A cross-validation-based approach for delimiting reliable home range estimates

Abstract

Background With decreasing costs of GPS telemetry devices, data repositories of animal movement paths are increasing almost exponentially in size. A series of complex statistical tools have been developed in conjunction with this increase in data. Each of these methods offers certain improvements over previously proposed methods, but each has certain assumptions or shortcomings that make its general application difficult. In the case of the recently developed Time Local Convex Hull (T-LoCoH) method, the subjectivity in parameter selection serves as one of the primary impediments to its more widespread use. While there are certain advantages to the flexibility it offers for question-driven research, the lack of an objective approach for parameter selection may prevent some users from exploring the benefits of the method.Results Here we present a cross-validation-based approach for selecting parameter values to optimize the T-LoCoH algorithm. We demonstrate the utility of the approach using a case study from the Etosha National Park anthrax system. Utilizing the proposed algorithm, rather than the guidelines in the T-LoCoH documentation, results in significantly different values for derived site fidelity metrics.Conclusions Due to its basis in principles of cross-validation, the application of this method offers a more objective approach than the relatively subjective guidelines set forth in the T-LoCoH documentation and enables a more accurate basis for the comparison of home ranges among individuals and species, as well as among studies.Keywords: Time Local Convex Hulls; T-LoCoH; Home Range; Epidemiology; Visitation; Duration; Cross-validation BackgroundDramatic advancements in GPS telemetry devices have enabled researchers to gain a more comprehensive understanding of animal movement behaviors [1]. The decreasing costs of such devices have resulted in their widespread deployment and a capacity for data collection at unprecedented spatial and temporal resolutions [2]. Movement ecology has emerged as a discipline in its own right [3], with numerous methods and tools being developed and disseminated to analyze the wealth of available data. Ecologists can now quantitatively characterize home ranges and space use patterns over time. Often, the purpose of applying such quantification methods to movement paths is comparison of space use among individuals or species in order to examine such processes as niche partitioning [4,5], optimal foraging [6,7], social aggregation [8], or even decision-making [9]. However, many methods require user-defined input parameters, and results are often highly sensitive to the selec-. CC-BY-ND 4.0 International license peer-reviewed) is the author/funder. It is made available under a The copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/168021 doi: bioRxiv preprint first posted online Jul. 25, 2017; Dougherty et al. Page 2 of 15tion of such values. For meaningful comparisons, standardization is required [10], yet protocols to achieve consistency acros...

Going through the motions: incorporating movement analyses into disease research

“…Two such metrics are the duration of a visit to a particular point or area of interest, known as the residence time, and the rate at which individuals return to them, known as the visitation or return rate. Used together, these metrics can offer a means of evaluating the relative risk of contact or exposure among individuals (Dougherty et al 2017). Site-fidelity metrics such as these could be particularly important in the case of indirectly transmitted pathogens because high levels of fidelity increase exposure risk if an infectious reservoir is present in the range but will buffer an individual from exposure if the range is free of relevant pathogens or parasites.…”

A comprehensive analysis of autocorrelation and bias in home range estimation

“…Note how previous conclusions on the influence of autocorrelation on home range estimation were based on weakly autocorrelated data that are not representative of the majority of modern GPS data. while others have simply shown empirical results without reference to their accuracy (e.g., De Solla et al 1999, Dougherty et al 2017. In the absence of the latter, researchers have been encouraged to obtain a threshold number of locations (Seaman et al 1999, Girard et al 2002) or days (e.g., B€ orger et al 2006, but the precise conditions under which autocorrelation and movement behavior can a priori be expected to influence these thresholds have remained obscure.…”

Spores and soil from six sides: interdisciplinarity and the environmental biology of anthrax (Bacillus anthracis)

“…Similarly, interspecific variation in movement still requires investigation, given the wealth of movement data collected in Etosha over the past two decades (Lyons, Turner & Getz, 2013;Mashintonio et al, 2014;Polansky, Kilian & Wittemyer, 2015;Dougherty et al, 2017Dougherty et al, , 2018aZidon et al, 2017); for example, elephants largely migrate away from the known anthrax areas of Etosha during the anthrax season, and return in the dry season. Intraspecific variation also requires further investigation; evidence suggests that there may be a link between partial migration of zebra herds in Etosha and avoidance of the anthrax season.…”

“…It has been suggested that this phenomenon could be linked to dominance structure, as dominant groups migrate, while resident (submissive) herds are encouraged to stay by decreased competition (Zidon et al, 2017). Movement data from Etosha has already been used to help develop anthrax-relevant analytic tools (Dougherty et al, 2017) and simulations (Dougherty et al, 2018a), and while some work in other systems has used movement data and tools to help map the link between environmental suitability and host exposure (Morris et al, 2016), similar work is still needed in Etosha [as it is needed in any system with the potential for transmission at the wildlife-livestock interface, where these tools are often the most useful for answering applied questions (Dougherty et al, 2018b)].…”