Behavioural adjustments of predators and prey to wind speed in the boreal forest

Studd, Emily K.; Peers, Michael J. L.; Menzies, Allyson K.; Derbyshire, Rachael; Majchrzak, Yasmine N.; Seguin, Jacob L.; Murray, Dennis L.; Dantzer, Ben; Lane, Jeffrey E.; McAdam, Andrew G.; Humphries, Murray M.; Boutin, Stan

doi:10.1007/s00442-022-05266-w

Cited by 3 publications

(1 citation statement)

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“…The snowshoe hare, a key prey species in the North American boreal forests [31,32], is a suitable species for the study because it is primarily active at night and inactive during the day (figure 1a) [33]. Hares are known to adjust foraging rates in response to perceived risk at various time scales [33][34][35][36], implying that hares perceive foraging as risky and thus a clear correlation between their activity and predation is expected. Hares are consumed by a diverse predator assemblage with different diel activity patterns [37][38][39] including diurnal northern goshawks (Accipiter gentilis) [40], nocturnal great horned owls (Bubo virginianus) [41], and diel-flexible (i.e.…”

Section: Introductionmentioning

confidence: 99%

When death comes: linking predator–prey activity patterns to timing of mortality to understand predation risk

et al. 2023

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The assumption that activity and foraging are risky for prey underlies many predator–prey theories and has led to the use of predator–prey activity overlap as a proxy of predation risk. However, the simultaneous measures of prey and predator activity along with timing of predation required to test this assumption have not been available. Here, we used accelerometry data on snowshoe hares ( Lepus americanus ) and Canada lynx ( Lynx canadensis ) to determine activity patterns of prey and predators and match these to precise timing of predation. Surprisingly we found that lynx kills of hares were as likely to occur during the day when hares were inactive as at night when hares were active. We also found that activity rates of hares were not related to the chance of predation at daily and weekly scales, whereas lynx activity rates positively affected the diel pattern of lynx predation on hares and their weekly kill rates of hares. Our findings suggest that predator–prey diel activity overlap may not always be a good proxy of predation risk, and highlight a need for examining the link between predation and spatio-temporal behaviour of predator and prey to improve our understanding of how predator–prey behavioural interactions drive predation risk.

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Section: Introductionmentioning

confidence: 99%

When death comes: linking predator–prey activity patterns to timing of mortality to understand predation risk

et al. 2023

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Food‐safety trade‐offs drive dynamic behavioural antipredator responses among snowshoe hares

Shiratsuru,

Pauli

2024

Journal of Animal Ecology

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Prey adopt various antipredator responses to minimize the risk of predation, and the fitness costs of antipredator responses can have emergent effects on the population dynamics of prey species. While the trade‐off between food acquisition and predation avoidance has long been recognized in predicting antipredator responses, less attention has been paid to the dynamics of the food‐safety trade‐off driven by temporal variation in multiple risk factors under changing seasonal conditions. Here, we monitored foraging and vigilance behaviour of a central prey species, snowshoe hares (Lepus americanus), at fine temporal scales over the winter with various types of predation risk, while also experimentally manipulating predation risk by attracting predators to foraging patches. Hares increased foraging and decreased vigilance over the winter, but hares under chronic risk decreased their antipredator efforts to a lesser degree, indicating that those individuals prioritized risk avoidance over food acquisition. Hares also decreased foraging and increased antipredator efforts in response to the temporal activity of predators and environmental cues of predation risk. However, the magnitude of the responses to the environmental cues was mediated by time of winter. While we did not detect a reactive response of hares to acute risk, we did find that hares exhibiting camouflage mismatch proactively increased vigilance. Overall, our results highlight the importance of species‐specific traits and changing seasonal conditions in addition to temporal variation in multiple risk factors in predicting antipredator responses and the context dependence of risk effects.

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A benchmark for computational analysis of animal behavior, using animal-borne tags

Hoffman,

Cusimano,

Baglione

et al. 2024

Mov Ecol

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Background Animal-borne sensors (‘bio-loggers’) can record a suite of kinematic and environmental data, which are used to elucidate animal ecophysiology and improve conservation efforts. Machine learning techniques are used for interpreting the large amounts of data recorded by bio-loggers, but there exists no common framework for comparing the different machine learning techniques in this domain. This makes it difficult to, for example, identify patterns in what works well for machine learning-based analysis of bio-logger data. It also makes it difficult to evaluate the effectiveness of novel methods developed by the machine learning community. Methods To address this, we present the Bio-logger Ethogram Benchmark (BEBE), a collection of datasets with behavioral annotations, as well as a modeling task and evaluation metrics. BEBE is to date the largest, most taxonomically diverse, publicly available benchmark of this type, and includes 1654 h of data collected from 149 individuals across nine taxa. Using BEBE, we compare the performance of deep and classical machine learning methods for identifying animal behaviors based on bio-logger data. As an example usage of BEBE, we test an approach based on self-supervised learning. To apply this approach to animal behavior classification, we adapt a deep neural network pre-trained with 700,000 h of data collected from human wrist-worn accelerometers. Results We find that deep neural networks out-perform the classical machine learning methods we tested across all nine datasets in BEBE. We additionally find that the approach based on self-supervised learning out-performs the alternatives we tested, especially in settings when there is a low amount of training data available. Conclusions In light of these results, we are able to make concrete suggestions for designing studies that rely on machine learning to infer behavior from bio-logger data. Therefore, we expect that BEBE will be useful for making similar suggestions in the future, as additional hypotheses about machine learning techniques are tested. Datasets, models, and evaluation code are made publicly available at https://github.com/earthspecies/BEBE, to enable community use of BEBE.

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Behavioural adjustments of predators and prey to wind speed in the boreal forest

Cited by 3 publications

References 61 publications

When death comes: linking predator–prey activity patterns to timing of mortality to understand predation risk

When death comes: linking predator–prey activity patterns to timing of mortality to understand predation risk

Food‐safety trade‐offs drive dynamic behavioural antipredator responses among snowshoe hares

A benchmark for computational analysis of animal behavior, using animal-borne tags

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