A future for models and data in environmental science.
Together, graphical models and the Bayesian paradigm provide powerful new tools that promise to change the way that environmental science is done. The capacity to merge theory with mechanistic understanding and empirical evidence, to assimilate diverse sources of information and to accommodate complexity will transform the collection and interpretation of data. As we discuss here, we specifically expect a shift from a focus on simple experiments with inflexible design and selection among models that embrace parts of processes to a synthesis of integrated process models. With this potential come new challenges, including some that are specific and technical and others that are general and will involve reexamination of the role of inference and prediction.
Duke Scholars
Altmetric Attention Stats
Dimensions Citation Stats
Published In
DOI
EISSN
ISSN
Publication Date
Volume
Issue
Start / End Page
Related Subject Headings
- Population
- Models, Biological
- Humans
- Forecasting
- Evolutionary Biology
- Ecology
- Data Collection
- Computer Simulation
- Bayes Theorem
- Animals
Citation
Published In
DOI
EISSN
ISSN
Publication Date
Volume
Issue
Start / End Page
Related Subject Headings
- Population
- Models, Biological
- Humans
- Forecasting
- Evolutionary Biology
- Ecology
- Data Collection
- Computer Simulation
- Bayes Theorem
- Animals