What drives spatially varying ecological relationships in a wide‐ranging species?

Abstract

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, nationallevel 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.

Guidelines for the use of spatially varying coefficients in species distribution models

“…When assessing spatial variability in species-environment relationships and/or trends, we recommend comparing SVC SDMs with simpler parametric SDMs that represent explicit hypotheses, as such comparisons can reveal the amount of support for different drivers of spatially varying effects/trends (Pease, Pacifici, Kays, & Reich, 2022). For example, in the eastern forest bird case study, the temperature model revealed a significant negative interaction of trend and breeding season maximum temperature for 18 species and a significant positive interaction for 8 species (Supplemental Information S3 Figure S1).…”

Spatio-temporal variability in the strength, directionality, and relative importance of climate on occupancy and population densities in a philopatric mammal, the American pika (Ochotona princeps)

“…Investigations into spatial nonstationarity in speciesenvironment relationships have been growing in recent years, with studies often documenting the greatest spatial nonstationarity in climate metrics specifically (Pease et al, 2022b). Identifying the key drivers of variability in these relationships is especially important for management decisionmaking and policy.…”

Guidelines for the use of spatially-varying coefficients in species distribution models

“…SDMs are increasingly applied across large spatial extents (i.e., macroscales) as a result of a growing interest in macroecological patterns, the emergence of large-scale citizen science programs (e.g., eBird, iNaturalist), and increasing availability of data from regional-to continentalscale monitoring programs. As the spatial extent of analysis increases, the common assumption of stationarity in species-environment relationships becomes less realistic (Pease et al, 2022b).…”

“…Despite increased recognition of the prevalence of nonstationary in macrosystems (Rollinson et al, 2021), the use of SVCs within SDMs is still scarce in many fields including wildlife ecology and conservation. Recent applications suggest increasing interest in this flexible framework for a variety of ecological applications (e.g., Meehan et al 2019;Pease et al 2022b;Sultaire et al 2022), yet a comprehensive understanding of the data requirements needed to estimate SVCs in SDMs is lacking. Typical SDM applications present many unique data complexities (e.g., binary response variable, imperfect detection) and there is little guidance on how such complexities impact the sample size requirements and reliability of predictions and inference from SDMs with SVCs (hereafter SVC SDMs).…”