EXTRACTING MORE OUT OF RELOCATION DATA: BUILDING MOVEMENT MODELS AS MIXTURES OF RANDOM WALKS

By J. Morales, Daniel T. Haydon, Jacqui Frair, Kent E. Holsinger, J. Fryxell. 2004

820 citations recorded by the source corpus.

Subfields: Ecology, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/910b9a1c92c7bda629f8c8b4e1fb4b0152adf58b

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In Ecology

In Nature and Landscape Conservation

Citations in the corpus

The pitfalls of ignoring behaviour when quantifying habitat selection (2014, methodology): “…in discrete time (McClintock et al. 2012): ½Zt jZt 1 ¼ k Categoricalðwk;1;wk;2Þ and wk;i ¼ PrðZt ¼ ijZt 1 ¼ kÞ; where wk,i is the probability of switching from state k at time t 1 to state i at time t, and PMi¼1 wk;i ¼ 1: This is equivalent to the ‘double-switch’ model in Morales et al. (2004).”
Corridors for Conservation: Integrating Pattern and Process (2006, methodology): “…summarizing movement pathways with turning angles, fractal dimensions, and step lengths; (b) modeling movement with random walks or their variations (Turchin 1998); and, (c) identifying patterns in movement data retrospectively to distinguish different movement types (e.g., Morales et al. 2004).”
Competition, predation, and migration: Individual choice patterns of Serengeti migrants captured by hierarchical models (2014, methodology): “We analyzed data from 17 female migratory wildebeest fitted with GPS radio collars between the years 2000 and 2008 (except 2002 during a transition period between funders) and 13 female zebra from 2005 to 2008 (see Appendix A for summary of collar statistics and Appendix B for details on animal capt”
Spatial memory and animal movement. (2013, methodology): “…whether memory partly explains observed movement paths (e.g. Avgar et al. 2013a; Gautestad et al. 2013) or to segment heterogeneous movement data into different, internally homogeneous activity periods (e.g. resting, moving, foraging) that can be more easily analysed (e.g. Morales et al. 2004 ).”
Analyzing animal movements using Brownian bridges. (2007, methodology): “For example, Morales et al. (2004) used characteristics of observed paths (i.e., turning angles and movement distances) and Jonsen et al. (2005) used state–space models to identify different movement states of animals (e.g., encamped vs. exploratory).”
A MULTI-SCALE TEST OF THE FORAGE MATURATION HYPOTHESIS IN A PARTIALLY MIGRATORY UNGULATE POPULATION (2008, background): “…115.7 maximize intermediate n.a. maximize minimize May 2008 157UNGULATE MIGRATION AND FORAGE MATURATION (Mirik et al. 2005), by distinguishing foraging behavior from GPS collar data (Morales et al. 2005), or by examining diet selection at the individual plant level (Baker and Hobbs 1982).”
Ecological Models and Data in R (2008, background): “It has already begun to appear in ecological journals (Jonsen et al., 2003; Morales et al., 2004; McCarthy and Parris, 2004; Okuyama and Bolker, 2005; Parris, 2006; Vesk, 2006), but statisticians continue to debate its exact meaning and appropriateness (both Spiegelhalter et al.”
momentuHMM: R package for generalized hidden Markov models of animal movement (2017, methodology): “The most widely used 2-state HMMs for animal movement include “encamped” (or “foraging”) and “exploratory” (or “transit”) states characterized by area-restricted-search-type movements and migratory-type movements, respectively (Morales et al. 2004; Jonsen et al. 2005).”
Flexibility in daily travel routines causes regional variation in bird migration speed (2008, background): “Linking migration to landscape properties is important to reveal the mechanisms underlying the apparent general mathematical principles that govern animal movement (e.g. Turchin 1988; Morales et al. 2004 ).”
Animal movements in heterogeneous landscapes: identifying profitable places and homogeneous movement bouts. (2008, methodology): “Jonsen et al.’s (2005, 2007) State Space Model (SSM) is, to a large extent, similar to the HMM model used by Morales et al. (2004), and is also fitted to the data using WinBUGS.”
Understanding movements of organisms: it's time to abandon the Lévy foraging hypothesis (2015, methodology): “…such situations, animal movement can be modelled as a mixture of specified processes (e.g. random walks), with unknown defining parameters and switch points, and then these unknown variables can be estimated using Bayesian inferential statistics (Morales et al. 2004; McClintock et al. 2012, 2013).”
Do fences create an edge-effect on the movement patterns of a highly mobile mega-herbivore? (2010, background): “Landscape level movement patterns can reflect foraging behaviour (Fortin, 2003; Johnson et al., 2002; Morales et al., 2004) and can be a response to habitat quality and landscape configuration (Forester et al.”