Predictive Ecology: Systems Approaches. Evans, M. R., Norris, K. J., & Benton, T. G. Philosophical transactions of the Royal Society of London. Series B, Biological sciences, 367(1586):163–169, January, 2012.
doi  abstract   bibtex   
The world is experiencing significant, largely anthropogenically induced, environmental change. This will impact on the biological world and we need to be able to forecast its effects. In order to produce such forecasts, ecology needs to become more predictive–to develop the ability to understand how ecological systems will behave in future, changed, conditions. Further development of process-based models is required to allow such predictions to be made. Critical to the development of such models will be achieving a balance between the brute-force approach that naively attempts to include everything, and over simplification that throws out important heterogeneities at various levels. Central to this will be the recognition that individuals are the elementary particles of all ecological systems. As such it will be necessary to understand the effect of evolution on ecological systems, particularly when exposed to environmental change. However, insights from evolutionary biology will help the development of models even when data may be sparse. Process-based models are more common, and are used for forecasting, in other disciplines, e.g. climatology and molecular systems biology. Tools and techniques developed in these endeavours can be appropriated into ecological modelling, but it will also be necessary to develop the science of ecoinformatics along with approaches specific to ecological problems. The impetus for this effort should come from the demand coming from society to understand the effects of environmental change on the world and what might be performed to mitigate or adapt to them.
@article{evansPredictiveEcologySystems2012,
  title = {Predictive Ecology: Systems Approaches},
  author = {Evans, Matthew R. and Norris, Ken J. and Benton, Tim G.},
  year = {2012},
  month = jan,
  volume = {367},
  pages = {163--169},
  issn = {1471-2970},
  doi = {10.1098/rstb.2011.0191},
  abstract = {The world is experiencing significant, largely anthropogenically induced, environmental change. This will impact on the biological world and we need to be able to forecast its effects. In order to produce such forecasts, ecology needs to become more predictive--to develop the ability to understand how ecological systems will behave in future, changed, conditions. Further development of process-based models is required to allow such predictions to be made. Critical to the development of such models will be achieving a balance between the brute-force approach that naively attempts to include everything, and over simplification that throws out important heterogeneities at various levels. Central to this will be the recognition that individuals are the elementary particles of all ecological systems. As such it will be necessary to understand the effect of evolution on ecological systems, particularly when exposed to environmental change. However, insights from evolutionary biology will help the development of models even when data may be sparse. Process-based models are more common, and are used for forecasting, in other disciplines, e.g. climatology and molecular systems biology. Tools and techniques developed in these endeavours can be appropriated into ecological modelling, but it will also be necessary to develop the science of ecoinformatics along with approaches specific to ecological problems. The impetus for this effort should come from the demand coming from society to understand the effects of environmental change on the world and what might be performed to mitigate or adapt to them.},
  journal = {Philosophical transactions of the Royal Society of London. Series B, Biological sciences},
  keywords = {*imported-from-citeulike-INRMM,~INRMM-MiD:c-10110410,anthropogenic-changes,ecology,ecosystem,extrapolation-error,global-change,local-over-complication,prediction-bias,system-of-systems,system-theory},
  lccn = {INRMM-MiD:c-10110410},
  number = {1586}
}
Downloads: 0