Fine-Tuned Ecological Niche Models Unveil Climatic Suitability and Association with Vegetation Groups for Selected Chaetocnema Species in South Africa (Coleoptera: Chrysomelidae)

Despite beetles (Coleoptera) representing most existing animal species, the ecological and biogeographical factors shaping their distribution are still unclear in many regions. We implemented state-of-the-art Ecological Niche Models (ENMs) and niche overlap analysis to investigate climate-occurrence patterns for five flea beetle species of the genus Chaetocnema in South Africa (C. brincki, C. danielssoni, C. darwini, C. gahani and C. natalensis). ENMs were fitted through Maxent and Random Forests, testing various parameterizations. For each species, tuned ENMs attaining good discrimination on spatially independent test data were selected to predict suitability across the study region and individuate its main climatic drivers. Percent coverage of climatically suitable areas by seventeen Afrotropical vegetation formations was also computed. Predicted suitable areas do not extend far away from known presence localities, except for C. brincki and C. gahani in northeastern South Africa. Temperate grasslands and shrublands cover most of suitable areas for C. brincki and C. gahani, along with warm temperate forests, as well as for C. danielssoni, in this case being followed by tropical flooded and swamp forests. Climatic suitability for C. darwini mainly relates to the Mediterranean grasslands and scrublands of the southern coastal region, while suitable areas for C. natalensis encompass various vegetation formations, coherently with its wide distribution. The environmental niche of C. danielssoni significantly overlaps with those of the wide-ranging C. darwini and C. natalensis, suggesting that historical factors, rather than low climatic tolerance, has determined its restricted distribution in the Western Cape Province. Maxent and Random Forests were confirmed to be of great help in disentangling the environment – occurrence relationships and predict suitability for the target species outside their known range, but they need to be properly tuned to perform at their best.