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bioRxiv2023

What explains the high island endemicity of Philippine Rafflesia? A species distribution modeling analysis of three threatened parasitic plant species and their hosts

PremiseRafflesia are rare holoparasitic plants. In the Philippines, all but one species are found only on single islands. This study aimed to better understand the factors contributing to this distribution pattern. Specifically, we sought to determine whether narrow environmental tolerances of host and/or parasite species might explain their island endemicity. MethodsWe used Maxent species distribution modeling to identify areas with suitable habitat for R. lagascae, R. lobata, and R. speciosa and their Tetrastigma host species. These analyses were carried out for current climate conditions as well as two future climate change scenarios. Key resultsWhereas species distribution models indicated suitable environmental conditions for the Tetrastigma host species in many parts of the Philippines, considerably fewer areas have suitable conditions for the three Rafflesia species. Some of these areas are found on islands from which they have not been reported. All three species will face significant threats as a result of climate change. ConclusionsOur results suggest that limited inter-island dispersal abilities and/or specific environmental requirements are likely responsible for the current pattern of island endemicity of the three Rafflesia species, rather than the environmental requirements of their Tetrastigma host species.

ecology↗

bioRxiv2022

Machine learning improves global models of plant diversity

Despite the paramount role of plant diversity for ecosystem functioning, biogeochemical cycles, and human welfare, knowledge of its global distribution is incomplete, hampering basic research and biodiversity conservation. Here, we used machine learning (random forests, extreme gradient boosting, neural networks) and conventional statistical methods (generalised linear models, generalised additive models) to model species richness and phylogenetic richness of vascular plants worldwide based on 830 regional plant inventories including c. 300,000 species and predictors of past and present environmental conditions. Machine learning showed an outstanding performance, explaining up to 80.9% of species richness and 83.3% of phylogenetic richness. Current climate and environmental heterogeneity emerged as the primary drivers, while past environmental conditions left only small but detectable imprints on plant diversity. Finally, we combined predictions from multiple modelling techniques (ensemble predictions) to reveal global patterns and centres of plant diversity at multiple resolutions down to 7,774 km2. Our predictive maps provide the most accurate estimates of global plant diversity available to date at grain sizes relevant for conservation and macroecology.

ecology↗