Incorporating intraspecific variation into species distribution models improves climate change analyses of a widespread West African tree species (Pterocarpus erinaceus Poir, Fabaceae)

Abstract

Climate change is predicted to affect species distribution worldwide. Most of the methods used to evaluate such impact so far assume that species respond to the environmental gradients in a uniform way along their distribution range. Because populations occupying different niches may differ in their response to climate change due to local adaptation, accounting for intraspecific variation in species distribution models (SDMs) may yield more reliable predictions for widely distributed species. Pterocarpus erinaceus Poir is a highly valued but endangered tree species, which occurs in the Sudanian (SZ), Sudano-Guinean (SGZ) and Guinean (GZ) ecological zones of Benin. Here, we used two (whole-species and intraspecific-level) SDM approaches to evaluate how local adaptation, quantified through niche differentiation, influences the potential impact of climate change on the distribution of P. erinaceus in Benin. The maximum entropy (MaxEnt) al gorithm was employed to simulate the current and future distributions of the species under various Shared Socioeconomic Pathways (SSPs) climate scenarios. The results showed three distinct populations of the species according to the ecological zones of Benin. The intraspecific populations displayed no niche overlap and thus were considered as locally adapted. Mean diurnal range was the main variable that determined the current distribution of the SZ population (percent contribution of 45.9%) while the distribution of the SGZ and GZ populations were determined by isothermality (percent contribution of 58.7% and 76.2%, respectively). While the whole-species SDMs showed that climate change would lead to significant reductions in the species suitable habitats in SZ under SSP2–4.5, SSP1–2.6, and SSP5–8.5, the SDMs based on intraspecific populations indicated a high decrease in habitat suitability in the GZ and an upward shift of the SGZ towards the SZ under the future climate scenarios. Our results suggest that incorporating intraspecific variation into SDMs improves predictions of the impact of climate change and helps to identify appropriate population-based conservation strategies.