Predicting animal population trajectories into the future has become a central exercise in both applied and fundamental ecology. Because demographic models classically assume population closure, they tend to provide inaccurate predictions when applied locally to interconnected subpopulations that are part of a larger metapopulation. Ideally, one should explicitly model dispersal among subpopulations, but in practice this is prevented by the difficulty of estimating dispersal rates in the wild. To forecast the local demography of connected subpopulations, we developed a new demographic model (hereafter, the two‐scale model) that disentangles two processes occurring at different spatial scales. First, at the larger scale, a closed population model describes changes in metapopulation size over time. Second, total metapopulation size is redistributed among subpopulations, using time‐varying proportionality parameters. This two‐step approach ensures that the long‐term growth of every subpopulation is constrained by the overall metapopulation growth rate. It implicitly accounts for the interconnectedness among subpopulations and avoids unrealistic trajectories. Using realistic simulations, we compared the performance of this new model with that of a classical closed population model at predicting subpopulations' trajectories over 30 years. While the classical model predicted future subpopulation sizes with an average bias of 30% and produced predictive errors sometimes >500%, the two‐scale model showed very little bias (<3%) and never produced predictive errors >20%. We also applied both models to a real dataset on European shags (Gulosus aristotelis) breeding along the Atlantic coast of France. Again, the classical model predicted highly unrealistic growths, as large as a 200‐fold increase over 30 years for some subpopulations. The two‐scale model predicted very sensible growths, never larger than a threefold increase over the 30‐year time horizon, which is more in accordance with this species' life history. This two‐scale model provides an effective solution to forecast the local demography of connected subpopulations in the absence of data on dispersal rates. In this context, it is a better alternative than closed population models and a more parsimonious option than full‐dispersal models. Because the only data required are simple counts, this model could be useful to many large‐scale wildlife monitoring programs.

中文翻译：

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一种预测元种群中本地人口趋势的建模方法


预测动物种群未来轨迹已成为应用生态学和基础生态学的核心练习。由于人口统计模型通常假设群体闭合，因此当它们在本地应用于属于较大元群体的互连子群体时，它们往往会提供不准确的预测。理想情况下，应该明确地模拟亚群之间的传播，但在实践中，由于难以估计野外的传播率，这被阻止了。为了预测相关亚群的当地人口统计学，我们开发了一种新的人口模型（以下简称双尺度模型），该模型解开了在不同空间尺度上发生的两个过程。首先，在更大的尺度上，封闭的种群模型描述了元种群大小随时间的变化。其次，使用时变比例参数在亚群之间重新分配总元群大小。这种两步法确保每个亚群的长期增长受到整体元种群增长率的限制。它隐含地解释了亚群之间的相互联系，并避免了不切实际的轨迹。使用逼真的模拟，我们将这个新模型与经典的封闭种群模型在预测亚群 30 年轨迹方面的性能进行了比较。虽然经典模型以 30% 的平均偏差预测未来的亚群规模，有时产生预测误差 >500%，但双尺度模型显示出非常小的偏差 （<3%），并且从未产生预测误差 >20%。我们还将这两个模型应用于法国大西洋沿岸欧洲粗毛 （Gulosus aristotelis） 繁殖的真实数据集。 同样，经典模型预测了非常不切实际的增长，某些亚群在 30 年内增长了 200 倍。双尺度模型预测了非常合理的生长，在 30 年的时间范围内从未超过三倍的增长，这更符合该物种的生活史。这个双尺度模型提供了一种有效的解决方案，可以在没有扩散率数据的情况下预测相关亚群的局部人口统计。在这种情况下，它是比封闭种群模型更好的选择，也是比完全分散模型更简洁的选择。由于唯一需要的数据是简单的计数，因此该模型可能对许多大规模野生动物监测计划有用。