cE3c-Centre for Ecology, Evolution and Environmental Changes, Faculty of Sciences, University of Lisbon, Campo Grande, C2, 1749-016, Lisbon, Portugal.
Department of Biology, University of Florence, Via Madonna del Piano 6, 50019, Sesto Fiorentino, Italy.
Sci Rep. 2021 Jan 27;11(1):2292. doi: 10.1038/s41598-021-82059-w.
The decomposition of beta-diversity (β-diversity) into its replacement (β) and richness (β) components in combination with a taxonomic and functional approach, may help to identify processes driving community composition along environmental gradients. We aimed to understand which abiotic and spatial variables influence ant β-diversity and identify which processes may drive ant β-diversity patterns in Mediterranean drylands by measuring the percentage of variation in ant taxonomic and functional β-diversity explained by local environmental, regional climatic and spatial variables. We found that taxonomic and functional replacement (β) primarily drove patterns in overall β-diversity (β). Variation partitioning analysis showed that respectively 16.8%, 12.9% and 21.6% of taxonomic β, β and β variation were mainly explained by local environmental variables. Local environmental variables were also the main determinants of functional β-diversity, explaining 20.4%, 17.9% and 23.2% of β, β and β variation, respectively. Findings suggest that niche-based processes drive changes in ant β-diversity, as local environmental variables may act as environmental filters on species and trait composition. While we found that local environmental variables were important predictors of ant β-diversity, further analysis should address the contribution of other mechanisms, e.g. competitive exclusion and resource partitioning, on ant β-diversity.
β 多样性（β-diversity）可分解为替代（β）和丰富度（β）成分，结合分类学和功能方法，可能有助于确定沿环境梯度驱动群落组成的过程。我们旨在通过测量蚂蚁分类和功能β多样性中由当地环境、区域气候和空间变量解释的变异百分比，了解哪些非生物和空间变量影响蚂蚁β多样性，并确定哪些过程可能驱动地中海干旱地区蚂蚁β多样性模式。我们发现，分类学和功能替代（β）主要驱动了总体β多样性（β）的模式。变异分解分析表明，分类学β、β和β变异性分别有 16.8%、12.9%和 21.6%主要由当地环境变量解释。局部环境变量也是功能β多样性的主要决定因素，分别解释了β、β和β变异性的 20.4%、17.9%和 23.2%。研究结果表明，基于生态位的过程驱动了蚂蚁β多样性的变化，因为当地环境变量可能对物种和特征组成起到环境过滤作用。虽然我们发现当地环境变量是蚂蚁β多样性的重要预测因子，但进一步的分析应该考虑其他机制的贡献，例如竞争排斥和资源分割，对蚂蚁β多样性的影响。
