William R Budnick

Thibault Leboucher

Jerome Beilard

Janne Soininen

Isabelle Lavoie

Katrina Pound

Aurélien Jamoneau

Juliette Tison-Rosebery

Evelyne Tales

Virpi Pajunen

Stephane Campeau

Sophia I Passy
email: sophia.passy@uta.edu

Running title: Land use effects on stream diversity LOCAL AND REGIONAL DRIVERS OF TAXONOMIC HOMOGENIZATION IN STREAM COMMUNITIES ALONG A LAND USE GRADIENT

Keywords: Stream diatoms, insects, and fish β-diversity, biodiversity loss, taxonomic homogenization, diatoms, fish, insects, land use, local assembly, spatial aggregation, species abundance distribution

Aim: The interaction of land use with local vs. regional processes driving biological homogenization (β-diversity loss) is poorly understood. We explored: i) stream β-diversity responses to land cover (forest vs. agriculture) in terms of physicochemistry and physicochemical heterogeneity, ii) whether these responses were constrained by the regional species pool, i.e. γ-diversity, or local assembly processes through local (α) diversity, iii) if local assembly operated through the regional species abundance distribution (SAD) or intraspecific spatial aggregation, and iv) the dependency on body size, dispersal capacity, and trophic level (producer vs. consumer).

INTRODUCTION

Landscape transformations from continuous undeveloped expanses to agricultural fields and urban sprawls have accelerated the global biodiversity decline [START_REF] Newbold | Global effects of land use on local terrestrial biodiversity[END_REF]. Human land use (hereafter land use) underlies declines in both regional richness, i.e. γ-diversity [START_REF] Barlow | Anthropogenic disturbance in tropical forests can double biodiversity loss from deforestation[END_REF], and dissimilarity among biological communities, i.e. β-diversity, resulting in taxonomic homogenization across space and time [START_REF] Petsch | Causes and consequences of biotic homogenization in freshwater ecosystems[END_REF]. Biodiversity losses from land use stem from habitat loss, fragmentation, eutrophication, and physicochemical stress, altogether considered among the primary threats facing global biodiversity [START_REF] Sala | Global Biodiversity Scenarios for the Year 2100[END_REF][START_REF] Devictor | Functional biotic homogenization of bird communities in disturbed landscapes[END_REF]. Preventing biodiversity losses and mitigating subsequent homogenization remain a top priority because both can translate into decreased biological integrity and ecosystem resilience [START_REF] De Juan | Counting on β-diversity to safeguard the resilience of estuaries[END_REF][START_REF] Socolar | How Should Beta-Diversity Inform Biodiversity Conservation?[END_REF]. Therefore, it is critical from a conservation planning standpoint to continue investigating how land use affects ecological processes underlying global diversity in order to mitigate the ongoing biodiversity crisis.

Land use effects on biodiversity occur across scales, operating either in a top-down or bottom-up fashion or both [START_REF] Flohre | Agricultural intensification and biodiversity partitioning in European landscapes comparing plants, carabids, and birds[END_REF]. Top-down mechanisms function through the regional species pool (γ-diversity), which is a product of speciation and extinction, large-scale dispersal, climate, and evolutionary, geological, and land use history [START_REF] Zobel | The species pool concept as a framework for studying patterns of plant diversity[END_REF]. Bottom-up mechanisms include local-level assembly processes, e.g. environmental filtering, interspecific interactions, and small-scale dispersal [START_REF] Márquez | Local and regional processes in community assembly[END_REF], which constrain local (α) diversity and subsequently affect site-to-site community dissimilarity. Studies across terrestrial and freshwater systems have reported a general decline in γ-diversity because of land use, but divergent patterns of α-diversity, including decreased αdiversity, owing to losses of sensitive and endemic species, and stable, or even increased αdiversity, owing to greater rates of species invasion and colonization [START_REF] Vellend | Global meta-analysis reveals no net change in local-scale plant biodiversity over time[END_REF][START_REF] Newbold | Global effects of land use on local terrestrial biodiversity[END_REF][START_REF] Gonzalez | Estimating local biodiversity change: a critique of papers claiming no net loss of local diversity[END_REF]. Thus, land use likely exerts differential impact on the species pool and local assembly processes that may cause γ-and α-diversity, respectively, to vary at different rates, which in turn influences β-diversity response [START_REF] Kraft | Disentangling the Drivers of β Diversity Along Latitudinal and Elevational Gradients[END_REF]. β-diversity is usually treated as a scalar linking average α-diversity with γ-diversity, thus reflecting spatial or temporal differences among localities. One can then measure the influence of α-and γ-diversity as proxies of local and regional drivers of β-diversity, respectively. Specifically, null models that constrain the observed species pool variation (i.e., γ-diversity) can assess the role of local assembly by calculating a β-diversity measure (β DEV ) corresponding solely to α-diversity variation (e.g., [START_REF] Kraft | Disentangling the Drivers of β Diversity Along Latitudinal and Elevational Gradients[END_REF] (Fig. 1a). β DEV can be further decomposed into fractions reflecting roles of intraspecific spatial aggregation (i.e., the spatial clumping pattern of individuals within species) and the regional species abundance distribution (SAD, vector of species abundances) [START_REF] Xu | Latitudinal differences in species abundance distributions, rather than spatial aggregation, explain beta-diversity along latitudinal gradients[END_REF] (Fig. 1b). Intraspecific spatial aggregation results from dispersal, competitive, and environmental mechanisms that cluster individuals of species across fewer sites, thus bolstering β-diversity [START_REF] Veech | Analyzing patterns of species diversity as departures from random expectations[END_REF]. However, regional SADs influence β-diversity because rare species are less likely to be locally sampled due to low regional abundance [START_REF] He | Species diversity patterns derived from species-area models[END_REF]. Although examined across latitudes, the two fractions of local assembly have not been studied in other contexts and it is unknown whether these components are responsive to strong ecological influences (e.g., land use).

Studying how local assembly and regional species pool processes interplay is an ongoing area of research in terrestrial systems because it may explain how β-diversity varies with land use [START_REF] Socolar | How Should Beta-Diversity Inform Biodiversity Conservation?[END_REF]. Surprisingly, little attention is focused on freshwater systems, even though freshwater biodiversity is more vulnerable to land use relative to terrestrial systems, particularly through habitat modification [START_REF] Sala | Global Biodiversity Scenarios for the Year 2100[END_REF][START_REF] Wiens | Climate-Related Local Extinctions Are Already Widespread among Plant and Animal Species[END_REF] and eutrophication from agriculture [START_REF] Withers | Agriculture and eutrophication: where do we go from here?[END_REF]. Although primary productivity in agricultural streams could increase with eutrophication, forest streams, which are usually low in nutrients and have more shading, tend to harbor higher biodiversity stemming from greater physical and environmental heterogeneity that translates into greater ecosystem complexity [START_REF] Penaluna | Aquatic biodiversity in forests: a weak link in ecosystem services resilience[END_REF]. Agriculture probably causes changes in physicochemical heterogeneity as well, but this subject is poorly explored. Thus, the scarcity of data, especially for aquatic taxa, has inhibited general understanding of how land use influences local and regional processes driving β-diversity.

Impacts of agricultural eutrophication on β DEV are not understood, although null models have been used to assess environmental disturbance (e.g., [START_REF] Myers | Disturbance alters beta-diversity but not the relative importance of community assembly mechanisms[END_REF]. We hypothesize β-diversity response to eutrophication, including variation in β DEV , depends on trophic level, body size, and dispersal capacity. For example, many unicellular producers, like diatoms, have high nutrient demands and may benefit from increased nutrients [START_REF] Passy | Continental diatom biodiversity in stream benthos declines as more nutrients become limiting[END_REF][START_REF] Soininen | Global patterns and drivers of species and trait composition in diatoms[END_REF]. Diatom microscopic size, high local abundance, and broad geographic distributions, allowing both in-stream and overland passive dispersal [START_REF] Finlay | Global dispersal of free-living microbial eukaryote species[END_REF], may result in weak β-diversity and β DEV response to agriculture. Smaller bodied macroscopic organisms, such as aquatic insects, may be more constrained in active dispersal capacity during larval stages but exhibit greater overland mobility during winged adult life stages, which could offset some harmful agricultural effects. In contrast, larger consumers with more limited geographic dispersal capacity, such as fish, may be negatively affected by eutrophication due to ammonia toxicity, loss of suitable habitat, and lower quality food sources [START_REF] Allan | Landscapes and Riverscapes: The Influence of Land Use on Stream Ecosystems[END_REF].

In this study, we compared spatial patterns of biodiversity and abundance in streams with watersheds dominated by agriculture vs. forest. Our objectives were to determine: i) how βdiversity and related biodiversity properties respond to agriculture (through nutrient enrichment or physicochemical heterogeneity), ii) if agriculture alters the relative contribution of local assembly effects to β-diversity, iii) whether agriculture differentially constrains the fractions of local assembly explained by spatial aggregation vs. the SAD, and iv) if the relationships outlined in i) to iii) vary across organismal groups (Table 1).

MATERIALS AND METHODS

Data sources and site selection

Our datasets (six in total) comprise stream organisms sampled from the US, France, and Canada (Fig. 2). Each dataset included community data and physicochemistry from watersheds dominated by either forest or agriculture. Only streams with ≥ 50% of their upstream watershed belonging to one of the two land cover categories were included in our analyses. We examined biodiversity patterns across three US datasets (diatoms, insects, and fish), two French datasets (diatoms and fish), and one Canadian dataset (diatoms), constructed as follows.

United States

US community data, spanning 19 latitudinal degrees and 55 longitudinal degrees, were obtained from the National Water-Quality Assessment (NAWQA) Program of the United States Geological Survey and the National Rivers and Streams Assessment (NRSA) of the United States Environmental Protection Agency. Communities were collected in the warm months during low flow conditions (July through September) to constrain seasonal succession and variation in temperature and flow. NAWQA communities (diatoms, insects, and fish) were sampled between [START_REF] Tilman | The maintenance of species richness in plant communities[END_REF]-2010, whereas NRSA communities (fish), whereas NRSA communities (fish), between 2011 and 2012.

Diatoms were collected from the richest-targeted habitats, encompassing hard substrates or macrophytes. Depending on available substrate, a defined area of 25 cobbles, 5 woody snags or 5 macrophyte beds was sampled within a stream reach and the samples were composited. Diatoms were identified generally to species in counts of 400-800 cells. Benthic insects (class Insecta) were composed of combined sieved samples taken from the richest-targeted habitats (i.e., riffles, main-channel, and natural-bed instream habitats). Insects were identified to the lowest possible category (order to species) in counts of 400-800 individuals. Both NAWQA and NRSA fish were generally identified to species in counts of 400-950 individuals taken from riffle, pool, and run habitats using electrofishing equipment with seines.

Land use and cover data were generated by the NAWQA and NRSA using National Land Cover Datasets 1992 and 2006, 30 m resolution. We selected 400 streams for diatoms and 126 streams for insects split equally between both land cover categories. Since fish communities and environmental data in both the NAWQA and NRSA data were sampled with similar methods, we combined both fish datasets into a single dataset comprising 231 streams (116 agricultural and 115 forested streams).

France

French diatom data were sourced from a national dataset including field collections of 200 streams from 2011. Algae were collected from stones during the low flow period in June through September with a standardized sampling method [START_REF] Afnor | T90-354, Qualite de l'eau[END_REF]. Diatoms were identified generally to species in counts of about 320-475 cells. The French fish dataset was collected by the French National Agency for Water and Aquatic Environments (ONEMA) during low flow periods between May and October 2011. The dataset comprised 200 streams with fish identified to species in counts of 10-3300 individuals sampled with electrofishers. For both French datasets, we used 100 agricultural and 100 forest streams, spanning 8 latitudinal and 14 longitudinal degrees. Land use cover data were obtained from the CORINE land cover database (European Environment Agency, 2013)

Canada

Canadian diatom data included 46 stream samples (23 streams in both land cover categories) collected in August to September during the low flow period between 2002 and 2009 [START_REF] Lavoie | Using diatoms to monitor stream biological integrity in Eastern Canada: An overview of 10 years of index development and ongoing challenges[END_REF] spanning 3 latitudinal and 6 longitudinal degrees. Samples were composites of rock scrapes (5-10 rocks) per stream reach, targeting riffles and runs. Diatoms were mainly identified to species in counts of at least 400 valves. Land use cover data were compiled from government GIS databases, including the Ecoforestry Information System, Annual Crop Inventory, and the Insured Crop Database.

Environmental data

All datasets had associated physicochemical and coordinate data (i.e., GCS coordinates reprojected with Lambert Conformal Conic). Environmental variables in our analyses included water temperature, air temperature, nitrite + nitrate (or total nitrogen when absent), ammonia, orthophosphate, total phosphorus, specific conductance, and pH (Appendix 1, Table S1.1 in Supplemental Information). Environmental data for the US datasets consisted of the average for the month of sample collection. Environmental data for French diatoms included the median of measurements obtained 30 days before and 15 days after the diatom sample date. The French fish environmental data represented the average of 12 monthly measurements prior to fish sampling.

Air temperature for French diatom data were not recorded at the time of sampling and were obtained from the WorldClim database [START_REF] Hijmans | Very high resolution interpolated climate surfaces for global land areas[END_REF], whereas air temperatures for French fish streams were measured at the stream. Canadian environmental data were seasonal averages calculated from water samples collected from July to September.

Diversity, spatial aggregation, and species abundance distribution

We calculated α-diversity (average richness across samples), γ-diversity (total richness per land use), and β-diversity of stream samples for both land cover categories for each dataset. We used equation (1) to calculate the observed β-diversity (β OBS ),

β OBS = 1 - α γ
(1) which indicated the average proportion of the species pool absent from a stream.

We used the null model framework developed by [START_REF] Xu | Latitudinal differences in species abundance distributions, rather than spatial aggregation, explain beta-diversity along latitudinal gradients[END_REF] to quantify i) the magnitude of the local assembly effect on β-diversity after controlling for γ-diversity and ii) the contributions of the SAD vs. intraspecific spatial aggregation to local assembly (Fig. 1b). First, the difference (i.e., β-deviance, β DEV ) between β OBS and the expected β-diversity (β EXP , i.e., βdiversity expected assuming completely random sampling, see Appendix S2) was taken to quantify local assembly absent the effect of γ-diversity. β DEV is bounded between 0 and 1, with larger β DEV corresponding to greater local control. Secondly, we calculated β-diversity predicted when intraspecific spatial aggregation is constant across all species (β PRED ). Then, the difference between β PRED and β EXP reveals what fraction of β DEV is contributed by the SAD (β SAD ), while the remaining fraction of β DEV is attributed to spatial aggregation (β AGG ). In this model, β SAD can exceed β DEV if β PRED exceeds β OBS. The corresponding aggregation fraction will in turn be negative because the sum of the two fractions, β SAD and β AGG , must equal 1, thus meaning that the pattern is less aggregated than expected by the null model. To test whether the two land covers differ in their magnitude of intraspecific spatial aggregation, we used maximum likelihood methods and calculated the aggregation parameter, k, across samples within each land cover (Appendix S3). Because smaller k corresponded to greater aggregation, we analyzed the reciprocal of the parameter for easier interpretation. In summary, the procedure yielded six measurements: β EXP, β PRED , β DEV, β SAD , β AGG, and 1/k. Regional SADs for both land cover categories was analyzed by summing abundances of each species across all stream samples and calculating the standard deviation (parameter σ) of the Poisson-lognormal distribution fit of the abundance data using the `sads´ R package [START_REF] Prado | sads": Maximum Likelihood Models for Species Abundance Distributions[END_REF]. Parameter σ reflects SAD evenness with greater σ values corresponding to increased unevenness. To determine if changes in σ were associated with prevalence of rare vs. common species, we also examined the relationship of σ with the skewness (`skewness´ function from R package `moments´, [START_REF] Komsta | moments: Moments, Cumulants, skewness, kurtosis, and related tests[END_REF] of the logtransformed regional species abundances for each land cover category. Skewness was significant if skewness divided by the standard error of the skewness (i.e., (6/n) 0.5 , where n = number of species) was greater than 2. Significant positive skew indicates greater prevalence of abundant species, while significant negative skew reveals higher number of rare species compared to the lognormal distribution.

Statistical analyses

Resampling scheme

Generally, the described procedures in our study typically produced a single value without any estimate of error, which inhibits statistical comparisons between datasets. Therefore, to test for abiotic and biotic differences between land covers, we conducted a resampling procedure where we randomly selected 50% of the streams within each land cover category for each dataset without replacement 999 times. Each loop calculated the median of each physicochemical variable, an estimate of physicochemical heterogeneity, biodiversity (α-, β-, and γ-diversity), SAD, and null model measures including the null model β-diversity values, and the within group intraspecific aggregation (1/k). This procedure generated six new datasets that contained resampled physiochemistry data and biotic measures, which were used further statistical analyses. R scripts are available as supplementary material for online publication only (see Appendices S3 and S4).

Eutrophication and physicochemical heterogeneity

We employed principal components analysis with all resampled, standardized median physicochemical variables (mean = 0, standard deviation = 1) to create a synthetic variable corresponding to the major physicochemical trend. The first PCA axis represented a eutrophication gradient and explained between 53.1% (French diatom samples) and 94.3% (Canadian diatom samples) of the variation among samples (Appendix 1, Fig. S1.1).

To estimate physicochemical heterogeneity within each land cover, we used permutational analysis of multivariate dispersion on standardized physiochemical data with the `betadisper´ function from R package `vegan´ [START_REF] Anderson | Multivariate dispersion as a measure of beta diversity[END_REF][START_REF] Oksanen | vegan: Community Ecology Package[END_REF]. In this procedure, physicochemical heterogeneity is calculated as the average distance from a multivariate group median (group = land cover) with larger distances corresponding to greater within-group heterogeneity.

Environmental effects

We determined how land use-driven eutrophication and physicochemical heterogeneity affected diversity components using a combination of univariate and multivariate techniques and variance partitioning. For each dataset, we used permutational MANOVA function `adonis´ from package `vegan´ to test for differences in the multivariate mean of the α-, β-, and γ-diversity between land covers. If the permutational MANOVA was significant, we followed with permutational ANOVA using the `perm.anova´ function provided in `RVAideMemore´ (999 permutations; [START_REF] Herve | RVAideMemoire: Testing and plotting for biostatistics[END_REF] for each dependent variable. We then used RDA-based variance partitioning models (`vegan´ function `varpart´) on each dataset to identify major explanatory factors underlying diversity patterns, with eutrophication, physicochemical heterogeneity, and land cover (coded as dummy variables) as predictors and the diversity measures (α-, β-, and γdiversity) as response variables.

We employed permutational MANOVA and permutational ANOVA to determine if the resampled β DEV , β SAD , β AGG , 1/k, σ, and skewness differed between land covers. Because total abundance and γ-diversity influence the shape of the regional SAD, we controlled their influences by regressing parameter σ against total abundance and γ-diversity of the resample and obtaining the residuals, which were then used in subsequent analyses. To further explore if β DEV was sensitive to variation in SAD unevenness (residual σ) and intraspecific spatial aggregation (1/k), we calculated Pearson correlations within both land cover categories for all datasets.

Pearson correlations were also used to assess whether residual σ correlated with skewness and 1/k. We then implemented variance partitioning to determine if eutrophication, physicochemical heterogeneity, land cover, or their covariance explained the variation in β DEV .

RESULTS

Eutrophication and Environmental Heterogeneity Effects on Diversity and the SAD

Permutational MANOVA and permutational ANOVAs of environmental data showed that all physiochemistry levels were significantly elevated (P < 0.05) in agricultural land use across all datasets. Permutational ANOVAs also indicated greater physicochemical heterogeneity among agricultural streams in all but the Canadian diatom dataset (higher in forest land cover) and the US Fish dataset (no differences, Fig. 3). MANOVA of α-, β-, and γ-diversity against land use revealed that land use significantly affected the diversity measures across all datasets. Following our first objective, we demonstrated that β-diversity declined with agriculture across all datasets.

Gamma diversity usually decreased, whereas α-diversity often increased with agriculture (Table 2). Except for French fish, SADs were generally significantly more uneven for agricultural land use than forest (higher residual σ), although the differences were mainly small (Fig. 4, columns 1-3). Intraspecific aggregation (1/k) was always greater in forest than in agriculture and was negatively correlated with residual σ, meaning more even SADs were always associated with higher aggregation (Appendix 1, Table S1.2). Skewness was significantly positive in the insect and all three diatom datasets, but non-significant in the two fish datasets. When positive, skewness correlated positively with residual σ regardless of land cover (although weakly for diatoms), indicating that SAD unevenness was generally characterized by greater abundances of more common species.

Our first objective was to determine how biodiversity explained by land use, eutrophication, and physicochemical heterogeneity. Variation in all diversity measures was primarily explained by covariance effects, while pure land cover, pure eutrophication, and pure physicochemical heterogeneity contributed minorly (Fig. 5). In general, covariance of eutrophication with land cover explained most of the variation, indicating that land use constrained biotic variability mainly through eutrophication rather than physicochemical heterogeneity. However, the insect dataset differed from the rest in that the covariance fraction of land cover, eutrophication, and physicochemical heterogeneity captured most of the variation.

Eutrophication-associated shifts in local assembly across organismal groups

For our second objective, we found local assembly weakly drove diatom and insect β-diversity (β DEV generally less than 0.26 across land covers) but had a relatively greater influence on fish βdiversity (β DEV between 0.38-0.45). β DEV differed significantly between forest and agriculture (permutational ANOVA) in all datasets except insects (no difference). However, the magnitude of the difference in β DEV was usually small (3.49 to 16.04%), with the direction of the difference depending on organismal group and biogeographic region (Fig. 4,column 4).

Contribution of the SAD vs. intraspecific spatial aggregation to β DEV

For objective three, the partitioning of β DEV revealed that β SAD generally exceeded 100% and β AGG was negative, regardless of land cover except for the US and French diatom datasets, which showed β SAD < 100% and positive β AGG for forest land use (Fig. 4, columns 5-6). As changes in β SAD correspond to equal and opposite changes in β AGG , we focus on β SAD for brevity. β SAD represented nearly all of β DEV regardless of dataset and land cover type (~ 90-110% of total deviance) and was significantly (although marginally) larger in agricultural land use than in forest cover. Further, β DEV was generally negatively correlated with residual σ, regardless of land cover or organismal group, implying that increased SAD unevenness was usually associated with greater contribution of the regional species pool (Appendix 1, Table S2). Variance partitioning of β DEV across datasets showed mixed patterns among and within organismal groups over what effects best explained β DEV (Fig. 6).

Variability across organismal groups

Consistent with our fourth objective, we demonstrated that smaller organisms (diatoms and insects) with greater dispersal capacity were more similar in terms of SAD and β DEV patterns, but diverged from fish. However, we also observed divergence in some ecological patterns between datasets within organismal groups (i.e., diatoms and fish) in that α-diversity, γ-diversity, SAD skewness, and β DEV responses varied between country of origin, which indicated context dependency of our results.

DISCUSSION

In this comprehensive study of stream organisms from two continents, agriculture and subsequent eutrophication were generally associated with reduced β-and γ-diversity and increased α-diversity. First, covariance of land use with physicochemical gradients, rather than with physicochemical heterogeneity, characterized regional biodiversity loss with land use.

Second, all datasets showed significant shifts in magnitude of β DEV with eutrophication but the direction (i.e., stronger or weaker local assembly effects) depended on organismal group and potentially biogeographical factors. Third, the regional SAD overrode intraspecific spatial aggregation in explaining β DEV and its influence and unevenness increased with agriculture.

Eutrophication and Environmental Heterogeneity Effects on Diversity and the SAD

With respect to objective one, regional biodiversity loss, local diversity gains, and increased community similarity in aquatic taxa were correlated with agricultural land use, consistent with patterns expected for taxonomic homogenization [START_REF] Petsch | Causes and consequences of biotic homogenization in freshwater ecosystems[END_REF]. Recently, Ribiero et al. (2015) explored the generality of floral homogenization consequential of agricultural land use and noted that too many studies focus on a single spatial scale or a single taxon. For aquatic taxa, agriculturally-associated changes in β-diversity have been reported, however, we have only begun to examine these changes at broader spatial scales. For example, [START_REF] Winegardner | Diatom diversity patterns over the past c. 150 years across the conterminous United States of America: Identifying mechanisms behind beta diversity[END_REF] attributed greater temporal β-diversity of diatoms across modified US landscapes to richness gains and losses stemming from disproportionate influence of contemporary vs. past land use, yet observed no changes in spatial β-diversity. In contrast, diatom spatial β-diversity declined with eutrophication in French streams [START_REF] Jamoneau | Beta diversity of diatom species and ecological guilds: Response to environmental and spatial mechanisms along the stream watercourse[END_REF]. Our investigation, exploring diatoms, insects, and fish across regional to subcontinental scales, demonstrates that the detrimental effects of agriculture on the regional biodiversity in stream ecosystems are independent of species biology or scale.

We further revealed that biodiversity variation between forest and agriculture was mainly driven by land use differences in physicochemistries rather than physicochemical heterogeneity, a result contrary to conventional wisdom that higher environmental heterogeneity brings greater turnover. While agriculture may homogenize the landscape, we show that it tended to lead to greater stream physicochemical heterogeneity, possibly due to variability in fertilization and landscape management regimes. Heterogeneity is an important mechanism of co-existence because it offsets competitive exclusion [START_REF] Tilman | The maintenance of species richness in plant communities[END_REF]. However, we observed that physicochemical heterogeneity poorly explained β-diversity, because eutrophication in agricultural streams may have exceeded the physiological thresholds of sensitive species and decoupled compositional and environmental variability [START_REF] Bini | Nutrient enrichment is related to two facets of beta diversity for stream invertebrates across the United States[END_REF]. The lack of a relationship may also be due to our measure of heterogeneity, which did not incorporate other aspects of heterogeneity, such as variability in substrate size, known to diminish with agriculture [START_REF] Allan | Landscapes and Riverscapes: The Influence of Land Use on Stream Ecosystems[END_REF].

Increased prevalence of common species over spatial and temporal scales is a hallmark of taxonomic homogenization [START_REF] Olden | On defining and quantifying biotic homogenization[END_REF], but our findings are restricted to the spatial dimension. Notably, while across datasets SADs were generally more uneven in agriculture, they were more positively skewed compared to forest only in two datasets, i.e. US insects and French diatoms. In these datasets, homogenization in agriculture was characterized by greater prevalence of common relative to rare species, which has also been observed in terrestrial arthropods [START_REF] Simons | Effects of land-use intensity on arthropod species abundance distributions in grasslands[END_REF][START_REF] Komonen | Ecological response hides behind the species abundance distribution: Community response to low-intensity disturbance in managed grasslands[END_REF].

However, SADs were more positively skewed in forest cover than in agriculture for two datasets (US and Canadian diatoms), and not skewed for both fish datasets. This suggested that stronger SAD unevenness in agriculture resulted from either buildup of common species or greater regional dominance by a relatively few species. Like recent terrestrial and tropical studies [START_REF] Vázquez | Rarity, commonness, and patterns of species richness: the mammals of Mexico[END_REF][START_REF] Lohbeck | The importance of biodiversity and dominance for multiple ecosystem functions in a human-modified tropical landscape[END_REF], we showed that SAD unevenness was associated with agriculturally-driven homogenization. Future research on homogenization should incorporate novel methods and procedures, like we employed, to elucidate how habitat modification and trait distribution contribute to the two forms of unevenness, i.e. asymmetry vs. dominance.

Land use-associated shifts in local assembly across organismal groups

Following objective two, we examined how local assembly (β DEV ) varied between forested and agricultural streams. In general, β DEV marginally differed between land covers, suggesting that the strength of local vs. regional mechanisms was relatively unaffected by physicochemical stressors, consistent with prior work, reporting that fire disturbance altered β-diversity but not its causes [START_REF] Myers | Disturbance alters beta-diversity but not the relative importance of community assembly mechanisms[END_REF]. Community comparisons revealed that the magnitude of β DEV usually increased with body size, which here was linked with dispersal capacity. Smaller β DEV values in diatoms and insects indicated that the observed species pool exerted greater influence on β-diversity relative to local assembly. These results are corroborated by earlier research showing that diatom and insect communities are unsaturated, whereby local richness is limited by the size of the regional pool as opposed to local interactions [START_REF] Passy | The relationship between local and regional diatom richness is mediated by the local and regional environment[END_REF][START_REF] Al-Shami | Drivers of beta diversity of macroinvertebrate communities in tropical forest streams[END_REF]but see Thornhill, Batty, Death, Friberg & Ledger, 2017). Therefore, it is possible that regional effects play a greater role in structuring local richness and β-diversity of smaller and more dispersive organisms than of larger and less dispersive organisms, and these relationships are not consistently affected by eutrophication.

In contrast, β DEV in both fish datasets approaching 0.50 suggested relatively similar local and regional control of β-diversity, in agreement with prior observations of comparable contributions of regional and local factors to fish richness [START_REF] Angermeier | Local vs. regional influences on local diversity in stream fish communities of Virginia[END_REF].

Taxonomic homogenization is a particularly prevalent phenomenon among freshwater fish [START_REF] Petsch | Causes and consequences of biotic homogenization in freshwater ecosystems[END_REF] and our study elucidated that the possible causes include both local and regional processes.

Other nearly uniform patterns, independent of land use, were the negative correlation of residual σ of the regional SAD and the positive correlation of intraspecific aggregation (1/k) with β DEV . These correlations indicated that more even regional SADs and increased intraspecific spatial aggregation were associated with stronger local constraints on β-diversity. Recent work has only begun to explore the relationship of SAD evenness with taxonomic homogenization, showing clear links between the two with implications for conservation (e.g., [START_REF] Simons | Effects of land-use intensity on arthropod species abundance distributions in grasslands[END_REF][START_REF] Komonen | Ecological response hides behind the species abundance distribution: Community response to low-intensity disturbance in managed grasslands[END_REF]. Our study is novel in that it demonstrates that local and regional processes controlling β-diversity are dependent on SAD evenness-a finding that could guide future stream conservation and management decisions, which need to be scale-explicit. For example, if preserving β-diversity, then adopting practices promoting abundance of less common species may be beneficial, given that SAD evenness is positively correlated with β-diversity.

The contribution of the SAD vs. intraspecific spatial aggregation to β DEV

To our knowledge, we are the first to explore how land use affects partitioning of β DEV into SAD vs. spatial aggregation fractions, i.e. β SAD vs. β AGG (objective three). β SAD accounted for most of β DEV , similar to observations for global tree communities [START_REF] Xu | Latitudinal differences in species abundance distributions, rather than spatial aggregation, explain beta-diversity along latitudinal gradients[END_REF], but opposite to findings, with a different null model, for Czech forests [START_REF] Sabatini | Beta-diversity of central European forests decreases along an elevational gradient due to the variation in local community assembly processes[END_REF]. We further discovered that β SAD largely exceeded β AGG across organismal groups, datasets, and land cover types. However, β SAD was significantly higher in agriculture compared to forest in all datasets. The two land covers also diverged in β AGG -less spatial aggregation than predicted by the null model (β AGG < 0) was detected in agriculture across all datasets, while some aggregation (β AGG > 0) was observed in forest streams in four out of six datasets. These results suggest that although land use did not constrain the magnitude of local assembly effects (β DEV ), it did control the mechanisms of local assembly, i.e. land use increased the role of the SAD, but diminished the influence of aggregation.

Organismal and geographic dependencies in biodiversity response to homogenization

In pursuit of our fourth objective, we found that organismal groups responded differently to land use, as reported by other studies (e.g., [START_REF] Angermeier | Local vs. regional influences on local diversity in stream fish communities of Virginia[END_REF][START_REF] Thornhill | Local and landscape scale determinants of macroinvertebrate assemblages and their conservation value in ponds across an urban land-use gradient[END_REF]. Insects resembled diatoms in biodiversity, SAD shape, and β DEV patterns, which suggested that body size and dispersal capacity may be more important than trophic position (autotroph vs. heterotroph) in predicting ecological responses to agricultural eutrophication. We generally expected consistent responses of these metrics to agriculture, regardless of country of origin (i.e., diatoms and fish). We reasoned that agriculture, being a major habitat alteration, will override all other influences, yet within both groups, there was divergence depending on region. We ensured that variation in individual counts and mean counts among samples and differences in geographic spread across datasets did not contribute to their dissimilarity (data not shown). Thus, our findings of within-taxon variability with respect to biodiversity and the SAD highlighted the importance of considering context dependency. Histories of land use disturbance among geographic regions can set biodiversity and relative abundance patterns on different trajectories by affecting processes underlying β-diversity [START_REF] Cramer | What's new about old fields? Land abandonment and ecosystem assembly[END_REF]. For example, European fish diversity has been historically depauperate relative to North American fauna owing particularly to differences in glacial influence [START_REF] Oberdorff | Is there an influence of historical events on contemporary fish species richness in rivers? Comparisons between Western Europe and North America[END_REF].

Furthermore, French aquatic communities have been impacted by agricultural activities far longer than their North American counterparts [START_REF] Hahn | Land-use history alters contemporary insect herbivore community composition and decouples plant-herbivore relationships[END_REF].

In summary, we determined eutrophication is a major driver of β-diversity losses among stream taxa, although the importance of geographic context was shown through the varied biodiversity responses within taxonomic groups. Local assembly generally was weakly affected by agriculture. However, in agriculture the regional SAD became significantly more uneven and its effect on local assembly significantly increased compared to forest, which may be the underlying causes of taxonomic homogenization. Biodiversity, SAD shape, and β DEV depended more strongly on body size and/or dispersal than trophic position. Future research should explore how local and regional processes operate in tandem with the SAD to uncover whether homogenization drivers are specific to organismal groups and the regions from which they were sampled. Although we examined β-diversity loss from a taxonomic perspective, we recommend future investigations on whether agriculture leads to phylogenetic and functional homogenization across space and time. Then, taxonomic, phylogenetic, and functional diversity responses to agriculture could be compared to generate more holistic understandings of the causes and patterns of biotic homogenization. 1) The SAD was the dominant fraction of β DEV and this pattern was independent of land use and organismal group. However, the SAD fraction was significantly higher in agriculture across all datasets, which may be the underlying factor of taxonomic homogenization;

2) Intraspecific spatial aggregation fraction was negative for agricultural streams and positive for forest streams, indicating that intraspecific aggregation was lower than expected across disturbed streams;

Figure 1 .

 1 Figure 1. a. Conceptual model depicting the land use effect on the species abundance

Figure 2 ,

 2 Figure2, a-f. Maps of diatom, macroinvertebrate, and fish sampling localities in the US,

Figure 3 ,

 3 Figure 3, a-f. Boxplots showing differences in resampled physicochemical heterogeneity

Figure 4 ,

 4 Figure 4, a-f. Boxplots of resampled SAD and null model metrics showing the differences

Figure 5

 5 Figure 5, a-f. Venn diagrams showing output of redundancy analysis-based variance partitioning of diversity measures (α-, β-, and γ-diversity). Values represent model adjusted R 2

Figure 6 ,

 6 Figure 6, a-f. Venn diagrams showing output of regression-based variance partitioning of β DEV . Values represent model adjusted R 2 values. Values in intersections represent covariance

Table 1 .

 1 Summary of procedures and analyses performed with corresponding expectations and observations.

	Procedures	Analyses	Expectations	Observations

Table 2 .

 2 Summary of the impact of agricultural land use on resampled diversity measures as positive or negative percent change relative to forest cover. Significant differences between land covers were detected in all comparisons (permutational MANOVA and ANOVA, P < 0.05).

	% Change from agriculture

α γ

ACKNOWLEDGEMENTS

We thank W. Xu for helpful comments on the null model. Support by the National Science Foundation (grant NSF DEB-1745348) to SIP is gratefully acknowledged.

ACKNOWLEDGEMENTS

We thank W. Xu for helpful comments on the null model. Support by the National Science Foundation (grant NSF DEB-1745348) to SIP is gratefully acknowledged.

DATA AVAILABILITY STATEMENT

Community and environmental datasets for the US are available for download from the USGS NAWQA Program via the Water Quality Portal (https://www.waterqualitydata.us/contact_us/) and EPA NRSA (https://www.epa.gov/national-aquatic-resource-surveys/data-national-aquaticresource-surveys) databases. DOI with associated URLs for all community and environmental datasets analyzed for this project will be made freely available for download from Dryad Digital Repository upon publication.

Author Biosketch: William R. Budnick is a Ph.D. candidate studying macroecology at the University of Texas at Arlington. His primary research interests lie with the application of cutting-edge numerical and spatial techniques to study and predict how global change forces will affect macroecological patterns particular to aquatic ecosystems. His interests also include integrating basic ecological theory with fisheries science to assist with the global conservation of threatened fish and crayfish fauna.