Evolutionary Zoology Laboratory, Department of Organisms and Ecosystems Research, National Institute of Biology, Ljubljana, Slovenia.
Jovan Hadži Institute of Biology, Research Centre of the Slovenian Academy of Sciences and Arts, Ljubljana, Slovenia.
BMC Biol. 2020 Aug 28;18(1):105. 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.
生物多样性科学的一个主要挑战是理解导致物种丰富度变化的因素——即群落或区域中不同物种的数量。多种生物和非生物因素被假设对物种丰富度有影响，并被用作其预测因子，但识别准确的预测因子并不简单。蜘蛛是一个高度多样化的群体，有 120 个科的约 48000 个物种；然而，近 75%的物种仅在最具物种多样性的 10 个科中发现。在这里，我们使用随机森林机器学习算法来测试不同变量对蜘蛛属物种丰富度的预测能力，这些变量假设会影响物种丰富度。
我们测试了来自蜘蛛形态、遗传、地理、生态和行为景观的 22 个变量对 45 个属的物种丰富度的预测能力，这些属被选择代表蜘蛛目系统发育和生物多样性。在这些变量中，随机森林分析发现体型（特别是雄性最小体型）最能预测物种丰富度。多元对应分析通过雄性体型与物种丰富度之间的负相关关系证实了这一结果。多元对应分析还进一步确定了同种物种的地理分布与属多样性呈正相关，而来自进化较老谱系的属则物种较少。在蜘蛛特有的特征中，气球行为的存在或性二型现象都不能预测物种丰富度。
我们表明，机器学习分析可用于破译与多样性模式相关的因素。由于没有蜘蛛特有的生物学特征可以预测物种丰富度，但生物学上普遍存在的体型可以，因此我们认为这些结论值得更广泛的生物学检验。对其他生物群体的进一步研究将确定物种丰富度与小体型和广泛地理分布的相关性是否更广泛。
