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.

中文翻译：

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机器学习方法将男性的体型确定为物种丰富度的最准确预测指标。

生物多样性科学的一项主要挑战是要了解可比生物分类谱系中物种丰富度变化的因素-社区或区域中不同物种的数量。据推测，多种生物和非生物因素会影响物种的丰富性，并已被用作其预测指标，但要确定准确的预测指标并非易事。蜘蛛是高度多样化的群体，在120个科中约有48,000种。然而，在仅有的10个最特殊的科中发现了所有物种的近75％。在这里，我们使用随机森林机器学习算法来测试假设影响蜘蛛属物种丰富度的不同变量的预测能力。我们从蜘蛛的形态，遗传，地理，生态和行为景观对45属物种丰富度的选择，代表了Araneae的系统发生和生物呼吸。在这些变量中，Random Forest分析发现了个体大小（特别是最小雄性个体大小），可以最好地预测物种丰富度。多重对应分析通过雄性个体大小与物种丰富度之间的负相关关系证实了这一结果。多重对应关系分析进一步证实，同属物种的地理分布与属多样性呈正相关，而在系统发育上较老的谱系中的属则物种较差。在蜘蛛特有的特征中，气球行为的存在和性大小的二态性都不能预测物种的丰富性。我们表明，机器学习分析可用于解密与多样性模式相关的因素。由于没有蜘蛛特异性生物学能够预测物种的丰富性，但是生物学上普遍存在的体形可以预测物种的丰富性，因此我们认为这些结论值得进行更广泛的生物学测试。未来在其他生物群上的工作将确定所检测到的物种丰富度与小体型和广泛地理范围之间的联系是否更广泛地适用。