Accounting for nonlinear responses to traits improves range shift predictions
Data files
Apr 03, 2024 version files 262.60 KB
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DataDescriptions_CannistraBuckley.pdf
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fish_Pinskyetal2013.csv
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mammals_Angertetal2011.csv
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MothsBirds_Hallfors2023.csv
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plants_Angertetal2011.csv
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README.md
Abstract
Accurately predicting species’ range shifts in response to environmental change is paramount for understanding ecological processes and global change. In synthetic analyses, traits emerge as significant but weak predictors of species’ range shifts across recent climate change. These studies assume linear responses to traits, while detailed empirical work often reveals trait responses that are unimodal and contain thresholds or other nonlinearities. We hypothesize that the use of linear modeling approaches fails to capture these nonlinearities and therefore may be under-powering traits to predict range shifts. We evaluate the predictive performance of approaches that can capture nonlinear relationships (ridge-regularized linear regression, support vector regression with linear and nonlinear kernels, and random forests). We apply our models using six multi-decadal range shift datasets for plants, moths, marine fish, birds, and small mammals. We show that nonlinear approaches can perform better than least-squares linear modeling in reproducing historical range shifts. Consistent with expectations, we identify dispersal and climatic niche traits as primary determinants of distribution shifts. Traits identified as important predictors and the direction of trait effects are generally consistent across models but there are notable exceptions. Among important predictors, there are more consistent responses to climatic niches than dispersal ability. Modest improvements in predictability when accounting for nonlinearities and interactions and the overall low amount of variance accounted for by trait predictors suggest limits to trait-based statistical predictive frameworks.
README: Accounting for nonlinear responses to traits improves range shift predictions
https://doi.org/10.5061/dryad.wstqjq2v8
We assess the performance of nonlinear models to predict climate-induced range shifts using six datasets encompassing a broad taxonomic range. The number of species per dataset ranges from 28 to 239 (mean=118, median=94), and range shifts were observed over periods ranging from 20 to 100+ years. Each dataset was derived from previous evaluations of traits as range shift predictors and consists of a list of focal species, associated species-level traits, and a range shift metric.
Description of the data and file structure
See the DataDescriptions_CannistraBuckley.pdf file for information on the data and structure. Refer to the references below for additional information on the datasets and please cite those papers if you use this data.
Sharing/Access information
Data was derived from the following sources:
- Angert, A. L., L. G. Crozier, L. J. Rissler, S. E. Gilman, J. J. Tewksbury, and A. J. Chunco. 2011. Do species’ traits predict recent shifts at expanding range edges? Ecology Letters. 14:677–689.
- Froese, R., and D. Pauly. 2010. FishBase. Fisheries Centre, University of British Columbia Los Baños, Philippines.
- Hällfors, M. H., R. K. Heikkinen, M. Kuussaari, A. Lehikoinen, M. Luoto, J. Pöyry, R. Virkkala, M. Saastamoinen, and H. Kujala. 2023. Recent range shifts of moths, butterflies, and birds are driven by the breadth of their climatic niche. Evolution Letters: qrad004.
- Pinsky, M. L., B. Worm, M. J. Fogarty, J. L. Sarmiento, and S. A. Levin. 2013. Marine Taxa Track Local Climate Velocities. Science. 341:1239–1242.
Code/Software
The data and all code for analyzing data are available on GitHub for both the R (https://github.com/lbuckley/cc_traits and the Python implementations (https://github.com/huckleylab/cc_traits.
Methods
We assess model performance using six datasets encompassing a broad taxonomic range. The number of species per dataset ranges from 28 to 239 (mean=118, median=94), and range shifts were observed over periods ranging from 20 to 100+ years. Each dataset was derived from previous evaluations of traits as range shift predictors and consists of a list of focal species, associated species-level traits, and a range shift metric.