@article{StillmanRailsbackGiskeetal.2015, author = {Stillman, Richard A. and Railsback, Steven Floyd and Giske, Jarl and Berger, Uta and Grimm, Volker}, title = {Making Predictions in a Changing World: The Benefits of Individual-Based Ecology}, series = {Bioscience}, volume = {65}, journal = {Bioscience}, number = {2}, publisher = {Oxford Univ. Press}, address = {Oxford}, issn = {0006-3568}, doi = {10.1093/biosci/biu192}, pages = {140 -- 150}, year = {2015}, abstract = {Ecologists urgently need a better ability to predict how environmental change affects biodiversity. We examine individual-based ecology (IBE), a research paradigm that promises better a predictive ability by using individual-based models (IBMs) to represent ecological dynamics as arising from how individuals interact with their environment and with each other. A key advantage of IBMs is that the basis for predictions-fitness maximization by individual organisms-is more general and reliable than the empirical relationships that other models depend on. Case studies illustrate the usefulness and predictive success of long-term IBE programs. The pioneering programs had three phases: conceptualization, implementation, and diversification. Continued validation of models runs throughout these phases. The breakthroughs that make IBE more productive include standards for describing and validating IBMs, improved and standardized theory for individual traits and behavior, software tools, and generalized instead of system-specific IBMs. We provide guidelines for pursuing IBE and a vision for future IBE research.}, language = {en} } @article{GrimmRevillaBergeretal.2005, author = {Grimm, Volker and Revilla, Eloy and Berger, Uta and Jeltsch, Florian and Mooij, Wolf M. and Railsback, Steven Floyd and Thulke, Hans-Hermann and Weiner, Jacob and Wiegand, Thorsten and DeAngelis, Donald L.}, title = {Pattern-oriented modeling of agend-based complex systems : lessons from ecology}, year = {2005}, abstract = {Agent-based complex systems are dynamic networks of many interacting agents; examples include ecosystems, financial markets, and cities. The search for general principles underlying the internal organization of such systems often uses bottom-up simulation models such as cellular automata and agent-based models. No general framework for designing, testing, and analyzing bottom-up models has yet been established, but recent advances in ecological modeling have come together in a general strategy we call pattern-oriented modeling. This strategy provides a unifying framework for decoding the internal organization of agent-based complex systems and may lead toward unifying algorithmic theories of the relation between adaptive behavior and system complexity}, language = {en} } @article{GrimmBergerBastiansenetal.2006, author = {Grimm, Volker and Berger, Uta and Bastiansen, Finn and Eliassen, Sigrunn and Ginot, Vincent and Giske, Jarl and Goss-Custard, John and Grand, Tamara and Heinz, Simone K. and Huse, Geir and Huth, Andreas and Jepsen, Jane U. and Jorgensen, Christian and Mooij, Wolf M. and Mueller, Birgit and Piou, Cyril and Railsback, Steven Floyd and Robbins, Andrew M. and Robbins, Martha M. and Rossmanith, Eva and Rueger, Nadja and Strand, Espen and Souissi, Sami and Stillman, Richard A. and Vabo, Rune and Visser, Ute and DeAngelis, Donald L.}, title = {A standard protocol for describing individual-based and agent-based models}, series = {Ecological modelling : international journal on ecological modelling and engineering and systems ecolog}, volume = {198}, journal = {Ecological modelling : international journal on ecological modelling and engineering and systems ecolog}, publisher = {Elsevier}, address = {Amsterdam}, issn = {0304-3800}, doi = {10.1016/j.ecolmodel.2006.04.023}, pages = {115 -- 126}, year = {2006}, abstract = {Simulation models that describe autonomous individual organisms (individual based models, IBM) or agents (agent-based models, ABM) have become a widely used tool, not only in ecology, but also in many other disciplines dealing with complex systems made up of autonomous entities. However, there is no standard protocol for describing such simulation models, which can make them difficult to understand and to duplicate. This paper presents a proposed standard protocol, ODD, for describing IBMs and ABMs, developed and tested by 28 modellers who cover a wide range of fields within ecology. This protocol consists of three blocks (Overview, Design concepts, and Details), which are subdivided into seven elements: Purpose, State variables and scales, Process overview and scheduling, Design concepts, Initialization, Input, and Submodels. We explain which aspects of a model should be described in each element, and we present an example to illustrate the protocol in use. In addition, 19 examples are available in an Online Appendix. We consider ODD as a first step for establishing a more detailed common format of the description of IBMs and ABMs. Once initiated, the protocol will hopefully evolve as it becomes used by a sufficiently large proportion of modellers. (c) 2006 Elsevier B.V. All rights reserved.}, language = {en} } @article{GrimmAugusiakFocksetal.2014, author = {Grimm, Volker and Augusiak, Jacqueline and Focks, Andreas and Frank, Beatrice M. and Gabsi, Faten and Johnston, Alice S. A. and Liu, Chun and Martin, Benjamin T. and Meli, Mattia and Radchuk, Viktoriia and Thorbek, Pernille and Railsback, Steven Floyd}, title = {Towards better modelling and decision support: Documenting model development, testing, and analysis using TRACE}, series = {Ecological modelling : international journal on ecological modelling and engineering and systems ecolog}, volume = {280}, journal = {Ecological modelling : international journal on ecological modelling and engineering and systems ecolog}, publisher = {Elsevier}, address = {Amsterdam}, issn = {0304-3800}, doi = {10.1016/j.ecolmodel.2014.01.018}, pages = {129 -- 139}, year = {2014}, abstract = {The potential of ecological models for supporting environmental decision making is increasingly acknowledged. However, it often remains unclear whether a model is realistic and reliable enough. Good practice for developing and testing ecological models has not yet been established. Therefore, TRACE, a general framework for documenting a model's rationale, design, and testing was recently suggested. Originally TRACE was aimed at documenting good modelling practice. However, the word 'documentation' does not convey TRACE's urgency. Therefore, we re-define TRACE as a tool for planning, performing, and documenting good modelling practice. TRACE documents should provide convincing evidence that a model was thoughtfully designed, correctly implemented, thoroughly tested, well understood, and appropriately used for its intended purpose. TRACE documents link the science underlying a model to its application, thereby also linking modellers and model users, for example stakeholders, decision makers, and developers of policies. We report on first experiences in producing TRACE documents. We found that the original idea underlying TRACE was valid, but to make its use more coherent and efficient, an update of its structure and more specific guidance for its use are needed. The updated TRACE format follows the recently developed framework of model 'evaludation': the entire process of establishing model quality and credibility throughout all stages of model development, analysis, and application. TRACE thus becomes a tool for planning, documenting, and assessing model evaludation, which includes understanding the rationale behind a model and its envisaged use. We introduce the new structure and revised terminology of TRACE and provide examples. (C) 2014 Elsevier B.V. All rights reserved.}, language = {en} } @article{AyllonRailsbackVincenzietal.2016, author = {Ayllon, Daniel and Railsback, Steven Floyd and Vincenzi, Simone and Groeneveld, Juergen and Almodoevar, Ana and Grimm, Volker}, title = {InSTREAM-Gen: Modelling eco-evolutionary dynamics of trout populations under anthropogenic environmental change}, series = {Ecological modelling : international journal on ecological modelling and engineering and systems ecolog}, volume = {326}, journal = {Ecological modelling : international journal on ecological modelling and engineering and systems ecolog}, publisher = {Elsevier}, address = {Amsterdam}, issn = {0304-3800}, doi = {10.1016/j.ecolmodel.2015.07.026}, pages = {36 -- 53}, year = {2016}, abstract = {Current rates of environmental change are exceeding the capacity of many populations to adapt to new conditions and thus avoid demographic collapse and ultimate extinction. In particular, cold-water freshwater fish species are predicted to experience strong selective pressure from climate change and a wide range of interacting anthropogenic stressors in the near future. To implement effective management and conservation measures, it is crucial to quantify the maximum rate of change that cold-water freshwater fish populations can withstand. Here, we present a spatially explicit eco-genetic individual-based model, inSTREAM-Gen, to predict the eco-evolutionary dynamics of stream-dwelling trout under anthropogenic environmental change. The model builds on a well-tested demographic model, which includes submodels of river dynamics, bioenergetics, and adaptive habitat selection, with a new genetic module that allows exploration of genetic and life-history adaptations to new environments. The genetic module models the transmission of two key traits, size at emergence and maturity size threshold. We parameterized the model for a brown trout (Salmo trutta L.) population at the warmest edge of its range to validate it and analyze its sensitivity to parameters under contrasting thermal profiles. To illustrate potential applications of the model, we analyzed the population's demographic and evolutionary dynamics under scenarios of (1) climate change-induced warming, and (2) warming plus flow reduction resulting from climate and land use change, compared to (3) a baseline of no environmental change. The model predicted severe declines in density and biomass under climate warming. These declines were lower than expected at range margins because of evolution towards smaller size at both emergence and maturation compared to the natural evolution under the baseline conditions. Despite stronger evolutionary responses, declining rates were substantially larger under the combined warming and flow reduction scenario, leading to a high probability of population extinction over contemporary time frames. Therefore, adaptive responses could not prevent extinction under high rates of environmental change. Our model demonstrates critical elements of next generation ecological modelling aiming at predictions in a changing world as it accounts for spatial and temporal resource heterogeneity, while merging individual behaviour and bioenergetics with microevolutionary adaptations.}, language = {en} }