Abiotic stress and biotic factors mediate range dynamics on opposing edges
Siren, Alexej (2022), Abiotic stress and biotic factors mediate range dynamics on opposing edges, Dryad, Dataset, https://doi.org/10.5061/dryad.q2bvq83j5
Aim: In the face of global change, understanding causes of range limits is 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 US
Methods: We evaluated two hypotheses of iRLT using occupancy and structural equation modeling (SEM) frameworks based on data collected over a six-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.
We used data from 257 camera-trap sites spaced in non-overlapping grids based on the home range size of the smallest carnivore species (Martes americana = 2x2 km). Each site included a remote camera positioned facing north on a tree, 1–2 m above the snow surface, and pointed at a slight downward angle towards a stake positioned 3–5 m from the camera. Commercial skunk lure and turkey feathers were used as attractants and placed directly on the snow stakes. Cameras were set to take 1–3 consecutive pictures every 1–10 sec when triggered, depending on the brand and model, and checked on average 3 (range = 1–9) times each season to download data, refresh attractants, and to ensure cameras were working properly.
We used camera data from autumn to spring (16 October–15 May) for each year (2014–2019). This seasonal range was chosen as it approximates demographic (i.e., births and deaths) and geographic closure (i.e., dispersal) and is based on species’ ecological responses to snowpack and leaf phenology of the region. We identified species in photographs by their unique morphology and field marks and used consensus from multiple observers when identification was uncertain. We organized camera data into weekly occasions using CPW Photo Warehouse and recorded whether or not each species was detected during the occasion.
The data (sem_dat.csv) contains best unbiased predictors of occurrence (BUPs) for each species at each site and year. The locations of the sites were not included as Canada lynx (Lynx canadensis) and American marten (Martes americana) are federally-threatened and state-endangered species, respectively, in our study region. BUPs for each species are listed by their common name and exogenous variables include unscaled values (depth, biomass) and scaled values (depth_s, biomass_s), the latter of which were used in the SEM. The siteID column is the site specific id of the each camera site and was specified as a random effect in each generalized linear mixed effects model (GLMM) in the SEM. The associated R code (JBI-20-0706R1) can be used to peform the SEM using the data.