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dc.contributor.authormarolla, Filippo
dc.contributor.authorHenden, John-André
dc.contributor.authorFuglei, Eva
dc.contributor.authorPedersen, Åshild Ønvik
dc.contributor.authorItkin, Mikhail
dc.contributor.authorIms, Rolf Anker
dc.date.accessioned2021-06-30T07:06:14Z
dc.date.available2021-06-30T07:06:14Z
dc.date.issued2021-01-14
dc.description.abstractTo 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) <i>explanatory predictions</i> to quantify the effect of potential drivers of ptarmigan population dynamics, and (2) <i>anticipatory predictions</i> 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.en_US
dc.identifier.citationmarolla, Henden, Fuglei, Pedersen, Itkin, Ims. Iterative model predictions for wildlife populations impacted by rapid climate change. Global Change Biology. 2021;27(8):1547-1559en_US
dc.identifier.cristinIDFRIDAID 1903190
dc.identifier.doi10.1111/gcb.15518
dc.identifier.issn1354-1013
dc.identifier.issn1365-2486
dc.identifier.urihttps://hdl.handle.net/10037/21626
dc.language.isoengen_US
dc.publisherWileyen_US
dc.relation.journalGlobal Change Biology
dc.relation.projectIDinfo:eu-repo/grantAgreement/RCN/FORINFRA/245638/Norway/Climate-Ecological Observatory for Arctic Tundra/COAT/en_US
dc.relation.projectIDinfo:eu-repo/grantAgreement/RCN/?/?/Norway//SUSTAIN/en_US
dc.relation.projectIDinfo:eu-repo/grantAgreement/RCN/?/?/Norway/the terrestrial flagship of FRAM—High North Research Centre for Climate and the Environment//en_US
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2021 The Author(s)en_US
dc.subjectVDP::Mathematics and natural science: 400en_US
dc.subjectVDP::Matematikk og Naturvitenskap: 400en_US
dc.titleIterative model predictions for wildlife populations impacted by rapid climate changeen_US
dc.type.versionpublishedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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