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Data from: Contingent tradeoff decisions with feedbacks in cyclical environments: testing alternative theories

Citation

Railsback, Steven; Harvey, Bret; Ayllón, Daniel (2020), Data from: Contingent tradeoff decisions with feedbacks in cyclical environments: testing alternative theories, Dryad, Dataset, https://doi.org/10.5061/dryad.kkwh70s2m

Abstract

This archive contains the model code and input for the simulation experiments published in: Railsback, Harvey, and Ayllón, in press. Contingent tradeoff decisions with feedbacks in cyclical environments: testing alternative theories. Behavioral Ecology. BEHECO-2019-0460.R3. That paper formulates and tests four alternative theories for how animals make tradeoff decisions throughout the circadian light cycle in a population context, with the behavior of each individual affecting the resources available to others. The example application is to stream trout selecting when and where to feed vs. hide, during dawn, day, dusk, and night. These theories are tested against observed patterns reported in the literature. The archive provides separate NetLogo program files for each theory and each simulation experiment. These files allow complete reproduction of the simulation experiments.

Methods

The archive contains 40 NetLogo code files that each contain the code, parameter values, and experiment setup for one of the simulation experiments reported in the article. They can be used to recreate and modify those experiments.

A basic understanding of the NetLogo software platform is all that is necessary to use these files. NetLogo is free (http://ccl.northwestern.edu/netlogo/), well-documented, and widely used. Railsback and Grimm (2019; http://www.railsback-grimm-abm-book.com) introduce its use in ecological modeling.

Usage Notes

The archive consists of one zip file that contains all 40 NetLogo files plus a subdirectory (“LJCLower”) containing the input files used in all the simulations.

See the file RailsbackEtAl_BehavEcol_DRYAD-Metadata.pdf for instructions for running models and reproducing simulation experiments.