DOI: 10.5061/dryad.wstqjq2v8
title: Accounting for nonlinear responses to traits improves range shift predictions

description: 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.
description: 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.
description: # 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. ## 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](https://github.com/lbuckley/cc_traits) and the Python implementations ([https://github.com/huckleylab/cc_traits](https://github.com/huckleylab/cc_traits).