The DOI matches its Crossref record; 97 of the 97 references it cites were found in a registry or an index.
AIC model selection and multimodel inference in behavioral ecology: some background, observations, and comparisons
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“AIC-based stepwise model selection was performed to select the best-fitting model and to diminish overfitting and favoring a more parsimonious model (Burnham et al., 2011).”
From Perceived restorativeness and environment quality in relation to well-being, residential satisfaction, and sense of community: an analysis in Northeast Italy (2025) · cites this paper as Burnham et al., 2011
Citations
Cites
A Mathematical Theory of Communication
Shannon, C. E. (1948) · Bell System Technical Journal · doi:10.1002/j.1538-7305.1948.tb01338.x · Cited by 88,486
A new look at the statistical model identification
Akaike, H. (1974) · IEEE Transactions on Automatic Control · doi:10.1109/tac.1974.1100705 · Cited by 51,879
Estimating the Dimension of a Model
Schwarz, G. (1978) · The Annals of Statistics · doi:10.1214/aos/1176344136 · Cited by 47,033
Model selection and multimodel inference: a practical information-theoretic approach.
Guthery, F. S., Burnham, K. P., & Anderson, D. R. (2003) · The Journal of Wildlife Management · doi:10.2307/3802723 · Cited by 41,495
Self-citationElements of Information Theory
Cover, T. M., & Thomas, J. A. (2001) · Wiley eBooks · doi:10.1002/0471200611 · Cited by 37,139
(2005) · Springer eBooks · doi:10.1007/0-387-27255-0_9 · Cited by 20,333
On Information and Sufficiency
Kullback, S., & Leibler, R. A. (1951) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177729694 · Cited by 20,193
Information Theory and an Extension of the Maximum Likelihood Principle
Akaike, H. (1998) · Springer series in statistics · doi:10.1007/978-1-4612-1694-0_15 · Cited by 18,636
McCullagh, P., & Nelder, J. A. (1989) · doi:10.1007/978-1-4899-3242-6 · Cited by 16,836
Elements of Information Theory
Cover, T. M., & Thomas, J. A. (2005) · doi:10.1002/047174882x · Cited by 13,791
Flagged first, then by citations
Research works printed but not linked: 1
- Akaike H (1983b) Information measures and model selection. Int Statistical Institute 44:277–291
Cited by
Sorted by citations
Items 101–200 of 1,003
Shuryak, I., Carlson, D. J., Brown, J. M., & Brenner, D. J. (2015) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2015.05.013 · Cited by 120
“Ranking of models by relative goodness of fit, taking into account sample size and parameter number, was based on the Akaike information criterion with sample size correction (AICc), which has gained widespread popularity for this purpose [33,34].”
MethodEvolutionary Paths That Expand Plasmid Host-Range: Implications for Spread of Antibiotic Resistance
Loftie‐Eaton, W., Yano, H., Burleigh, S., Simmons, R. S., Hughes, J. M., Rogers, L. M., Hunter, S. S., Settles, M. L., Forney, L. J., Ponciano, J. M., et al. (2015) · Molecular Biology and Evolution · doi:10.1093/molbev/msv339 · Cited by 130
“…cutoffs not only change with the models and sample size, but in the case of Raftery (1995), they correspond to the case where the null hypothesis is assumed to be the true data-generating mechanism (for details, see Taper et al. 2008; Burnham et al. 2011; Aho et al. 2014; Taper and Ponciano 2016).”
BackgroundMilatz, A., Lüftenegger, M., & Schober, B. (2015) · Frontiers in Psychology · doi:10.3389/fpsyg.2015.01949 · Cited by 171
Lescord, G. L., Johnston, T. A., Branfireun, B. A., & Gunn, J. M. (2018) · Environmental Toxicology and Chemistry · doi:10.1002/etc.4233 · Cited by 117
“Coefficient results from the top models (defined as any model with a delta AICc value<4; Burnham and Anderson 2002; Burnham et al. 2011) were averaged as per Grueber et al.”
MethodAn Introduction to Phylogenetic Path Analysis
González‐Voyer, A., & von Hardenberg, A. (2014) · Springer eBooks · doi:10.1007/978-3-662-43550-2_8 · Cited by 117
“…data L (gi|data), provides information regarding the relative strength of evidence for a model compared to the others and can be computed, following Burnham et al. (2011): ‘ ¼ L gijdatað Þ ¼ exp"ð1=2ÞDCICcið Þ ð8:4Þ Finally, CICc weights, the probability of each path model gi, given the data and…”
MethodImpacts of tropical selective logging on carbon storage and tree species richness: A meta-analysis
Martin, P. A., Newton, A. C., Pfeifer, M., Khoo, M., & Bullock, J. M. (2015) · Forest Ecology and Management · doi:10.1016/j.foreco.2015.07.010 · Cited by 131
Galipaud, M., Gillingham, M. A. F., & Dechaume‐Moncharmont, F. (2017) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.12835 · Cited by 121
“One common but incorrect practice consists in considering all models that fall within Δ AIC < 2 of the top ranked model as being truly com - petitive (Arnold, 2010; Burnham et al., 2011).”
BackgroundMethodInfluentialWood, C. L., Sokolow, S. H., Jones, I. J., Chamberlin, A. J., Lafferty, K. D., Kuris, A. M., Jocqué, M., Hopkins, S. R., Adams, G., Buck, J. C., et al. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1903698116 · Cited by 113
“Using a model comparison approach (63), we investigated whether snail and snail–habitat variables could describe and predict the probability that a child became reinfected after praziquantel treatment (logistic generalized linear mixed model [GLMM] with logit link; function glmer in package lme4; ref.”
MethodTheodoridis, S., Fordham, D. A., Brown, S. C., Li, S., Rahbek, C., & Nogués‐Bravo, D. (2020) · Nature Communications · doi:10.1038/s41467-020-16449-5 · Cited by 125
Guo, J., Feng, H., McNie, P. M., Liu, Q., Xu, X., Pan, C., Yan, K., Feng, L., Goitom, E., & Yu, Y. (2022) · CATENA · doi:10.1016/j.catena.2022.106723 · Cited by 116
Colony size predicts division of labour in attine ants
Ferguson‐Gow, H., Sumner, S., Bourke, A. F. G., & Jones, K. E. (2014) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.2014.1411 · Cited by 115
“7 were considered uninformative and were discarded [68].”
BackgroundNocturnal light environments and species ecology: implications for nocturnal color vision in forests
Veilleux, C. C., & Cummings, M. E. (2012) · Journal of Experimental Biology · doi:10.1242/jeb.071415 · Cited by 115
“In general, models with AIC <2 are considered ‘almost as good’ as the best model, while models with AIC >9 have relatively little to no support (Burnham and Anderson, 2004; Burnham et al., 2011; Symonds and Moussalli, 2011).”
BackgroundMethodInfluentialBottom-up and top-down control of dispersal across major organismal groups
Fronhofer, E. A., Legrand, D., Altermatt, F., Ansart, A., Blanchet, S., Bonte, D., Chaine, A. S., Dahirel, M., Laender, F. D., Raedt, J. D., et al. (2018) · Nature Ecology & Evolution · doi:10.1038/s41559-018-0686-0 · Cited by 115
Climatic and edaphic controls over tropical forest diversity and vegetation carbon storage
Hofhansl, F., Chacón‐Madrigal, E., Fuchslueger, L., Jenking, D., Morera‐Beita, A., Plutzar, C., Silla, F., Andersen, K. M., Buchs, D. M., Dullinger, S., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-61868-5 · Cited by 124
Modelling Local Attitudes to Protected Areas in Developing Countries
Bragagnolo, C., Malhado, A. C. M., Jepson, P. R., & Ladle, R. J. (2016) · Conservation and Society · doi:10.4103/0972-4923.191161 · Cited by 113
Negrey, J. D., Reddy, R. B., Scully, E. J., Phillips-Garcia, S., Owens, L. A., Langergraber, K. E., Mitani, J. C., Thompson, M. E., Wrangham, R. W., Muller, M. N., et al. (2019) · Emerging Microbes & Infections · doi:10.1080/22221751.2018.1563456 · Cited by 114
“We analysed risk factors for morbidity and mortality using a generalized linear model (GLM) with binomial distribution and log-link function g in R [51] .”
MethodThe chaos in calibrating crop models
Wallach, D., Palosuo, T., Thorburn, P. J., Hochman, Z., Gourdain, E., Andrianasolo, F. N., Asseng, S., Basso, B., Buis, S., Crout, N. M. J., et al. (2020) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2021.105206 · Cited by 123
Goßner, M. M., Wende, B., Levick, S. R., Schall, P., Floren, A., Linsenmair, K. E., Steffan-Dewenter, I. D., Schulze, E., & Weisser, W. W. (2016) · Biological Conservation · doi:10.1016/j.biocon.2016.06.032 · Cited by 113
“…information criteria corrected for small sample size (AICc) we selected those models fromall possible models with Δ AICc b 2 (substantialsupport)and Δ AICc 2 – 7 (some support) as suggested by Burnham et al. (2011) by using the get.models function implemented in the package MuMIn (Barto ń , 2016).”
Schlacher, T. A., Baco, A. R., Rowden, A. A., O’Hara, T. D., Clark, M. R., Kelley, C., & Dower, J. F. (2013) · Diversity and Distributions · doi:10.1111/ddi.12142 · Cited by 111
“…variable in the DISTLM analysis was assessed using a multimodel inference approach, where AIC weights were summed for each variable across all models in which the variable was present, thereby giving relative probabilities of variable importance (Burnham & Anderson, 2002; Burnham et al., 2011).”
MethodSturgeon survival: The role of swimming performance and fish passage research
Katopodis, C., Cai, L., & Johnson, D. (2019) · Fisheries Research · doi:10.1016/j.fishres.2018.12.027 · Cited by 117
Ecological Niche Modeling: An Introduction for Veterinarians and Epidemiologists
Escobar, L. E. (2020) · Frontiers in Veterinary Science · doi:10.3389/fvets.2020.519059 · Cited by 113
“, Akaike’s information criterion) (19) (Figure 1A).”
Facial Width-To-Height Ratio (fWHR) Is Not Associated with Adolescent Testosterone Levels
Hodges‐Simeon, C. R., Sobraske, K. N. H., Samore, T., Gurven, M., & Gaulin, S. J. C. (2016) · PLoS ONE · doi:10.1371/journal.pone.0153083 · Cited by 108
Plant Functional Traits Predict Green Roof Ecosystem Services
Lundholm, J., Tran, S., & Gebert, L. (2015) · Environmental Science & Technology · doi:10.1021/es505426z · Cited by 109
Addressing human-tiger conflict using socio-ecological information on tolerance and risk
Struebig, M. J., Linkie, M., Deere, N. J., Martyr, D. J., Milliyanawati, B., Faulkner, S., Comber, S. C. L., Mangunjaya, F. M., Leader‐Williams, N., McKay, J. E., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-05983-y · Cited by 113
Gender Divisions of Paid and Unpaid Work in Contemporary UK Couples
McMunn, A., Bird, L., Webb, E. A., & Sacker, A. (2019) · Work Employment and Society · doi:10.1177/0950017019862153 · Cited by 109
Isaksson, C., Sepil, I., Baramidze, V., & Sheldon, B. C. (2013) · BMC Ecology · doi:10.1186/1472-6785-13-15 · Cited by 108
“This analysis compares all possible combination of models in a given data set and ranks them by their AIC value; the model with the lowest AIC value is the best fitting model [63,64].”
MethodBiocrust as one of multiple stable states in global drylands
Chen, N., Yu, K., Jia, R., Teng, J., & Zhao, C. (2020) · Science Advances · doi:10.1126/sciadv.aay3763 · Cited by 108
Pham, T. D., Yokoya, N., Nguyen, T. T. T., Le, N. N., Ha, N. T., Xia, J., Takeuchi, W., & Pham, T. D. (2020) · GIScience & Remote Sensing · doi:10.1080/15481603.2020.1857623 · Cited by 108
Utesch, T., Dreiskämper, D., Naul, R., & Geukes, K. (2018) · Scientific Reports · doi:10.1038/s41598-018-24139-y · Cited by 106
Information-theoretic approaches to statistical analysis in behavioural ecology: an introduction
Garamszegi, L. Z. (2010) · Behavioral Ecology and Sociobiology · doi:10.1007/s00265-010-1028-7 · Cited by 107
Developing multiple hypotheses in behavioral ecology
Dochtermann, N. A., & Jenkins, S. H. (2010) · Behavioral Ecology and Sociobiology · doi:10.1007/s00265-010-1039-4 · Cited by 107
Impact of chronic noise exposure on antipredator behavior: an experiment in breeding house sparrows
Meillère, A., Brischoux, F., & Angelier, F. (2015) · Behavioral Ecology · doi:10.1093/beheco/aru232 · Cited by 106
Amyloid duration is associated with preclinical cognitive decline and tau PET
Koscik, R. L., Betthauser, T. J., Jonaitis, E. M., Allison, S. L., Clark, L. R., Hermann, B. P., Cody, K. A., Engle, J. W., Barnhart, T. E., Stone, C. K., et al. (2020) · Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring · doi:10.1002/dad2.12007 · Cited by 103
Organic phosphorus availability shapes the diversity of phoD-harboring bacteria in agricultural soil
Wei, X., Hu, Y., Cai, G., Yao, H., Ye, J., Sun, Q., Veresoglou, S. D., Li, Y., Zhu, Z., Guggenberger, G., et al. (2021) · Soil Biology and Biochemistry · doi:10.1016/j.soilbio.2021.108364 · Cited by 104
Species decline under nitrogen fertilization increases community-level competence of fungal diseases
Liu, X., Lyu, S., Sun, D., Bradshaw, C. J. A., & Zhou, S. (2017) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.2016.2621 · Cited by 102
Primate mosaic brain evolution reflects selection on sensory and cognitive specialization
DeCasien, A. R., & Higham, J. P. (2019) · Nature Ecology & Evolution · doi:10.1038/s41559-019-0969-0 · Cited by 105
A global overview of the trophic structure within microbiomes across ecosystems
Xiong, W., Jousset, A., Li, R., Delgado‐Baquerizo, M., Bahram, M., Logares, R., Wilden, B., de Groot, G. A., Amacker, N., Kowalchuk, G. A., et al. (2021) · Environment International · doi:10.1016/j.envint.2021.106438 · Cited by 103
“We identified the best model for the regression by the lowest Akaike Information Criteria (Burnham et al., 2011; Peruggia, 2003).”
MethodModeling nonlinear relationships in ERP data using mixed‐effects regression with R examples
Tremblay, A., & Newman, A. J. (2014) · Psychophysiology · doi:10.1111/psyp.12299 · Cited by 101
“We refer the interested reader to Burnham, Anderson, and Huyvaert (2011) and Symonds and Moussalli (2011) for more details about AIC and model selection.”
MethodLeigh, C., Bonada, N., Boulton, A. J., Hugueny, B., Larned, S. T., Vorste, R. V., & Datry, T. (2015) · Aquatic Sciences · doi:10.1007/s00027-015-0427-2 · Cited by 101
“For each response variable, we selected the optimal model based on the change in Akaike’s Information Criterion, corrected for finite sample sizes (AICc; Burnham et al. 2011), between each of the following full models: the relevant above-described model as dependent on the question being explored (Q1 or Q2–3) with (i)…”
MethodGroup-level cooperation in chimpanzees is shaped by strong social ties
Samuni, L., Crockford, C., & Wittig, R. M. (2021) · Nature Communications · doi:10.1038/s41467-020-20709-9 · Cited by 108
Blue carbon benefits from global saltmarsh restoration
Mason, V. G., Burden, A., Epstein, G., Jupe, L. L., Wood, K. A., & Skov, M. W. (2023) · Global Change Biology · doi:10.1111/gcb.16943 · Cited by 100
Amazon forest biogeography predicts resilience and vulnerability to drought
Chen, S., Stark, S. C., Nobre, A. D., Cuartas, L. A., Amore, D. J., Restrepo‐Coupé, N., Smith, M. N., Chitra‐Tarak, R., Ko, H., Nelson, B., et al. (2024) · Nature · doi:10.1038/s41586-024-07568-w · Cited by 108
Agentic narcissism, communal narcissism, and prosociality.
Nehrlich, A. D., Gebauer, J. E., Sedikides, C., & Schoel, C. (2019) · Journal of Personality and Social Psychology · doi:10.1037/pspp0000190 · Cited by 106
Borella, E., Carbone, E., Pastore, M., Béni, R. D., & Carretti, B. (2017) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2017.00099 · Cited by 112
“, an estimate of the likelihood of a model making the best prediction on new data, conditional on the set of models considered (Burnham et al., 2011; McElreath, 2016).”
MethodPLS-Based Model Selection: The Role of Alternative Explanations in Information Systems Research
Sharma, P. N., Sarstedt, M., Galit Shmueli, A., Kim, K. H., & Thiele, K. O. (2019) · tub.dok (Hamburg University of Technology) · doi:10.17005/1.jais.00538 · Cited by 98
Individual variation in early‐life telomere length and survival in a wild mammal
van Lieshout, S. H. J., Bretman, A. J., Newman, C., Buesching, C. D., Macdonald, D. W., & Dugdale, H. L. (2019) · Molecular Ecology · doi:10.1111/mec.15212 · Cited by 102
Betini, G. S., & Norris, D. R. (2011) · Animal Behaviour · doi:10.1016/j.anbehav.2011.10.018 · Cited by 98
Biodiversity attracts visitors to national parks
Siikamäki, P., Kangas, K., Paasivaara, A., & Schroderus, S. (2015) · Biodiversity and Conservation · doi:10.1007/s10531-015-0941-5 · Cited by 98
“As the number of species typically increases with area (MacArthur and Wilson 1967), the area of the NPs was included in the analyses to control for the effects of the species–area relationship.”
BackgroundEnvironment-sensitivity functions for gross primary productivity in light use efficiency models
Bao, S., Wutzler, T., Koirala, S., Cuntz, M., Ibrom, A., Besnard, S., Walther, S., Šigut, L., Moreno‐Martínez, Á., Weber, U., et al. (2021) · Agricultural and Forest Meteorology · doi:10.1016/j.agrformet.2021.108708 · Cited by 98
Changes in behaviour of dairy cows with clinical mastitis
Sepúlveda-Varas, P., Proudfoot, K. L., Weary, D. M., & von Keyserlingk, M. A. G. (2014) · Applied Animal Behaviour Science · doi:10.1016/j.applanim.2014.09.022 · Cited by 96
“The correlation between repeated measures was modelled with compound symmetry or auto-regressive based on the lowest Akaike’s information criterion (Burnham et al., 2011). Residuals were examined to verify normality and homogeneity of variances. After visual inspection of the graphs, specific comparisons were ehaviour…”
Matos, F. A. R., Magnago, L. F. S., Gastauer, M., Carreiras, J. M. B., Simonelli, M., Meira‐Neto, J. A. A., & Edwards, D. P. (2016) · Journal of Ecology · doi:10.1111/1365-2745.12661 · Cited by 98
“The Akaike Information Criterion of Second Order (AICc indicated for small sample sizes), with ∆AICc≤2, was used to select our best models (Burnham et al. 2011), although we also consider all selected models.”
MethodRegos, A., Gagne, L., Alcaraz‐Segura, D., Honrado, J. P., & Domínguez, J. (2019) · Scientific Reports · doi:10.1038/s41598-019-40766-5 · Cited by 96
Would protecting tropical forest fragments provide carbon and biodiversity cobenefits under REDD +?
Magnago, L. F. S., Magrach, A., Laurance, W. F., Martins, S. V., Meira‐Neto, J. A. A., Simonelli, M., & Edwards, D. P. (2015) · Global Change Biology · doi:10.1111/gcb.12937 · Cited by 99
“…richness and abundance of IUCN Red-listed and endemic species, we used an information theoretical approach based on the Akaike information criterion of second order (AICc), which is indicated for small sample sizes, and the best model was indicated by the lowest AICc value (Burnham et al., 2011).”
MethodSharma, K., Bayrakçısmith, R., Tumursukh, L., Johansson, Ö., Purevsuren, S., McCarthy, T., & Mishra, C. (2014) · PLoS ONE · doi:10.1371/journal.pone.0101319 · Cited by 95
The effects of wind and fuel stores on stopover departure behavior across a migratory barrier
Dossman, B. C., Mitchell, G. W., Norris, D. R., Taylor, P. D., Guglielmo, C. G., Matthews, S. N., & Rodewald, P. G. (2015) · Behavioral Ecology · doi:10.1093/beheco/arv189 · Cited by 94
Formidable females and the power trajectories of socially integrated male vervet monkeys
Young, C., McFarland, R., Barrett, L., & Henzi, S. P. (2017) · Animal Behaviour · doi:10.1016/j.anbehav.2017.01.006 · Cited by 93
“5 of the best model were then used to derive averaged model outcomes (Burnham, Anderson, & Huyvaert, 2011).”
MethodSpatio‐temporal variation in lifelong telomere dynamics in a long‐term ecological study
Spurgin, L. G., Bebbington, K., Fairfield, E. A., Hammers, M., Komdeur, J., Burke, T. A., Dugdale, H. L., & Richardson, D. S. (2017) · Journal of Animal Ecology · doi:10.1111/1365-2656.12741 · Cited by 94
I-nice: A new approach for identifying the number of clusters and initial cluster centres
Masud, M. A., Huang, J. Z., Wei, C., Wang, J., Khan, I., & Ming, Z. (2018) · Information Sciences · doi:10.1016/j.ins.2018.07.034 · Cited by 99
McCune, J. L., & Vellend, M. (2013) · Journal of Ecology · doi:10.1111/1365-2745.12156 · Cited by 93
Behavioral correlates of supplementary feeding of wildlife: Can general conclusions be drawn?
Steyaert, S. M. J. G., Kindberg, J., Klemen Jerina, A., Krofel, M., Stergar, M., Swenson, J. E., & Zedrosser, A. (2014) · Basic and Applied Ecology · doi:10.1016/j.baae.2014.10.002 · Cited by 92
Fitak, R. R., & Johnsen, S. (2017) · Journal of Experimental Biology · doi:10.1242/jeb.167056 · Cited by 92
“These methods rely on the probability of data given a particular model, or likelihood, and are preferred for quantifying strength of evidence in favor of a hypothesis (Burnham and Anderson, 2002; Burnham et al., 2011).”
BackgroundMethodInfluentialIntensification of shifting cultivation reduces forest resilience in the northern Amazon
Villa, P. M., Martins, S. V., Neto, S. N. D. O., Rodrigues, A. C., Martorano, L. G., Monsanto, L. D., Cancio, N. M., & Gastauer, M. (2018) · Forest Ecology and Management · doi:10.1016/j.foreco.2018.08.014 · Cited by 100
Effects of body size on estimation of mammalian area requirements
Noonan, M. J., Fleming, C. H., Tucker, M. A., Kays, R., Harrison, A., Crofoot, M. C., Abrahms, B. L., Alberts, S. C., Ali, A. H., Jeanne Altmann, A., et al. (2020) · Conservation Biology · doi:10.1111/cobi.13495 · Cited by 92
Cornée, S. (2019) · Journal of Small Business Management · doi:10.1111/jsbm.12318 · Cited by 91
“To compare models on the basis of the AIC, we compute an evidence ratio as suggested by Burnham, Anderson, and Huyvaert (2011).15 According to this ratio, the SI and HISI models are, respectively, 97.03 and 139.77 times more likely than the HI model to be closer to the best model.”
BackgroundMethodInfluentialThurfjell, H., Ciuti, S., & Boyce, M. S. (2017) · PLoS ONE · doi:10.1371/journal.pone.0178082 · Cited by 90
Lavoie, R. A., Amyot, M., & Lapierre, J. (2019) · Journal of Geophysical Research - Biogeosciences · doi:10.1029/2018jg004896 · Cited by 67
Rabone, M., Wiethase, J. H., Allan, F., Gouvras, A., Pennance, T., Hamidou, A. A., Webster, B. L., Labbo, R., Emery, A. M., Garba, A., et al. (2019) · Parasites & Vectors · doi:10.1186/s13071-019-3745-8 · Cited by 90
Garrido, M., Hansen, S. K., Yaari, R., & Hawlena, H. (2021) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.13742 · Cited by 92
“However, since it is recommended not to mix dichotomous hypothesis testing with MSA (Burnham et al., 2011), we propose to use here any MSA as well (e.g. comparison of models including or excluding a given path or calculation of the relative importance of specific paths by model averaging).”
BackgroundMethodEdwards, E. K., Mitchell, N. J., & Ridley, A. R. (2015) · Ostrich · doi:10.2989/00306525.2015.1034219 · Cited by 90
“Using individual identity (nested in group identity) as random terms to account for the potential influence of repeated measures on the distribution of the data, a series of models were tested, with each model representing a biological hypothesis (Burnham et al. 2011).”
MethodFamiliarity breeds content: assessing bird species popularity with culturomics
Correia, R. A., Jepson, P. R., Malhado, A. C. M., & Ladle, R. J. (2016) · PeerJ · doi:10.7717/peerj.1728 · Cited by 90
Chabot, D., Carignan, V., & Bird, D. M. (2014) · Wetlands · doi:10.1007/s13157-014-0518-1 · Cited by 89
“We then used a model-selection approach based on Akaike’s information criterion for finite sample sizes (AICc; Burnham and Anderson 2002; Burnham et al. 2011) to compare candidate regression models with number of vocalizing males detected as the dependent variable and the various habitat parameters…”
Li, Z., Fotheringham, A. S., Oshan, T. M., & Wolf, L. J. (2020) · Annals of the American Association of Geographers · doi:10.1080/24694452.2019.1704680 · Cited by 90
Temperate agroforestry systems provide greater pollination service than monoculture
Varah, A., Jones, H., Smith, J., & Potts, S. G. (2020) · Agriculture Ecosystems & Environment · doi:10.1016/j.agee.2020.107031 · Cited by 89
Adam, S., Anteneh, H., Hornisch, M., Wagner, V., Lu, J., Radde, N. E., Bashtrykov, P., Song, J., & Jeltsch, A. (2020) · Nature Communications · doi:10.1038/s41467-020-17531-8 · Cited by 88
Spatial clustering and risk factors of malaria infections in Ratanakiri Province, Cambodia
Sluydts, V., Heng, S., Coosemans, M., van Roey, K. J., Gryseels, C., Canier, L., Kim, S., Khim, N., Sovannaroth, S., Mean, V., et al. (2014) · Malaria Journal · doi:10.1186/1475-2875-13-387 · Cited by 89
Lounibos, L. P., Bargielowski, I., Carrasquilla, M. C., & Nishimura, N. (2016) · Journal of Medical Entomology · doi:10.1093/jme/tjw122 · Cited by 88
A tutorial on Bayesian multi-model linear regression with BAS and JASP
van den Bergh, D., Clyde, M. A., Gupta, A. R. K. N., de Jong, T., Gronau, Q. F., Marsman, M., Ly, A., & Wagenmakers, E. (2021) · Behavior Research Methods · doi:10.3758/s13428-021-01552-2 · Cited by 90
“Despite the advantages of multi-model inference (e.g., Burnham, Anderson, & Huyvaert, 2011; Hinne, Gronau, van den Bergh, & Wagenmakers, 2020; Hoeting et al. 1999) and its successes in fields such as machine learning (Breiman, 2001), cosmology (Trotta, 2008), and climate prediction (Tebaldi &…”
Band, N., Kadmon, R., Mandel, M., & DeMalach, N. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2112010119 · Cited by 90
Nazaries, L., Tottey, W., Robinson, L. J., Khachane, A. N., Al‐Soud, W. A., Sørensen, S. J., & Singh, B. K. (2015) · Soil Biology and Biochemistry · doi:10.1016/j.soilbio.2015.06.027 · Cited by 88
“This approach relies on ordinary leastsquares (OLS) linear regression analysis and is considered better thanmultiple linear regression as it does not rely on null hypothesis testing (Burnham et al., 2011; Mundry, 2011).”
MethodSchloss, I. R., Wasiłowska, A., Dumont, D., Almandoz, G. O., Hernando, M. P., Michaud-Tremblay, C.-A., Saravia, L. A., Rzepecki, M., Monien, P., Monien, D., et al. (2014) · Limnology and Oceanography · doi:10.4319/lo.2014.59.1.0195 · Cited by 88
“Synthesis of the results from the two models—In a study based on a 19 yr time series in PC, Schloss et al. (2012) found that air temperature presented significant positive correlations with surface-water temperature and negative correlations with salinity.”
BackgroundMethodResultInfluentialWalker, R. H., King, A. J., McNutt, J. W., & Jordan, N. R. (2017) · Proceedings of the Royal Society B: Biological Sciences · doi:10.1098/rspb.2017.0347 · Cited by 92
Responses of anurans to composition and configuration of agricultural landscapes
Collins, S. J., & Fahrig, L. (2017) · Agriculture Ecosystems & Environment · doi:10.1016/j.agee.2016.12.038 · Cited by 87
“Prior to model selection we assessed the fit of the global models by examining residual plots and calculating the adjusted R(2) (Burnham et al., 2011).”
MethodEvolution of female promiscuity in Passerides songbirds
Lifjeld, J. T., Gohli, J., Albrecht, T., García‐del‐Rey, E., Johannessen, L. E., Kleven, O., Marki, P. Z., Omotoriogun, T. C., Rowe, M., & Johnsen, A. (2019) · BMC Evolutionary Biology · doi:10.1186/s12862-019-1493-1 · Cited by 86
Strong Evidence for an Intraspecific Metabolic Scaling Coefficient Near 0.89 in Fish
Jerde, C. L., Kraskura, K., Eliason, E. J., Csik, S. R., Stier, A. C., & Taper, M. L. (2019) · Frontiers in Physiology · doi:10.3389/fphys.2019.01166 · Cited by 107
Intra‐ and inter‐annual variation in gray whale body condition on a foraging ground
Soledade Lemos, L., Burnett, J. D., Chandler, T. E., Sumich, J. L., & Torres, L. G. (2020) · Ecosphere · doi:10.1002/ecs2.3094 · Cited by 86
Is logarithmic transformation necessary in allometry?
Packard, G. C. (2013) · Biological Journal of the Linnean Society · doi:10.1111/bij.12038 · Cited by 86
Ecomorphological convergence in planktivorous surgeonfishes
Friedman, S. T., Price, S. A., Hoey, A. S., & Wainwright, P. C. (2016) · Journal of Evolutionary Biology · doi:10.1111/jeb.12837 · Cited by 86
Food availability affects adult survival trajectories depending on early developmental conditions
Briga, M., Koetsier, E., Boonekamp, J. J., Jimeno, B., & Verhulst, S. (2017) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.2016.2287 · Cited by 86
Murphy, S. M., Augustine, B. C., Ulrey, W. A., Guthrie, J. M., Scheick, B. K., McCown, J. W., & Cox, J. J. (2017) · PLoS ONE · doi:10.1371/journal.pone.0181849 · Cited by 86
Balasubramaniam, K., Beisner, B., Berman, C. M., Marco, A. D., Duboscq, J., Koirala, S., Majolo, B., MacIntosh, A., McFarland, R., Molesti, S., et al. (2018) · American Journal of Primatology · doi:10.1002/ajp.22727 · Cited by 75
“As recommended by I-T approaches (Burnham et al., 2011; Grueber et al., 2011), our global models were constructed by the inclusion of just those covariates that catered to testing our specific predictions (Q2-Q4).”
MethodParallels in the sequential organization of birdsong and human speech
Sainburg, T., Theilman, B. H., Thielk, M., & Gentner, T. Q. (2019) · Nature Communications · doi:10.1038/s41467-019-11605-y · Cited by 85
Effects of Temperature and Water Availability on Northern European Boreal Forests
Ruiz-Pérez, G., & Vico, G. (2020) · Frontiers in Forests and Global Change · doi:10.3389/ffgc.2020.00034 · Cited by 84
Measuring human wellbeing: A protocol for selecting local indicators
Loveridge, R., Sallu, S. M., Pesha, I. J., & Marshall, A. R. (2020) · Environmental Science & Policy · doi:10.1016/j.envsci.2020.09.002 · Cited by 86
Flammability traits of Cape shrubland species with different post-fire recruitment strategies
Burger, N., & Bond, W. J. (2015) · South African Journal of Botany · doi:10.1016/j.sajb.2015.05.026 · Cited by 84
Ecological niche modeling re‐examined: A case study with the Darwin's fox
Escobar, L. E., Qiao, H., Cabello, J., & Peterson, A. T. (2018) · Ecology and Evolution · doi:10.1002/ece3.4014 · Cited by 83
“AIC is a penalized likelihood criterion expressed as AIC = 2* K − 2 * ln( L ), where L is the maximized value of the likelihood function for a model and K is the number of parameters employed in the model (Burnham et al., 2011).”
Cohn, M., Chen, C.-Y., & Yu, Z. (2019) · Annual Meeting of the Special Interest Group on Discourse and Dialogue · doi:10.18653/v1/w19-5935 · Cited by 84
“Model comparisons revealed that the model with the fixed effects of Valence and Emotion Orientation best fit the data ( AIC c =1689.9), relative to the model including Function and Emotion Orientation ( AIC c =1694.78).”
MethodInfluentialMature Andean forests as globally important carbon sinks and future carbon refuges
Duque, Á., Peña, M. A., Cuesta, F., González‐Caro, S., Kennedy, P. G., Phillips, O. L., Calderón-Loor, M. R., Blundo, C., Carilla, J., Cayola, L., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-22459-8 · Cited by 87
Costs of territoriality: a review of hypotheses, meta-analysis, and field study
Ord, T. J. (2021) · Oecologia · doi:10.1007/s00442-021-05068-6 · Cited by 83
Human recreation alters behaviour profiles of non-breeding birds on open-coast sandy shores
Schlacher, T. A., Nielsen, T., & Weston, M. A. (2013) · Estuarine Coastal and Shelf Science · doi:10.1016/j.ecss.2012.12.016 · Cited by 83
“The criterion to evaluate the ‘performance’ of models was the corrected Akaike information criterion (AICc) (Burnham et al., 2011; Symonds and Moussalli, 2011).”
MethodStructure and organization of an avian haemosporidian assemblage in a Neotropical savanna in Brazil
Fecchio, A., Lima, M. R., Svensson‐Coelho, M., Marini, M. Â., & Ricklefs, R. E. (2012) · Parasitology · doi:10.1017/s0031182012001412 · Cited by 82
“Multi-model inference was carried out in the a priori set of models that presented ΔAICc equal or lower than 7 (Burnham et al. 2011).”
BackgroundMethod
Metadata
- Authors
- Burnham, K. P., Anderson, D. R., & Huyvaert, K. P.
- Affiliations
- Colorado State University (US)
- Corresponding author
- Burnham, Kenneth P.
- ORCID
- Huyvaert, Kathryn P.
- Venue
- Behavioral Ecology and Sociobiology
- Published
- 2010-08-18
- Volume
- 65
- Issue
- 1
- Pages
- 23-35
- ISSN
- 0340-5443 (ISSN-L)
- 1432-0762
- OpenAlex
- W2096650374
- Semantic Scholar
- 33e08c84858e01d75d94592e03272dd4073316ff
- Cited by
- 4,104
- References
- 95
- Citations by year
- 2026: 228 · 2025: 300 · 2024: 327 · 2023: 365 · 2022: 358 · 2021: 434 · 2020: 404 · 2019: 335 · 2018: 340 · 2017: 251 · 2016: 239 · 2015: 192 · 2014: 154 · 2013: 98 · 2012: 53
- FWCI
- 44.95
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Behavioral Ecology and Sociobiology (publishedVersion)
- Funders
- Colorado State University
- Innovative Research Group Project of the National Natural Science Foundation of China
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
- Keywords
- AIC model selection, multi-model inference, alternative hypothesis, hypothesis testing, behavioral ecology, statistical inference, probability models
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
