Niche models tell half the story: spatial context and life‐history traits influence species responses to global change

Abstract

Aim While niche models are typically used to assess the vulnerability of species to climate change, they have been criticized for their limited assessment of threats other than climate change. We attempt to evaluate this limitation by combining niche models with life‐history models to investigate the relative influence of climate change and a range of fire regimes on the viability of a long‐lived plant population. Specifically, we investigate whether range shift due to climate change is a greater threat to an obligate seeding fire‐prone shrub than altered fire frequency and how these two threatening processes might interact. Location Australian sclerophyll woodland and heathland. Methods The study species is Leucopogon setiger, an obligate seeding fire‐prone shrub. A spatially explicit stochastic matrix model was constructed for this species and linked with a dynamic niche model and fire risk functions representing a suite of average fire return intervals. We compared scenarios with a variety of hypothetical patches, a patch framework based upon current habitat suitability and one with dynamic habitat suitability based on climate change scenarios A1FI and A2. Results Leucopogon setiger was found to be sensitive to fire frequency, with shorter intervals reducing expected minimum abundances (EMAs). Spatial decoupling of fires across the landscape reduced the vulnerability of the species to shortened fire frequencies. Shifting habitat, while reducing EMAs, was less of a threat to the species than frequent fire. Main conclusions Altered fire regime, in particular more frequent fires relative to the historical regime, was predicted to be a strong threat to this species, which may reflect a vulnerability of obligate seeders in general. Range shifts induced by climate change were a secondary threat when habitat reductions were predicted. Incorporating life‐history traits into habitat suitability models by linking species distribution models with population models allowed for the population‐level evaluation of multiple stressors that affect population dynamics and habitat, ultimately providing a greater understanding of the impacts of global change than would be gained by niche models alone. Further investigations of this type could elucidate how particular bioecological factors can affect certain types of species under global change.

Potential influence of wildfire in modulating climate-induced forest redistribution in a central Rocky Mountain landscape

“…However, the complexity of such models can obscure fundamental species-climate relationships that determine future climate niche distributions and may be relevant to formulating future conservation actions (e.g., assisted migration). An integrated approach allows utilization of the benefits of both types of models (e.g., Swab et al 2012, Halofsky et al 2014 and an opportunity to directly enhance climate niche projections via additional species response and disturbance dynamics.…”

Knowing the past to forecast the future: a case study on a relictual, endemic species of the SW Alps, Berardia subacaulis

“…Our results should be considered with caution due to some limitations of the approach we adopted. First, climate is only one of several determinants of species distribution (Nogués-Bravo 2009;Thuiller et al 2009;Chuine 2010;Swab et al 2012). For example, a restricted distribution may result from dispersal limitation, competition with established vegetation, effects of past, large-scale events and human impacts Nogués-Bravo 2009, Svenning and.…”

Beyond connectivity: how the structure of dispersal influences metacommunity dynamics

“…Without accounting for the effects of spatial structure, approaches assuming that species distributions are driven primarily by environmental features may fail to accurately identify the importance of habitat patches for conservation or predict the effects of environmental changes on species (Keith et al 2008;Swab et al 2012). When spatial heterogeneity is produced through pattern formation, the structure as a whole is responsible for determining what patterns are possible and the resulting effects on species.…”