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检测温带雨林树种之间的非加性生物相互作用并评估其生物学相关性。

Detecting Nonadditive Biotic Interactions and Assessing Their Biological Relevance among Temperate Rainforest Trees.

出版信息

Am Nat. 2024 Aug;204(2):105-120. doi: 10.1086/730807. Epub 2024 Jun 20.

DOI:10.1086/730807
PMID:39008837
Abstract

AbstractInteractions between and within abiotic and biotic processes generate nonadditive density-dependent effects on species performance that can vary in strength or direction across environments. If ignored, nonadditivities can lead to inaccurate predictions of species responses to environmental and compositional changes. While there are increasing empirical efforts to test the constancy of pairwise biotic interactions along environmental and compositional gradients, few assess both simultaneously. Using a nationwide forest inventory that spans broad ambient temperature and moisture gradients throughout New Zealand, we address this gap by analyzing the diameter growth of six focal tree species as a function of neighbor densities and climate, as well as neighbor × climate and neighbor × neighbor statistical interactions. The most complex model featuring all interaction terms had the highest predictive accuracy. Compared with climate variables, biotic interactions typically had stronger effects on diameter growth, especially when subjected to nonadditivities from local climatic conditions and the density of intermediary species. Furthermore, statistically strong (or weak) nonadditivities could be biologically irrelevant (or significant) depending on whether a species pair typically interacted under average or more extreme conditions. Our study highlights the importance of considering both the statistical potential and the biological relevance of nonadditive biotic interactions when assessing species performance under global change.

摘要

摘要

生物和非生物过程之间以及内部的相互作用对物种表现产生非加性的密度依赖性影响,这种影响在不同环境中的强度或方向可能会有所不同。如果忽略这些相互作用,可能会导致对物种对环境和组成变化的反应的预测不准确。虽然越来越多的实证研究试图检验生物相互作用在环境和组成梯度上的恒定性,但很少有研究同时评估这两个方面。利用一项覆盖新西兰广泛环境温度和湿度梯度的全国性森林清查数据,我们通过分析六个焦点树种的直径生长与邻居密度和气候的关系,以及邻居与气候和邻居与邻居之间的统计相互作用,解决了这一差距。具有所有交互项的最复杂模型具有最高的预测准确性。与气候变量相比,生物相互作用通常对直径生长有更强的影响,尤其是当受到来自当地气候条件和中介物种密度的非加性影响时。此外,统计上强(或弱)的非加性可能在生物学上是无关紧要的(或重要的),这取决于物种对通常在平均条件还是更极端条件下相互作用。我们的研究强调了在评估全球变化下物种表现时,既要考虑非加性生物相互作用的统计可能性,也要考虑其生物学相关性。

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