Iterative model predictions for wildlife populations impacted by rapid climate change

• Published in: Global Change Biology
• Main Authors: Marolla, Filippo, Henden, John‐André, Fuglei, Eva, Pedersen, Åshild Ø., Itkin, Mikhail, Ims, Rolf A.
• Other Authors: Universitetet i Tromsø, Norges Forskningsråd, Norsk Polarinstitutt
• Format: Article in Journal/Newspaper
• Language: English
• Published: Wiley 2021
• Subjects: Arctic; Svalbard; Climate change; rock ptarmigan; Svalbard Rock Ptarmigan
• Online Access: http://dx.doi.org/10.1111/gcb.15518; https://onlinelibrary.wiley.com/doi/pdf/10.1111/gcb.15518; https://onlinelibrary.wiley.com/doi/full-xml/10.1111/gcb.15518

Abstract 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 ...