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 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. Conclusions: 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.

Perspectives in machine learning for wildlife conservation

“…Spatially explicit linear regression models have been conventionally used to predict species and community distribution based on explanatory variables such as climate and topography 124,125 . Non-parametric ML techniques like Random Forest 126 have been successfully used to predict species richness and have shown significant error reduction with respect to the traditional counterparts used in ecology, for example in the estimation of richness distributions of fishes 127,128 , spiders 129 , and small mammals 130 . Tree-based techniques have also been used to predict species interactions: for example, regression trees significantly outperformed classical generalized linear models in predicting plant-pollinator interactions 33 .…”

Seeing biodiversity: perspectives in machine learning for wildlife conservation

“…Spatially explicit linear regression models have been conventionally used to predict species and community distribution based on explanatory variables such as climate and topography (128,129). Non-parametric ML techniques like Random Forest (130) have been successfully used to predict species richness and have shown significant error reduction with respect to the traditional counterparts used in ecology, for example in the estimation of richness distributions of fishes (131,132), spiders (133), and small mammals (134).…”

Biogeography of Long-Jawed Spiders Reveals Multiple Colonization of the Caribbean

“…Spiders represent an ancient lineage with nearly 50 thousand species from 129 families [34] and inhabit most terrestrial ecosystems. Interestingly, various taxonomic groups of comparable ranks within spiders exhibit highly variable dispersal abilities, degrees of endemism, species richness and species distributions [35,36] Our study focuses on long-jawed spiders (genus Tetragnatha, family Tetragnathidae), with special emphasis on the Caribbean archipelago. This diverse genus includes 323 described species [34] (and probably numerous undescribed ones) and has been extensively used in biogeographic research, notably in Hawaii and other Pacific archipelagos [37][38][39][40].…”