Dataset Information

Migratory restlessness in captive individuals predicts actual departure in the wild.

ABSTRACT: In captivity, migratory birds show increased activity during the time that they would normally migrate. The phenology and intensity of such 'migratory restlessness' has been shown to mirror species- and population-specific migration patterns observed in the wild and has consequently been used as a proxy for the motivation to migrate. Many studies doing so, however, were aiming to explain among-individual variation in migratory behaviour or traits, and not species- or population-specific traits. These studies thus assumed that, also at the level of the individual, migratory restlessness is an accurate proxy for the motivation to migrate. We tested this assumption for the first time and found that it holds; individuals showing very little migratory restlessness remained at stopover for longer than one night, whereas most individuals showing more restlessness departed sooner. This finding validates the use of migratory restlessness as a proxy for the motivation to migrate, thereby justifying the conclusions made in a large body of research on avian migration.

SUBMITTER: Eikenaar C

PROVIDER: S-EPMC4013702 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Migratory restlessness in captive individuals predicts actual departure in the wild.

Eikenaar Cas C Klinner Thomas T Szostek K Lesley KL Bairlein Franz F

Biology letters 20140409 4

In captivity, migratory birds show increased activity during the time that they would normally migrate. The phenology and intensity of such 'migratory restlessness' has been shown to mirror species- and population-specific migration patterns observed in the wild and has consequently been used as a proxy for the motivation to migrate. Many studies doing so, however, were aiming to explain among-individual variation in migratory behaviour or traits, and not species- or population-specific traits. ...[more]

PMID: 24718095

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Project description:1. Remotely tracking distinct behaviours of animals using acceleration data and machine learning has been carried out successfully in several species in captive settings. In order to study the ecology of animals in natural habitats, such behaviour classification models need to be transferred to wild individuals. However, at present, the development of those models usually requires direct observation of the target animals. 2. The goal of this study was to infer the behaviour of wild, free-roaming animals from acceleration data by training behaviour classification models on captive individuals, without the necessity to observe their wild conspecifics. We further sought to develop methods to validate the credibility of the resulting behaviour extrapolations. 3. We trained two machine learning algorithms proposed by the literature, Random Forest (RF) and Support Vector Machine (SVM), on data from captive red foxes (Vulpes vulpes) and later applied them to data from wild foxes. We also tested a new advance for behaviour classification, by applying a moving window to an Artificial Neural Network (ANN). Finally, we investigated four strategies to validate our classification output. 4. While all three machine learning algorithms performed well under training conditions (Kappa values: RF (0.82), SVM (0.78), ANN (0.85)), the established methods, RF and SVM, failed in classifying distinct behaviours when transferred from captive to wild foxes. Behaviour classification with the ANN and a moving window, in contrast, inferred distinct behaviours and showed consistent results for most individuals. 5. Our approach is a substantial improvement over the methods previously proposed in the literature as it generated plausible results for wild fox behaviour. We were able to infer the behaviour of wild animals that have never been observed in the wild and to further illustrate the credibility of the output. This framework is not restricted to foxes but can be applied to infer the behaviour of many other species and thus empowers new advances in behavioural ecology.

Dataset Information

Migratory restlessness in captive individuals predicts actual departure in the wild.

Publications

Migratory restlessness in captive individuals predicts actual departure in the wild.

Similar Datasets

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