Although species distribution models (SDMs) are commonly used to hindcast fine-scale population metrics, there remains a paucity of information about how well these models predict future responses to climate. Many conventional SDMs rely on spatially-explicit but time-invariant conditions to quantify species distributions and densities. We compared these status quo ‘static' models with more climate-informed 'dynamic' SDMs to assess whether the addition of time-varying processes would improve hindcast performance and/or forecast skill. Here, we present two groundfish case studies from the Bering Sea – a high latitude system that has recently undergone considerable warming. We relied on conventional statistics (R², % deviance explained, UBRE or GCV) to evaluate hindcast performance for presence–absence, numerical abundance and biomass of arrowtooth flounder Atheresthes stomias and walleye pollock Gadus chalcogrammus. We then used retrospective skill testing to evaluate near-term forecast skill. Retrospective skill testing enables direct comparisons between forecasts and observations through a process of fitting and forecasting nested submodels within a given time series. We found that the inclusion of time-varying covariates improved hindcasts. However, dynamic models either did not improve or decreased forecast skill relative to static SDMs. This is likely a result of rapidly changing temperatures within the ecosystem, which required models to predict species responses to environmental conditions that were outside the range of observed values. Until additional model development allows for fully dynamic predictions, static model forecasts (or persistence forecasts from dynamic models) may serve as reliable placeholders, especially when anomalous conditions are anticipated. Nonetheless, our findings demonstrate support for the use of retrospective skill testing rather than selecting forecast models a priori based on their ability to quantify species–habitat associations in the past.

中文翻译：

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气候信息模型有利于后报，但在预测物种-栖息地关联时存在挑战

尽管物种分布模型 (SDM) 通常用于后测精细的种群指标，但关于这些模型预测未来对气候反应的程度的信息仍然很少。许多传统的 SDM 依赖于空间明确但时间不变的条件来量化物种分布和密度。我们将这些现状“静态”模型与更具气候信息的“动态”SDM 进行比较，以评估添加时变过程是否会提高后报性能和/或预测技能。在这里，我们介绍了来自白令海的两个底层鱼类案例研究——一个最近经历了相当大的变暖的高纬度系统。我们依靠常规统计（R ², % 偏差解释, UBRE 或 GCV) 来评估箭齿比目鱼Atheresthes stomias和大眼鳕Gadus chalcogrammus的存在-不存在、数值丰度和生物量的后报性能. 然后，我们使用回顾性技能测试来评估近期预测技能。回顾性技能测试通过在给定时间序列内拟合和预测嵌套子模型的过程，可以直接比较预测和观察结果。我们发现包含时变协变量改善了后报。然而，相对于静态 SDM，动态模型要么没有提高，要么降低了预测技能。这可能是生态系统内温度迅速变化的结果，这需要模型来预测物种对观察值范围之外的环境条件的反应。在其他模型开发允许完全动态预测之前，静态模型预测（或来自动态模型的持久性预测）可以作为可靠的占位符，尤其是在预计会出现异常情况时。尽管如此，我们的研究结果表明支持使用回顾性技能测试，而不是根据过去量化物种-栖息地关联的能力先验地选择预测模型。