气候变化领域集成服务门户

A conspicuous season-diversity relationship (SDR) can be seen in seasonal environments, often with a defined peak in active species diversity in the growing season. We ask is this a general pattern and are other patterns possible? In addition, we ask what is the ultimate cause of this pattern and can we understand it using existing ecological theory? To accomplish this task, we assembled a global database on changes in species diversity through time in seasonal environments for different taxa and habitats and also conducted a modeling study in an attempt to replicate observed patterns. Our global database includes terrestrial and aquatic habitats, temperate, tropical, and polar environments, and taxa from disparate groups including vertebrates, insects, and plankton. We constructed nine alternative models that vary in assumptions on type of seasonal forcing, responses to that forcing, species niches, and types of species interactions. We found that most guilds of species exhibit a repeatable SDR across years. For north temperate ecosystems, active species diversity generally peaks mid-year. The peak for a guild is generally more pronounced in terrestrial habitats than aquatic habitats and more pronounced in temperate and polar regions than the tropics. We now have evidence that at least several different habitat and taxa types are likely to have multiple peaks in diversity in a year, for example, guilds of both aquatic microbes and desert vertebrates can show a bimodal or multimodal SDR. We compared all nine candidate models in their ability to explain the patterns and match their assumptions to the data. Some performed considerably better than others in being able to match the different patterns. We conclude that a model that includes both temperature niches and environmental feedbacks is necessary to explain the different SDRs. We use such a model to make predictions about how the SDR could be impacted by climate change. More effort should be put into documenting and understanding baseline seasonal patterns in diversity in order to predict future responses to global change.