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

Abstract

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.

Integrating demography, dispersal and interspecific interactions into bird distribution models

“…Unfortunately, although constituting a major advance in biodiversity modelling, it is not clear yet in how far JSDMs could fulfil our wish to model interspecific interactions. Specifically, the residual co‐occurrence (joint distribution) may indicate interspecific interactions but may also be caused by missing or sub‐scale environmental covariates (Clark et al , Pollock et al , Harris ). Especially the latter is an important source of bias that is inherent in common species inventory data such as breeding bird atlases that are gridded to a certain resolution and may not necessarily reflect the spatial requirements of the many different species (Zurell et al ).…”

Vulnerability of eastern US tree species to climate change

“…Yet these require expert knowledge on parameters that are often data-limited; as a result, they are typically applied to plant functional types or a small number of species. However, SDMs contain notable limitations that are widely discussed in the literature (Pearson & Dawson, 2003;Guisan & Thuiller, 2005;Aitken et al, 2008;Morin & Thuiller, 2009;Franklin, 2010;Fordham et al, 2012;Clark et al, 2014;Thuiller et al, 2014). SDMs are rooted in the theory that large-scale plant distributions are fundamentally controlled by climate and, to a lesser extent, soils (Woodward, 1987;Davis & Shaw, 2001;Willis & Whittaker, 2002;Soberon & Nakamura, 2009;Gallien et al, 2010;Soberon, 2010).…”

Comparing the prediction of joint species distribution models with respect to characteristics of sampling data

“…However, species are closely associated with each other through biological processes such as predation, competition and mutualism, and the misspecification of biotic interactions may limit the understanding of assembly processes, leading to substantial bias in predicting community structure. Therefore, multispecies distribution modelling has drawn broad interests in classical and recent community ecology (Legendre and Legendre 2012, Clark et al 2014, Warton et al 2015a, Ovaskainen et al 2017b, Thorson and Barnett 2017. On the other hand, when species distributions are related, one species can provide information for predicting the distribution of other species, implying that modelling the distribution of multiple species simultaneously is an efficient way of using available data (Latimer et al 2009, Ovaskainen et al 2010, Pollock et al 2014.…”