Iterative model predictions for wildlife populations impacted by rapid climate change
© 2021 The Authors. Global Change Biology published by John Wiley & Sons Ltd.
| Veröffentlicht in: | Global change biology. - 1999. - 27(2021), 8 vom: 12. Apr., Seite 1547-1559 |
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| 1. Verfasser: | |
| Weitere Verfasser: | , , , , |
| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2021
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| Zugriff auf das übergeordnete Werk: | Global change biology |
| Schlagworte: | Journal Article Research Support, Non-U.S. Gov't Arctic Svalbard climate change management near-term forecasting prediction ptarmigan winter temperature |
| Zusammenfassung: | © 2021 The Authors. Global Change Biology published by John Wiley & Sons Ltd. 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 and management of wildlife populations exposed to rapid climate change. We provide recommendations for how to improve this approach |
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| Beschreibung: | Date Completed 23.04.2021 Date Revised 23.04.2021 published: Print-Electronic Citation Status MEDLINE |
| ISSN: | 1365-2486 |
| DOI: | 10.1111/gcb.15518 |