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

• Published in: BMC biology 2020-08, Vol.18 (1), p.105-105, Article 105
• Main Authors: Čandek, Klemen, Pristovšek Čandek, Urška, Kuntner, Matjaž
• Format: Article
• Language: English
• Subjects: Abiotic factors; Algorithms; Analysis; Animal behavior; Animals; Biodiversity; Biology; Body Size; Data mining; Decision trees; Dimorphism; Ecology - methods; Ecosystem components; Endangered & extinct species; Extinction; Genera; Genetic diversity; Geographical distribution; Learning algorithms; Lineage diversity; Machine Learning; Male; Morphology; Phylogenetic diversity; Phylogenetics; Phylogeny; Random variables; Sexual behavior; Sexual dimorphism; Species distribution; Species diversity; Species richness; Species traits; Spiders; Taxonomy; Variables
• ISSN: 1741-7007; 1741-7007
• DOI: 10.1186/s12915-020-00835-y

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. We test the predictive power of 22 variables from spiders' morphological, genetic, geographic, ecological and behavioral landscapes on species richness of 45 genera selected to represent the phylogenetic and biological breath of Araneae. Among the variables, Random Forest analyses find body size (specifically, minimum male body size) to best predict species richness. Multiple Correspondence analysis confirms this outcome through a negative relationship between male body size and species richness. Multiple Correspondence analyses furthermore establish that geographic distribution of congeneric species is positively associated with genus diversity, and that genera from phylogenetically older lineages are species poorer. Of the spider-specific traits, neither the presence of ballooning behavior, nor sexual size dimorphism, can predict species richness. We show that machine learning analyses can be used in deciphering the factors associated with diversity patterns. Since no spider-specific biology could predict species richness, but the biologically universal body size did, we believe these conclusions are worthy of broader biological testing. Future work on other groups of organisms will establish whether the detected associations of species richness with small body size and wide geographic ranges hold more broadly.