DOI: 10.5061/dryad.k0p2ngf9j
title: Forecasting species distributions: Correlation does not equal causation

description: 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.
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 (DDI-2020-0432R2) can be used to peform the SEM using the
 data.