Modelling animal movement as a persistent random walk in two dimensions: expected magnitude of net displacement

By Hsin-i Wu, Bai-lian Li, T. Springer, W. H. Neill. 2000

98 citations recorded by the source corpus.

Subfields: Genetics, Modeling and Simulation, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/0e1ea697ecc368dd34214af0c8aeba8dfd544d2b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Genetics

1. Sergei V. Petrovskii
2. Michael J. Plank
3. W. J. Sutherland
4. Bai-lian Li
5. Emmanuel Paradis
6. Horst Malchow
7. Edward A. Codling
8. S. Benhamou

In Modeling and Simulation

1. Sergei V. Petrovskii
2. Michael J. Plank
3. Edward A. Codling
4. S. Benhamou

In Molecular Biology

1. Michael J. Plank
2. Edward A. Codling
3. Emmanuel Paradis
4. S. Benhamou
5. G. Pyke
6. Liam J. Revell
7. David D. Ackerly
8. Nathan J B Kraft

Citations in the corpus

• Active Brownian particles (2012, methodology): “So we can solve the sum in (200) and get a formula for the mean squared displacement after n steps of a 2D correlated random walk like in [247] and [168], which was derived previously by Kareiva and Shigesada [182]:”
• Random walk models in biology (2008, background): “6) causes problems and means that calculating the MDD directly is mathematically very difficult (McCulloch & Cain 1989; Wu et al. 2000; Byers 2001).”
• Modeling large-scale dispersal distances (2002, background): “However, it seems possible to derive the probability distributions of dispersal distances from Wu et al. (2000) model, making thus possible the likelihood approach presented here.”
• A state-space model to derive bluefin tuna movement and habitat from archival tags (2005, methodology): “constant speed distribution), it is possible to relate such a model to the standard diffusion by computing the expected square displacement using the formulae proposed by Wu et al. (2000).”
• Spatiotemporal Complexity of Plankton and Fish Dynamics (2002, background): “The motion of fish schools is controlled by various biotic and abiotic environmental factors such as light, temperature, salinity, and nutrient (e.g., plankton) supply (Radakov, 1973; Bocharov, 1990; Wu et al., 2000).”
• Modeling animal movements using stochastic differential equations (2004, background): “…to dispersal, home ranges, foraging strategies, habitat grain, natural disturbance and herbivory (Turner et al., 1993; Gross et al., 1995; Focardi et al., 1996; Lima and Zollner, 1996; Carter and Finn, 1999; Moorcroft et al., 1999; Zollner and Lima, 1999; Bergman et al., 2000; Wu et al., 2000).”