Appendix A — References

Aho, K., Derryberry, D. and Peterson, T. (2014) ‘Model selection for ecologists: The worldviews of AIC and BIC’, Ecology, 95(3), pp. 631–636.

Arel-Bundock, V., Greifer, N. and Heiss, A. (2024) ‘How to interpret statistical models using marginaleffects for R and Python’, Journal of Statistical Software, 111(9), pp. 1–32.

Bates, D. (2011) ‘Computational methods for mixed models’, Vignette for lme4 [Preprint].

Bates, S., Hastie, T. and Tibshirani, R. (2024) ‘Cross-validation: What does it estimate and how well does it do it?’, Journal of the American Statistical Association, 119(546), pp. 1434–1445.

Bengio, Y. and Grandvalet, Y. (2004) ‘No unbiased estimator of the variance of k-fold cross-validation’, Journal of Machine Learning Research, 5, pp. 1089–1105. Available at: https://www.jmlr.org/papers/v5/grandvalet04a.html.

Berg, S.M. van den (2025) Analysing data using linear models: With applications in R. bookdown.org. Available at: https://bookdown.org/pingapang9/linear_models_bookdown/ (Accessed: 3 October 2026).

Besag, J. (1974) ‘Spatial interaction and the statistical analysis of lattice systems’, Journal of the Royal Statistical Society: Series B (Methodological), 36(2), pp. 192–225.

Bhaskaran, K. and Smeeth, L. (2014) ‘What is the difference between missing completely at random and missing at random?’, International journal of epidemiology, 43(4), pp. 1336–1339.

Blanchet, F.G., Cazelles, K. and Gravel, D. (2020) ‘Co-occurrence is not evidence of ecological interactions’, Ecology Letters, 23(7), pp. 1050–1063.

Bolker, B.M. (2015) ‘Linear and generalized linear mixed models’, in Ecological statistics. Oxford University Press, pp. 309–333.

Chernozhukov, V. et al. (2018) ‘Double/debiased machine learning for treatment and structural parameters’, The Econometrics Journal, 21(1), pp. C1–C68.

Damgaard, C.F. and Irvine, K.M. (2019) ‘Using the beta distribution to analyse plant cover data’, Journal of Ecology, 107(6), pp. 2747–2759.

Dormann, C.F. et al. (2013) ‘Collinearity: A review of methods to deal with it and a simulation study evaluating their performance’, Ecography, 36(1), pp. 27–46.

Douma, J.C. and Weedon, J.T. (2019) ‘Analysing continuous proportions in ecology and evolution: A practical introduction to beta and dirichlet regression’, Methods in Ecology and Evolution, 10(9), pp. 1412–1430.

Dumelle, M. et al. (2025) ‘Missing data in ecology: Syntheses, clarifications, and considerations’, Ecological Monographs, 95(4), p. e70037.

Felsenstein, J. (1985) ‘Phylogenies and the comparative method’, The American Naturalist, 125(1), pp. 1–15.

Fox, J. and Weisberg, S. (2019) An R companion to applied regression. 3rd edn. Thousand Oaks, CA: Sage.

Gelman, A. (2008) ‘Scaling regression inputs by dividing by two standard deviations’, Statistics in Medicine, 27(15), pp. 2865–2873.

Gelman, A. and Stern, H. (2006) ‘The difference between “significant” and “not significant” is not itself statistically significant’, The American Statistician, 60(4), pp. 328–331.

Grace, J.B. and Keeley, J.E. (2006) ‘A structural equation model analysis of postfire plant diversity in California shrublands’, Ecological Applications, 16(2), pp. 503–514.

Green, P. and MacLeod, C.J. (2016) ‘SIMR: An R package for power analysis of generalized linear mixed models by simulation’, Methods in Ecology and Evolution, 7(4), pp. 493–498.

Harrison, X.A. et al. (2018) ‘A brief introduction to mixed effects modelling and multi-model inference in ecology’, PeerJ, 6, p. e4794.

Hartig, F. (2024) ‘Introduction to Bayesian statistics with R’. Available at: https://florianhartig.github.io/LearningBayes/.

Hawkes, N. (2018) ‘Sixty seconds on … P-hacking’, BMJ, 362, p. k4039.

Hector, A., Von Felten, S. and Schmid, B. (2010) ‘Analysis of variance with unbalanced data: An update for ecology & evolution’, Journal of animal ecology, 79(2), pp. 308–316.

Hilborn, R. and Mangel, M. (1997) The ecological detective: Confronting models with data. Princeton, NJ: Princeton University Press (Monographs in population biology, 28).

Hippel, P.T. von (2007) ‘Regression with missing Ys: An improved strategy for analyzing multiply imputed data’, Sociological Methodology, 37(1), pp. 83–117.

Hurlbert, S.H. (1984) ‘Pseudoreplication and the design of ecological field experiments’, Ecological Monographs, 54(2), pp. 187–211.

Jung, K. et al. (2014) ‘Female hurricanes are deadlier than male hurricanes’, Proceedings of the National Academy of Sciences, 111(24), pp. 8782–8787.

Korhonen, P. et al. (2024) ‘A comparison of joint species distribution models for percent cover data’, Methods in Ecology and Evolution, 15(12), pp. 2359–2372.

Kubinec, R. (2023) ‘Ordered beta regression: A parsimonious, well-fitting model for continuous data with lower and upper bounds’, Political Analysis, 31(4), pp. 519–536.

Kuznetsova, A., Brockhoff, P.B. and Christensen, R.H.B. (2017) ‘lmerTest package: Tests in linear mixed effects models’, Journal of Statistical Software, 82(13), pp. 1–26.

Lai, J., Zou, Y., Zhang, J., et al. (2022) ‘Generalizing hierarchical and variation partitioning in multiple regression and canonical analyses using the rdacca.hp R package’, Methods in Ecology and Evolution, 13(4), pp. 782–788.

Lai, J., Zou, Y., Zhang, S., et al. (2022) ‘Glmm.hp: An R package for computing individual effect of predictors in generalized linear mixed models’, Journal of Plant Ecology, 15(6), pp. 1302–1307.

Langsrud, Ø. (2003) ‘ANOVA for unbalanced data: Use type II instead of type III sums of squares’, Statistics and Computing, 13(2), pp. 163–167.

Laubach, Z.M. et al. (2021) ‘A biologist’s guide to model selection and causal inference’, Proceedings of the Royal Society B, 288(1943), p. 20202815.

Lederer, D.J. et al. (2019) ‘Control of confounding and reporting of results in causal inference studies. Guidance for authors from editors of respiratory, sleep, and critical care journals’, Annals of the American Thoracic Society, 16(1), pp. 22–28.

Leite, M. de S. et al. (2024) ‘Major axes of variation in tree demography across global forests’, Ecography, 2024(6), p. e07187.

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

McGillycuddy, M. et al. (2025) ‘Parsimoniously fitting large multivariate random effects in glmmTMB’, Journal of Statistical Software, 112(1).

Nakagawa, S. and Freckleton, R.P. (2008) ‘Missing inaction: The dangers of ignoring missing data’, Trends in Ecology & Evolution, 23(11), pp. 592–596.

Nakagawa, S., Johnson, P.C.D. and Schielzeth, H. (2017) ‘The coefficient of determination R² and intra-class correlation coefficient from generalized linear mixed-effects models revisited and expanded’, Journal of The Royal Society Interface, 14(134), p. 20170213.

Nakagawa, S. and Schielzeth, H. (2013) ‘A general and simple method for obtaining R² from generalized linear mixed-effects models’, Methods in Ecology and Evolution, 4(2), pp. 133–142.

Nelder, J.A. (1977) ‘A reformulation of linear models’, Journal of the Royal Statistical Society: Series A (General), 140(1).

Neuhaus, J.M., McCulloch, C.E. and Boylan, R. (2013) ‘Estimation of covariate effects in generalized linear mixed models with a misspecified distribution of random intercepts and slopes’, Statistics in Medicine, 32(14), pp. 2419–2429.

Oberpriller, J., Souza Leite, M. de and Pichler, M. (2022) ‘Fixed or random? On the reliability of mixed-effects models for a small number of levels in grouping variables’, Ecology and Evolution, 12(7), p. e9062.

Pearl, J. (2009) Causality: Models, reasoning, and inference. 2nd edn. Cambridge University Press.

Rabone, M. et al. (2019) ‘Freshwater snails of biomedical importance in the niger river valley: Evidence of temporal and spatial patterns in abundance, distribution and infection with schistosoma spp.’, Parasites & vectors, 12(1), pp. 1–20.

Roberts, D.R. et al. (2017) ‘Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure’, Ecography, 40(8), pp. 913–929.

Schad, D.J. et al. (2020) ‘How to capitalize on a priori contrasts in linear (mixed) models: A tutorial’, Journal of Memory and Language, 110, p. 104038.

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

Shaw, R.G. and Mitchell-Olds, T. (1993) ‘ANOVA for unbalanced data: An overview’, Ecology, 74(6), pp. 1638–1645.

Silberzahn, R. et al. (2018) ‘Many analysts, one data set: Making transparent how variations in analytic choices affect results’, Advances in Methods and Practices in Psychological Science, 1(3), pp. 337–356.

Simmons, J.P., Nelson, L.D. and Simonsohn, U. (2011) ‘False-positive psychology: Undisclosed flexibility in data collection and analysis allows presenting anything as significant’, Psychological Science, 22(11), pp. 1359–1366.

Smithson, M. and Verkuilen, J. (2006) ‘A better lemon squeezer? Maximum-likelihood regression with beta-distributed dependent variables’, Psychological Methods, 11(1), pp. 54–71.

Verbyla, A.P. (2019) ‘A note on model selection using information criteria for general linear models estimated using REML’, Australian & New Zealand Journal of Statistics, 61(1), pp. 39–50.

Warton, D.I. and Hui, F.K.C. (2011) ‘The arcsine is asinine: The analysis of proportions in ecology’, Ecology, 92(1), pp. 3–10.