State-space model for Svalbard ptarmigan

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 c...