Forecasting species distributions: Correlation does not equal causation ...

• Main Authors: Sirén, Alexej, Sutherland, Chris S., Karmalkar, Ambarish V., Duveneck, Matthew J., Morelli, Toni Lyn
• Format: Dataset
• Language: English
• Published: Dryad 2022
• Subjects: FOS Biological sciences; abiotic; biotic; Carnivores; Climate change; competition; ecological modeling; Snow; Species Distribution Modeling; Species interactions; structural equation modeling; Ecology, Evolution, Behavior and Systematics; Martes americana
• Online Access: https://dx.doi.org/10.5061/dryad.k0p2ngf9j; https://datadryad.org/stash/dataset/doi:10.5061/dryad.k0p2ngf9j

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 ... : 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 ...