Department of Arctic and Marine Biology, UiT The Arctic University of Norway, Tromsø, Norway.
Norwegian Polar Institute, Fram Centre, Tromsø, Norway.
Glob Chang Biol. 2021 Apr;27(8):1547-1559. doi: 10.1111/gcb.15518. Epub 2021 Jan 28.
To improve understanding and management of the consequences of current rapid environmental change, ecologists advocate using long-term monitoring data series to generate iterative near-term predictions of ecosystem responses. This approach allows scientific evidence to increase rapidly and management strategies to be tailored simultaneously. Iterative near-term forecasting may therefore be particularly useful for adaptive monitoring of ecosystems subjected to rapid climate change. Here, we show how to implement near-term forecasting in the case of a harvested population of rock ptarmigan in high-arctic Svalbard, a region subjected to the largest and most rapid climate change on Earth. We fitted state-space models to ptarmigan counts from point transect distance sampling during 2005-2019 and developed two types of predictions: (1) explanatory predictions to quantify the effect of potential drivers of ptarmigan population dynamics, and (2) anticipatory predictions to assess the ability of candidate models of increasing complexity to forecast next-year population density. Based on the explanatory predictions, we found that a recent increasing trend in the Svalbard rock ptarmigan population can be attributed to major changes in winter climate. Currently, a strong positive effect of increasing average winter temperature on ptarmigan population growth outweighs the negative impacts of other manifestations of climate change such as rain-on-snow events. Moreover, the ptarmigan population may compensate for current harvest levels. Based on the anticipatory predictions, the near-term forecasting ability of the models improved nonlinearly with the length of the time series, but yielded good forecasts even based on a short time series. The inclusion of ecological predictors improved forecasts of sharp changes in next-year population density, demonstrating the value of ecosystem-based monitoring. Overall, our study illustrates the power of integrating near-term forecasting in monitoring systems to aid understanding and management of wildlife populations exposed to rapid climate change. We provide recommendations for how to improve this approach.
为了提高对当前快速环境变化后果的理解和管理，生态学家提倡使用长期监测数据系列来生成对生态系统响应的迭代短期预测。这种方法可以使科学证据迅速增加，同时调整管理策略。因此，迭代短期预测对于快速气候变化下的生态系统自适应监测可能特别有用。在这里，我们展示了如何在高北极斯瓦尔巴群岛的一种已收获的岩石松鸡种群的情况下实施短期预测，该地区正经历着地球上最大和最快的气候变化。我们根据 2005-2019 年的点样带距离抽样对岩石松鸡的计数拟合了状态空间模型，并开发了两种类型的预测：（1）解释性预测，以量化岩石松鸡种群动态的潜在驱动因素的影响，以及（2）预期性预测，以评估候选模型的复杂性不断增加的预测下一年种群密度的能力。基于解释性预测，我们发现斯瓦尔巴群岛岩石松鸡种群最近呈上升趋势，这归因于冬季气候的重大变化。目前，平均冬季温度升高对岩石松鸡种群增长的强烈正效应超过了气候变化的其他表现形式的负面影响，如雨夹雪事件。此外，岩石松鸡种群可能会弥补当前的收获水平。基于预期性预测，模型的短期预测能力随着时间序列长度的非线性增加而提高，但即使基于较短的时间序列也能产生良好的预测。生态预测因子的纳入提高了下一年种群密度的急剧变化的预测，证明了基于生态系统的监测的价值。总体而言，我们的研究说明了将短期预测纳入监测系统的力量，以帮助理解和管理暴露于快速气候变化的野生动物种群。我们提供了如何改进这种方法的建议。
