Macroecological context predicts species' responses to climate warming

Abstract

Context-dependencies in species' responses to the same climate change frustrate attempts to generalize and make predictions based on experimental and observational approaches in biodiversity science. Here, we propose predictability may be enhanced by explicitly incorporating macroecological context into analyses of species' responses to climate manipulations. We combined vascular plant species' responses to an 8-year, 12-site turf transplant climate change experiment set in southwestern Norway with climate niche data from the observed 151 species. We used the difference between a species' mean climate across their range and climate conditions at the transplant site ("climate differences") to predict colonization probability, extinction probability, and change in abundance of a species at a site. In analyses across species that ignore species-specific patterns, colonization success increased as species' distribution optima were increasingly warmer than the experimental target site. Extinction probability increased as species' distribution optima were increasingly colder than the target site. These patterns were reflected in change in abundance analyses. We found weak responses to increased precipitation in these oceanic climates. Climate differences were better predictors of species' responses to climate manipulations than range size. Interestingly, similar patterns were found when analyses focused on variation in species-specific responses across sites. These results provide an experimental underpinning to observational studies that report thermophilization of communities and suggest that space-for-time substitutions may be valid for predicting species' responses to climate warming, given other conditions are accounted for (e.g., soil nutrients). Finally, we suggest that this method of putting climate change experiments into macroecological context has the potential to generalize and predict species' responses to climate manipulations globally.

Keywords: Bayesian modeling; abundance; biodiversity; climate niche; colonization; extinction; macroecological experiment; range size; transplant; vascular plants.