Iterative model predictions for wildlife populations impacted by rapid climate change

Area of Science:

• Ecology
• Climate Change Biology
• Population Dynamics

Background:

• Ecologists advocate for long-term monitoring data series to predict ecosystem responses to environmental change.
• Iterative near-term forecasting is crucial for adaptive management of ecosystems under rapid climate change.

Purpose of the Study:

• To implement and evaluate near-term forecasting for a harvested rock ptarmigan population in Svalbard.
• To quantify climate drivers of ptarmigan population dynamics and assess forecasting model performance.

Main Methods:

• Fitted state-space models to rock ptarmigan counts (2005-2019) using point transect distance sampling.
• Developed explanatory predictions to identify population drivers and anticipatory predictions for population density forecasting.

Main Results:

• Increasing winter temperatures positively impact rock ptarmigan population growth, outweighing negative climate effects.
• Near-term forecasting accuracy improved with time series length and inclusion of ecological predictors.
• The population demonstrated resilience to harvest, with models showing good predictive ability even with short data series.

Conclusions:

• Near-term forecasting effectively aids understanding and management of wildlife populations under rapid climate change.
• Integrating ecological predictors enhances forecasting of population density shifts.
• This approach supports adaptive monitoring and management of Arctic ecosystems like Svalbard.