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231225s2021 xx |||||o 00| ||eng c |
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|a 10.1111/gcb.15518
|2 doi
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|a pubmed24n1067.xml
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|a (DE-627)NLM320112942
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|a (NLM)33448074
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|a DE-627
|b ger
|c DE-627
|e rakwb
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|a eng
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| 100 |
1 |
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|a Marolla, Filippo
|e verfasserin
|4 aut
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| 245 |
1 |
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|a Iterative model predictions for wildlife populations impacted by rapid climate change
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| 264 |
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|c 2021
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| 336 |
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
|b c
|2 rdamedia
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| 338 |
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|a ƒa Online-Ressource
|b cr
|2 rdacarrier
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| 500 |
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|a Date Completed 23.04.2021
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|a Date Revised 23.04.2021
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|a published: Print-Electronic
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|a Citation Status MEDLINE
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|a © 2021 The Authors. Global Change Biology published by John Wiley & Sons Ltd.
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|a 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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4 |
|a Journal Article
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| 650 |
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4 |
|a Research Support, Non-U.S. Gov't
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| 650 |
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4 |
|a Arctic
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| 650 |
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4 |
|a Svalbard
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| 650 |
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4 |
|a climate change
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| 650 |
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4 |
|a management
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| 650 |
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4 |
|a near-term forecasting
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| 650 |
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4 |
|a prediction
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| 650 |
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4 |
|a ptarmigan
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| 650 |
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4 |
|a winter temperature
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| 700 |
1 |
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|a Henden, John-André
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Fuglei, Eva
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Pedersen, Åshild Ø
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Itkin, Mikhail
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Ims, Rolf A
|e verfasserin
|4 aut
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| 773 |
0 |
8 |
|i Enthalten in
|t Global change biology
|d 1999
|g 27(2021), 8 vom: 12. Apr., Seite 1547-1559
|w (DE-627)NLM098239996
|x 1365-2486
|7 nnns
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| 773 |
1 |
8 |
|g volume:27
|g year:2021
|g number:8
|g day:12
|g month:04
|g pages:1547-1559
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| 856 |
4 |
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|u http://dx.doi.org/10.1111/gcb.15518
|3 Volltext
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