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 >35% of variance in climate change exposure and >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.

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. 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 et al., 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). Mapping Species Distributions: Spatial Inference and Prediction. Cambridge University Press.

Araújo, M. B., & New, M. (2007). Ensemble forecasting of species distributions. Trends in Ecology & Evolution, 22(1), 42–47.

Muscatello, A., Elith, J., & Kujala, H. (2021). How decisions about fitting species distribution models affect conservation outcomes. Conservation Biology, 35(4), 1309–1320.