More than the sum of the parts: forest climate response from joint species distribution models

• Published in: Ecological applications Vol. 24; no. 5; p. 990
• Main Authors: Clark, James S, Gelfand, Alan E, Woodall, Christopher W, Zhu, Kai
• Format: Journal Article
• Language: English
• Published: United States 01.07.2014
• Subjects: Climate Change; Forests; Models, Biological; Temperature; United States
• DOI: 10.1890/13-1015.1
• ISSN: 1051-0761

The perceived threat of climate change is often evaluated from species distribution models that are fitted to many species independently and then added together. This approach ignores the fact that species are jointly distributed and limit one another. Species respond to the same underlying climatic variables, and the abundance of any one species can be constrained by competition; a large increase in one is inevitably linked to declines of others. Omitting this basic relationship explains why responses modeled independently do not agree with the species richness or basal areas of actual forests. We introduce a joint species distribution modeling approach (JSDM), which is unique in three ways, and apply it to forests of eastern North America. First, it accommodates the joint distribution of species. Second, this joint distribution includes both abundance and presence-absence data. We solve the common issue of large numbers of zeros in abundance data by accommodating zeros in both stem counts and basal area data, i.e., a new approach to zero inflation. Finally, inverse prediction can be applied to the joint distribution of predictions to integrate the role of climate risks across all species and identify geographic areas where communities will change most (in terms of changes in abundance) with climate change. Application to forests in the eastern United States shows that climate can have greatest impact in the Northeast, due to temperature, and in the Upper Midwest, due to temperature and precipitation. Thus, these are the regions experiencing the fastest warming and are also identified as most responsive at this scale.