Climate variables are not the dominant predictor of Arctic shorebird distributions

large portion of explained deviance in shorebird occupancy is related to direct effects of addi- tional predictors, such as elevation and land cover, and that these effects are not well captured as indirect effects of climate. It is therefore unwise to assume that climate models are always sufficient for explaining habitat suitability. By making these assumptions, we place unneces- sary limits on our understanding of species relationships with their environment. Incorrect conclusions from habitat suitability models could have important management implications for designating protected areas and assessing threats like climate change and human develop- ment. As many countries seek to expand their protected areas networks to meet international commitments under the Convention on Biological Diversity [52], a more complete under- standing of species’ distributions can have important management implications now and espe- cially in the future, under scenarios of climatic change. Supporting information S1 Fig. Maps of northern Canada showing where each species of breeding shorebirds was present during PRISM surveys 1994–2018. Reprinted from ArcGIS under a CC BY license, with permission from Esri, original Copyright 2022 Esri (Basemaps supported by Esri, HERE, Garmin, OpenStreetMap contributors, and the GIS User Community). (PDF) S2 Fig. AIC values for all tested radii to determine the optimal scale of effect at which land cover and snow cover affect shorebird occupancy. (PDF) S1 Table. Years in which each PRISM region was surveyed. See [29] for map of survey regions. (DOCX)

S2 Table. Range of values for environmental predictors. (DOCX) S3 Table. Radius (m) of the optimal scale of effect at which land cover and snow cover affect shorebird occupancy, determined by AIC. (DOCX) S4 Table. Full results of GLMs used for deviance partitioning. (TXT) S5 Table. Path analysis: Summary of the significant total effects of predictors on shorebird occupancy. (CSV) S6 Table. Path analysis: Full results of the 17 species-specific path models, including sum- maries of the direct, indirect and total effects of the paths between all variables. (CSV) Acknowledgments We thank the field crews who conducted shorebird surveys for the PRISM program, and the Polar Continental Shelf program for providing logistical support. Author Contributions Conceptualization: Christine M. Anderson, Lenore Fahrig, Paul A. Smith. PLOS ONE Climate variables are not the dominant predictor of Arctic shorebird distributions PLOS ONE | https://doi.org/10.1371/journal.pone.0285115 May 17, 2023 11 / 14