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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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Gao, Y., Wallach, D., Hasegawa, T., Tang, L. R., Zhang, R., Asseng, S., Kahveci, T., Liu, L., He, J., & Hoogenboom, G. (2021) · Agricultural and Forest Meteorology · doi:10.1016/j.agrformet.2021.108686 · Cited by 50
Temperature and not landscape composition shapes wild bee communities in an urban environment
Geppert, C., Cappellari, A., Corcos, D., Caruso, V., Cerretti, P., Mei, M., & Marini, L. (2022) · Insect Conservation and Diversity · doi:10.1111/icad.12602 · Cited by 54
“Multi-model inference analyses were performed with the ‘MuMIn’ package (Barton, 2020; Burnham et al., 2010).”
MethodA critical thermal transition driving spring phenology of Northern Hemisphere conifers
Huang, J., Zhang, Y., Wang, M., Yu, X., Deslauriers, A., Fonti, P., Liang, E., Mäkinen, H. H., Oberhuber, W., Rathgeber, C., et al. (2022) · Global Change Biology · doi:10.1111/gcb.16543 · Cited by 52
“The validity of the assumptions of normality and homoscedasticity was examined using residual plots (Burnham et al., 2011).”
MethodA global systematic review of the cultural ecosystem services provided by wetlands
Wood, K. A., Jupe, L. L., Aguiar, F. C., Collins, A., Davidson, S. J., Freeman, W., Kirkpatrick, L., Magalhães, T. L., McKinley, E., Nuño, A., et al. (2024) · Ecosystem Services · doi:10.1016/j.ecoser.2024.101673 · Cited by 63
Laskowski, K. L., Pearish, S., Bensky, M. K., & Bell, A. M. (2015) · Advances in ecological research/Advances in Ecological Research · doi:10.1016/bs.aecr.2015.01.004 · Cited by 52
“Akaike’s weight is the relative probabilities of each model given the data and sums to one over the set of models (Burnham et al., 2011; Johnson and Omland, 2004).”
BackgroundMethodCutini, A., Chianucci, F., Chirichella, R., Donaggio, E., Mattioli, L., & Apollonio, M. (2013) · Annals of Forest Science · doi:10.1007/s13595-013-0282-z · Cited by 52
“We used the Akaike information criterion modified for small sample sizes (AICc; Burnham et al. 2011; Symonds and Mousalli 2011) to select the best predicting model.”
MethodLeaf and Life History Traits Predict Plant Growth in a Green Roof Ecosystem
Lundholm, J., Heim, A., Tran, S., & Smith, T. W. (2014) · PLoS ONE · doi:10.1371/journal.pone.0101395 · Cited by 52
“Since many models had Di values of below 7 for both response variables, this suggested that there was no best model, so model averaging was used to create a predictive model (using the subset of models with Di values ,7)[36,37].”
MethodWhy are we not evaluating multiple competing hypotheses in ecology and evolution?
Betini, G. S., Avgar, T., & Fryxell, J. M. (2017) · Royal Society Open Science · doi:10.1098/rsos.160756 · Cited by 56
“While often used as a tool for statistical inference, a set of regression models containing partially overlapping sets of explanatory variables does not necessarily represent a valid evaluation of multiple hypotheses unless each statistical model represents a unique research hypothesis [31].”
BackgroundMethodMagrach, A., Holzschuh, A., Bartomeus, Í., Riedinger, V., Roberts, S. P. M., Rundlöf, M., Vujić, A., Wickens, J. B., Wickens, V. J., Bommarco, R., et al. (2017) · Ecography · doi:10.1111/ecog.02847 · Cited by 48
“If more than one plausible model existed (i.e. when ΔAICc < 6 for more than one model, Burnham et al. 2011) we computed average estimates for each variable across all models in which each variable was retained.”
Spillover of managed honeybees from mass-flowering crops into natural habitats
González‐Varo, J. P., & Vilà, M. (2017) · Biological Conservation · doi:10.1016/j.biocon.2017.06.018 · Cited by 52
Strydom, M., Veldtman, R., Ngwenya, M. Z., & Esler, K. J. (2017) · PLoS ONE · doi:10.1371/journal.pone.0181763 · Cited by 52
“Models were compared through the use of information theoretic (I-T) model procedures based on Akaike’s information criterion (AIC) [40].”
MethodInfluentialBlomqvist, M., Kosunen, M., Starr, M., Kantola, T., Holopainen, M., & Lyytikäinen‐Saarenmaa, P. (2018) · European Journal of Forest Research · doi:10.1007/s10342-018-1133-0 · Cited by 52
“For further testing of the relative goodness of the final models, we used Akaike information criterion (AIC, Akaike 1987) and calculated ΔAICs and Akaike weights (Burnham et al. 2011).”
MethodRodrigues, A. C., Villa, P. M., Ali, A., Júnior, W. G. F., & Neri, A. V. (2019) · Journal of Forestry Research · doi:10.1007/s11676-019-00994-x · Cited by 52
Cellulose dominantly affects soil fauna in the decomposition of forest litter: A meta-analysis
Xu, X., Sun, Y., Sun, J., Cao, P., Wang, Y., Chen, H. Y. H., Wang, W., & Ruan, H. (2020) · Geoderma · doi:10.1016/j.geoderma.2020.114620 · Cited by 54
Xu, Y., Park, Y., Park, J. D., & Cho, W. (2020) · Journal of Management Analytics · doi:10.1080/23270012.2020.1832925 · Cited by 52
Botzas‐Coluni, J., Crockett, E. T. H., Rieb, J. T., & Bennett, E. M. (2021) · Agriculture Ecosystems & Environment · doi:10.1016/j.agee.2021.107661 · Cited by 62
Taking the elephant out of the room and into the corridor: can urban corridors work?
Adams, T. S. F., Chase, M. J., Rogers, T. L., & Leggett, K. E. A. (2016) · Oryx · doi:10.1017/s0030605315001246 · Cited by 51
Spatial ecology of perceived predation risk and vigilance behavior in white-faced capuchins
Campos, F. A., & Fedigan, L. M. (2014) · Behavioral Ecology · doi:10.1093/beheco/aru005 · Cited by 51
Bekkby, T., Rinde, E., Gundersen, H., Norderhaug, K. M., Gitmark, J., & Christie, H. (2014) · Marine Ecology Progress Series · doi:10.3354/meps10778 · Cited by 51
Murphy, S. M., Cox, J. J., Augustine, B. C., Hast, J. T., Guthrie, J. M., Wright, J., McDermott, J., Maehr, S. C., & Plaxico, J. H. (2016) · Journal of Wildlife Management · doi:10.1002/jwmg.21144 · Cited by 50
Low, S. K., Smolkowski, K., Cook, C. R., & Desfosses, D. A. (2018) · Developmental Psychology · doi:10.1037/dev0000621 · Cited by 64
Behavioural development in southern right whale calves
Nielsen, M., Sprogis, K. R., Bejder, L., Madsen, P. T., & Christiansen, F. (2019) · Marine Ecology Progress Series · doi:10.3354/meps13125 · Cited by 51
“Model selection was based on Akaike’s information criterion (AIC) (Burnham et al. 2011).”
MethodRomero-Álvarez, D., Peterson, A. T., Salzer, J. S., Pittiglio, C., Shadomy, S. V., Traxler, R. M., Vieira, A. R., Bower, W. A., Walke, H. T., & Campbell, L. P. (2020) · PLoS neglected tropical diseases · doi:10.1371/journal.pntd.0008131 · Cited by 53
“Finally, models selected through both metrics were further discriminated using the Akaike information criterion corrected for sample size (AICc) to obtain models with low complexity and good fit to the underlying data [64,68,69].”
MethodVilla, P. M., Ali, A., Martins, S. V., Neto, S. N. D. O., Rodrigues, A. C., Teshome, M., Carvalho, F. A., Heringer, G., & Gastauer, M. (2020) · Forest Ecology and Management · doi:10.1016/j.foreco.2020.118481 · Cited by 52
Sutherland, K., Ndlovu, M., & Pérez‐Rodríguez, A. (2018) · African Journal of Wildlife Research · doi:10.3957/056.048.023003 · Cited by 50
“We used the command dredge of the package MuMIn to calculate the AIC values of all possible models nested within the full model, looking for the set of candidate models that better adjusted to our dataset (the model with the lowest AIC value, and all other models whose AIC values differing by less than two units from…”
MethodInfluentialDenan, N., Zaki, W. M. W., Norhisham, A. R., Sanusi, R., Nasir, D. M., Nobilly, F., Ashton‐Butt, A., Lechner, A. M., & Azhar, B. (2019) · Ecology and Evolution · doi:10.1002/ece3.5856 · Cited by 51
Souza‐Silva, M., Cerqueira, R. F. V., Pellegrini, T. G., & Ferreira, R. L. (2021) · Biodiversity and Conservation · doi:10.1007/s10531-021-02302-8 · Cited by 50
Conley, J. M., Lambright, C. S., Evans, N., Medlock-Kakaley, E., Dixon, A., Hill, D., McCord, J. P., Strynar, M. J., Ford, J., & Gray, L. E. (2022) · Environment International · doi:10.1016/j.envint.2022.107631 · Cited by 50
“AICc weighted means (Wm) were calculated as a probabilistic approach to determine the mixture model with the best fit compared to the observed best fit regression model (Burnham et al. 2010).”
MethodEffects of multicollinearity and data granularity on regression models of stream temperature
DERTLİ, H. İ., Hayes, D. B., & Zorn, T. G. (2024) · Journal of Hydrology · doi:10.1016/j.jhydrol.2024.131572 · Cited by 50
The role of maternal behavior and offspring development in the survival of mountain goat kids
Théoret-Gosselin, R., Hamel, S., & Côté, S. D. (2015) · Oecologia · doi:10.1007/s00442-014-3198-x · Cited by 54
“We considered all models with a mean ΔAICc of 4 or less (sensu Burnham et al. 2011) as providing plausible support to the data (9 models for summer survival and 22 for winter survival; see ESM2).”
Gambín, B. L., Coyos, T., Mauro, G. D., Borrás, L., & Garibaldi, L. A. (2016) · Agricultural Systems · doi:10.1016/j.agsy.2016.03.011 · Cited by 50
Roches, A. D. B. D., Veissier, I., Boivin, X., Gilot‐Fromont, E., & Mounier, L. (2016) · Journal of Dairy Science · doi:10.3168/jds.2015-10633 · Cited by 50
“The simplification was stopped when no factor that would decrease the AIC value could be removed (Burnham et al., 2011).”
BackgroundThe Evolution of Gods’ Minds in the Tyva Republic
Purzycki, B. G. (2016) · Current Anthropology · doi:10.1086/685729 · Cited by 54
Intra- and inter-annual variability in metabolism in an oligotrophic lake
Richardson, D. C., Carey, C. C., Bruesewitz, D. A., & Weathers, K. C. (2016) · Aquatic Sciences · doi:10.1007/s00027-016-0499-7 · Cited by 59
“We ranked the environmental predictors that exhibited corrected AIC (AICc) values that were within 11 units of the top model fit (Burnham et al. 2011).”
BackgroundMethodInfluentialRicardo, T., Bergero, L. C., Bulgarella, E., & Previtali, M. A. (2018) · PLoS Neglected Tropical Diseases · doi:10.1371/journal.pntd.0006470 · Cited by 50
Benussi, A., Alberici, A., Ferrari, C., Cantoni, V., Dell’Era, V., Turrone, R., Cotelli, M. S., Binetti, G. G., Paghera, B., Koch, G., et al. (2018) · Alzheimer s Research & Therapy · doi:10.1186/s13195-018-0423-6 · Cited by 50
Safar, N. V. H., Magnago, L. F. S., & Schaefer, C. E. G. R. (2020) · Forest Ecology and Management · doi:10.1016/j.foreco.2020.118183 · Cited by 58
Scheper, J. A., Bukovinszky, T., Huigens, M. E., & Kleijn, D. (2021) · Basic and Applied Ecology · doi:10.1016/j.baae.2021.08.014 · Cited by 49
Plant Species Richness is Associated with Canopy Height and Topography in a Neotropical Forest
Wolf, J. A., Fricker, G. A., Meyer, V., Hubbell, S. P., Gillespie, T. W., & Saatchi, S. S. (2012) · Remote Sensing · doi:10.3390/rs4124010 · Cited by 50
Overcoming extinction: understanding processes of recovery of the Tibetan antelope
Leclerc, C., Bellard, C., Luque, G. M., & Courchamp, F. (2015) · Ecosphere · doi:10.1890/es15-00049.1 · Cited by 48
“From the DAICc, we computed the Akaike weights of the models, which can be interpreted as the estimated relative strength of evidence that each model in the set is the ‘‘best model’’ (Burnham et al. 2011).”
MethodExtinction risk in extant marine species integrating palaeontological and biodistributional data
Collins, K. S., Edie, S. M., Hunt, G., Roy, K., & Jablonski, D. (2018) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.2018.1698 · Cited by 48
Rattray, J. B., Thomas, S. A., Wang, Y., Molotkova, E., Gurney, J., Varga, J. J., & Brown, S. P. (2022) · mBio · doi:10.1128/mbio.00745-22 · Cited by 49
“To confirm the lack of threshold behavior we assessed alternate statistical 118 models including threshold functions, and found that a linear fit model supports the data better 119 than a step-function fit (AIC linear: 89, AIC step-function: 190; relative likelihood that the linear 120 model is the best fit compared t…”
Somers, S. E., Davidson, G. L., Johnson, C. N., Reichert, M. S., Crane, J. M. S., Ross, R. P., Stanton, C., & Quinn, J. L. (2023) · Molecular Ecology · doi:10.1111/mec.16919 · Cited by 48
Wu, Y., Wu, Y., Guo, J., Wang, T., Wang, T., Li, W., Wang, X., Cui, H., Hu, X., & Qi, L. (2023) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2023.169346 · Cited by 51
Chen, Q., Peng, C., Xie, R., Xu, H., Su, Z., Yilihan, G., Wei, X., Yang, S., Shen, Y., Ye, C., et al. (2024) · Journal of Hazardous Materials · doi:10.1016/j.jhazmat.2024.135527 · Cited by 55
Kelly, C. D. (2011) · Biological Journal of the Linnean Society · doi:10.1111/j.1095-8312.2011.01714.x · Cited by 49
Conservation gone to the dogs: when canids rule the beach in small coastal reserves
Schlacher, T. A., Weston, M. A., Lynn, D., Schoeman, D. S., Huijbers, C. M., Olds, A. D., Masters, S., & Connolly, R. M. (2014) · Biodiversity and Conservation · doi:10.1007/s10531-014-0830-3 · Cited by 49
“…approach was employed to assess the contributions of individual variables based on their summed Akaike weights (Anderson 2008); summed AICc weights (w ?) provide relative probabilities of variable importance, with variables \0.3 likely to be of minor or no importance (Burnham and Anderson 2002).”
MethodInfluentialKaburu, S., & Newton‐Fisher, N. E. (2015) · Behavioral Ecology and Sociobiology · doi:10.1007/s00265-015-1917-x · Cited by 52
“Models that fit the data well have low Δ and AIC c values (Burnham et al. 2011).”
Distinguishing individual quality from habitat preference and quality in a territorial passerine
Germain, R. R., & Arcese, P. (2013) · Ecology · doi:10.1890/13-0467.1 · Cited by 49
“We chose a cutoff of DAIC 7 rather than DAIC 2 to incorporate all models that fall within the range of plausibility described by Burnham and Anderson (2002) and Burnham et al. (2011).”
MethodMHC class II‐assortative mate choice in European badgers (Meles meles)
Sin, S. Y. W., Annavi, G., Newman, C., Buesching, C. D., Burke, T. A., Macdonald, D. W., & Dugdale, H. L. (2015) · Molecular Ecology · doi:10.1111/mec.13217 · Cited by 49
“Multimodel inference (Burnham & Anderson 2002) was performed for models with DAICc <7 (Burnham et al. 2011).”
MethodCoupé, C. (2018) · Frontiers in Psychology · doi:10.3389/fpsyg.2018.00513 · Cited by 48
“Information criteria are hence useful when one tries to select the most appropriate model for a given set of observations and possible predictors (Burnham et al., 2011).”
BackgroundMay, R. F., Masden, E. A., Bennet, F., & Perron, M. (2018) · Journal of Environmental Management · doi:10.1016/j.jenvman.2018.09.062 · Cited by 49
Mayer, M., Ullmann, W., Sunde, P., Fischer, C., & Blaum, N. (2018) · Ecology and Evolution · doi:10.1002/ece3.4613 · Cited by 52
Global patterns of body size evolution are driven by precipitation in legless amphibians
Pincheira‐Donoso, D., Meiri, S., Jara, M., Olalla‐Tárraga, M. Á., & Hodgson, D. J. (2019) · Ecography · doi:10.1111/ecog.04644 · Cited by 49
“This procedure is robust given that information is contained in 191 well-fitting but non-optimal models for parameter estimates (with confidence intervals), which would, in 192 contrast, be lost with a single best-fit model (i.e., a step-wise approach) (Burnham et al., 2011).”
MethodDominance rank predicts social network position across developmental stages in rhesus monkeys
Wooddell, L. J., Kaburu, S., & Dettmer, A. M. (2019) · American Journal of Primatology · doi:10.1002/ajp.23024 · Cited by 49
Oddi, F. J., Miguez, F. E., Ghermandi, L., Bianchi, L. O., & Garibaldi, L. A. (2019) · Ecology and Evolution · doi:10.1002/ece3.5543 · Cited by 48
“We used delta AIC > 2, but other cutoffs could be chosen as rule of thumb (Harrison et al., 2018).”
InfluentialBattisti, D. D., & Griffin, J. N. (2019) · Annals of Botany · doi:10.1093/aob/mcz125 · Cited by 53
Kamoske, A., Dahlin, K. M., Serbin, S. P., & Stark, S. C. (2020) · Ecological Applications · doi:10.1002/eap.2230 · Cited by 49
Brandmeier, J., Reininghaus, H., Pappagallo, S., Karley, A. J., Kiær, L. P., & Scherber, C. (2021) · Basic and Applied Ecology · doi:10.1016/j.baae.2021.02.011 · Cited by 57
How can we estimate optimum fertilizer rates with accuracy and precision?
Miguez, F. E., & Poffenbarger, H. (2022) · Agricultural & Environmental Letters · doi:10.1002/ael2.20075 · Cited by 48
Individual personality differences in P ort J ackson sharks H eterodontus portusjacksoni
Byrnes, E. E., & Brown, C. (2016) · Journal of Fish Biology · doi:10.1111/jfb.12993 · Cited by 48
San‐Jose, L. M., Ducrest, A., Ducret, V., Simon, C., Richter, H., Wakamatsu, K., & Roulin, A. (2016) · Molecular Ecology · doi:10.1111/mec.13861 · Cited by 48
“We ran a set of competing models for each genotype and ranked them based on the Akaike’s information criterion corrected for small sample sizes (AICc: Burnham et al. 2011).”
MethodNiebuhr, C. N., Poulin, R., & Tompkins, D. M. (2016) · PLoS ONE · doi:10.1371/journal.pone.0165918 · Cited by 48
Ojha, K. S., Kerry, J. P., & Tiwari, B. K. (2017) · LWT · doi:10.1016/j.lwt.2017.03.011 · Cited by 48
Marino, N. A. C., Romero, G. Q., & Farjalla, V. F. (2018) · Ecology Letters · doi:10.1111/ele.12913 · Cited by 47
Ihlen, E. A. F., van Schooten, K. S., Bruijn, S. M., van Dieën, J. H., Vereijken, B., Helbostad, J. L., & Pijnappels, M. A. G. M. (2018) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2018.00044 · Cited by 55
“A significant difference 1AICc = AICc1−AICc2 was identified as a relative likelihood less than 0.05 (i.e., RL = exp([AICc1−AICc2]/2) 0.05) (Burnham et al., 2011).”
BackgroundInfluentialWang, Y., Gao, C. D., Chen, L., Ji, N., Wu, B., Li, X., Lü, P., Zheng, Y., & Guo, L. (2019) · FEMS Microbiology Ecology · doi:10.1093/femsec/fiz037 · Cited by 48
Luque, G. M., Giraud, T., & Courchamp, F. (2013) · Journal of Animal Ecology · doi:10.1111/1365-2656.12091 · Cited by 49
Du, L., Langhough, R. E., Wilson, R. E., Reyes, R. E. R., Hermann, B. P., Jonaitis, E. M., Betthauser, T. J., Chin, N. A., Christian, B., Chaby, L., et al. (2024) · Alzheimer's & Dementia · doi:10.1002/alz.14100 · Cited by 46
“57 The ∆ values for any given model are linked to the evidence ratio for the best model as exp{ − (1/2) Δ }.”
MethodWalder, F., Büchi, L., Wagg, C., Colombi, T., Banerjee, S., Hirte, J., Mayer, J., Six, J., Keller, T., Charles, R., et al. (2023) · Journal of Applied Ecology · doi:10.1111/1365-2664.14484 · Cited by 50
Condition-dependent reproductive effort in frogs infected by a widespread pathogen
Roznik, E. A., Sapsford, S. J., Pike, D. A., Schwarzkopf, L., & Alford, R. A. (2015) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.2015.0694 · Cited by 48
“Because the selected threshold for model selection should be based on all models in the set, rather than an arbitrary cutoff [44], we averaged the top four models that were most strongly supported by our data and had a total Akaike weight of 65% (table 2).”
Almeida‐Gomes, M., & Rocha, C. F. D. (2014) · Biotropica · doi:10.1111/btp.12168 · Cited by 47
“We calculated the evidence ratio (ER) dividing the weight of evidence (wi) of each model by the wi of the null model (Burnham et al. 2011).”
MethodIs non-loglinear allometry a statistical artifact?
Packard, G. C. (2012) · Biological Journal of the Linnean Society · doi:10.1111/j.1095-8312.2012.01995.x · Cited by 47
Pomara, L. Y., LeDee, O. E., Martin, K. J., & Zuckerberg, B. (2013) · Global Change Biology · doi:10.1111/gcb.12510 · Cited by 50
“0 to be strongly supported, and we assessed variable importance with summed Akaike weights for the full model set (Burnham & Anderson, 2002; Burnham et al., 2011).”
MethodInfluentialExploring the universal ecological responses to climate change in a univoltine butterfly
Fenberg, P. B., Self, A. E., Stewart, J. R., Wilson, R. J., & Brooks, S. J. (2016) · Journal of Animal Ecology · doi:10.1111/1365-2656.12492 · Cited by 47
Hagemeyer, B., Schönbrodt, F. D., Neyer, F. J., Neberich, W., & Asendorpf, J. B. (2015) · Journal of Personality and Social Psychology · doi:10.1037/pspi0000031 · Cited by 47
Trajectories of cannabis use disorder: risk factors, clinical characteristics and outcomes
Kosty, D. B., Seeley, J. R., Farmer, R. F., Stevens, J. J., & Lewinsohn, P. M. (2016) · Addiction · doi:10.1111/add.13557 · Cited by 47
A Long Look at MCG-5-23-16 with NuSTAR. I. Relativistic Reflection and Coronal Properties
Zoghbi, A., Matt, G., Miller, J. M., Lohfink, A. M., Walton, D. J., Ballantyne, D. R., García, J. A., Stern, D. K., Koss, M., Farrah, D., et al. (2017) · The Astrophysical Journal · doi:10.3847/1538-4357/aa582c · Cited by 47
“We assess the significance of the parameter changes using the sample-corrected Akaike Information Criterion (cAIC; Akaike 1974; Burnham et al. 2011), where we compare models in which the parameter of interest is either fixed or allowed to change between observations.”
MethodPredicting road traffic accidents using artificial neural network models
de Soto, B. G., Bumbacher, A., Deublein, M., & Adey, B. (2018) · Infrastructure Asset Management · doi:10.1680/jinam.17.00028 · Cited by 7
Schmitz, D., Schaefer, C. E. G. R., Putzke, J., Francelino, M. R., Ferrari, F. R., Corrêa, G. R., & Villa, P. M. (2019) · Ecological Indicators · doi:10.1016/j.ecolind.2019.105726 · Cited by 47
“The model with the lowest AICc was considered to be the best one, but all models that differed less than four units from the best model were considered as equally good models (Burnham et al., 2011).”
MethodDo ecosystem service frameworks represent people’s values?
Maund, P., Irvine, K. N., Dallimer, M., Fish, R. D., Austen, G. E., & Davies, Z. G. (2020) · Ecosystem Services · doi:10.1016/j.ecoser.2020.101221 · Cited by 47
“4 candidate models and identify the most parsimonious solution (Burnham et al., 2011).”
BackgroundIndividual variation in energy‐saving heterothermy affects survival and reproductive success
Dammhahn, M., Landry‐Cuerrier, M., Réale, D. J. F., Garant, D., & Humphries, M. M. (2016) · Functional Ecology · doi:10.1111/1365-2435.12797 · Cited by 47
Kamoske, A., Dahlin, K. M., Read, Q. D., Record, S., Stark, S. C., Serbin, S. P., & Zarnetske, P. L. (2022) · Global Ecology and Biogeography · doi:10.1111/geb.13516 · Cited by 50
Toward an improved understanding of causation in the ecological sciences
Addicott, E. T., Fenichel, E. P., Bradford, M. A., Pinsky, M. L., & Wood, S. A. (2022) · Frontiers in Ecology and the Environment · doi:10.1002/fee.2530 · Cited by 45
Njana, M. A., Bollandsås, O. M., Eid, T., Zahabu, E. M., & Malimbwi, R. E. (2015) · Annals of Forest Science · doi:10.1007/s13595-015-0524-3 · Cited by 46
“The existing models were ranked without stratification into model type or predictor variable included since AIC as a model selection criteria is capable of detecting such differences (Burnham et al. 2011).”
MethodSharma, K., Wright, B., Joseph, T., & Desai, N. (2014) · Biological Conservation · doi:10.1016/j.biocon.2014.08.016 · Cited by 46
Annavi, G., Newman, C., Dugdale, H. L., Buesching, C. D., Sin, S. Y. W., Burke, T. A., & Macdonald, D. W. (2014) · Journal of Evolutionary Biology · doi:10.1111/jeb.12473 · Cited by 46
“We employed an information-theoretic (IT) approach to select sets of plausible models and to estimate the overall importance of each fixed effect (Burnham et al., 2011).”
MethodIdentification and Characterization of Androgen-Responsive Genes in Zebrafish Embryos
Fetter, É., Smetanová, S., Baldauf, L., Lidzba, A., Altenburger, R., Schüttler, A., & Scholz, S. (2015) · Environmental Science & Technology · doi:10.1021/acs.est.5b01034 · Cited by 46
Habitat‐related seed germination traits in alpine habitats
Isanta, M. T., Fernández‐Pascual, E., Wijayasinghe, M. M., Orsenigo, S., Rossi, G., Pritchard, H. W., & Mondoni, A. (2017) · Ecology and Evolution · doi:10.1002/ece3.3539 · Cited by 46
Thompson, M. E., Machanda, Z. P., Scully, E. J., Enigk, D. K., Otali, E., Muller, M. N., Goldberg, T. L., Chapman, C. A., & Wrangham, R. W. (2018) · Royal Society Open Science · doi:10.1098/rsos.180840 · Cited by 46
Nyirenda, V. R., Nkhata, B. A., Tembo, O., & Siamundele, S. (2018) · Sustainability · doi:10.3390/su10103572 · Cited by 46
“Furthermore, the analysis framework for model selection and calculations of model weights, and evidence ratios for ranking followed [63,64].”
MethodInfluentialHippocampal subfields alterations in adolescents with post‐traumatic stress disorder
Postel, C., Viard, A., André, C., Guenolé, F., de Florès, R., Baleyte, J., Gérardin, P., Eustache, F., Dayan, J., & Guillery‐Girard, B. (2018) · Human Brain Mapping · doi:10.1002/hbm.24443 · Cited by 47
Potential Solar Radiation as a Driver for Bark Beetle Infestation on a Landscape Scale
Mezei, P., Potterf, M., Škvarenina, J., Rasmussen, J. G., & Jakuš, R. (2019) · Forests · doi:10.3390/f10070604 · Cited by 48
Greenhouse gas flux from stormwater ponds in southeastern Virginia (USA)
Gorsky, A. L., Racanelli, G. A., Belvin, A. C., & Chambers, R. M. (2019) · Anthropocene · doi:10.1016/j.ancene.2019.100218 · Cited by 46
Sun, L., Luo, J., Qian, L., Deng, T., & Sun, H. (2020) · Global Ecology and Conservation · doi:10.1016/j.gecco.2020.e01053 · Cited by 46
Going off trails: How dispersed visitor use affects alpine vegetation
Barros, A., Aschero, V., Mazzolari, A. C., Cavieres, L. A., & Pickering, C. M. (2020) · Journal of Environmental Management · doi:10.1016/j.jenvman.2020.110546 · Cited by 46
Artificial light at night as a driver of urban colonization by an avian predator
Rodríguez, A., Orozco-Valor, P. M., & Sarasola, J. H. (2020) · Landscape Ecology · doi:10.1007/s10980-020-01132-3 · Cited by 46
Brambilla, A., Keller, L. F., Bassano, B., & Grossen, C. (2017) · Evolutionary Applications · doi:10.1111/eva.12575 · Cited by 43
“We then performed model selection following the approach proposed by Grueber, Nakagawa, Laws, and Jamieson (2011) and Burnham, Anderson, and Huyvaert (2011).”
MethodTrubin, A., Mezei, P., Zabihi, K., Surový, P., & Jakuš, R. (2021) · Forest Ecology and Management · doi:10.1016/j.foreco.2021.119829 · Cited by 45
Meta-analysis reveals glucocorticoid levels reflect variation in metabolic rate, not ‘stress’
Jimeno, B., & Verhulst, S. (2023) · eLife · doi:10.7554/elife.88205 · Cited by 52
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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