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Increasing ground-layer plant taxonomic diversity masks declining phylogenetic diversity along a silvicultural disturbance gradient

机译:增加地层植物分类学多样性面部沿造林扰动梯度下降了系统发育多样性

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摘要

Most plant diversity in temperate deciduous forests is found in the ground layer, but nearly all studies comparing plant community assembly using taxonomic, trait, and phylogenetic diversity indices are limited to woody plants. To examine the relationship between short-term ground-layer plant community assembly and disturbance severity, we leveraged a silvicultural experiment that applied a combination of harvest and site preparation treatments in a northern hardwood forest in Michigan, USA. We predicted that after two growing seasons, plant communities would be less sensitive to harvest treatments when compared with site preparation treatments that disturb the rhizosphere and modify rooting substrate. We also predicted that an increase in taxonomic diversity would accompany a decline in trait diversity and phylogenetic diversity. Instead, plant species composition responded similarly to harvest treatment and site preparation treatment. However, our measure of disturbance severity was positively correlated with both trait diversity and taxonomic diversity but negatively correlated with phylogenetic diversity, indicating that increasingly diverse traits and taxonomies along this disturbance severity gradient were comprised of more phylogenetically simple plant communities. Informed management decisions should therefore consider the underlying value of each diversity measure, as taxonomic diversity alone may not be the best metric for assessing plant community assembly.
机译:温带落叶林中的大多数植物多样性都存在于底层,但几乎所有利用分类学、性状和系统发育多样性指数比较植物群落组合的研究都局限于木本植物。为了研究短期底层植物群落组合与干扰严重程度之间的关系,我们在美国密歇根州北部的一片阔叶林中进行了一项造林试验,采用了收割和整地处理相结合的方法。我们预测,经过两个生长季,与干扰根际和改变生根基质的整地处理相比,植物群落对收获处理的敏感性较低。我们还预测,分类多样性的增加将伴随着性状多样性和系统发育多样性的下降。相反,植物物种组成对收获处理和整地处理的反应类似。然而,我们对干扰严重程度的测量与性状多样性和分类多样性均呈正相关,但与系统发育多样性呈负相关,这表明沿着干扰严重程度梯度越来越多样化的性状和分类由系统发育上更简单的植物群落组成。因此,明智的管理决策应考虑每个多样性措施的潜在价值,因为分类多样性可能不是评估植物群落聚集的最佳尺度。

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