Global Patterns Predict Local Biodiversity Shifts in a Climate Change Hotspot

Jake Lawlor, Amelia Hesketh, Julien Beaulieu, Nicole Knight, Tianna Peller, Laura Super, Alexis Pereira, Ellen Bledsoe, Joseph B Burant , Jennifer Dijkstra, Kylla Benes, Jarrett E. K. Byrnes

Climate change is redistributing life on Earth, and global-scale biogeographical patterns can inform expectations for local ecological responses. As thermal envelopes shift towards higher absolute latitudes and deeper depths in the ocean, fixed locations are experiencing changes in their niche space, driving changes in abundance, occurrence, and community composition. Here, we examine intertidal population and community change in a climate-warming hotspot using a dataset of intertidal surveys collected over a 42-year duration on Appledore Island in the Gulf of Maine, USA, paired with global occupancy-derived estimates of species’ thermal niches. We quantify changes that have occurred and test whether observed changes align with expectations informed by global-scale trends. We detect several signals consistent with climate-driven biodiversity change: appearance and increased abundance of warm-affinity species, disappearance and decreased abundance of cold-affinity species, overall increases in species richness, shifts of species richness towards lower tidal elevations, and increases in community temperature affinity, which notably lags the rate of temperature change by a factor of six. In contrast, we find little evidence for shifts in individual species’ distributions across intertidal elevations, suggesting that community change overall and across intertidal elevations might be driven more strongly by within-range abundance shifts than by wholesale redistributions. These findings exemplify that community-level metrics can be more sensitive indicators of climate-driven change than species-level distribution shifts, particularly given limits on data resolution. Overall, our results provide strong evidence of climate-driven reorganization in Appledore Island’s intertidal community and demonstrate how long-term datasets from static locations can be contextualized with global-scale data to infer broad biological responses to climate change.