Associations between space-use behaviour and temperature-humidity index in barn-housed dairy cows - Kareemah Chopra; Chenyu Zhang; Chunde Liu; Zhiwen Luo; Christopher K. Reynolds; Jonathan R. Amory; Zoe E. Barker; Andy Thurman; Edward A. Codling (Applied Animal Behaviour Science, 2026)

Heat stress monitoring, modelling, and mitigation in a dairy cattle building in reading, UK: Impacts of current and projected heatwaves - Chunde Liu; Yiran Cao; Zhiwen Luo; Yiqing Liu; Christopher K. Reynolds; David Humphries; Chenyu Zhang; Edward A. Codling; Kareemah Chopra; Jonathan Amory; Zoe Barker (Building and Environment, 2025)

Navigational efficiency in a biased and correlated random walk model of individual animal movement - Joseph D. Bailey; Jamie Wallis; Edward A. Codling (Ecology, 2018)
Understanding how an individual animal is able to navigate through its environment is a key question in movement ecology that can give insight into observed movement patterns and the mechanisms behind them. Efficiency of navigation is important for behavioral processes at a range of different spatio‐temporal scales, including foraging and migration. Random walk models provide a standard framework for modeling individual animal movement and navigation. Here we consider a vector‐weighted biased and correlated random walk ( BCRW ) model for directed movement (taxis), where external navigation cues are balanced with forward persistence. We derive a mathematical approximation of the expected navigational efficiency for any BCRW of this form and confirm the model predictions using simulations. We demonstrate how the navigational efficiency is related to the weighting given to forward persistence and external navigation cues, and highlight the counter‐intuitive result that for low (but realistic) levels of error on forward persistence, a higher navigational efficiency is achieved by giving more weighting to this indirect navigation cue rather than direct navigational cues. We discuss and interpret the relevance of these results for understanding animal movement and navigation strategies.

Distinguishing between Lévy walks and strong alternative models: comment - Marie Auger-Méthé; Michael J. Plank; Edward A. Codling (Ecology, 2014)

Classifying area-restricted search (ARS) using a partial sum approach - Andrew S. Knell; Edward A. Codling (Theoretical Ecology, 2012)

Turn designation, sampling rate and the misidentification of power laws in movement path data using maximum likelihood estimates - Edward A. Codling; Michael J. Plank (Theoretical Ecology, 2011)

Diffusion about the mean drift location in a biased random walk - Edward A. Codling; Rachel N. Bearon; Graeme J. Thorn (Ecology, 2010)
Random walks are used to model movement in a wide variety of contexts: from the movement of cells undergoing chemotaxis to the migration of animals. In a two‐dimensional biased random walk, the diffusion about the mean drift position is entirely dependent on the moments of the angular distribution used to determine the movement direction at each step. Here we consider biased random walks using several different angular distributions and derive expressions for the diffusion coefficients in each direction based on either a fixed or variable movement speed, and we use these to generate a probability density function for the long‐time spatial distribution. We demonstrate how diffusion is typically anisotropic around the mean drift position and illustrate these theoretical results using computer simulations. We relate these results to earlier studies of swimming microorganisms and explain how the results can be generalized to other types of animal movement.

On scientists’ discomfort in fisheries advisory science: the example of simulation‐based fisheries management‐strategy evaluations - Sarah B M Kraak; Ciaran J Kelly; Edward A Codling; Emer Rogan (Fish and Fisheries, 2010)
Scientists feel discomfort when they are asked to create certainty, where none exists, for use as an alibi in policy‐making. Recently, the scientific literature has drawn attention to some pitfalls of simulation‐based fisheries management‐strategy evaluation (MSE). For example, while estimates concerning central tendencies of distributions of simulation outcomes are usually fairly robust because they are conditioned on ample data, estimates concerning the tails of distributions (such as the probability of falling below a critical biomass) are usually conditional on few data and thus often rely on assumptions that have no strong knowledge base. The clients of scientific advice, such as the European Commission, are embracing the mechanization of the evaluation of proposed Harvest Control Rules against the precautionary principle and management objectives. Where the fisheries management institutions aim for simple answers from the scientists, giving ‘green/red light’ to a proposed management strategy, the scientists are forced into a split position between satisfying the demands of their advisory role and living up to the standards of scientific rigour. We argue against the mechanization of scientific advice that aims to incorporate all relevant processes into one big model algorithm that, after construction, can be run without circumspection. We rather encourage that fisheries advice should be a dynamic process of expert judgement, incorporating separate parallel concurrent, lines of scientific evidence, from quantitative and qualitative modelling exercises and factual knowledge of the biology and the fishery dynamics. This process can be formalized to a certain degree and can easily accommodate stakeholder viewpoints.

Sampling rate and misidentification of Lévy and non‐Lévy movement paths - Michael J. Plank; Edward A. Codling (Ecology, 2009)
A large number of empirical studies have attributed Lévy search patterns to the foraging movements of animals. Typically, this is done by fitting a power‐law distribution with an exponent of 1

Individual-based movement behaviour in a simple marine reserve—fishery system: why predictive models should be handled with care - Edward A. Codling (Hydrobiologia, 2008)