Abstract

The status and trend estimates derived from the North American Breeding Bird Survey (BBS) are critical sources of information for bird conservation. However, the estimates are partly dependent on the statistical model used. Therefore, multiple models are useful because not all of the varied uses of these estimates (e.g., inferences about long-term change, annual fluctuations, population cycles, and recovery of once-declining populations) are supported equally well by a single statistical model. Here we describe Bayesian hierarchical generalized additive models (GAMs) for the BBS, which share information on the pattern of population change across a species’ range. We demonstrate the models and their benefits using data from a selection of species, and we run full cross-validation of the GAMs against 2 other models to compare the predictive fit. The GAMs have a better predictive fit than the standard model for all species studied here and comparable predictive fit to an alternative first difference model. In addition, one version of the GAM described here (GAMYE) estimates a population trajectory that can be decomposed into a smooth component and the annual fluctuations around that smooth component. This decomposition allows trend estimates based only on the smooth component, which are more stable between years and are therefore particularly useful for trend-based status assessments, such as those by the International Union for the Conservation of Nature. It also allows for the easy customization of the model to incorporate covariates that influence the smooth component separately from those that influence annual fluctuations (e.g., climate cycles vs. annual precipitation). For these reasons and more, this GAMYE model is a particularly useful model for the BBS-based status and trend estimates.

中文翻译：

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使用灵活的贝叶斯层次广义加性模型，北美种鸟调查状况和趋势估计可满足广泛的保护需求

抽象的

来自北美种禽调查（BBS）的状态和趋势估计值是保护鸟类的重要信息来源。但是，估计值部分取决于所使用的统计模型。因此，多个模型很有用，因为单个统计模型不能很好地支持这些估计的所有变化用法（例如，有关长期变化，年度波动，人口周期和曾经下降的人口恢复的推论）。在这里，我们描述了BBS的贝叶斯分层广义加性模型（GAM），该模型共享有关物种范围内种群变化模式的信息。我们使用来自选定物种的数据演示了模型及其益处，并且针对其他2个模型对GAM进行了全面交叉验证，以比较预测的拟合度。对于此处研究的所有物种，GAM均具有比标准模型更好的预测拟合，并且与替代的第一差异模型具有可比的预测拟合。另外，这里描述的GAM（GAMYE）的一种版本估算了人口轨迹，该轨迹可以分解为平滑分量和围绕该平滑分量的年波动。这种分解允许仅基于平滑分量进行趋势估计，这种平滑估计在几年之间更稳定，因此对于基于趋势的状态评估（例如国际自然保护联盟的评估）特别有用。它还允许轻松定制模型，以合并影响平滑分量的协变量和影响年度波动（例如，气候周期与年降水量）的协变量。