Urbanization changes the richness and homogenizes fungal and invertebrate communities in California

Urbanization changes the richness and homogenizes fungal and invertebrate communities in California | 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 Urbanization changes the richness and homogenizes fungal and invertebrate communities in California Ariel Simons, Austin Baker, Christopher Timlin-Broussard, Avicka Willis, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8663064/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract A large proportion of nutrient cycling and ecosystem services are supported by the activities of fungal and invertebrate communities. Though these communities have been comparatively understudied compared to more charismatic groups such as birds and mammals, there is a growing body of evidence that the structure of these communities is also impacted by urbanization. Here we analyzed species occurrences derived from environmental DNA (eDNA), taken from over 20,000 samples in California, to investigate patterns of diversity for a variety of fungal and invertebrate orders. We investigated their differences in both taxonomic richness and turnover by comparing communities sampled in urban and non-urban areas. We found taxonomic richness was significantly lower within fungal orders sampled in urban areas, and most invertebrate orders displayed a similar pattern. Both invertebrate and fungal communities were found to have undergone a significant level of biotic homogenization in urban areas. We demonstrated that the composition of fungal and invertebrate communities, classified using eDNA, is significantly affected by the process of urbanization. environmental DNA eDNA urban biodiversity invertebrate diversity fungal diversity biotic homogenization Figures Figure 1 Figure 2 Introduction Humanity has recently become a predominantly urban species, with the geographic scope of urban land cover expected to continue its rapid expansion over the coming century (McDonald, 2008 ; Sun et al., 2020 ). Urbanization has been found to significantly shape ecological conditions ranging from local climate (Zhang et al., 2021 ) to patterns of biodiversity (Sidemo-Holm et al., 2022 ). These landscape modifications, and their ecological impacts, are significant enough that urban areas can be considered a distinct class of biomes (Fleming and Bateman, 2018 ; Teixeira and Fernandes, 2020 ), containing their own unique ecosystems (Avolio et al., 2020 ; de Barros Ruas et al., 2022 ). Changes in ecological conditions associated with urbanization impact human well-being (Marselle et al., 2021 ; Pataki et al., 2021 ). Urban areas have also become increasingly interconnected through networks of trade and resource extraction, affecting biodiversity at large (Spotswood et al., 2021 ; Li et al., 2022 ), and driving interest in studying urban areas as ecological factors in their own right (Montero, 2020 ; Uchida et al., 2021 ). The rise in the scale and intensity of these anthropogenic stressors, and their subsequent impacts on ecosystems, make the need for tracking biodiversity in urban environments more pressing (Xu et al. 2021 ). This need for monitoring biodiversity covers everything ranging from establishing baselines (Linares, 2022 ), monitoring spatiotemporal patterns (Magurran et al., 2010 ), determining extinction risks (Raimondo et al., 2023 ), and identifying predictors of community composition (Stupariu et al., 2022 ). With traditionally understudied groups, in particular fungi and invertebrates, there are significant gaps in our monitoring efforts (Hochkirch et al., 2021 ; Niskanen et al., 2023 ). Despite their ecological importance, both fungal and invertebrate communities are less likely to be studied than a variety of plant and animal groups because they are hyperdiverse groups, and are more difficult to track and distinguish visually and morphologically (Frøslev et al., 2019 ; Lücking et al., 2020 ; Hending, 2024 ). However, the rapid growth of environmental DNA (eDNA) allows us to better track patterns and changes in their biodiversity. Fragments of eDNA can be obtained from a variety of substrates, such as sediment or water, and can complement current biomonitoring efforts with the simultaneous identification of thousands of species (Stat et al., 2019 ; Lin et al., 2021 ; Nørgaard et al., 2021 ), including many fungal or invertebrate taxa which may otherwise go unnoticed (Shirouzu et al., 2020 ; van der Heyde et al., 2023 ). eDNA monitoring can complement individual based sampling for both fungal and invertebrate communities and can enable greater taxonomic resolution due to the large amount of identified dark taxa (Kirse et al., 2021 ; Tordoni et al., 2021 ; Suren et al., 2024 ). This study investigates the role of urban areas in shaping patterns of biodiversity for both fungal and invertebrate communities in California, as classified using eDNA. We focus on California as it covers a diverse range of environmental conditions and biological communities (Myers et al., 2000 ), including biodiversity hotspots (Calsbeek et al., 2003 ). Additionally, California is of particular interest in studying the effects of urbanization on ecosystems as it is heavily urbanized (McDonald et al., 2013 ; Gillespie et al., 2018 ), and recently so (Harrison et al., 2024 ). Though there is growing interest in studying the role of urbanization in shaping ecosystems, there are significant taxonomic biases in the groups being studied; with birds being the most overrepresented by far (Lokatis and Jeschke, 2022 ). Conversely, both fungal and invertebrate communities represent large gaps in studies of urbanization and ecology (Hochkirch et al., 2021 ), which are only recently being addressed (Lewthwaite et al., 2024 ). Given the general importance of both fungal (Bahram and Netherway, 2022 ; Seena et al., 2023 ) and invertebrate (Griffiths et al., 2021 ; Mermillod-Blondin et al., 2023 ) communities in shaping ecosystems, this is a gap which needs to be addressed to better understand the effects of urbanization on these taxa and ecosystems as a whole. Given the historically recent decline in global biodiversity (Pereira et al., 2024 ), addressing this gap is of particular urgency as the ecological impacts of anthropogenic stressors can often be tracked using the compositions of both fungal (Zaghloul et al., 2020 ; Galitskaya et al., 2021 ) and invertebrate communities (Borges et al., 2021 ; Sumudumali and Jayawardana, 2021 ). Considering the presence of highly diverse, but understudied, biological communities present in landscapes containing recently developed urban areas, we propose to investigate the following questions: Is urbanization shaping patterns of fungal or invertebrate richness in California? Specifically, is there a significant difference in the richness of fungal and invertebrate communities sampled via eDNA between urban and non-urban areas, and if so what environmental factors may be driving these differences? Is urbanization shaping the compositions of fungal or invertebrate communities in California? Are the composition of biological communities significantly distinct between urban and non-urban areas, and, if so, what environmental factors may be driving these differences? Methods Obtaining biodiversity data The biodiversity data used in this study are derived from all samples with associated sequence data in the Global Biodiversity Information Facility (GBIF), gathered in California during the period 2000 through 2024 (GBIF, 2025). This initial data set contains 1,368,210 unique occurrences collected at 32,313 unique points in space and time, hereafter referred to as samples. We filtered this data set to only contain occurrences where the taxonomy could be resolved to species, and which could be placed within a list of either fungal or invertebrate phyla. The list of fungal phyla includes: Ascomycota, Basidiomycota, Entorrhizomycetes, Blastocladiomycota, Chytridiomycota, Cryptomycota, Microsporidia, Mucoromycota, Nephridiophaga, Olpidiomycota, Sanchytriomycota, and Zoopagomycota. The list of invertebrate phyla includes: Platyhelminthes, Nemertea, Rotifera, Gastrotricha, Acanthocephala, Nematoda, Nematomorpha, Priapulida, Kinorhyncha, Loricifera, Entoprocta, Cycliophora, Gnathostomulida, Micrognathozoa, Chaetognatha, Hemichordata, Bryozoa, Brachiopoda, Phoronida, Annelida, Mollusca, and Arthropoda. This filtering produced a set of 252,535 species-level occurrences, found across 22,782 samples. The filtered set of occurrences included 35,527 associated with fungal species, and 217,008 associated with invertebrate species. These occurrences were then assembled into phyloseq objects using the function phyloseq within the R package phyloseq v1.38.0 (McMurdie and Holmes, 2013 ). Defining urban and non-urban samples and taxa The boundaries used to define California urban areas were generated by first downloading the shapefiles for 2023 United States Metropolitan Statistical Areas (US Census Bureau, 2023 ) and the 2016 California boundaries (US Census Bureau, 2016 ). While samples were collected between the period 2000 to 2024 (Table S1 ), most were collected towards the end of this period with the median sample year being 2016 for invertebrate species and 2023 for fungal species. The boundaries for the Metropolitan Statistical Areas (MSAs) were clipped to California using the function st_intersection in the R package sf v1.0.9 (Pebesma, 2018 ). Samples were then designated as urban if their coordinates overlapped California urban areas and non-urban if they did not, which was determined using the sf function st_join . Hereafter we refer to the urban versus non-urban status of a sample as its ‘area category’. While there were sample locations which did shift their urban versus non-urban status over the period 2000–2023 using the 2000 United States Metropolitan Statistical Area boundaries (US Census Bureau, 2000 ), we found these status values to be largely unchanged (Table 1 ). Table 1 The urban versus non-urban status for eDNA samples 2000 versus 2023. Community 2000 Urban boundaries 2023 Urban boundaries Fungi - Urban 4229 3765 Fungi - NonUrban 18553 19017 Invertebrates - Urban 4229 3765 Invertebrates - NonUrban 18553 19017 Testing factors influencing urban and non-urban taxonomic richness For all decontaminated and filtered samples taxonomic richness was calculated as Chao-1 alpha diversity (Chao, 1984 ) via the phyloseq function plot_richness . We also calculated iChao1 (Chiu et al., 2014 ) and Chao1-bc (Chao et al., 2016 ) using the function ChaoRichness within the R package SpadeR v0.1.1 (Chao et al., 2016 ). Using the function chart.Correlation in the R package PerformanceAnalytics v2.0.4 (Carl et al., 2010 ) we found all richness metrics to be highly collinear with Chao-1 (Fig. 1 ). To test if taxonomic richness is significantly affected by area category or sampling year, we ran linear models of taxonomic richness as a function of area category, sampling year, and their interaction for all available fungal and invertebrate orders. To then test if taxonomic richness of orders tended to be higher or lower in urban environments, we then ran linear models of taxonomic richness as a function of area category through the function emmeans in the R package emmeans v1.10.5 (Lenth, 2021 ). The output from emmeans was then further analyzed using the emmeans function summary . Mapping taxonomic richness Using the st_as_sf function sf , a set of spatial points objects were constructed from data frames containing the location and taxonomic richness of each sample. These points were then colored and sized by their taxonomic richness values, along with shapefiles representing the boundaries of urban areas in 2023, using the R packages leaflet (Cheng et al., 2019 ), leafletlegend (Roh, 2024 ), and ggplot2 (Wickham and Wickham, 2016 ). Testing factors influencing urban and non-urban beta diversity To test if beta diversity varies significantly based on area category and sampling year we first generated Chao beta diversity distance matrices using the distance function in phyloseq on all fungal or invertebrate samples. We then ran a PERMANOVA, with 999 permutations, on these distance matrices with area category and sampling year as covariates using the function adonis2 in the R package vegan v2.6.4 (Oksanen et al., 2022 ). In order to test if beta diversity is greater in urban or non-urban areas, we calculated the dispersion of these beta diversity matrices using the vegan function betadisper , with the parameter bias.adjust set to true in order to correct for unequal sample sizes between groups. A series of multiple comparisons were then run on these dispersion outputs using Tukey HSD tests and the stats function TukeyHSD . Assessing local contributions to beta diversity To compare how much each sample contributes to the unique fungal or invertebrate communities, we calculated the local contribution to beta diversity (LCBD). This was done using the function LCBD.comp from the R package adespatial v0.3.21 (Dray et al., 2018 ), with fungal or invertebrate Chao beta diversity distance matrices used as input. We then created linear models of LCBD values as a function of area category, and tested if LCBD values for urban and non-urban samples were significantly different using the function summary . Results Lower richness for urban fungal and invertebrate communities For the data used in this study, most of the fungal and invertebrate species were found to be exclusive to non-urban over urban areas, with approximately twice as many unique invertebrate species recorded as fungal ones (Table 2 ). Table 2 Number of fungal and invertebrate species, and the number which are exclusive to urban or non-urban areas. Community Number of species Number of exclusively urban species Number of exclusively non-urban species Fungi 2760 183 2102 Invertebrates 5988 421 4135 We also found evidence of urbanization significantly influencing richness for both fungal and invertebrate communities in non-urban areas, with all fungal orders and most invertebrate orders containing greater richness in non-urban samples than urban ones (Table 3 ). Table 3 Pairwise comparisons of Chao-1 alpha diversity of non-urban versus urban samples for fungal and invertebrate orders (Significant results, p < 0.05, only). Positive EMM values indicate a greater alpha diversity in non-urban samples. contrast Estimated marginal mean (EMM) Standard error (SE) Degrees of freedom t ratio order Fungi 1.64 0.20 100 8.29 Eurotiales Fungi 1.14 0.28 60 4.13 Pleosporales Fungi 1.42 0.44 50 3.19 Chaetothyriales Fungi 0.95 0.36 310 2.65 Helotiales Fungi 0.51 0.20 316 2.61 Hypocreales Fungi 0.71 0.31 113 2.27 Thelephorales Fungi 0.37 0.16 42 2.31 Capnodiales Fungi 0.26 0.12 6854 2.20 Agaricales Fungi 0.23 0.11 801 2.11 Russulales Invertebrates 0.69 9.06 x 10 − 17 2 7.65 x 10 15 Sarcoptiformes Invertebrates 1.10 3.36 x 10 − 16 2 3.27 x 10 15 Polydesmida Invertebrates 0.85 0.09 2013 9.49 Hymenoptera Invertebrates 0.51 0.09 2126 5.96 Diptera Invertebrates 0.44 0.08 3283 5.27 Lepidoptera Invertebrates -0.59 0.12 28 -4.87 Stylommatophora Invertebrates -0.19 0.05 374 -4.02 Entomobryomorpha Invertebrates 0.29 0.08 1466 3.63 Hemiptera Invertebrates 0.72 0.21 63 3.44 Amphipoda Invertebrates 0.64 0.25 71 2.56 Decapoda Invertebrates 0.52 0.22 497 2.34 Araneae Invertebrates 0.17 0.08 482 2.10 Thysanoptera Invertebrates -0.59 0.29 33 -2.07 Trochida Geographic trends in taxonomic richness In mapping the taxonomic richness of fungi we tended to find its highest levels along the Sierra Nevada mountain range, while most of the sampled richness is relatively low elsewhere (Fig. 2 A). For invertebrates we found sampled locations with a mix of high and low richness in most locations, except for most of the southern half of the Central Valley and the Mojave desert, which were largely undersampled (Fig. 2 B). Consistent lower richness of urban fungal communities With the available data we found, we could build linear models of taxonomic richness as a function of urban/non-urban category and sample year for 74 fungal and invertebrate orders. From this initial list we found that 41 orders have significant variation with both urban/non-urban area category and sample year (Table S2). Urban fungal communities were found to have consistently lower richness than their non-urban counterparts, however there was no consistent pattern with regards to this urban/non-urban split over time for invertebrate communities (Table 4 ). Table 4 Pairwise comparisons of Chao-1 alpha diversity of non-urban versus urban samples for fungi and invertebrates on an annual basis (Significant results, p < 0.05, only). Positive EMM values indicate a greater alpha diversity in non-urban samples. Estimated marginal mean (EMM) Standard error (SE) Degrees of freedom t ratio year type 3.22 0.32 363 10.07 2022 fungi 2.72 0.60 41 4.54 2016 fungi 2.94 0.80 124 3.68 2018 fungi 2.33 0.66 184 3.55 2019 fungi 0.35 0.14 32 2.51 2017 fungi 1.51 0.64 77 2.36 2012 fungi 1.53 0.70 47 2.20 2015 fungi -2.66 0.15 227 -17.47 2015 invertebrates -1.90 0.17 313 -10.96 2017 invertebrates 2.34 0.56 286 4.22 2021 invertebrates 1.53 0.38 248 4.08 2020 invertebrates -0.82 0.29 125 -2.83 2001 invertebrates -0.57 0.29 365 -1.98 2016 invertebrates Homogenization of urban fungal and invertebrate communities We found that both fungal and invertebrate communities tended to be more homogenized (lower beta diversity) in urban than non-urban areas, with beta diversity varying significantly more for non-urban as opposed to urban samples for all fungal orders and most invertebrate ones (Table S3). For both communities we also found urban, as opposed to non-urban, samples tended to contribute more to overall beta diversity (Table S4). Temporal trends in beta diversity We found 82 orders of fungi and invertebrates with enough data to build linear models of beta diversity as a function of urban/non-urban category and sample year. Of this initial list, we found that 67 orders have significant variation with both urban/non-urban category and sample year (Table S5). We also found that both fungal and invertebrate communities tended to be more homogenized (lower beta diversity) in urban than non-urban areas on a year-by-year basis (Table 5 ). Table 5 Difference in mean distance to median Chao beta diversity for non-urban versus urban samples for fungi and invertebrates split by year. Note: Only results with adjusted significance values of less than 0.05 are included. Difference in mean distance to median beta diversity (Urban - Non-urban) year type Number Non-Urban Samples Number Urban Samples -4.23 x 10 − 4 2023 fungi 4867 369 -8.10 x 10 − 4 2024 fungi 3876 533 -3.53 x 10 − 3 2020 fungi 132 84 -2.66 x 10 − 2 2009 invertebrates 334 40 -3.10 x 10 − 2 2020 invertebrates 185 65 -4.80 x 10 − 3 2010 invertebrates 313 58 -7.64 x 10 − 3 2005 invertebrates 187 121 -1.27 x 10 − 2 2016 invertebrates 170 197 -3.60 x 10 − 3 2012 invertebrates 264 56 -3.76 x 10 − 2 2015 invertebrates 95 134 -3.10 x 10 − 2 2014 invertebrates 117 141 -7.36 x 10 − 3 2004 invertebrates 183 70 -1.10 x 10 − 2 2022 invertebrates 329 25 -5.39 x 10 − 3 2007 invertebrates 275 18 -5.89 x 10 − 2 2002 invertebrates 98 11 8.11 x 10 − 3 2006 invertebrates 244 101 Discussion The taxonomic richness of fungal and invertebrate communities were found to both be significantly impacted by urban areas, albeit with distinct trends between these communities. Both communities contained relatively few taxa which were exclusive to urban areas in California, which indicates that most of the taxa detected in urban areas in this study are urban-tolerant rather than urban-exclusive. This may reflect prior observations of urban environments being preferential to the spread of generalist taxa, both with invertebrate (Diamond et al., 2018 ; Callaghan et al., 2021 ) and fungal communities (Abrego et al., 2020 ; Yiallouris et al., 2024 ). For both communities we found the highest levels of taxonomic richness to be found along the Sierra Nevada mountain range in the northern half of the state. This area is relatively cool, wet, and undisturbed by changes in land cover associated with urbanization or agriculture. This area also largely corresponds to relatively diverse plant communities (Kling et al., 2019 ) and the potential distribution of mixed conifer forests in California, in the absence of human activities (Fenn et al., 2010 ). This spatial pattern in diversity appears to have an ecological basis, as evidence indicates significant correlations between the diversity of plant and fungal (Kivlin and Hawkes, 2011 ; Gao et al., 2013 ; Nguyen et al., 2016 ), as well as plant and invertebrate (Parkhurst et al., 2022 ; Wan et al., 2022 ), communities. With the exception of members of Stylommatophora, an order of terrestrial gastropods, and Entomobryomorpha, an order of springtails, we found the taxonomic richness of urban invertebrates to be generally lower than their non-urban counterparts. Such urban versus non-urban patterns of invertebrate diversity have been observed in a variety of studies (Simons et al., 2019 ; Piano et al., 2020 ; Ji et al., 2022 ; Kotze et al., 2022 ). However, when looking at invertebrate richness as a whole over time we do not see a consistent split between urban and non-urban communities. Some years urban invertebrate communities appear to have significantly lower richness than their non-urban counterparts, and some years the opposite is observed. These variations in part may be driven by differences in sampling substrates and locations between urban and non-urban samples on a year by year basis. For example, invertebrate eDNA sampled from streams running through urban environments may be capturing eDNA from across the entirety of the upstream extent of the watershed (Deiner et al., 2016 ). There is significant variability in the distance of eDNA transport along streams (Barnes and Turner, 2016 ), but readily detectable amounts have been found at distances of tens of kilometers from the release location (Deiner and Altermatt, 2014 ; Pont et al., 2018 ). However, for some orders, elevated taxonomic richness of urban invertebrate communities may sometimes reflect biological reality (Tournayre et al., 2025 ). Urban areas are the hubs of transportation networks, and they may have higher invertebrate diversity as a result of elevated rates of species introductions, which are conducive to the successful spread of generalist taxa (Campos et al., 2024 ). Excess resources available in urban areas may also be a factor in elevating urban invertebrate richness (Yao et al., 2023 ), especially given prior observations of urban irrigation supporting the growth of invertebrate communities (Ge et al., 2019 ; Szabó et al., 2023 ). With fungal communities we found taxonomic richness to be significantly and consistently lower in urban as opposed to non-urban areas. Similar declines in fungal diversity have been associated with urbanization in the past(Abrego et al., 2020 ). These declines have been connected with disturbances to plant communities and their fungal symbionts due to land-use changes associated with urbanization (Epp Schmidt et al., 2017 ; Delgado-Baquerizo et al., 2021 ). Changes in air temperature and rainfall, both influenced by the urban heat-island effect, have been found to be important drivers of fungal taxonomic richness (Delgado-Baquerizo et al., 2021 ; Gómez-Hernández et al., 2021 ). In terms of beta diversity, for both fungal and invertebrate communities we found significant evidence of urbanization influencing beta diversity. This result reflects prior evidence of a significant role for urbanization in shaping beta diversity for both invertebrate (Braschler et al., 2020 ) and fungal (Epp Schmidt et al., 2017 ; Rusterholz and Baur, 2023 ) communities. However, as with other ecological studies using eDNA, the strength and significance of this potential relationship varies by the substrates from which eDNA are sampled (Koziol et al., 2019 ; Zhao and Andermann, 2024 ). We found significant evidence of biotic homogenization in both fungal and invertebrate communities in association with urbanization. This is consistent with prior observations of urbanization homogenizing a range of biological communities, ranging from birds to plants, across a variety of habitats globally (McKinney and Lockwood, 1999 ; McKinney, 2006 ; Carlon and Dominoni, 2024 ). Furthermore, as this study utilizes thousands of eDNA samples, it greatly enhances the geographic scale and taxonomic depth relative to prior studies of invertebrate (Knop, 2016 ; Piano et al., 2020 ; Liu et al., 2022 ) and fungal (Epp Schmidt et al., 2017 ; Delgado-Baquerizo et al., 2021 ) communities becoming more homogenized as a result of urbanization. There are likely additional factors not explicitly measured in this study that may further account for the observed variations in beta diversity. The observed homogenization of urban biological communities may relate to prior land conversion associated with urbanization. Land conversion can influence both local climate, as well as types of soil available which can result in changes in beta diversity for both invertebrate (Liu et al., 2022 ; Montràs-Janer et al., 2024 ) and fungal (Epp Schmidt et al., 2017 ; Delgado-Baquerizo et al., 2021 ; Mikryukov et al., 2023 ) communities. Urbanization has led to a great increase in the transport of soil. Soil transportation can result in both the transport of introduced species, as well as the homogenization of soil communities, thereby reducing beta diversity (LaSorte et al., 2014 ; Blouin et al., 2019 ; Wang et al., 2021 ). However, these results are derived from a wide variety of sampling efforts across California and not a single systematic effort. As a result, we found strong skews in how evenly distributed eDNA samples were gathered across California. For both invertebrate and fungal communities there have been relatively few eDNA samples gathered from the southern half of the Central Valley or the Mojave desert. These findings strongly suggest the need for more extensive sampling, from communities in regions such as the Mojave Desert or the northern portion of the Central Valley, in order to better capture a more representative picture of fungal and invertebrate diversity in California. Limitations While we used eDNA to better characterize understudied patterns of invertebrate and fungal diversity classified via morphotaxonomy, we do recognize that the use of eDNA can introduce its own biases due to differences in sampling substrates and choice of primers. Commonly used sampling substrates (e.g. water, sediment, decomposing plant material) will all produce a different picture of biodiversity (Koziol et al., 2019 ; Van Der Hyde et al., 2020; Ryan et al., 2022) due to a variety of factors ranging from material transport to exposure to sunlight and air (Synder et al., 2023; Guthrie et al., 2024 ). There is no single optimal sampling substrate for using eDNA to characterize biodiversity, rather there is growing evidence for the concurrent use of multiple sampling substrates within field projects to help build a more complete picture (Lavrador et al., 2024 ; Runnel et al., 2024 ). The volume of eDNA data used in studies such as this has been growing rapidly in recent years. This rapid growth has the potential to enhance traditionally understudied communities, such as fungi and invertebrates, by complimenting diversity described solely by morphotaxonomy (Frøslev et al., 2019 ; Leandro et al., 2024 ; Runnel et al., 2024 ; Suren et al., 2024 ). Though eDNA can help capture some of this traditionally overlooked diversity it is not without its own gaps. This is particularly the case in the use of metabarcoding primers used to classify species from eDNA, many of which have well-documented biases related to their ability to differentiate various taxonomic groups (Elbrecht and Leese, 2017 ; Kauserud, 2023 ). While the primer set used to track invertebrate diversity may be designed to be ‘universal’, it does not equally distinguish different invertebrate groups. For example, many nematode species will look identical when using a particular primer to target the CO1 gene (Deagle et al., 2014 ). Similarly, a common choice of primer to target the ITS1 gene and track fungal diversity has been found to be biased towards distinguishing members of the phylum Basidiomycota (Bellemain et al., 2010 ). Given variations in the performance of different primers, there is growing use of the integration of multiple sets of primers for better tracking of the compositions of biological communities (Ficetola et al., 2021 ; Reji Chacko et al., 2023 ). Beyond the use of metabarcoding, metagenomics can capture much larger portions of genomes sampled via eDNA, and with it the potential to help overcome a number of these classification biases found with the use of metabarcoding (Lofgren and Stajich, 2021 ; Paul-Chima et al., 2024 ). Beyond potential biases associated with sampling substrates and primer sets, there is the issue of uneven sampling effort over time. The eDNA data used in this study are quite recent, predominantly collected within a period of a few years (Kelly et al., 2024 ). These data were aggregated to better enable calculations of biodiversity metrics, although such aggregation may obscure a number of ecological patterns. For example, seasonal variations may be bigger drivers of biodiversity patterns than urbanization, or influence biodiversity signals derived from eDNA through wind and rainfall patterns. Such geospatial sampling biases may even out in the near future as the volume of eDNA data is expected to continue its rapid growth. Conclusions There is a growing need to monitor the ecological effects in an increasingly urbanized world. Of particular importance are the impacts of urbanization on the structure of fungal and invertebrate communities, which underpin the functioning of many ecosystems. Despite potential biases between eDNA sampling substrates in capturing biodiversity, or large variations in sampling efforts over space and time, we demonstrate the potential to integrate such data sets to track the impact of urbanization on the diversity of such foundational communities. We have found evidence for urbanization being associated with a significant decline in the richness of fungal communities, and mixed but generally negative impacts on invertebrate richness. For both types of communities, we found significantly higher levels of homogenization in urban areas. However, the strength and significance of these potential relationships are likely being influenced by factors such as the substrate from which eDNA is sampled, seasonal variations, and sampling biases across space and time. We therefore recommend more eDNA-based biodiversity surveys, publishing such data in common platforms such as GBIF, as well as publishing field and lab data to better enable corrections for potential sequencing and sampling biases. Declarations Conflicts of interest/Competing interests: Not applicable Funding: Not applicable Author Contribution All authors contribution to the original draft, as well as subsequent edits. Conceptualization of this project was performed by A.LS. Methodology was performed by A.L.S and A.B. Software development was performed by A.L.S. Acknowledgement We would like to acknowledge Dr. Rachel Meyer, Dr. Laura Melissa Guzman, Ajith Seresinghe, and Julien Pometta for their support in helping to develop the ideas within this project. Data Availability Availability of data and material: All of the urban area boundaries are available as shapefiles from: https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.html . All species observations were obtained from https://www.gbif.org using the archived query: https://doi.org/10.15468/dl.pnhtw9 .Code availability: The analysis script used in this project is available here: https://github.com/levisimons/WLAC/blob/main/CIB_Urban3.R References Abrego N, Crosier B, Somervuo P et al (2020) Fungal communities decline with urbanization—more in air than in soil. ISME J 14:2806–2815 Avolio M, Pataki DE, Jenerette GD et al (2020) Urban plant diversity in Los Angeles, California: Species and functional type turnover in cultivated landscapes. Plants People Planet 2:144–156 Bahram M, Netherway T (2022) Fungi as mediators linking organisms and ecosystems. FEMS Microbiol Rev 46:fuab058 Barnes MA, Turner CR (2016) The ecology of environmental DNA and implications for conservation genetics. Conserv Genet 17:1–17 Bellemain E, Carlsen T, Brochmann C et al (2010) ITS as an environmental DNA barcode for fungi: an in silico approach reveals potential PCR biases. BMC Microbiol 10:1–9 Blouin D, Pellerin S, Poulin M (2019) Increase in non-native species richness leads to biotic homogenization in vacant lots of a highly urbanized landscape. Urban Ecosyst 22:879–892 Borges FLG, da Rosa Oliveira M, de Almeida TC et al (2021) Terrestrial invertebrates as bioindicators in restoration ecology: A global bibliometric survey. Ecol Indic 125:107458 Braschler B, Gilgado JD, Zwahlen V et al (2020) Ground-dwelling invertebrate diversity in domestic gardens along a rural-urban gradient: Landscape characteristics are more important than garden characteristics. PLoS ONE 15:e0240061 Callaghan CT, Bowler DE, Pereira HM (2021) Thermal flexibility and a generalist life history promote urban affinity in butterflies. Glob Chang Biol 27:3532–3546 Calsbeek R, Thompson JN, Richardson JE (2003) Patterns of molecular evolution and diversification in a biodiversity hotspot: the California Floristic Province. Mol Ecol 12:1021–1029 Campos CO, Almeida SFP, Serra SRQ et al (2024) The overlooked margins: how cities impact diversity of plants and terrestrial invertebrates along urban streams. Urban Ecosyst 1–16 Carl P, Peterson BG, Peterson MBG (2010) Package ‘PerformanceAnalytics.’ Retrieved March 29:2011 Carlon E, Dominoni DM (2024) The role of urbanization in facilitating the introduction and establishment of non-native animal species: a comprehensive review. J Urban Ecol 10:juae015 Chao A (1984) Nonparametric estimation of the number of classes in a population. Scand J Stat 265–270 Chao A, Ma KH, Hsieh TC, Chiu C-H (2016) User’s guide for online program SpadeR (Species-richness prediction and diversity estimation in R). Natl Tsing Hua Univ Hsinchu, Taiwan 88 Cheng J, Karambelkar B, Xie Y et al (2019) Package ‘leaflet.’ R Packag version 2:1 Chiu C-H, Wang Y-T, Walther BA, Chao A (2014) An improved nonparametric lower bound of species richness via a modified good–turing frequency formula. Biometrics 70:671–682 Deagle BE, Jarman SN, Coissac E et al (2014) DNA metabarcoding and the cytochrome c oxidase subunit I marker: not a perfect match. Biol Lett 10:20140562 Deiner K, Altermatt F (2014) Transport distance of invertebrate environmental DNA in a natural river. PLoS ONE 9:e88786 Deiner K, Fronhofer EA, Mächler E et al (2016) Environmental DNA reveals that rivers are conveyer belts of biodiversity information. Nat Commun 7:12544 Delgado-Baquerizo M, Eldridge DJ, Liu Y-R et al (2021) Global homogenization of the structure and function in the soil microbiome of urban greenspaces. Sci Adv 7:eabg5809 Diamond SE, Chick LD, Perez A et al (2018) Evolution of plasticity in the city: urban acorn ants can better tolerate more rapid increases in environmental temperature. Conserv Physiol 6:coy030 Dray S, Blanchet G, Borcard D et al (2018) Package ‘adespatial.’ R Packag 2018:3–8 Elbrecht V, Leese F (2017) Validation and development of COI metabarcoding primers for freshwater macroinvertebrate bioassessment. Front Environ Sci 5:11 Epp Schmidt DJ, Pouyat R, Szlavecz K et al (2017) Urbanization erodes ectomycorrhizal fungal diversity and may cause microbial communities to converge. Nat Ecol Evol 1:0123 Fenn ME, Allen EB, Weiss SB et al (2010) Nitrogen critical loads and management alternatives for N-impacted ecosystems in California. J Environ Manage 91:2404–2423 Ficetola GF, Boyer F, Valentini A et al (2021) Comparison of markers for the monitoring of freshwater benthic biodiversity through DNA metabarcoding. Mol Ecol 30:3189–3202 Fleming PA, Bateman PW (2018) Novel predation opportunities in anthropogenic landscapes. Anim Behav 138:145–155 Frøslev TG, Kjøller R, Bruun HH et al (2019) Man against machine: Do fungal fruitbodies and eDNA give similar biodiversity assessments across broad environmental gradients? Biol Conserv 233:201–212 Galitskaya P, Biktasheva L, Blagodatsky S, Selivanovskaya S (2021) Response of bacterial and fungal communities to high petroleum pollution in different soils. Sci Rep 11:164 Gao C, Shi N, Liu Y et al (2013) Host plant genus-level diversity is the best predictor of ectomycorrhizal fungal diversity in a Chinese subtropical forest. Mol Ecol 22:3403–3414 GBIF.org (2025) GBIF Occurrence Download Ge B, Mehring AS, Levin LA (2019) Urbanization alters belowground invertebrate community structure in semi-arid regions: A comparison of lawns, biofilters and sage scrub. Landsc Urban Plan 192:103664 Gillespie TW, Ostermann-Kelm S, Dong C et al (2018) Monitoring changes of NDVI in protected areas of southern California. Ecol Indic 88:485–494 Gómez-Hernández M, Avendaño-Villegas E, Toledo-Garibaldi M, Gándara E (2021) Impact of urbanization on functional diversity in macromycete communities along an urban ecosystem in Southwest Mexico. PeerJ 9:e12191 Griffiths HM, Ashton LA, Parr CL, Eggleton P (2021) The impact of invertebrate decomposers on plants and soil. New Phytol 231:2142–2149 Guthrie AM, Cooper CE, Bateman PW et al (2024) A quantitative analysis of vertebrate environmental DNA degradation in soil in response to time, UV light, and temperature. Environ DNA 6:e581 Harrison S, Franklin J, Hernandez RR et al (2024) Climate change and California’s terrestrial biodiversity. Proc Natl Acad Sci 121:e2310074121 Hending D (2024) Cryptic species conservation: a review. Biol Rev Hochkirch A, Samways MJ, Gerlach J et al (2021) A strategy for the next decade to address data deficiency in neglected biodiversity. Conserv Biol 35:502–509 Ji F, Han D, Yan L et al (2022) Assessment of benthic invertebrate diversity and river ecological status along an urbanized gradient using environmental DNA metabarcoding and a traditional survey method. Sci Total Environ 806:150587 Kauserud H (2023) ITS alchemy: on the use of ITS as a DNA marker in fungal ecology. Fungal Ecol 101274 Kelly RP, Lodge DM, Lee KN et al (2024) Toward a national eDNA strategy for the United States. Environ DNA 6:e432 Kirse A, Bourlat SJ, Langen K, Fonseca VG (2021) Unearthing the potential of soil eDNA metabarcoding—Towards best practice advice for invertebrate biodiversity assessment. Front Ecol Evol 9:630560 Kivlin SN, Hawkes CV (2011) Differentiating between effects of invasion and diversity: impacts of aboveground plant communities on belowground fungal communities. New Phytol 189:526–535 Kling MM, Mishler BD, Thornhill AH et al (2019) Facets of phylodiversity: evolutionary diversification, divergence and survival as conservation targets. Philos Trans R Soc B 374:20170397 Knop E (2016) Biotic homogenization of three insect groups due to urbanization. Glob Chang Biol 22:228–236 Kotze DJ, Lowe EC, MacIvor JS et al (2022) Urban forest invertebrates: how they shape and respond to the urban environment. Urban Ecosyst 25:1589–1609 Koziol A, Stat M, Simpson T et al (2019) Environmental DNA metabarcoding studies are critically affected by substrate selection. Mol Ecol Resour 19:366–376 La Sorte FA, Aronson MFJ, Williams NSG et al (2014) Beta diversity of urban floras among E uropean and non-E uropean cities. Glob Ecol Biogeogr 23:769–779 Lavrador AS, Amaral FG, Moutinho J et al (2024) Comprehensive DNA metabarcoding-based detection of non-indigenous invertebrates in recreational marinas through a multi-substrate approach. Mar Environ Res 200:106660 Leandro C, Jay-Robert P, Pétillon J (2024) eDNA for monitoring and conserving terrestrial arthropods: Insights from a systematic map and barcode repositories assessments. Insect Conserv Divers Lenth RV (2021) emmeans: estimated marginal means, aka least-squares means https://CRAN. R-project org/package = emmeans Lewthwaite JMM, Baiotto TM, Brown BV et al (2024) Drivers of arthropod biodiversity in an urban ecosystem. Sci Rep 14:390 Li G, Fang C, Li Y et al (2022) Global impacts of future urban expansion on terrestrial vertebrate diversity. Nat Commun 13:1628 Lin M, Simons AL, Harrigan RJ et al (2021) Landscape analyses using eDNA metabarcoding and Earth observation predict community biodiversity in California. Ecol Appl 31:e02379 Linares LMD (2022) The awkward question: What baseline should be used to measure biodiversity loss? The role of history, biology and politics in setting up an objective and fair baseline for the international biodiversity regime. Environ Sci Policy 135:137–146 Liu Z, Zhou T, Heino J et al (2022) Land conversion induced by urbanization leads to taxonomic and functional homogenization of a river macroinvertebrate metacommunity. Sci Total Environ 825:153940 Lofgren LA, Stajich JE (2021) Fungal biodiversity and conservation mycology in light of new technology, big data, and changing attitudes. Curr Biol 31:R1312–R1325 Lokatis S, Jeschke JM (2022) Urban biotic homogenization: Approaches and knowledge gaps. Ecol Appl 32:e2703 Lücking R, Aime MC, Robbertse B et al (2020) Unambiguous identification of fungi: where do we stand and how accurate and precise is fungal DNA barcoding? IMA Fungus 11:14 Magurran AE, Baillie SR, Buckland ST et al (2010) Long-term datasets in biodiversity research and monitoring: assessing change in ecological communities through time. Trends Ecol Evol 25:574–582 Marselle MR, Lindley SJ, Cook PA, Bonn A (2021) Biodiversity and health in the urban environment. Curr Environ Heal Rep 8:146–156 McDonald RI (2008) Global urbanization: can ecologists identify a sustainable way forward? Front Ecol Environ 6:99–104 McDonald RI, Marcotullio PJ, Güneralp B (2013) Urbanization and global trends in biodiversity and ecosystem services Urban Biodivers Ecosyst Serv challenges Oppor a Glob Assess 31–52 McKinney ML (2006) Urbanization as a major cause of biotic homogenization. Biol Conserv 127:247–260 McKinney ML, Lockwood JL (1999) Biotic homogenization: a few winners replacing many losers in the next mass extinction. Trends Ecol Evol 14:450–453 McMurdie PJ, Holmes S (2013) phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8:e61217 Mermillod-Blondin F, Hose GC, Simon KS et al (2023) Role of invertebrates in groundwater ecosystem processes and services. Groundwater ecology and evolution. Elsevier, pp 263–281 Mikryukov V, Dulya O, Zizka A et al (2023) Connecting the multiple dimensions of global soil fungal diversity. Sci Adv 9:eadj8016 Montero S (2020) Leveraging Bogotá: Sustainable development, global philanthropy and the rise of urban solutionism. Urban Stud 57:2263–2281 Montràs-Janer T, Suggitt AJ, Fox R et al (2024) Anthropogenic climate and land-use change drive short-and long-term biodiversity shifts across taxa. Nat Ecol Evol 8:739–751 Myers N, Mittermeier RA, Mittermeier CG et al (2000) Biodiversity hotspots for conservation priorities. Nature 403:853–858 Nguyen NH, Williams LJ, Vincent JB et al (2016) Ectomycorrhizal fungal diversity and saprotrophic fungal diversity are linked to different tree community attributes in a field-based tree experiment. Mol Ecol 25:4032–4046 Niskanen T, Lücking R, Dahlberg A et al (2023) Pushing the frontiers of biodiversity research: Unveiling the global diversity, distribution, and conservation of fungi. Annu Rev Environ Resour 48:149–176 Nørgaard L, Olesen CR, Trøjelsgaard K et al (2021) eDNA metabarcoding for biodiversity assessment, generalist predators as sampling assistants. Sci Rep 11:6820 Oksanen J, Simpson G, Blanchet FG et al (2022) Vegan: Community Ecology Package, Version 2.6-4. 2022 Parkhurst T, Prober SM, Hobbs RJ, Standish RJ (2022) Global meta-analysis reveals incomplete recovery of soil conditions and invertebrate assemblages after ecological restoration in agricultural landscapes. J Appl Ecol 59:358–372 Pataki DE, Alberti M, Cadenasso ML et al (2021) The benefits and limits of urban tree planting for environmental and human health. Front Ecol Evol 9:603757 Paul-Chima UO, Ugo AE, Ben OM (2024) The Role of Environmental DNA (EDNA) in Biodiversity Conservation Pebesma EJ (2018) Simple features for R: standardized support for spatial vector data. R J 10:439 Pereira HM, Martins IS, Rosa IMD et al (2024) Global trends and scenarios for terrestrial biodiversity and ecosystem services from 1900 to 2050. Science 80–:384:458–465 Piano E, Souffreau C, Merckx T et al (2020) Urbanization drives cross-taxon declines in abundance and diversity at multiple spatial scales. Glob Chang Biol 26:1196–1211 Pont D, Rocle M, Valentini A et al (2018) Environmental DNA reveals quantitative patterns of fish biodiversity in large rivers despite its downstream transportation. Sci Rep 8:10361 Raimondo D, Young BE, Brooks TM et al (2023) Using Red List Indices to monitor extinction risk at national scales. Conserv Sci Pract 5:e12854 Reji Chacko M, Altermatt F, Fopp F et al (2023) Catchment-based sampling of river eDNA integrates terrestrial and aquatic biodiversity of alpine landscapes. Oecologia 202:699–713 Roh T (2024) leaflegend: Create Custom Legends for Leaflet. Comput software] https//leaflegend delveds com Ruas RDB, Costa LMS, Bered F (2022) Urbanization driving changes in plant species and communities—A global view. Glob Ecol Conserv 38:e02243 Runnel K, Lõhmus P, Küngas K et al (2024) Aerial eDNA contributes vital information for fungal biodiversity assessment. J Appl Ecol Rusterholz H-P, Baur B (2023) Changes in Soil Fungal Diversity and Composition along a Rural–Urban Gradient. Forests 14:2226 Seena S, Baschien C, Barros J et al (2023) Ecosystem services provided by fungi in freshwaters: a wake-up call. Hydrobiologia 850:2779–2794 Shirouzu T, Matsuoka S, Doi H et al (2020) Complementary molecular methods reveal comprehensive phylogenetic diversity integrating inconspicuous lineages of early-diverged wood-decaying mushrooms. Sci Rep 10:3057 Sidemo-Holm W, Ekroos J, Reina García S et al (2022) Urbanization causes biotic homogenization of woodland bird communities at multiple spatial scales. Glob Chang Biol 28:6152–6164 Simons AL, Mazor R, Stein ED, Nuzhdin S (2019) Using alpha, beta, and zeta diversity in describing the health of stream-based benthic macroinvertebrate communities. Ecol Appl 29:e01896 Snyder ED, Tank JL, Brandão-Dias PFP et al (2023) Environmental DNA (eDNA) removal rates in streams differ by particle size under varying substrate and light conditions. Sci Total Environ 903:166469 Spotswood EN, Beller EE, Grossinger R et al (2021) The biological deserts fallacy: cities in their landscapes contribute more than we think to regional biodiversity. Bioscience 71:148–160 Stat M, John J, DiBattista JD et al (2019) Combined use of eDNA metabarcoding and video surveillance for the assessment of fish biodiversity. Conserv Biol 33:196–205 Stupariu M-S, Cushman SA, Pleşoianu A-I et al (2022) Machine learning in landscape ecological analysis: a review of recent approaches. Landsc Ecol 37:1227–1250 Sumudumali RGI, Jayawardana J (2021) A review of biological monitoring of aquatic ecosystems approaches: with special reference to macroinvertebrates and pesticide pollution. Environ Manage 67:263–276 Sun L, Chen J, Li Q, Huang D (2020) Dramatic uneven urbanization of large cities throughout the world in recent decades. Nat Commun 11:5366 Suren AM, Burdon FJ, Wilkinson SP (2024) eDNA is a useful environmental monitoring tool for assessing stream ecological health. Environ DNA 6:e596 Szabó B, Korányi D, Gallé R et al (2023) Urbanization decreases species richness, and increases abundance in dry climates whereas decreases in wet climates: a global meta-analysis. Sci Total Environ 859:160145 Teixeira CP, Fernandes CO (2020) Novel ecosystems: a review of the concept in non-urban and urban contexts. Landsc Ecol 35:23–39 Tordoni E, Ametrano CG, Banchi E et al (2021) Integrated eDNA metabarcoding and morphological analyses assess spatio-temporal patterns of airborne fungal spores. Ecol Indic 121:107032 Tournayre O, Littlefair JE, Garrett NR et al (2025) Contrasted effects of human pressure on biodiversity in the UK: a multi-taxonomic assessment using airborne environmental DNA. Ecography (Cop) e08196 U.S, Census Bureau (2023) 2023 TIGER/Line Shapefiles. In: U.S. Dep. Commer U.S, Census Bureau (2000) 2000 TIGER/Line Shapefiles. In: U.S. Dep. Commer Census Bureau US (2016) 2016 TIGER/Line Shapefiles. In: U.S. Dep. Commer Uchida K, Blakey RV, Burger JR et al (2021) Urban biodiversity and the importance of scale. Trends Ecol Evol 36:123–131 van der Heyde M, Alexander J, Nevill P et al (2023) Rapid detection of subterranean fauna from passive sampling of groundwater eDNA. Environ DNA 5:1706–1719 Van Der Heyde M, Bunce M, Wardell-Johnson G et al (2020) Testing multiple substrates for terrestrial biodiversity monitoring using environmental DNA metabarcoding. Mol Ecol Resour 20:732–745 Wan N-F, Fu L, Dainese M et al (2022) Plant genetic diversity affects multiple trophic levels and trophic interactions. Nat Commun 13:7312 Wang X, Svenning J-C, Liu J et al (2021) Regional effects of plant diversity and biotic homogenization in urban greenspace–The case of university campuses across China. Urban Urban Green 62:127170 Wickham H, Wickham H (2016) Data analysis. Springer Xu H, Cao Y, Yu D et al (2021) Ensuring effective implementation of the post-2020 global biodiversity targets. Nat Ecol Evol 5:411–418 Yao H, Li Z, Geisen S et al (2023) Degree of urbanization and vegetation type shape soil biodiversity in city parks. Sci Total Environ 899:166437 Yiallouris A, Pana ZD, Marangos G et al (2024) Fungal diversity in the soil Mycobiome: Implications for ONE health. One Heal 100720 Zaghloul A, Saber M, Gadow S, Awad F (2020) Biological indicators for pollution detection in terrestrial and aquatic ecosystems. Bull Natl Res Cent 44:1–11 Zhang P, Ren G, Qin Y et al (2021) Urbanization effects on estimates of global trends in mean and extreme air temperature. J Clim 34:1923–1945 Zhao B, Andermann T (2024) Properties and limitations of eDNA. substrates for terrestrial animal monitoring Additional Declarations No competing interests reported. Supplementary Files UrbanBiodiversitySI.docx Cite Share Download PDF Status: Posted Version 1 posted 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. 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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-8663064","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":594947411,"identity":"44476f8c-6717-403b-a17f-4c00e486008d","order_by":0,"name":"Ariel Simons","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2klEQVRIiWNgGAWjYBACPiA+wGAAYjIfABISMgS1sCG0sCWAtPAQpQUKeMAaidDC3mN48EfBNjlz/jOfX92oseBhYD98dANeLTxnDA7zGNw2tpyRu8065xjQYTxpaTfwapFISzjMYHA7ccMN3m3GOWxALRI8Zvi1yD9LOPgDpOX8mWfGOf+I0SLBfOAAD0jLgRzmx7ltxGjhST4A9ovBjTQz5tw+CR42Qn7hZz/Y/PHHn9tyBucPP/6c861Ojp/98DG8WlAdCSaJVQ4CzB9IUT0KRsEoGAUjBwAACNNG7EESVZEAAAAASUVORK5CYII=","orcid":"","institution":"West Los Angeles College","correspondingAuthor":true,"prefix":"","firstName":"Ariel","middleName":"","lastName":"Simons","suffix":""},{"id":594947412,"identity":"8a85c826-2e8f-481b-8c7f-07eb46d1e5cd","order_by":1,"name":"Austin Baker","email":"","orcid":"","institution":"Natural History Museum of Los Angeles County","correspondingAuthor":false,"prefix":"","firstName":"Austin","middleName":"","lastName":"Baker","suffix":""},{"id":594947413,"identity":"f1de2492-312e-434e-9bc6-eafb209fa7b8","order_by":2,"name":"Christopher Timlin-Broussard","email":"","orcid":"","institution":"Los Angeles City College","correspondingAuthor":false,"prefix":"","firstName":"Christopher","middleName":"","lastName":"Timlin-Broussard","suffix":""},{"id":594947414,"identity":"cb9efe6e-cfbc-4cd1-8625-633ad496d6b8","order_by":3,"name":"Avicka Willis","email":"","orcid":"","institution":"Santa Monica College","correspondingAuthor":false,"prefix":"","firstName":"Avicka","middleName":"","lastName":"Willis","suffix":""},{"id":594947415,"identity":"5217abf9-024f-4d80-8c9c-50d611ded6b4","order_by":4,"name":"Jerelyn Lee","email":"","orcid":"","institution":"West Los Angeles College","correspondingAuthor":false,"prefix":"","firstName":"Jerelyn","middleName":"","lastName":"Lee","suffix":""},{"id":594947416,"identity":"78733b18-6b38-43c0-87d5-98c01258097b","order_by":5,"name":"Riley Wells","email":"","orcid":"","institution":"Santa Monica College","correspondingAuthor":false,"prefix":"","firstName":"Riley","middleName":"","lastName":"Wells","suffix":""}],"badges":[],"createdAt":"2026-01-21 19:23:52","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8663064/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8663064/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103332387,"identity":"9332f23e-e066-449e-b8cd-be4b021c09b9","added_by":"auto","created_at":"2026-02-24 14:05:16","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":739278,"visible":true,"origin":"","legend":"\u003cp\u003ePearson correlation coefficients between Chao1, iChao1, and Chao1-bc richness for fungal (A) and invertebrate (B) diversity (p \u0026lt; 10\u003csup\u003e-4\u003c/sup\u003e).\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8663064/v1/cb710e41c575638500426698.png"},{"id":103506763,"identity":"562e62ef-504f-4e87-8ad2-6468756cb2f0","added_by":"auto","created_at":"2026-02-26 13:39:23","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1390059,"visible":true,"origin":"","legend":"\u003cp\u003eChao-1 richness, colored and sized by the log\u003csub\u003e10\u003c/sub\u003e of richness, of fungal (A) and invertebrate (B) communities. Urban areas are marked in magenta. eDNA Samples collected from 2000 to 2024.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8663064/v1/79b301d1f7e3e86df8b1471d.png"},{"id":103509922,"identity":"7df94815-4f58-4ac3-bb37-17d2c5ff63bb","added_by":"auto","created_at":"2026-02-26 14:02:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3279980,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8663064/v1/940fb56f-f137-4190-99ce-a04e4731c6a3.pdf"},{"id":103332389,"identity":"9d7e9292-af21-4e5d-82f7-ce8ff43368e9","added_by":"auto","created_at":"2026-02-24 14:05:16","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":64590,"visible":true,"origin":"","legend":"","description":"","filename":"UrbanBiodiversitySI.docx","url":"https://assets-eu.researchsquare.com/files/rs-8663064/v1/58053525fdfe860e11a72c0d.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Urbanization changes the richness and homogenizes fungal and invertebrate communities in California","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHumanity has recently become a predominantly urban species, with the geographic scope of urban land cover expected to continue its rapid expansion over the coming century (McDonald, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Sun et al., \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Urbanization has been found to significantly shape ecological conditions ranging from local climate (Zhang et al., \u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) to patterns of biodiversity (Sidemo-Holm et al., \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These landscape modifications, and their ecological impacts, are significant enough that urban areas can be considered a distinct class of biomes (Fleming and Bateman, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Teixeira and Fernandes, \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), containing their own unique ecosystems (Avolio et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; de Barros Ruas et al., \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Changes in ecological conditions associated with urbanization impact human well-being (Marselle et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Pataki et al., \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Urban areas have also become increasingly interconnected through networks of trade and resource extraction, affecting biodiversity at large (Spotswood et al., \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and driving interest in studying urban areas as ecological factors in their own right (Montero, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Uchida et al., \u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe rise in the scale and intensity of these anthropogenic stressors, and their subsequent impacts on ecosystems, make the need for tracking biodiversity in urban environments more pressing (Xu et al. \u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This need for monitoring biodiversity covers everything ranging from establishing baselines (Linares, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), monitoring spatiotemporal patterns (Magurran et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), determining extinction risks (Raimondo et al., \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and identifying predictors of community composition (Stupariu et al., \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWith traditionally understudied groups, in particular fungi and invertebrates, there are significant gaps in our monitoring efforts (Hochkirch et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Niskanen et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Despite their ecological importance, both fungal and invertebrate communities are less likely to be studied than a variety of plant and animal groups because they are hyperdiverse groups, and are more difficult to track and distinguish visually and morphologically (Fr\u0026oslash;slev et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; L\u0026uuml;cking et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Hending, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). However, the rapid growth of environmental DNA (eDNA) allows us to better track patterns and changes in their biodiversity. Fragments of eDNA can be obtained from a variety of substrates, such as sediment or water, and can complement current biomonitoring efforts with the simultaneous identification of thousands of species (Stat et al., \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Lin et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; N\u0026oslash;rgaard et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), including many fungal or invertebrate taxa which may otherwise go unnoticed (Shirouzu et al., \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; van der Heyde et al., \u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). eDNA monitoring can complement individual based sampling for both fungal and invertebrate communities and can enable greater taxonomic resolution due to the large amount of identified dark taxa (Kirse et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Tordoni et al., \u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Suren et al., \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study investigates the role of urban areas in shaping patterns of biodiversity for both fungal and invertebrate communities in California, as classified using eDNA. We focus on California as it covers a diverse range of environmental conditions and biological communities (Myers et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2000\u003c/span\u003e), including biodiversity hotspots (Calsbeek et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Additionally, California is of particular interest in studying the effects of urbanization on ecosystems as it is heavily urbanized (McDonald et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Gillespie et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), and recently so (Harrison et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThough there is growing interest in studying the role of urbanization in shaping ecosystems, there are significant taxonomic biases in the groups being studied; with birds being the most overrepresented by far (Lokatis and Jeschke, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Conversely, both fungal and invertebrate communities represent large gaps in studies of urbanization and ecology (Hochkirch et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), which are only recently being addressed (Lewthwaite et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Given the general importance of both fungal (Bahram and Netherway, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Seena et al., \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and invertebrate (Griffiths et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Mermillod-Blondin et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) communities in shaping ecosystems, this is a gap which needs to be addressed to better understand the effects of urbanization on these taxa and ecosystems as a whole. Given the historically recent decline in global biodiversity (Pereira et al., \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), addressing this gap is of particular urgency as the ecological impacts of anthropogenic stressors can often be tracked using the compositions of both fungal (Zaghloul et al., \u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Galitskaya et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and invertebrate communities (Borges et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Sumudumali and Jayawardana, \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eConsidering the presence of highly diverse, but understudied, biological communities present in landscapes containing recently developed urban areas, we propose to investigate the following questions:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eIs urbanization shaping patterns of fungal or invertebrate richness in California?\u003c/em\u003e Specifically, is there a significant difference in the richness of fungal and invertebrate communities sampled via eDNA between urban and non-urban areas, and if so what environmental factors may be driving these differences?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eIs urbanization shaping the compositions of fungal or invertebrate communities in California?\u003c/em\u003e Are the composition of biological communities significantly distinct between urban and non-urban areas, and, if so, what environmental factors may be driving these differences?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eObtaining biodiversity data\u003c/h2\u003e \u003cp\u003eThe biodiversity data used in this study are derived from all samples with associated sequence data in the Global Biodiversity Information Facility (GBIF), gathered in California during the period 2000 through 2024 (GBIF, 2025). This initial data set contains 1,368,210 unique occurrences collected at 32,313 unique points in space and time, hereafter referred to as samples. We filtered this data set to only contain occurrences where the taxonomy could be resolved to species, and which could be placed within a list of either fungal or invertebrate phyla. The list of fungal phyla includes: Ascomycota, Basidiomycota, Entorrhizomycetes, Blastocladiomycota, Chytridiomycota, Cryptomycota, Microsporidia, Mucoromycota, Nephridiophaga, Olpidiomycota, Sanchytriomycota, and Zoopagomycota. The list of invertebrate phyla includes: Platyhelminthes, Nemertea, Rotifera, Gastrotricha, Acanthocephala, Nematoda, Nematomorpha, Priapulida, Kinorhyncha, Loricifera, Entoprocta, Cycliophora, Gnathostomulida, Micrognathozoa, Chaetognatha, Hemichordata, Bryozoa, Brachiopoda, Phoronida, Annelida, Mollusca, and Arthropoda. This filtering produced a set of 252,535 species-level occurrences, found across 22,782 samples. The filtered set of occurrences included 35,527 associated with fungal species, and 217,008 associated with invertebrate species. These occurrences were then assembled into phyloseq objects using the function \u003cem\u003ephyloseq\u003c/em\u003e within the R package \u003cem\u003ephyloseq v1.38.0\u003c/em\u003e (McMurdie and Holmes, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDefining urban and non-urban samples and taxa\u003c/h3\u003e\n\u003cp\u003eThe boundaries used to define California urban areas were generated by first downloading the shapefiles for 2023 United States Metropolitan Statistical Areas (US Census Bureau, \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and the 2016 California boundaries (US Census Bureau, \u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). While samples were collected between the period 2000 to 2024 (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e), most were collected towards the end of this period with the median sample year being 2016 for invertebrate species and 2023 for fungal species.\u003c/p\u003e \u003cp\u003eThe boundaries for the Metropolitan Statistical Areas (MSAs) were clipped to California using the function \u003cem\u003est_intersection\u003c/em\u003e in the R package \u003cem\u003esf v1.0.9\u003c/em\u003e (Pebesma, \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Samples were then designated as urban if their coordinates overlapped California urban areas and non-urban if they did not, which was determined using the \u003cem\u003esf\u003c/em\u003e function \u003cem\u003est_join\u003c/em\u003e. Hereafter we refer to the urban versus non-urban status of a sample as its \u0026lsquo;area category\u0026rsquo;. While there were sample locations which did shift their urban versus non-urban status over the period 2000\u0026ndash;2023 using the 2000 United States Metropolitan Statistical Area boundaries (US Census Bureau, \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e2000\u003c/span\u003e), we found these status values to be largely unchanged (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\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\u003eThe urban versus non-urban status for eDNA samples 2000 versus 2023.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCommunity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2000 Urban boundaries\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2023 Urban boundaries\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFungi - Urban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3765\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFungi - NonUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18553\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInvertebrates - Urban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3765\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInvertebrates - NonUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18553\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eTesting factors influencing urban and non-urban taxonomic richness\u003c/h3\u003e\n\u003cp\u003eFor all decontaminated and filtered samples taxonomic richness was calculated as Chao-1 alpha diversity (Chao, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1984\u003c/span\u003e) via the \u003cem\u003ephyloseq\u003c/em\u003e function \u003cem\u003eplot_richness\u003c/em\u003e. We also calculated iChao1 (Chiu et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) and Chao1-bc (Chao et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) using the function \u003cem\u003eChaoRichness\u003c/em\u003e within the R package \u003cem\u003eSpadeR v0.1.1\u003c/em\u003e (Chao et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Using the function \u003cem\u003echart.Correlation\u003c/em\u003e in the R package \u003cem\u003ePerformanceAnalytics\u003c/em\u003e v2.0.4 (Carl et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) we found all richness metrics to be highly collinear with Chao-1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo test if taxonomic richness is significantly affected by area category or sampling year, we ran linear models of taxonomic richness as a function of area category, sampling year, and their interaction for all available fungal and invertebrate orders. To then test if taxonomic richness of orders tended to be higher or lower in urban environments, we then ran linear models of taxonomic richness as a function of area category through the function \u003cem\u003eemmeans\u003c/em\u003e in the R package \u003cem\u003eemmeans v1.10.5\u003c/em\u003e (Lenth, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The output from \u003cem\u003eemmeans\u003c/em\u003e was then further analyzed using the \u003cem\u003eemmeans\u003c/em\u003e function \u003cem\u003esummary\u003c/em\u003e.\u003c/p\u003e\n\u003ch3\u003eMapping taxonomic richness\u003c/h3\u003e\n\u003cp\u003eUsing the \u003cem\u003est_as_sf\u003c/em\u003e function \u003cem\u003esf\u003c/em\u003e, a set of spatial points objects were constructed from data frames containing the location and taxonomic richness of each sample. These points were then colored and sized by their taxonomic richness values, along with shapefiles representing the boundaries of urban areas in 2023, using the R packages \u003cem\u003eleaflet\u003c/em\u003e (Cheng et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), \u003cem\u003eleafletlegend\u003c/em\u003e (Roh, \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), and \u003cem\u003eggplot2\u003c/em\u003e (Wickham and Wickham, \u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eTesting factors influencing urban and non-urban beta diversity\u003c/h3\u003e\n\u003cp\u003eTo test if beta diversity varies significantly based on area category and sampling year we first generated Chao beta diversity distance matrices using the \u003cem\u003edistance\u003c/em\u003e function in \u003cem\u003ephyloseq\u003c/em\u003e on all fungal or invertebrate samples. We then ran a PERMANOVA, with 999 permutations, on these distance matrices with area category and sampling year as covariates using the function \u003cem\u003eadonis2\u003c/em\u003e in the R package \u003cem\u003evegan v2.6.4\u003c/em\u003e (Oksanen et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn order to test if beta diversity is greater in urban or non-urban areas, we calculated the dispersion of these beta diversity matrices using the \u003cem\u003evegan\u003c/em\u003e function \u003cem\u003ebetadisper\u003c/em\u003e, with the parameter \u003cem\u003ebias.adjust\u003c/em\u003e set to true in order to correct for unequal sample sizes between groups. A series of multiple comparisons were then run on these dispersion outputs using Tukey HSD tests and the \u003cem\u003estats\u003c/em\u003e function \u003cem\u003eTukeyHSD\u003c/em\u003e.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eAssessing local contributions to beta diversity\u003c/h2\u003e \u003cp\u003eTo compare how much each sample contributes to the unique fungal or invertebrate communities, we calculated the local contribution to beta diversity (LCBD). This was done using the function \u003cem\u003eLCBD.comp\u003c/em\u003e from the R package \u003cem\u003eadespatial v0.3.21\u003c/em\u003e (Dray et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), with fungal or invertebrate Chao beta diversity distance matrices used as input. We then created linear models of LCBD values as a function of area category, and tested if LCBD values for urban and non-urban samples were significantly different using the function \u003cem\u003esummary\u003c/em\u003e.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eLower richness for urban fungal and invertebrate communities\u003c/h2\u003e \u003cp\u003eFor the data used in this study, most of the fungal and invertebrate species were found to be exclusive to non-urban over urban areas, with approximately twice as many unique invertebrate species recorded as fungal ones (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\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\u003eNumber of fungal and invertebrate species, and the number which are exclusive to urban or non-urban areas.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCommunity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of species\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber of exclusively urban species\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNumber of exclusively non-urban species\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFungi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2760\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2102\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInvertebrates\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5988\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e421\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4135\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\u003eWe also found evidence of urbanization significantly influencing richness for both fungal and invertebrate communities in non-urban areas, with all fungal orders and most invertebrate orders containing greater richness in non-urban samples than urban ones (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\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\u003ePairwise comparisons of Chao-1 alpha diversity of non-urban versus urban samples for fungal and invertebrate orders (Significant results, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, only). Positive EMM values indicate a greater alpha diversity in non-urban samples.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003econtrast\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimated marginal mean (EMM)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStandard error (SE)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDegrees of freedom\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003et ratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eorder\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFungi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEurotiales\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFungi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePleosporales\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFungi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eChaetothyriales\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFungi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHelotiales\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFungi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e316\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHypocreales\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFungi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eThelephorales\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFungi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCapnodiales\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFungi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6854\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAgaricales\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFungi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e801\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRussulales\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInvertebrates\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.06 x 10\u003csup\u003e\u0026minus;\u0026thinsp;17\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.65 x 10\u003csup\u003e15\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSarcoptiformes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInvertebrates\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.36 x 10\u003csup\u003e\u0026minus;\u0026thinsp;16\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.27 x 10\u003csup\u003e15\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePolydesmida\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInvertebrates\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHymenoptera\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInvertebrates\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDiptera\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInvertebrates\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLepidoptera\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInvertebrates\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-4.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eStylommatophora\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInvertebrates\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e374\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-4.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEntomobryomorpha\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInvertebrates\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1466\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHemiptera\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInvertebrates\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAmphipoda\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInvertebrates\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDecapoda\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInvertebrates\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e497\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAraneae\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInvertebrates\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e482\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eThysanoptera\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInvertebrates\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTrochida\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eGeographic trends in taxonomic richness\u003c/h2\u003e \u003cp\u003eIn mapping the taxonomic richness of fungi we tended to find its highest levels along the Sierra Nevada mountain range, while most of the sampled richness is relatively low elsewhere (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). For invertebrates we found sampled locations with a mix of high and low richness in most locations, except for most of the southern half of the Central Valley and the Mojave desert, which were largely undersampled (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eConsistent lower richness of urban fungal communities\u003c/h2\u003e \u003cp\u003eWith the available data we found, we could build linear models of taxonomic richness as a function of urban/non-urban category and sample year for 74 fungal and invertebrate orders. From this initial list we found that 41 orders have significant variation with both urban/non-urban area category and sample year (Table S2). Urban fungal communities were found to have consistently lower richness than their non-urban counterparts, however there was no consistent pattern with regards to this urban/non-urban split over time for invertebrate communities (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\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\u003ePairwise comparisons of Chao-1 alpha diversity of non-urban versus urban samples for fungi and invertebrates on an annual basis (Significant results, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, only). Positive EMM values indicate a greater alpha diversity in non-urban samples.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEstimated marginal mean (EMM)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStandard error (SE)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDegrees of freedom\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003et ratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003etype\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003efungi\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003efungi\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003efungi\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003efungi\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003efungi\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003efungi\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003efungi\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-2.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-17.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003einvertebrates\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-1.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e313\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-10.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003einvertebrates\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003einvertebrates\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003einvertebrates\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003einvertebrates\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e365\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003einvertebrates\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eHomogenization of urban fungal and invertebrate communities\u003c/h2\u003e \u003cp\u003eWe found that both fungal and invertebrate communities tended to be more homogenized (lower beta diversity) in urban than non-urban areas, with beta diversity varying significantly more for non-urban as opposed to urban samples for all fungal orders and most invertebrate ones (Table S3). For both communities we also found urban, as opposed to non-urban, samples tended to contribute more to overall beta diversity (Table S4).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eTemporal trends in beta diversity\u003c/h2\u003e \u003cp\u003eWe found 82 orders of fungi and invertebrates with enough data to build linear models of beta diversity as a function of urban/non-urban category and sample year. Of this initial list, we found that 67 orders have significant variation with both urban/non-urban category and sample year (Table S5). We also found that both fungal and invertebrate communities tended to be more homogenized (lower beta diversity) in urban than non-urban areas on a year-by-year basis (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\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\u003eDifference in mean distance to median Chao beta diversity for non-urban versus urban samples for fungi and invertebrates split by year. Note: Only results with adjusted significance values of less than 0.05 are included.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDifference in mean distance to median beta diversity (Urban - Non-urban)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eyear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003etype\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNumber Non-Urban Samples\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNumber Urban Samples\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-4.23 x 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003efungi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e369\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-8.10 x 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003efungi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3876\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e533\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-3.53 x 10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003efungi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-2.66 x 10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003einvertebrates\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-3.10 x 10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003einvertebrates\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-4.80 x 10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003einvertebrates\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e313\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-7.64 x 10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003einvertebrates\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e121\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-1.27 x 10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003einvertebrates\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e197\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-3.60 x 10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003einvertebrates\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-3.76 x 10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003einvertebrates\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e134\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-3.10 x 10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003einvertebrates\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e141\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-7.36 x 10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003einvertebrates\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-1.10 x 10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003einvertebrates\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e329\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-5.39 x 10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003einvertebrates\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-5.89 x 10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003einvertebrates\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8.11 x 10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003einvertebrates\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e101\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe taxonomic richness of fungal and invertebrate communities were found to both be significantly impacted by urban areas, albeit with distinct trends between these communities. Both communities contained relatively few taxa which were exclusive to urban areas in California, which indicates that most of the taxa detected in urban areas in this study are urban-tolerant rather than urban-exclusive. This may reflect prior observations of urban environments being preferential to the spread of generalist taxa, both with invertebrate (Diamond et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Callaghan et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and fungal communities (Abrego et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Yiallouris et al., \u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFor both communities we found the highest levels of taxonomic richness to be found along the Sierra Nevada mountain range in the northern half of the state. This area is relatively cool, wet, and undisturbed by changes in land cover associated with urbanization or agriculture. This area also largely corresponds to relatively diverse plant communities (Kling et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and the potential distribution of mixed conifer forests in California, in the absence of human activities (Fenn et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). This spatial pattern in diversity appears to have an ecological basis, as evidence indicates significant correlations between the diversity of plant and fungal (Kivlin and Hawkes, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Gao et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Nguyen et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), as well as plant and invertebrate (Parkhurst et al., \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Wan et al., \u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), communities.\u003c/p\u003e \u003cp\u003eWith the exception of members of Stylommatophora, an order of terrestrial gastropods, and Entomobryomorpha, an order of springtails, we found the taxonomic richness of urban invertebrates to be generally lower than their non-urban counterparts. Such urban versus non-urban patterns of invertebrate diversity have been observed in a variety of studies (Simons et al., \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Piano et al., \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Ji et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Kotze et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, when looking at invertebrate richness as a whole over time we do not see a consistent split between urban and non-urban communities. Some years urban invertebrate communities appear to have significantly lower richness than their non-urban counterparts, and some years the opposite is observed. These variations in part may be driven by differences in sampling substrates and locations between urban and non-urban samples on a year by year basis. For example, invertebrate eDNA sampled from streams running through urban environments may be capturing eDNA from across the entirety of the upstream extent of the watershed (Deiner et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). There is significant variability in the distance of eDNA transport along streams (Barnes and Turner, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), but readily detectable amounts have been found at distances of tens of kilometers from the release location (Deiner and Altermatt, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Pont et al., \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHowever, for some orders, elevated taxonomic richness of urban invertebrate communities may sometimes reflect biological reality (Tournayre et al., \u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Urban areas are the hubs of transportation networks, and they may have higher invertebrate diversity as a result of elevated rates of species introductions, which are conducive to the successful spread of generalist taxa (Campos et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Excess resources available in urban areas may also be a factor in elevating urban invertebrate richness (Yao et al., \u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), especially given prior observations of urban irrigation supporting the growth of invertebrate communities (Ge et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Szab\u0026oacute; et al., \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWith fungal communities we found taxonomic richness to be significantly and consistently lower in urban as opposed to non-urban areas. Similar declines in fungal diversity have been associated with urbanization in the past(Abrego et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These declines have been connected with disturbances to plant communities and their fungal symbionts due to land-use changes associated with urbanization (Epp Schmidt et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Delgado-Baquerizo et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Changes in air temperature and rainfall, both influenced by the urban heat-island effect, have been found to be important drivers of fungal taxonomic richness (Delgado-Baquerizo et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; G\u0026oacute;mez-Hern\u0026aacute;ndez et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn terms of beta diversity, for both fungal and invertebrate communities we found significant evidence of urbanization influencing beta diversity. This result reflects prior evidence of a significant role for urbanization in shaping beta diversity for both invertebrate (Braschler et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and fungal (Epp Schmidt et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Rusterholz and Baur, \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) communities. However, as with other ecological studies using eDNA, the strength and significance of this potential relationship varies by the substrates from which eDNA are sampled (Koziol et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Zhao and Andermann, \u003cspan citationid=\"CR121\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe found significant evidence of biotic homogenization in both fungal and invertebrate communities in association with urbanization. This is consistent with prior observations of urbanization homogenizing a range of biological communities, ranging from birds to plants, across a variety of habitats globally (McKinney and Lockwood, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; McKinney, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Carlon and Dominoni, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Furthermore, as this study utilizes thousands of eDNA samples, it greatly enhances the geographic scale and taxonomic depth relative to prior studies of invertebrate (Knop, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Piano et al., \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and fungal (Epp Schmidt et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Delgado-Baquerizo et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) communities becoming more homogenized as a result of urbanization.\u003c/p\u003e \u003cp\u003eThere are likely additional factors not explicitly measured in this study that may further account for the observed variations in beta diversity. The observed homogenization of urban biological communities may relate to prior land conversion associated with urbanization. Land conversion can influence both local climate, as well as types of soil available which can result in changes in beta diversity for both invertebrate (Liu et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Montr\u0026agrave;s-Janer et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and fungal (Epp Schmidt et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Delgado-Baquerizo et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Mikryukov et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) communities. Urbanization has led to a great increase in the transport of soil. Soil transportation can result in both the transport of introduced species, as well as the homogenization of soil communities, thereby reducing beta diversity (LaSorte et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Blouin et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHowever, these results are derived from a wide variety of sampling efforts across California and not a single systematic effort. As a result, we found strong skews in how evenly distributed eDNA samples were gathered across California. For both invertebrate and fungal communities there have been relatively few eDNA samples gathered from the southern half of the Central Valley or the Mojave desert. These findings strongly suggest the need for more extensive sampling, from communities in regions such as the Mojave Desert or the northern portion of the Central Valley, in order to better capture a more representative picture of fungal and invertebrate diversity in California.\u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eWhile we used eDNA to better characterize understudied patterns of invertebrate and fungal diversity classified via morphotaxonomy, we do recognize that the use of eDNA can introduce its own biases due to differences in sampling substrates and choice of primers. Commonly used sampling substrates (e.g. water, sediment, decomposing plant material) will all produce a different picture of biodiversity (Koziol et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Van Der Hyde et al., 2020; Ryan et al., 2022) due to a variety of factors ranging from material transport to exposure to sunlight and air (Synder et al., 2023; Guthrie et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). There is no single optimal sampling substrate for using eDNA to characterize biodiversity, rather there is growing evidence for the concurrent use of multiple sampling substrates within field projects to help build a more complete picture (Lavrador et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Runnel et al., \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe volume of eDNA data used in studies such as this has been growing rapidly in recent years. This rapid growth has the potential to enhance traditionally understudied communities, such as fungi and invertebrates, by complimenting diversity described solely by morphotaxonomy (Fr\u0026oslash;slev et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Leandro et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Runnel et al., \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Suren et al., \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Though eDNA can help capture some of this traditionally overlooked diversity it is not without its own gaps. This is particularly the case in the use of metabarcoding primers used to classify species from eDNA, many of which have well-documented biases related to their ability to differentiate various taxonomic groups (Elbrecht and Leese, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Kauserud, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). While the primer set used to track invertebrate diversity may be designed to be \u0026lsquo;universal\u0026rsquo;, it does not equally distinguish different invertebrate groups. For example, many nematode species will look identical when using a particular primer to target the CO1 gene (Deagle et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Similarly, a common choice of primer to target the ITS1 gene and track fungal diversity has been found to be biased towards distinguishing members of the phylum Basidiomycota (Bellemain et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGiven variations in the performance of different primers, there is growing use of the integration of multiple sets of primers for better tracking of the compositions of biological communities (Ficetola et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Reji Chacko et al., \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Beyond the use of metabarcoding, metagenomics can capture much larger portions of genomes sampled via eDNA, and with it the potential to help overcome a number of these classification biases found with the use of metabarcoding (Lofgren and Stajich, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Paul-Chima et al., \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBeyond potential biases associated with sampling substrates and primer sets, there is the issue of uneven sampling effort over time. The eDNA data used in this study are quite recent, predominantly collected within a period of a few years (Kelly et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These data were aggregated to better enable calculations of biodiversity metrics, although such aggregation may obscure a number of ecological patterns. For example, seasonal variations may be bigger drivers of biodiversity patterns than urbanization, or influence biodiversity signals derived from eDNA through wind and rainfall patterns. Such geospatial sampling biases may even out in the near future as the volume of eDNA data is expected to continue its rapid growth.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThere is a growing need to monitor the ecological effects in an increasingly urbanized world. Of particular importance are the impacts of urbanization on the structure of fungal and invertebrate communities, which underpin the functioning of many ecosystems. Despite potential biases between eDNA sampling substrates in capturing biodiversity, or large variations in sampling efforts over space and time, we demonstrate the potential to integrate such data sets to track the impact of urbanization on the diversity of such foundational communities. We have found evidence for urbanization being associated with a significant decline in the richness of fungal communities, and mixed but generally negative impacts on invertebrate richness. For both types of communities, we found significantly higher levels of homogenization in urban areas. However, the strength and significance of these potential relationships are likely being influenced by factors such as the substrate from which eDNA is sampled, seasonal variations, and sampling biases across space and time. We therefore recommend more eDNA-based biodiversity surveys, publishing such data in common platforms such as GBIF, as well as publishing field and lab data to better enable corrections for potential sequencing and sampling biases.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflicts of interest/Competing interests:\u003c/h2\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eNot applicable\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll authors contribution to the original draft, as well as subsequent edits. Conceptualization of this project was performed by A.LS. Methodology was performed by A.L.S and A.B. Software development was performed by A.L.S.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe would like to acknowledge Dr. Rachel Meyer, Dr. Laura Melissa Guzman, Ajith Seresinghe, and Julien Pometta for their support in helping to develop the ideas within this project.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAvailability of data and material: All of the urban area boundaries are available as shapefiles from: https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.html . All species observations were obtained from https://www.gbif.org using the archived query: https://doi.org/10.15468/dl.pnhtw9 .Code availability: The analysis script used in this project is available here: https://github.com/levisimons/WLAC/blob/main/CIB_Urban3.R\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbrego N, Crosier B, Somervuo P et al (2020) Fungal communities decline with urbanization\u0026mdash;more in air than in soil. ISME J 14:2806\u0026ndash;2815\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAvolio M, Pataki DE, Jenerette GD et al (2020) Urban plant diversity in Los Angeles, California: Species and functional type turnover in cultivated landscapes. Plants People Planet 2:144\u0026ndash;156\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBahram M, Netherway T (2022) Fungi as mediators linking organisms and ecosystems. FEMS Microbiol Rev 46:fuab058\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarnes MA, Turner CR (2016) The ecology of environmental DNA and implications for conservation genetics. Conserv Genet 17:1\u0026ndash;17\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBellemain E, Carlsen T, Brochmann C et al (2010) ITS as an environmental DNA barcode for fungi: an in silico approach reveals potential PCR biases. BMC Microbiol 10:1\u0026ndash;9\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBlouin D, Pellerin S, Poulin M (2019) Increase in non-native species richness leads to biotic homogenization in vacant lots of a highly urbanized landscape. Urban Ecosyst 22:879\u0026ndash;892\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBorges FLG, da Rosa Oliveira M, de Almeida TC et al (2021) Terrestrial invertebrates as bioindicators in restoration ecology: A global bibliometric survey. Ecol Indic 125:107458\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBraschler B, Gilgado JD, Zwahlen V et al (2020) Ground-dwelling invertebrate diversity in domestic gardens along a rural-urban gradient: Landscape characteristics are more important than garden characteristics. PLoS ONE 15:e0240061\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCallaghan CT, Bowler DE, Pereira HM (2021) Thermal flexibility and a generalist life history promote urban affinity in butterflies. Glob Chang Biol 27:3532\u0026ndash;3546\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCalsbeek R, Thompson JN, Richardson JE (2003) Patterns of molecular evolution and diversification in a biodiversity hotspot: the California Floristic Province. Mol Ecol 12:1021\u0026ndash;1029\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCampos CO, Almeida SFP, Serra SRQ et al (2024) The overlooked margins: how cities impact diversity of plants and terrestrial invertebrates along urban streams. Urban Ecosyst 1\u0026ndash;16\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarl P, Peterson BG, Peterson MBG (2010) Package \u0026lsquo;PerformanceAnalytics.\u0026rsquo; Retrieved March 29:2011\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarlon E, Dominoni DM (2024) The role of urbanization in facilitating the introduction and establishment of non-native animal species: a comprehensive review. J Urban Ecol 10:juae015\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChao A (1984) Nonparametric estimation of the number of classes in a population. Scand J Stat 265\u0026ndash;270\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChao A, Ma KH, Hsieh TC, Chiu C-H (2016) User\u0026rsquo;s guide for online program SpadeR (Species-richness prediction and diversity estimation in R). Natl Tsing Hua Univ Hsinchu, Taiwan 88\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCheng J, Karambelkar B, Xie Y et al (2019) Package \u0026lsquo;leaflet.\u0026rsquo; R Packag version 2:1\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChiu C-H, Wang Y-T, Walther BA, Chao A (2014) An improved nonparametric lower bound of species richness via a modified good\u0026ndash;turing frequency formula. Biometrics 70:671\u0026ndash;682\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeagle BE, Jarman SN, Coissac E et al (2014) DNA metabarcoding and the cytochrome c oxidase subunit I marker: not a perfect match. Biol Lett 10:20140562\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeiner K, Altermatt F (2014) Transport distance of invertebrate environmental DNA in a natural river. PLoS ONE 9:e88786\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeiner K, Fronhofer EA, M\u0026auml;chler E et al (2016) Environmental DNA reveals that rivers are conveyer belts of biodiversity information. Nat Commun 7:12544\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDelgado-Baquerizo M, Eldridge DJ, Liu Y-R et al (2021) Global homogenization of the structure and function in the soil microbiome of urban greenspaces. Sci Adv 7:eabg5809\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDiamond SE, Chick LD, Perez A et al (2018) Evolution of plasticity in the city: urban acorn ants can better tolerate more rapid increases in environmental temperature. Conserv Physiol 6:coy030\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDray S, Blanchet G, Borcard D et al (2018) Package \u0026lsquo;adespatial.\u0026rsquo; R Packag 2018:3\u0026ndash;8\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eElbrecht V, Leese F (2017) Validation and development of COI metabarcoding primers for freshwater macroinvertebrate bioassessment. Front Environ Sci 5:11\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEpp Schmidt DJ, Pouyat R, Szlavecz K et al (2017) Urbanization erodes ectomycorrhizal fungal diversity and may cause microbial communities to converge. Nat Ecol Evol 1:0123\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFenn ME, Allen EB, Weiss SB et al (2010) Nitrogen critical loads and management alternatives for N-impacted ecosystems in California. J Environ Manage 91:2404\u0026ndash;2423\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFicetola GF, Boyer F, Valentini A et al (2021) Comparison of markers for the monitoring of freshwater benthic biodiversity through DNA metabarcoding. Mol Ecol 30:3189\u0026ndash;3202\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFleming PA, Bateman PW (2018) Novel predation opportunities in anthropogenic landscapes. Anim Behav 138:145\u0026ndash;155\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFr\u0026oslash;slev TG, Kj\u0026oslash;ller R, Bruun HH et al (2019) Man against machine: Do fungal fruitbodies and eDNA give similar biodiversity assessments across broad environmental gradients? Biol Conserv 233:201\u0026ndash;212\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGalitskaya P, Biktasheva L, Blagodatsky S, Selivanovskaya S (2021) Response of bacterial and fungal communities to high petroleum pollution in different soils. Sci Rep 11:164\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGao C, Shi N, Liu Y et al (2013) Host plant genus-level diversity is the best predictor of ectomycorrhizal fungal diversity in a Chinese subtropical forest. Mol Ecol 22:3403\u0026ndash;3414\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGBIF.org (2025) GBIF Occurrence Download\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGe B, Mehring AS, Levin LA (2019) Urbanization alters belowground invertebrate community structure in semi-arid regions: A comparison of lawns, biofilters and sage scrub. Landsc Urban Plan 192:103664\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGillespie TW, Ostermann-Kelm S, Dong C et al (2018) Monitoring changes of NDVI in protected areas of southern California. Ecol Indic 88:485\u0026ndash;494\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eG\u0026oacute;mez-Hern\u0026aacute;ndez M, Avenda\u0026ntilde;o-Villegas E, Toledo-Garibaldi M, G\u0026aacute;ndara E (2021) Impact of urbanization on functional diversity in macromycete communities along an urban ecosystem in Southwest Mexico. PeerJ 9:e12191\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGriffiths HM, Ashton LA, Parr CL, Eggleton P (2021) The impact of invertebrate decomposers on plants and soil. New Phytol 231:2142\u0026ndash;2149\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuthrie AM, Cooper CE, Bateman PW et al (2024) A quantitative analysis of vertebrate environmental DNA degradation in soil in response to time, UV light, and temperature. Environ DNA 6:e581\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHarrison S, Franklin J, Hernandez RR et al (2024) Climate change and California\u0026rsquo;s terrestrial biodiversity. Proc Natl Acad Sci 121:e2310074121\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHending D (2024) Cryptic species conservation: a review. Biol Rev\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHochkirch A, Samways MJ, Gerlach J et al (2021) A strategy for the next decade to address data deficiency in neglected biodiversity. Conserv Biol 35:502\u0026ndash;509\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJi F, Han D, Yan L et al (2022) Assessment of benthic invertebrate diversity and river ecological status along an urbanized gradient using environmental DNA metabarcoding and a traditional survey method. Sci Total Environ 806:150587\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKauserud H (2023) ITS alchemy: on the use of ITS as a DNA marker in fungal ecology. Fungal Ecol 101274\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKelly RP, Lodge DM, Lee KN et al (2024) Toward a national eDNA strategy for the United States. Environ DNA 6:e432\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKirse A, Bourlat SJ, Langen K, Fonseca VG (2021) Unearthing the potential of soil eDNA metabarcoding\u0026mdash;Towards best practice advice for invertebrate biodiversity assessment. Front Ecol Evol 9:630560\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKivlin SN, Hawkes CV (2011) Differentiating between effects of invasion and diversity: impacts of aboveground plant communities on belowground fungal communities. New Phytol 189:526\u0026ndash;535\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKling MM, Mishler BD, Thornhill AH et al (2019) Facets of phylodiversity: evolutionary diversification, divergence and survival as conservation targets. Philos Trans R Soc B 374:20170397\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKnop E (2016) Biotic homogenization of three insect groups due to urbanization. Glob Chang Biol 22:228\u0026ndash;236\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKotze DJ, Lowe EC, MacIvor JS et al (2022) Urban forest invertebrates: how they shape and respond to the urban environment. Urban Ecosyst 25:1589\u0026ndash;1609\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKoziol A, Stat M, Simpson T et al (2019) Environmental DNA metabarcoding studies are critically affected by substrate selection. Mol Ecol Resour 19:366\u0026ndash;376\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLa Sorte FA, Aronson MFJ, Williams NSG et al (2014) Beta diversity of urban floras among E uropean and non-E uropean cities. Glob Ecol Biogeogr 23:769\u0026ndash;779\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLavrador AS, Amaral FG, Moutinho J et al (2024) Comprehensive DNA metabarcoding-based detection of non-indigenous invertebrates in recreational marinas through a multi-substrate approach. Mar Environ Res 200:106660\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLeandro C, Jay-Robert P, P\u0026eacute;tillon J (2024) eDNA for monitoring and conserving terrestrial arthropods: Insights from a systematic map and barcode repositories assessments. Insect Conserv Divers\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLenth RV (2021) emmeans: estimated marginal means, aka least-squares means https://CRAN. R-project org/package\u0026thinsp;=\u0026thinsp;emmeans\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLewthwaite JMM, Baiotto TM, Brown BV et al (2024) Drivers of arthropod biodiversity in an urban ecosystem. Sci Rep 14:390\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi G, Fang C, Li Y et al (2022) Global impacts of future urban expansion on terrestrial vertebrate diversity. Nat Commun 13:1628\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLin M, Simons AL, Harrigan RJ et al (2021) Landscape analyses using eDNA metabarcoding and Earth observation predict community biodiversity in California. Ecol Appl 31:e02379\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLinares LMD (2022) The awkward question: What baseline should be used to measure biodiversity loss? The role of history, biology and politics in setting up an objective and fair baseline for the international biodiversity regime. Environ Sci Policy 135:137\u0026ndash;146\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu Z, Zhou T, Heino J et al (2022) Land conversion induced by urbanization leads to taxonomic and functional homogenization of a river macroinvertebrate metacommunity. Sci Total Environ 825:153940\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLofgren LA, Stajich JE (2021) Fungal biodiversity and conservation mycology in light of new technology, big data, and changing attitudes. Curr Biol 31:R1312\u0026ndash;R1325\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLokatis S, Jeschke JM (2022) Urban biotic homogenization: Approaches and knowledge gaps. Ecol Appl 32:e2703\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eL\u0026uuml;cking R, Aime MC, Robbertse B et al (2020) Unambiguous identification of fungi: where do we stand and how accurate and precise is fungal DNA barcoding? IMA Fungus 11:14\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMagurran AE, Baillie SR, Buckland ST et al (2010) Long-term datasets in biodiversity research and monitoring: assessing change in ecological communities through time. Trends Ecol Evol 25:574\u0026ndash;582\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarselle MR, Lindley SJ, Cook PA, Bonn A (2021) Biodiversity and health in the urban environment. Curr Environ Heal Rep 8:146\u0026ndash;156\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcDonald RI (2008) Global urbanization: can ecologists identify a sustainable way forward? Front Ecol Environ 6:99\u0026ndash;104\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcDonald RI, Marcotullio PJ, G\u0026uuml;neralp B (2013) Urbanization and global trends in\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ebiodiversity and ecosystem services Urban Biodivers Ecosyst Serv challenges Oppor a Glob Assess 31\u0026ndash;52\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcKinney ML (2006) Urbanization as a major cause of biotic homogenization. Biol Conserv 127:247\u0026ndash;260\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcKinney ML, Lockwood JL (1999) Biotic homogenization: a few winners replacing many losers in the next mass extinction. Trends Ecol Evol 14:450\u0026ndash;453\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcMurdie PJ, Holmes S (2013) phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8:e61217\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMermillod-Blondin F, Hose GC, Simon KS et al (2023) Role of invertebrates in groundwater ecosystem processes and services. Groundwater ecology and evolution. Elsevier, pp 263\u0026ndash;281\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMikryukov V, Dulya O, Zizka A et al (2023) Connecting the multiple dimensions of global soil fungal diversity. Sci Adv 9:eadj8016\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMontero S (2020) Leveraging Bogot\u0026aacute;: Sustainable development, global philanthropy and the rise of urban solutionism. Urban Stud 57:2263\u0026ndash;2281\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMontr\u0026agrave;s-Janer T, Suggitt AJ, Fox R et al (2024) Anthropogenic climate and land-use change drive short-and long-term biodiversity shifts across taxa. Nat Ecol Evol 8:739\u0026ndash;751\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMyers N, Mittermeier RA, Mittermeier CG et al (2000) Biodiversity hotspots for conservation priorities. Nature 403:853\u0026ndash;858\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNguyen NH, Williams LJ, Vincent JB et al (2016) Ectomycorrhizal fungal diversity and saprotrophic fungal diversity are linked to different tree community attributes in a field-based tree experiment. Mol Ecol 25:4032\u0026ndash;4046\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNiskanen T, L\u0026uuml;cking R, Dahlberg A et al (2023) Pushing the frontiers of biodiversity research: Unveiling the global diversity, distribution, and conservation of fungi. Annu Rev Environ Resour 48:149\u0026ndash;176\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eN\u0026oslash;rgaard L, Olesen CR, Tr\u0026oslash;jelsgaard K et al (2021) eDNA metabarcoding for biodiversity assessment, generalist predators as sampling assistants. Sci Rep 11:6820\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOksanen J, Simpson G, Blanchet FG et al (2022) Vegan: Community Ecology Package, Version 2.6-4. 2022\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eParkhurst T, Prober SM, Hobbs RJ, Standish RJ (2022) Global meta-analysis reveals incomplete recovery of soil conditions and invertebrate assemblages after ecological restoration in agricultural landscapes. J Appl Ecol 59:358\u0026ndash;372\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePataki DE, Alberti M, Cadenasso ML et al (2021) The benefits and limits of urban tree planting for environmental and human health. Front Ecol Evol 9:603757\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePaul-Chima UO, Ugo AE, Ben OM (2024) The Role of Environmental DNA (EDNA) in Biodiversity Conservation\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePebesma EJ (2018) Simple features for R: standardized support for spatial vector data. R J 10:439\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePereira HM, Martins IS, Rosa IMD et al (2024) Global trends and scenarios for terrestrial biodiversity and ecosystem services from 1900 to 2050. Science 80\u0026ndash;:384:458\u0026ndash;465\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePiano E, Souffreau C, Merckx T et al (2020) Urbanization drives cross-taxon declines in abundance and diversity at multiple spatial scales. Glob Chang Biol 26:1196\u0026ndash;1211\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePont D, Rocle M, Valentini A et al (2018) Environmental DNA reveals quantitative patterns of fish biodiversity in large rivers despite its downstream transportation. Sci Rep 8:10361\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRaimondo D, Young BE, Brooks TM et al (2023) Using Red List Indices to monitor extinction risk at national scales. Conserv Sci Pract 5:e12854\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eReji Chacko M, Altermatt F, Fopp F et al (2023) Catchment-based sampling of river eDNA integrates terrestrial and aquatic biodiversity of alpine landscapes. Oecologia 202:699\u0026ndash;713\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoh T (2024) leaflegend: Create Custom Legends for Leaflet. Comput software] https//leaflegend delveds com\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRuas RDB, Costa LMS, Bered F (2022) Urbanization driving changes in plant species and communities\u0026mdash;A global view. Glob Ecol Conserv 38:e02243\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRunnel K, L\u0026otilde;hmus P, K\u0026uuml;ngas K et al (2024) Aerial eDNA contributes vital information for fungal biodiversity assessment. J Appl Ecol\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRusterholz H-P, Baur B (2023) Changes in Soil Fungal Diversity and Composition along a Rural\u0026ndash;Urban Gradient. Forests 14:2226\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSeena S, Baschien C, Barros J et al (2023) Ecosystem services provided by fungi in freshwaters: a wake-up call. Hydrobiologia 850:2779\u0026ndash;2794\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShirouzu T, Matsuoka S, Doi H et al (2020) Complementary molecular methods reveal comprehensive phylogenetic diversity integrating inconspicuous lineages of early-diverged wood-decaying mushrooms. Sci Rep 10:3057\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSidemo-Holm W, Ekroos J, Reina Garc\u0026iacute;a S et al (2022) Urbanization causes biotic homogenization of woodland bird communities at multiple spatial scales. Glob Chang Biol 28:6152\u0026ndash;6164\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSimons AL, Mazor R, Stein ED, Nuzhdin S (2019) Using alpha, beta, and zeta diversity in describing the health of stream-based benthic macroinvertebrate communities. Ecol Appl 29:e01896\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSnyder ED, Tank JL, Brand\u0026atilde;o-Dias PFP et al (2023) Environmental DNA (eDNA) removal rates in streams differ by particle size under varying substrate and light conditions. Sci Total Environ 903:166469\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSpotswood EN, Beller EE, Grossinger R et al (2021) The biological deserts fallacy: cities in their landscapes contribute more than we think to regional biodiversity. Bioscience 71:148\u0026ndash;160\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStat M, John J, DiBattista JD et al (2019) Combined use of eDNA metabarcoding and video surveillance for the assessment of fish biodiversity. Conserv Biol 33:196\u0026ndash;205\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStupariu M-S, Cushman SA, Pleşoianu A-I et al (2022) Machine learning in landscape ecological analysis: a review of recent approaches. Landsc Ecol 37:1227\u0026ndash;1250\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSumudumali RGI, Jayawardana J (2021) A review of biological monitoring of aquatic ecosystems approaches: with special reference to macroinvertebrates and pesticide pollution. Environ Manage 67:263\u0026ndash;276\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun L, Chen J, Li Q, Huang D (2020) Dramatic uneven urbanization of large cities throughout the world in recent decades. Nat Commun 11:5366\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSuren AM, Burdon FJ, Wilkinson SP (2024) eDNA is a useful environmental monitoring tool for assessing stream ecological health. Environ DNA 6:e596\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSzab\u0026oacute; B, Kor\u0026aacute;nyi D, Gall\u0026eacute; R et al (2023) Urbanization decreases species richness, and increases abundance in dry climates whereas decreases in wet climates: a global meta-analysis. Sci Total Environ 859:160145\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTeixeira CP, Fernandes CO (2020) Novel ecosystems: a review of the concept in non-urban and urban contexts. Landsc Ecol 35:23\u0026ndash;39\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTordoni E, Ametrano CG, Banchi E et al (2021) Integrated eDNA metabarcoding and morphological analyses assess spatio-temporal patterns of airborne fungal spores. Ecol Indic 121:107032\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTournayre O, Littlefair JE, Garrett NR et al (2025) Contrasted effects of human pressure on biodiversity in the UK: a multi-taxonomic assessment using airborne environmental DNA. Ecography (Cop) e08196\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eU.S, Census Bureau (2023) 2023 TIGER/Line Shapefiles. In: U.S. Dep. Commer\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eU.S, Census Bureau (2000) 2000 TIGER/Line Shapefiles. In: U.S. Dep. Commer\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCensus Bureau US (2016) 2016 TIGER/Line Shapefiles. In: U.S. Dep. Commer\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUchida K, Blakey RV, Burger JR et al (2021) Urban biodiversity and the importance of scale. Trends Ecol Evol 36:123\u0026ndash;131\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evan der Heyde M, Alexander J, Nevill P et al (2023) Rapid detection of subterranean fauna from passive sampling of groundwater eDNA. Environ DNA 5:1706\u0026ndash;1719\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVan Der Heyde M, Bunce M, Wardell-Johnson G et al (2020) Testing multiple substrates for terrestrial biodiversity monitoring using environmental DNA metabarcoding. Mol Ecol Resour 20:732\u0026ndash;745\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWan N-F, Fu L, Dainese M et al (2022) Plant genetic diversity affects multiple trophic levels and trophic interactions. Nat Commun 13:7312\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang X, Svenning J-C, Liu J et al (2021) Regional effects of plant diversity and biotic homogenization in urban greenspace\u0026ndash;The case of university campuses across China. Urban Urban Green 62:127170\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWickham H, Wickham H (2016) Data analysis. Springer\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu H, Cao Y, Yu D et al (2021) Ensuring effective implementation of the post-2020 global biodiversity targets. Nat Ecol Evol 5:411\u0026ndash;418\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYao H, Li Z, Geisen S et al (2023) Degree of urbanization and vegetation type shape soil biodiversity in city parks. Sci Total Environ 899:166437\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYiallouris A, Pana ZD, Marangos G et al (2024) Fungal diversity in the soil Mycobiome: Implications for ONE health. One Heal 100720\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZaghloul A, Saber M, Gadow S, Awad F (2020) Biological indicators for pollution detection in terrestrial and aquatic ecosystems. Bull Natl Res Cent 44:1\u0026ndash;11\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang P, Ren G, Qin Y et al (2021) Urbanization effects on estimates of global trends in mean and extreme air temperature. J Clim 34:1923\u0026ndash;1945\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao B, Andermann T (2024) Properties and limitations of eDNA. substrates for terrestrial animal monitoring\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"environmental DNA, eDNA, urban biodiversity, invertebrate diversity, fungal diversity, biotic homogenization","lastPublishedDoi":"10.21203/rs.3.rs-8663064/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8663064/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eA large proportion of nutrient cycling and ecosystem services are supported by the activities of fungal and invertebrate communities. Though these communities have been comparatively understudied compared to more charismatic groups such as birds and mammals, there is a growing body of evidence that the structure of these communities is also impacted by urbanization. Here we analyzed species occurrences derived from environmental DNA (eDNA), taken from over 20,000 samples in California, to investigate patterns of diversity for a variety of fungal and invertebrate orders. We investigated their differences in both taxonomic richness and turnover by comparing communities sampled in urban and non-urban areas.\u003c/p\u003e \u003cp\u003eWe found taxonomic richness was significantly lower within fungal orders sampled in urban areas, and most invertebrate orders displayed a similar pattern. Both invertebrate and fungal communities were found to have undergone a significant level of biotic homogenization in urban areas. We demonstrated that the composition of fungal and invertebrate communities, classified using eDNA, is significantly affected by the process of urbanization.\u003c/p\u003e","manuscriptTitle":"Urbanization changes the richness and homogenizes fungal and invertebrate communities in California","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-24 14:05:11","doi":"10.21203/rs.3.rs-8663064/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"98187766-f8ca-4a6c-98f9-2b1e77f20536","owner":[],"postedDate":"February 24th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-24T14:05:11+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-24 14:05:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8663064","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8663064","identity":"rs-8663064","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}