Abiotic stress and biotic factors mediate range dynamics on opposing edges

Abstract

Aim In the face of global change, understanding causes of range limits are one of the most pressing needs in biogeography and ecology. A prevailing hypothesis is that abiotic stress forms cold (upper latitude/altitude) limits, whereas biotic interactions create warm (lower) limits. A new framework – Interactive Range‐Limit Theory (iRLT) – asserts that positive biotic factors such as food availability can ameliorate abiotic stress along cold edges, whereas abiotic stress can have a positive effect and mediate biotic interactions (e.g., competition) along warm limits. Location Northeastern United States Taxon Carnivora Methods We evaluated two hypotheses of iRLT using occupancy and structural equation modeling (SEM) frameworks based on data collected over a 6‐year period (2014–2019) of six carnivore species across a broad latitudinal (42.8–45.3°N) and altitudinal (3–1451 m) gradient. Results We found that snow directly limits populations, but prey or habitat availability can influence range dynamics along cold edges. For example, bobcats (Lynx rufus) and coyotes (Canis latrans) were limited by deep snow and long winters, but the availability of prey had a strong positive effect. Conversely, snow had a strong positive effect on the warm limits of Canada lynx (Lynx canadensis), countering the negative effect of competition with the phylogenetically similar bobcat and with coyotes, highlighting how climate mediates competition between species. Main conclusions We used an integrated dataset that included competitors and prey species collected at the same spatial and temporal scale. As such, this design, along with a causal modeling framework (SEM), allowed us to evaluate community‐wide hypotheses at macroecological scales and identify coarse‐scale drivers of species' range limits. Our study supports iRLT and underscores the need to consider direct and indirect mechanisms for studying range dynamics and species' responses to global change.

Spatial variation in bioclimatic relationships for a snow‐adapted species along a discontinuous southern range boundary

“…This finding highlights the potential benefit of assessing space varying species–environment relationships in SDMs. Furthermore, our results lend additional support to the interactive nature of environmental drivers of species range boundaries, in which multiple factors contribute to the formation of a range boundary in a given region (Siren et al, 2021). However, our prediction of more different occurrence–climate relationship for the isolated Lower Peninsula population was not strongly supported (Figure 2).…”

Forecasting species distributions: Correlation does not equal causation

“…This study focused on data from a large-scale, 6-year camera trapping effort at 257 sites across the U.S. states of Vermont and New Hampshire (Figure 1). Although other carnivores, such as coyotes and fishers, (Pekania pennanti) negatively influence lynx (Bayne et al, 2008;McLellan et al, 2018), our previous work, and that from other regions, indicates that bobcats have the greatest impact on lynx occurrence (Peers et al, 2013;Scully et al, 2018;Sirén et al, 2021). Furthermore, we only found coyotes, rather than fishers, to have a negative effect on marten occurrence (Sirén et al, 2021), supporting recent work in the region (Jensen & Humphries, 2019).…”

“…Although other carnivores, such as coyotes and fishers, ( Pekania pennanti ) negatively influence lynx (Bayne et al, 2008; McLellan et al, 2018), our previous work, and that from other regions, indicates that bobcats have the greatest impact on lynx occurrence (Peers et al, 2013; Scully et al, 2018; Sirén et al, 2021). Furthermore, we only found coyotes, rather than fishers, to have a negative effect on marten occurrence (Sirén et al, 2021), supporting recent work in the region (Jensen & Humphries, 2019). However, the indirect effect of snow on martens through coyotes was weaker than that of lynx vs. bobcats (Sirén et al, 2021).…”

“…Accordingly, our models predict that marten distribution will retract northward in the region due to the decline in snow depth projected by GCMs (see Figure 5, Figure S10). Previous research has indicated that martens have a strong positive association with snow due to its potential mediating effect on competitors (Jensen & Humphries, 2019;Sirén et al, 2021;Zielinski et al, 2017) and its role as habitat for foraging and thermoregulation (Buskirk et al, 1989). Thus, a decline in snow depth will likely have a detrimental impact and result in range contraction along its trailing edge (Carroll, 2007;Wasserman et al, 2012).…”

“…For the causal model, we used structural equation modeling (SEM) (Lefcheck, 2016) and a modification of the causal system described by Sirén et al (2021) that included only snow depth and forest biomass as exogenous variables and bobcats and coyotes as competitors/predators of lynx and martens, respectively. SEM is generally described as a series of univariate regressions within a causal graph or network of paths that provides for the evaluation of complex and competing hypotheses (Grace, 2008).…”