Machine learning approaches identify male body size as the most accurate predictor of species richness.

ABSTRACT:

Background

A major challenge in biodiversity science is to understand the factors contributing to the variability of species richness -the number of different species in a community or region - among comparable taxonomic lineages. Multiple biotic and abiotic factors have been hypothesized to have an effect on species richness and have been used as its predictors, but identifying accurate predictors is not straightforward. Spiders are a highly diverse group, with some 48,000 species in 120 families; yet nearly 75% of all species are found within just the ten most speciose families. Here we use a Random Forest machine learning algorithm to test the predictive power of different variables hypothesized to affect species richness of spider genera.

Results

We test the predictive p

SUBMITTER: Candek K 

PROVIDER: S-EPMC7453550 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature