Gregory Killian A, Francesiaz Charlotte, Jiguet Frédéric, Besnard Aurélien
Master de Biologie, École Normale Supérieure de Lyon, Université Claude Bernard Lyon 1, Université de Lyon, Lyon, France.
CESCO, MNHN-CNRS-Sorbonne Université, Paris, France.
Mov Ecol. 2023 Oct 27;11(1):69. doi: 10.1186/s40462-023-00388-z.
Migration movements connect breeding and non-breeding bird populations over the year. Such links, referred to as migratory connectivity, have important implications for migratory population dynamics as they dictate the consequences of localised events for the whole population network. This calls for concerted efforts to understand migration processes for large-scale conservation. Over the last 20 years, the toolbox to investigate connectivity patterns has expanded and studies now consider migratory connectivity over a broader range of species and contexts. Here, we summarise recent developments in analysing migratory connectivity, focusing on strategies and challenges to pooling various types of data to both optimise and broaden the scope of connectivity studies. We find that the different approaches used to investigate migratory connectivity still have complementary strengths and weaknesses, whether in terms of cost, spatial and temporal resolution, or challenges in obtaining large sample sizes or connectivity estimates. Certain recent developments offer particularly promising prospects: robust quantitative models for banding data, improved precision of geolocators and accessibility of telemetry tracking systems, and increasingly precise probabilistic assignments based on genomic markers or large-scale isoscapes. In parallel, studies have proposed various ways to combine the information of different datasets, from simply comparing the connectivity patterns they draw to formally integrating their analyses. Such data combinations have proven to be more accurate in estimating connectivity patterns, particularly for integrated approaches that offer promising flexibility. Given the diversity of available tools, future studies would benefit from a rigorous comparative evaluation of the different methodologies to guide data collection to complete migration atlases: where and when should data be collected during the migratory cycle to best describe connectivity patterns? Which data are most favourable to combine, and under what conditions? Are there methods for combining data that are better than others? Can combination methods be improved by adjusting the contribution of the various data in the models? How can we fully integrate connectivity with demographic and environmental data? Data integration shows strong potential to deepen our understanding of migratory connectivity as a dynamic ecological process, especially if the gaps can be bridged between connectivity, population and environmental models.
一年之中,迁徙活动将繁殖期和非繁殖期的鸟类种群联系起来。这种联系,即所谓的迁徙连通性,对迁徙种群动态具有重要影响,因为它们决定了局部事件对整个种群网络的影响。这就需要各方共同努力,以了解迁徙过程,实现大规模的保护。在过去20年里,用于研究连通性模式的工具不断扩充,现在的研究在更广泛的物种和背景下考虑迁徙连通性。在此,我们总结了分析迁徙连通性方面的最新进展,重点关注汇集各类数据以优化和拓宽连通性研究范围的策略与挑战。我们发现,用于研究迁徙连通性的不同方法在成本、空间和时间分辨率方面,或者在获取大样本量或连通性估计值所面临的挑战方面,仍然各有互补的优缺点。近期的某些进展提供了特别有前景的方向:针对环志数据的稳健定量模型、地理定位器精度的提高以及遥测追踪系统的可及性,以及基于基因组标记或大规模等风景观越来越精确的概率分配。与此同时,研究提出了多种整合不同数据集信息的方法,从简单比较它们得出的连通性模式到正式整合分析。事实证明,这种数据整合在估计连通性模式时更加准确,特别是对于具有良好灵活性的整合方法。鉴于可用工具的多样性,未来的研究将受益于对不同方法进行严格的比较评估,以指导数据收集来完成迁徙图谱:在迁徙周期的何时何地收集数据,才能最好地描述连通性模式?哪些数据最适合组合,在什么条件下组合?是否存在比其他方法更好的数据组合方法?能否通过调整模型中各种数据的贡献来改进组合方法?我们如何将连通性与人口统计学和环境数据完全整合?数据整合具有强大潜力,可加深我们对作为动态生态过程的迁徙连通性的理解,特别是如果能够弥合连通性、种群和环境模型之间的差距。