Estimating space‐use and habitat preference from wildlife telemetry data

By G. Aarts, Monique Lea Mackenzie, Bernie McConnell, M. Fedak, Jason Matthiopoulos. 2008

480 citations recorded by the source corpus.

Subfields: Ecology

View Paper: https://www.semanticscholar.org/paper/bfc76b5ee9a5ba7ab2e36e3faad5d523132bffb5

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In Ecology

Citations in the corpus

Establishing the link between habitat selection and animal population dynamics (2015, methodology): “Here and elsewhere (Aarts et al. 2008, Matthiopoulos et al. 2011), we define a habitat x as a point in E-space, the combination of specific values for K environmental August 2015 415LINKING HABITAT TO POPULATION GROWTH variables (e.g., geomorphology and climate variables combining into the…”
Quantifying habitat use and preferences of pelagic seabirds using individual movement data: a review (2009, methodology): “…penguins, pinnipeds, fish and turtles, which have also been the subject of recent modeling studies (Jonsen et al. 2003, Royer et al. 2005, Jonsen et al. 2007, Aarts et al. 2008, Eckert et al. 2008, Gurarie et al. 2009) are more similar to pelagic seabirds in that they travel through a fluid medium.”
Functional connectivity within conservation networks: Delineating corridors for African elephants (2013, background): “…Reserve is connected by the Selous-Niassa Corridor to Selous Game Reserve in southern Tanzania (Hofer et al. 2004; Figure 3-1), and elephants in this region could benefit from being connected to and managed collectively with those in the Niassa and Selous Game Reserves (see Baldus et al. 2003).”
Basking sharks and oceanographic fronts: quantifying associations in the north‐east Atlantic (2015, background): “The use of presence-only, serially autocorrelated tracking data to infer habitat preference has inherent complications (Aarts et al. 2008; Warton & Aarts 2013).”
Generalized estimating equations and generalized linear mixed-effects models for modelling resource selection (2009, background): “2002) biases results towards data-rich individuals (Gillies et al. 2006; Aarts et al. 2008), if data are not missing at random.”
Circumpolar habitat use in the southern elephant seal: implications for foraging success and population trajectories (2016, background): “The development of miniaturized logging and satellite- linked monitoring equipment that can be attached to marine animals has revolutionized how we gather information in extreme environments like the Southern Ocean (Aarts et al. 2008).”
Practical guidance on characterizing availability in resource selection functions under a use-availability design. (2013, background): “However, these methods offer a number of methodological challenges (e.g., Aarts et al. 2008).”
Comparative interpretation of count, presence–absence and point methods for species distribution models (2012, background): “2005; Webb, Hebblewhite & Merrill 2008) and address questions regarding how preference for environmental conditions may depend on the characteristics of individuals under study (Aarts et al. 2008).”
Correlation and studies of habitat selection: problem, red herring or opportunity? (2010, methodology): “2006; Aarts et al. 2008; Fortin et al. 2009; Godvik et al. 2009). Gillies et al. (2006) suggested adding random”
The influences of wolf predation, habitat loss, and human activity on caribou and moose in the Alberta oil sands (2011, unknown): “Constraining availability in this way is common in resource selection studies (Arts et al. 2008).”
Habitat modelling of tracking data from multiple marine predators identifies important areas in the Southern Indian Ocean (2018, methodology): “We adopted the aforementioned case–control design (Aarts et al., 2008), where location estimates from a real track were treated as presences (1) and the pseudo- locations from the 20 simulated tracks were treated as habitat potentially available to an individual (0).”
Sexual segregation in a wide-ranging marine predator is a consequence of habitat selection (2015, methodology): “HSFs compare habitat usage to availability using a logistic-regression 192 based approach with a case-control design (Aarts et al. 2008).”