DOI: 10.5061/dryad.gf1vhhmw6
title: Data from: Rarity, geography, and plant exposure to global change in the California Floristic Province

description: Aim: Rarity and geographic aspects of species distributions
 mediate their vulnerability to global change. We explore the relationships
 between species rarity and geography and their exposure to climate and
 land use change in a biodiversity hotspot. Location: California,
 USA. Taxa: One hundred and six terrestrial plants.
 Methods: We estimated four rarity traits: range size, niche
 breadth, number of habitat patches, and patch isolation; and three
 geographic traits: mean elevation, topographic heterogeneity, and distance
 to coast. We used species distribution models to measure species
 exposure—predicted change in continuous habitat suitability within
 currently occupied habitat—under climate and land use change scenarios.
 Using regression models, decision-tree models and variance partitioning,
 we assessed the relationships between species rarity, geography, and
 exposure to climate and land use change. Results: Rarity,
 geography and greenhouse gas emissions scenario explained &gt;35% of
 variance in climate change exposure and &gt;61% for land use change
 exposure. While rarity traits (range size and number of habitat patches)
 were most important for explaining species exposure to climate change,
 geographic traits (elevation and topographic heterogeneity) were more
 strongly associated with species' exposure to land use change. Main
 conclusions: Species with restricted range sizes and low
 topographic heterogeneity across their distributions were predicted to be
 the most exposed to climate change, while species at low elevations were
 the most exposed to habitat loss via land use change. However, even some
 broadly distributed species were projected to lose &gt;70% of their
 currently suitable habitat due to climate and land use change if they are
 in geographically vulnerable areas, emphasizing the need to consider both
 species rarity traits and geography in vulnerability assessments.
description: This dataset includes the spatial outputs for the species
 distribution models described in Rose, M. B., Velazco, S. J. E., Regan, H.
 M., & Franklin, J. (2022). Rarity, geography, and plant exposure
 to global change in the California Floristic Province. <em>Global
 Ecology and Biogeography</em>. https://doi.org/10.1111/geb.13618. We selected eight SDM algorithms for ensemble predictions: generalized linear models, generalized additive models, boosted regression trees, random forests, artificial neural networks, support vector machines, maximum entropy, and gaussian process (Franklin 2010). The last two algorithms were only used for presence-only models. Ensembles, in which predictions of individual algorithms are combined to produce a consensus distribution, can reduce model uncertainty and improve model transferability (Araújo & New, 2007). For each model, we applied the model-specific suitability value that maximized the sum of sensitivity and specificity as a threshold, retaining continuous suitability values above the threshold and assigning 0 suitability values to those cells below the threshold. This method removes areas with low habitat suitability while retaining variation in suitability within species’ habitat (Muscatello <em>et al</em>., 2021), and allowed us to later define discrete species’ ranges from which to calculate the number of patches and patch isolation. This dataset includes three sets of SDM habitat suitability projections: 1) climate change only (current land use patterns accounted for), 2) land use change only and 3) climate and land use change combined. References: Franklin, J. (2010). <em>Mapping Species Distributions: Spatial Inference and Prediction</em>. Cambridge University Press. Araújo, M. B., & New, M. (2007). Ensemble forecasting of species distributions. <em>Trends in Ecology & Evolution</em>, <em>22</em>(1), 42–47. Muscatello, A., Elith, J., & Kujala, H. (2021). How decisions about fitting species distribution models affect conservation outcomes. <em>Conservation Biology</em>, <em>35</em>(4), 1309–1320.
description: # SDM outputs for: Rarity, geography, and plant exposure to global change
 in the California Floristic Province
 [https://doi.org/10.5061/dryad.gf1vhhmw6](https://doi.org/10.5061/dryad.gf1vhhmw6) This dataset includes the spatial outputs for the species distribution models described in Rose, M. B., Velazco, S. J. E., Regan, H. M., & Franklin, J. (2022). Rarity, geography, and plant exposure to global change in the California Floristic Province. *Global Ecology and Biogeography*. [https://doi.org/10.1111/geb.13618](https://doi.org/10.1111/geb.13618). Importantly, the maps included in this dataset include habitat suitability projections for the extent of the Californian portion of the California Floristic Province. Results in the paper are based on habitat suitability patterns within species currently occupied range. To replicate these results, users can use the occupied habitat maps in each species "01_current" folder found in each zip file to crop the maps of projected future habitat suitability. Habitat suitability maps reflect continuous habitat suitability (0-1). ## Description of the data and file structure Each zip file has the naming structure "species scientific name". Within each species folder, there are three folders: "species scientific name_CC" (habitat suitability maps under future climate change, accounting for current land use patterns), "species scientific name_LUC" (habitat suitability maps under future land use change - based on currently occupied habitat), and 3) "species scientific name_CC_LUC" (habitat suitability maps under future climate and land use change). Within each of these folders, there are 5 additional folders: 01_current: includes .asc file for species currently occupied habitat area, based on consensus SDM predictions and occurrence records 02_cnrm_rcp45: .asc files for species habitat suitability under CNRM-CM5 RCP 4.5 scenario for two time periods: 2040-2069 ("2055") and 2070-2099 ("2085"). 2055 and 2085 reflect the mid-points for the 30 year climate averages used to produce these maps. 03_cnrm_rcp85: .asc files for species habitat suitability under CNRM-CM5 RCP 8.5 scenario for two time periods: 2040-2069 ("2055") and 2070-2099 ("2085"). 04_hades_rcp45: .asc files for species habitat suitability under HadGEM2-ES RCP 4.5 scenario for two time periods: 2040-2069 ("2055") and 2070-2099 ("2085"). 05_hades_rcp85: .asc files for species habitat suitability under HadGEM2-ES RCP 8.5 scenario for two time periods: 2040-2069 ("2055") and 2070-2099 ("2085"). ## Code/Software R script used in our final analyses relating exposure to species traits are included with our manuscript as Supporting Information. We included additional scripts used to build species distribution models, assess the impact of land use change, and calculate exposure in a GitHub repository ([https://github.com/mrose048/sp_traits_exposure](https://github.com/mrose048/sp_traits_exposure)). ## Version history: 2024-07-13: Quercus douglasii files were updated to correct an error discovered (previously labeled as Quercus wislizeni). 2023-12-05: Initial database submission to Dryad.