2008
DOI: 10.1111/j.2007.0906-7590.05236.x
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Estimating space‐use and habitat preference from wildlife telemetry data

Abstract: Management and conservation of populations of animals requires information on where they are, why they are there, and where else they could be. These objectives are typically approached by collecting data on the animals' use of space, relating these positional data to prevailing environmental conditions and employing the resulting statistical models to predict usage at other geographical regions. Technical advances in wildlife telemetry have accomplished manifold increases in the amount and quality of availabl… Show more

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Cited by 387 publications
(511 citation statements)
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References 72 publications
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“…Hence, habitat accessibility is a function of distance from the colony (Matthiopoulos, 2003;Aarts et al, 2008), together with wind velocity and direction, which affect air speed and energy cost per unit time (Weimerskirch et al, 2000;Wakefield et al, 2009b). We assumed that accessibility may not vary linearly with distance (it may increase faster, per unit distance, at greater distances from the colony) because of the need to provide food for offspring at regular intervals.…”
Section: Accessibility Factormentioning
confidence: 99%
See 1 more Smart Citation
“…Hence, habitat accessibility is a function of distance from the colony (Matthiopoulos, 2003;Aarts et al, 2008), together with wind velocity and direction, which affect air speed and energy cost per unit time (Weimerskirch et al, 2000;Wakefield et al, 2009b). We assumed that accessibility may not vary linearly with distance (it may increase faster, per unit distance, at greater distances from the colony) because of the need to provide food for offspring at regular intervals.…”
Section: Accessibility Factormentioning
confidence: 99%
“…In all cases, we concluded the model fitted the data correctly and provided valuable insight into novel configurations of the predictors. The use of a Poisson-gamma GLM is a suitable alternative to the Generalized Additive Models (GAMs), also successfully used in these contexts (Aarts et al, 2008;Wakefield et al, 2011). GAMs are more flexible, but they lack a parametric formulation and so they are less useful for application with other datasets.…”
Section: Model Strengths and Weaknessesmentioning
confidence: 99%
“…In recent years, evolutionary ecologists have postulated that life-history trade-offs may lead to the evolution and maintenance of such variation [10][11][12][13], resulting in the prediction that different personality types should engage in different life-history strategies. There is empirical support to suggest that personality correlates with single life-history traits, such as survival or fecundity [12,[14][15][16], and some evidence that there is an interaction between age and personality on fitness, with bolder individuals having higher reproductive success in old age [17]. However, while life-history predicts that an individual's personality will correlate with the level of energy allocation in current versus future reproduction, and that high allocation corresponds to an investment involving increased senescence at old age [10 -12], to our knowledge, no study to date has examined the relationship between personality and reproductive senescence in the wild.…”
Section: Introductionmentioning
confidence: 99%
“…The female model had poor fit; consequently, models should be used with caution and corridors should be further validated with field data. In our analysis, we attempted to limit unrealistic predictions by bounding all variables to values only seen within the model; however, this does not compensate for changes in the proportion of each variable (Aarts et al 2008;Matthiopoulos et al 2011). For the current flow maps, we suspect that adding absolute barriers using the 99 th percentile of elephant locations produced more realistic predictions.…”
Section: Discussionmentioning
confidence: 99%