The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
ABC as a flexible framework to estimate demography over space and time: some cons, many pros
Open accessAbstract
The analysis of genetic variation to estimate demographic and historical parameters and to quantitatively compare alternative scenarios recently gained a powerful and flexible approach: the Approximate Bayesian Computation (ABC). The likelihood functions does not need to be theoretically specified, but posterior distributions can be approximated by simulation even assuming very complex population models including both natural and human‐induced processes. Prior information can be easily incorporated and the quality of the results can be analysed with rather limited additional effort. ABC is not a statistical analysis per se, but rather a statistical framework and any specific application is a sort of hybrid between a simulation and a data‐analysis study. Complete software packages performing the necessary steps under a set of models and for specific genetic markers are already available, but the flexibility of the method is better exploited combining different programs. Many questions relevant in ecology can be addressed using ABC, but adequate amount of time should be dedicated to decide among alternative options and to evaluate the results. In this paper we will describe and critically comment on the different steps of an ABC analysis, analyse some of the published applications of ABC and provide user guidelines.
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Cites
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Markov chain Monte Carlo without likelihoods
Marjoram, P., Molitor, J., Plagnol, V., & Tavaré, S. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0306899100 · Cited by 1,199
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Adaptive approximate Bayesian computation
Beaumont, M. A., Cornuet, J.-M., Marin, J.-M., & Robert, C. P. (2009) · Biometrika · doi:10.1093/biomet/asp052 · Cited by 614 · cited 8×
“Recently, two implementations of non-standard ABC (using MCMC and PMC) have become available for general users within the set of programs called ABCtoolbox (Wegmann et al. 2010).”
MethodInfluentialThe Origins of Lactase Persistence in Europe
Itan, Y., Powell, A., Beaumont, M. A., Burger, J., & Thomas, M. G. (2009) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000491 · Cited by 569 · cited 1×
“Forward genetic simulations have the advantage to substantially simplify the implementation of natural selection and for this reason they may spread for specific ABC implementations (e.g. Itan et al. 2009).”
BackgroundThornton, K., & Andolfatto, P. (2006) · Genetics · doi:10.1534/genetics.105.048223 · Cited by 256
Bayesian Computation and Model Selection Without Likelihoods
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Cited by
Approximate Bayesian Computation in Evolution and Ecology
Beaumont, M. A. (2010) · Annual Review of Ecology, Evolution, and Systematics · doi:10.1146/annurev-ecolsys-102209-144621 · Cited by 1,114
Reconstructing routes of invasion using genetic data: why, how and so what?
ESTOUP, A., & GUILLEMAUD, T. (2010) · Molecular Ecology · doi:10.1111/j.1365-294x.2010.04773.x · Cited by 649
Approximate Bayesian Computation
Sunnåker, M., Busetto, A. G., Numminen, E., Corander, J., Foll, M., & Dessimoz, C. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002803 · Cited by 623
Reliability of genetic bottleneck tests for detecting recent population declines
PEERY, M. Z., KIRBY, R., REID, B. N., STOELTING, R., DOUCET‐BËER, E., ROBINSON, S., VÁSQUEZ‐CARRILLO, C., PAULI, J. N., & PALSBØLL, P. J. (2012) · Molecular Ecology · doi:10.1111/j.1365-294x.2012.05635.x · Cited by 578
Statistical inference for stochastic simulation models - theory and application
Hartig, F., Calabrese, J. M., Reineking, B., Wiegand, T., & Huth, A. (2011) · Ecology Letters · doi:10.1111/j.1461-0248.2011.01640.x · Cited by 384
Metadata
- Authors
- BERTORELLE, G., BENAZZO, A., & MONA, S.
- Venue
- Molecular Ecology
- Publisher
- Wiley
- Published
- 2010-06-29
- Volume
- 19
- Issue
- 13
- Pages
- 2609-2625
- ISSN
- 0962-1083 (ISSN-L)
- 1365-294X
- OpenAlex
- W2136336930
- Cited by
- 464
- References
- 11
- FWCI
- 19.62
- Type
- journal article
- Language
- en
- Open access
- bronze
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
