Aim: Identifying the mechanisms influencing species’ distributions is critical for accurate climate change forecasts. However, current approaches are limited by correlative models that cannot distinguish between direct and indirect effects. Location: New Hampshire and Vermont, USA. Methods: Using causal and correlational models and new theory on range limits, we compared current (2014–2019) and future (2080s) distributions of ecologically important mammalian carnivores and competitors along range limits in the northeastern US under two global climate models (GCMs) and a high-emissions scenario (RCP8.5) of projected snow and forest biomass change. Results: Our hypothesis that causal models of climate-mediated competition would result in different distribution predictions than correlational models, both in the current and future periods, was well-supported by our results; however, these patterns were prominent only for species pairs that exhibited strong interactions. The causal model predicted the current distribution of Canada lynx (Lynx canadensis) more accurately, likely because it incorporated the influence of competitive interactions mediated by snow with the closely related bobcat (Lynx rufus). Both modeling frameworks predicted an overall decline in lynx occurrence in the central high elevation regions and increased occurrence in the northeastern region in the 2080s due to changes in land use that provided optimal habitat. However, these losses and gains were less substantial in the causal model due to the inclusion of an indirect buffering effect of snow on lynx. Main conclusions: Our comparative analysis indicates that a causal framework, steeped in ecological theory, can be used to generate spatially-explicit predictions of species distributions. This approach can be used to disentangle correlated predictors that have previously hampered understanding of range limits and species’ response to climate 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 (DDI-2020-0432R2) can be used to peform the SEM using the data.