Phenological overlap of interacting species in a changing climate: an assessment of available approaches.
Concern regarding the biological effects of climate change has led to a recent surge in research to understand the consequences of phenological change for species interactions. This rapidly expanding research program is centered on three lines of inquiry: (1) how the phenological overlap of interacting species is changing, (2) why the phenological overlap of interacting species is changing, and (3) how the phenological overlap of interacting species will change under future climate scenarios. We synthesize the widely disparate approaches currently being used to investigate these questions: (1) interpretation of long-term phenological data, (2) field observations, (3) experimental manipulations, (4) simulations and nonmechanistic models, and (5) mechanistic models. We present a conceptual framework for selecting approaches that are best matched to the question of interest. We weigh the merits and limitations of each approach, survey the recent literature from diverse systems to quantify their use, and characterize the types of interactions being studied by each of them. We highlight the value of combining approaches and the importance of long-term data for establishing a baseline of phenological synchrony. Future work that scales up from pairwise species interactions to communities and ecosystems, emphasizing the use of predictive approaches, will be particularly valuable for reaching a broader understanding of the complex effects of climate change on the phenological overlap of interacting species. It will also be important to study a broader range of interactions: to date, most of the research on climate-induced phenological shifts has focused on terrestrial pairwise resource-consumer interactions, especially those between plants and insects.
['Rafferty|Nicole E|NE|', 'Caradonna|Paul J|PJ|', 'Burkle|Laura A|LA|', 'Iler|Amy M|AM|', 'Bronstein|Judith L|JL|']
phenology Climate change observation simulation models long-term data community demography experiment life history