@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{LiuSiblyGrimmetal.2013,
  author    = {Liu, Chun and Sibly, Richard M. and Grimm, Volker and Thorbek, Pernille},
  title     = {Linking pesticide exposure and spatial dynamics an individual-based model of wood mouse (Apodemus sylvaticus) populations in agricultural landscapes},
  series = {Ecological modelling : international journal on ecological modelling and engineering and systems ecolog},
  volume    = {248},
  journal   = {Ecological modelling : international journal on ecological modelling and engineering and systems ecolog},
  number    = {2},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {0304-3800},
  doi       = {10.1016/j.ecolmodel.2012.09.016},
  pages     = {92 -- 102},
  year      = {2013},
  abstract  = {The wood mouse is a common and abundant species in agricultural landscape and is a focal species in pesticide risk assessment. Empirical studies on the ecology of the wood mouse have provided sufficient information for the species to be modelled mechanistically. An individual-based model was constructed to explicitly represent the locations and movement patterns of individual mice. This together with the schedule of pesticide application allows prediction of the risk to the population from pesticide exposure. The model included life-history traits of wood mice as well as typical landscape dynamics in agricultural farmland in the UK. The model obtains a good fit to the available population data and is fit for risk assessment purposes. It can help identify spatio-temporal situations with the largest potential risk of exposure and enables extrapolation from individual-level endpoints to population-level effects. Largest risk of exposure to pesticides was found when good crop growth in the "sink" fields coincided with high "source" population densities in the hedgerows.},
  language  = {en}
}