Density and spatial pattern of Parkia biglobosa under climate change: the case of Benin

Abstract

Parkia biglobosa (locust bean) is an indigenous species which, traditionally contributes to the resilience of the agricultural production system in terms of food security, source of income, poverty reduction and agro-ecosystem stability. Therefore, it is important to improve knowledge on its density, current and future spatial distribution. The main objective of this study is to evaluate the tree density, the climate change effects on the spatial distribution of the species in the future for better conservation. The modeling of the current and future geographical distribution of the species was based on the principle of Maximum Entropy (MaxEnt) using a total of 286 occurrence points from field work and the Global Biodiversity Information Facility GBIF-Data Portal. Two climatic models (HadGEM2_ES and Csiro_mk3_6_0) were used under two scenarios RCP 2.6 and RCP 8.5 for the projection of the species distribution at the horizon 2050. Correlation analyses and Jackknife test helped to identify seven variables which are less correlated (r < 0.80) with highest contribution to the model. Soil, annual precipitation and temperature (diurnal average Deviation) are the variables which have mostly contributed to the models. Currently, 53% of national territory of Benin, spread from north to south is very suitable for P. biglobosa. At the temporal horizon 2050, the scenarios have projected loss of habitats, which are currently very suitable for P. biglobosa. 51% and 57% are the highest proportions of this habitat lost, which has been registered with HadGEM2_ES model under two scenarios. In order to limit damages such as decreased in productivity and extirpation, some appropriate solutions must be found. It is important to plan the introduction of Parkia biglobosa in reforestation programs and the protection of its potential habitat at national level by the forestry administration which is an asset for a better conservation of this significant NTFP.