Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 2,263

The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.

Multimodel inference in ecology and evolution: challenges and solutions

GRUEBER, C. E., NAKAGAWA, S., LAWS, R. J., JAMIESON, I. G.·2011·Journal of Evolutionary Biology
DOI 10.1111/j.1420-9101.2010.02210.x
Open this paper in CoChat — free account

Abstract

Information theoretic approaches and model averaging are increasing in popularity, but this approach can be difficult to apply to the realistic, complex models that typify many ecological and evolutionary analyses. This is especially true for those researchers without a formal background in information theory. Here, we highlight a number of practical obstacles to model averaging complex models. Although not meant to be an exhaustive review, we identify several important issues with tentative solutions where they exist (e.g. dealing with collinearity amongst predictors; how to compute model-averaged parameters) and highlight areas for future research where solutions are not clear (e.g. when to use random intercepts or slopes; which information criteria to use when random factors are involved). We also provide a worked example of a mixed model analysis of inbreeding depression in a wild population. By providing an overview of these issues, we hope that this approach will become more accessible to those investigating any process where multiple variables impact an evolutionary or ecological response.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1111/j.1420-9101.2010.02210.x

Cites

7 of 7 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 7 references

Research works printed but not linked: 63

  1. Akaike, 1973, Second International Symposium on Information Theory, 267
  2. Anderson, 2002, Avoiding pitfalls when using information-theoretic methods, J. Wildl. Manage., 66, 912, 10.2307/3803155 · DOI · Paper page
  3. Anderson, 2000, Null hypothesis testing: problems, prevalence, and an alternative, J. Wildl. Manage., 64, 912, 10.2307/3803199 · DOI · Paper page
  4. Bolker, 2009, Learning hierarchical models: advice for the rest of us, Ecol. Appl., 19, 588, 10.1890/08-0639.1 · DOI
  5. Bolker, 2009, Generalized linear mixed models: a practical guide for ecology and evolution, Trends Ecol. Evol., 24, 127, 10.1016/j.tree.2008.10.008 · DOI · Paper page
  6. Bring, 1994, How to standardize regression coefficients, Am. Stat., 48, 209, 10.2307/2684719 · DOI
  7. Browne, 2005, Variance partitioning in multilevel logistic models that exhibit overdispersion, J. R. Stat. Soc. Ser. A-Stat. Soc., 168, 599, 10.1111/j.1467-985X.2004.00365.x · DOI · Paper page
  8. Carstens, 2009, An information-theoretical approach to phylogeography, Mol. Ecol., 18, 4270, 10.1111/j.1365-294X.2009.04327.x · DOI
  9. Claeskens, 2003, The focused information criterion, J. Am. Stat. Assoc., 98, 900, 10.1198/016214503000000819 · DOI · Paper page
  10. Claeskens, 2007, Prediction-focused model selection for autoregressive models, Aus. N. Z. J. Stat., 49, 359, 10.1111/j.1467-842X.2007.00487.x · doi:10.1111/j.1467-842x.2007.00487.x
  11. Congdon, 2006a, Bayesian model choice based on Monte Carlo estimates of posterior model probabilities, Comput. Stat. Data Anal., 50, 346, 10.1016/j.csda.2004.08.001 · doi:10.1016/j.csda.2004.08.001
  12. Congdon, 2006b, Bayesian Statistical Modelling, 10.1002/9780470035948 · DOI · Paper page
  13. Darwin, 1876, The Effects of Crossing and Self-fertilization in the Vegetable Kingdom, 10.5962/bhl.title.110800 · DOI · Paper page
  14. Dochtermann, 2010, Developing and evaluating multiple hypotheses in behavioral ecology, Behav. Ecol. Sociobiol.
  15. Faraway, 2005, Linear Models with R
  16. Fisher, 1948, The Theory of Inbreeding
  17. Forslund, 1995, Age and reproduction in birds - hypotheses and tests, Trends Ecol. Evol., 10, 374, 10.1016/S0169-5347(00)89141-7 · DOI · Paper page
  18. Freckleton, 2010, Dealing with collinearity in behavioral and ecological data: model averaging and the problems of measurement error, Behav. Ecol. Sociobiol., 65, 91, 10.1007/s00265-010-1045-6 · DOI · Paper page
  19. Gelman, 2008, Scaling regression inputs by dividing by two standard deviations, Stat. Med., 27, 2865, 10.1002/sim.3107 · DOI · Paper page
  20. Gelman, 2007, Data Analysis Using Regression and Multileval/Hierarchical Models
  21. Grueber, 2008, Quantifying and managing the loss of genetic variation through the use of pedigrees in a non-captive endangered species, Conserv. Genet., 9, 645, 10.1007/s10592-007-9390-3 · DOI
  22. Grueber, 2010, Inbreeding depression accumulates across life-history stages of the endangered takahe, Conserv. Biol., 24, 1617, 10.1111/j.1523-1739.2010.01549.x · DOI
  23. Haldane, 1924, The mathematical theory of natural and artificial selection. Part II: the influence of partial self-fertilization, inbreeding, assortative mating, and selective fertilization on the composition of Mendelian populations, and on natural selection, Proceedings of the Cambridge Philosophical Society, 1
  24. Harrell, 2001, Regression Modeling Strategies: With Applications to Linear Models, Logisitic Regression, and Survival Analysis, 10.1007/978-1-4757-3462-1 · DOI · Paper page
  25. Hereford, 2004, Comparing strengths of directional selection: how strong is strong?, Evolution, 58, 2133, 10.1111/j.0014-3820.2004.tb01592.x · DOI · Paper page
  26. Hurvich, 1989, Regression and time series model selection in small samples, Biometrika, 76, 297, 10.1093/biomet/76.2.297 · DOI · Paper page
  27. Jamieson, 2003, Sex specific consequences of recent inbreeding in an ancestrally inbred population of New Zealand takahe, Conserv. Biol., 17, 708, 10.1046/j.1523-1739.2003.01400.x · doi:10.1046/j.1523-1739.2003.01400.x
  28. Jefferys, 1961, Theory of Probability
  29. Johnson, 2004, Model selection in ecology and evolution, Trends Ecol. Evol., 19, 101, 10.1016/j.tree.2003.10.013 · DOI · Paper page
  30. Keller, 2002, Inbreeding effects in wild populations, Trends Ecol. Evol., 17, 230, 10.1016/S0169-5347(02)02489-8 · DOI · Paper page
  31. Keller, 2008, Testing evolutionary models of senescence in a natural population: age and inbreeding effects on fitness components in song sparrows, Proc. R. Soc. B, 275, 597, 10.1098/rspb.2007.0961 · DOI
  32. King, 1986, How not to lie with statistics: avoiding common mistakes in quantitative political science, Am. J. Pol. Sci., 30, 666, 10.2307/2111095 · DOI · Paper page
  33. Konishi, 2008, Information Criteria and Statistical Modeling, 10.1007/978-0-387-71887-3 · DOI · Paper page
  34. Laws , R.J. Jamieson , I.G. 2010 Is lack of evidence of inbreeding depression in a threatened New Zealand robin indicative of reduced genetic load? 10.1111/j.1469-1795.2010.00388.x · DOI
  35. Laws , R.J. Townsend , S.M. Nakagawa , S. Jamieson , I.G. 2010 Limited inbreeding depression in a bottlenecked population is age but not environment dependent 10.1111/j.1600-048X.2010.05164.x · DOI
  36. Lebreton, 1992, Modeling survival and testing biological hypotheses using marked animals - a unified approach with case studies, Ecol. Monogr., 62, 67, 10.2307/2937171 · DOI · Paper page
  37. Liang, 2008, A note on conditional AIC for linear mixed-effects models, Biometrika, 95, 773, 10.1093/biomet/asn023 · DOI
  38. Link, 2006, Model weights and the foundations of multimodel inference, Ecology, 87, 2626, 10.1890/0012-9658(2006)87[2626:MWATFO]2.0.CO;2 · DOI · Paper page
  39. Lukacs, 2007, Concerns regarding a call for pluralism of information theory and hypothesis testing, J. Appl. Ecol., 44, 456, 10.1111/j.1365-2664.2006.01267.x · DOI · Paper page
  40. Lukacs, 2010, Model selection bias and Freedman’s paradox, Ann. Inst. Stat. Math., 62, 117, 10.1007/s10463-009-0234-4 · DOI · Paper page
  41. McCarthy, 2007, Bayesian Methods for Ecology, 10.1017/CBO9780511802454 · DOI · Paper page
  42. Morton, 1956, An estimate of the mutational damage in man from data on consanguineous marriages, Proc. Natl. Acad. Sci. USA, 42, 855, 10.1073/pnas.42.11.855 · DOI · Paper page
  43. Murtaugh, 2009, Performance of several variable-selection methods applied to real ecological data, Ecol. Lett., 12, 1061, 10.1111/j.1461-0248.2009.01361.x · DOI · Paper page
  44. Nakagawa, 2010, Model averaging, missing data and multiple imputation: a case study for behavioural ecology, Behav. Ecol. Sociobiol., 65, 103, 10.1007/s00265-010-1044-7 · DOI · Paper page
  45. Paterson, 2003, Mixed models: getting the best use of parasitological data, Trends. Parasitol., 19, 370, 10.1016/S1471-4922(03)00149-1 · DOI
  46. van de Pol, 2009, A simple method for distinguishing within- versus between-subject effects using mixed models, Anim. Behav., 77, 753, 10.1016/j.anbehav.2008.11.006 · DOI · Paper page
  47. Richards, 2005, Testing ecological theory using the information-theoretic approach: examples and cautionary results, Ecology, 86, 2805, 10.1890/05-0074 · DOI · Paper page
  48. Richards, 2008, Dealing with overdispersed count data in applied ecology, J. Appl. Ecol., 45, 218, 10.1111/j.1365-2664.2007.01377.x · DOI · Paper page
  49. Richards, 2010, Model selection and model averaging in behavioural ecology: the utility of the IT-AIC framework, Behav. Ecol. Sociobiol., 65, 77, 10.1007/s00265-010-1035-8 · DOI · Paper page
  50. Schielzeth, 2009, Conclusions beyond support: overconfident estimates in mixed models, Behav. Ecol., 20, 416, 10.1093/beheco/arn145 · DOI · Paper page
  51. Schwarz, 1978, Estimating the dimension of a model, Ann. Stat., 6, 461, 10.1214/aos/1176344136 · DOI · Paper page
  52. Schwarz, 1999, Estimating animal abundance: review III, Stat. Sci., 14, 427, 10.1214/ss/1009212521 · DOI · Paper page
  53. Spiegelhalter, 2002, Bayesian measures of model complexity and fit, J. R. Stat. Soc. B, 64, 583, 10.1111/1467-9868.00353 · DOI · Paper page
  54. Stephens, 2005, Information theory and hypothesis testing: a call for pluralism, J. Appl. Ecol., 42, 4, 10.1111/j.1365-2664.2005.01002.x · DOI · Paper page
  55. Stephens, 2007, A call for statistical pluralism answered, J. Appl. Ecol., 44, 461, 10.1111/j.1365-2664.2007.01302.x · doi:10.1111/j.1365-2664.2007.01302.x
  56. Symonds, 2008, Species richness and evenness in Australian birds, Am. Nat., 171, 480, 10.1086/528960 · DOI
  57. Symonds, 2010, A brief guide to model selection, multimodel inference and model averaging in behavioural ecology using Akaike’s information criterion, Behav. Ecol. Sociobiol., 65, 13, 10.1007/s00265-010-1037-6 · DOI · Paper page
  58. Tukey, 1977, Exploratory Data Analysis
  59. Vaida, 2005, Conditional Akaike information for mixed-effects models, Biometrika, 92, 351, 10.1093/biomet/92.2.351 · DOI · Paper page
  60. Whittingham, 2006, Why do we still use stepwise modelling in ecology and behaviour?, J. Anim. Ecol., 75, 1182, 10.1111/j.1365-2656.2006.01141.x · DOI · Paper page
  61. Wright, 1922, The effects of inbreeding and crossbreeding on guinea pigs III: crosses between highly inbred families, US Dept. Agric. Bull., 1121, 1
  62. Yang, 2005, Can the strengths of AIC and BIC be shared? A conflict between model indentification and regression estimation, Biometrika, 92, 937, 10.1093/biomet/92.4.937 · DOI · Paper page
  63. Zuur, 2010, A protocol for data exploration to avoid common statistical problems, Meth. Ecol. Evol., 1, 3, 10.1111/j.2041-210X.2009.00001.x · DOI · Paper page

Cited by

See all 2,263

Metadata

Authors
GRUEBER, C. E., NAKAGAWA, S., LAWS, R. J., & JAMIESON, I. G.
Venue
Journal of Evolutionary Biology
Publisher
Oxford University Press (OUP)
Published
2011-01-27
Volume
24
Issue
4
Pages
699-711
ISSN
1010-061X (ISSN-L)
DOI
10.1111/j.1420-9101.2010.02210.x
OpenAlex
W1584343945
Cited by
2,263
References
7
FWCI
68.16
Type
journal article
Language
en
Update notices
erratum (2011-06-03)
Open access
closed
Licence
doi.wiley.com/10.1002/tdm_license_1.1 (tdm, 1678 d)

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Multimodel inference in ecology and evolution: challenges and solutions. https://app.cochat.ai/papers/10.1111/j.1420-9101.2010.02210.x
The paper
GRUEBER, C. E., NAKAGAWA, S., LAWS, R. J., & JAMIESON, I. G. (2011). Multimodel inference in ecology and evolution: challenges and solutions. Journal of Evolutionary Biology. https://doi.org/10.1111/j.1420-9101.2010.02210.x

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

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.