Accurate forecasts of how animals respond to climate-driven environmental change are needed to prepare for future redistributions, however, it is unclear which temporal scales of environmental variability give rise to predictability of species distributions. We examined the temporal scales of environmental variability that best predicted spatial abundance of a marine predator, swordfish Xiphias gladius, in the California Current. To understand which temporal scales of environmental variability provide biological predictability, we decomposed physical variables into three components: a monthly climatology (long-term average), a low frequency component representing interannual variability, and a high frequency (sub-annual) component that captures ephemeral features. We then assessed each component's contribution to predictive skill for spatially-explicit swordfish catch. The monthly climatology was the primary source of predictability in swordfish spatial catch, reflecting the spatial distribution associated with seasonal movements in this region. Importantly, we found that the low frequency component (capturing interannual variability) provided significant skill in predicting anomalous swordfish distribution and catch, which the monthly climatology cannot. The addition of the high frequency component added only minor improvement in predictability. By examining models' ability to predict species distribution anomalies, we assess the models in a way that is consistent with the goal of distribution forecasts – to predict deviations of species distributions from their average historical locations. The critical importance of low frequency climate variability in describing anomalous swordfish distributions and catch matches the target timescales of physical climate forecasts, suggesting potential for skillful ecological forecasts of swordfish distributions across short (seasonal) and long (climate) timescales. Understanding sources of prediction skill for species environmental responses gives confidence in our ability to accurately predict species distributions and abundance, and to know which responses are likely less predictable, under future climate change. This is important as climate change continues to cause an unprecedented redistribution of life on Earth.

中文翻译：

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探索物种分布可预测性的时间尺度

需要准确预测动物如何应对气候驱动的环境变化，为未来的重新分布做准备，然而，尚不清楚环境变化的哪些时间尺度会导致物种分布的可预测性。我们研究了最能预测海洋捕食者剑鱼Xiphias Gladius空间丰度的环境变化的时间尺度，在加利福尼亚洋流中。为了了解环境变化的哪些时间尺度提供生物可预测性，我们将物理变量分解为三个部分：月气候学（长期平均值）、代表年际变化的低频分量和捕捉到的高频（次年）分量短暂的特征。然后，我们评估了每个组件对空间显式箭鱼捕获预测技能的贡献。每月气候是箭鱼空间捕捞量可预测性的主要来源，反映了与该地区季节性运动相关的空间分布。重要的是，我们发现低频分量（捕捉年际变化）提供了预测异常旗鱼分布和捕获量的重要技能，月气候学不能。高频分量的添加仅增加了可预测性的微小改进。通过检查模型预测物种分布异常的能力，我们以与分布预测目标一致的方式评估模型——预测物种分布与其平均历史位置的偏差。低频气候变异在描述异常旗鱼分布和捕获量方面的关键重要性与物理气候预测的目标时间尺度相匹配，这表明在短（季节性）和长（气候）时间尺度上对箭鱼分布进行巧妙的生态预测的潜力。了解物种环境响应预测技能的来源使我们有信心准确预测物种分布和丰度，并了解在未来气候变化下哪些响应可能不太可预测。这一点很重要，因为气候变化继续导致地球上前所未有的生命重新分配。