Abstract:
Modelling measures of biodiversity for understudied taxa or regions is one method to address taxonomic and geographic biases in biodiversity data. However, modelling biodiversity metrics, such as species richness, to unsampled areas is only useful if predictions are reliable. As a result, testing the transferability of richness models is necessary for assessing the potential for models to predict to unsampled areas. Here we test the transferability of plant richness models between two reserves to examine if the richness-environment relationship from one reserve can accurately estimate richness patterns in the other reserve, using the vascular plant species richness of the Waterberg region (savanna biome; northern South Africa) as a model system. Six richness response variables (total species, grass species, herb species, woody species, genus, and family richness) and a set of 16 predictor variables were analysed with three modelling approaches and two statistical techniques to build: (1) models comprising all available predictor variables, (2) models using a subset of predictor variables chosen based on model performance, and (3) models using a subset of predictor variables that reduce the difference in the environmental conditions between the two reserves. The models’ performance in the training area varied considerably, but soil variables were consistently the most important predictors of plant richness. However, the transferability of all the models was consistently poor across all modelling approaches and both techniques, possibly reflecting the degree to which each reserve contains novel environments absent from the other reserve (despite being separated by only c. 60 km and sharing vegetation types). Due to the poor performance of these richness models, they are currently not useful for predicting richness to other areas in the vicinity of the reserves or in the broader region. However, in areas, like the Waterberg region, that have high plant diversity and are poorly sampled, there may be value in continued development of richness models to address biodiversity gaps, thereby providing better data to inform conservation decisions.
