This book is in Open Review. I want your feedback to make the book better for you and other readers. To add your annotation, select some text and then click the on the pop-up menu. To see the annotations of others, click the in the upper right hand corner of the page


Abdi, H., & Williams, L. J. (2010). Tukey’s honestly significant difference (hsd) test. Encyclopedia of Research Design, 3(1), 1–5.

Agresti, A. (2018). An introduction to categorical data analysis. John Wiley & Sons.

Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6), 716–723.

Allison, T., & Cicchetti, D. V. (1976). Sleep in mammals: Ecological and constitutional correlates. Science, 194(4266), 732–734.

Anderson, D., & Burnham, K. (2004). Model selection and multi-model inference. Second. NY: Springer-Verlag, 63.

ArchMiller, A. A., Dorazio, R. M., Clair, K. S., & Fieberg, J. R. (2018). Time series sightability modeling of animal populations. PloS One, 13(1).

Arif, S., & MacNeil, A. (2022). Predictive models aren’t for causal inference. Ecology Letters, n/a(n/a). doi:

Arnold, T. W. (2010). Uninformative parameters and model selection using akaike’s information criterion. The Journal of Wildlife Management, 74(6), 1175–1178.

Arnqvist, G. (2020). Mixed models offer no freedom from degrees of freedom. Trends in Ecology & Evolution, 35(4), 329–335.

Babyak, M. A. (2004). What you see may not be what you get: A brief, nontechnical introduction to overfitting in regression-type models. Psychosomatic Medicine, 66(3), 411–421.

Barr, D. J., Levy, R., Scheepers, C., & Tily, H. J. (2013). Random effects structure for confirmatory hypothesis testing: Keep it maximal. Journal of Memory and Language, 68(3), 255–278.

Barton, K. (2020). MuMIn: Multi-model inference. Retrieved from

Bates, D., Mächler, M., Bolker, B., & Walker, S. (2015). Fitting linear mixed-effects models using lme4. Journal of Statistical Software, 67(1), 1–48. doi:10.18637/jss.v067.i01

Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society: Series B (Methodological), 57(1), 289–300.

Beverton, R. J., & Holt, S. J. (2012). On the dynamics of exploited fish populations (Vol. 11). Springer Science & Business Media.

Bigler, C. (2016). Trade-offs between growth rate, tree size and lifespan of mountain pine (pinus montana) in the swiss national park. PloS One, 11(3), e0150402.

Bliss, C. I. (1935). The calculation of the dosage-mortality curve. Annals of Applied Biology, 22(1), 134–167.

Bolker, B. (2020). Emdbook: Ecological models and data in R.

Bolker, B. M. (2008). Ecological models and data in R. Princeton University Press.

Bolker, B., & R Development Core Team. (2020). Bbmle: Tools for general maximum likelihood estimation. Retrieved from

Bolker, B., & Robinson, D. (2021). Broom.mixed: Tidying methods for mixed models. Retrieved from

Bramwell, R., West, H., & Salmon, P. (2006). Health professionals’ and service users’ interpretation of screening test results: Experimental study. Bmj, 333(7562), 284.

Breheny, P., & Burchett, W. (2013). Visualization of regression models using visreg. R Package, 1–15.

Brenning, A. (2012). Spatial cross-validation and bootstrap for the assessment of prediction rules in remote sensing: The r package ’sperrorest’. In IEEE international symposium on geoscience and remote sensing igarss. doi:10.1109/igarss.2012.6352393

Brewer, M. J., Butler, A., & Cooksley, S. L. (2016). The relative performance of aic, aicc and bic in the presence of unobserved heterogeneity. Methods in Ecology and Evolution, 7(6), 679–692.

Brooks, M. E., Kristensen, K., van Benthem, K. J., Magnusson, A., Berg, C. W., Nielsen, A., … Bolker, B. M. (2017). glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. The R Journal, 9(2), 378–400. Retrieved from

Buckland, S. T., Burnham, K. P., & Augustin, N. H. (1997). Model selection: An integral part of inference. Biometrics, 603–618.

Cade, B. S. (2015). Model averaging and muddled multimodel inferences. Ecology, 96(9), 2370–2382.

Carpenter, B., Gelman, A., Hoffman, M. D., Lee, D., Goodrich, B., Betancourt, M., … Riddell, A. (2017). Stan: A probabilistic programming language. Journal of Statistical Software, 76(1).

Carrière, I., & Bouyer, J. (2002). Choosing marginal or random-effects models for longitudinal binary responses: Application to self-reported disability among older persons. BMC Medical Research Methodology, 2(1), 1–10.

Casella, G., Ghosh, M., Gill, J., & Kyung, M. (2010). Penalized regression, standard errors, and bayesian lassos. Bayesian Analysis, 5(2), 369–411.

Chung, Y., Gelman, A., Rabe-Hesketh, S., Liu, J., & Dorie, V. (2015). Weakly informative prior for point estimation of covariance matrices in hierarchical models. Journal of Educational and Behavioral Statistics, 40(2), 136–157.

Cohen, J. (1992). Things i have learned (so far). In Annual convention of the american psychological association, 98th, aug, 1990, boston, ma, us; presented at the aforementioned conference. American Psychological Association.

Conn, P. B., Johnson, D. S., Williams, P. J., Melin, S. R., & Hooten, M. B. (2018). A guide to bayesian model checking for ecologists. Ecological Monographs, 88(4), 526–542.

Conor, E. F., & McCoy, E. D. (2013). Species–area relationships. In S. A. Levin (Ed.), Encyclopedia of biodiversity (second edition) (Second Edition, pp. 640–650). Waltham: Academic Press. doi:

Cooke, S. J., Donaldson, M. R., Hinch, S. G., Crossin, G. T., Patterson, D. A., Hanson, K. C., … Farrell, A. P. (2009). Is fishing selective for physiological and energetic characteristics in migratory adult sockeye salmon? Evolutionary Applications, 2(3), 299–311.

Copas, J., & Long, T. (1991). Estimating the residual variance in orthogonal regression with variable selection. Journal of the Royal Statistical Society: Series D (the Statistician), 40(1), 51–59.

Crainiceanu, C. M., & Ruppert, D. (2004). Likelihood ratio tests in linear mixed models with one variance component. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 66(1), 165–185.

Crawley, M. J. (2012). The r book. John Wiley & Sons.

Cummings, J. W., Hague, M. J., Patterson, D. A., & Peterman, R. M. (2011). The impact of different performance measures on model selection for fraser river sockeye salmon. North American Journal of Fisheries Management, 31(2), 323–334.

Curtis, S. M. (2018). Mcmcplots: Create plots from mcmc output. Retrieved from

Cutler, D. R., Edwards Jr, T. C., Beard, K. H., Cutler, A., Hess, K. T., Gibson, J., & Lawler, J. J. (2007). Random forests for classification in ecology. Ecology, 88(11), 2783–2792.

Çetinkaya-Rundel, M., Diez, D., Bray, A., Kim, A. Y., Baumer, B., Ismay, C., … Barr, C. (2021). Openintro: Data sets and supplemental functions from ’openintro’ textbooks and labs. Retrieved from

Dablander, F. (2020). An introduction to causal inference.

Dahlgren, J. P. (2010). Alternative regression methods are not considered in murtaugh (2009) or by ecologists in general. Ecology Letters, 13(5), E7–E9.

Davison, A. C., & Hinkley, D. V. (1997). Bootstrap methods and their application. Cambridge university press.

DelGiudice, G. D., Riggs, M. R., Joly, P., & Pan, W. (2002). Winter severity, survival, and cause-specific mortality of female white-tailed deer in north-central minnesota. The Journal of Wildlife Management, 698–717.

Dennis, B. (1996). Discussion: Should ecologists become bayesians? Ecological Applications, 6(4), 1095–1103.

De Valpine, P. (2014). The common sense of p values. Ecology, 95(3), 617–621.

Dickie, M., Serrouya, R., Avgar, T., McLoughlin, P., McNay, R., DeMars, C., … Ford, A. (2022). Resource exploitation efficiency collapses the home range of an apex predator. Ecology, e3642.

Diggle, P., Liang, K.-Y., & Zeger, S. L. (1994). Longitudinal data analysis. New York: Oxford University Press, 5, 13.

Ditmer, M. A., Garshelis, D., Noyce, K., Laske, T., Iaizzo, P. A., Burk, T., … Fieberg, J. R. (2015). Behavioral and physiological responses of american black bears to landscape features within an agricultural region. Ecosphere, 6(3), 1–21.

Dorazio, R. M. (2016). Bayesian data analysis in population ecology: Motivations, methods, and benefits. Population Ecology, 58(1), 31–44.

Dormann, C. F., Elith, J., Bacher, S., Buchmann, C., Carl, G., Carré, G., … others. (2013). Collinearity: A review of methods to deal with it and a simulation study evaluating their performance. Ecography, 36(1), 27–46.

Efron, B., Halloran, E., & Holmes, S. (1996). Bootstrap confidence levels for phylogenetic trees. Proceedings of the National Academy of Sciences, 93(14), 7085–7090.

Elith, J., Leathwick, J. R., & Hastie, T. (2008). A working guide to boosted regression trees. Journal of Animal Ecology, 77(4), 802–813.

Faraway, J. J. (2016). Extending the linear model with r: Generalized linear, mixed effects and nonparametric regression models. CRC press.

Felsenstein, J. (1985). Confidence limits on phylogenies: An approach using the bootstrap. Evolution, 39(4), 783–791.

Fieberg, J. (2012). Estimating population abundance using sightability models: R sightabilitymodel package. Journal of Statistical Software, 51(9), 1–20.

Fieberg, J. (2021). Data4Ecologists: Data assoicated with statistics for ecologists.

Fieberg, J., Alexander, M., Tse, S., & St. Clair, K. (2013). Abundance estimation with sightability data: A bayesian data augmentation approach. Methods in Ecology and Evolution, 4(9), 854–864.

Fieberg, J., & Johnson, D. H. (2015). MMI: Multimodel inference or models with management implications? The Journal of Wildlife Management, 79(5), 708–718.

Fieberg, J., Rieger, R. H., Zicus, M. C., & Schildcrout, J. S. (2009). Regression modelling of correlated data in ecology: Subject-specific and population averaged response patterns. Journal of Applied Ecology, 46(5), 1018–1025.

Fieberg, J. R., Vitense, K., & Johnson, D. H. (2020). Resampling-based methods for biologists. PeerJ, 8, e9089.

Fieberg, J., Signer, J., Smith, B., & Avgar, T. (2021). A ‘how to’ guide for interpreting parameters in habitat-selection analyses. Journal of Animal Ecology, 90(5), 1027–1043.

Fijorek, K., & Sokolowski, A. (2012). Separation-resistant and bias-reduced logistic regression: Statistica macro. Journal of Statistical Software, 47, 1–12.

Firth, D. (1993). Bias reduction of maximum likelihood estimates. Biometrika, 80(1), 27–38.

Forstmeier, W., Wagenmakers, E.-J., & Parker, T. H. (2017). Detecting and avoiding likely false-positive findings–a practical guide. Biological Reviews, 92(4), 1941–1968.

Fox, J. (2003). Effect displays in R for generalised linear models. Journal of Statistical Software, 8(15), 1–27. Retrieved from

Fox, J., & Weisberg, S. (2018). Visualizing fit and lack of fit in complex regression models with predictor effect plots and partial residuals. Journal of Statistical Software, 87(9), 1–27. doi:10.18637/jss.v087.i09

Fox, J., & Weisberg, S. (2019a). An R companion to applied regression (Third). Thousand Oaks CA: Sage. Retrieved from

Fox, J., & Weisberg, S. (2019b). An r companion to applied regression (3rd ed.). Thousand Oaks CA: Sage. Retrieved from

Friederichs, S. J., Zimmer, K. D., Herwig, B. R., Hanson, M. A., & Fieberg, J. R. (2011). Total phosphorus and piscivore mass as drivers of food web characteristics in shallow lakes. Oikos, 120(5), 756–765.

Friedman, J., Hastie, T., & Tibshirani, R. (2010). Regularization paths for generalized linear models via coordinate descent. Journal of Statistical Software, 33(1), 1.

García, L. V. (2004). Escaping the bonferroni iron claw in ecological studies. Oikos, 105(3), 657–663.

Gelman, A., & Hill, J. (2006). Data analysis using regression and multilevel/hierarchical models. Cambridge university press.

Gelman, A., Jakulin, A., Pittau, M. G., Su, Y.-S., & others. (2008). A weakly informative default prior distribution for logistic and other regression models. The Annals of Applied Statistics, 2(4), 1360–1383.

Gelman, A., Rubin, D. B., & others. (1992). Inference from iterative simulation using multiple sequences. Statistical Science, 7(4), 457–472.

Giesbrecht, F. G., & Burns, J. C. (1985). Two-stage analysis based on a mixed model: Large-sample asymptotic theory and small-sample simulation results. Biometrics, 477–486.

Giudice, J. H., Fieberg, J. R., & Lenarz, M. S. (2012). Spending degrees of freedom in a poor economy: A case study of building a sightability model for moose in northeastern minnesota. The Journal of Wildlife Management, 76(1), 75–87.

Glymour, M., Pearl, J., & Jewell, N. P. (2016). Causal inference in statistics: A primer. John Wiley & Sons.

Goode, K., & Rey, K. (2019). GgResidpanel: Panels and interactive versions of diagnostic plots using ’ggplot2’. Retrieved from

Grace, J. B. (2008). Structural equation modeling for observational studies. The Journal of Wildlife Management, 72(1), 14–22.

Graham, M. H. (2003). Confronting multicollinearity in ecological multiple regression. Ecology, 84(11), 2809–2815.

Gray, B. R. (2005). Selecting a distributional assumption for modelling relative densities of benthic macroinvertebrates. Ecological Modelling, 185(1), 1–12.

Greven, S., & Kneib, T. (2010). On the behaviour of marginal and conditional aic in linear mixed models. Biometrika, 97(4), 773–789.

Halekoh, U., & Højsgaard, S. (2014). A kenward-roger approximation and parametric bootstrap methods for tests in linear mixed models – the R package pbkrtest. Journal of Statistical Software, 59(9), 1–30. Retrieved from

Halekoh, U., Højsgaard, S., & Yan, J. (2006). The r package geepack for generalized estimating equations. Journal of Statistical Software, 15/2, 1–11.

Harrell Jr, F. E. (2015). Regression modeling strategies: With applications to linear models, logistic and ordinal regression, and survival analysis. Springer.

Harrell Jr, F. E. (2021). Rms: Regression modeling strategies. Retrieved from

Harrison, X. A. (2014). Using observation-level random effects to model overdispersion in count data in ecology and evolution. PeerJ, 2, e616.

Hartig, F. (2021). DHARMa: Residual diagnostics for hierarchical (multi-level / mixed) regression models. Retrieved from

Heagerty, P. J., & Zeger, S. L. (1998). Lorelogram: A regression approach to exploring dependence in longitudinal categorical responses. Journal of the American Statistical Association, 93(441), 150–162.

Hedeker, D., Toit, S. H. du, Demirtas, H., & Gibbons, R. D. (2018). A note on marginalization of regression parameters from mixed models of binary outcomes. Biometrics, 74(1), 354–361.

Hefley, T. J., Broms, K. M., Brost, B. M., Buderman, F. E., Kay, S. L., Scharf, H. R., … Hooten, M. B. (2017). The basis function approach for modeling autocorrelation in ecological data. Ecology, 98(3), 632–646.

Hegyi, G., & Garamszegi, L. Z. (2011). Using information theory as a substitute for stepwise regression in ecology and behavior. Behavioral Ecology and Sociobiology, 65(1), 69–76.

Heinze, G., Wallisch, C., & Dunkler, D. (2018). Variable selection–a review and recommendations for the practicing statistician. Biometrical Journal, 60(3), 431–449.

Henry, L., & Wickham, H. (2020). Purrr: Functional programming tools. Retrieved from

Herbranson, W. T., & Schroeder, J. (2010). Are birds smarter than mathematicians? Pigeons (columba livia) perform optimally on a version of the monty hall dilemma. Journal of Comparative Psychology, 124(1), 1.

Hesterberg, T. C. (2015). What teachers should know about the bootstrap: Resampling in the undergraduate statistics curriculum. The American Statistician, 69(4), 371–386.

Hilbe, J. M., & Hardin, J. W. (2008). Generalized estimating equations for longitudinal panel analysis. Handbook of Longitudinal Research: Design, Measurement, and Analysis, 1, 467–474.

Hoerl, A. E., & Kennard, R. W. (1970). Ridge regression: Biased estimation for nonorthogonal problems. Technometrics, 12(1), 55–67.

Hoffman, M. D., & Gelman, A. (2014). The no-u-turn sampler: Adaptively setting path lengths in hamiltonian monte carlo. Journal of Machine Learning Research, 15(1), 1593–1623.

Hooten, M. B., & Hobbs, N. T. (2015). A guide to bayesian model selection for ecologists. Ecological Monographs, 85(1), 3–28.

Hosmer Jr, D. W., Lemeshow, S., & Sturdivant, R. X. (2013). Applied logistic regression (Vol. 398). John Wiley & Sons.

Hrong-Tai Fai, A., & Cornelius, P. L. (1996). Approximate f-tests of multiple degree of freedom hypotheses in generalized least squares analyses of unbalanced split-plot experiments. Journal of Statistical Computation and Simulation, 54(4), 363–378.

Iannarilli, F., Arnold, T. W., Erb, J., & Fieberg, J. R. (2019). Using lorelograms to measure and model correlation in binary data: Applications to ecological studies. Methods in Ecology and Evolution, 10(12), 2153–2162.

Iannarilli, F., Erb, J., Arnold, T. W., & Fieberg, J. R. (2021). Evaluating species-specific responses to camera-trap survey designs. Wildlife Biology, 2021(1).

Ioannidis, J. P. (2005). Why most published research findings are false. PLoS Medicine, 2(8), e124.

Janssen, G., & Mulder, S. (2004). De ecologie van de zandige kust van nederland: Inventarisatie van het marcobenthos van zand en brandingszone. Rapportnr.: 2004.033.

Janssen, G., & Mulder, S. (2005). Zonation of macrofauna across sandy beaches and surf zones along the dutch coast. Oceanologia, 47(2).

Johnson, D. H. (1999). The insignificance of statistical significance testing. The Journal of Wildlife Management, 763–772.

Johnson, M. P., & Raven, P. H. (1973). Species number and endemism: The galápagos archipelago revisited. Science, 179(4076), 893–895.

Kaplan, D. (2009). Statistical modeling: A fresh approach. CreateSpace Independent Publishing Platform. Retrieved from

Kass, J. M., Muscarella, R., Galante, P. J., Bohl, C. L., Pinilla-Buitrago, G. E., Boria, R. A., … Anderson, R. P. (2021). ENMeval 2.0: Redesigned for customizable and reproducible modeling of species’ niches and distributions. Methods in Ecology and Evolution. Retrieved from

Keele, L., & Keele, L. (2008). Semiparametric regression for the social sciences. Wiley Online Library.

Kenward, M. G., & Roger, J. H. (1997). Small sample inference for fixed effects from restricted maximum likelihood. Biometrics, 983–997.

Kéry, M. (2010). Introduction to winbugs for ecologists: Bayesian approach to regression, anova, mixed models and related analyses. Academic Press.

Knief, U., & Forstmeier, W. (2021). Violating the normality assumption may be the lesser of two evils. Behavior Research Methods, 1–15.

Kuhn, M. (2021). Caret: Classification and regression training. Retrieved from

Kuhn, M., & Wickham, H. (2020). Tidymodels: A collection of packages for modeling and machine learning using tidyverse principles. Retrieved from

Kuiper, S., & Sklar, J. (2012). Practicing statistics: Guided investigations for the second course. Pearson Higher Ed.

Kutner, M. H., Nachtsheim, C. J., & Neter, J. (2004). Applied linear regression models. McGraw-Hill.

Kuznetsova, A., Brockhoff, P. B., & Christensen, R. H. B. (2017). lmerTest package: Tests in linear mixed effects models. Journal of Statistical Software, 82(13), 1–26. doi:10.18637/jss.v082.i13

Laubach, Z. M., Murray, E. J., Hoke, K. L., Safran, R. J., & Perng, W. (2021). A biologist’s guide to model selection and causal inference. Proceedings of the Royal Society B, 288(1943), 20202815.

Lele, S. R. (2020). Consequences of lack of parameterization invariance of non-informative bayesian analysis for wildlife management: Survival of san joaquin kit fox and declines in amphibian populations. Frontiers in Ecology and Evolution, 7, 501.

Lele, S. R., & Dennis, B. (2009). Bayesian methods for hierarchical models: Are ecologists making a faustian bargain? Ecological Applications, 19(3), 581–584.

Lele, S. R., Dennis, B., & Lutscher, F. (2007). Data cloning: Easy maximum likelihood estimation for complex ecological models using bayesian markov chain monte carlo methods. Ecology Letters, 10(7), 551–563.

Lele, S. R., Keim, J. L., & Solymos, P. (2019). ResourceSelection: Resource selection (probability) functions for use-availability data. Retrieved from

Lenth, R. V. (2021). Emmeans: Estimated marginal means, aka least-squares means. Retrieved from

Lever, J., Krzywinski, M., & Altman, N. (2016). Points of significance: Regularization. Nature Methods, 13(10), 803–805.

Liang, K.-Y., & Zeger, S. L. (1986). Longitudinal data analysis using generalized linear models. Biometrika, 73(1), 13–22.

Little, R. J. (2006). Calibrated bayes: A bayes/frequentist roadmap. The American Statistician, 60(3), 213–223.

Little, R. J., & Rubin, D. B. (2019). Statistical analysis with missing data (Vol. 793). John Wiley & Sons.

Lock, R. (2017). Lock5Data: Datasets for "statistics: UnLocking the power of data". Retrieved from

Lock, R. H., Lock, P. F., Morgan, K. L., Lock, E. F., & Lock, D. F. (2020). Statistics: Unlocking the power of data. John Wiley & Sons.

Lucas, T. C. (2020). A translucent box: Interpretable machine learning in ecology. Ecological Monographs, 90(4), e01422.

Lukacs, P. M., Burnham, K. P., & Anderson, D. R. (2010). Model selection bias and freedman’s paradox. Annals of the Institute of Statistical Mathematics, 62(1), 117.

Luke, S. G. (2017). Evaluating significance in linear mixed-effects models in r. Behavior Research Methods, 49(4), 1494–1502.

Luque-Fernandez, M. A., Schomaker, M., Redondo-Sanchez, D., Jose Sanchez Perez, M., Vaidya, A., & Schnitzer, M. E. (2019). Educational note: Paradoxical collider effect in the analysis of non-communicable disease epidemiological data: A reproducible illustration and web application. International Journal of Epidemiology, 48(2), 640–653.

Lüdecke, D. (2018). Ggeffects: Tidy data frames of marginal effects from regression models. Journal of Open Source Software, 3(26), 772. doi:10.21105/joss.00772

Lüdecke, D. (2021). SjPlot: Data visualization for statistics in social science. Retrieved from

Lüdecke, D., Ben-Shachar, M. S., Patil, I., Waggoner, P., & Makowski, D. (2021). performance: An R package for assessment, comparison and testing of statistical models. Journal of Open Source Software, 6(60), 3139. doi:10.21105/joss.03139

Lyon, A. (2010). Philosophy of probability. Philosophies of the Sciences: A Guide, 92–125.

MacKenzie, D. I., Nichols, J. D., Royle, J. A., Pollock, K. H., Bailey, L., & Hines, J. E. (2017). Occupancy estimation and modeling: Inferring patterns and dynamics of species occurrence. Elsevier.

Manly, B. (1991). Randomization and monte carlo methods in biology london. UK: Chapman and Hall.

Marchetti, G. M., Drton, M., & Sadeghi, K. (2020). Ggm: Graphical markov models with mixed graphs. Retrieved from

McCullagh, P., & Nelder, J. A. (1989). Generalized linear models, second edition. Chapman & Hall. Retrieved from\_FfBkC

McCulloch, C. E., & Neuhaus, J. M. (2005). Generalized linear mixed models. Encyclopedia of Biostatistics, 4.

McElreath, R. (2020). Statistical rethinking: A bayesian course with examples in r and stan. CRC press.

McFadden, D. (1977). Quantitative methods for analyzing travel behavior of individuals: Some recent 857 developments: Institute of transportation studies. University of California, 858.

Mech, L. D., Fieberg, J., & Barber-Meyer, S. (2018). An historical overview and update of wolf–moose interactions in northeastern minnesota. Wildlife Society Bulletin, 42(1), 40–47.

Meier, U. (2006). A note on the power of fisher’s least significant difference procedure. Pharmaceutical Statistics: The Journal of Applied Statistics in the Pharmaceutical Industry, 5(4), 253–263.

Middleton, K. M., & Pruim, R. (2015). Abd: The analysis of biological data. Retrieved from

Moran, M. D. (2003). Arguments for rejecting the sequential bonferroni in ecological studies. Oikos, 100(2), 403–405.

Morris, W., Doak, D., Groom, M., Kareiva, P., Fieberg, J., Gerber, L., … Thomson, D. (1999). A practical handbook for population viability analysis. The Nature Conservancy.

Mosteller, F., & Tukey, J. W. (1968). Data analysis, including statistics. Handbook of Social Psychology, 2, 80–203.

Moya-Laraño, J., & Corcobado, G. (2008). Plotting partial correlation and regression in ecological studies. Web Ecology, 8(1), 35–46.

Muff, S., Held, L., & Keller, L. F. (2016). Marginal or conditional regression models for correlated non-normal data? Methods in Ecology and Evolution, 7(12), 1514–1524.

Muff, S., Signer, J., & Fieberg, J. (2020). Accounting for individual-specific variation in habitat-selection studies: Efficient estimation of mixed-effects models using bayesian or frequentist computation. Journal of Animal Ecology, 89(1), 80–92.

Murdoch, D., & Adler, D. (2021). Rgl: 3D visualization using opengl. Retrieved from

Murtaugh, P. A. (1989). Size and species composition of zooplankton in experimental ponds with and without fishes. Journal of Freshwater Ecology, 5(1), 27–38.

Murtaugh, P. A. (2007). Simplicity and complexity in ecological data analysis. Ecology, 88(1), 56–62.

Murtaugh, P. A. (2014). In defense of p values. Ecology, 95(3), 611–617.

Nakagawa, S. (2004). A farewell to bonferroni: The problems of low statistical power and publication bias. Behavioral Ecology, 15(6), 1044–1045.

Nakagawa, S., & Schielzeth, H. (2013). A general and simple method for obtaining r2 from generalized linear mixed-effects models. Methods in Ecology and Evolution, 4(2), 133–142.

Oberpriller, J., Souza Leite, M. de, & Pichler, M. (2021). Fixed or random? On the reliability of mixed-effect models for a small number of levels in grouping variables. bioRxiv.

Ogle, D. H., Doll, J. C., Wheeler, P., & Dinno, A. (2021). FSA: Fisheries stock analysis. Retrieved from

O’Hara, R., & Kotze, J. (2010). Do not log-transform count data. Nature Precedings, 1–1.

Otis, D. L., Burnham, K. P., White, G. C., & Anderson, D. R. (1978). Statistical inference from capture data on closed animal populations. Wildlife Monographs, (62), 3–135.

Pearl, J. (1995). Causal diagrams for empirical research. Biometrika, 82(4), 669–688. doi:10.1093/biomet/82.4.669

Pearl, J. (2000). Causality: Models, reasoning, and inference. Cambridge University Press.

Pearl, J., Glymour, M., & Jewell, N. P. (2016). Causal inference in statistics: A primer. John Wiley & Sons.

Pearl, J., & Mackenzie, D. (2018). The book of why: The new science of cause and effect. Basic books.

Perneger, T. V. (1998). What’s wrong with bonferroni adjustments. Bmj, 316(7139), 1236–1238.

Perperoglou, A., Sauerbrei, W., Abrahamowicz, M., & Schmid, M. (2019). A review of spline function procedures in r. BMC Medical Research Methodology, 19(1), 1–16.

Pewsey, A., Neuhäuser, M., & Ruxton, G. D. (2013). Circular statistics in r. Oxford University Press.

Piegorsch, W. W., & Bailer, A. J. (2005). Analyzing environmental data. John Wiley & Sons.

Pike, N. (2011). Using false discovery rates for multiple comparisons in ecology and evolution. Methods in Ecology and Evolution, 2(3), 278–282.

Pinheiro, J., & Bates, D. (2006). Mixed-effects models in s and s-plus. Springer Science & Business Media.

Pinheiro, J., Bates, D., DebRoy, S., Sarkar, D., & R Core Team. (2021). nlme: Linear and nonlinear mixed effects models. Retrieved from

Plummer, M., & others. (2003). JAGS: A program for analysis of bayesian graphical models using gibbs sampling. In Proceedings of the 3rd international workshop on distributed statistical computing (Vol. 124, pp. 1–10). Vienna, Austria.

Pollock, K. H., Nichols, J. D., Brownie, C., & Hines, J. E. (1990). Statistical inference for capture-recapture experiments. Wildlife Monographs, 3–97.

Ponisio, L. C., Valpine, P. de, Michaud, N., & Turek, D. (2020). One size does not fit all: Customizing mcmc methods for hierarchical models using nimble. Ecology and Evolution, 10(5), 2385–2416.

Puth, M.-T., Neuhäuser, M., & Ruxton, G. D. (2015). On the variety of methods for calculating confidence intervals by bootstrapping. Journal of Animal Ecology, 84(4), 892–897.

R Core Team. (2021). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. Retrieved from

Reitan, T., & Nielsen, A. (2016). Do not divide count data with count data; a story from pollination ecology with implications beyond. PloS One, 11(2).

Ritz, J., & Spiegelman, D. (2004). Equivalence of conditional and marginal regression models for clustered and longitudinal data. Statistical Methods in Medical Research, 13(4), 309–323.

Rizopoulos, D. (2021). GLMMadaptive: Generalized linear mixed models using adaptive gaussian quadrature. Retrieved from

Roback, P., & Legler, J. (2021). Beyond multiple linear regression: Applied generalized linear models and multilevel models in r. Chapman; Hall/CRC.

Roberts, D. R., Bahn, V., Ciuti, S., Boyce, M. S., Elith, J., Guillera-Arroita, G., … others. (2017). Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure. Ecography, 40(8), 913–929.

Robinson, D., Hayes, A., & Couch, S. (2021). Broom: Convert statistical objects into tidy tibbles. Retrieved from

Rok Blagus. (2017). Abe: Augmented backward elimination. Retrieved from

Rossman, A. J. (1994). Televisions, physicians, and life expectancy. Journal of Statistics Education, 2(2).

Ruxton, G. D., & Neuhäuser, M. (2010). When should we use one-tailed hypothesis testing? Methods in Ecology and Evolution, 1(2), 114–117.

Sackett, D. L. (1979). Bias in analytic research. In The case-control study consensus and controversy (pp. 51–63). Elsevier.

Savage, V. M., & West, G. B. (2007). A quantitative, theoretical framework for understanding mammalian sleep. Proceedings of the National Academy of Sciences, 104(3), 1051–1056.

Schad, D. J., Vasishth, S., Hohenstein, S., & Kliegl, R. (2020). How to capitalize on a priori contrasts in linear (mixed) models: A tutorial. Journal of Memory and Language, 110, 104038.

Scheipl, F., Greven, S., & Kuechenhoff, H. (2008). Size and power of tests for a zero random effect variance or polynomial regression in additive and linear mixed models. Computational Statistics & Data Analysis, 52(7), 3283–3299.

Schielzeth, H. (2010). Simple means to improve the interpretability of regression coefficients. Methods in Ecology and Evolution, 1(2), 103–113.

Schielzeth, H., & Forstmeier, W. (2009). Conclusions beyond support: Overconfident estimates in mixed models. Behavioral Ecology, 20(2), 416–420.

Scotson, L., Fredriksson, G., Ngoprasert, D., Wong, W.-M., & Fieberg, J. (2017). Projecting range-wide sun bear population trends using tree cover and camera-trap bycatch data. PloS One, 12(9).

Self, S. G., & Liang, K.-Y. (1987). Asymptotic properties of maximum likelihood estimators and likelihood ratio tests under nonstandard conditions. Journal of the American Statistical Association, 82(398), 605–610.

Shipley, B. (2002). Cause and correlation in biology: A user’s guide to path analysis, structural equations and causal inference. Cambridge University Press.

Shmueli, G. (2010). To explain or to predict? Statistical Science, 25(3), 289–310.

Signer, J., Fieberg, J., & Avgar, T. (2019). Animal movement tools (amt): R package for managing tracking data and conducting habitat selection analyses. Ecology and Evolution, 9(2), 880–890.

Sileshi, G. (2008). The excess-zero problem in soil animal count data and choice of appropriate models for statistical inference. Pedobiologia, 52(1), 1–17.

Silk, M. J., Harrison, X. A., & Hodgson, D. J. (2020). Perils and pitfalls of mixed-effects regression models in biology. PeerJ, 8, e9522.

Singer, J. D., Willett, J. B., Willett, J. B., & others. (2003). Applied longitudinal data analysis: Modeling change and event occurrence. Oxford university press.

Stan Development Team. (2019). RStan: The R interface to Stan. Retrieved from

Statisticat, & LLC. (2021). LaplacesDemon: Complete environment for bayesian inference. Retrieved from

Stewart, P. S., Stephens, P. A., Hill, R. A., Whittingham, M. J., & Dawson, W. (2022). Model selection in occupancy models: Inference versus prediction. bioRxiv.

Su, Y.-S., & Masanao Yajima. (2021). R2jags: Using r to run ’jags’. Retrieved from

Su, Z., & He, J. X. (2013). Analysis of lake huron recreational fisheries data using models dealing with excessive zeros. Fisheries Research, 148, 81–89.

Thompson, S. J., Johnson, D. H., Niemuth, N. D., & Ribic, C. A. (2015). Avoidance of unconventional oil wells and roads exacerbates habitat loss for grassland birds in the north american great plains. Biological Conservation, 192, 82–90.

Tibshirani, R. (2011). Regression shrinkage and selection via the lasso: A retrospective. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 73(3), 273–282.

Toms, J. D., & Lesperance, M. L. (2003). Piecewise regression: A tool for identifying ecological thresholds. Ecology, 84(8), 2034–2041.

Tredennick, A. T., Hooker, G., Ellner, S. P., & Adler, P. B. (2021). A practical guide to selecting models for exploration, inference, and prediction in ecology. Ecology, 102(6), e03336.

Vaida, F., & Blanchard, S. (2005). Conditional akaike information for mixed-effects models. Biometrika, 92(2), 351–370.

Valavi, R., Elith, J., Lahoz-Monfort, J. J., & Guillera-Arroita, G. (2019). BlockCV: An r package for generating spatially or environmentally separated folds for k-fold cross-validation of species distribution models. Methods in Ecology and Evolution, 10(2), 225–232.

Vaughan, D. (2021). Workflows: Modeling workflows. Retrieved from

Vehtari, A., Gabry, J., Magnusson, M., Yao, Y., Bürkner, P.-C., Paananen, T., & Gelman, A. (2020). Loo: Efficient leave-one-out cross-validation and waic for bayesian models. Retrieved from

Vehtari, A., Gelman, A., & Gabry, J. (2017). Practical bayesian model evaluation using leave-one-out cross-validation and waic. Statistics and Computing, 27(5), 1413–1432. doi:10.1007/s11222-016-9696-4

Vehtari, A., & Lampinen, J. (2002). Bayesian model assessment and comparison using cross-validation predictive densities. Neural Computation, 14(10), 2439–2468.

Venables, W. N., & Ripley, B. D. (2002). Modern applied statistics with s (Fourth). New York: Springer. Retrieved from

Verhoeven, K. J., Simonsen, K. L., & McIntyre, L. M. (2005). Implementing false discovery rate control: Increasing your power. Oikos, 108(3), 643–647.

Warton, D. I. (2005). Many zeros does not mean zero inflation: Comparing the goodness-of-fit of parametric models to multivariate abundance data. Environmetrics: The Official Journal of the International Environmetrics Society, 16(3), 275–289.

Watanabe, S., & Opper, M. (2010). Asymptotic equivalence of bayes cross validation and widely applicable information criterion in singular learning theory. Journal of Machine Learning Research, 11(12).

Weber, P., Binder, K., & Krauss, S. (2018). Why can only 24% solve bayesian reasoning problems in natural frequencies: Frequency phobia in spite of probability blindness. Frontiers in Psychology, 9, 1833.

White, H. (1982). Maximum likelihood estimation of misspecified models. Econometrica: Journal of the Econometric Society, 1–25.

Whitlock, M., & Schluter, D. (2015). The analysis of biological data. Roberts Publishers.

Whitman, K., Starfield, A. M., Quadling, H. S., & Packer, C. (2004). Sustainable trophy hunting of african lions. Nature, 428(6979), 175.

Whittingham, M. J., Stephens, P. A., Bradbury, R. B., & Freckleton, R. P. (2006). Why do we still use stepwise modelling in ecology and behaviour? Journal of Animal Ecology, 75(5), 1182–1189.

Wickham, H. (2016). Ggplot2: Elegant graphics for data analysis. Springer-Verlag New York. Retrieved from

Wickham, H. (2021). Tidyr: Tidy messy data. Retrieved from

Wickham, H., François, R., Henry, L., & Müller, K. (2021). Dplyr: A grammar of data manipulation. Retrieved from

Williamson, J. M. (2014). Informative cluster size. Wiley StatsRef: Statistics Reference Online, 1–2.

Wood, S. N. (2004). Stable and efficient multiple smoothing parameter estimation for generalized additive models. Journal of the American Statistical Association, 99(467), 673–686.

Wood, S. N. (2017). Generalized additive models: An introduction with r (2nd ed.). Chapman; Hall/CRC.

Wood, S. N. (2017). Generalized additive models: An introduction with r. Chapman; Hall/CRC.

Wright, S. (1921). Correlation and causation. Journal of Agricultural Research, 20, 557–585.

Yackulic, C. B., Dodrill, M., Dzul, M., Sanderlin, J. S., & Reid, J. A. (2020). A need for speed in bayesian population models: A practical guide to marginalizing and recovering discrete latent states. Ecological Applications, 30(5), e02112.

Yan, J. (2002). Geepack: Yet another package for generalized estimating equations. R-News, 2/3, 12–14.

Young, M. L., Preisser, J. S., Qaqish, B. F., & Wolfson, M. (2007). Comparison of subject-specific and population averaged models for count data from cluster-unit intervention trials. Statistical Methods in Medical Research, 16(2), 167–184.

Youngflesh, C. (2018). MCMCvis: Tools to visualize, manipulate, and summarize mcmc output. Journal of Open Source Software, 3(24), 640. doi:10.21105/joss.00640

Zeger, S. L., Liang, K.-Y., & Albert, P. S. (1988). Models for longitudinal data: A generalized estimating equation approach. Biometrics, 1049–1060.

Zicus, M. C., Fieberg, J., & Rave, D. P. (2003). Does mallard clutch size vary with landscape composition: A different view. The Wilson Journal of Ornithology, 115(4), 409–414.

Zicus, M. C., Rave, D. P., Das, A., Riggs, M. R., & Buitenwerf, M. L. (2006a). Influence of land use on mallard nest-structure occupancy. The Journal of Wildlife Management, 70(5), 1325–1333.

Zicus, M. C., Rave, D. P., & Fieberg, J. R. (2006b). Cost-effectiveness of single-versus double-cylinder over-water nest structures. Wildlife Society Bulletin, 34(3), 647–655.

Zuur, A., Ieno, E. N., Walker, N., Saveliev, A. A., & Smith, G. M. (2009). Mixed effects models and extensions in ecology with r. Springer Science & Business Media.