Making Predictions in a Changing World: The Benefits of Individual-Based Ecology

This data was imported from PubMed:

Authors: Stillman, R.A., Railsback, S.F., Giske, J., Berger, U. and Grimm, V.

http://eprints.bournemouth.ac.uk/21809/

Journal: Bioscience

Volume: 65

Issue: 2

Pages: 140-150

ISSN: 0006-3568

DOI: 10.1093/biosci/biu192

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.

This source preferred by Richard Stillman

This data was imported from Scopus:

Authors: Stillman, R.A., Railsback, S.F., Giske, J., Berger, U. and Grimm, V.

http://eprints.bournemouth.ac.uk/21809/

Journal: BioScience

Volume: 65

Issue: 2

Pages: 140-150

eISSN: 1525-3244

ISSN: 0006-3568

DOI: 10.1093/biosci/biu192

© 2014 The Author(s). 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.

This data was imported from Web of Science (Lite):

Authors: Stillman, R.A., Railsback, S.F., Giske, J., Berger, U. and Grimm, V.

http://eprints.bournemouth.ac.uk/21809/

Journal: BIOSCIENCE

Volume: 65

Issue: 2

Pages: 140-150

eISSN: 1525-3244

ISSN: 0006-3568

DOI: 10.1093/biosci/biu192

This data was imported from Europe PubMed Central:

Authors: Stillman, R.A., Railsback, S.F., Giske, J., Berger, U. and Grimm, V.

http://eprints.bournemouth.ac.uk/21809/

Journal: Bioscience

Volume: 65

Issue: 2

Pages: 140-150

eISSN: 1525-3244

ISSN: 0006-3568

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.

The data on this page was last updated at 04:40 on November 22, 2017.