Individual‐based Modeling and Ecology

By G. Huse. 2008

948 citations recorded by the source corpus.

Subfields: Global and Planetary Change, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/f9f175cbdab12da21722e51789d70c7dcfb807de

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In Global and Planetary Change

In Nature and Landscape Conservation

Citations in the corpus

The Robust Volterra Principle* (2008, methodology): “(See also Donalson & Nisbet, 1999; Grimm & Railsback, 2005; DeAngelis & Mooij, 2005) They are not a substitute for population-based models, but they can be used to relax assumptions made by generating population-based models.”
A standard protocol for describing individual-based and agent-based models (2006, methodology): “agent-based models Volker Grimma,∗, Uta Bergerb, Finn Bastiansena, Sigrunn Eliassenc, Vincent Ginotd, Jarl Giskec, John Goss-Custarde, Tamara Grandf, Simone K. Heinzc, Geir Huseg, Andreas Hutha, Jane U. Jepsena, Christian Jørgensenc, Wolf M. Mooijh, Birgit Müllera, Guy Pe’er i, Cyril Pioub, Steven ”
Agent-Based Modeling in Marketing: Guidelines for Rigor (2011, methodology): “information systems Heppenstall et al. (2006) Brown et al.”
Facilitating Parameter Estimation and Sensitivity Analysis of Agent-Based Models: A Cookbook Using NetLogo and 'R' (2014, methodology): “…or optimisation methods with so-called inverse modelling, also referred to as pattern-oriented parameterisation/modelling (POM; Grimm & Railsback 2005), or Monte Carlo Filtering, as the patterns are applied as filters to separate good from bad sets of parameter values (Grimm & Railsback 2005).”
Towards better modelling and decision support: Documenting model development, testing, and analysis using TRACE (2014, positive): “…foraging or habitat selection, are parameterized for a wider range of environmental conditions, population-level phenomena are no longer imposed but emerge and the population model can be expected to predict responses to new conditions more reliably (Grimm and Railsback, 2005; supporting evidence.”
The virtual ecologist approach: simulating data and observers (2010, methodology): “They range from grid-based models and patch network models (Hanski 1998) to individual-based models (Grimm 1999, Grimm and Railsback 2005).”
RangeShifter: a platform for modelling spatial eco‐evolutionary dynamics and species' responses to environmental changes (2014, background): “…development [e.g. on causes and consequences of dispersal (Clobert et al. 2012)] and for (typically localized) species-specific tactical models (Grimm & Railsback 2005), their application inmulti-purpose,multi-scale simulationmodelling for investigating species’ responses to environmental…”
Representing the acquisition and use of energy by individuals in agent‐based models of animal populations (2013, positive): “The ways that individuals respond to their individual circumstances in a mapped landscape are modelled in detail, and the emergent dynamics of the population are then studied by computer simulation (e.g. DeAngelis & Mooij 2005; Grimm & Railsback 2005, 2012; Grimm et al. 2005; Railsback&Grimm2012).”
Companion Modelling: A Participatory Approach to Support Sustainable Development (2010, background): “Remember that the model objective is essential, as this overview is useful for anyone encountering the model for the first time to understand why certain aspects of the system studied have been ignored (Grimm and Railsback 2005).”
Dispersal, demography and spatial population models for conservation and control management (2008, background): “These models allow individuals to be tracked (DeAngelis and Gross, 1992; Grimm and Railsback, 2005), but normally sacrifice analytic results (but see Dieckmann et al.”
A new tool for epidemiology: the usefulness of dynamic-agent models in understanding place effects on health. (2008, methodology): “A solution to this challenge is to follow recently developed standard protocols for conducting and describing agentbased models (14, 62).”
How to fit nonlinear plant growth models and calculate growth rates: an update for ecologists (2012, methodology): “These models consider growth algorithmically or as an iterative process of carbon acquisition, allocation to various compartments and biomass gain (Tilman 1988; Grimm & Railsback 2005).”
Social-ecological systems as complex adaptive systems: modeling and policy implications (2012, background): “Agent-based models or individual-based models allow each individual to have unique behaviors that may change in response to others’ actions, and the possibly slow evolution of macroscopic variables (Bonabeau, 2002; Couzin et al., 2005; Grimm and Railsback, 2005).”
Factors and mechanisms explaining spatial heterogeneity: a review of methods for insect populations (2011, positive): “When local movements and individual behaviour are considered as important processes affecting the spatial pattern, an individual-based modelling (IBM) approach will better describe the system, based on emergence of population properties from individual behaviour (Grimm & Railsback 2005).”