Abstract
The development of GPS tags for tracking wildlife has revolutionised the study of home ranges, habitat use and behaviour. Concomitantly, there have been rapid developments in methods for estimating habitat use from GPS data. In combination, these changes can cause challenges in choosing the best methods for estimating home ranges. In primatology, this issue has received little attention, as there have been few GPS collar-based studies to date. However, as advancing technology is making collaring studies more feasible, there is a need for the analysis to advance alongside the technology. Here, using a high quality GPS collaring data set from 10 proboscis monkeys (Nasalis larvatus), we aimed to: 1) compare home range estimates from the most commonly used method in primatology, the grid-cell method, with three recent methods designed for large and/or temporally correlated GPS data sets; 2) evaluate how well these methods identify known physical barriers (e.g. rivers); and 3) test the robustness of the different methods to data containing either less frequent or random losses of GPS fixes. Biased random bridges had the best overall performance, combining a high level of agreement between the raw data and estimated utilisation distribution with a relatively low sensitivity to reduced fixed frequency or loss of data. It estimated the home range of proboscis monkeys to be 24–165 ha (mean 80.89 ha). The grid-cell method and approaches based on local convex hulls had some advantages including simplicity and excellent barrier identification, respectively, but lower overall performance. With the most suitable model, or combination of models, it is possible to understand more fully the patterns, causes, and potential consequences that disturbances could have on an animal, and accordingly be used to assist in the management and restoration of degraded landscapes.
Highlights
The development of global positioning system (GPS) tags for tracking wildlife has revolutionised the study of home ranges, habitat use and behaviour [1]
Grid-cell method (GCM) utilisation distributions were clearly separated from the other three methods, which were usually very similar to one another: a-local convex hull nonparametric kernel method (LoCoH) and time local convex hull (T-LoCoH) models were generally concordant, whilst half of the biased random bridges (BRB) models overlapped the LoCoH models, and the remaining half were still closer to the LoCoH methods than to the GCMs (Fig 2)
Despite being commonly used in primatology, GCM is not recommended for determining home range boundaries, especially when animals cannot be followed intensively [18], or for a species that use areas with hard barriers, such as proboscis monkeys, who spend a large proportion of the time along water edges
Summary
The development of global positioning system (GPS) tags for tracking wildlife has revolutionised the study of home ranges, habitat use and behaviour [1]. The larger data sets and more frequent sampling intervals compared to traditional tracking technologies challenge the validity and feasibility of established data analysis methods, stimulating the development of new methods to reveal movement patterns, behaviour and estimate home ranges [3,4]. These are important developments, yet relatively little guidance is available to help researchers choose between them. Characterising these different aspects of home ranges, and understanding the processes of habitat selection, movement and activity patterns and how they respond to environmental and anthropogenic changes, are all important for the conservation management of wild populations [11,13,14]
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