Diversifying modelling techniques to disentangle the complex patterns of species richness and diversity in the protected afromontane grasslands

Abstract

Ecological research has focused on the importance of environmental factors on spatial biodiversity variations and organisation. This is important because of scant conservation resources. We used stepwise backward selection and random feature selection (RFE) to identify a parsimonious model that can predict species richness and diversity metrics in response to three models; biotic, abiotic, and topo-edaphic. Our results show that both metrics are good predictors of one another, mainly because species diversity is a combination of species richness and abundance, and further highlights the importance of biotic variables in predicting species distribution. The two modelling techniques selected soil texture and its interactions with topographic variables as the most important variables. However, random forest performed worse than multiple linear regression in the prediction of diversity metrics. This research highlights the importance of topographically controlled edaphic factors as drivers of species richness and diversity in mountainous grasslands where topography inherently controls the geomorphic, hydrological, and, as a result, ecological processes.