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AIC model selection and multimodel inference in behavioral ecology: some background, observations, and comparisons
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“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
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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
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- Akaike H (1983b) Information measures and model selection. Int Statistical Institute 44:277–291
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High Throughput Measurement of γH2AX DSB Repair Kinetics in a Healthy Human Population
Sharma, P. M., Ponnaiya, B., Taveras, M., Shuryak, I., Turner, H. C., & Brenner, D. J. (2015) · PLoS ONE · doi:10.1371/journal.pone.0121083 · Cited by 87
“The relative degree of support provided by the data for each scenario was assessed by the information-theoretic (I-T) approach, using the Akaike information criterion corrected for sample size and parameter number (AICc) [35, 36].”
Chen, L.-I., Guh, J., Wu, K., Chen, Y., Kuo, M., Hwang, S., Chen, T.-H., & Chen, H. (2014) · PLoS ONE · doi:10.1371/journal.pone.0099645 · Cited by 81
“Ordinal logistic regressions (Cindefined CKD category and eGFR was the dependent and independent variable, respectively) (Methods S1) were compared by the Akaike information criterion (AIC) where a model with the lowest AIC is the best model [29].”
BackgroundMethodInfluentialLiu, X., Chen, L., Liu, M., García‐Guzmán, G., Gilbert, G. S., & Zhou, S. (2020) · Oikos · doi:10.1111/oik.07027 · Cited by 85
Analyzing the popularity of YouTube videos that violate mountain gorilla tourism regulations
Otsuka, R., & Yamakoshi, G. (2020) · PLoS ONE · doi:10.1371/journal.pone.0232085 · Cited by 81
How to Quantify Urbanization When Testing for Urban Evolution?
Szulkin, M., Garroway, C. J., Corsini, M., Kotarba, A. Z., & Dominoni, D. M. (2020) · doi:10.1093/oso/9780198836841.003.0002 · Cited by 81
Unbounding the mental number line—new evidence on children's spatial representation of numbers
Link, T., Huber, S., Nuerk, H., & Moeller, K. (2014) · Frontiers in Psychology · doi:10.3389/fpsyg.2013.01021 · Cited by 81
“Models were compared by calculating AICc (Akaike information criterion with a correction for finite sample sizes) values for each participant (e.g., Burnham et al., 2011; see also Cohen and Blanc-Goldhammer, 2011, for a similar procedure).”
MethodEffects of Rising Temperature on the Growth, Stoichiometry, and Palatability of Aquatic Plants
Zhang, P., Grutters, B. M. C., van Leeuwen, C. H. A., Xu, J., Petruzzella, A., van den Berg, R. F., & Bakker, E. S. (2019) · Frontiers in Plant Science · doi:10.3389/fpls.2018.01947 · Cited by 79
“The significance of included terms was tested by model selection based on AICc values (Burnham and Anderson, 2002; Burnham et al., 2011).”
MethodPositive effects of afforestation efforts on the health of urban soils
Oldfield, E. E., Felson, A. J., Wood, S. A., Hallett, R. A., Strickland, M. S., & Bradford, M. A. (2013) · Forest Ecology and Management · doi:10.1016/j.foreco.2013.11.027 · Cited by 80
“To select the best models for soil response variables, we used the model with the lowest Akaike information criterion (AIC) score (Burnham et al., 2011).”
MethodPham, T. D., Yoshino, K., & Bui, D. T. (2016) · GIScience & Remote Sensing · doi:10.1080/15481603.2016.1269869 · Cited by 79
“Both AIC and BIC provide simple and effective methods for selecting and comparing regression models (Burnham and Anderson 2004; Burnham, Anderson, and Huyvaert 2011).”
MethodWarming affects foliar fungal diseases more than precipitation in a Tibetan alpine meadow
Liu, X., Ma, Z., Cadotte, M. W., Chen, F., He, J., & Zhou, S. (2018) · New Phytologist · doi:10.1111/nph.15460 · Cited by 80
Phylogenomics reveals the history of host use in mosquitoes
Soghigian, J., Sither, C. B., Justi, S. A., Morinaga, G., Cassel, B. K., Vitek, C. J., LIVDAHL, T. P., Xia, S., Gloria‐Soria, A., Powell, J. R., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-41764-y · Cited by 82
“We compared these models against one another, calculating Akaike Information Criterion 89 (AIC) from their likelihoods, and compared their weights 90 (AIC w ).”
Plant functional trait effects on runoff to design herbaceous hedges for soil erosion control
Kervroëdan, L., Armand, R., Saunier, M., Ouvry, J.-F., & Faucon, M. (2018) · Ecological Engineering · doi:10.1016/j.ecoleng.2018.04.024 · Cited by 78
“Models with ∆AICc varying between 2 and 7, indicating 275 less support, were also analysed as recommended by Burnham et al. (2011).”
MethodGetting fat or getting help? How female mammals cope with energetic constraints on reproduction
Heldstab, S. A., van Schaik, C. P., & Isler, K. (2017) · Frontiers in Zoology · doi:10.1186/s12983-017-0214-0 · Cited by 78
“We accounted for uncertainty in the models by performing model averaging [99] in the candidate model set including models with ΔAICc <2 [100].”
MethodSpatial expansions and travelling waves of rabies in vampire bats
Benavides, J. A., Valderrama, W., & Streicker, D. G. (2016) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.2016.0328 · Cited by 78
Eco-Labeled Seafood: Determinants for (Blue) Green Consumption
Jonell, M., Crona, B., Brown, K., Rönnbäck, P., & Troell, M. F. (2016) · Sustainability · doi:10.3390/su8090884 · Cited by 88
“∆AIC can be translated to evidence ratios, which indicate the relative likelihood of model i versus model j [52].”
BackgroundMethodInfluentialNorder, S. J., Πρόιος, Κ., Whittaker, R. J., Alonso, M. R., Borges, P. A. V., Borregaard, M. K., Cowie, R. H., Florens, F. B. V., Martins, A. M. D. F., Ibáñez, M., et al. (2018) · Global Ecology and Biogeography · doi:10.1111/geb.12835 · Cited by 77
Geppert, C., Hass, A. L., Földesi, R., Donkó, B., Akter, A., Tscharntke, T., & Batáry, P. (2020) · Journal of Applied Ecology · doi:10.1111/1365-2664.13682 · Cited by 75
Dawe, K., Bayne, E. M., & Boutin, S. A. (2014) · Canadian Journal of Zoology · doi:10.1139/cjz-2013-0262 · Cited by 77
Morozova, O., Levina, O. S., Uusküla, A., & Heimer, R. (2015) · BMC Medical Research Methodology · doi:10.1186/s12874-015-0066-2 · Cited by 77
“it still fails to account for model uncertainty and treats a selected model as if it was pre-specified, thus returning biased standard errors [53].”
BackgroundThe multi-peak adaptive landscape of crocodylomorph body size evolution
Godoy, P. L., Benson, R., Bronzati, M., & Butler, R. J. (2019) · BMC Evolutionary Biology · doi:10.1186/s12862-019-1466-4 · Cited by 79
“suboptimal, given that AIC procedures might face difficulties [153], which have previously demonstrated for other datasets (e.”
BackgroundParameter identifiability and model selection for sigmoid population growth models
Simpson, M. J., Browning, A. P., Warne, D. J., Maclaren, O. J., & Baker, R. E. (2021) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2021.110998 · Cited by 76
Humberg, S., Dufner, M., Schönbrodt, F. D., Geukes, K., Hutteman, R., Küfner, A. C. P., van Zalk, M. H. W., Denissen, J. J. A., Nestler, S., & Back, M. D. (2018) · Journal of Personality and Social Psychology · doi:10.1037/pspp0000204 · Cited by 95
Mills, D., Fattebert, J., Hunter, L., & Slotow, R. (2019) · PLoS ONE · doi:10.1371/journal.pone.0216447 · Cited by 75
“Models were evaluated using the Akaike’s Information Criterion corrected for small sample sizes (AICc) [44].”
MethodKnapp, S., & Kühn, I. (2012) · Ecology Letters · doi:10.1111/j.1461-0248.2012.01787.x · Cited by 75
“We reduced the fitted model to its minimal adequate version by stepwise backward selection, using Akaike’s Information Criterion (AIC; cf. Burnham et al. 2011).”
MethodSocial network- and community-level influences on contraceptive use: evidence from rural Poland
Colleran, H., & Mace, R. (2015) · Proceedings of the Royal Society of London. Biological Sciences · doi:10.1098/rspb.2015.0398 · Cited by 79
Stroeve, J. C., Jenouvrier, S., Campbell, G. G., Barbraud, C., & Delord, K. (2016) · The Cryosphere · doi:10.5194/tc-10-1823-2016 · Cited by 75
“To select the covariate that most impacts the breeding success of snow petrels, we applied the information-theoretic (I-T) approaches (Burnham et al., 2011).”
MethodMarchese, C., Albouy, C. P., Tremblay, J., Dumont, D., D’Ortenzio, F., Vissault, S., & Bélanger, S. (2017) · Polar Biology · doi:10.1007/s00300-017-2095-2 · Cited by 75
Cañedo‐Argüelles, M., Gutiérrez‐Cánovas, C., Acosta, R., Castro‐López, D., Cid, N., Fortuño, P., Munné, A., Múrria, C., Pimentão, A. R., Sarremejane, R., et al. (2020) · Journal of Biogeography · doi:10.1111/jbi.13913 · Cited by 76
Eye for an eyespot: how iridescent plumage ocelli influence peacock mating success
Dakin, R., & Montgomerie, R. (2013) · Behavioral Ecology · doi:10.1093/beheco/art045 · Cited by 75
Thermodynamic equilibrium of 4-hydroxy-2,5-dimethyl-3(2H)-furanone in different solvent systems
Xu, J., Wang, Y., Wang, G., Huang, C., Hao, H., & Yin, Q. (2015) · The Journal of Chemical Thermodynamics · doi:10.1016/j.jct.2015.08.035 · Cited by 74
Fiery frills: carotenoid-based coloration predicts contest success in frillneck lizards
Hamilton, D. G., Whiting, M. J., & Pryke, S. R. (2013) · Behavioral Ecology · doi:10.1093/beheco/art041 · Cited by 74
“…the fit of the model against the number of parameters and was used to objectively compare different models and select the best-fitting and most parsimonious model; the model with the lowest AIC C value (and a ∆AIC C of ≤ 2) is accepted as the model best fitting the data (Burnham et al. 2011).”
RetractedValidation of the protein kinase PfCLK3 as a multistage cross-species malarial drug target
Alam, M. M., Sánchez-Azqueta, A., Janha, O., Flannery, E., Mahindra, A., Mapesa, K., Char, A. B., Sriranganadane, D., Brancucci, N. M. B., Antonova-Koch, Y., et al. (2019) · Science · doi:10.1126/science.aau1682 · Cited by 75
Effects of forest structure, management and landscape on bird and bat communities
Bouvet, A., Paillet, Y., Archaux, F., Tillon, L., DENIS, P., Gilg, O., & Gosselin, F. (2016) · Environmental Conservation · doi:10.1017/s0376892915000363 · Cited by 73
“As our sample was relatively small, we selected the best models based on Akaike Information Criteria corrected for small samples (AICc; Burnham et al. 2011).”
MethodPredicting the seasonal dynamics of bird communities along an urban-rural gradient using NDVI
Leveau, L. M., Isla, F. I., & Bellocq, M. I. (2018) · Landscape and Urban Planning · doi:10.1016/j.landurbplan.2018.04.007 · Cited by 73
Purpura, D. J., King, Y. A., Rolan, E. P., Hornburg, C. B., Schmitt, S. A., Hart, S. A., & Ganley, C. M. (2020) · Frontiers in Psychology · doi:10.3389/fpsyg.2020.01925 · Cited by 91
“Lower AIC and BIC values—typically differences of 10 or more—indicated a better fitting model (Kass and Raftery, 1995; Hu and Bentler, 1999; Frontiers in Psychology | www.frontiersin.org 6 August 2020 | Volume 11 | Article 1925 Burnham et al., 2011).”
BackgroundHoney bees and climate explain viral prevalence in wild bee communities on a continental scale
Piot, N., Schweiger, O., Meeus, I., Yañez, O., Straub, L., Villamar‐Bouza, L., la Rúa, P. D., Jara, L., Ruíz, C., Malmstrøm, M., et al. (2022) · Scientific Reports · doi:10.1038/s41598-022-05603-2 · Cited by 75
Milicev, J., McCann, M., Simpson, S. A., Biello, S. M., & Gardani, M. (2023) · Current Psychology · doi:10.1007/s12144-021-02309-y · Cited by 56
Invasion Expansion: Time since introduction best predicts global ranges of marine invaders
Byers, J. E., Smith, R. S., Pringle, J. M., Clark, G. F., Gribben, P. E., Hewitt, C. L., Inglis, G. J., Johnston, E. L., Ruiz, G. M., Stachowicz, J. J., et al. (2015) · Scientific Reports · doi:10.1038/srep12436 · Cited by 72
Diversity begets diversity: A global perspective on gender equality in scientific society leadership
Potvin, D. A., Burdfield‐Steel, E., Potvin, J. M., & Heap, S. M. (2018) · PLoS ONE · doi:10.1371/journal.pone.0197280 · Cited by 72
Weather influences feed intake and feed efficiency in a temperate climate
Hill, D. L., & Wall, E. (2017) · Journal of Dairy Science · doi:10.3168/jds.2016-11047 · Cited by 71
“For each response variable, we determined the best model with respect to timescale and weather metric based on the lowest AIC, and considered 7 AIC units to be a meaningful difference (Burnham et al., 2011).”
MethodPredictive Model of Avian Electrocution Risk on Overhead Power Lines
Dwyer, J. F., Harness, R. E., & Donohue, K. (2013) · Conservation Biology · doi:10.1111/cobi.12145 · Cited by 71
“We used AICc scores to rank and weight models (Burnham & Anderson 2004; Burnham et al. 2011).”
MethodMason, N. A., & Burns, K. J. (2015) · Biological Journal of the Linnean Society · doi:10.1111/bij.12455 · Cited by 71
“We used the most recent molecular phylogeny to delimit a monophyletic Thraupidae for taxonomic sampling (Burns et al., 2014). To identify species for comparative analyses, we followed the species-level taxonomy of Clements et al. (2013). However, we also included Sicalis luteiventris and Poospiza whitii because they w…”
MethodGonzález‐Quevedo, C., Davies, R. G., & Richardson, D. S. (2014) · Journal of Animal Ecology · doi:10.1111/1365-2656.12214 · Cited by 74
Multimodal signalling in the North American barn swallow: a phenotype network approach
Wilkins, M. R., Shizuka, D., Joseph, M. B., Hubbard, J. K., & Safran, R. J. (2015) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.2015.1574 · Cited by 71
Rodríguez, A., Börner, M., Pabijan, M., Gehara, M., Haddad, C. F. B., & Miguel Vences, B. (2015) · Evolutionary Ecology · doi:10.1007/s10682-015-9774-7 · Cited by 71
“Model selection uncertainty was assessed by comparing DAICc values, Akaike weights and evidence ratios (Burnham and Anderson 2002).”
MethodPanigutti, C., Tizzoni, M., Bajardi, P., Smoreda, Z., & Vittoria Colizza, A. (2017) · Royal Society Open Science · doi:10.1098/rsos.160950 · Cited by 65
“Compared to the best model with AICmin, we found 30 and 45 models to be within the range AIC < 2, which represents a traditional threshold for model selection [46].”
MethodMutual assessment during ritualized fighting in mantis shrimp (Stomatopoda)
Green, P. A., & Patek, S. N. (2018) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.2017.2542 · Cited by 70
“We ran all possible models including and excluding these terms and identified which models had the lowest Akaike information criterion (AIC) scores (within 2 DAIC, [39]).”
MethodSakamaki, T., Ryu, H., Toda, K., Tokuyama, N., & Furuichi, T. (2018) · International Journal of Primatology · doi:10.1007/s10764-018-0058-2 · Cited by 70
Understanding and Modeling Forest Disturbance Interactions at the Landscape Level
Sturtevant, B. R., & Fortin, M. (2021) · Frontiers in Ecology and Evolution · doi:10.3389/fevo.2021.653647 · Cited by 75
“The authors used generalized additive models to allow detection of non-linear relationships, and applied multi-model inference (Burnham et al., 2011) to select from a set of candidate models reflecting plausible alternative hypotheses of underlying processes.”
MethodMultimodel-Inference in Comparative Analyses
Garamszegi, L. Z., & Mundry, R. (2014) · Springer eBooks · doi:10.1007/978-3-662-43550-2_12 · Cited by 70
“The validity of the analysis is conditional on this set, and if the candidate model set is not a reasonable one, the results will be deceiving (Burnham and Anderson 2002; Burnham et al. 2011).”
BackgroundDominance, pair bonds and boldness determine social-foraging tactics in rooks, Corvus frugilegus
Jolles, J. W., Ostojić, L., & Clayton, N. S. (2013) · Animal Behaviour · doi:10.1016/j.anbehav.2013.03.013 · Cited by 70
“Minimal adequate models were obtained by comparing models with dropped terms using Akaike’s information criterion (AIC), with the lowest AICc value indicating the best model fit, which is considered the preferable method for observational data (Burnham et al. 2011).”
MethodBalasubramaniam, K. N., Beisner, B. A., Vandeleest, J. J., Atwill, E. R., & McCowan, B. (2016) · PeerJ · doi:10.7717/peerj.2630 · Cited by 72
Long-Term Simulated Atmospheric Nitrogen Deposition Alters Leaf and Fine Root Decomposition
Xia, M., Talhelm, A. F., & Pregitzer, K. S. (2017) · Ecosystems · doi:10.1007/s10021-017-0130-3 · Cited by 72
Effect of diabetes status and hyperglycemia on global DNA methylation and hydroxymethylation
Pinzón-Cortés, J. A., Chaux, A. P., Villamizar, N. S. R., Díaz‐Basabe, A., Polanía-Villanueva, D., Jácome, M. F., Mendivil, C. O., Groot, H., & López-Segura, V. (2017) · Endocrine Connections · doi:10.1530/ec-17-0199 · Cited by 72
Genomic signatures of selection in bats surviving white‐nose syndrome
Gignoux‐Wolfsohn, S., Pinsky, M. L., Kerwin, K., Herzog, C., Hall, M., Bennett, A. B., Fefferman, N. H., & Maslo, B. (2021) · Molecular Ecology · doi:10.1111/mec.15813 · Cited by 74
“To reduce model selection bias and uncertainty, we averaged all models contributing to cumulative w ≥ 0.85 (top five models) and calculated parameter estimates based on weighted averages of the parameter estimates in the top models (Burnham & Anderson, 2002; Burnham et al., 2011).”
MethodInfluentialLandscape context determines soil fungal diversity in a fragmented habitat
Su, N., Jarvie, S., Yan, Y., Gong, X., Li, F., Han, P., & Zhang, Q. (2022) · CATENA · doi:10.1016/j.catena.2022.106163 · Cited by 69
Growing the urban forest: tree performance in response to biotic and abiotic land management
Oldfield, E. E., Felson, A. J., Auyeung, D. S. N., Crowther, T. W., Sonti, N. F., Harada, Y., Maynard, D. S., Sokol, N. W., Ashton, M. S., Warren, R. J., et al. (2015) · Restoration Ecology · doi:10.1111/rec.12230 · Cited by 72
Schlacher, T. A., Weston, M. A., Lynn, D., & Connolly, R. M. (2013) · PLoS ONE · doi:10.1371/journal.pone.0071200 · Cited by 69
“Model performance was evaluated using the corrected Akaike Information Criterion (AICc) based on all possible combinations of environmental variables used in model building [47,48].”
Chia, E. K., Bassett, M., Leonard, S., Holland, G. J., Ritchie, E. G., Clarke, M. F., & Bennett, A. F. (2015) · Forest Ecology and Management · doi:10.1016/j.foreco.2015.12.008 · Cited by 77
“If there wasno single bestmodel (i.e. all AICcweights 0.90),model averaging was performed on all models for whichDAICc 6 because these models have some support (Burnham et al., 2011).”
MethodInfluentialClimatic warming destabilizes forest ant communities
Diamond, S. E., Nichols, L. M., Pelini, S. L., Penick, C. A., Barber, G. W., Cahan, S. H., Dunn, R. R., Ellison, A. M., Sanders, N. J., & Gotelli, N. J. (2016) · Science Advances · doi:10.1126/sciadv.1600842 · Cited by 69
Bravo, J. J. P., Villanueva, M. E., Sanchez, L. M., Ollier, R. P., Álvarez, V. A., & Copello, G. J. (2020) · Applied Clay Science · doi:10.1016/j.clay.2020.105703 · Cited by 69
Large diurnal temperature range increases bird sensitivity to climate change
Briga, M., & Verhulst, S. (2015) · Scientific Reports · doi:10.1038/srep16600 · Cited by 68
Do biodiversity offsets achieve No Net Loss? An evaluation of offsets in a French department
Bezombes, L., Kerbiriou, C., & Spiegelberger, T. (2019) · Biological Conservation · doi:10.1016/j.biocon.2019.01.004 · Cited by 79
Coelho, A. J. P., Magnago, L. F. S., Matos, F. A. R., Mota, N. M., Diniz, É. S., & Meira‐Neto, J. A. A. (2020) · Biodiversity and Conservation · doi:10.1007/s10531-020-02013-6 · Cited by 69
“The multi-model inference approach was used to identify the models that best explained the behavior of the response variables, based on the second-order Akaike Information Criterion (AICc), which is recommended for small samples (Burnham et al. 2011).”
MethodCapilla‐Lasheras, P., Thompson, M. J., Sánchez‐Tójar, A., Haddou, Y., Branston, C. J., Réale, D. J. F., Charmantier, A., & Dominoni, D. M. (2022) · Ecology Letters · doi:10.1111/ele.14099 · Cited by 68
Rinde, E., Christie, H., Fagerli, C. W., Bekkby, T., Gundersen, H., Norderhaug, K. M., & Hjermann, D. O. (2014) · PLoS ONE · doi:10.1371/journal.pone.0100222 · Cited by 68
Routti, H., Aars, J., Fuglei, E., Hanssen, L., Lone, K., Polder, A., Pedersen, Å. Ø., Tartu, S., Welker, J. M., & Yoccoz, N. G. (2017) · Environmental Science & Technology · doi:10.1021/acs.est.7b03585 · Cited by 69
“δ(15)N and δ(13)C) were not included in the same 176 model as recommended.(66) To make inference from all candidate models and subsequently from 177 all predictor variables, we used model averaging based on Akaike’s Information Criterion 178 (AIC)(67) using the R-package MuMIn.”
MethodHelfrich-Förster, C., Monecke, S., Spiousas, I., Hovestadt, T., Mitesser, O., & Wehr, T. A. (2021) · Science Advances · doi:10.1126/sciadv.abe1358 · Cited by 70
Flammability of the keystone savanna bunchgrass Aristida stricta
Fill, J. M., Moule, B. M., Varner, J. M., & Mousseau, T. A. (2016) · Plant Ecology · doi:10.1007/s11258-016-0574-0 · Cited by 69
Autoregressive Model-Based Signal Reconstruction for Automotive Radar Interference Mitigation
Rameez, M., Dahl, M., & Pettersson, M. I. (2020) · IEEE Sensors Journal · doi:10.1109/jsen.2020.3042061 · Cited by 67
Data-driven methods for present and future pandemics: Monitoring, modelling and managing
Álamo, T., Reina, D. G., Millán, P., Preciado, V. M., & Giordano, G. (2021) · Annual Reviews in Control · doi:10.1016/j.arcontrol.2021.05.003 · Cited by 69
“Under similar adjustment to observations, simpler models are preferred since they are more robust from an information-theoretic point of view [108].”
MethodClimate change is an important predictor of extinction risk on macroevolutionary timescales
Malanoski, C. M., Farnsworth, A., Lunt, D. J., Valdes, P. J., & Saupe, E. E. (2024) · Science · doi:10.1126/science.adj5763 · Cited by 71
Decoupling social status and status certainty effects on health in macaques: a network approach
Vandeleest, J. J., Beisner, B. A., Hannibal, D. L., Nathman, A. C., Capitanio, J. P., Hsieh, F., Atwill, E. R., & McCowan, B. (2016) · PeerJ · doi:10.7717/peerj.2394 · Cited by 70
Quantifying habitat selection and variability in habitat suitability for juvenile white sharks
White, C. F., Lyons, K., Jorgensen, S. J., O'Sullivan, J., Winkler, C., Weng, K. C., & Lowe, C. G. (2019) · PLoS ONE · doi:10.1371/journal.pone.0214642 · Cited by 66
Schürch, R., Couvillon, M. J., Burns, D. D. R., Tasman, K., Waxman, D., & Ratnieks, F. L. W. (2013) · Journal of Comparative Physiology A · doi:10.1007/s00359-013-0860-4 · Cited by 66
Sin, S. Y. W., Annavi, G., Dugdale, H. L., Newman, C., Burke, T. A., & Macdonald, D. W. (2014) · Molecular Ecology · doi:10.1111/mec.12917 · Cited by 66
“Multimodel inference (Burnham & Anderson 2002) was performed for models with DAICc < 7 (Burnham et al. 2011).”
MethodHumans and seasonal climate variability threaten large-bodied coral reef fish with small ranges
Mellin, C., Mouillot, D., Kulbicki, M., McClanahan, T. R., Vigliola, L., Bradshaw, C. J. A., Brainard, R. E., Chabanet, P., Edgar, G. J., Fordham, D. A., et al. (2016) · Nature Communications · doi:10.1038/ncomms10491 · Cited by 69
Current spring warming as a driver of selection on reproductive timing in a wild passerine
Marrot, P., Charmantier, A., Blondel, J., & Garant, D. (2018) · Journal of Animal Ecology · doi:10.1111/1365-2656.12794 · Cited by 69
Historical trends and extremes in boreal Alaska river basins
Bennett, K. E., Cannon, A. J., & Hinzman, L. D. (2015) · Journal of Hydrology · doi:10.1016/j.jhydrol.2015.04.065 · Cited by 63
Murphy, R. R., Keisman, J., Harcum, J., Karrh, R. R., Lane, M., Perry, E. S., & Zhang, Q. (2021) · Environmental Science & Technology · doi:10.1021/acs.est.1c05388 · Cited by 67
The communicative significance of song frequency and song length in territorial chiffchaffs
Linhart, P., Slabbekoorn, H., & Fuchs, R. (2012) · Behavioral Ecology · doi:10.1093/beheco/ars127 · Cited by 65
Paranoia and the social representation of others: a large-scale game theory approach
Raihani, N., & Bell, V. (2017) · Scientific Reports · doi:10.1038/s41598-017-04805-3 · Cited by 68
The X-ray properties of Be/X-ray pulsars in quiescence
Tsygankov, S. S., Wijnands, R. A. D., Lutovinov, A. A., Degenaar, N., & Poutanen, J. (2017) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stx1255 · Cited by 65
“…conclusion about the preference of one of the models for a particular source we used the Akaike information criterion (AIC; Akaike 1974; Burnham et al. 2011) used both in the cosmological tasks (see e.g. Liddle 2007) and to discriminate between different spectral models (see e.g.…”
MethodIrvine, L. G., Thums, M., Hanson, C. E., McMahon, C. R., & Hindell, M. A. (2017) · Royal Society Open Science · doi:10.1098/rsos.160290 · Cited by 65
Gilbert, S. L., Hundertmark, K. J., Person, D. K., Lindberg, M. S., & Boyce, M. S. (2016) · Journal of Mammalogy · doi:10.1093/jmammal/gyw167 · Cited by 65
“…is not possible if a covariate is not available to an individual animal, yet Akaike’s information criterion (AIC)-based model selection dictates that underlying data (i.e., relocations of individuals) be identical across the models being compared (Burnham and Anderson 2002; Burnham et al. 2011).”
BackgroundMethodKlefoth, T., Skov, C., Kuparinen, A. K., & Arlinghaus, R. (2017) · Evolutionary Applications · doi:10.1111/eva.12504 · Cited by 65
Time constraints imposed by anthropogenic environments alter social behaviour in longtailed macaques
Marty, P. R., Beisner, B. A., Kaburu, S., Balasubramaniam, K. N., Bliss‐Moreau, E., Ruppert, N., Sah, S. A. M., Ismail, A. S., Arlet, M. E., Atwill, E. R., et al. (2019) · Animal Behaviour · doi:10.1016/j.anbehav.2019.02.010 · Cited by 64
Severe below-maintenance feed intake increases methane yield from enteric fermentation in cattle
Goopy, J. P., Korir, D., Pelster, D. E., Ali, A. I. M., Wassie, S. E., Schlecht, E., Dickhoefer, U., Merbold, L., & Butterbach‐Bahl, K. (2020) · British Journal Of Nutrition · doi:10.1017/s0007114519003350 · Cited by 65
“The ‘best’ model was selected after examination of the χ2 values as well as the Akaike information criterion(30), in order to assess if the extra complexity explained sufficient variability to justify its use.”
MethodChen, W., Kelly, P. L., Diego, J. M., Oguri, M., Williams, L. L. R., Zitrin, A., Treu, T. L., Smith, N., Broadhurst, T. J., Kaiser, N., et al. (2019) · The Astrophysical Journal · doi:10.3847/1538-4357/ab297d · Cited by 64
Shirey, V. M., Belitz, M. W., Barve, V., & Guralnick, R. (2021) · Ecography · doi:10.1111/ecog.05396 · Cited by 64
Human impacts as the main driver of tropical forest carbon
Pyles, M. V., Magnago, L. F. S., Maia, V. A., Pinho, B. X., Pitta, G. R., de Gasper, A. L., Vibrans, A. C., dos Santos, R. M., van den Berg, E., & de Lima, R. A. F. (2022) · Science Advances · doi:10.1126/sciadv.abl7968 · Cited by 64
“…model had support from the data to be considered as the single best model (AIC weights < 0.10), the first 40 equally plausible candidate models (i.e., delta AIC ≤ 4 and AIC weights sum > 0.77) were averaged to address the uncertainty in the selection of the best candidate covariables ( 68 ).”
InfluentialGlobal Climate Change Increases Terrestrial Soil CH4 Emissions
Guo, J., Feng, H., Peng, C. H., Chen, H., Xu, X., Ma, X., Li, L., Kneeshaw, D., Ruan, H., Yang, H., et al. (2023) · Global Biogeochemical Cycles · doi:10.1029/2021gb007255 · Cited by 64
Cryptic female choice enhances fertilization success and embryo survival in chinook salmon
Rosengrave, P., Montgomerie, R., & Gemmell, N. J. (2016) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.2016.0001 · Cited by 64
Resting high frequency heart rate variability selectively predicts cooperative behavior
Beffara, B., Bret, A., Vermeulen, N., & Mermillod, M. (2016) · Physiology & Behavior · doi:10.1016/j.physbeh.2016.06.011 · Cited by 64
New Perspectives on Frontal Variability in the Southern Ocean
Chapman, C. (2017) · Journal of Physical Oceanography · doi:10.1175/jpo-d-16-0222.1 · Cited by 64
Intuitive proportion judgment in number-line estimation: Converging evidence from multiple tasks
Slusser, E. B., & Barth, H. C. (2017) · Journal of Experimental Child Psychology · doi:10.1016/j.jecp.2017.04.010 · Cited by 64
The importance of artificial wetlands for birds: A case study from Cyprus
Giosa, E., Mammides, C., & Zotos, S. (2018) · PLoS ONE · doi:10.1371/journal.pone.0197286 · Cited by 67
Major roads have important negative effects on insectivorous bat activity
Claireau, F., Bas, Y., Pauwels, J., Barré, K., Machon, N., Allegrini, B., Puechmaille, S. J., & Kerbiriou, C. (2019) · Biological Conservation · doi:10.1016/j.biocon.2019.04.002 · Cited by 64
How conservation initiatives go to scale
Mills, M., Bode, M., Mascia, M. B., Weeks, R., Gelcich, S., Dudley, N., Govan, H., Archibald, C. L., Romero‐de‐Diego, C., Holden, M. H., et al. (2019) · Nature Sustainability · doi:10.1038/s41893-019-0384-1 · Cited by 62
Global determinants and conservation of evolutionary and geographic rarity in land vertebrates
Murali, G., Gumbs, R., Meiri, S., & Roll, U. (2021) · Science Advances · doi:10.1126/sciadv.abe5582 · Cited by 61
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
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