Accounting for nonlinear responses to traits improves range shift predictions

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 b..., 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., , # Accounting for nonlinear responses to traits improves range shift predictions

[https://doi.org/10.5061/dryad.wstqjq2v8](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.

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Data was derived from the following sources:

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