Dispersal syndromes allow understanding but not predicting dispersal ability across the tree of life

Guillermo Fandos, Robert A. Robinson, Damaris Zurell
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Abstract

Dispersal is fundamental to many ecological and evolutionary processes, yet understanding the determinants and predictability of dispersal remains a crucial challenge. Dispersal syndromes, which describe patterns in the covariation of traits associated with dispersal, can help to gain a deeper understanding of the evolutionary dynamics of dispersal and its implications for range dynamics and ecosystem functioning in the face of global change. However, the extent to which such dispersal syndromes are generalizable across a large taxonomic scale has been hampered by low availability of standardized dispersal data across species. In this study, we used the most comprehensive and up-to-date empirical dispersal dataset for European birds to investigate the formation of dispersal syndromes and their effectiveness in predicting dispersal across species. We found body mass, feeding guild, and life-history traits shape dispersal syndromes in birds. Yet, only body mass and life history accurately predicted dispersal for unassessed species, while even multi-trait dispersal syndromes poorly predicted dispersal for phylogenetically distant species. These results shed light on the complex nature of bird dispersal and emphasize the need for refined approaches in elucidating the mechanisms and constraints underlying dispersal evolution. Our study highlights the importance of considering multiple factors and expanding empirical datasets to enhance our understanding of dispersal in avian populations.
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通过扩散综合征可以了解但无法预测生命树的扩散能力
扩散是许多生态和进化过程的基本要素,但了解扩散的决定因素和可预测性仍然是一个重要的挑战。散布综合征描述了与散布相关的性状的共变模式,有助于深入了解散布的进化动态及其在全球变化中对分布区动态和生态系统功能的影响。然而,由于物种间标准化散布数据的可用性较低,这种散布综合征在大的分类尺度上的通用性受到了阻碍。在这项研究中,我们使用了最全面、最新的欧洲鸟类经验性扩散数据集来研究扩散综合征的形成及其在预测不同物种扩散方面的有效性。我们发现鸟类的体质、觅食习性和生活史特征塑造了鸟类的扩散综合征。然而,只有体重和生活史能准确预测未评估物种的扩散,而即使是多特征扩散综合征也不能很好地预测系统发育较远物种的扩散。这些结果揭示了鸟类散布的复杂性,并强调在阐明鸟类散布进化的机制和制约因素时需要精细的方法。我们的研究强调了考虑多种因素和扩大经验数据集的重要性,以加深我们对鸟类种群扩散的理解。
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