Moving beyond static species distribution models in support of conservation biogeography

Abstract

Aim To demonstrate that multi‐modelling methods have effectively been used to combine static species distribution models (SDM), predicting the geographical pattern of suitable habitat, with dynamic landscape and population models to forecast the impacts of environmental change on species’ status, an important goal of conservation biogeography. Methods Three approaches were considered: (1) incorporating models of species migration to understand the ability of a species to occupy suitable habitat in new locations; (2) linking models of landscape disturbance and succession to models of habitat suitability; and (3) fully linking models of habitat suitability, habitat dynamics and spatially explicit population dynamics. Results Linking species–environment relationships, landscape dynamics and population dynamics in a multi‐modelling framework allows the combined impacts of climate change (affecting species distribution and vital rates) and land cover dynamics (land use change, altered disturbance regimes) on species to be predicted. This approach is only feasible if the life history parameters and habitat requirements of the species are well understood. Main conclusions Forecasts of the impacts of global change on species may be improved by considering multiple causes. A range of methods are available to address the interactions of changing habitat suitability, habitat dynamics and population response that vary in their complexity, realism and data requirements.

Divergence in Forest-Type Response to Climate and Weather: Evidence for Regional Links Between Forest-Type Evenness and Net Primary Productivity

“…In addition, GLO-PEM NPP means and standard deviations are similar to NPP means and standard deviations from field measurements in several biomes across the globe (Zheng and others 2003). The 20-year dataset used in this study has been used to assess the global patterns in NPP variability and long-term NPP trends (Franklin 2010;others 2002, 2003). The work presented here focuses on the influence of vegetation composition on NPP patterns within a single forested region.…”

What are the roles of species distribution models in conservation planning?

“…It is these future projections that have drawn the most criticism of SDMs, primarily because of inherent variability of the environmental parameters, the unknown migration ability of the species, and model uncertainty, which are not always incorporated into the predictions (Cayuela et al 2009;Coreau et al 2009;Elith & Leatherwick 2009;Franklin 2010;Sinclair et al 2010). Many of the same limits to static SDMs are at issue when linking SDMs to climate models.…”

“…In addition, anthropogenic stresses may have complex interactions with projected changes in climate parameters (Singh & Milner-Gulland 2011) and the uncertainty values of critical parameters makes future predictions challenging for some species (Carvalho et al 2011). Of particular note is that the persistence of organisms in future landscapes depends partially on their ability to migrate into newly suitable habitats, abilities that are poorly understood for most species (Franklin 2009(Franklin , 2010.…”

“…SDMs only reach their full potential when they bring together scientists, public stakeholders and policy makers, and are used as an adaptive management tool to understand complex landscapes that are undergoing short-and long-term change. Excellent reviews of the evolution of SDMs exist, and all aspects of the models will not be covered here (Guisan & Thuiller 2005;Elith & Leatherwick 2009;Franklin 2009Franklin , 2010Iverson et al 2011). The major points are that distribution models for single or multiple species are created based on survey data across a range of environmental variables.…”

“…PHVAs have been incorporated into a metapopulation schema, where sources and sinks for populations are temporally and spatially variable due to shifting resources (Bonnot et al 2011). This linking of population parameters with environmental change across a landscape holds much potential for SDMs that seek to predict the future viability of critical species (Elith & Leatherwick 2009;Franklin 2010).…”