Evaluating the performance of individual-based animal movement models in novel environments

Authored by Katherine Shepard Watkins, Kenneth A Rose

Date Published: 2013

DOI: 10.1016/j.ecolmodel.2012.11.011

Sponsors: United States National Oceanic and Atmospheric Administration (NOAA) United States National Science Foundation (NSF) Center for Sponsored Coastal Ocean Research (CSCOR)

Platforms: No platforms listed

Model Documentation: Other Narrative Mathematical description

Model Code URLs: Model code not found

Abstract

Simulating animal movement in spatially explicit individual-based models (IBMs) is both challenging and critically important to accurately estimating population dynamics. A number of different approaches have been developed that make different assumptions about how individuals move in their environment and use different mathematics to translate movement cues into a behavioral response. Properly calibrated movement models should produce realistic movement in both conditions encountered during calibration and in novel conditions; however, most studies to date have not tested movement models in novel conditions. We compared four distinct movement approaches or sub-models (restricted-area search, kinesis, event-based, and run and tumble) using an IBM loosely based on a small pelagic fish (e.g. Engraulidae) that simulated growth, mortality, and movement of a cohort on a 2-dimensional grid. We trained the sub-models with a genetic algorithm in one set of environmental conditions and then tested them in other three environments. The sub-models generally performed well in novel environments, except restricted-area search and event-based that needed to be trained in environments with gradients similar to the test environment. Also, run and tumble produced near-random distributions in all training environments except the one with the steepest habitat quality gradient, and it produced random distributions in all novel test environments. In selecting a movement sub-model, researchers should consider the assumptions of potential sub-models, the observed movement patterns of the species of interest, and the shape and steepness of the underlying habitat quality gradient. (C) 2012 Elsevier B.V. All rights reserved.

Tags

behavior Management Simulation-model Population-dynamics Fish Random-walk Spatially-explicit Anchovy anchoa-mitchilli Bay anchovy Migrations