Print PagePredicting which species will decline in the face of predicted climate change represents a major challenge for ecologists, with important implications for setting future conservation priorities. Shifting phenology (i.e. timing) observed worldwide provides some of the best evidence that species and are already responding to climate change, but there is tremendous variation among species in their observed phenological shifts. Here we set out to test the hypothesis that species which do not phenologically track climate will be at the greatest risk of declines. To do this, we synthesized data on species responses in warming experiments, where researchers measured both responses of phenology and performance (including relative abundance, biomass, or reproductive output). A quantile regression approach was utilized to predict relative performance ((warming – control)/control) on the basis of phenological responsiveness (days of shift in flowering, budburst, or seedset per degree of warming above ambient conditions).
Results/Conclusions
The relationship between phenological responsiveness and species performance under warmed conditions was generally positive but displayed large variation, and was asymmetric between those species with accelerated versus delayed phenological responses. Species with accelerated phenology in response to warming varied greatly in their performance; some increased in abundance while others decreased. In contrast, those species with delayed phenology always had decreased performance under warmed conditions. These results indicate that the ability of a species to track climate is an important trait that confers the potential to maintain performance under changing climatic conditions, but that many other factors likely play important roles as well, including the responses of competitors, natural enemies and mutualists to climate change. Our results also suggest that species which do not phenologically track climate change are likely to decline in abundance; this may be an important indicator for future conservation prioritization.
