DOI: 10.5061/dryad.q2bvq83j5
title: Abiotic stress and biotic factors mediate range dynamics on opposing edges

description: 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 Taxon: Carnivora 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.
description: We used data from 257 camera-trap sites spaced in non-overlapping
 grids based on the home range size of the smallest carnivore species
 (<em>Martes americana</em> = 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.
description: 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 (<em>Lynx
 canadensis</em>) and American marten (<em>Martes
 americana</em>) 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.