Quantifying the strength of migratory connectivity
Quantifying the strength of migratory connectivity
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DOI：
10.1111/2041-210x.12916
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发表时间：
2018-03-01
影响因子：
6.6
通讯作者：
Marra, Peter P.
中科院分区：
文献类型：
--
作者：
Cohen, Emily B.;Hostetler, Jeffrey A.;Marra, Peter P.
技术进步促进了对迁徙联系的研究，即个人和人口在年周期不同季节之间的联系的研究。迁移连接的强度是衡量种群在整个年度周期中共同出现的一个指标，可以用一个季节和另一个季节个体之间距离的相关性来表示（曼特尔相关性，r（M））。然而，季节性分布的测量通常涉及不完整的抽样和使用的技术在准确性和精确度上各不相同。由于这些原因，我们扩展了r（M），以衡量迁移连接（MC）的强度与人口特定的转移概率，可以从许多数据类型和不均匀的sampling.We探索MC可能的输入参数的真实世界的变化的敏感性：转移概率，丰富的区域，区域的空间布局，和样本大小。我们比较了MC和r（M），提出了一系列将输入值中的不确定性传播到MC和r（M）估计中的方法，并利用鸟类跟踪数据验证了该方法。当种群在季节之间相距较远时，迁移连接性为负，当种群在季节之间保持在一起时，迁移连接性为正，当种群在季节之间没有分布模式时，迁移连接性为零。MC是最敏感的过渡概率和空间布局的区域，并执行优于r（M）时，采样工作是不成比例的真实丰度，当迁移连接的强度变化的范围内的物种。我们对MC和r（M）的估计在几种数据类型中表现良好。我们希望这些方法和MigConnectivity r包将有助于跨研究，数据类型和分类群的迁移连接的定量比较，以更好地了解种群季节分布的原因和后果。从我们的模拟中出现了几个研究设计建议：（1）当采样不成比例时，将区域之间的丰度合并;（2）在尽可能多的范围内测量转移概率;（3）使用关于种群划分的生物信息定义研究区域，或使用离散的研究位置作为区域的质心;以及（4）估计和报告来自适当来源的采样和过程误差的不确定性。
Technological advancements have spurred rapid growth in the study of migratory connectivity, the linkage of individuals and populations between seasons of the annual cycle. The strength of migratory connectivity is a measure of the co-occurrence of populations throughout the annual cycle and can be represented by a correlation of the distances between individuals during one season and another (Mantel correlation, r(M)). However, measurement of seasonal distributions most often involves incomplete sampling and use of technologies that vary in accuracy and precision. For these reasons, we expanded r(M) to measure the strength of migratory connectivity (MC) with population-specific transition probabilities that can be derived from many data types and uneven sampling.We explore the sensitivity of MC to possible real-world variation in input parameters: transition probabilities, abundance among regions, spatial arrangement of regions, and sample sizes. We compare MC to r(M), present a series of resampling approaches for propagating uncertainty in input values into estimation of MC and r(M), and validate the method with bird tracking data.Migratory connectivity was negative when populations are further apart between seasons, positive when populations remain together between seasons, and zero when populations have no patterns in distribution between seasons. MC is most sensitive to transition probabilities and spatial arrangement of regions and performs better than r(M) when sampling effort is not proportional to true abundance, and when the strength of migratory connectivity varies across the range of the species. Our estimators for MC and r(M) performed well across several data types.We hope that these methods and the MigConnectivity r package will facilitate quantitative comparisons of migratory connectivity across studies, data types, and taxa to better understand the causes and consequences of the seasonal distributions of populations. Several study design recommendations emerge from our simulations: (1) incorporate abundance among regions when sampling is not proportional; (2)measure transition probabilities across as much of the range as logistically possible; (3) define study regions with either biological information about population delineation, or use discrete study locations as centroids of regions; and (4) estimate and report uncertainty from appropriate sources of sampling and process errors.