Forecasting species distributions: Correlation does not equal causation ...
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 ca...
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ftdatacite:10.5061/dryad.k0p2ngf9j 2024-02-04T10:02:02+01:00 Forecasting species distributions: Correlation does not equal causation ... Sirén, Alexej Sutherland, Chris S. Karmalkar, Ambarish V. Duveneck, Matthew J. Morelli, Toni Lyn 2022 https://dx.doi.org/10.5061/dryad.k0p2ngf9j https://datadryad.org/stash/dataset/doi:10.5061/dryad.k0p2ngf9j en eng Dryad https://dx.doi.org/10.1111/ddi.13480 https://dx.doi.org/10.5281/zenodo.5830947 Creative Commons Zero v1.0 Universal https://creativecommons.org/publicdomain/zero/1.0/legalcode cc0-1.0 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 Dataset dataset 2022 ftdatacite https://doi.org/10.5061/dryad.k0p2ngf9j10.1111/ddi.1348010.5281/zenodo.5830947 2024-01-05T04:51:50Z 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 ... Dataset Martes americana DataCite Metadata Store (German National Library of Science and Technology) |
institution |
Open Polar |
collection |
DataCite Metadata Store (German National Library of Science and Technology) |
op_collection_id |
ftdatacite |
language |
English |
topic |
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 |
spellingShingle |
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 Sirén, Alexej Sutherland, Chris S. Karmalkar, Ambarish V. Duveneck, Matthew J. Morelli, Toni Lyn Forecasting species distributions: Correlation does not equal causation ... |
topic_facet |
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 |
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 ... : 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 ... |
format |
Dataset |
author |
Sirén, Alexej Sutherland, Chris S. Karmalkar, Ambarish V. Duveneck, Matthew J. Morelli, Toni Lyn |
author_facet |
Sirén, Alexej Sutherland, Chris S. Karmalkar, Ambarish V. Duveneck, Matthew J. Morelli, Toni Lyn |
author_sort |
Sirén, Alexej |
title |
Forecasting species distributions: Correlation does not equal causation ... |
title_short |
Forecasting species distributions: Correlation does not equal causation ... |
title_full |
Forecasting species distributions: Correlation does not equal causation ... |
title_fullStr |
Forecasting species distributions: Correlation does not equal causation ... |
title_full_unstemmed |
Forecasting species distributions: Correlation does not equal causation ... |
title_sort |
forecasting species distributions: correlation does not equal causation ... |
publisher |
Dryad |
publishDate |
2022 |
url |
https://dx.doi.org/10.5061/dryad.k0p2ngf9j https://datadryad.org/stash/dataset/doi:10.5061/dryad.k0p2ngf9j |
genre |
Martes americana |
genre_facet |
Martes americana |
op_relation |
https://dx.doi.org/10.1111/ddi.13480 https://dx.doi.org/10.5281/zenodo.5830947 |
op_rights |
Creative Commons Zero v1.0 Universal https://creativecommons.org/publicdomain/zero/1.0/legalcode cc0-1.0 |
op_doi |
https://doi.org/10.5061/dryad.k0p2ngf9j10.1111/ddi.1348010.5281/zenodo.5830947 |
_version_ |
1789968356185473024 |