Spatio-temporal transferability of environmentally-dependent population models: Insights from the intrinsic predictabilities of Adélie penguin abundance time series
Spatio-temporal transferability of environmentally-dependent population models: Insights from the intrinsic predictabilities of Adélie penguin abundance time series
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环境依赖性种群模型的时空可转移性：阿德利企鹅丰度时间序列内在可预测性的见解
DOI：
10.1016/j.ecolind.2023.110239
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发表时间：
2023
影响因子：
6.9
通讯作者：
Lynch, Heather J.
中科院分区：
文献类型：
--
作者：
Şen, Bilgecan;Che-Castaldo, Christian;Krumhardt, Kristen M.;Landrum, Laura;Holland, Marika M.;LaRue, Michelle A.;Long, Matthew C.;Jenouvrier, Stéphanie;Lynch, Heather J.
生态预测对于检验假设的调节物种种群动态的过程是否可跨时间和空间推广是必要的。为了证明普适性，模型预测应该在一个或多个维度上是可转移的，其中可转移是对模型数据界限之外的反应的成功预测。虽然很多人都知道是什么使得面向空间的模型具有可转移性，但对于生态时间序列模型的时空可转移性还没有达成普遍共识。在这里，我们使用在南极洲周围24个殖民地收集的Adélie企鹅繁殖丰度时间序列的特殊长期数据集，检查时间序列的内在可预测性(以其复杂性衡量)是否会限制这种可转移性。对于每个群体，我们从共同体地球系统模型第2版中选择一套环境变量来预测人口增长率，然后评估这些环境依赖的人口模型在时间上的转移情况以及时间信号在空间中复制的可靠性。我们发现，加权置换熵(WPE)是最近引入生态学的一种无模型的内在可预测性度量，它在不同的Adélie企鹅种群之间存在空间差异，可能是对随机环境事件的响应。我们证明WPE可以强烈地限制时间预测性能，尽管如果内在可预测性随着时间的推移不恒定，这种关系可能会被削弱。最后，我们证明了WPE还可以限制空间预测视界，我们将空间预测视界定义为空间预测性能相对于焦点群体和预测群体之间的物理距离的衰减。不管内在的可预测性如何，本研究中包括的所有Adélie企鹅繁殖群体的空间预测范围都令人惊讶地短，与基于长期平均增长率的零模型相比，我们的种群模型通常具有类似的时间和空间预测性能。对于由WPE测量的时间序列是复杂的，并且生物激励的机械模型的可转移性较差的情况，我们建议使用零模型来代替预测。这些模型可能更善于捕捉平均增长率和长期环境状况之间更具概括性的关系。最后，我们建议WPE可以在评估模型性能、设计采样或监测计划或评估先前存在的数据集的适当性以做出环境变化的保护管理决策时提供有价值的见解。
Ecological predictions are necessary for testing whether processes hypothesized to regulate species population dynamics are generalizable across time and space. In order to demonstrate generalizability, model predictions should be transferable in one or more dimensions, where transferability is the successful prediction of responses outside of the model data bounds. While much is known as to what makes spatially-oriented models transferable, there is no general consensus as to the spatio-temporal transferability of ecological time series models. Here, we examine whether the intrinsic predictability of a time series, as measured by its complexity, could limit such transferability using an exceptional long-term dataset of Adélie penguin breeding abundance time series collected at 24 colonies around Antarctica. For each colony, we select a suite of environmental variables from the Community Earth System Model, version 2 to predict population growth rates, before assessing how well these environmentally-dependent population models transfer temporally and how reliably temporal signals replicate through space. We show that weighted permutation entropy (WPE), a model-free measure of intrinsic predictability recently introduced to ecology, varies spatially across Adélie penguin populations, perhaps in response to stochastic environmental events. We demonstrate that WPE can strongly limit temporal predictive performance, although this relationship could be weakened if intrinsic predictability is not constant over time. Lastly, we show that WPE can also limit spatial forecast horizon, which we define as the decay in spatial predictive performance with respect to the physical distance between focal colony and predicted colony. Irrespective of intrinsic predictability, spatial forecast horizons for all Adélie penguin breeding colonies included in this study are surprisingly short and our population models often have similar temporal and spatial predictive performance compared to null models based on long-term average growth rates. For cases where time series are complex, as measured by WPE, and the transferability of biologically-motivated mechanistic models are poor, we advise that null models should instead be used for prediction. These models are likely better at capturing more generalizable relationships between average growth rates and long-term environmental conditions. Lastly, we recommend that WPE can provide valuable insights when evaluating model performance, designing sampling or monitoring programs, or assessing the appropriateness of preexisting datasets for making conservation management decisions in response to environmental change.
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