relationship between non-native and native species in subtropical wetlands. Applied Vegetation Science 14: 210-220.\u003c/li\u003e\n\u003cli\u003eCarl, T. and S. Blumenshine. 2005. Relationships among vernal pool invertebrate assemblages with habitat morphology and distribution. Bios 76: 145-152.\u003c/li\u003e\n\u003cli\u003eCasanova, M.T. and M.A. Brock. 2000. How do depth, duration and frequency of flooding influence the establishment of wetland plant communities? Plant Ecology 147: 237-250.\u003c/li\u003e\n\u003cli\u003eCoelho, M.T.P., Barreto, E., Rangel, T.F. \u003cem\u003eet al.\u003c/em\u003e 2023. The geography of climate and the global patterns of species diversity. Nature 622, 537\u0026ndash;544. https://doi.org/10.1038/s41586-023-06577-5 \u003c/li\u003e\n\u003cli\u003eColburn, E.A., S.C. Weeks, and S.K. Reed. 2007. Diversity and ecology of vernal pool invertebrates. \u003cem\u003eScience and conservation of vernal pools in northeastern North America. \u003c/em\u003eCRC Press, Boca Raton, pp. 105-126.\u003c/li\u003e\n\u003cli\u003eCollinge, S.K., C. Ray, and F. Gerhardt. 2011. Long-term dynamics of biotic and abiotic resistance to exotic species invasion in restored vernal pool plant communities. Ecological Applications 21: 2105-2118.\u003c/li\u003e\n\u003cli\u003eCooper, S.D., P.S. Lake, S. Sabater, J.M. Melack, and J.L. Sabo. 2013. The effects of land use changes on streams and rivers in mediterranean climates. Hydrobiologia 719: 383-425. \u003c/li\u003e\n\u003cli\u003eCurrie, D.J., G.M. Mittelbach, H.V. Cornell, R. Field, J. Guegan, B.A. Hawkins, D.M. Kaufman, J.T. Kerr, T. Oberdorff, E.O\u0026rsquo;Brien, and J.R.G. Turner. 2004. Predictions and tests of climate-based hypotheses of broad-scale variation in taxonomic richness. Ecology Letters 7: 1121-1134.\u003c/li\u003e\n\u003cli\u003eDewitz, J., 2019, National Land Cover Database (NLCD) 2016 Products: U.S. Geological Survey data release, https://doi.org/10.5066/P96HHBIE.\u003c/li\u003e\n\u003cli\u003eDickman, C.R. 1987. Habitat fragmentation and vertebrate species richness in an urban environment. Journal of Applied Ecology 24: 337-351.\u003c/li\u003e\n\u003cli\u003eDudgeon, D., A.H. Arthington, M.O. Gessner, Z. Kawabata, D.J. Knowler, C. Leveque, R.J. Naiman, A. Prieur-Richard, D. Soto, M.L.J. Stiassny, and C.A. Sullivan. 2006. Freshwater biodiversity: importance, threats, status and conservation challenges. Biological Reviews 81: 163-182.\u003c/li\u003e\n\u003cli\u003eDunn, P.K. and G.K. Smyth. 2005. Series evaluation of Tweedie exponential dispersion models. Statistics and Computing 15: 267-280.\u003c/li\u003e\n\u003cli\u003eDuffy, W.G. and S.N. Kahara. 2011. Wetland ecosystem services in California\u0026rsquo;s Central Valley and implications for the Wetland Reserve Program. Ecological Applications 21: 18-30.\u003c/li\u003e\n\u003cli\u003eEbert, T.A. and M.L. Balko. 1987. Temporary pools as islands in space and in time: the biota of vernal pools in San Diego, Southern California, USA. Hydrobiologia 110: 101-123. \u003c/li\u003e\n\u003cli\u003eEl Madihi, M., L. Rhazi, M. V. den Broeck, M. Rhazi, A. Waterkeyn, E. Saber, S. Bouahim, M. Arahou, A. Zouahri, A. Guelmami, S.D. Muller, L. Brendonck, and P. Grillas. 2017. Plant community patterns in Moroccan temporary ponds along latitudinal and anthropogenic disturbance gradients. Plant Ecology and Diversity 10:197-215.\u003c/li\u003e\n\u003cli\u003eEpele, L.B., M.G. Grech, E.A. Williams-Subiza, C. Sternert, K. McLean, H.S. Greig, L. Maltchik, M.M. Pires, M.S. Bird, A. Boissezon, D. Boix, E. Demierre, P.E. Garcia, S. Gascon, M. Jeffries, J.M. Kneitel, O. Loskutova, L.M. Manzo, G\u0026gt; Mataloni, M.C. Mlambo, B. Oertli, J. Sala, E.E. Scheibler, H. Wu, S.A. Wissinger, and D.P. Batzer. 2022. Perils of life on the edge: climatic threats to global patterns of wetland macroinvertebrates. Science of the Total Environment 820: 153052.\u003c/li\u003e\n\u003cli\u003eEpele, L.B. Grech. M.G., Williams-Subiza, E.A., Stenert C., McLean, K., Greig, H.S., Maltchik L., Marques Pires, M., Bird, M.S., Boissezon A., Boix, D., Demierre E., Garcia, P.E., Gascon, S., Jeffries, M., Kneitel, J.M., Loskutova, O., Manzo, L.M., Mataloni, G.M., Mlambo, M.C., Oertli, B., Sala, J., Scheibler, E. E., Wu, H., Wissinger, S.A. \u0026amp; D. P. Batzer. 2024. A global assessment of environmental and climatic controls on wetland macroinvertebrate structure and function. Global Change Biology: https://doi.org/10.1111/gcb.17173. \u003c/li\u003e\n\u003cli\u003eFaist, A.M. and S.C. Beals. 2018. Invasive plant feedbacks promote alternative states in California vernal pools. Restoration Ecology 26: 255-263.\u003c/li\u003e\n\u003cli\u003eFeld, C.K., S. Birk, D. Eme, M. Gerisch, D. Hering, M. Kernan, K. Maileht, U. Mischke, I. Ott, F. Pletterbauer, S. Poikane, J. Salgado, C.D. Sayer, J. van Wichelen, and F. Malard. 2016. Disentangling the effects of land use and geo-climatic factors on diversity in European freshwater ecosystems. Ecological Indicators 60: 71-83.\u003c/li\u003e\n\u003cli\u003eFick, S.E. and R.J. Hijmans, 2017. WorldClim 2: new 1km spatial resolution climate surfaces for global land areas. International Journal of Climatology 37 (12): 4302-4315.\u003c/li\u003e\n\u003cli\u003eFoley, J.A., R. DeFries, G.A. Asner, C. Barford, G. Bonan, S.R. Carpenter, F.S. Chapin, M.T. Coe, G.C. Daily, H.K. Gibbs, J.H. Helkowski, T. Holloway, E.A. Howard, C.J. Kucharik, C. Monfreda, J.A. Patz, I.C. Prentice, N. Ramankutty, and P.K. Snyder. 2005. Global consequences of land use. Science 309: 570-574. \u003c/li\u003e\n\u003cli\u003eFox, J. and S. Weisberg. 2019. An {R} companion to applied regression, Third Edition. Thousand Oaks, California. Sage. \u003c/li\u003e\n\u003cli\u003eGerhardt, F., \u0026amp; Collinge, S. K. 2007. Abiotic constraints eclipse biotic resistance in determining invasibility along experimental vernal pool gradients. Ecological Applications 17 (3): 922-933.Gerstner, K., \u003cu\u003eC.F.\u003c/u\u003e Dormann, A. Stein, \u003cu\u003eA.M.\u003c/u\u003e Manceur and R. Seppelt. 2014. Effects of land use on plant diversity\u0026ndash;A global meta‐analysis. Journal of Applied Ecology 51\u003cem\u003e: \u003c/em\u003e1690-1700.\u003c/li\u003e\n\u003cli\u003eGibbs, J.P. 2000. Wetland loss and biodiversity conservation. Conservation Biology 14: 314-317.\u003c/li\u003e\n\u003cli\u003eGalatowitsch, S.M., D.C. Whited, R. Lehtinen, J. Husveth, and K. Schik. 2000. The vegetation of wet meadows in relation to their land-use. Environmental Monitoring and Assessment 60: 121-144.\u003c/li\u003e\n\u003cli\u003eGerhardt, F. and S.K. Collinge. 2007. Abiotic constraints eclipse biotic resistance in determining invasability along experimental vernal pool gradients. Ecological Applications 17: 922-933.\u003c/li\u003e\n\u003cli\u003eGonzalez, P., R.P. Neilson, J.M. Lenihan, and R.J. Drapek. 2010. Global patterns in the vulnerability of ecosystems to vegetation shifts due to climate change. Global Ecology and Biogeography 19: 755-768.\u003c/li\u003e\n\u003cli\u003eGonz\u0026aacute;lez‐Meg\u0026iacute;as, A., J. Mar\u0026iacute;a G\u0026oacute;mez and F. S\u0026aacute;nchez‐Pi\u0026ntilde;ero. 2007. Diversity‐habitat heterogeneity relationship at different spatial and temporal scales. Ecography 30\u003cem\u003e: \u003c/em\u003e31-41. \u003c/li\u003e\n\u003cli\u003eGuerin, G.R., H. Wen, and A.J. Lowe. 2012. Leaf morphology shift linked to climate change. Biology Letters 8: 882-886.\u003c/li\u003e\n\u003cli\u003eHankins, D.L. 2013. The effects of indigenous prescribed fire on riparian vegetation in central California. Ecological Processes 2:24.\u003c/li\u003e\n\u003cli\u003eHansen, M.J. and A.P. Clevenger. 2005. The influence of disturbance and habitat on the presence of non-native plant species along transport corridors. Biological Conservation 125: 249-259.\u003c/li\u003e\n\u003cli\u003eHeino, J. 2013. The importance of metacommunity ecology for environmental assessment research in the freshwater realm. Biological Reviews 88: 166-178. \u003c/li\u003e\n\u003cli\u003eHillebrand, H. 2004. On the generality of the latitudinal diversity gradient. The American Naturalist 163: 192-211.\u003c/li\u003e\n\u003cli\u003eHof, C., M. Br\u0026auml;ndle, and R. Brandl. 2008. Latitudinal variation of diversity in European freshwater invertebrates is not concordant across habitat types. Global Ecology and Biogeography DOI: 10.1111/j.1466-8238.2008.00394.x\u003c/li\u003e\n\u003cli\u003eHolland, R.F. Talk titled \u003cem\u003eGlimmerings from the Hogwallows\u003c/em\u003e. Presented at the International Conference on Mediterranean Temporary Ponds, Menorca, 5-8 May 2009.\u003c/li\u003e\n\u003cli\u003eHolland, R.F. and S.K. Jain. 2018. Insular biogeography of vernal pools in the central valley of California. The American Naturalist 117: 24-37.\u003c/li\u003e\n\u003cli\u003eHomer, C., J. Dewitz, S. Jin, G. Xian, C. Costello, P. Danielson, L. Gass, M. Funk, J. Wickham, S. Stehman, R. Auch, and K. Riitters. 2020. Conterminous United States land cover change patterns 2001\u0026ndash;2016 from the 2016 National Land Cover Database: ISPRS Journal of Photogrammetry and Remote Sensing, v. 162, p. 184\u0026ndash;199, at https://doi.org/10.1016/j.isprsjprs.2020.02.019\u003c/li\u003e\n\u003cli\u003eHoulahan, J.E., P.A. Keddy, K. Makkay, and C.S. Findlay. 2006. Effects of adjacent land use on wetland species richness and community composition. Society of Wetland Scientists 26: 79-96.\u003c/li\u003e\n\u003cli\u003eIPCC, 2023. Climate Change 2023: Synthesis Report. Contribution of Working Groups I, II and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team, H. Lee and J. Romero (eds.)]. IPCC, Geneva, Switzerland, pp. 35-115, doi: 10.59327/IPCC/AR6-9789291691647\u003c/li\u003e\n\u003cli\u003eJunk, W.J., S. An, C.M. Finlayson, B. Gopal, J. Kvet, S.A. Mitchell, W.J. Mitsch, and R.D. Roberts, 2013. Current state of knowledge regarding the world\u0026rsquo;s wetlands and their future under global climate change: a synthesis. Aquatic Sciences 75: 151-167.\u003c/li\u003e\n\u003cli\u003eKeeler-Wolf, T., D.R. Elam, K. Lewis, and S.A. Flint. 1998. California vernal pool assessment: a preliminary report. Resources Agency, California Department of Fish and Game, Sacramento, CA.\u003c/li\u003e\n\u003cli\u003eKeeley, J.E. and P.H. Zedler. 1998. Characterization and global distribution of vernal pools. Ecology, conservation, and management of vernal pool ecosystems- proceedings from a 1996 conference, California Native Plant Society, Sacramento, CA.\u003c/li\u003e\n\u003cli\u003eKido, R.R. and J.M. Kneitel. 2019. Eutrophication effects differ among functional groups in vernal pool invertebrate communities. Hydrobiologia 848: 1659-1673.\u003c/li\u003e\n\u003cli\u003eKing, C.M. and S.M. Hovick. 2020. Wetland plant community variation across replicate urban to rural gradients: non-native species as both drivers and passengers in systems impacted by anthropogenic land-use. Urban Ecosystems https://doi.org/10.1007/s11252-020-01012-3.\u003c/li\u003e\n\u003cli\u003eKing, J.L., M.A. Simovich, and R.C. Brusca. 1996. Species richness, endemism and ecology of crustacean assemblages in northern California vernal pools. Hydrobiologia 328: 85-116.\u003c/li\u003e\n\u003cli\u003eKinlock, N.L., L. Prowant, E.M. Herstoff \u003cem\u003eet al. \u003c/em\u003e2018. Explaining global variation in the latitudinal diversity gradient: meta-analysis confirms known patterns and uncovers new ones. Global Ecology and Biogeography 27: 125-141.\u003c/li\u003e\n\u003cli\u003eKneitel, J.M. 2016. Climate-driven habitat size determines the latitudinal diversity gradient in temporary ponds. Ecology 97: 961-968.\u003c/li\u003e\n\u003cli\u003eKneitel, J.M. 2018. Occupancy and environmental responses of habitat specialists and generalists depend on dispersal traits. Ecosphere 9:e02143.\u003c/li\u003e\n\u003cli\u003eKohn, D.D. and D.M. Walsh. 1994. Plant species richness- The effect of island size and habitat diversity. Journal of Ecology 82: 367-377.\u003c/li\u003e\n\u003cli\u003eL\u0026aacute;zaro-Lobo, A. and G.N. Ervin, 2021. Native and exotic plant species respond differently to ecosystem characteristics at both local and landscape scales. Biological Invasions 23: 143-156.\u003c/li\u003e\n\u003cli\u003eLeibold, M.A., M. Holyoak, N. Mouquet, P. Amarasekare, J.M. Chase, M.F. Hoopes, R.D. Holt, R.D., J. B. Shurin, R. Law, D. Tilman, and M. Loreau, 2004. The metacommunity concept: a framework for multi‐scale community ecology. Ecology Letters 7: 601-613.\u003c/li\u003e\n\u003cli\u003eMacArthur, R.H. and E.O. Wilson. 1967. The theory of island biogeography. Princeton University Press, Princeton, New Jersey.\u003c/li\u003e\n\u003cli\u003eMarty, J.T. 2005. Effects of cattle grazing on diversity in ephemeral wetlands. Conservation Biology 19: 1626-1632. \u003c/li\u003e\n\u003cli\u003eMarty, J.T. 2015. Loss of biodiversity and hydrologic function in seasonal wetlands persists over 10 years of livestock grazing removal. Restoration Ecology 23: 548-554. \u003c/li\u003e\n\u003cli\u003eMichaels, J.S., K.W. Tate, and V.T. Eviner. 2022. Vernal pool wetlands respond to livestock grazing, exclusion and reintroduction. Journal of Applied Ecology 59: 67-78.\u003c/li\u003e\n\u003cli\u003eMillennium Ecosystem Assessment, 2005. \u003cem\u003eEcosystems and Human Well-Being: Wetlands and Water Synthesis. \u003c/em\u003eWorld Resources Institute, Washington, DC.\u003c/li\u003e\n\u003cli\u003eMittelbach, G.G., D.W. Schemske, H.V. Cornell \u003cem\u003eet al. \u003c/em\u003e2007. Evolution and the latitudinal diversity gradient: speciation, extinction and biogeography. Ecology Letters 10: 315-331.\u003c/li\u003e\n\u003cli\u003eMoomaw, W.R., G.L. Chmura, G.T. Davies, C.M. Finlayson, B.A. Middleton, S.M. Natali, J.E. Perry, N. Roulet, A.E. Sutton-Grier. 2018. Wetlands in a changing climate: science, policy and management. Wetlands 38: 183-205.\u003c/li\u003e\n\u003cli\u003eO\u0026rsquo;Brien, S.M., B.P. Helm, T.M. Davidson, and J.M. Kneitel. 2023. Invertebrate community composition differs between restored and natural vernal pools. Society for Ecological Restoration doi: 10.1111/rec.14080\u003c/li\u003e\n\u003cli\u003eOertli, B., Joye, D.A., Castella, E., Juge, R., Cambin, D. and Lachavanne, J.B., 2002. Does size matter? The relationship between pond area and biodiversity. \u003cem\u003eBiological conservation\u003c/em\u003e, \u003cem\u003e104\u003c/em\u003e(1), pp.59-70.\u003c/li\u003e\n\u003cli\u003ePardini, R., E. Nichols, and T. Puttker. 2017. Biodiversity response to habitat loss and fragmentation. \u003cem\u003eEncyclopedia of the Anthropocene\u003c/em\u003e, 3, pp. 229-239.\u003cem\u003e \u003c/em\u003eElsevier.\u003c/li\u003e\n\u003cli\u003ePires, M.M., P.E. Garcia, L. Maltchik, C. Stenert, L.B. Epele, K.I. McLean, J.M. Kneitel, S. Racey, D.P. Batzer. 2024. Searching for the Achilles heel(s) for maintaining invertebrate biodiversity across complexes of depressional wetlands. Journal of Nature Conservation: https://doi.org/10.1016/j.jnc.2023.126332\u003c/li\u003e\n\u003cli\u003eRahel, F.J. and J.D. Olden. 2007. Assessing the effects of climate change on aquatic invasive species. Conservation Biology 22: 521-533.\u003c/li\u003e\n\u003cli\u003eReddy, K.R., R. DeLaune, and C.B. Craft. 2010. Nutrients in wetlands: implications to water quality under changing climatic conditions. Final Report submitted to U.S. Environmental Protection Agency. EPA Contract No. EP-C-09-001.\u003c/li\u003e\n\u003cli\u003eReindl, S., K.I. McLean, J.M. Kneitel, D.A. Bell, and D.P. Batzer. 2023. Doing the same thing over and over again and getting the same results: assessing variance in wetland invertebrate assemblages. Wetlands 43: 84 https://doi.org/10.1007/s13157-023-01734-y\u003c/li\u003e\n\u003cli\u003eReis, V., V. Hermoso, S.K. Hamilton, D. Ward, E. Fluet-Chouinard, B. Lehner, and S. Linke. 2017. A global assessment of inland wetland conservation status. BioScience 67: 523-533.\u003c/li\u003e\n\u003cli\u003eRenaud, S., J. Michauz, D.N. Schmidt, J. Aguilar, P. Mein, and J. Auffray. 2005. Morphological evolution, ecological diversification and climate change in rodents. Proceedings of Biological Science 272: 609-617.\u003c/li\u003e\n\u003cli\u003eRhazi, L., P. Grillas, A.M. Toure, L.T. Ham. 2001. Impact of land use in catchment and human activities on water, sediment and vegetation of Mediterranean temporary pools. Life Sciences 324: 165-177.\u003c/li\u003e\n\u003cli\u003eRipley, B.J. and M.A. Simovich. 2009. Species richness on islands in time: variation in ephemeral pond crustacean communities in relation to habitat duration and size. Hydrobiologia 617: 181-196.\u003c/li\u003e\n\u003cli\u003eRam\u0026iacute;rez-Delgado, J.P., Di Marco, M., Watson, J.E., Johnson, C.J., Rondinini, C., Corredor Llano, X., Arias, M. and Venter, O. 2022. Matrix condition mediates the effects of habitat fragmentation on species extinction risk. Nature Communications 13: 595.\u003c/li\u003e\n\u003cli\u003eSch\u0026ouml;pke, B., Heinze, J., P\u0026auml;tzig, M. and Heinken, T., 2019. Do dispersal traits of wetland plant species explain tolerance against isolation effects in naturally fragmented habitats?. \u003cem\u003ePlant Ecology\u003c/em\u003e, \u003cem\u003e220\u003c/em\u003e(9), pp.801-815.\u003c/li\u003e\n\u003cli\u003eSchuler, M.S., J.M. Chase, and T.M. Knight. 2017. Habitat size modulates the influence of heterogeneity on species richness patterns in a model zooplankton community. Ecology 98: 1651-1659.\u003c/li\u003e\n\u003cli\u003eSharitz, R.R. and S.C. Pennings. 2019. Development of wetland plant communities. Pages 133-150 \u003cem\u003ein: \u003c/em\u003eEcology of Freshwater and Estuarine Wetlands. University of California Press. https://doi.org/10.1525/9780520959118-007\u003c/li\u003e\n\u003cli\u003eSilver, C.A. and S.M. Vamosi. 2012. Macroinvertebrate community composition of temporary prairie wetlands: a preliminary test of the effect of rotational grazing. Wetlands 32: 185-197.\u003c/li\u003e\n\u003cli\u003eSmith, V.H., G.D. Tilman, and J.C. Nekola. 1999. Eutrophication: impacts of excess nutrient inputs on freshwater, marine, and terrestrial ecosystems. Environmental Pollution 100: 179-196.\u003c/li\u003e\n\u003cli\u003eSpear, S.F. and A. Storfer. 2010. Anthropogenic and natural disturbance lead to differing patterns of gene flow in the Rocky Mountain tailed frog, \u003cem\u003eAscaphus montanus\u003c/em\u003e. Biological Conservation 143: 778-786.\u003c/li\u003e\n\u003cli\u003eSpencer, M., L. Blaustein, S.S. Schwartz, and J.E. Cohen. 1999. Species richness and the proportion of predatory invertebrate species in temporary freshwater pools: relationships with habitat size and permanence. Ecology Letters 2: 157-166.\u003c/li\u003e\n\u003cli\u003eStein, A., K. Gerstner, and H. Kreft. 2014. Environmental heterogeneity as a universal driver of species richness across taxa, biomes and spatial scales. Ecology Letters doi: 10.1111/ele.12277\u003c/li\u003e\n\u003cli\u003eSteinman, A.D., J. Conklin, P.J. Bohlen, and \u003cu\u003eD.G.\u003c/u\u003e Uzarski. 2003. Influence of cattle grazing and pasture land use on macroinvertebrate communities in freshwater wetlands. Wetlands 23\u003cem\u003e: \u003c/em\u003e877-889.\u003c/li\u003e\n\u003cli\u003eTang, J., M. Nolan, C. D\u0026rsquo;Antonio, S.D. Cooper, and L. Stratton. 2023. Reinvasion of restored California vernal pools reveals the importance of long-term restoration planning. Restoration Ecology 31: e13991.Tangen, B.A., M.G. Butler, and M.J. Ell. 2003. Weak correspondence between macroinvertebrate assemblages and land use in prairie pothole region wetlands, USA. Wetlands 23:104\u0026ndash;115 \u003c/li\u003e\n\u003cli\u003eThomas, C.D. 2010. Climate, climate change and range boundaries. Diversity and Distributions 16: 488-495.\u003c/li\u003e\n\u003cli\u003eTornwall, B.M., A.L. Pitt, B.L. Brown, J. Hawley-Howard. And R.F. Baldwin. 2020. Diversity patterns associated with varying dispersal capabilities as a function of spatial and local environmental variables in small wetlands in forested ecosystems. Forests 11: 1146.\u003c/li\u003e\n\u003cli\u003eTran, C.P., R.W. Bode, A.J. Smith, and G.S. Kleppel. 2010. Land-use proximity as a basis for assessing stream water quality in New York State (USA). Ecological Indicators 10: 727-733.\u003c/li\u003e\n\u003cli\u003eUnderwood, E.C., J.H. Viers, K.R. Klausmeyer, R.L. Cox, and M.R. Shaw. 2009. Threats and biodiversity in the mediterranean biome. Diversity and Distributions 15: 188-197.\u003c/li\u003e\n\u003cli\u003eUSDA, NRCS. 2023. The PLANTS database (http://plants.usda.gov). National Plant Data Team, Greensboro, NC USA.\u003c/li\u003e\n\u003cli\u003eU.S. Geological Survey Gap Analysis Program, 20160513, GAP/LANDFIRE National Terrestrial Ecosystems 2011: U.S. Geological Survey, https://doi.org/10.5066/F7ZS2TM0\u003c/li\u003e\n\u003cli\u003eVan den Broeck, M., L. Rhazi, A. Waterkeyn, M. El Madihi, P. Grillas, J.M. Kneitel, and L. Brendonck. 2019. Livestock disturbances in Mediterranean temporary ponds: a mesocosm experiment with sheep manure and simulated trampling. Freshwater Biology 64: 856-869. \u003c/li\u003e\n\u003cli\u003eVanschoenwinkel, B., A. Hulsmans, E. De Roeck, C. De Vries, M. Seaman, and L. Brendonck. 2009. Community structure in temporary freshwater pools: disentangling the effects of habitat size and hydroregime. Freshwater Biology 54: 1487-1500.\u003c/li\u003e\n\u003cli\u003eWarren, R., J. VanDerWal, J. Price, J.A. Welbergen, I. Atkinson, J. Ramirez-Villegas, T.J. Osborn, A. Jarvis, L.P. Shoo, S.E. Williams, and J. Lowe. 2013. Quantifying the benefit of early climate change mitigation in avoiding biodiversity loss. Nature Climate Change DOI: 10.1038/NCLIMATE1887\u003c/li\u003e\n\u003cli\u003eWaterkeyn, A., P. Grillas, B. Vanschoenwinkel, and L. Brendonck. 2008.. Invertebrate community patterns in Mediterranean temporary wetlands along hydroperiod and salinity gradients. Freshwater Biology 53: 1808-1822.\u003c/li\u003e\n\u003cli\u003eWeiskopf, S.R., M.A. Rubenstein, L.G. Corzier, S. Gaichas, R. Griffis, J.E. Halofsky, K.J.W. Hyde, T.L. Morelli, J.T. Morisette, R.C. Munoz, A.J. Pershing, D.L. Peterson, R. Poudel, M.D. Staudinger, A.E. Sutton-Grier, L. Thompson, J. Vose, J.F. Weltzin, and K.P. Whyte. 2020. Climate change effects on biodiversity, ecosystems, ecosystem services, and natural resource management in the United States. Science of the Total Environment 733: 137782.\u003c/li\u003e\n\u003cli\u003eWellborn, G.A., D.K. Skelly, and E.E. Werner. 1996. Mechanisms creating community structure across a freshwater habitat gradient. Annual Review of Ecology and Systematics 27: 337-363.\u003c/li\u003e\n\u003cli\u003eWiggins, G. B., R. J. Mackay, and I. M. Smith. 1980. Evolutionary and ecological strategies of invertebrates in annual temporary pools. Archiv fur Hydrobiologie 58(Suppl.): 97\u0026ndash;206.\u003c/li\u003e\n\u003cli\u003eWillig, M.R., D.M. Kaufman, R.D. Stevens. 2003. Latitudinal gradients of biodiversity: pattern, process, scale, and synthesis. Annual Review of Ecology, Evolution, and Systematics 34: 273-309.\u003c/li\u003e\n\u003cli\u003eWilson, T.S., Matchett, E., Byrd, K.B., Conlisk, E., Reiter, M.E., Wallace, C., Flint, L.E., Flint, A.L., Joyce, B. and Moritsch, M.M., 2022. Climate and land change impacts on future managed wetland habitat: a case study from California\u0026rsquo;s Central Valley. \u003cem\u003eLandscape Ecology\u003c/em\u003e 37: 1-21.\u003c/li\u003e\n\u003cli\u003eWinikoff, S. G., and J. C. Finlay. 2023. Water-quality outcomes of wetland restoration depend on hydroperiod rather than restoration strategy. Freshwater Science 42 (1): 70-87.\u003c/li\u003e\n\u003cli\u003eZacharias, I. and M. Zamparas. 2010. Mediterranean temporary ponds. A disappearing ecosystem. Biodiversity Conservation 19: 3827-3834.\u003c/li\u003e\n\u003cli\u003eZambrano, J., Garzon-Lopez, C.X., Yeager, L., Fortunel, C., Cordeiro, N.J. and Beckman, N.G., 2019. The effects of habitat loss and fragmentation on plant functional traits and functional diversity: what do we know so far?. \u003cem\u003eOecologia\u003c/em\u003e, \u003cem\u003e191\u003c/em\u003e(3), pp.505-518. \u003c/li\u003e\n\u003cli\u003eZedler, J.B. and S. Kercher. 2004. Causes and consequences of invasive plants in wetlands: opportunities, opportunities, and outcomes. Critical Reviews in Plant Sciences 23: 431-452.\u003c/li\u003e\n\u003cli\u003eZedler, J.B. and S. Kercher. 2005. Wetland resources: status, trends, ecosystem services, and restorability. Annual Review of Environmental Resources 30: 39-74.\u003c/li\u003e\n\u003cli\u003eZhang, D. 2024. _rsq: R-squared and related measures_. R package version 2.7. https://CRAN.R-project.org/package=rsq \u003c/li\u003e\n\u003cli\u003eZimova, M., L.S. Mills, and J.J. Nowak. 2016. High fitness costs of climate change-induced camouflage mismatch. Ecology Letters 19: 299-307.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"aquatic-sciences","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"aqsc","sideBox":"Learn more about [Aquatic Sciences](http://link.springer.com/journal/27)","snPcode":"27","submissionUrl":"https://submission.nature.com/new-submission/27/3","title":"Aquatic Sciences","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Functional groups, Invertebrates, Latitude, Plants, Seasonal wetlands, Species diversity","lastPublishedDoi":"10.21203/rs.3.rs-9097717/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9097717/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBiodiversity at different spatial scales is affected by many processes, including habitat size, climate, and human activities. Vernal pools are seasonal freshwater wetlands found throughout California\u0026rsquo;s Central Valley. They support high levels of endemism and only 10% of this habitat remains in California. This study used two previously collected vernal pool plant and invertebrate datasets to understand how land-use, climate, and pool size influence diversity of vernal pool taxonomic and functional groups at local, regional, and biogeographic scales. Plant richness generally increased with latitude and decreased with measures of pool size while invertebrate richness generally increased with measures of pool size. Native plant gamma diversity was the only factor with a negative association with land use. Latitude, climate, pool characteristics, and land-use all contributed to richness in at least one of the functional groups. This study highlights how diversity of plant and invertebrate functional groups responds to factors at different spatial scales, which should also be considered in restoration and management.\u003c/p\u003e","manuscriptTitle":"Climate, Habitat and Land-use Effects on California Vernal Pool Communities","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-19 13:22:52","doi":"10.21203/rs.3.rs-9097717/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-08T16:47:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"335172465105381599608679661231809798462","date":"2026-04-17T15:23:16+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-17T10:49:23+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-14T12:52:53+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-13T06:00:29+00:00","index":"","fulltext":""},{"type":"submitted","content":"Aquatic Sciences","date":"2026-03-11T19:26:46+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"aquatic-sciences","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"aqsc","sideBox":"Learn more about [Aquatic Sciences](http://link.springer.com/journal/27)","snPcode":"27","submissionUrl":"https://submission.nature.com/new-submission/27/3","title":"Aquatic Sciences","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"f24602e5-154e-4d15-a298-56f31a237e93","owner":[],"postedDate":"March 19th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-08T16:47:09+00:00","index":34,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-19T13:22:52+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-19 13:22:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9097717","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9097717","identity":"rs-9097717","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}