State-space model for Svalbard ptarmigan

• Main Authors: Marolla, Filippo, Henden, John-Andre, Fuglei, Eva, Pedersen, Ashild Ønvik, Itkin, Mikhail, Ims, Rolf Anker
• Format: Dataset
• Language: unknown
• Published: 2021
• Subjects: Bayesian state-space modelling; Svalbard; rock ptarmigan; Arctic; Climate change; Svalbard Rock Ptarmigan
• Online Access: https://zenodo.org/record/4435265; https://doi.org/10.5061/dryad.ngf1vhht0

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 non-linearly 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 ...