Natural populations are increasingly threatened with collapse at the hands of anthropogenic effects. Predicting population collapse with the help of generic early warning signals (EWS) may provide a prospective tool for identifying species or populations at highest risk. However, pattern-to-process methods such as EWS have a multitude of challenges to overcome to be useful, including the low signal to noise ratio of ecological systems and the need for high quality time-series data. The inclusion of trait dynamics with EWS has been proposed as a more robust tool to predict population collapse. However, the length and resolution of available time series are highly variable from one system to another, especially when generation time is considered. As yet it remains unknown how this variability with regards to generation time will alter the efficacy of EWS. Here we take both a simulation- and experimental-based approach to assess the impacts of relative time-series length and resolution on the forecasting ability of EWS. We show that EWS' performance decreases with decreasing time series length. However, there was no evident decrease in EWS performance as resolution decreased. Our simulations suggest a relative time-series length between ten and five generations as a minimum requirement for accurate forecasting by abundance-based EWS. However, when trait information is included alongside abundance-based EWS, we find positive signals at lengths half of what was required without them. We suggest that, in systems where specific traits are known to affect demography, trait data should be monitored and included alongside abundance data to improve forecasting reliability.

中文翻译：

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时间序列长度和分辨率对基于丰度和性状的种群衰退预警信号的影响

自然种群越来越受到人为影响而崩溃的威胁。在通用预警信号 (EWS) 的帮助下预测种群崩溃可能为识别风险最高的物种或种群提供前瞻性工具。然而，像 EWS 这样的模式到处理方法需要克服许多挑战才能发挥作用，包括生态系统的低信噪比和对高质量时间序列数据的需求。已经提出将特征动态与 EWS 结合起来作为预测种群崩溃的更强大的工具。但是，从一个系统到另一个系统，可用时间序列的长度和分辨率变化很大，尤其是在考虑生成时间时。目前尚不清楚这种关于世代时间的可变性将如何改变 EWS 的功效。在这里，我们采用基于模拟和实验的方法来评估相对时间序列长度和分辨率对 EWS 预测能力的影响。我们表明 EWS 的性能随着时间序列长度的减少而降低。然而，随着分辨率的降低，EWS 性能没有明显下降。我们的模拟表明，10 到 5 代之间的相对时间序列长度是基于丰度的 EWS 进行准确预测的最低要求。然而，当特征信息与基于丰度的 EWS 一起包含时，我们发现积极信号的长度只有没有它们所需的一半。我们建议，在已知特定特征会影响人口统计学的系统中，