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 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
management of wildlife populations exposed to rapid climate change. We
provide recommendations for how to improve this approach.