2008
DOI: 10.1890/08-0162.1
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Animal Movements in Heterogeneous Landscapes: Identifying Profitable Places and Homogeneous Movement Bouts

Abstract: Because of the heterogeneity of natural landscapes, animals have to move through various types of areas that are more or less suitable with respect to their current needs. The locations of the profitable places actually used, which may be only a subset of the whole set of suitable areas available, are usually unknown, but can be inferred from movement analysis by assuming that these places correspond to the limited areas where the animals spend more time than elsewhere. Identifying these intensively used areas… Show more

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Cited by 284 publications
(328 citation statements)
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“…We used residence time (RT [48]) to identify ARS bouts in all foraging tracks (adehabitatLT R package [49]). To avoid artificial inflation of RTs, we excluded tracking locations recorded during hours of darkness (because gannets are diurnal foragers) and all locations within a radius of 1 km of the colony (because gannets do not forage here but do frequently rest on the water).…”
Section: Behavioural Classificationmentioning
confidence: 99%
See 1 more Smart Citation
“…We used residence time (RT [48]) to identify ARS bouts in all foraging tracks (adehabitatLT R package [49]). To avoid artificial inflation of RTs, we excluded tracking locations recorded during hours of darkness (because gannets are diurnal foragers) and all locations within a radius of 1 km of the colony (because gannets do not forage here but do frequently rest on the water).…”
Section: Behavioural Classificationmentioning
confidence: 99%
“…[44]; average scale of search 9.1 + 1.9 km, with nested finer-scale search at 1.5 + 0.8 km). We used RT at each interpolated location to distinguish ARS from transit using an approach based on Lavielle segmentation [48], using both the mean and variance of each series with an 'Lmin' value of 3 (minimum number of observations in each segment) and a 'Kmax' value of 10 (maximum number of segments in movement burst; electronic supplementary material, figure S1). We classified segments as periods of ARS or transit using a custom-written R function that identifies each segment as either above or below a threshold of RT (seconds), with thresholds specified as mean values across all trips at each radius, resulting in a binary response variable (i.e.…”
Section: Behavioural Classificationmentioning
confidence: 99%
“…That individual movement trajectories represent an amalgamation of different behavioural states is increasingly recognized [22]. A useful biological characterization of a particular behavioural state is the mean (determinism) and variance (stochasticity) of Figure 1.…”
Section: Pairwise Movement Synchrony and A Movement Coherence Spectrummentioning
confidence: 99%
“…These percentiles were chosen because they represent the core area of use and the home-range of an individual respectively (Burt 1943, Barraquand & Benhamou 2008. This matrix of overlapping proportions is asymmetrical which means that, for example, animal id13"s 50% isopleth overlaps 20% (0.2) with animal id28, but id28"s 50%…”
Section: Inter-individual Foraging Site Fidelity: Differences In Corementioning
confidence: 99%