DOI: 10.5061/dryad.cfxpnvxff
title: A non-equilibrium species distribution model reveals unprecedented depth of time lag responses to past environmental change trajectories

description: Previous studies have demonstrated legacy effects of current species
 distributions to past environmental conditions, but the temporal extent of
 such time lag dynamics remains unknown. Here, we have developed a
 non-equilibrium SDM approach quantifying the temporal extent that must be
 taken into account to capture 95 % of the effect that a given time series
 of past environmental conditions has on the current distribution of a
 species. We applied this approach to the distribution of 92 European
 forest birds in response to past trajectories of change in forest cover
 and climate. We found that non-equilibrium SDMs outperformed traditional
 SDMs for 95% of the species. Non-equilibrium SDMs suggest unprecedented
 long-lasting effects of past global changes (average time lag extent
 ranged from 9 years to 231 years). This framework can help to relax the
 equilibrium hypothesis of traditional SDMs and to improve future
 predictions of biodiversity redistribution in response to global changes.
description: # A non-equilibrium species distribution model reveals unprecedented depth
 of time lag responses to past environmental change trajectories.
 [https://doi.org/10.5061/dryad.cfxpnvxff](https://doi.org/10.5061/dryad.cfxpnvxff) The goal of this study is to evaluate the effects of current and past climate and land-use conditions on the continental distribution of 92 European forest bird species, sampled during the period 2013-2017. Our approach to address this goal was first to use generalized linear regressions considering environmental predictors over 2013-2017. These null models assume an equilibrium of species distribution with current conditions (no time-lag effect). Second, using temporally-weighted generalized linear regressions, we quantified the temporal extent of the time-lag in explaining the current distribution of a given species in response to past land-use (over 850-2017) and climate changes (over 1850-2017). In these models, the effect size of each focal environmental predictor is associated to a weighting parameter that defines: (i) the temporal extent of the time lag that must be considered to capture the majority of the effects of past conditions on current species distribution, and (ii) the shape of an exponential negative curve that describes the temporal decrease of the effect size as we go back in time. ## Description of the data and file structure The directories are organised as follows: ### ./data/inputs/: includes the bird data, the temporal matrices associated to the predictors, and a background map for country delineation. * AVONET/AVONET1_Birdlife_selec_red: includes two columns for the name of species and their preferential habitats. * EBBA2/ebba2_data_occurrence_50km_selec_red: includes four columns that are the reference code of the mesh of the EBBA2 database, the reference code and name of the species, and the occurrence always = 1 meaning that this matrix includes all the combination of mesh x species (in rows) for which species are present. * EBBA2/ebba2_grid50x50_v1: all the files associated with the shapefile of the meshes provided with the EBBA2 database. * gadm/gadm36_adm0_r5_pk.rds: background map of country delination produced from the R package 'geodata'. * temporal.matrix*: a file is associated to each predictor and includes the predictor values for each date (in row) x each EBBA2'mesh (in column), except for the first column that must be named 'dist' and includes 'delta t' for each date (each row). The bird and the predictor datasets were retrieved from the Chelsa, LUH2 and EBBA2 repositories at: [https://chelsa-climate.org/](https://chelsa-climate.org/) [https://luh.umd.edu/](https://luh.umd.edu/) [https://ebba2.info/](https://ebba2.info/) The associated articles linked to the original datasets are are: * Karger, D., Schmatz, D. R., Dettling, G., & Zimmermann, N. E. (2019). High resolution monthly precipitation and temperature timeseries for the period 2006-2100. arXiv, arXiv-1912.doi:10.48550/arXiv.1912.06037 * Hurtt, G. C., Chini, L., Sahajpal, R., Frolking, S., Bodirsky, B. L., Calvin, K., ... & Zhang, X. (2020). Harmonization of global land use change and management for the period 850–2100 (LUH2) for CMIP6. Geoscientific Model Development, 13(11), 5425-5464. [https://doi.org/10.5194/gmd-13-5425-2020](https://doi.org/10.5194/gmd-13-5425-2020) * EBCC (2022). European Breeding Bird Atlas 2 website. European Bird Census Council. Accessed from: [http://ebba2.info](http://ebba2.info) (19/05/2024). * Keller, V., Herrando, S., Voríšek, P., Franch, M., Kipson, M., Milanesi, P., ... & Foppen, R. P. B. (2020). European breeding bird atlas 2: distribution, abundance and change. This repository include processed data, from these previously mentionned original datasets, after extaction and adaptation to the spatial extent, the spatial and the thematic resolution of the study. ### ./R/: includes the R script associated to the data to produce the outputs of the article. * weighting_funct: function from Lowe et al. (2022). The adaptation to the temporal dimension consists to use the temporal matrices, the first column being delta t as defined in the article. * Lowe, E. B., Iuliano, B., Gratton, C., & Ives, A. R. (2022). ‘Scalescape’: an R package for estimating distance-weighted landscape effects on an environmental response. Landscape Ecology, 37(7), 1771-1785. [https://doi.org/10.1007/s10980-022-01437-5](https://doi.org/10.1007/s10980-022-01437-5) * Temporally_weigthed_regressions: this script includes the fitting of all the models (equilibrium models, non-equilibrium models with Gaussian weighting function, non-equilibrium models with exponential weighting function). * Graphical_and_quantitative_results: this script includes all qualitative (habitat selection) and quantitative analyses (estimate comparison, best model selection and validation) as well as the graphical outputs (plots of the weighting functions and the maps of species richness extinction debt and immigration credit). ### ./data/outputs/: includes the fitted models saved as RDS files (one model for one species) and somme associated results. * Temporally_weigthed_regressions_exponential/*.rds: RDS file for the non-equilibrium models with exponential weighting function. The RDS files can be opened using the 'readRDS' function of the R programming language. One RDS file is related to one species and it includes two compiled models (the equilibrium and the non-equilibrium models). * Temporally_weigthed_regressions_exponential/2024_03_11_Temporally_weigthed_regressions_sp.csv: a matrix including for each species (in row) the name of the species, the preferential habitat, the parameters beta and gamma for each predictors (primf: primary forest, secf: secondary forest, pr: precipitation, tasmax: temperature, ts: indicates gamma parameters), the mean AUC (Area Under the Curve, between O.5 and 1), sensitivity (true positive rate) and specificity (true negative rate) of the non-equilibrium models, and the threshold values used for binarization of the predictions in order to compare the equilibrium and the non-equilibrium models (threhold presence probability). * Temporally_weigthed_regressions_gaussian/*.rds: RDS file for the non-equilibrium models with Gaussian weighting function. * Temporally_weigthed_regressions_gaussian/2024_03_11_Temporally_weigthed_regressions_sp.csv: same description than for '2024_03_11_Temporally_weigthed_regressions_sp.csv' but for the results obtained with a Gaussian weighting function. * Temporally_weigthed_regressions_gaussian/*_prediction*.csv: includes the prediction ('*prediction0.csv': equilibrium models, '*prediction.csv': non-equilibrium models) saved as csv files (one file for one species), the first column is the rank of the EBBA's mesh (after excluding some NA values) and the second column is the predicted probability of presence. ## Sharing/Access information The bird and the predictor datasets were retrieved from the Chelsa, LUH2 and EBBA2 repositories at: [https://chelsa-climate.org/](https://chelsa-climate.org/) [https://luh.umd.edu/](https://luh.umd.edu/) [https://ebba2.info/](https://ebba2.info/) The associated articles and doi are: Karger, D., Schmatz, D. R., Dettling, G., & Zimmermann, N. E. (2019). High resolution monthly precipitation and temperature timeseries for the period 2006-2100. arXiv, arXiv-1912.doi:10.48550/arXiv.1912.06037 Hurtt, G. C., Chini, L., Sahajpal, R., Frolking, S., Bodirsky, B. L., Calvin, K., ... & Zhang, X. (2020). Harmonization of global land use change and management for the period 850–2100 (LUH2) for CMIP6. Geoscientific Model Development, 13(11), 5425-5464. [https://doi.org/10.5194/gmd-13-5425-2020](https://doi.org/10.5194/gmd-13-5425-2020) EBCC (2022). European Breeding Bird Atlas 2 website. European Bird Census Council. Accessed from: [http://ebba2.info](http://ebba2.info) (19/05/2024). Keller, V., Herrando, S., Voríšek, P., Franch, M., Kipson, M., Milanesi, P., ... & Foppen, R. P. B. (2020). European breeding bird atlas 2: distribution, abundance and change. ## Code/Software All the analyses were performed using the R software v. 4.3.3