Simulating individual-based movement in dynamic environments
Authored by Katherine Shepard Watkins, Kenneth A Rose
Date Published: 2017
DOI: 10.1016/j.ecolmodel.2017.03.025
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Abstract
The accuracy of spatially-explicit individual-based models (IBMs) often
depends on the realistic simulation of the movement of organisms, which
is especially challenging when movement cues (e.g., environmental
conditions; prey and predator abundances) vary in time and space. A
number of approaches or sub-models have been developed for simulating
movement in IBMs. We evaluated four movement sub-models (restricted-area
search, kinesis, event-based, and run and tumble) in a spatially
explicit cohort IBM in which the prey and predators were both dynamic
(varying across cells and over time) and responsive to the dynamics of
the cohort individuals. Movement, growth, and mortality were simulated
every 25 min for 30 12-h days (single generation) on a 2.7 x 2.7 km(2)
grid with 625 m(2) cells, and egg production was calculated based on
weight and survival of individuals at the end of 30 days. We based the
cohort model on small pelagic coastal fish, and the prey was based on
zooplankton and the predators based on a typical piscivorous fish.
Movement sub-models were calibrated with a genetic algorithm in dynamic
and static versions of the prey and predator-defined environments. Prey
and predator fields were fixed in the static environment; in the dynamic
environment, prey density was reduced based on consumption and predators
actively sought out cohort individuals. Static-trained sub-models were
then tested in the dynamic environments and vice versa. The four
movement sub-models were successfully trained and performed reasonably
well in terms of egg production (a measure of individual fitness) when
trained and tested in the same type of environment. However, the type of
environment affected calibration success, and static-trained models did
not perform well when tested in dynamic environments because cohort
individuals moved in response to both prey and predator cues rather than
primarily avoiding fixed-in-space high mortality cells. Use of movement
sub-models in IBMs should carefully consider how the conditions assumed
for calibration relates to the dynamic conditions the model will be used
to address. (C) 2017 Elsevier B.V. All rights reserved.
Tags
Individual-based model
behavior
models
animal
ecology
patterns
Climate-change
Resolution
Fish
Genetic
algorithm
Migrations
Sardine
Fish movement
Behavioral movement
Tools