A General Framework for the Analysis of Animal Resource Selection from Telemetry Data

Johnson, Devin S.; Thomas, Dana L.; Hoef, Jay M. Ver; Christ, Aaron M.

doi:10.1111/j.1541-0420.2007.00943.x

Cited by 116 publications

(201 citation statements)

References 24 publications

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“…Furthermore, the ecological justification for application of the methodology for dealing with the bias data does not necessarily apply to wildlife since successive locations from free roaming animals may have variable distances, times, and related predictor variables. The ongoing development of statistical and modeling techniques to capitalize on the lack of independence between successive GPS fixes from free ranging wildlife in habitat and space use studies [50], will refine the ability to develop ecologically meaningful habitat models and to deal with the large amount of data accrued by GPS collars.…”

Section: Discussionmentioning

confidence: 99%

GPS Bias Correction and Habitat Selection by Mountain Goats

et al. 2011

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Abstract:In Washington State, USA, mountain goats (Oreamnos americanus) have experienced a long-term population decline. To assist management, we created annual and seasonal (summer and winter) habitat models based on 2 years of data collected from 38 GPS-collared (GPS plus collar v6, Vectronic-Aerospace GmbH, Berlin, Germany) mountain goats in the western Cascades. To address GPS bias of position acquisition, we evaluated habitat and physiographic effects on GPS collar performance at 543 sites in the Cascades. In the western Cascades, total vegetation cover and the quadratic mean diameter of trees were shown to effect GPS performance. In the eastern Cascades, aspect and total vegetation cover were found to influence GPS performance. To evaluate the influence of bias correction on the analysis of habitat selection, we created resource selection functions with and without bias correction for mountain goats in the western Cascades. We examined how well the resultant habitat models performed with reserved data (25% of fixes from 38 study animals) and with data from 9 other GPS-collared mountain goats that were both temporally and spatially independent. The statistical properties of our GPS bias correction model were similar to those previously reported explaining between 20 and 30% of the variation, however, application of bias correction improved the accuracy of the mountain goat habitat model by only 1-2% on average and did not alter parameter estimates in a meaningful, or consistent manner. Despite statistical limitations, our habitat models, most OPEN ACCESSRemote Sens. 2011, 3 436 notably during the winter, provided the widest extent and most detailed models of the distribution of mountain goat habitat in the Cascades yet developed.

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Section: Discussionmentioning

confidence: 99%

GPS Bias Correction and Habitat Selection by Mountain Goats

et al. 2011

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“…Resource selection functions typically are fit in a use-availability framework, whereby environmental covariates (e.g., elevation) at the locations where the animal was present (the used locations) are contrasted with covariates at random locations taken from an area deemed to be available for selection (the availability sample [Manly et al 2002, Johnson et al 2006). Such methods are inherently based on models for spatial point processes (as are many species distribution models; e.g., Warton and Shepherd [2010]), however logistic regression, which asymptotically approximates a point process model (Johnson et al 2006, Aarts et al 2012, typically is used to estimate coefficients (but see Baddeley and Turner [2000], Lele and Keim [2006], Johnson et al [2008], and Aarts et al [2012] for alternate approaches). Logistic regression allows researchers to easily obtain inference on selection or avoidance of covariates and to generate maps for use in subsequent analysis (Boyce and McDonald 1999).…”

Section: Introductionmentioning

confidence: 99%

Practical guidance on characterizing availability in resource selection functions under a use–availability design

Northrup

Hooten

Anderson

et al. 2013

Ecology

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Abstract. Habitat selection is a fundamental aspect of animal ecology, the understanding of which is critical to management and conservation. Global positioning system data from animals allow fine-scale assessments of habitat selection and typically are analyzed in a useavailability framework, whereby animal locations are contrasted with random locations (the availability sample). Although most use-availability methods are in fact spatial point process models, they often are fit using logistic regression. This framework offers numerous methodological challenges, for which the literature provides little guidance. Specifically, the size and spatial extent of the availability sample influences coefficient estimates potentially causing interpretational bias. We examined the influence of availability on statistical inference through simulations and analysis of serially correlated mule deer GPS data. Bias in estimates arose from incorrectly assessing and sampling the spatial extent of availability. Spatial autocorrelation in covariates, which is common for landscape characteristics, exacerbated the error in availability sampling leading to increased bias. These results have strong implications for habitat selection analyses using GPS data, which are increasingly prevalent in the literature. We recommend that researchers assess the sensitivity of their results to their availability sample and, where bias is likely, take care with interpretations and use cross validation to assess robustness.

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“…This approach allows for nonlinear models, but the forms of nonlinearity are restricted to exponential and logistic functions, and the movement response is based on a single probability density. Recently, animal movement models that combine movement, resource selection, and home range of an animal have been developed (e.g., Dalziel et al 2008, Johnson et al 2008, Forester et al 2009). Most of these models incorporate covariates in a probability density function of bivariate locations rather than turn angles and move length, and it is not always straightforward to assess the effect of landscape features.…”

Section: Introductionmentioning

confidence: 99%

Mapping behavioral landscapes for animal movement: a finite mixture modeling approach

Tracey

Zhu

Boydston

et al. 2013

Ecological Applications

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Abstract. Because of its role in many ecological processes, movement of animals in response to landscape features is an important subject in ecology and conservation biology. In this paper, we develop models of animal movement in relation to objects or fields in a landscape. We took a finite mixture modeling approach in which the component densities are conceptually related to different choices for movement in response to a landscape feature, and the mixing proportions are related to the probability of selecting each response as a function of one or more covariates. We combined particle swarm optimization and an expectationmaximization (EM) algorithm to obtain maximum-likelihood estimates of the model parameters. We used this approach to analyze data for movement of three bobcats in relation to urban areas in southern California, USA. A behavioral interpretation of the models revealed similarities and differences in bobcat movement response to urbanization. All three bobcats avoided urbanization by moving either parallel to urban boundaries or toward less urban areas as the proportion of urban land cover in the surrounding area increased. However, one bobcat, a male with a dispersal-like large-scale movement pattern, avoided urbanization at lower densities and responded strictly by moving parallel to the urban edge. The other two bobcats, which were both residents and occupied similar geographic areas, avoided urban areas using a combination of movements parallel to the urban edge and movement toward areas of less urbanization. However, the resident female appeared to exhibit greater repulsion at lower levels of urbanization than the resident male, consistent with empirical observations of bobcats in southern California. Using the parameterized finite mixture models, we mapped behavioral states to geographic space, creating a representation of a behavioral landscape. This approach can provide guidance for conservation planning based on analysis of animal movement data using statistical models, thereby linking connectivity evaluations to empirical data.

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A General Framework for the Analysis of Animal Resource Selection from Telemetry Data

Cited by 116 publications

References 24 publications

GPS Bias Correction and Habitat Selection by Mountain Goats

GPS Bias Correction and Habitat Selection by Mountain Goats

Practical guidance on characterizing availability in resource selection functions under a use–availability design

Mapping behavioral landscapes for animal movement: a finite mixture modeling approach

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