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Aim Decades of research on species distributions has revealed geographic variation in species‐environment relationships for a given species. That is, the way a species uses the local environment varies across geographic space. However, the drivers underlying this variation are contested and still largely unexplored. Niche traits that are conserved should reflect the evolutionary history of a species whereas more flexible ecological traits could vary at finer scales, reflecting local adaptation. Location North America. Methods We used mammal observations during a 5‐year period from the iNaturalist biodiversity database and a local ensemble modelling approach to explore spatial variation in American black bear (Ursus americanus) relationships with eight ecological correlates. We tested four biologically driven hypotheses to explain the patterns of local adaptation. We evaluated non‐stationarity in ecological relationships using a Stationarity Index and tested predictive performance using an independent, national‐level animal occurrence data set. Results We documented considerable spatial non‐stationarity in all eight environmental relationships, with the greatest spatial variation occurring in bear's relationship to climatic factors. Notably, the greatest variation in environmental relationships tended to occur along the current boundaries of the species' range, potentially representing the ecological limits to the species geographic range. We additionally documented that spatial variation in relationships with land cover and anthropogenic factors were best explained by niche conservatism at the subspecies level, whereas climatic relationships were better explained by local adaptation. Main Conclusions Based on these results, we propose that the current distribution of American black bear is determined by an evolutionary legacy of habitat relationships unique to each subspecies combined with more fine‐scale local adaptation to climatic conditions. This result suggests that black bears should be adaptable to climatic changes over the 21st century and that management of habitat and human‐bear relationships could be considered at the subspecies level.

Issues of residual spatial autocorrelation (RSA) and spatial scale are critical to the study of species-environment relationships, because RSA invalidates many statistical procedures, while the scale of analysis affects the quantification of these relationships. Although these issues independently are widely covered in the literature, only sparse attention is given to their integration. This paper focuses on the interplay between RSA and the spatial scaling of species-environment relationships. Using a hypothetical species in an artificial landscape, we show that a mismatch between the scale of analysis and the scale of a species' response to its environment leads to a decrease in the portion of variation explained by environmental predictors. Moreover, it results in RSA and biased regression coefficients. This bias stems from error-predictor dependencies due to the scale mismatch, the magnitude of which depends on the interaction between the scale of landscape heterogeneity and the scale of a species' response to this heterogeneity. We show that explicitly considering scale effects on RSA can reveal the characteristic scale of a species' response to its environment. This is important, because the estimation of species-environment relationships using spatial regression methods proves to be erroneous in case of a scale mismatch, leading to spurious conclusions when scaling issues are not explicitly considered. The findings presented here highlight the importance of examining the appropriateness of the spatial scales used in analyses, since scale mismatches affect the rigor of statistical analyses and thereby the ability to understand the processes underlying spatial patterning in ecological phenomena.

Insects and spiders comprise more than two-thirds of the Earth's total species diversity. There is wide concern, however, that the global diversity of arthropods may be declining even more rapidly than the diversity of vertebrates and plants. For adequate conservation planning, ecologists need to understand the driving factors for arthropod communities and devise methods that provide reliable predictions when resources do not permit exhaustive ground surveys. Which factor most successfully predicts arthropod community structure is still a matter of debate, however. The purpose of this study was to identify the factor best predicting arthropod assemblage composition. We investigated the species composition of seven functionally different arthropod groups (epigeic spiders, grasshoppers, ground beetles, weevils, hoppers, hoverflies, and bees) at 47 sites in The Netherlands comprising a range of seminatural grassland types and one heathland type. We then compared the actual arthropod composition with predictions based on plant species composition, vegetation structure, environmental data, flower richness, and landscape composition. For this we used the recently published method of predictive co-correspondence analysis, and a predictive variant of canonical correspondence analysis, depending on the type of predictor data. Our results demonstrate that local plant species composition is the most effective predictor of arthropod assemblage composition, for all investigated groups. In predicting arthropod assemblages, plant community composition consistently outperforms both vegetation structure and environmental conditions (even when the two are combined), and also performs better than the surrounding landscape. These results run against a common expectation of vegetation structure as the decisive factor. Such expectations, however, have always been biased by the fact that until recently no methods existed that could use an entire (plant) species composition in the explanatory role. Although more recent experimental diversity work has reawakened interest in the role of plant species, these studies still have not used (or have not been able to use) entire species compositions. They only consider diversity measures, both for plant and insect assemblages, which may obscure relationships. The present study demonstrates that the species compositions of insect and plant communities are clearly linked.

1 Disturbance may cause community composition across sites to become more or less homogenous, depending on the importance of different processes involved in community assembly. In north-eastern North America and Europe local (alpha) diversity of forest plants is lower in forests growing on former agricultural fields (recent forests) than in older (ancient) forests, but little is known about the influence of land-use history on the degree of compositional differentiation among sites (beta diversity). 2 Here we analyse data from 1446 sites in ancient and recent forests across 11 different landscapes in north-eastern North America and Europe to demonstrate decreases in beta diversity and in the strength of species-environment relationships in recent vs. ancient forests. 3 The magnitude of environmental variability among sites did not differ between the two forest types. This suggests the difference in beta diversity between ancient and recent forests was not due to different degrees of environmental heterogeneity, but rather to dispersal filters that constrain the pool of species initially colonizing recent forests. 4 The observed effects of community homogenization and weakened relationships between species distributions and environmental gradients appear to persist for decades or longer. The legacy of human land-use history in spatial patterns of biodiversity may endure, both within individual sites and across sites, for decades if not centuries.

Two main aspects of the study of diversity can be distinguished: the first is related to the inventory of living organisms, the second is related to the organization of life at the level of biotic communities. Quantitative assessment of diversity is two-components as the richness of elements and their evenness. A model of the ecosystem continuum is proposed. The greatest indicators of diversity should be expected in the middle part of the environmental gradients with temporal stability. Study of producers and consumers in water bodies of Ukraine showed a regular change in their community structure in the gradient of saprobity indices. The decreasing of community diversity estimated by the Shannon index and by species richness was found at both high and low values of the saprobity indices. The fundamental coincidence of the empirical point fields of the Shannon index for the communities of invertebrates and phytoplankton with the field points of the empirical model indicates the universality of the bimodal distribution of diversity indicators in the trophic gradient. It is shown that the estimates by zoobenthos overestimate organic pollution compared with the calculations of the same indicators by phytoplankton.

Understanding complexities in biodiversity is one of the fundamental goals of ecology and its monitoring is significant for ecosystem sustainability, maintenance, and conservation. However, biodiversity monitoring needs improvement to handle complex datasets and their analyses. This study attempts to understand these ecological complexities quickly, efficiently, and easily. The aim is to provide an alternative to ecologists, researchers, instructors, and stakeholders for biodiversity monitoring with the flexibility to visualize and customize outputs without software knowledge. A novel web‐based technique is applied to monitor the biodiversity of a complex mountain ecosystem using a national database. The species–environment relationships of different vegetation types across a mountain ecosystem's elevation gradient are investigated using open‐source climatic, physiographic, and socioeconomic variables. The proposed interactive tool to monitor biodiversity and understand its complexities is designed to visualize the data structure, summary, correlations, and sampling effectiveness quickly and easily. Plant species richness patterns and life forms (herb, shrub, and tree) across elevational gradients are investigated. We highlight the preliminary investigation of the data structure and their spatial distribution and apply the multicollinearity test to select variables for modeling. The drop‐down menu helps users browse different datasets and select those datasets for instant visualization. Preliminary investigations on interactions between variables and species richness of vegetation types along elevation gradient interactively displayed with options to select variables, plant richness, and an elevational range. Species–environment relationships are investigated using multiple modeling protocols, and results are interactively displayed with options to download in different file formats and colors at the click of a button. This visualization tool helps to understand ecosystem structure, species richness patterns and species–environment relationships easily and efficiently. The R‐codes used in this tool are reproducible and can be implemented with multiple datasets to monitor ecosystems. Understanding complex datasets efficiently and easily using visualization tools is useful for quick decision‐making. The proposed interactive tool is designed to study the data structure, summary, correlations, and sampling efficiency. It also helps to understand the ecosystem structure, species richness patterns, and species–environment relationships. The R‐codes used in this tool are reproducible for datasets of multiple ecosystems. The tool could help planning for conservation be more meaningful and easy.

Understanding variation in community composition across space, or beta diversity, is of longstanding interest in ecology, yet the determinants of beta diversity remain poorly known. In part, this results from a lack of manipulative tests of hypothesized drivers. The size of species pools is one putative driver, but few studies have provided a direct test of this mechanism through manipulation of clearly defined species pools independent of local communities. Furthermore, we know little about underlying mechanisms, such as enhanced species sorting, or whether a species pool size‐beta diversity relationship is scale‐dependent or modified by environmental conditions. Here, we evaluate 29 prairie plant communities restored from bare soil with known species pools (seed mixes) to address those questions. To address the generality of beta diversity drivers across scales, we investigated how the size of species pools during restoration influenced beta diversity in the plant community at two scales: among prairies and within prairies (among plots). Among a group of prairies sown with larger species pools, among‐site beta diversity was greater than among a group of prairies assembled from smaller pools, but not because of enhanced species sorting. We found an interaction between species pool size and an environmental filter, whereby beta diversity was higher among prairies restored with species‐rich seed mixes, but only when soil moisture was also high. We detected neither greater beta diversity nor stronger species sorting among plots within prairies sown with species‐rich mixes. Synthesis. This work provides what is to our knowledge the first large‐scale manipulative test of how species pool size influences beta diversity. We found higher beta diversity among restored prairies sown with species‐rich seed mixes, but little evidence for species sorting as a causal mechanism. Our results, based on manipulated real‐world communities, provide an important link between previous theoretical and observational studies and small‐scale experimental approaches. Of applied importance, our findings show that by creating communities of high beta diversity, ecological restoration can counteract widespread anthropogenic biotic homogenization.

Aim: The aim of our study was to reveal relationships between richness patterns of native vs. alien plant species and spatial heterogeneity across varying landscape patterns at a regional scale. Location: The study was carried out in the administrative district of Dessau (Germany), covering around$4000 km^2$. Methods: Data on plant distribution of the German vascular flora available in grid cells covering 5' longitude and 3' latitude ($c. 32 km^2$) were divided into three status groups: native plants, archaeophytes (pre 1500 AD aliens) and neophytes (post 1500 AD aliens). Land use and abiotic data layers were intersected with 125 grid cells comprising the selected area. Using novel landscape ecological methods, we calculated 38 indices of landscape composition and configuration for each grid cell. Principal components analysis (PCA) with a set of 29 selected, low correlated landscape indices was followed by multiple linear regression analysis. Results: PCA reduced 29 indices to eight principal components (PCs) that explained 80% cumulative variance. Multiple linear regression analysis was highly significant and explained 41% to 60% variance in plant species distribution (adjusted R2) with three significant PCs (tested for spatial autocorrelation) expressing moderate to high disturbance levels and high spatial heterogeneity. Comparing the significance of the PCs for the species groups, native plant species richness is most strongly associated with riverine ecosystems, followed by urban ecosystems, and then small-scale rural ecosystems. Archaeophyte and neophyte richness are most strongly associated with urban ecosystems, followed by small-scale rural ecosystems and riverine ecosystems for archaeophytes, and riverine ecosystems and small-scale rural ecosystems for neophytes. Main conclusions: Our overall results suggest that species richness of native and alien plants increases with moderate levels of natural and/or anthropogenic disturbances, coupled with high levels of habitat and structural heterogeneity in urban, riverine, and small-scale rural ecosystems. Despite differences in the order of relevance of PCs for the three plant groups, we conclude that at the regional scale species richness patterns of native plants as well as alien plants are promoted by similar factors.

The design and objective of a community study imply the selection of the appropriate ordination technique in terms of species response models and weighting options. In this paper, we start from the observation that existing two-table ordination techniques and related measures of niche breadth inevitably weight a sample in proportion to its abundance. We introduce a new multivariate method, which gives a more even weight to all sampling units, including those which are species poor or individual poor. We use this new method of analysis which we call OMI (for Outlying Mean Index) to address the question of niche separation and niche breadth. The Outlying Mean Index, or species marginality, measures the distance between the mean habitat conditions used by species (species centroid), and the mean habitat conditions of the sampling area (origin of the niche hyperspace), and OMI analysis places species along habitat conditions using a maximization of their mean OMI. Therefore, the position of the species depends on their niche deviation from a reference, which represents neither the mean nor the most abundant species, but a theoretical ubiquitous species that tolerates the most general habitat conditions (i.e., a hypothetical species uniformly distributed among habitat conditions). We demonstrate that OMI analysis is well suited for the investigation of multidimensional niche breadths in the case of strong limiting factors (e.g., meteorological conditions) or strong driving forces (e.g., longitudinal stream gradient). Furthermore, the analysis helps in finding which ecological factors are most important for community structure and organization and provides a separation of species based on their niche characteristics.

A pair of ecological tables is made of one table containing environmental variables (in columns) and another table containing species data (in columns). The rows of these two tables are identical and correspond to the sites where environmental variables and species data have been measured. Such data are used to analyze the relationships between species and their environment. If sampling is repeated over time for both tables, one obtains a sequence of pairs of ecological tables. Analyzing this type of data is a way to assess changes in species-environment relationships, which can be important for conservation Ecology or for global change studies. We present a new data analysis method adapted to the study of this type of data, and we compare it with two other methods on the same data set. All three methods are implemented in the ade4 package for the R environment.