Data for: Predicting berry plant habitat under climate change in Bristol Bay, AK ...

• Main Authors: Hamilton, Casey, Smithwick, Erica, Spellman, Katie, Baltensperger, Andrew, Spellman, Blaine, Chi, Guangqing
• Format: Dataset
• Language: English
• Published: Dryad 2023
• Subjects: FOS Biological sciences; Berries; Boreal; Climate change; Empetrum nigrum; habitat suitability; Random Forests; Rubus chamaemorus; Vaccinium uliginosum; Vaccinium vitis-idaea; Viburnum edule; Arctic; Alaska
• Online Access: https://dx.doi.org/10.5061/dryad.7wm37pvxz; https://datadryad.org/stash/dataset/doi:10.5061/dryad.7wm37pvxz

Aim: Climate change is altering suitable habitat distributions of many species in high latitudes. Fleshy fruit-producing plants (hereafter “berry plants”), important in arctic food webs and as subsistence resources for human communities, may be impacted, but their response to a warming and increasingly variable climate at a landscape scale has not yet been examined. Here, we identified influential environmental determinants of berry plant distribution and produced predictions on how climate change might shift these distributions. Location: Bristol Bay and Togiak NRCS Survey Areas, Alaska. Methods: We built species distribution models using the Random Forests algorithm to identify key characteristics and predict the spatial distribution of habitats suitable for five berry plant species: Vaccinium uliginosum L., Empetrum nigrum L., Rubus chamaemorus L., Vaccinium vitis-idaea L., and Viburnum edule (Michx.) Raf. Then, we used future climate projections (2081-2100; representative concentration pathways 4.5, 6.0, ... : We used presence and absence data for five berry plant species collected by the USDA Natural Resources Conservation Service (NRCS) to build species distribution models. The NRCS data were collected between the years 2006-2013 as part of routine soil surveys conducted throughout the state. These location data were used in tandem with geospatial predictor variables (topography, soils), climate data from Alaska-specific climate models, and the Random Forests algorithm to assess and predict berry plant habitat suitability across the Bristol Bay landscape under current (2006-2013) and future projected (2081-2100; representative concentration pathways 4.5, 6.0, and 8.5) climate conditions. ...