Priorities of woody species trait-climate associations at continental scale

Priorities of woody species trait-climate associations at continental scale | 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 Article Priorities of woody species trait-climate associations at continental scale Nidhi Vinod, Camila Medeiros, Marvin Browne, Anna Ongjoco, Hannes Deurwaerder, and 20 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9188810/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Plant functional traits underpin ecological strategies and determine ecosystem responses to climate change, yet the key climate drivers of traits remain unresolved at continental scale. Key questions are whether across large gradients species traits are driven more strongly by temperature or precipitation—and by annual or by growing season mean climates. Across 16 ecosystems across the continental USA and northern Mexico, we measured 328 species-site combinations, including 246 unique woody species, for 59 traits relating to hydraulic and photosynthetic physiology, leaf and wood structure and anatomy, and nutrient and isotope concentrations using standard protocols, and tested associations with the macroclimate of both the species’ natural distributions and of the sampling sites, i.e., growing season and mean annual temperature, precipitation, potential evapotranspiration, and aridity index. Across species-site combinations, ninety-eight percent of traits were associated with one or more climate variables. More traits were correlated, and showed stronger correlations, with mean and growing season precipitation than temperature, and more trait variation was explained by precipitation than temperature in multiple regressions. Potential evapotranspiration and aridity index were also strong trait predictors. Seasonal and annual means did not differ in the strength of trait correlations for precipitation, whereas growing season temperature was a stronger predictor of traits than mean annual temperature. Our findings highlight the importance of mean annual precipitation a driver of the distribution of plant traits, indicating the strong sensitivity of species to ongoing shifts in moisture availability at continental scale. Earth and environmental sciences/Ecology/Ecosystem ecology Earth and environmental sciences/Ecology/Ecophysiology Figures Figure 1 Figure 2 Figure 3 Introduction Climate plays a critical role in shaping species adaptation and natural distributions. The global diversity of plants can be considered in terms of functional traits—i.e., the morphological, phenological and physiological characteristics that determine reproduction, growth, and survival strategies 1 , which are frequently aligned with the environment, including macro- and microclimate. Thus, climate shapes the assembly and evolution of species’ traits, and, conversely, a species’ traits determine the climates in which the species can recruit, survive, compete and regenerate 2–6 . Indeed, plant trait-climate associations have been studied at least since Von Humboldt (1807) described how vegetation becomes shorter at higher elevations and latitudes, and a wealth of classical studies have highlighted many factors that underlie the dramatic patterns in plant adaptation and ecological strategies, with temperature and precipitation arguably the most analyzed variables 7–14 . Temperature and moisture influence tissue temperature and hydration, and thereby impact gene expression, photosynthetic capacity (photosynthetic rate, respiration), hydraulic function, and growth (tissue expansion, relative growth rates) 15–24 . Further, exposure to either extremely high or low temperatures or moisture supply can damage or kill plants 23,25–27 . Plant traits may contribute to stress-tolerance, through “resistance” mechanisms, i.e., enabling the maintenance of function during stress, and/or through “avoidance” mechanisms, i.e., reducing function during stress and mitigating the loss with rapid growth during periods of favorable climate 6,28,29 . Thus, within and across species, numerous traits correlate with temperature and/or precipitation, including leaf size, leaf mass per area, leaf nitrogen and phosphorous concentrations, photosynthetic rate, wood density, seed mass, plant height, and water use efficiency 11,16,30–35,35–40 . While plants can respond to given climate variables semi-independently, ultimately performance would depend on their combined influence 6,39,41 . Thus, given natural covariation of environmental factors, plant species may be adapted, assembled and/or adjusted plastically, according to the combinations of macro- and microclimate variables that they experience, in addition to other abiotic and biotic factors 42–44 . A topic of strong current interest is parsing out key climatic determinants of biogeographic trait distributions, which would reveal how specific pressures structure plant functional strategies, enable more accurate trait-based predictions, and advance the understanding of adaptation limits 45 . Thus, many studies have focused on the associations of plant traits with the mean climate of species’ or ecotypes’ native ranges, at a range of scales, across closely-related species or ecotypes 46–49 , or across species from diverse ecosystems, within or across biomes 6,11,40 . Yet, few studies have tested the relative numbers and strengths of the associations of traits with climate variables, i.e., the “priorities” of trait-climate associations.Indeed, the importance of temperature versus precipitation as a driver of species’ trait associations across large spatial extents has become a topic of debate. A highly-cited study reported that annual temperature is a better predictor than precipitation of plant traits globally, considering 21 traits from a compiled trait database for 447,961 site-species combinations 11 . However, ecological patterns at continental scales may differ from those at global scales (Table 1), given contrasting ranges of variation in traits and in climate variables, which are influenced by multiple interacting factors. For example, at a global scale, trait–climate associations correspond to broad latitudinal gradients in which temperature and moisture are positively associated from poles to tropics, and represent high species turnover and phylogenetic heterogeneity, while within continents, aridity gradients may be stronger due to positive association of dry and warm environments, and trait-climate associations may be strengthened by shared floristic histories and more limited species pools. A focus on the adaptation and assembly of ecosystems at continental scale has been established as a Grand Challenge for predicting and mitigating the impacts of climatic shifts 50 , as such systems are models for understanding shifts under hotter, drier future climates 51 . One study at continental scale in Australia found mean annual precipitation was a strong predictor of eight key functional traits 52 . Here we provide a strong test of the central outstanding question of the priorities of trait-climate associations for woody species across ecosystems of the continental USA and Mexico. We focused on four climate variables commonly considered in ecology— temperature, precipitation, potential evapotranspiration, and aridity index (i.e., the ratio of precipitation to potential evapotranspiration). We considered 59 traits including hydraulic, morphological, structural, photosynthesis and compositional traits, expected to contribute to stress resistance and/or stress avoidance (see hypotheses and references in Table S1), We focused on 328 species-site combinations including 246 unique species, sampled in 16 diverse woody-dominated ecosystems representative of larger regions (Fig. 1a; Table 1; Tables S2 and S3). We overcame several limitations of previous studies that would weaken trait-climate associations at large spatial scales. Previous studies have analyzed databases compiled for plants measured at many different locations and times, sometimes using differing methods, with a focus on few traits, especially relating to fast vs. slow growth (“leaf economic spectrum” traits) or plant size, which globally constitute orthogonal axes, often independent of climate 11,39,42,52 . These methodological considerations are among a larger set of reasons that would lead to weakened trait-climate correlations, also including trait-climate mismatch, trait multifunctionality, many-to-one trait mapping, disequilibrium effects, the influence of microclimate, intraspecific trait and climate variation, and climate sampling bias 6,54 .We focused on measurements for plants coexisting in ecosystems across the gradient, and measured a wide range of traits using standard methods, as the incorporation of additional mechanistic traits (including hydraulic and compositional traits) can increase power for resolving trait-climate relationships, especially when common protocols are applied to plants grown together in one or few common environments 6,44,55 . We developed the first continental-scale analysis of the priorities of trait-climate relationships across the United States of America and northern Mexico. We tested association of traits with the mean climate of species’ native ranges, focusing on four key climatic variables known to shape plant and community distributions and diversity and community, considering annual and growing season means: growing season and mean annual temperature (GST and MAT, respectively), growing season and mean annual precipitation (GSP and MAP), growing season and mean annual potential evapotranspiration (GSPET and MAPET), and growing season and mean annual aridity index (GSAI and MAAI) (Fig. 1b). First, we hypothesized that across these sites, traits would correlate most closely with the aridity gradient, and thus, more strongly with precipitation, PET and aridity index, than with temperature. Second, we hypothesized that traits would correlate more closely with growing season than annual climate variables, under the expectation that traits would have strongest influence during the active growth period 56–58 . Methods Data collection Our study focused on data from the CommuniTraits Database (Medeiros et al., to be submitted ). This database includes traits measured with standard methodology on 19 sites including 12 ecosystem types distributed across diverse climates throughout the United States: eight sites of the California floristic province that include alpine, desert, coastal sage scrub, shrubland, chaparral, montane wet forest, mixed riparian woodland and mixed evergreen-deciduous forest ecosystems; nine additional woody-dominated temperate evergreen, deciduous and mixed evergreen-deciduous forests from East to West coasts, and Hawaiian montane wet and lowland dry forests. For this comparative study, we focused on woody species and thus excluded the species of an alpine site dominated by herbaceous species, and given our focus on a continental gradient, we excluded data from two tropical Hawaii forests. Permits were obtained for work in the reserves through direct communication with the reserve directors for the sites part of the University of California Natural Reserve System (UCNRS), the National Ecological and Observatory Network (NEON), and the Forest Global Earth Observatory (ForestGEO). For the Yosemite Forest Dynamics Plot and the Great Smoky Mountains National Park, the permits were obtained through the United States Department of the Interior National Park Service (Permits #YOSE-2017-SCI-0009 and #GRSM-2023-SCI-2199, respectively). Field sampling and trait measurements Our database included the biomass-dominant and other common species at each site, selected based on guidance from reserve managers and, when available, using forest census data. The species included in this study are taxonomically diverse (Table S3), including 246 unique species from 79 families, with 328 species-site combinations across the 16 sites. At each site, we sampled 3–5 individuals per species, and 14-26 species per site. Individual trees and shrubs were sampled across the landscape and we avoided sampling adjacent individuals of the same species. We collected sun-exposed leaves from non-epicormic branches, with no signs of damage or herbivory using aerial lifts, pole pruners or a slingshot. Branches were transported to the lab in dark plastic bags with moist paper and rehydrated overnight in a saturated atmosphere before harvesting current-year grown, fully expanded leaves for subsequent analyses. For compound-leafed species, whole leaves were used. We measured 59 traits using standard protocols (traits listed in Table S1; detailed methods described in Methods S1). For epidermal morphology, we measured one leaf per individual (epidermal cell areas, stomatal size, density, index, maximum anatomical conductance, trichome density). For leaf structure, we averaged values for three leaves (small, medium and large sizes) per individual (leaf area, mass per area, thickness, density, dry matter content, saturated water content and mass per area, petiole area to leaf area ratio, petiole mass to leaf mass ratio and petiole mass per area), and for stem structure and allocation we measured one branch per individual (wood density and Huber value). For leaf elemental and isotopic composition, we analyzed a mixture of 10-20 ground leaves per individual (concentrations per leaf mass of carbon, nitrogen, phosphorus, potassium, calcium, magnesium, iron, and trace metals, concentration per leaf area of nitrogen, and chlorophyll, and carbon: nitrogen and nitrogen: phosphorus ratios, and carbon isotope discrimination). For plant hydraulic traits, we measured two leaves per individual 3-5 individuals per species for turgor loss point, and 3-7 biomass-dominant species from eight sites, we measured 15-48 leaves from 3-5 individuals per species for maximum leaf hydraulic conductance and leaf water potential at 80% decline of conductance. For 3-12 biomass-dominant species from six sites, we measured photosynthetic CO 2 response curves and calculated photosynthetic maximum electron transport rate and maximum carboxylation rate traits for 3-27 leaves from 1-12 individuals per species (see Table 2 for site abbreviations, and number of species per trait in Table S3). Based on nutrient composition, isotope ratio and stomatal data, we derived additional modelled trait values for time-integrated photosynthetic assimilation and stomatal conductance, and maximum stomatal conductance: nitrogen concentration ratio. Additionally, we compiled maximum plant height and seed mass from the TRY database 59 . Climate variables We focused on trait associations with species’ native climate, using the mean climate variables of species’ native distributions based on the assumption that due to gene flow within a species’ native range, average trait values should correspond with average climatic conditions 6,60 . Additionally, we tested trait associations with the climate at the sites at which species were sampled. We estimated the climate of species’ native ranges using occurrence data from the Global Biodiversity Information Facility 6,29,60–63 . Environmental variables were extracted and computed using R software (version 4.2.1 64 ). Species occurrence data were obtained via the ‘rgbif’ R package 65 , and filtered to retain herbarium records collected since 1950. Records lacking latitude or longitude, duplicates, and non-wild occurrences (such as botanical gardens or cultivated urban trees) were excluded 6,65,66 . Species data were further limited to observations from the United States, Canada, and Mexico, unless the species had a known global distribution. After screening with the clean_coordinates function of the ‘CoordinateCleaner’ package to remove points with quality issues (i.e, coordinates that represent administrative capitals, country centroids, equal or zero latitude and longitude, or a 1-degree radius around the GBIF headquarters), descriptive climate statistics were calculated at the species level. As our goal was to test trait–climate relationships, we opted to use direct occurrence points rather than species distribution maps based on ecological niche models to determine species’ climatic envelopes to avoid bias in our analyses 6 . For the coordinates of each species’ occurrences, and also for the coordinates of the study sites, four environmental variables were extracted from publicly available raster datasets, mean annual temperature and precipitation, (MAT and MAP, respectively; WorldClim 67 ), and potential evapotranspiration and aridity index (MAPET and MAAI, respectively; CGIAR-CSI, NCAR-UCAR 68 . From the monthly data of these four variables, we calculated the growing season temperature (GST), growing season precipitation (GSP), growing season potential evapotranspiration (GSPET) and growing season aridity index (GSAI). The growing season was determined as the months with abundant soil moisture (mean precipitation in mm ≥ 2 × mean temperature in degrees Celsius) and minimum temperature above 4ºC, the temperature below which water becomes too viscous to pass through membranes 69 . The raster layers were combined using the ‘stack’ function from the ‘raster’ package 70 , and environmental data for each occurrence point were extracted using the extract function from the ‘dismo’ package 67 . To characterize the climate of the sampling location, we extracted the same eight variables from 1×1 km grid cell around the centroid of each site (Table S2). While these datasets effectively capture broad-scale environmental trends, they are not suited for fine-scale variation such as microclimate factors—e.g., local temperature, light, moisture, or soil composition 71 (Complete dataset provided in Medeiros et al. to be submitted ). Statistical analyses Statistical analyses and visualizations were conducted using R software (versions 3.4.4 and 4.0.2 64 ) along with packages from the CRAN repository (detailed stepwise methods are found in https://github.com/NidhiVinod/Traits-and-Climate). To establish the significance of species-level trait variation within and across sites, we performed two analyses of variance (ANOVA). First, we tested the variation across species in each trait, and second, we used a nested analysis of variance to test for variation across sites, and variation among species within sites. Trait data were log-transformed to improve normality and homoscedasticity before the analysis. For traits containing zero or negative values, a constant of the absolute value of the minimum value plus 1 was added prior to transformation. We used packages car, MASS, dplyr, tidyverse, and TukeyC for this analysis. To summarize the covariation of climate variables, we conducted a principal components analysis (PCA) using ‘stats’ package 64 . Given our interest in the absolute predictive and explanatory power of climate variables, rather than evolutionary processes, we applied ahistoric (nonphylogenetic) analyses. We applied four approaches to test the numbers and strength of associations of traits with the eight climatic variables (GSP, GST, GSAI, GSPET, MAP, MAT, MAAI, MAPET). First, for each trait-climate association we conducted Spearman rank correlations and Pearson correlations for both untransformed and log-transformed data, and considered the highest of the three r -values (“highest r- value”). We then determined the proportion of trait-climate associations that was significant for each climate variable. To assess whether these proportions differed between climate variables, we conducted pairwise proportion tests using the prop.test function in R versions 3.4.4 and 4.0.2 64 . Second, for each climate variable, we conducted analyses of variance to test for differences among the climate variables in their highest r -values. Third, we tested for differences among each pair of climate variables in the strength of all trait correlations (including both significant and nonsignificant) using a meta-analysis of the highest r -values 11 adapted from MetaWin 72 using the package ‘metafor’ 73 . Finally, we tested the relative importance of MAT and MAP in co-determining traits, by fitting multiple regression models that included MAT and MAP as independent variables and functional traits as dependent variables; we then performed a hierarchical partitioning analysis using the ‘hier.part’ package to calculate the percentage contribution of each variable to the prediction of traits 74 . These models were fitted to untransformed and log-transformed data, and we selected the highest R2 based between log-transformed and untransformed data for each trait. We calculated the percentage of traits that were best explained by MAT or MAP, and the mean percent contribution of MAT and MAP to the determination of each trait. Each of the above tests were conducted across all the species-site combinations, first considering trait associations with the mean climate of individual species’ native ranges, and second, considering associations with the climate of species’ sampling sites. We tested the relative variation in the climate variables of species’ native ranges and of the study sites using coefficients of variation for log-transformed data after applying a constant of the absolute value of the minimum value plus 1, and tested for differences among these using standardized likelihood ratio test (SLRT 75 ). Results We found strong variation across species for vast majority of the traits (ANOVAs; P < 0.001 to P < 0.05 for most traits; Table S4). The majority of variance, on average 92%, was explained by species-differences (ANOVAs; P < 0.001 to P < 0.05; Table S4). Considering species nested within site, 35% of variation was explained by site differences, 61% by species within sites, and 4% by variation among individuals within species (Table S4). We conducted a principal components analysis (PCA) to clarify the pattern of variation in the mean climate variables of species’ native distributions (Fig. 1c). The first two axes, Climate-PC1 and Climate-PC2, accounted for 60.4% and 21.8% of the variation, respectively, and 82% in total (Table S5). Climate-PC1 was related to the aridity gradient from cool, wetter sites to hotter, drier sites, i.e., from low to high MAT, MAPET and MAAI, and from high to low GSP and MAP. Climate-PC2 captured orthogonal variation in climatic warmth and evaporative demand, i.e., MAT and MAPET (Table S5, Fig 1). Both PCA axes were related to latitude, though stronger for Climate-PC2 than Climate-PC1 ( r = 0.86 and 0.32, respectively; P <0.001; Table S5; Fig. S1). In the comparison of the relative variation in MAP and MAT across species’ mean climates and site mean climates, MAT showed the stronger variation, with coefficients of variation ± standard error of 0.145±0.0080 and 0.175± 0.00973 respectively, relative to 0.00792±0.0044 and 0.00923± 0.00511 for MAP (Table S16, Table S17). Across species, 58 of the 59 measured plant traits were related to one or more climate variables representing the mean of species’ climate distributions, all but foliar carbon: nitrogen ratio (Fig. 3; Tables S7 and S8). Similarly, when considering the mean climate of species’ sampling sites, 57 of 59 measured traits were related to one or more climate variables, all but leaf dry matter content and bark thickness to stem diameter ratio (Table S9 and S10). Overall, traits were more strongly correlated with precipitation than with temperature. The overall findings for stronger relationships with climatic precipitation than temperature at continental scale is illustrated with five example traits, wood density (WD), turgor loss point (TLP), carbon isotope discrimination (Δ 13 C), leaf area (LA), foliar nitrogen content per mass ( N mass ), and leaf mass per area (LMA) (Fig. 2). Thus, TLP, Δ 13 C, N mass and LMA, had stronger associations with GSP than GST, and TLP, Δ 13 C, LA, N mass and LMA had stronger associations with MAP than MAT. In our analyses across all the traits, considering associations with the mean climate variables for species’ native ranges, a greater proportion of trait correlations were significant for MAP versus MAT (proportion tests; Table 3). Further, trait-climate correlations were stronger for MAP versus MAT when considering only significant correlations (ANOVAs; Fig. 3; Table S11), and for MAP versus MAT and also for GSP versus GST, when considering all their correlations regardless of significance (meta-analysis; Table 3). Further, multiple regression analyses of each trait with respect to MAT and MAP also supported the greater importance of MAP (Table S12). Across the 59 traits, the multiple regressions showed a mean ± standard error of the adjusted r 2 of 0.14 ± 0.015; 84% of traits showed a higher contribution of MAP, and 15% of MAT, and across all the traits, the mean ± standard error of the percent contribution of MAP was 77.6 ± 3.23% whereas that of MAT was 22.3 ± 3.23%. Trait variation was also strongly explained by climatic variables beyond temperature and precipitation, i.e., potential evapotranspiration and aridity index. Indeed, MAPET and MAAI both had higher proportions of significant trait correlations and stronger trait correlations than MAT (Table 3; Fig. 3). Considering growing season variables, GSPET and GSAI showed significantly stronger overall trait correlations than GST (Table 3). Comparing growing season and annual climate variables, for temperature, the growing season mean had a greater proportion of significant trait correlations and stronger correlations overall than the annual mean (Table 3; Fig. 3), and for aridity index, the growing season mean had stronger overall trait correlations and stronger significant correlations than the annual mean (Table 3; Fig. 3; Table S11). For precipitation and potential evapotranspiration, growing season and annual means did not differ in the proportion or strength of correlations (Table 3; Fig. 3; Table S11). The findings were similar when considering trait associations with the climate of species’ sampling sites, rather than the mean climates of species’ native ranges. Thus, a greater proportion of trait correlations were significant, and overall correlations, and significant correlations were stronger, for MAP versus MAT (Table S13 and S14; Fig. S2).Further, MAAI and MAPET both had a higher proportion of significant trait correlations, and stronger associations than MAT, whether considering all correlations or only significant ones (Table S13 and S14; Fig. S3). Considering growing season variables, GST was statistically similar in the proportion and strengths of significant and overall trait correlations as GSP, GSPET and GSAI (Table S13 and S14; Fig. S3). Multiple regression analyses of each trait with respect to MAT and MAP of the climate of species’ sampling sites also supported the greater importance of MAP (Table S15). Across the 59 traits, the multiple regressions showed a mean ± standard error of the adjusted r 2 of 0.12 ± 0.016; 78.0% of traits showed a higher contribution of MAP, and 22% of MAT. Across all the traits, the mean ± standard error of the percent contribution of MAP was 79.7 ± 3.64% whereas that of MAT was 20.3 ± 3.64% (Table S10). Comparing growing season and annual climate variables, GST had a higher proportion of trait correlations, and stronger overall and significant correlations than MAT. For aridity index, the annual mean had a higher proportion of trait correlations, and stronger overall and significant correlations than the growing season mean (Tables S13 and S14; Fig. S3). For precipitation and potential evapotranspiration, growing season and annual means did not differ in the proportions or strength of correlations (Table S13 and S14; Fig. S3). Discussion Switching of patterns from global scale to the continental scale across the contiguous USA and northern Mexico The important role of both precipitation and temperature in predicting traits across the contiguous USA and northern Mexico are consistent with several previous studies at continental and global scales 39,43,49,52 . Yet, we found a strongly novel finding with MAP being a better predictor than MAT, contrasting with the pattern shown in global studies, in which temperature is a better predictor of traits than precipitation 11,38,76,77 . Indeed, across all our analyses of continental-scale trait-climate associations, we found that while traits have substantial associations with temperature, precipitation, potential evapotranspiration and aridity index, precipitation is a stronger predictor of plant traits than temperature. Consistent with our findings, a recent continental analysis for over 7 types of ecosystems spanning from arid, tropical to temperate in Australia highlighted the importance of MAP, though without a comparison of the relative strengths of trait associations with MAT vs. MAP 52 . The difference in the findings of continental from global tests of trait-climate associations would emerge for multiple reasons associated with the disparity in scales (Table 1). At first sight, one might assume that the relative strengths of trait-climate associations may reflect the variation in specific climate variables, and thus that the stronger trait associations with temperature than precipitation at global scale corresponds to the greater variation in temperature, whereas the stronger trait associations with precipitation at continental scale reflects a greater variation in precipitation than temperature. However, in our study, and in a global-scale trait-climate study 11 , MAT exhibited greater climatic variation compared to MAP. The stronger role of MAP than MAT as a driver of trait variation continentally may rather be linked with the associations of climate traits. Globally, the dominant climatic gradient is latitudinal, with variation from colder, drier climates at high latitudes to warmer, wetter climates in the tropics. By contrast, for the continental gradient in our study, a climatic aridity gradient is dominant, from cooler, moister climates to drier, warmer climates, due to continentality and elevational variation 78 ; such a gradient would reinforce the importance of moisture, as water stress in combination with high temperature represents a stronger compound stress. Indeed, in our principal components analysis of climate variation across the species’ native ranges, temperature, and precipitation partly inter-related, with Climate-PC1 showing a strong aridity gradient, with temperature and precipitation negatively correlated, and Climate-PC2 showed additional variation in temperature, showing the influence of the latitudinal gradient, here independent of precipitation. The importance of climatic rainfall as a trait driver is also consistent with its influence on climatic temperature at regional and continental scales; for example, reduced water levels can contribute to high growing season climatic temperatures 79–81 . We note that stronger trait-climate associations would also arise continentally than globally given the relative floristic similarity by contrast with a global analysis 52 , though potentially weaker relationships might be expected given the more constrained species variation 11 . Notably, the global-scale study by Moles et al. 2014 focused on all life forms—including herbaceous and woody species—our study focused on woody species. Microclimate, specifically MAT and MAP experienced by understory herbaceous species varies drastically from woody canopy species 82 , and thus trait relationships with macroclimate variables may be weaker for herbaceous than woody species, especially as the life cycle and phenology of herbaceous species might reduce the time of year in which they are present and/or active, altering trait-climate relationships 61,83 . We conclude from the strong divergence of our continental study from global studies of the priorities of trait-climate associations that the global pattern is not generalizable across continents. However, we also note that the specific pattern found across the sites in our study, may differ from that of other continents, depending on the floristic composition of the continent, the specific ranges of climate variables, and their inter-relationships. Importantly, the trends observed continentally may be expected to be more potentially actionable, relative to global trends, which combine disparate ecosystems with similar climates 11 . Another novel aspect of our study was the test of whether growing season climate variables would be stronger predictors of traits than annual variables. A greater importance of growing season climate would be expected in considering that in temperate regions the leaf expansion and development, and the bulk of photosynthetic activity occurs during the growing season 84 . Indeed, with GST had a greater proportion of significant trait correlations and stronger correlations than MAT, likely because temperature directly constrains developmental and physiological processes such as leaf expansion, nutrient assimilation and photosynthesis during periods of active growth. However, for precipitation and potential evapotranspiration, annual and growing-season variables showed similar proportions of significant trait associations and comparable association strengths, and for aridity index, the annual mean showed stronger correlations than the growing season mean (Table 3 and Tables S11, S13 and S14). These findings are consistent with the pronounced importance of precipitation as a driver of trait associations across our continental gradient. For woody species, given deep soils, the availability of moisture in the growing season may depend on precipitation falling year-round, with water stored from non-growing season periods remaining accessible during the growing season, buffering plants against short-term drought. Additionally, traits associated with survival during the non-growing season—such as cold tolerance, drought resistance during late summer, or maintenance of perennial structures—may be shaped by climatic conditions across the entire year, reducing the contrast between growing season and annual variables as separable predictors for MAP. Similarly, aridity index would reflect cumulative water balance and atmospheric demand across the entire year, such that plant traits respond more strongly to annual than to growing-season aridity (Table 1). Mechanistic associations of traits with precipitation and aridity gradients Our findings of precipitation as the strongest driver of plant traits reflects multiple functional trait adaptations along a continental aridity gradient, that would confer stress resistance and/or stress avoidance (Table S1). Species associated with lower precipitation levels have a range of traits shown in previous studies to be adaptive, including shorter stature, smaller cell sizes, thicker leaves with higher leaf mass per area, and leaf dry mater content, smaller leaf size, denser trichomes lower stomatal density, lower TLP, lower Δ 13 C, higher wood density, higher Huber value, lower saturated water content, lower hydraulic conductance, safer hydraulic thresholds and lower nitrogen mass per area compared to plants adapted to moister regions (Fig. 2; Table S7). These examples of the associations of traits across many plant levels of organization and functional roles highlight the extraordinary importance of precipitation, and aridity, as important predictors of plant traits at continental scale (Fig. 2, Table S7). Limitations of the analyses Our study highlights important relationships among traits and climate variables, and importantly expands on previous studies in the number of traits measured according to standard protocols for a diversity of ecosystems included for trait-climate associations at continental scale. Our analysis was based on standardized field-collected data for 59 traits across 16 ecosystems, capturing diverse woody species across ecosystems at the continental scale. Notably, previous large-scale trait–climate studies have often relied on databases that include non-standardized trait measurements, often compiled for different plants of each species growing in disparate locations. However, our analysis requires key caveats, pointing to avenues for future studies to achieve yet higher resolution. Our climate data were modelled from species occurrences, a widely used approach, though relatively coarse, and which may limit the precision of trait-climate relations; finer-resolution climate data could enhance the relationships observed in our study. Indeed, microclimatic temperature often differs from regional weather stations, and modelled macroclimatic temperature 85 . Further, climatic moisture variables may have a complex relationship to species’ water availability in the field. The availability of water to plants depends on a suite of factors, including the seasonal distribution of rainfall, hydrology, soil depth, soil type (including soil moisture-holding capacity and the soil moisture characteristic curve), access to groundwater and on temperature, which determines both whether the precipitation falls as rain or snow and the level of evaporative demand 44,86–88 . Notably, the growing season variables are estimated by thresholds, therefore it is likely may not necessarily correspond to actual ranges of water availability experienced by the plants in their growing seasons. Additionally, our use of aggregated climate variables (e.g., mean annual or growing season values) may mask the effects of extreme events or intra-seasonal variability—factors that can strongly influence trait adaption and assembly. While we tested for differences between growing season and annual climate variables, the temporal resolution of the available data constrains our ability to evaluate carry-over effects from previous years or to separate the impacts of long-term climate trends from short-term anomalies. Our finding of stronger trait associations with precipitation than temperature, in contrast to global studies, emphasizes the importance of considering scale and specific systems in examining the priorities of trait-climate associations. That point remains robust, though, as noted above, the strength of given climatic variables in driving trat associations would vary across other continents that differ in the ranges of climate variables and their inter-relationships and in their floras. Further, the designation and sampling of species within continents would also play a role in the specific trait-climate associations discerned. Thus, despite our including a wide range of species and representative ecosystems from the contiguous USA and northern Mexico, a wider definition and greater sampling across of the North American continent, including Canada and Mexico would likely shift the specific findings for relative trait associations to a degree. However, our finding of a stronger role for precipitation than temperature in our studied continental gradient remains a robust demonstration of an important departure from global patterns. Overall conclusions and practical applications Our findings highlight the ecological importance of multiple climate variables, and in particular, of precipitation as a stronger trait predictor than temperature in shaping plant functional strategies continentally, differing from the findings globally where temperature can more strongly predict species traits. Precipitation as a driver of traits continentally would influence species occurrences and distributions. As droughts become more frequent and intense, species may face limits to their adaptive capacity associated with their traits, restricting their ability to persist outside certain precipitation ranges. The importance of precipitation continentally points to the need for future studies to analyze continental climate-trait patterns across the globe. Models projecting species and trait distribution for future climate scenarios should incorporate precipitation as a driver of traits regionally and continentally rather than relying primarily on temperature, though often modelled scenarios show greater resolution of potential shifts in temperature compared to precipitation 89,90 . Precipitation-driven variation in species and traits also impacts ecosystem scale functions such as ecosystem carbon and water fluxes, since traits can directly impact ecosystem productivity and fluxes 91 with feedbacks on future precipitation regimes. Our study suggests that the consideration of the wide range of trait-climate associations will improve predictions of across scales, from species-distributions to the terrestrial climate cycle, to future climate, and locally, improve restoration efforts through incorporating species with drought tolerance in regions prone to droughts and heatwaves. Declarations Acknowledgements We are grateful to the indigenous peoples that stewarded the land and plants studied in this project throughout millenia, including the Kizh, Tongva, Chumash and Chemehuevi peoples in Southern California and Mojave Desert, Kumeyaay (Diegueño) and Micqanaqa’n peoples in Ensenada, Baja California region, Ahwahnechee (Southern Sierra Miwok and Mono) indegenious to Yosemite region, Yokuts, Miwok and Wintun communities in Central Valley and coast range, California; Ute community indegenious to Colorado Niwot region, Eastern Shashone with Arapaho community in Pacific Northwest, Algonquian-speaking peoples, Nipmuc, and Wampanoag Piscataway, Monocan, Pohatan, Lenape in North East, Osage Nation in Missouri region, Apalachee, Timucua, Creek and Seminole peoples in North-Central Florida. We are grateful to J. André, R. Argaez, R. Barreto, S. Dannet Diaz de Leon Guerrero, L. Fletcher, S. Germain, C. Garnica-Díaz, J. Laoué, L. Magee, M. Ochoa, A. Pivovaroff, K. Svoboda, L. Azaryan, A. Berrol, S. Boyadzhyan, S. Ebrahimi, G. Grewal, E. Huang, S. Kady, J. Kim, J. Laguardia, E. Lam, B. Leyva, V. Liu, M. Mehta, R. Min, L. Nasrallah, L. Ngau, J. Ortiguerra, A. Perez, C. Rai, J. Smith, E. Solis, S. Tagaryan, A. Varnado, A. Verma, A. Yadegarian, M. Yu, and K. Zhang for helping with sampling, trait measurements and comments on early versions of the manuscript. This work was funded by National Science Foundation award 2017949 and the Laboratory Directed Research and Development Program of Oak Ridge National Laboratory (ORNL); ORNL is managed by the University of Tennessee-Battelle, LLC, under contract DE-AC05-00OR22725 with the U.S. Department of Energy. Author Contributions Conceptualization of the project: LS, CM, NV, MD, ATT, NK; writing of original draft: NV and LS; formal analyses: NV, CM, KCC and LS; investigation- species sampling: CM, NV, AO, MB, MD, HD, JZ, CH, ST, LS; investigation- collection of trait data: CM, NV, AO, MB, MD, HD, LS; investigation- site logistics and species abundance: LS, JF, NMH, CM, SM, GPJ, DJ, JAL, RMA, JDW, KAT, MD, ATT, NK; data visualization: NV and CM; review & editing: all. Conflict of Interest None. Data and Materials Availability All code used for this analysis is available on GitHub Trait-Climate repository: https://github.com/NidhiVinod/Traits-and-Climate References Violle, C. et al. Let the concept of trait be functional! Oikos 116 , 882–892 (2007). Rolhauser, A. G., Waller, D. M. & Tucker, C. M. Complex trait‒environment relationships underlie the structure of forest plant communities. Journal of Ecology 109 , 3794–3806 (2021). Laughlin, D. C. & Messier, J. 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Contrasts in elements of pattern and processes between global and continental scale gradients that would influence the priorities of trait-climate association across species, and their generalizability for prediction. Pattern or process element Global Scale Continental Scale Potential influence on across-species trait-climate associations Concurrence of temperature and precipitation gradients The dominant climatic gradient is latitudinal, with variation from colder, drier climates (high latitudes) to warmer, wetter climates (tropical). 92–95 Climatic aridity gradients are often found, from cooler, moister climates to drier, warmer climates, due to continentality and elevational variation 78 The global latitudinal gradient aligns adaptation to low/high moisture with low/high temperature, whereas aridity gradients as typical at continental scale emphasize the importance of moisture, as water stress is stronger when combined with high temperatures Climatic overlap Similar climates can exist in extremely floristically distinct regions (e.g., Mediterranean vs. Australian shrublands) 96 Floristics more cohesive typically for given climates within a continent 97,98 Trait-climate associations may be stronger continentally than globally Biogeographic context Strong floristic/evolutionary differences across the spatial extent (Erwin 2009) Stronger influence of shared evolutionary and biogeographic history 97 Trait-climate associations may be stronger continentally than globally Species pool Higher species variation due to global species turnover and greater phylogenetic heterogeneity 99 More constrained species variation due to consistent regional species pools and lower phylogenetic heterogeneity 100 . Trait-climate associations may be stronger globally than continentally Generalizability of trait-climate associations May obscure local/regional patterns (Towers et al. 2024) Captures variation without overgeneralization (Towers et al. 2024) Trait-climate associations may be more generalizable continentally than globally, and thus more actionable and policy-relevant (e.g., national/continental planning) Table 2. Study sites across the continental USA and northern Mexico, ordered from West to East with site codes (Fig. 1). Sites with forest type, number of species, growing season temperature (GST), growing season precipitation (GSP), growing season potential evapotranspiration (GSPET), growing season aridity (GSAI), mean annual temperature (MAT), mean annual potential evapotranspiration (MAPET), and mean annual aridity (MAAI). Site Vegetation type # sp. GST GSP GSPET GSAI MAT MAP MAPET MAAI Sweeney Granite Mountains Desert Research Center (SGMD) Desert 28 8.68 122 542 0.228 16.6 263 2738 0.095 Stunt Ranch Santa Monica Mountains Reserve (SRSM) Chaparral 26 12.9 392 734 0.563 16.4 412 1926 0.213 Centro de Investigación Científica y de Educación Superior de Ensenada (ENSE) Coastal sage scrub 22 13.0 183 408 0.336 16.4 256 1588 0.121 Yosemite Forest Dynamics Plot (YFDP) Montane wet forest 20 7.87 605 720 1.09 10.7 977 1812 0.539 San Joaquin Experimental Range (SJER) Shrubland, evergreen forest 14 10.3 494 569 1.03 16.6 552 2118 0.260 Onion Creek (OC) Mixed riparian woodland 19 9.75 137 441 0.330 6.46 1122 1486 0.754 Angelo Coast Range Reserve (ACRR) Mixed evergreen-deciduous forest 21 8.85 1557 641 3.30 11.4 1613 1365 1.18 Wind River Forest Dynamics Plot (WFDP) Evergreen forest 18 10.8 1086 820 1.96 9.05 2005 1081 1.85 Missouri Ozark AmeriFlux (MOFL) Deciduous forest 20 16.6 858 1239 0.753 12.4 1013 1374 0.737 Niwot Ridge Mountain Research Station (NIWO) Evergreen forest 12 8.58 218 616 0.352 0.039 668 1202 0.555 Harvard Forest (HARV) Mixed evergreen-deciduous forest 26 13.8 656 809 0.852 7.01 1105 1004 1.10 Rutgers University Pinelands Field Station (RPFS) Atlantic coastal pine barrens 18 15.3 895 1088 0.897 11.7 1159 1237 0.937 Smithsonian Conservation Biology Institute (SCBI) Mixed evergreen-deciduous forest 12 15.1 828 1080 0.825 11.5 1038 1232 0.841 Smithsonian Environmental Research Center (SERC) Deciduous forest 22 16.7 860 1189 0.768 13.2 1103 1356 0.813 Great Smoky Mountains National Park (GRSM) Deciduous forest 26 14.6 1163 1175 1.08 12.7 1392 1295 1.07 Ordway Swisher Biological Station (OSBS) Woody evergreen forest 24 20.5 1297 1625 0.802 20.5 1297 1632 0.794 Table 3. Tests of differences in the numbers and strengths of correlations and of 59 traits with mean climate variables for 328 species’ site combinations across 16 ecosystems of the continental USA and northern Mexico. For tests of proportions of significant correlations, pairwise proportion tests were used; tests of correlations strength were based on meta-analysis (log-ratio tests); Significant differences at P < 0.05 are highlighted in bold and with an asterisk for P<0.05. Number of asterisks follow standard convention of p-levels (* <0.05; ** <0.01; *** <0.001). Rows are ordered within sections from the highest to lowest proportion of significant trait correlations Climate Variable 1 Climate Variable 2 Proportion of significant trait correlations P -value (proportion tests) Meta-analysis log ratio of variable 1 / variable 2 (95% confidence intervals) P -value (meta-analyses) Variable 1 Variable 2 Annual climate variables MAP MAT 0.881 0.492 <0.001 1.37 (0.75, 1.367) <0.001 MAAI MAT 0.831 0.492 <0.001 1.39 (0.858, 1.928) <0.001 MAAI MAP 0.831 0.881 0.70 0.015 (0.2, 0.238) 0.89 MAPET MAAI 0.712 0.831 0.094 0.242 (0.031, 0.514) 0.082 MAPET MAP 0.712 0.881 0.98 0.282 (-0.665, 0.101) 0.15 MAPET MAT 0.712 0.492 0.012 1.18 (0.634, 1.183) <0.001 Growing season climate variables GSP GSPET 0.797 0.746 0.33 0.163 (0.064, 0.262) 0.0012 GSP GST 0.797 0.678 0.10 0.835 (0.452, 1.219) <0.001 GSP GSAI 0.797 0.695 0.14 0.914 (0.577, 1.251) <0.001 GSPET GSAI 0.746 0.695 0.24 0.735 (0.362, 1.107) <0.001 GSPET GST 0.746 0.678 0.73 0.651 (0.283, 1.018) <0.001 GST GSAI 0.678 0.695 0.50 0.052 (-0.523, 0.627) 0.86 Annual vs growing season climate variables MAAI GSAI 0.831 0.695 0.94 0.851 (0.463,1.239) <0.001 MAP GSP 0.797 0.881 0.84 -0.048 (0.411, 0.316) 0.80 MAPET GSPET 0.712 0.746 0.42 0.069 (-0.276, 0.414) 0.70 GST MAT 0.678 0.492 0.031* 0.770 (0.151,1.389) 0.015 Additional Declarations There is NO Competing Interest. 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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-9188810","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":612622094,"identity":"26894d6c-255a-4b05-9671-80e7f98eb9b8","order_by":0,"name":"Nidhi Vinod","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAsElEQVRIiWNgGAWjYDACZgYDIGkDxIyNB0jRkgbS0kCkFgawlsNgFnFazNmZt33m3XHebm37YaAtNTbRBLVYNrMVz+Y9czt525lEoJZjabkNBF11mMeYmbftdrLZAaAWxobDRGs5l2x2/iFpWg7Ymd0g3ha2Ysa5Z5ITzG4AbUkgyi/nD29meLvDzt7sfPrDBx9qbAhrAQPGBoZEsMoEopRDtdgTrXgUjIJRMApGHgAAXNxESKiRHtEAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0003-2726-4105","institution":"UCLA","correspondingAuthor":true,"prefix":"","firstName":"Nidhi","middleName":"","lastName":"Vinod","suffix":""},{"id":612622095,"identity":"74339405-ec76-4096-99b1-923c31e28092","order_by":1,"name":"Camila Medeiros","email":"","orcid":"","institution":"Department of Botany, Federal University of Pernambuco, Recife, Brazil","correspondingAuthor":false,"prefix":"","firstName":"Camila","middleName":"","lastName":"Medeiros","suffix":""},{"id":612622096,"identity":"47ac4dc6-8baa-456a-8077-9e72fbc51a6d","order_by":2,"name":"Marvin Browne","email":"","orcid":"","institution":"Department of Biology, Stanford University, Stanford, CA, USA","correspondingAuthor":false,"prefix":"","firstName":"Marvin","middleName":"","lastName":"Browne","suffix":""},{"id":612622097,"identity":"2192b572-6ce1-4f42-9f34-728f0e5c4394","order_by":3,"name":"Anna Ongjoco","email":"","orcid":"","institution":"UCLA","correspondingAuthor":false,"prefix":"","firstName":"Anna","middleName":"","lastName":"Ongjoco","suffix":""},{"id":612622098,"identity":"ce6b130d-d27c-46d2-811d-3cb8d9817c22","order_by":4,"name":"Hannes Deurwaerder","email":"","orcid":"","institution":"Department of Forest Resources and Environmental Conservation Virginia Tech, USA","correspondingAuthor":false,"prefix":"","firstName":"Hannes","middleName":"","lastName":"Deurwaerder","suffix":""},{"id":612622099,"identity":"f0175909-cd11-47cf-ae9f-f1ae4713ca0e","order_by":5,"name":"Joseph Zailaa","email":"","orcid":"","institution":"Yale School of the Environment, Yale University, New Haven, CT, USA","correspondingAuthor":false,"prefix":"","firstName":"Joseph","middleName":"","lastName":"Zailaa","suffix":""},{"id":612622100,"identity":"a097480b-19a6-4ee8-a66f-3120b2ea94f7","order_by":6,"name":"Christian Henry","email":"","orcid":"","institution":"UCLA","correspondingAuthor":false,"prefix":"","firstName":"Christian","middleName":"","lastName":"Henry","suffix":""},{"id":612622101,"identity":"06a0bfec-691a-415b-9fec-64e387ff4ae3","order_by":7,"name":"Santiago Trueba","email":"","orcid":"","institution":"AMAP, Univ Montpellier, IRD, CIRAD, CNRS, INRAE, Montpellier, France","correspondingAuthor":false,"prefix":"","firstName":"Santiago","middleName":"","lastName":"Trueba","suffix":""},{"id":612622102,"identity":"2bd865c5-3547-47bc-9952-cf28adaabea0","order_by":8,"name":"Norbert Kunert","email":"","orcid":"","institution":"Functional and Tropical Plant Ecology, University of Bayreuth, Bayreuth, Germany","correspondingAuthor":false,"prefix":"","firstName":"Norbert","middleName":"","lastName":"Kunert","suffix":""},{"id":612622103,"identity":"76691ffc-67e1-4c1d-a875-278f5c417e0f","order_by":9,"name":"KC Cushman","email":"","orcid":"","institution":"Oak Ridge National Laboratory","correspondingAuthor":false,"prefix":"","firstName":"KC","middleName":"","lastName":"Cushman","suffix":""},{"id":612622104,"identity":"5daf60ff-ea6e-4b0a-90a1-10689e0956f9","order_by":10,"name":"Jennifer A. Franklin","email":"","orcid":"https://orcid.org/0000-0002-7158-5953","institution":"Department of Forestry, Wildlife and Fisheries, University of Tennessee, Knoxville, TN, USA","correspondingAuthor":false,"prefix":"","firstName":"Jennifer","middleName":"A.","lastName":"Franklin","suffix":""},{"id":612622105,"identity":"55246b3c-af91-4f18-943f-8fface617b9f","order_by":11,"name":"N Michele Holbrook","email":"","orcid":"https://orcid.org/0000-0003-3325-5395","institution":"Harvard University","correspondingAuthor":false,"prefix":"","firstName":"N","middleName":"Michele","lastName":"Holbrook","suffix":""},{"id":612622106,"identity":"42e8e83a-b103-4594-8bf1-21d40645d3a4","order_by":12,"name":"David Orwig","email":"","orcid":"https://orcid.org/0000-0001-7822-3560","institution":"Harvard University","correspondingAuthor":false,"prefix":"","firstName":"David","middleName":"","lastName":"Orwig","suffix":""},{"id":612622107,"identity":"8c8b8ed9-a6f7-468c-91cf-406133b675c0","order_by":13,"name":"Sean McMahon","email":"","orcid":"https://orcid.org/0000-0001-8302-6908","institution":"Smithsonian Tropical Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Sean","middleName":"","lastName":"McMahon","suffix":""},{"id":612622108,"identity":"a7049bd4-9b85-4075-be0a-afa6af153270","order_by":14,"name":"Jess Shue","email":"","orcid":"","institution":"Smithsonian Environmental Research Center, Edgewater, MD, USA","correspondingAuthor":false,"prefix":"","firstName":"Jess","middleName":"","lastName":"Shue","suffix":""},{"id":612622109,"identity":"2af2e74c-482e-45aa-97eb-84cba1abc105","order_by":15,"name":"Grace John","email":"","orcid":"","institution":"13.\tSchool of Forest, Fisheries, and Geomatics Sciences, University of Florida, Gainesville, FL, USA","correspondingAuthor":false,"prefix":"","firstName":"Grace","middleName":"","lastName":"John","suffix":""},{"id":612622110,"identity":"1d9c25db-3400-478d-b890-355fde660113","order_by":16,"name":"Daniel Johnson","email":"","orcid":"https://orcid.org/0000-0002-8585-2143","institution":"University of Florida","correspondingAuthor":false,"prefix":"","firstName":"Daniel","middleName":"","lastName":"Johnson","suffix":""},{"id":612622111,"identity":"096119a9-67f0-4647-bfda-768f7b7c12f1","order_by":17,"name":"Nathan Kraft","email":"","orcid":"","institution":"University of California, Los Angeles","correspondingAuthor":false,"prefix":"","firstName":"Nathan","middleName":"","lastName":"Kraft","suffix":""},{"id":612622112,"identity":"b64c8528-c2df-4eaf-a1af-b05be17f88ee","order_by":18,"name":"James Lutz","email":"","orcid":"https://orcid.org/0000-0002-2560-0710","institution":"Utah State University","correspondingAuthor":false,"prefix":"","firstName":"James","middleName":"","lastName":"Lutz","suffix":""},{"id":612622113,"identity":"c3bc0302-9dc5-467e-82b6-f24dc17751b0","order_by":19,"name":"Rodrigo Alonzo","email":"","orcid":"","institution":"Ensenada Center for Scientific Research and Higher Education, Ensenada, Mexico","correspondingAuthor":false,"prefix":"","firstName":"Rodrigo","middleName":"","lastName":"Alonzo","suffix":""},{"id":612622114,"identity":"5a2236ca-ca74-46e5-9c73-cf4c8d7dd1e2","order_by":20,"name":"Anna Trugman","email":"","orcid":"https://orcid.org/0000-0002-7903-9711","institution":"University of California Santa Barbara","correspondingAuthor":false,"prefix":"","firstName":"Anna","middleName":"","lastName":"Trugman","suffix":""},{"id":612622115,"identity":"6fbda11d-8536-4400-ba45-eb24c76848d1","order_by":21,"name":"Jeffrey D. Wood","email":"","orcid":"","institution":"School of Natural Resources, University of Missouri, Columbia, MO, USA","correspondingAuthor":false,"prefix":"","firstName":"Jeffrey","middleName":"D.","lastName":"Wood","suffix":""},{"id":612622116,"identity":"8ab2f83a-7e51-45fa-891e-599d4b392e0e","order_by":22,"name":"Kristina Anderson-Teixeira","email":"","orcid":"https://orcid.org/0000-0001-8461-9713","institution":"Smithsonian Conservation Biology Institute","correspondingAuthor":false,"prefix":"","firstName":"Kristina","middleName":"","lastName":"Anderson-Teixeira","suffix":""},{"id":612622117,"identity":"3ce10c9a-02e6-47e0-9b06-496056e9d1b2","order_by":23,"name":"Matteo Detto","email":"","orcid":"https://orcid.org/0000-0003-0494-188X","institution":"Princeton University","correspondingAuthor":false,"prefix":"","firstName":"Matteo","middleName":"","lastName":"Detto","suffix":""},{"id":612622118,"identity":"c38297d0-758d-4e9e-8bd7-fd20891ed128","order_by":24,"name":"Lawren Sack","email":"","orcid":"https://orcid.org/0000-0002-7009-7202","institution":"Department of Ecology and Evolutionary Biology, UCLA","correspondingAuthor":false,"prefix":"","firstName":"Lawren","middleName":"","lastName":"Sack","suffix":""}],"badges":[],"createdAt":"2026-03-22 03:30:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9188810/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9188810/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107484591,"identity":"67817282-8364-4c66-8c0f-5666594c158f","added_by":"auto","created_at":"2026-04-22 02:32:27","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":382415,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Mean coordinates of the species’ native distributions for 328 species-site combinations for 246 species sampled from 16 ecosystems 246 unique species in this study and the 16 sites sampled across the USA and northern Mexico (see Table 2 for site codes) (b) Flowchart of climatic variables included in this study, i.e., mean annual precipitation (MAP), temperature (MAT), potential evapotranspiration (MAPET), and aridity (MAAI) and growing season mean precipitation (GSP), temperature (GST), potential evapotranspiration (GSPET), and aridity (GSAI). (c) Principal components analysis of the climate variables; the points represent the mean climate of species’ native distributions, and are colored according to the species’ sampling locations.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9188810/v1/c04fb581a5d080742e3aa81d.png"},{"id":107484386,"identity":"3213196c-8ff8-441c-a50c-e5786757b93b","added_by":"auto","created_at":"2026-04-22 02:31:48","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1272804,"visible":true,"origin":"","legend":"\u003cp\u003eIllustrative relationships of traits with the mean climate of species’ native distributions for 328 species-site combinations sampled from 16 ecosystems of the continental USA and northern Mexico. : relationships of wood density (WD), turgor loss point (TLP), carbon isotope discrimination (D\u003csup\u003e13\u003c/sup\u003eC), leaf area (LA), foliar nitrogen content per mass (\u003cem\u003eN\u003c/em\u003e\u003csub\u003emass\u003c/sub\u003e), leaf mass per area (LMA), with growing season temperature (GST), growing season precipitation (GSP), growing season potential evapotranspiration (GSPET), growing season aridity index (GSAI), mean annual temperature (MAT), mean annual precipitation (MAP), mean annual potential evapotranspiration (MAP), mean annual aridity index (MAAI).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9188810/v1/ec0b541f4e870ed2e3eaef4f.png"},{"id":107306988,"identity":"335c6993-cf6c-443a-b9a2-882c1136d831","added_by":"auto","created_at":"2026-04-20 08:31:28","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":225169,"visible":true,"origin":"","legend":"\u003cp\u003eMean correlation coefficients (\u003cem\u003er\u003c/em\u003e values) ± standard error) for significant associations of traits with the mean climate of species’ native distributions, across 328 species’ site combinations sampled from 16 ecosystems of the continental USA and northern Mexico. Mean annual and growing season climate variables for: temperature (MAT and GST, respectively), precipitation (MAP and GSP), potential evapotranspiration (MAPET and GSPET), and aridity (MAAI and GSAI). Letters above each bar indicate significant differences among mean annual or growing season mean variables based on Tukey comparisons, such that; bars with different letters within mean annual or growing season mean variables are significantly different at \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05 (Table S11). Asterisks above the bars indicate significant differences between growing season and mean annual values (ANOVAs; p\u0026lt;0.05; Table S11).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9188810/v1/56461098a169ec5a233e9357.png"},{"id":107487021,"identity":"507e073f-21ba-4a57-8eb2-f12e8d39b60a","added_by":"auto","created_at":"2026-04-22 02:39:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2504242,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9188810/v1/f4f2ab51-cdb8-4d5e-80d7-c2904c475c84.pdf"},{"id":107306986,"identity":"49e5d1f1-5e25-45b4-9f6d-11b18d3b4fa1","added_by":"auto","created_at":"2026-04-20 08:31:28","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1390920,"visible":true,"origin":"","legend":"Supplementary Information for","description":"","filename":"TraitClimateSI.docx","url":"https://assets-eu.researchsquare.com/files/rs-9188810/v1/f6d6ae14f7e6c29e6f3aec51.docx"},{"id":107306987,"identity":"d533c18d-7414-4e89-b174-2917f88ea20d","added_by":"auto","created_at":"2026-04-20 08:31:28","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":2430478,"visible":true,"origin":"","legend":"Supplementary Information for","description":"","filename":"TraitClimateSI.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9188810/v1/d9b776b135444e2da6e7ab7b.xlsx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Priorities of woody species trait-climate associations at continental scale","fulltext":[{"header":"Introduction","content":"\u003cp\u003eClimate plays a critical role in shaping species adaptation and natural distributions. The global diversity of plants can be considered in terms of functional traits\u0026mdash;i.e., the morphological, phenological and physiological characteristics that determine reproduction, growth, and survival strategies\u003csup\u003e1\u003c/sup\u003e, which are frequently aligned with the environment, including macro- and microclimate. Thus, climate shapes the assembly and evolution of species\u0026rsquo; traits, and, conversely, a species\u0026rsquo; traits determine the climates in which the species can recruit, survive, compete and regenerate\u003csup\u003e2\u0026ndash;6\u003c/sup\u003e. Indeed, plant trait-climate associations have been studied at least since Von Humboldt (1807) described how vegetation becomes shorter at higher elevations and latitudes, and a wealth of classical studies have highlighted many factors that underlie the dramatic patterns in plant adaptation and ecological strategies, with temperature and precipitation arguably the most analyzed variables\u003csup\u003e7\u0026ndash;14\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eTemperature and moisture influence tissue temperature and hydration, and thereby impact gene expression, photosynthetic capacity (photosynthetic rate, respiration), hydraulic function, and growth (tissue expansion, relative growth rates)\u003csup\u003e15\u0026ndash;24\u003c/sup\u003e. Further, exposure to either extremely high or low temperatures or moisture supply can damage or kill plants\u003csup\u003e23,25\u0026ndash;27\u003c/sup\u003e. Plant traits may contribute to stress-tolerance, through \u0026ldquo;resistance\u0026rdquo; mechanisms, i.e., enabling the maintenance of function during stress, and/or through \u0026ldquo;avoidance\u0026rdquo; mechanisms, i.e., reducing function during stress and mitigating the loss with rapid growth during periods of favorable climate\u003csup\u003e6,28,29\u003c/sup\u003e. Thus, within and across species, numerous traits correlate with temperature and/or precipitation, including leaf size, leaf mass per area, leaf nitrogen and phosphorous concentrations, photosynthetic rate, wood density, seed mass, plant height, and water use efficiency\u003csup\u003e11,16,30\u0026ndash;35,35\u0026ndash;40\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eWhile plants can respond to given climate variables semi-independently, ultimately performance would depend on their combined influence\u003csup\u003e6,39,41\u003c/sup\u003e. Thus, given natural covariation of environmental factors, plant species may be adapted, assembled and/or adjusted plastically, according to the combinations of macro- and microclimate variables that they experience, in addition to other abiotic and biotic factors\u003csup\u003e42\u0026ndash;44\u003c/sup\u003e. A topic of strong current interest is parsing out key climatic determinants of biogeographic trait distributions, which would reveal how specific pressures structure plant functional strategies, enable more accurate trait-based predictions, and advance the understanding of adaptation limits \u003csup\u003e45\u003c/sup\u003e. Thus, many studies have focused on the associations of plant traits with the mean climate of species\u0026rsquo; or ecotypes\u0026rsquo; native ranges, at a range of scales, across closely-related species or ecotypes\u003csup\u003e46\u0026ndash;49\u003c/sup\u003e, or across species from diverse ecosystems, within or across biomes\u003csup\u003e6,11,40\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eYet, few studies have tested the relative numbers and strengths of the associations of traits with climate variables, i.e., the \u0026ldquo;priorities\u0026rdquo; of trait-climate associations.Indeed, the importance of temperature versus precipitation as a driver of species\u0026rsquo; trait associations across large spatial extents has become a topic of debate. A highly-cited study reported that annual temperature is a better predictor than precipitation of plant traits globally, considering 21 traits from a compiled trait database for 447,961 site-species combinations \u003csup\u003e11\u003c/sup\u003e. However, ecological patterns at continental scales may differ from those at global scales (Table 1), given contrasting ranges of variation in traits and in climate variables, which are influenced by multiple interacting factors. For example, at a global scale, trait\u0026ndash;climate associations correspond to broad latitudinal gradients in which temperature and moisture are positively associated from poles to tropics, and represent high species turnover and phylogenetic heterogeneity, while within continents, aridity gradients may be stronger due to positive association of dry and warm environments, and trait-climate associations may be strengthened by shared floristic histories and more limited species pools. A focus on the adaptation and assembly of ecosystems at continental scale has been established as a Grand Challenge for predicting and mitigating the impacts of climatic shifts\u003csup\u003e50\u003c/sup\u003e, as such systems are models for understanding shifts under hotter, drier future climates\u003csup\u003e51\u003c/sup\u003e. One study at continental scale in Australia found mean annual precipitation was a strong predictor of eight key functional traits\u003csup\u003e52\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eHere we provide a strong test of the central outstanding question of the priorities of trait-climate associations for woody species across ecosystems of the continental USA and Mexico. We focused on four climate variables commonly considered in ecology\u0026mdash; temperature, precipitation, potential evapotranspiration, and aridity index (i.e., the ratio of precipitation to potential evapotranspiration). We considered 59 traits including hydraulic, morphological, structural, photosynthesis and compositional traits, expected to contribute to stress resistance and/or stress avoidance (see hypotheses and references in Table S1), We focused on 328 species-site combinations including 246 unique species, sampled in 16 diverse woody-dominated ecosystems representative of larger regions (Fig. 1a; Table 1; Tables S2 and S3). We overcame several limitations of previous studies that would weaken trait-climate associations at large spatial scales. Previous studies have analyzed databases compiled for plants measured at many different locations and times, sometimes using differing methods, with a focus on few traits, especially relating to fast vs. slow growth (\u0026ldquo;leaf economic spectrum\u0026rdquo; traits) or plant size, which globally constitute orthogonal axes, often independent of climate\u003csup\u003e11,39,42,52\u003c/sup\u003e. These methodological considerations are among a larger set of reasons that would lead to weakened trait-climate correlations, also including trait-climate mismatch, trait multifunctionality, many-to-one trait mapping, disequilibrium effects, the influence of microclimate, intraspecific trait and climate variation, and climate sampling bias\u003csup\u003e6,54\u003c/sup\u003e .We focused on measurements for plants coexisting in ecosystems across the gradient, and measured a wide range of traits using standard methods, as the incorporation of additional mechanistic traits (including hydraulic and compositional traits) can increase power for resolving trait-climate relationships, especially when common protocols are applied to plants grown together in one or few common environments\u003csup\u003e6,44,55\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eWe developed the first continental-scale analysis of the priorities of trait-climate relationships across the United States of America and northern Mexico. We tested association of traits with the mean climate of species\u0026rsquo; native ranges, focusing on four key climatic variables known to shape plant and community distributions and diversity and community, considering annual and growing season means: growing season and mean annual temperature (GST and MAT, respectively), growing season and mean annual precipitation (GSP and MAP), growing season and mean annual potential evapotranspiration (GSPET and MAPET), and growing season and mean annual aridity index (GSAI and MAAI) (Fig. 1b). First, we hypothesized that across these sites, traits would correlate most closely with the aridity gradient, and thus, more strongly with precipitation, PET and aridity index, than with temperature. Second, we hypothesized that traits would correlate more closely with growing season than annual climate variables, under the expectation that traits would have strongest influence during the active growth period\u003csup\u003e56\u0026ndash;58\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eData collection\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur study focused on data from the CommuniTraits Database (Medeiros et al., \u003cem\u003eto be submitted\u003c/em\u003e). This database includes traits measured with standard methodology on 19 sites including 12 ecosystem types distributed across diverse climates throughout the United States: eight sites of the California floristic province that include alpine, desert, coastal sage scrub, shrubland, chaparral, montane wet forest, mixed riparian woodland and mixed evergreen-deciduous forest ecosystems; nine additional woody-dominated temperate evergreen, deciduous and mixed evergreen-deciduous forests from East to West coasts, and Hawaiian montane wet and lowland dry forests. For this comparative study, we focused on woody species and thus excluded the species of an alpine site dominated by herbaceous species, and given our focus on a continental gradient, we excluded data from two tropical Hawaii forests. Permits were obtained for work in the reserves through direct communication with the reserve directors for the sites part of the University of California Natural Reserve System (UCNRS), the National Ecological and Observatory Network (NEON), and the Forest Global Earth Observatory (ForestGEO). For the Yosemite Forest Dynamics Plot and the Great Smoky Mountains National Park, the permits were obtained through the United States Department of the Interior National Park Service (Permits #YOSE-2017-SCI-0009 and #GRSM-2023-SCI-2199, respectively).\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eField sampling and trait measurements\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur database included the biomass-dominant and other common species at each site, selected based on guidance from reserve managers and, when available, using forest census data. The species included in this study are taxonomically diverse (Table S3), including 246 unique species from 79 families, with 328 species-site combinations across the 16 sites. At each site, we sampled 3\u0026ndash;5 individuals per species, and 14-26 species per site. Individual trees and shrubs were sampled across the landscape and we avoided sampling adjacent individuals of the same species. We collected sun-exposed leaves from non-epicormic branches, with no signs of damage or herbivory using aerial lifts, pole pruners or a slingshot. Branches were transported to the lab in dark plastic bags with moist paper and rehydrated overnight in a saturated atmosphere before harvesting current-year grown, fully expanded leaves for subsequent analyses. For compound-leafed species, whole leaves were used. We measured 59 traits using standard protocols (traits listed in Table S1; detailed methods described in Methods S1). For epidermal morphology, we measured one leaf per individual (epidermal cell areas, stomatal size, density, index, maximum anatomical conductance, trichome density). For leaf structure, we averaged values for three leaves (small, medium and large sizes) per individual (leaf area, mass per area, thickness, density, dry matter content, saturated water content and mass per area, petiole area to leaf area ratio, petiole mass to leaf mass ratio and petiole mass per area), and for stem structure and allocation we measured one branch per individual (wood density and Huber value). For leaf elemental and isotopic composition, we analyzed a mixture of 10-20 ground leaves per individual (concentrations per leaf mass of carbon, nitrogen, phosphorus, potassium, calcium, magnesium, iron, and trace metals, concentration per leaf area of nitrogen, and chlorophyll, and carbon: nitrogen and nitrogen: phosphorus ratios, and carbon isotope discrimination). For plant hydraulic traits, we measured two leaves per individual 3-5 individuals per species for turgor loss point, and 3-7 biomass-dominant species from eight sites, we measured 15-48 leaves from 3-5 individuals per species for maximum leaf hydraulic conductance and leaf water potential at 80% decline of conductance. For 3-12 biomass-dominant species from six sites, we measured photosynthetic CO\u003csub\u003e2\u003c/sub\u003e response curves and calculated photosynthetic maximum electron transport rate and maximum carboxylation rate traits for 3-27 leaves from 1-12 individuals per species (see Table 2 for site abbreviations, and number of species per trait in Table S3). Based on nutrient composition, isotope ratio and stomatal data, we derived additional modelled trait values for time-integrated photosynthetic assimilation and stomatal conductance, and maximum stomatal conductance: nitrogen concentration ratio. Additionally, we compiled maximum plant height and seed mass from the TRY database\u003csup\u003e59\u003c/sup\u003e.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eClimate variables\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe focused on trait associations with species\u0026rsquo; native climate, using the mean climate variables of species\u0026rsquo; native distributions based on the assumption that due to gene flow within a species\u0026rsquo; native range, average trait values should correspond with average climatic conditions\u003csup\u003e6,60\u003c/sup\u003e. Additionally, we tested trait associations with the climate at the sites at which species were sampled. \u003c/p\u003e\n\u003cp\u003eWe estimated the climate of species\u0026rsquo; native ranges using occurrence data from the Global Biodiversity Information Facility\u003csup\u003e6,29,60\u0026ndash;63\u003c/sup\u003e. Environmental variables were extracted and computed using R software (version 4.2.1\u003csup\u003e64\u003c/sup\u003e). Species occurrence data were obtained via the \u0026lsquo;rgbif\u0026rsquo; R package \u003csup\u003e65\u003c/sup\u003e, and filtered to retain herbarium records collected since 1950. Records lacking latitude or longitude, duplicates, and non-wild occurrences (such as botanical gardens or cultivated urban trees) were excluded \u003csup\u003e6,65,66\u003c/sup\u003e. Species data were further limited to observations from the United States, Canada, and Mexico, unless the species had a known global distribution. After screening with the \u003cem\u003eclean_coordinates\u003c/em\u003e function of the \u0026lsquo;CoordinateCleaner\u0026rsquo; package to remove points with quality issues (i.e, coordinates that represent administrative capitals, country centroids, equal or zero latitude and longitude, or a 1-degree radius around the GBIF headquarters), descriptive climate statistics were calculated at the species level. As our goal was to test trait\u0026ndash;climate relationships, we opted to use direct occurrence points rather than species distribution maps based on ecological niche models to determine species\u0026rsquo; climatic envelopes to avoid bias in our analyses\u003csup\u003e6\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eFor the coordinates of each species\u0026rsquo; occurrences, and also for the coordinates of the study sites, four environmental variables were extracted from publicly available raster datasets, mean annual temperature and precipitation, (MAT and MAP, respectively; WorldClim\u003csup\u003e67\u003c/sup\u003e), and potential evapotranspiration and aridity index (MAPET and MAAI, respectively; CGIAR-CSI, NCAR-UCAR\u003csup\u003e68\u003c/sup\u003e. From the monthly data of these four variables, we calculated the growing season temperature (GST), growing season precipitation (GSP), growing season potential evapotranspiration (GSPET) and growing season aridity index (GSAI). The growing season was determined as the months with abundant soil moisture (mean precipitation in mm \u0026ge; 2 \u0026times; mean temperature in degrees Celsius) and minimum temperature above 4\u0026ordm;C, the temperature below which water becomes too viscous to pass through membranes \u003csup\u003e69\u003c/sup\u003e. The raster layers were combined using the \u003cem\u003e\u0026lsquo;stack\u0026rsquo;\u003c/em\u003e function from the \u0026lsquo;raster\u0026rsquo; package\u003csup\u003e70\u003c/sup\u003e, and environmental data for each occurrence point were extracted using the \u003cem\u003eextract\u003c/em\u003e function from the \u0026lsquo;dismo\u0026rsquo; package\u003csup\u003e67\u003c/sup\u003e. To characterize the climate of the sampling location, we extracted the same eight variables from 1\u0026times;1 km grid cell around the centroid of each site (Table S2). While these datasets effectively capture broad-scale environmental trends, they are not suited for fine-scale variation such as microclimate factors\u0026mdash;e.g., local temperature, light, moisture, or soil composition\u003csup\u003e71\u003c/sup\u003e (Complete dataset provided in Medeiros et al. \u003cem\u003eto be submitted\u003c/em\u003e).\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eStatistical analyses\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStatistical analyses and visualizations were conducted using R software (versions 3.4.4 and 4.0.2\u003csup\u003e64\u003c/sup\u003e) along with packages from the CRAN repository (detailed stepwise methods are found in https://github.com/NidhiVinod/Traits-and-Climate). \u003c/p\u003e\n\u003cp\u003eTo establish the significance of species-level trait variation within and across sites, we performed two analyses of variance (ANOVA). First, we tested the variation across species in each trait, and second, we used a nested analysis of variance to test for variation across sites, and variation among species within sites. Trait data were log-transformed to improve normality and homoscedasticity before the analysis. For traits containing zero or negative values, a constant of the absolute value of the minimum value plus 1 was added prior to transformation. We used packages car, MASS, dplyr, tidyverse, and TukeyC for this analysis.\u003c/p\u003e\n\u003cp\u003eTo summarize the covariation of climate variables, we conducted a principal components analysis (PCA) using \u0026lsquo;stats\u0026rsquo; package\u003csup\u003e64\u003c/sup\u003e. \u003c/p\u003e\n\u003cp\u003eGiven our interest in the absolute predictive and explanatory power of climate variables, rather than evolutionary processes, we applied ahistoric (nonphylogenetic) analyses. We applied four approaches to test the numbers and strength of associations of traits with the eight climatic variables (GSP, GST, GSAI, GSPET, MAP, MAT, MAAI, MAPET). First, for each trait-climate association we conducted Spearman rank correlations and Pearson correlations for both untransformed and log-transformed data, and considered the highest of the three \u003cem\u003er\u003c/em\u003e-values (\u0026ldquo;highest \u003cem\u003er-\u003c/em\u003evalue\u0026rdquo;). We then determined the proportion of trait-climate associations that was significant for each climate variable. To assess whether these proportions differed between climate variables, we conducted pairwise proportion tests using the \u003cem\u003eprop.test\u003c/em\u003e function in R versions 3.4.4 and 4.0.2\u003csup\u003e64\u003c/sup\u003e. Second, for each climate variable, we conducted analyses of variance to test for differences among the climate variables in their highest \u003cem\u003er\u003c/em\u003e-values. Third, we tested for differences among each pair of climate variables in the strength of all trait correlations (including both significant and nonsignificant) using a meta-analysis of the highest \u003cem\u003er\u003c/em\u003e-values\u003csup\u003e11\u003c/sup\u003e adapted from \u003cem\u003eMetaWin\u003c/em\u003e\u003csup\u003e72\u003c/sup\u003e using the package \u0026lsquo;metafor\u0026rsquo;\u003csup\u003e73\u003c/sup\u003e. Finally, we tested the relative importance of MAT and MAP in co-determining traits, by fitting multiple regression models that included MAT and MAP as independent variables and functional traits as dependent variables; we then performed a hierarchical partitioning analysis using the \u0026lsquo;hier.part\u0026rsquo; package to calculate the percentage contribution of each variable to the prediction of traits\u003csup\u003e74\u003c/sup\u003e. These models were fitted to untransformed and log-transformed data, and we selected the highest R2 based between log-transformed and untransformed data for each trait. We calculated the percentage of traits that were best explained by MAT or MAP, and the mean percent contribution of MAT and MAP to the determination of each trait. Each of the above tests were conducted across all the species-site combinations, first considering trait associations with the mean climate of individual species\u0026rsquo; native ranges, and second, considering associations with the climate of species\u0026rsquo; sampling sites.\u003c/p\u003e\n\u003cp\u003eWe tested the relative variation in the climate variables of species\u0026rsquo; native ranges and of the study sites using coefficients of variation for log-transformed data after applying a constant of the absolute value of the minimum value plus 1, and tested for differences among these using standardized likelihood ratio test (SLRT\u003csup\u003e75\u003c/sup\u003e).\u003c/p\u003e\n"},{"header":"Results","content":"\u003cp\u003eWe found strong variation across species for vast majority of the traits (ANOVAs; \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001 to \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05 for most traits; Table S4). The majority of variance, on average 92%, was explained by species-differences (ANOVAs; \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001 to \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05; Table S4). Considering species nested within site, 35% of variation was explained by site differences, 61% by species within sites, and 4% by variation among individuals within species (Table S4). \u003c/p\u003e\n\u003cp\u003eWe conducted a principal components analysis (PCA) to clarify the pattern of variation in the mean climate variables of species\u0026rsquo; native distributions (Fig. 1c). The first two axes, Climate-PC1 and Climate-PC2, accounted for 60.4% and 21.8% of the variation, respectively, and 82% in total (Table S5). Climate-PC1 was related to the aridity gradient from cool, wetter sites to hotter, drier sites, i.e., from low to high MAT, MAPET and MAAI, and from high to low GSP and MAP. Climate-PC2 captured orthogonal variation in climatic warmth and evaporative demand, i.e., MAT and MAPET (Table S5, Fig 1). Both PCA axes were related to latitude, though stronger for Climate-PC2 than Climate-PC1 (\u003cem\u003er\u003c/em\u003e = 0.86 and 0.32, respectively; \u003cem\u003eP\u003c/em\u003e \u0026lt;0.001; Table S5; Fig. S1). In the comparison of the relative variation in MAP and MAT across species\u0026rsquo; mean climates and site mean climates, MAT showed the stronger variation, with coefficients of variation \u0026plusmn; standard error of 0.145\u0026plusmn;0.0080 and 0.175\u0026plusmn; 0.00973 respectively, relative to 0.00792\u0026plusmn;0.0044 and 0.00923\u0026plusmn; 0.00511 for MAP (Table S16, Table S17). \u003c/p\u003e\n\u003cp\u003eAcross species, 58 of the 59 measured plant traits were related to one or more climate variables representing the mean of species\u0026rsquo; climate distributions, all but foliar carbon: nitrogen ratio (Fig. 3; Tables S7 and S8). Similarly, when considering the mean climate of species\u0026rsquo; sampling sites, 57 of 59 measured traits were related to one or more climate variables, all but leaf dry matter content and bark thickness to stem diameter ratio (Table S9 and S10).\u003c/p\u003e\n\u003cp\u003eOverall, traits were more strongly correlated with precipitation than with temperature. The overall findings for stronger relationships with climatic precipitation than temperature at continental scale is illustrated with five example traits, wood density (WD), turgor loss point (TLP), carbon isotope discrimination (\u0026Delta;\u003csup\u003e13\u003c/sup\u003eC), leaf area (LA), foliar nitrogen content per mass (\u003cem\u003eN\u003c/em\u003e\u003csub\u003emass\u003c/sub\u003e), and leaf mass per area (LMA) (Fig. 2). Thus, TLP, \u0026Delta;\u003csup\u003e13\u003c/sup\u003eC,\u003cem\u003e N\u003c/em\u003e\u003csub\u003emass\u003c/sub\u003e and LMA, had stronger associations with GSP than GST, and TLP, \u0026Delta;\u003csup\u003e13\u003c/sup\u003eC, LA, \u003cem\u003eN\u003c/em\u003e\u003csub\u003emass\u003c/sub\u003e and LMA had stronger associations with MAP than MAT. \u003c/p\u003e\n\u003cp\u003eIn our analyses across all the traits, considering associations with the mean climate variables for species\u0026rsquo; native ranges, a greater proportion of trait correlations were significant for MAP versus MAT (proportion tests; Table 3). Further, trait-climate correlations were stronger for MAP versus MAT when considering only significant correlations (ANOVAs; Fig. 3; Table S11), and for MAP versus MAT and also for GSP versus GST, when considering all their correlations regardless of significance (meta-analysis; Table 3). Further, multiple regression analyses of each trait with respect to MAT and MAP also supported the greater importance of MAP (Table S12). Across the 59 traits, the multiple regressions showed a mean \u0026plusmn; standard error of the adjusted \u003cem\u003er\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e of 0.14 \u0026plusmn; 0.015; 84% of traits showed a higher contribution of MAP, and 15% of MAT, and across all the traits, the mean \u0026plusmn; standard error of the percent contribution of MAP was 77.6 \u0026plusmn; 3.23% whereas that of MAT was 22.3 \u0026plusmn; 3.23%.\u003c/p\u003e\n\u003cp\u003eTrait variation was also strongly explained by climatic variables beyond temperature and precipitation, i.e., potential evapotranspiration and aridity index. Indeed, MAPET and MAAI both had higher proportions of significant trait correlations and stronger trait correlations than MAT (Table 3; Fig. 3). Considering growing season variables, GSPET and GSAI showed significantly stronger overall trait correlations than GST (Table 3). \u003c/p\u003e\n\u003cp\u003eComparing growing season and annual climate variables, for temperature, the growing season mean had a greater proportion of significant trait correlations and stronger correlations overall than the annual mean (Table 3; Fig. 3), and for aridity index, the growing season mean had stronger overall trait correlations and stronger significant correlations than the annual mean (Table 3; Fig. 3; Table S11). For precipitation and potential evapotranspiration, growing season and annual means did not differ in the proportion or strength of correlations (Table 3; Fig. 3; Table S11). \u003c/p\u003e\n\u003cp\u003eThe findings were similar when considering trait associations with the climate of species\u0026rsquo; sampling sites, rather than the mean climates of species\u0026rsquo; native ranges. Thus, a greater proportion of trait correlations were significant, and overall correlations, and significant correlations were stronger, for MAP versus MAT (Table S13 and S14; Fig. S2).Further, MAAI and MAPET both had a higher proportion of significant trait correlations, and stronger associations than MAT, whether considering all correlations or only significant ones (Table S13 and S14; Fig. S3). Considering growing season variables, GST was statistically similar in the proportion and strengths of significant and overall trait correlations as GSP, GSPET and GSAI (Table S13 and S14; Fig. S3). Multiple regression analyses of each trait with respect to MAT and MAP of the climate of species\u0026rsquo; sampling sites also supported the greater importance of MAP (Table S15). Across the 59 traits, the multiple regressions showed a mean \u0026plusmn; standard error of the adjusted \u003cem\u003er\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e of 0.12 \u0026plusmn; 0.016; 78.0% of traits showed a higher contribution of MAP, and 22% of MAT. Across all the traits, the mean \u0026plusmn; standard error of the percent contribution of MAP was 79.7 \u0026plusmn; 3.64% whereas that of MAT was 20.3 \u0026plusmn; 3.64% (Table S10). Comparing growing season and annual climate variables, GST had a higher proportion of trait correlations, and stronger overall and significant correlations than MAT. For aridity index, the annual mean had a higher proportion of trait correlations, and stronger overall and significant correlations than the growing season mean (Tables S13 and S14; Fig. S3). For precipitation and potential evapotranspiration, growing season and annual means did not differ in the proportions or strength of correlations (Table S13 and S14; Fig. S3).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSwitching of patterns from global scale to the continental scale across the contiguous USA and northern Mexico\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe important role of both precipitation and temperature in predicting traits across the contiguous USA and northern Mexico\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eare consistent with several previous studies at continental and global scales\u003csup\u003e39,43,49,52\u003c/sup\u003e. Yet, we found a strongly novel finding with MAP being a better predictor than MAT, contrasting with the pattern shown in global studies, in which temperature is a better predictor of traits than precipitation\u003csup\u003e11,38,76,77\u003c/sup\u003e. Indeed, across all our analyses of continental-scale trait-climate associations, we found that while traits have substantial associations with temperature, precipitation, potential evapotranspiration and aridity index, precipitation is a stronger predictor of plant traits than temperature. Consistent with our findings, a recent continental analysis for over 7 types of ecosystems spanning from arid, tropical to temperate in Australia highlighted the importance of MAP, though without a comparison of the relative strengths of trait associations with MAT vs. MAP\u003csup\u003e52\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe difference in the findings of continental from global tests of trait-climate associations would emerge for multiple reasons associated with the disparity in scales (Table 1). At first sight, one might assume that the relative strengths of trait-climate associations may reflect the variation in specific climate variables, and thus that the stronger trait associations with temperature than precipitation at global scale corresponds to the greater variation in temperature, whereas the stronger trait associations with precipitation at continental scale reflects a greater variation in precipitation than temperature. However, in our study, and in a global-scale trait-climate study\u003csup\u003e11\u003c/sup\u003e, MAT exhibited greater climatic variation compared to MAP. The stronger role of MAP than MAT as a driver of trait variation continentally may rather be linked with the associations of climate traits. Globally, the dominant climatic gradient is latitudinal, with variation from colder, drier climates at high latitudes to warmer, wetter climates in the tropics. By contrast, for the continental gradient in our study, a climatic aridity gradient is dominant, from cooler, moister climates to drier, warmer climates, due to continentality and elevational variation\u003csup\u003e78\u003c/sup\u003e; such a gradient would reinforce the importance of moisture, as water stress in combination with high temperature represents a stronger compound stress. Indeed, in our principal components analysis of climate variation across the species\u0026rsquo; native ranges, temperature, and precipitation partly inter-related, with Climate-PC1 showing a strong aridity gradient, with temperature and precipitation negatively correlated, and Climate-PC2 showed additional variation in temperature, showing the influence of the latitudinal gradient, here independent of precipitation. The importance of climatic rainfall as a trait driver is also consistent with its influence on climatic temperature at regional and continental scales; for example, reduced water levels can contribute to high growing season climatic temperatures\u003csup\u003e79\u0026ndash;81\u003c/sup\u003e. We note that stronger trait-climate associations would also arise continentally than globally given the relative floristic similarity by contrast with a global analysis\u003csup\u003e52\u003c/sup\u003e, though potentially weaker relationships might be expected given the more constrained species variation\u003csup\u003e11\u003c/sup\u003e. Notably, the global-scale study by Moles et al. 2014 focused on all life forms\u0026mdash;including herbaceous and woody species\u0026mdash;our study focused on woody species. Microclimate, specifically MAT and MAP experienced by understory herbaceous species varies drastically from woody canopy species\u003csup\u003e82\u003c/sup\u003e, and thus trait relationships with macroclimate variables may be weaker for herbaceous than woody species, especially as the life cycle and phenology of herbaceous species might reduce the time of year in which they are present and/or active, altering trait-climate relationships\u003csup\u003e61,83\u003c/sup\u003e. We conclude from the strong divergence of our continental study from global studies of the priorities of trait-climate associations that the global pattern is not generalizable across continents. However, we also note that the specific pattern found across the sites in our study, may differ from that of other continents, depending on the floristic composition of the continent, the specific ranges of climate variables, and their inter-relationships. Importantly, the trends observed continentally may be expected to be more potentially actionable, relative to global trends, which combine disparate ecosystems with similar climates\u003csup\u003e11\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eAnother novel aspect of our study was the test of whether growing season climate variables would be stronger predictors of traits than annual variables. A greater importance of growing season climate would be expected in considering that in temperate regions the leaf expansion and development, and the bulk of photosynthetic activity occurs during the growing season\u003csup\u003e84\u003c/sup\u003e. Indeed, with GST had a greater proportion of significant trait correlations and stronger correlations than MAT, likely because temperature directly constrains developmental and physiological processes such as leaf expansion, nutrient assimilation and photosynthesis during periods of active growth. However, for precipitation and potential evapotranspiration, annual and growing-season variables showed similar proportions of significant trait associations and comparable association strengths, and for aridity index, the annual mean showed stronger correlations than the growing season mean (Table 3 and Tables S11, S13 and S14). These findings are consistent with the pronounced importance of precipitation as a driver of trait associations across our continental gradient. For woody species, given deep soils, the availability of moisture in the growing season may depend on precipitation falling year-round, with water stored from non-growing season periods remaining accessible during the growing season, buffering plants against short-term drought. Additionally, traits associated with survival during the non-growing season\u0026mdash;such as cold tolerance, drought resistance during late summer, or maintenance of perennial structures\u0026mdash;may be shaped by climatic conditions across the entire year, reducing the contrast between growing season and annual variables as separable predictors for MAP. Similarly, aridity index would reflect cumulative water balance and atmospheric demand across the entire year, such that plant traits respond more strongly to annual than to growing-season aridity (Table 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eMechanistic associations of traits with precipitation and aridity gradients\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur findings of precipitation as the strongest driver of plant traits reflects multiple functional trait adaptations along a continental aridity gradient, that would confer stress resistance and/or stress avoidance (Table S1). Species associated with lower precipitation levels have a range of traits shown in previous studies to be adaptive, including shorter stature, smaller cell sizes, thicker leaves with higher leaf mass per area, and leaf dry mater content, smaller leaf size, denser trichomes lower stomatal density, lower TLP, lower \u0026Delta;\u003csup\u003e13\u003c/sup\u003eC, higher wood density, higher Huber value, lower saturated water content, lower hydraulic conductance, safer hydraulic thresholds and lower nitrogen mass per area compared to plants adapted to moister regions (Fig. 2; Table S7). These examples of the associations of traits across many plant levels of organization and functional roles highlight the extraordinary importance of precipitation, and aridity, as important predictors of plant traits at continental scale (Fig. 2, Table S7).\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eLimitations of the analyses\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur study highlights important relationships among traits and climate variables, and importantly expands on previous studies in the number of traits measured according to standard protocols for a diversity of ecosystems included for trait-climate associations at continental scale. Our analysis was based on standardized field-collected data for 59 traits across 16 ecosystems, capturing diverse woody species across ecosystems at the continental scale. Notably, previous large-scale trait\u0026ndash;climate studies have often relied on databases that include non-standardized trait measurements, often compiled for different plants of each species growing in disparate locations.\u003c/p\u003e\n\u003cp\u003eHowever, our analysis requires key caveats, pointing to avenues for future studies to achieve yet higher resolution. Our climate data were modelled from species occurrences, a widely used approach, though relatively coarse, and which may limit the precision of trait-climate relations; finer-resolution climate data could enhance the relationships observed in our study. Indeed, microclimatic temperature often differs from regional weather stations, and modelled macroclimatic temperature \u003csup\u003e85\u003c/sup\u003e. Further, climatic moisture variables may have a complex relationship to species\u0026rsquo; water availability in the field. The availability of water to plants depends on a suite of factors, including the seasonal distribution of rainfall, hydrology, soil depth, soil type (including soil moisture-holding capacity and the soil moisture characteristic curve), access to groundwater and on temperature, which determines both whether the precipitation falls as rain or snow and the level of evaporative demand\u003csup\u003e44,86\u0026ndash;88\u003c/sup\u003e. Notably, the growing season variables are estimated by thresholds, therefore it is likely may not necessarily correspond to actual ranges of water availability experienced by the plants in their growing seasons. Additionally, our use of aggregated climate variables (e.g., mean annual or growing season values) may mask the effects of extreme events or intra-seasonal variability\u0026mdash;factors that can strongly influence trait adaption and assembly. While we tested for differences between growing season and annual climate variables, the temporal resolution of the available data constrains our ability to evaluate carry-over effects from previous years or to separate the impacts of long-term climate trends from short-term anomalies.\u003c/p\u003e\n\u003cp\u003eOur finding of stronger trait associations with precipitation than temperature, in contrast to global studies, emphasizes the importance of considering scale and specific systems in examining the priorities of trait-climate associations. That point remains robust, though, as noted above, the strength of given climatic variables in driving trat associations would vary across other continents that differ in the ranges of climate variables and their inter-relationships and in their floras. Further, the designation and sampling of species within continents would also play a role in the specific trait-climate associations discerned. Thus, despite our including a wide range of species and representative ecosystems from the contiguous USA and northern Mexico, a wider definition and greater sampling across of the North American continent, including Canada and Mexico would likely shift the specific findings for relative trait associations to a degree. However, our finding of a stronger role for precipitation than temperature in our studied continental gradient remains a robust demonstration of an important departure from global patterns.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eOverall conclusions and practical applications\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur findings highlight the ecological importance of multiple climate variables, and in particular, of precipitation as a stronger trait predictor than temperature in shaping plant functional strategies continentally, differing from the findings globally where temperature can more strongly predict species traits. Precipitation as a driver of traits continentally would influence species occurrences and distributions. As droughts become more frequent and intense, species may face limits to their adaptive capacity associated with their traits, restricting their ability to persist outside certain precipitation ranges. The importance of precipitation continentally points to the need for future studies to analyze continental climate-trait patterns across the globe. Models projecting species and trait distribution for future climate scenarios should incorporate precipitation as a driver of traits regionally and continentally rather than relying primarily on temperature, though often modelled scenarios show greater resolution of potential shifts in temperature compared to precipitation\u003csup\u003e89,90\u003c/sup\u003e. Precipitation-driven variation in species and traits also impacts ecosystem scale functions such as ecosystem carbon and water fluxes, since traits can directly impact ecosystem productivity and fluxes\u003csup\u003e91\u003c/sup\u003e with feedbacks on future precipitation regimes. Our study suggests that the consideration of the wide range of trait-climate associations will improve predictions of across scales, from species-distributions to the terrestrial climate cycle, to future climate, and locally, improve restoration efforts through incorporating species with drought tolerance in regions prone to droughts and heatwaves.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are grateful to the indigenous peoples that stewarded the land and plants studied in this project throughout millenia, including the Kizh, Tongva, Chumash and Chemehuevi peoples in Southern California and Mojave Desert, Kumeyaay (Diegue\u0026ntilde;o) and Micqanaqa\u0026rsquo;n peoples in Ensenada, Baja California region, Ahwahnechee (Southern Sierra Miwok and Mono) indegenious to Yosemite region, Yokuts, Miwok and Wintun communities in Central Valley and coast range, California; Ute community indegenious to Colorado Niwot region, Eastern Shashone with Arapaho community in Pacific Northwest, Algonquian-speaking peoples, Nipmuc, and Wampanoag Piscataway, Monocan, Pohatan, Lenape in North East, Osage Nation in Missouri region, Apalachee, Timucua, Creek and Seminole peoples in North-Central Florida. We are grateful to J. Andr\u0026eacute;, R. Argaez, R. Barreto, S. Dannet Diaz de Leon Guerrero, L. Fletcher, S. Germain, C. Garnica-D\u0026iacute;az, J. Laou\u0026eacute;, L. Magee, M. Ochoa, A. Pivovaroff, K. Svoboda, L. Azaryan, A. Berrol, S. Boyadzhyan, S. Ebrahimi, G. Grewal, E. Huang, S. Kady, J. Kim, J. Laguardia, E. Lam, B. Leyva, V. Liu, M. Mehta, R. Min, L. Nasrallah, L. Ngau, J. Ortiguerra, A. Perez, C. Rai, J. Smith, E. Solis, S. Tagaryan, A. Varnado, A. Verma, A. Yadegarian, M. Yu, and K. Zhang for helping with sampling, trait measurements and comments on early versions of the manuscript. This work was funded by National Science Foundation award 2017949 and the Laboratory Directed Research and Development Program of Oak Ridge National Laboratory (ORNL); ORNL is managed by the University of Tennessee-Battelle, LLC, under contract DE-AC05-00OR22725 with the U.S. Department of Energy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization of the \u0026nbsp;project: LS, CM, NV, MD, ATT, NK; writing of original draft: NV and LS; formal analyses: \u0026nbsp;NV, CM, KCC and LS; investigation- species sampling: CM, NV, AO, MB, MD, HD, JZ, CH, ST, LS; investigation- collection of trait data: CM, NV, AO, MB, MD, HD, LS; investigation- site logistics and species abundance: LS, JF, NMH, CM, SM, GPJ, DJ, JAL, RMA, JDW, KAT, MD, ATT, NK; data visualization: NV and CM; review \u0026amp; editing: all.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData and Materials Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll code used for this analysis is available on GitHub Trait-Climate repository: https://github.com/NidhiVinod/Traits-and-Climate\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eViolle, C. \u003cem\u003eet al.\u003c/em\u003e Let the concept of trait be functional! \u003cem\u003eOikos\u003c/em\u003e \u003cstrong\u003e116\u003c/strong\u003e, 882\u0026ndash;892 (2007).\u003c/li\u003e\n\u003cli\u003eRolhauser, A. 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Is regional species diversity bounded or unbounded? https://onlinelibrary.wiley.com/doi/10.1111/j.1469-185X.2012.00245.x (2012). \u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1.\u0026nbsp;\u003c/strong\u003eContrasts in elements of pattern and processes between global and continental scale gradients that would influence the priorities of trait-climate association across species, and their generalizability for prediction.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"846\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.5674%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePattern or process element\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.3688%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGlobal Scale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4043%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eContinental Scale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27.6596%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePotential influence on across-species trait-climate associations\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.5674%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eConcurrence of temperature and precipitation gradients\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.3688%;\"\u003e\n \u003cp\u003eThe dominant climatic gradient is latitudinal, with variation from colder, drier climates (high latitudes) to warmer, wetter climates (tropical). \u003csup\u003e92\u0026ndash;95\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4043%;\"\u003e\n \u003cp\u003eClimatic aridity gradients are often found, from cooler, moister climates to drier, warmer climates, due to continentality and elevational variation \u003csup\u003e78\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27.6596%;\"\u003e\n \u003cp\u003eThe global latitudinal gradient aligns adaptation to low/high moisture with low/high temperature, whereas aridity gradients as typical at continental scale emphasize the importance of moisture, as water stress is stronger when combined with high temperatures\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.5674%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eClimatic overlap\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.3688%;\"\u003e\n \u003cp\u003eSimilar climates can exist in extremely floristically distinct regions (e.g., Mediterranean vs. Australian shrublands) \u003csup\u003e96\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4043%;\"\u003e\n \u003cp\u003eFloristics more cohesive typically for given climates within a continent \u003csup\u003e97,98\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27.6596%;\"\u003e\n \u003cp\u003eTrait-climate associations may be stronger continentally than globally\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.5674%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBiogeographic context\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.3688%;\"\u003e\n \u003cp\u003eStrong floristic/evolutionary differences across the spatial extent (Erwin 2009)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4043%;\"\u003e\n \u003cp\u003eStronger influence of shared evolutionary and biogeographic history \u003csup\u003e97\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27.6596%;\"\u003e\n \u003cp\u003eTrait-climate associations may be stronger continentally than globally\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.5674%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpecies pool\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.3688%;\"\u003e\n \u003cp\u003eHigher species variation due to global species turnover and greater phylogenetic heterogeneity \u003csup\u003e99\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4043%;\"\u003e\n \u003cp\u003eMore constrained species variation due to consistent regional species pools and lower phylogenetic heterogeneity \u003csup\u003e100\u003c/sup\u003e.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27.6596%;\"\u003e\n \u003cp\u003eTrait-climate associations may be stronger globally than continentally\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.5674%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGeneralizability of trait-climate associations\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.3688%;\"\u003e\n \u003cp\u003eMay obscure local/regional patterns (Towers et al. 2024)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4043%;\"\u003e\n \u003cp\u003eCaptures variation without overgeneralization (Towers et al. 2024)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27.6596%;\"\u003e\n \u003cp\u003eTrait-climate associations may be more generalizable continentally than globally, and thus more actionable and policy-relevant (e.g., national/continental planning)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e Study sites across the continental USA and northern Mexico, ordered from West to East with site codes (Fig. 1). Sites with forest type, number of species, growing season temperature (GST), growing season precipitation (GSP), growing season potential evapotranspiration (GSPET), growing season aridity (GSAI), mean annual temperature (MAT), mean annual potential evapotranspiration (MAPET), and mean annual aridity (MAAI).\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 24.0385%;\"\u003e\n \u003cp\u003eSite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 15.3846%;\"\u003e\n \u003cp\u003eVegetation type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 5.76923%;\"\u003e\n \u003cp\u003e# sp.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.76923%;\"\u003e\n \u003cp\u003eGST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 5.76923%;\"\u003e\n \u003cp\u003eGSP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 7.69231%;\"\u003e\n \u003cp\u003eGSPET\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 6.73077%;\"\u003e\n \u003cp\u003eGSAI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 6.73077%;\"\u003e\n \u003cp\u003eMAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 6.73077%;\"\u003e\n \u003cp\u003eMAP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.65385%;\"\u003e\n \u003cp\u003eMAPET\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 6.73077%;\"\u003e\n \u003cp\u003eMAAI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 24.0385%;\"\u003e\n \u003cp\u003eSweeney Granite Mountains Desert Research Center (SGMD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 15.3846%;\"\u003e\n \u003cp\u003eDesert\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 5.76923%;\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.76923%;\"\u003e\n \u003cp\u003e8.68\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 5.76923%;\"\u003e\n \u003cp\u003e122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 7.69231%;\"\u003e\n \u003cp\u003e542\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 6.73077%;\"\u003e\n \u003cp\u003e0.228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 6.73077%;\"\u003e\n \u003cp\u003e16.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 6.73077%;\"\u003e\n \u003cp\u003e263\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.65385%;\"\u003e\n \u003cp\u003e2738\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 6.73077%;\"\u003e\n \u003cp\u003e0.095\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 24.0385%;\"\u003e\n \u003cp\u003eStunt Ranch Santa Monica Mountains Reserve (SRSM)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 15.3846%;\"\u003e\n \u003cp\u003eChaparral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 5.76923%;\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.76923%;\"\u003e\n \u003cp\u003e12.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 5.76923%;\"\u003e\n \u003cp\u003e392\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 7.69231%;\"\u003e\n \u003cp\u003e734\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 6.73077%;\"\u003e\n \u003cp\u003e0.563\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 6.73077%;\"\u003e\n \u003cp\u003e16.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 6.73077%;\"\u003e\n \u003cp\u003e412\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.65385%;\"\u003e\n \u003cp\u003e1926\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 6.73077%;\"\u003e\n \u003cp\u003e0.213\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 24.0385%;\"\u003e\n \u003cp\u003eCentro de Investigaci\u0026oacute;n Cient\u0026iacute;fica y de Educaci\u0026oacute;n Superior de Ensenada (ENSE)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 15.3846%;\"\u003e\n \u003cp\u003eCoastal sage scrub\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 5.76923%;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.76923%;\"\u003e\n \u003cp\u003e13.0\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 5.76923%;\"\u003e\n \u003cp\u003e183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 7.69231%;\"\u003e\n \u003cp\u003e408\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 6.73077%;\"\u003e\n \u003cp\u003e0.336\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 6.73077%;\"\u003e\n \u003cp\u003e16.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 6.73077%;\"\u003e\n \u003cp\u003e256\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.65385%;\"\u003e\n \u003cp\u003e1588\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 6.73077%;\"\u003e\n \u003cp\u003e0.121\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 24.0385%;\"\u003e\n \u003cp\u003eYosemite Forest Dynamics Plot (YFDP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 15.3846%;\"\u003e\n \u003cp\u003eMontane wet forest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 5.76923%;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.76923%;\"\u003e\n \u003cp\u003e7.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 5.76923%;\"\u003e\n \u003cp\u003e605\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 7.69231%;\"\u003e\n \u003cp\u003e720\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 6.73077%;\"\u003e\n \u003cp\u003e1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 6.73077%;\"\u003e\n \u003cp\u003e10.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 6.73077%;\"\u003e\n \u003cp\u003e977\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.65385%;\"\u003e\n \u003cp\u003e1812\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 6.73077%;\"\u003e\n \u003cp\u003e0.539\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 24.0385%;\"\u003e\n \u003cp\u003eSan Joaquin Experimental Range (SJER)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 15.3846%;\"\u003e\n \u003cp\u003eShrubland, evergreen forest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 5.76923%;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.76923%;\"\u003e\n \u003cp\u003e10.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 5.76923%;\"\u003e\n \u003cp\u003e494\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 7.69231%;\"\u003e\n \u003cp\u003e569\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 6.73077%;\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 6.73077%;\"\u003e\n \u003cp\u003e16.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 6.73077%;\"\u003e\n \u003cp\u003e552\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.65385%;\"\u003e\n \u003cp\u003e2118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 6.73077%;\"\u003e\n \u003cp\u003e0.260\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 24.0385%;\"\u003e\n \u003cp\u003eOnion Creek (OC)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 15.3846%;\"\u003e\n \u003cp\u003eMixed riparian woodland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 5.76923%;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.76923%;\"\u003e\n \u003cp\u003e9.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 5.76923%;\"\u003e\n \u003cp\u003e137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 7.69231%;\"\u003e\n \u003cp\u003e441\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 6.73077%;\"\u003e\n \u003cp\u003e0.330\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 6.73077%;\"\u003e\n \u003cp\u003e6.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 6.73077%;\"\u003e\n \u003cp\u003e1122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.65385%;\"\u003e\n \u003cp\u003e1486\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 6.73077%;\"\u003e\n \u003cp\u003e0.754\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 24.0385%;\"\u003e\n \u003cp\u003eAngelo Coast Range Reserve (ACRR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 15.3846%;\"\u003e\n \u003cp\u003eMixed evergreen-deciduous forest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 5.76923%;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.76923%;\"\u003e\n \u003cp\u003e8.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 5.76923%;\"\u003e\n \u003cp\u003e1557\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 7.69231%;\"\u003e\n \u003cp\u003e641\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 6.73077%;\"\u003e\n \u003cp\u003e3.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 6.73077%;\"\u003e\n \u003cp\u003e11.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 6.73077%;\"\u003e\n \u003cp\u003e1613\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.65385%;\"\u003e\n \u003cp\u003e1365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 6.73077%;\"\u003e\n \u003cp\u003e1.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 24.0385%;\"\u003e\n \u003cp\u003eWind River Forest Dynamics Plot (WFDP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 15.3846%;\"\u003e\n \u003cp\u003eEvergreen forest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 5.76923%;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.76923%;\"\u003e\n \u003cp\u003e10.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 5.76923%;\"\u003e\n \u003cp\u003e1086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 7.69231%;\"\u003e\n \u003cp\u003e820\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 6.73077%;\"\u003e\n \u003cp\u003e1.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 6.73077%;\"\u003e\n \u003cp\u003e9.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 6.73077%;\"\u003e\n \u003cp\u003e2005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.65385%;\"\u003e\n \u003cp\u003e1081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 6.73077%;\"\u003e\n \u003cp\u003e1.85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 24.0385%;\"\u003e\n \u003cp\u003eMissouri Ozark AmeriFlux (MOFL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 15.3846%;\"\u003e\n \u003cp\u003eDeciduous forest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 5.76923%;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.76923%;\"\u003e\n \u003cp\u003e16.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 5.76923%;\"\u003e\n \u003cp\u003e858\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 7.69231%;\"\u003e\n \u003cp\u003e1239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 6.73077%;\"\u003e\n \u003cp\u003e0.753\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 6.73077%;\"\u003e\n \u003cp\u003e12.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 6.73077%;\"\u003e\n \u003cp\u003e1013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.65385%;\"\u003e\n \u003cp\u003e1374\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 6.73077%;\"\u003e\n \u003cp\u003e0.737\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 24.0385%;\"\u003e\n \u003cp\u003eNiwot Ridge Mountain Research Station (NIWO)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 15.3846%;\"\u003e\n \u003cp\u003eEvergreen forest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 5.76923%;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.76923%;\"\u003e\n \u003cp\u003e8.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 5.76923%;\"\u003e\n \u003cp\u003e218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 7.69231%;\"\u003e\n \u003cp\u003e616\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 6.73077%;\"\u003e\n \u003cp\u003e0.352\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 6.73077%;\"\u003e\n \u003cp\u003e0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 6.73077%;\"\u003e\n \u003cp\u003e668\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.65385%;\"\u003e\n \u003cp\u003e1202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 6.73077%;\"\u003e\n \u003cp\u003e0.555\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 24.0385%;\"\u003e\n \u003cp\u003eHarvard Forest (HARV)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 15.3846%;\"\u003e\n \u003cp\u003eMixed evergreen-deciduous forest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 5.76923%;\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.76923%;\"\u003e\n \u003cp\u003e13.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 5.76923%;\"\u003e\n \u003cp\u003e656\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 7.69231%;\"\u003e\n \u003cp\u003e809\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 6.73077%;\"\u003e\n \u003cp\u003e0.852\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 6.73077%;\"\u003e\n \u003cp\u003e7.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 6.73077%;\"\u003e\n \u003cp\u003e1105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.65385%;\"\u003e\n \u003cp\u003e1004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 6.73077%;\"\u003e\n \u003cp\u003e1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 24.0385%;\"\u003e\n \u003cp\u003eRutgers University Pinelands Field Station (RPFS)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 15.3846%;\"\u003e\n \u003cp\u003eAtlantic coastal pine barrens\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 5.76923%;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.76923%;\"\u003e\n \u003cp\u003e15.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 5.76923%;\"\u003e\n \u003cp\u003e895\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 7.69231%;\"\u003e\n \u003cp\u003e1088\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 6.73077%;\"\u003e\n \u003cp\u003e0.897\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 6.73077%;\"\u003e\n \u003cp\u003e11.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 6.73077%;\"\u003e\n \u003cp\u003e1159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.65385%;\"\u003e\n \u003cp\u003e1237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 6.73077%;\"\u003e\n \u003cp\u003e0.937\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 24.0385%;\"\u003e\n \u003cp\u003eSmithsonian Conservation Biology Institute (SCBI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 15.3846%;\"\u003e\n \u003cp\u003eMixed evergreen-deciduous forest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 5.76923%;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.76923%;\"\u003e\n \u003cp\u003e15.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 5.76923%;\"\u003e\n \u003cp\u003e828\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 7.69231%;\"\u003e\n \u003cp\u003e1080\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 6.73077%;\"\u003e\n \u003cp\u003e0.825\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 6.73077%;\"\u003e\n \u003cp\u003e11.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 6.73077%;\"\u003e\n \u003cp\u003e1038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.65385%;\"\u003e\n \u003cp\u003e1232\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 6.73077%;\"\u003e\n \u003cp\u003e0.841\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 24.0385%;\"\u003e\n \u003cp\u003eSmithsonian Environmental Research Center (SERC)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 15.3846%;\"\u003e\n \u003cp\u003eDeciduous forest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 5.76923%;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.76923%;\"\u003e\n \u003cp\u003e16.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 5.76923%;\"\u003e\n \u003cp\u003e860\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 7.69231%;\"\u003e\n \u003cp\u003e1189\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 6.73077%;\"\u003e\n \u003cp\u003e0.768\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 6.73077%;\"\u003e\n \u003cp\u003e13.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 6.73077%;\"\u003e\n \u003cp\u003e1103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.65385%;\"\u003e\n \u003cp\u003e1356\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 6.73077%;\"\u003e\n \u003cp\u003e0.813\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 24.0385%;\"\u003e\n \u003cp\u003eGreat Smoky Mountains National Park (GRSM)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 15.3846%;\"\u003e\n \u003cp\u003eDeciduous forest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 5.76923%;\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.76923%;\"\u003e\n \u003cp\u003e14.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 5.76923%;\"\u003e\n \u003cp\u003e1163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 7.69231%;\"\u003e\n \u003cp\u003e1175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 6.73077%;\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 6.73077%;\"\u003e\n \u003cp\u003e12.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 6.73077%;\"\u003e\n \u003cp\u003e1392\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.65385%;\"\u003e\n \u003cp\u003e1295\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 6.73077%;\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 24.0385%;\"\u003e\n \u003cp\u003eOrdway Swisher Biological Station\u0026nbsp;(OSBS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 15.3846%;\"\u003e\n \u003cp\u003eWoody evergreen forest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 5.76923%;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.76923%;\"\u003e\n \u003cp\u003e20.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 5.76923%;\"\u003e\n \u003cp\u003e1297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 7.69231%;\"\u003e\n \u003cp\u003e1625\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 6.73077%;\"\u003e\n \u003cp\u003e0.802\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 6.73077%;\"\u003e\n \u003cp\u003e20.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 6.73077%;\"\u003e\n \u003cp\u003e1297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.65385%;\"\u003e\n \u003cp\u003e1632\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 6.73077%;\"\u003e\n \u003cp\u003e0.794\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u0026nbsp;\u003c/strong\u003eTests of differences in the numbers and strengths of correlations and of 59 traits with mean climate variables for 328 species\u0026rsquo; site combinations across 16 ecosystems of the continental USA and northern Mexico. For tests of proportions of significant correlations, pairwise proportion tests were used; tests of correlations strength were based on meta-analysis (log-ratio tests); Significant differences at P \u0026lt; 0.05 are highlighted in bold and with an asterisk for P\u0026lt;0.05. Number of asterisks follow standard convention of p-levels (* \u0026lt;0.05; ** \u0026lt;0.01; *** \u0026lt;0.001). Rows are ordered within sections from the highest to lowest proportion of significant trait correlations\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"575\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eClimate Variable 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eClimate Variable 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eProportion of significant trait correlations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value (proportion tests)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003eMeta-analysis log ratio of variable 1 / variable 2 (95% confidence intervals)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003cp\u003e(meta-analyses)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003eVariable 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003eVariable 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eAnnual climate variables\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eMAP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eMAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.881\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.492\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.37 (0.75, 1.367)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eMAAI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eMAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.831\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.492\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.39 \u0026nbsp;(0.858, 1.928)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eMAAI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eMAP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e0.831\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e0.881\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e0.015 (0.2, 0.238)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eMAPET\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eMAAI\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e0.712\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e0.831\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e0.242 (0.031, 0.514)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.082\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eMAPET\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eMAP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e0.712\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e0.881\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e0.282 (-0.665, 0.101)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eMAPET\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;MAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.712\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.492\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.012\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.18 (0.634, 1.183)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eGrowing season climate variables\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eGSP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eGSPET\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e0.797\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e0.746\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.163 (0.064, 0.262)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0012\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eGSP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eGST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e0.797\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e0.678\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.835 (0.452, 1.219)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eGSP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eGSAI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e0.797\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e0.695\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.914 (0.577, 1.251)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eGSPET\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eGSAI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e0.746\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e0.695\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.735 (0.362, 1.107)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eGSPET\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eGST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e0.746\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e0.678\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.651 (0.283, 1.018)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eGST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eGSAI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e0.678\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e0.695\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e0.052 (-0.523, 0.627)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eAnnual vs growing season climate variables\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eMAAI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eGSAI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e0.831\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e0.695\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.851 (0.463,1.239)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eMAP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eGSP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e0.797\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e0.881\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e-0.048 (0.411, 0.316)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 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\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-9188810/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9188810/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Plant functional traits underpin ecological strategies and determine ecosystem responses to climate change, yet the key climate drivers of traits remain unresolved at continental scale. Key questions are whether across large gradients species traits are driven more strongly by temperature or precipitation—and by annual or by growing season mean climates. Across 16 ecosystems across the continental USA and northern Mexico, we measured 328 species-site combinations, including 246 unique woody species, for 59 traits relating to hydraulic and photosynthetic physiology, leaf and wood structure and anatomy, and nutrient and isotope concentrations using standard protocols, and tested associations with the macroclimate of both the species’ natural distributions and of the sampling sites, i.e., growing season and mean annual temperature, precipitation, potential evapotranspiration, and aridity index. Across species-site combinations, ninety-eight percent of traits were associated with one or more climate variables. More traits were correlated, and showed stronger correlations, with mean and growing season precipitation than temperature, and more trait variation was explained by precipitation than temperature in multiple regressions. Potential evapotranspiration and aridity index were also strong trait predictors. Seasonal and annual means did not differ in the strength of trait correlations for precipitation, whereas growing season temperature was a stronger predictor of traits than mean annual temperature. Our findings highlight the importance of mean annual precipitation a driver of the distribution of plant traits, indicating the strong sensitivity of species to ongoing shifts in moisture availability at continental scale.","manuscriptTitle":"Priorities of woody species trait-climate associations at continental scale","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-20 08:31:23","doi":"10.21203/rs.3.rs-9188810/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-communications","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"NCOMMS","sideBox":"Learn more about [Nature Communications](http://www.nature.com/ncomms/)","snPcode":"","submissionUrl":"https://mts-ncomms.nature.com/","title":"Nature Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Communications","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"790f90a7-c5eb-4595-b619-36ae8ee4322c","owner":[],"postedDate":"April 20th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":65184933,"name":"Earth and environmental sciences/Ecology/Ecosystem ecology"},{"id":65184934,"name":"Earth and environmental sciences/Ecology/Ecophysiology"}],"tags":[],"updatedAt":"2026-04-20T08:31:24+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-20 08:31:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9188810","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9188810","identity":"rs-9188810","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}