Accelerometery is revolutionising the field of behavioural ecology through its capacity to detect the fine-scale movements of animals resulting from their behaviour. Because it is often difficult to infer the behaviour of wildlife on a continuous basis, particularly for cryptic species, accelerometers potentially provide powerful tools for remote monitoring of their behavioural responses to the environment. The goal of this study was to provide a detailed, calibrated methodology, including practical guidelines, to infer the behaviour of free-ranging animals from acceleration data. This approach can be employed to reliably infer the time budget of species that are difficult to observe in certain environments or at certain times of the day. To this end, we trained several behavioural classification algorithms with accelerometer data obtained on captive roe deer, then validated these algorithms with data obtained on free-ranging roe deer, and finally predicted the time-budgets of a substantial sample of unobserved free-ranging roe deer in a human-dominated landscape. The best classification algorithm was the Random Forest which predicted five behavioural classes with a high overall level of accuracy (approx. 90%). Except for grooming (34-38%), we were able to predict the behaviour of free-ranging roe deer over the course of a day with high accuracy, in particular, foraging head down, running, walking and standing (68-94%). Applied to free-ranging individuals, the classification allowed us to estimate, for example, that roe deer spent about twice as much time foraging head-down, walking or running during dawn and dusk than during daylight or nighttime. By integrating step by step calibration and validation of accelerometer data prior to application in the wild, our approach is transferable to other free-ranging animals for predicting key behaviours in cryptic species.
The behavioural trade-off between foraging and risk avoidance is expected to be particularly acute during gestation and lactation, when the energetic demands of reproduction peak. We investigated how female roe deer, an income breeding ungulate, adjust their management of this trade-off during the birth period in terms of foraging activity and habitat use. We showed that activity levels of reproductive females more than doubled immediately following parturition, when energy demand is highest. Moreover, reproductive females increased their use of open habitat during daytime and ranged closer to roads, but slightly further from refuge woodland, compared to non-reproductive females. However, these post-partum modifications in behaviour were particularly pronounced in late-parturient females who adopted a more risk prone tactic, presumably to compensate for the fitness handicap of their late-born offspring. In income breeders, individuals that give birth late may be forced to trade risk avoidance for resource acquisition during peak allocation to reproduction, likely with significant fitness consequences.
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