Stochastic formulation of ecological models and their applications
Trends in Ecology & Evolution, ISSN: 0169-5347, Vol: 27, Issue: 6, Page: 337-345
2012
- 181Citations
- 494Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Metrics Details
- Citations181
- Citation Indexes181
- CrossRef181
- 175
- Captures494
- Readers494
- 494
Review Description
The increasing use of computer simulation by theoretical ecologists started a move away from models formulated at the population level towards individual-based models. However, many of the models studied at the individual level are not analysed mathematically and remain defined in terms of a computer algorithm. This is not surprising, given that they are intrinsically stochastic and require tools and techniques for their study that may be unfamiliar to ecologists. Here, we argue that the construction of ecological models at the individual level and their subsequent analysis is, in many cases, straightforward and leads to important insights. We discuss recent work that highlights the importance of stochastic effects for parameter ranges and systems where it was previously thought that such effects would be negligible.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S016953471200033X; http://dx.doi.org/10.1016/j.tree.2012.01.014; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=84861331629&origin=inward; http://www.ncbi.nlm.nih.gov/pubmed/22406194; https://linkinghub.elsevier.com/retrieve/pii/S016953471200033X; https://dx.doi.org/10.1016/j.tree.2012.01.014
Elsevier BV
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