@misc{SiblyGrimmMartinetal.2013,
  author    = {Sibly, Richard M. and Grimm, Volker and Martin, Benjamin T. and Johnston, Alice S. A. and Kulakowska, Katarzyna and Topping, Christopher J. and Calow, Peter and Nabe-Nielsen, Jacob and Thorbek, Pernille and DeAngelis, Donald L.},
  title     = {Representing the acquisition and use of energy by individuals in agent-based models of animal populations},
  series = {Methods in ecology and evolution : an official journal of the British Ecological Society},
  volume    = {4},
  journal   = {Methods in ecology and evolution : an official journal of the British Ecological Society},
  number    = {2},
  publisher = {Wiley-Blackwell},
  address   = {Hoboken},
  issn      = {2041-210X},
  doi       = {10.1111/2041-210x.12002},
  pages     = {151 -- 161},
  year      = {2013},
  abstract  = {Agent-based models (ABMs) are widely used to predict how populations respond to changing environments. As the availability of food varies in space and time, individuals should have their own energy budgets, but there is no consensus as to how these should be modelled. Here, we use knowledge of physiological ecology to identify major issues confronting the modeller and to make recommendations about how energy budgets for use in ABMs should be constructed. Our proposal is that modelled animals forage as necessary to supply their energy needs for maintenance, growth and reproduction. If there is sufficient energy intake, an animal allocates the energy obtained in the order: maintenance, growth, reproduction, energy storage, until its energy stores reach an optimal level. If there is a shortfall, the priorities for maintenance and growth/reproduction remain the same until reserves fall to a critical threshold below which all are allocated to maintenance. Rates of ingestion and allocation depend on body mass and temperature. We make suggestions for how each of these processes should be modelled mathematically. Mortality rates vary with body mass and temperature according to known relationships, and these can be used to obtain estimates of background mortality rate. If parameter values cannot be obtained directly, then values may provisionally be obtained by parameter borrowing, pattern-oriented modelling, artificial evolution or from allometric equations. The development of ABMs incorporating individual energy budgets is essential for realistic modelling of populations affected by food availability. Such ABMs are already being used to guide conservation planning of nature reserves and shell fisheries, to assess environmental impacts of building proposals including wind farms and highways and to assess the effects on nontarget organisms of chemicals for the control of agricultural pests.},
  language  = {en}
}
@article{ToppingAlroeFarrelletal.2015,
  author    = {Topping, Christopher J. and Alroe, Hugo Fjelsted and Farrell, Katharine N. and Grimm, Volker},
  title     = {Per Aspera ad Astra: Through Complex Population Modeling to Predictive Theory},
  series = {The American naturalist : a bi-monthly journal devoted to the advancement and correlation of the biological sciences},
  volume    = {186},
  journal   = {The American naturalist : a bi-monthly journal devoted to the advancement and correlation of the biological sciences},
  number    = {5},
  publisher = {Univ. of Chicago Press},
  address   = {Chicago},
  issn      = {0003-0147},
  doi       = {10.1086/683181},
  pages     = {669 -- 674},
  year      = {2015},
  abstract  = {Population models in ecology are often not good at predictions, even if they are complex and seem to be realistic enough. The reason for this might be that Occam's razor, which is key for minimal models exploring ideas and concepts, has been too uncritically adopted for more realistic models of systems. This can tic models too closely to certain situations, thereby preventing them from predicting the response to new conditions. We therefore advocate a new kind of parsimony to improve the application of Occam's razor. This new parsimony balances two contrasting strategies for avoiding errors in modeling: avoiding inclusion of nonessential factors (false inclusions) and avoiding exclusion of sometimes-important factors (false exclusions). It involves a synthesis of traditional modeling and analysis, used to describe the essentials of mechanistic relationships, with elements that arc included in a model because they have been reported to be or can arguably be assumed to be important under certain conditions. The resulting models should be able to reflect how the internal organization of populations change and thereby generate representations of the novel behavior necessary for complex predictions, including regime shifts.},
  language  = {en}
}