The ODD protocol for describing agent-based and other simulation models: A second update to improve clarity, replication, and structural realism

© 2020, University of Surrey. All rights reserved. The Overview, Design concepts and Details (ODD) protocol for describing Individual-and Agent-Based Models (ABMs) is now widely accepted and used to document such models in journal articles. As a standardized document for providing a consistent, logical and readable account of the structure and dynamics of ABMs, some research groups also find it useful as a workflow for model design. Even so, there are still limitations to ODD that obstruct its more widespread adoption. Such limitations are discussed and addressed in this paper: the limited availability of guidance on how to use ODD; the length of ODD documents; limitations of ODD for highly complex models; lack of sufficient details of many ODDs to enable reimplementation without access to the model code; and the lack of provision for sections in the document structure covering model design ratio-nale, the model’s underlying narrative, and the means by which the model’s fitness for purpose is evaluated. We document the steps we have taken to provide better guidance on: structuring complex ODDs and an ODD summary for inclusion in a journal article (with full details in supplementary material; Table 1); using ODD to point readers to relevant sections of the model code; update the document structure to include sections on model rationale and evaluation. We also further advocate the need for standard descriptions of simulation experiments and argue that ODD can in principle be used for any type of simulation model. Thereby ODD would provide a lingua franca for simulation modelling

Model for the movement and distribution of fish in a body of water

A Monte Carlo mathematical model tracks the movement of fish in a body of water (e.g., a pond or reservoir) which is represented by a two-dimensional grid. For the case of a long, narrow reservoir, depth and length along the reservoir are the logical choices for coordinate axes. In the model, it is assumed that the movement of fish is influenced by gradients of temperature and dissolved oxygen, as well as food availability and habitat preference. The fish takes one spatial ''step'' at a time, the direction being randomly selected, but also biased by the above factors. In trial simulations, a large number of simulated fish were allowed to distribute themselves in a hypothetical body of water. Assuming only temperature was influencing the movements of the fish, the resultant distributions are compared with experimental data on temperature preferences

Uncertainty in spatially explicit animal dispersal models

Uncertainty in estimates of survival of dispersing animals is a vexing difficulty in conservation biology. The current notion is that this uncertainty decreases the usefulness of spatially explicit population models in particular. We examined this problem by comparing dispersal models of three levels of complexity: (1) an event-based binomial model that considers only the occurrence of mortality or arrival, (2) a temporally explicit exponential model that employs mortality and arrival rates, and (3) a spatially explicit grid-walk model that simulates the movement of animals through an artificial landscape. Each model was fitted to the same set of field data. A first objective of the paper is to illustrate how the maximum-likelihood method can be used in all three cases to estimate the means and confidence limits for the relevant model parameters, given a particular set of data on dispersal survival. Using this framework we show that the structure of the uncertainty for all three models is strikingly similar. In fact, the results of our unified approach imply that spatially explicit dispersal models, which take advantage of information on landscape details, suffer less from uncertainly than do simpler models. Moreover, we show that the proposed strategy of model development safeguards one from error propagation in these more complex models. Finally, our approach shows that all models related to animal dispersal, ranging from simple to complex, can be related in a hierarchical fashion, so that the various approaches to modeling such dispersal can be viewed from a unified perspective [KEYWORDS: dispersal models, uncertainty; dispersal mortality; dispersal success; individual-based modeling; landscape details; likelihood, maximum; managing endangered species; model complexity; population models; random grid-walk models; spatially explicit modeling; suitable habitat]