Towards a global understanding of the drivers of marine and terrestrial biodiversity.

ABSTRACT: Understanding the distribution of life's variety has driven naturalists and scientists for centuries, yet this has been constrained both by the available data and the models needed for their analysis. Here we compiled data for over 67,000 marine and terrestrial species and used artificial neural networks to model species richness with the state and variability of climate, productivity, and multiple other environmental variables. We find terrestrial diversity is better predicted by the available environmental drivers than is marine diversity, and that marine diversity can be predicted with a smaller set of variables. Ecological mechanisms such as geographic isolation and structural complexity appear to explain model residuals and also identify regions and processes that deserve further atte

SUBMITTER: Gagne TO 

PROVIDER: S-EPMC7001915 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature