Fire and life history affect the distribution of plant species in a biodiversity hotspot

Abstract

AbstractAimSpecies distribution models (SDMs) provide valuable insights into species–environment relationships and potential climate change impacts on diversity. Most SDMs do not account for the role of natural disturbance regimes such as fire in determining current and future species distributions, or how species traits mediate their response to these stressors. Here, we investigate the importance of fire in determining the distributions of species in fire‐prone fynbos vegetation, and how this varies in relation to different life history traits (growth form and fire‐response strategy).LocationCape Floristic Region, South Africa.MethodsWe modelled the distribution of 104 plant species with different life history traits, using Maxent. The model included five climatic variables, one edaphic and one fire variable. Post hoc analyses of model output and permutation procedures were conducted to assess variable importance across different life history traits. We accounted for phylogenetic autocorrelation using sister species comparisons.ResultsPermutation importance scores identified fire return interval as a major determinant of fynbos species’ distributions. Linear mixed effect analyses revealed that seeder species were significantly more sensitive to fire than resprouters. Coefficients from the (linear) response curves of the different predictors indicated that the occurrence of species across all life histories was negatively associated with longer fire return intervals.Main conclusionsFire and life history traits governing species’ response to fire are key factors determining species distributions in our study system. SDMs that ignore the role of fire in driving species distributions, and how this varies across different life history types, compromise our ability to understand species–environment relationships in fire‐prone ecosystems. There is great need for better spatial data describing historical, current and future fire regimes and for models that can incorporate different responses based on species life histories, to improve vulnerability assessments for fire‐prone ecosystems.