Experimental Design and Data Analysis for Biologists
Abstract
An essential textbook for any student or researcher in biology needing to design experiments, sample programs or analyse the resulting data. The text begins with a revision of estimation and hypothesis testing methods, covering both classical and Bayesian philosophies, before advancing to the analysis of linear and generalized linear models. Topics covered include linear and logistic regression, simple and complex ANOVA models (for factorial, nested, block, split-plot and repeated measures and covariance designs), and log-linear models. Multivariate techniques, including classification and ordination, are then introduced. Special emphasis is placed on checking assumptions, exploratory data analysis and presentation of results. The main analyses are illustrated with many examples from published papers and there is an extensive reference list to both the statistical and biological literature. The book is supported by a website that provides all data sets, questions for each chapter and links to software.
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VIFs did not reveal collinearity problems, with the largest value being <4 across models.
“VIFs did not reveal collinearity problems, as indicated by the largest value being <4 (51) (event model 2.59; anticipation model 2.43; defection model 1.98; intergroup conflict type model 2.74; persistence model 2.03; reduced event model 2.72).”
From Oxytocin reactivity during intergroup conflict in wild chimpanzees (2017) · cites this paper as [51]
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Generalized linear mixed models: a practical guide for ecology and evolution
Bolker, B. M., Brooks, M. E., Clark, C. J., Geange, S. W., Poulsen, J. R., Stevens, M. H. H., & White, J.-S. S. (2009) · Trends in Ecology & Evolution · doi:10.1016/j.tree.2008.10.008 · Cited by 8,910 · see citing works
A protocol for data exploration to avoid common statistical problems: <i>Data exploration</i>
Zuur, A. F., Ieno, E. N., & Elphick, C. S. (2010) · Methods in Ecology and Evolution · doi:10.1111/j.2041-210x.2009.00001.x · Cited by 8,228 · see citing works
Effect size, confidence interval and statistical significance: a practical guide for biologists
Nakagawa, S., & Cuthill, I. C. (2007) · Biological Reviews · doi:10.1111/j.1469-185x.2007.00027.x · Cited by 3,835 · see citing works
“A confidence interval (CI) is usually interpreted as the range of values that encompass the population or ‘true’ value, estimated by a certain statistic, with a given probability (e.g. Cohen, 1990; Rice, 1995; Zar, 1999; Quinn & Keough, 2002).”
BackgroundSimple means to improve the interpretability of regression coefficients
Schielzeth, H. (2010) · Methods in Ecology and Evolution · doi:10.1111/j.2041-210x.2010.00012.x · Cited by 3,027 · see citing works
“Centring the input variables before fitting the model will largely remove this correlation (e.g. r = )0Æ12 and )0Æ05 for the same input variables; Aiken & West 1991; Neter et al. 1996; Quinn & Keough 2002).”
BackgroundMethodInfluentialBivariate line‐fitting methods for allometry
Warton, D. I., Wright, I. J., Falster, D. S., & Westoby, M. (2006) · Biological reviews/Biological reviews of the Cambridge Philosophical Society · doi:10.1017/s1464793106007007 · Cited by 2,318 · see citing works
A brief introduction to mixed effects modelling and multi-model inference in ecology
Harrison, X. A., Donaldson, L., Correa-Cano, M. E., Evans, J., Fisher, D. N., Goodwin, C. E. D., Robinson, B. S., Hodgson, D. J., & Inger, R. (2018) · PeerJ · doi:10.7717/peerj.4794 · Cited by 2,094 · see citing works
“…parameters to be estimated can result in overparameterised models, where there are insufficient data to estimate coefficients robustly (Southwood & Henderson 2000, Quinn & Keough 2002, Crawley 2013 expected fitted lines of a 3-way interaction means refraining from fitting a model containing one.”
BackgroundMultivariate Analysis of Ecological Data
Lepš, J., & Šmilauer, P. (2006) · Bulletin of the Ecological Society of America · doi:10.1890/0012-9623(2006)87[193:maoed]2.0.co;2 · Cited by 2,080 · see citing works
Ruxton, G. D. (2006) · Behavioral Ecology · doi:10.1093/beheco/ark016 · Cited by 1,988 · see citing works
Bee foraging ranges and their relationship to body size
Greenleaf, S. S., Williams, N. M., Winfree, R., & Kremen, C. (2007) · Oecologia · doi:10.1007/s00442-007-0752-9 · Cited by 1,790 · see citing works
“inXuence of potential outliers using Cook’s D (Quinn and Keough 2002).”
MethodInfluentialPlant Species Richness and Ecosystem Multifunctionality in Global Drylands
Maestre, F. T., Quero, J. L., Gotelli, N. J., Escudero, A., Ochoa, V., Delgado-Baquerizo, M., García-Gómez, M., Bowker, M. A., Soliveres, S., Escolar, C., et al. (2012) · Science · doi:10.1126/science.1215442 · Cited by 1,730 · see citing works
Robustness of linear mixed-effects models to violations of distributional assumptions
Schielzeth, H., Dingemanse, N. J., Nakagawa, S., Westneat, D. F., Allegue, H., Teplitsky, C., Réale, D., Dochtermann, N. A., Garamszegi, L. Z., & Araya‐Ajoy, Y. G. (2020) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.13434 · Cited by 1,393 · see citing works
“Warnings about correlated fixed effects are abound (Freckleton, 2011; Quinn & Keough, 2002; Zuur, Ieno, & Elphick, 2010), but the impact of such collinearity is much more subtle than commonly assumed.”
BackgroundBerkelmans, R., & van Oppen, M. J. H. (2006) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.2006.3567 · Cited by 1,305 · see citing works
“008 in accordance with the Dunn–Sidak procedure to account for the number of multiple comparisons undertaken (Quinn & Keough 2002).”
MethodA Quantitative Survey of Local Adaptation and Fitness Trade‐Offs
Hereford, J. (2009) · The American Naturalist · doi:10.1086/597611 · Cited by 1,259 · see citing works
“I standardized the phenotypes of all populations by calculating Euclidean distances between the phenotypes of each pair of populations at each field site.”
BackgroundMethodModel selection for ecologists: the worldviews of AIC and BIC
Aho, K., Derryberry, D., & Peterson, T. (2014) · Ecology · doi:10.1890/13-1452.1 · Cited by 1,165 · see citing works
“Scientists often do not consider a single data set to be adequate for research hypothesis rejection (Quinn and Keough 2002:35), particularly for complex hypotheses with a low degree of falsifiability (i.e., Popper 1959:266).”
BackgroundImpact of invasive plants on the species richness, diversity and composition of invaded communities
Hejda, M., Pyšek, P., & Jarošík, V. (2009) · Journal of Ecology · doi:10.1111/j.1365-2745.2009.01480.x · Cited by 1,172 · see citing works
“…species were then examined by regressing simple slopes on increasing differences between the cover of invading and dominant native species at varying values of the cover of the invading species: its mean, and mean plus and minus its sample standard deviation (Quinn & Keough 2002, pp. 131–133).”
BackgroundMethodInfluentialCatford, J. A., Jansson, R., & Nilsson, C. F. (2008) · Diversity and Distributions · doi:10.1111/j.1472-4642.2008.00521.x · Cited by 1,139 · see citing works
Forstmeier, W., & Schielzeth, H. (2010) · Behavioral Ecology and Sociobiology · doi:10.1007/s00265-010-1038-5 · Cited by 1,117 · see citing works
“In observational studies, model selection procedures are commonly applied in order to find a parsimonious combination of predictors to explain some phenomenon of interest (Miller 1984; Quinn and Keough 2002).”
Bottom-up effects of plant diversity on multitrophic interactions in a biodiversity experiment
Scherber, C., Eisenhauer, N., Weisser, W. W., Schmid, B., Voigt, W., Fischer, M., Schulze, E., Roscher, C., Weigelt, A., Allan, E., et al. (2010) · Nature · doi:10.1038/nature09492 · Cited by 1,086 · see citing works
Biomagnification of mercury in aquatic food webs: a worldwide meta-analysis.
Lavoie, R. A., Jardine, T., Chumchal, M., Kidd, K., & Campbell, L. (2013) · Environmental Science and Technology · doi:10.1021/es403103t · Cited by 919 · see citing works
Characterization of the gut microbiome using 16S or shotgun metagenomics
Jovel, J., Patterson, J., Wang, W., Hotte, N., O'Keefe, S., Mitchel, T., Perry, T., Kao, D., Mason, A. L., Madsen, K. L., et al. (2016) · Frontiers in Microbiology · doi:10.3389/fmicb.2016.00459 · Cited by 942 · see citing works
“…ecology are non-metric multidimensional scaling (NMDS) and principal coordinate analyses (PCoA), also known as metric multidimensional scaling (Quinn and Keough, 2002; Frontiers in Microbiology | www.frontiersin.org 8 April 2016 | Volume 7 | Article 459 FIGURE 4 | Popular techniques for…”
MethodConclusions beyond support: overconfident estimates in mixed models
Schielzeth, H., & Forstmeier, W. (2009) · Behavioral Ecology · doi:10.1093/beheco/arn145 · Cited by 952 · see citing works
“The problem is most easily illustrated for designs, in which some factor varies between subjects, but other factors (or covariates) vary within subjects (e.g., split-plot and repeatedmeasures designs; Quinn and Keough 2002).”
BackgroundThe Influence of Late Quaternary Climate-Change Velocity on Species Endemism
Sandel, B., Arge, L., Dalsgaard, B., Davies, R. G., Gaston, K. J., Sutherland, W. J., & Svenning, J. (2011) · Science · doi:10.1126/science.1210173 · Cited by 921 · see citing works
Jing, X., Sanders, N. J., Shi, Y., Chu, H., Classen, A. T., Zhao, K., Chen, L., Shi, Y., Jiang, Y., & He, J.-S. (2015) · Nature Communications · doi:10.1038/ncomms9159 · Cited by 895 · see citing works
A protocol for conducting and presenting results of regression‐type analyses
Zuur, A. F., & Ieno, E. N. (2016) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.12577 · Cited by 832 · see citing works
“Violation of these assumptionsmay result in biased parameter estimates and type I errors (Quinn & Keough 2002).”
BackgroundInfluentialDisentangling Genetic Variation for Resistance and Tolerance to Infectious Diseases in Animals
Råberg, L., Sim, D. G., & Read, A. F. (2007) · Science · doi:10.1126/science.1148526 · Cited by 798 · see citing works
“Thus, resistance and tolerance have contrasting effects on the epidemiology of infectious diseases (5, 7).”
BackgroundEngqvist, L. (2005) · Animal Behaviour · doi:10.1016/j.anbehav.2005.01.016 · Cited by 782 · see citing works
“2001; Quinn & Keough 2002): (1) nonsignificant covariate interaction terms must be removed before re-running the final analysis; (2) when there are significant interaction terms, which should not be removed, it is incorrect to interpret the response to the factor as an overall or average main…”
BackgroundMethodInfluentialWooster, M. J., Roberts, G., Perry, G. L. W., & Kaufman, Y. J. (2005) · Journal of Geophysical Research Atmospheres · doi:10.1029/2005jd006318 · Cited by 774 · see citing works
“Interaction terms [Quinn and Keough, 2002] indicate that the slopes of the regression lines for the three independent combustion stages differ significantly (F = 8.1039, p 0.0001, df = 2), and the data suggest that during the smoldering (typically final) stage of each fire there is a tendency for…”
BackgroundInfluentialDouma, J. C., & Weedon, J. T. (2019) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.13234 · Cited by 728 · see citing works
“For proportions that are derived from discrete counts, logistic or binomial regression are appropriate techniques which are well treated in most introductory biostatistics textbooks (Quinn & Keough, 2002; Zuur, Ieno, Walker, Saveliev, & Smith, 2009).”
BackgroundMethodResultInfluentialWidespread deoxygenation of temperate lakes
Jane, S. F., Hansen, G. J. A., Kraemer, B. M., Leavitt, P. R., Mincer, J. L., North, R. L., Pilla, R. M., Stetler, J. T., Williamson, C. E., Woolway, R. I., et al. (2021) · Nature · doi:10.1038/s41586-021-03550-y · Cited by 709 · see citing works
Phylogenetic beta diversity: linking ecological and evolutionary processes across space in time
Graham, C. H., & Fine, P. V. A. (2008) · Ecology Letters · doi:10.1111/j.1461-0248.2008.01256.x · Cited by 683 · see citing works
“…research where it is not possible to linearize data and recent work focused on modelling species environment interactions indicate that nonlinear methods, such as general additive models, perform better than linear models (i.e. general linear models; e.g. Quinn & Keough 2002; Elith et al. 2006).”
Reconceptualizing synergism and antagonism among multiple stressors
Piggott, J. J., Townsend, C. R., & Matthaei, C. D. (2015) · Ecology and Evolution · doi:10.1002/ece3.1465 · Cited by 682 · see citing works
“We emphasize the need for reconsideration by the ecological community of the interpretation of synergism and antagonism in situations where individual stressor effects oppose each other or where cumulative effects are reversed and enhanced.”
BackgroundOry, N. C., Sobral, P., Ferreira, J. L., & Thiel, M. (2017) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2017.01.175 · Cited by 676 · see citing works
“Bootstrap estimates with 1000 resamplings (if not indicated otherwise) of the data were used to estimate the standard error of the mean and of the median (see Quinn, 2002).”
MethodThe determination of standard metabolic rate in fishes
Chabot, D., Steffensen, J. F., & Farrell, A. P. (2016) · Journal of Fish Biology · doi:10.1111/jfb.12845 · Cited by 667 · see citing works
“The effect of experimental duration on SMR estimation was then assessed.”
BackgroundViolating the normality assumption may be the lesser of two evils
Knief, U., & Forstmeier, W. (2021) · Behavior Research Methods · doi:10.3758/s13428-021-01587-5 · Cited by 654 · see citing works
A solution to minimum sample size for regressions
Jenkins, D. G., & Quintana-Ascencio, P. F. (2020) · PLOS ONE · doi:10.1371/journal.pone.0229345 · Cited by 665 · see citing works
“That practice is ill-suited to select among alternative models, especially if models differ in the number of parameters and if regression assumptions are violated [38,42,46].”
BackgroundThe imprint of the geographical, evolutionary and ecological context on species–area relationships
Drakare, S., Lennon, J. J., & Hillebrand, H. (2005) · Ecology Letters · doi:10.1111/j.1461-0248.2005.00848.x · Cited by 632 · see citing works
Rochman, C. M., Hentschel, B. T., & Teh, S. J. (2014) · PLoS ONE · doi:10.1371/journal.pone.0085433 · Cited by 624 · see citing works
Critical temperatures for xylogenesis in conifers of cold climates
Rossi, S., Deslauriers, A., Gričar, J., et al. (2008) · Global Ecology and Biogeography · doi:10.1111/j.1466-8238.2008.00417.x · Cited by 622 · see citing works
“Model verification included χ2 of the likelihood ratio, Wald’s χ2 for regression parameter and goodness of fit and the Hosmer–Lemeshow Ç for eventual lack of fit log ( ) ln it π π π β βx x x jx= − = + 1 0 1 (Quinn & Keough, 2002).”
MethodDisentangling the role of environmental and human pressures on biological invasions across Europe
Pyšek, P., Jaros̆ı́k, V., Hulme, P. E., Kühn, I., Wild, J., Αριανούτσου, Μ., Bacher, S., Chiron, F., Didžiulis, V., Essl, F., et al. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1002314107 · Cited by 607 · see citing works
“Because it was impossible to fit the full models directly due to a high number of explanatory variables, the analyseswerefirst runwithin the factors/ classes of variables related to geography, climate, land cover, and human factors (67).”
Are there any consistent predictors of invasion success?
Hayes, K. R., & Barry, S. C. (2007) · Biological Invasions · doi:10.1007/s10530-007-9146-5 · Cited by 603 · see citing works
“Most of the studies reviewed here adopt a single method, most notably linear models such as least squares regression, generalised linear models with a logit link function (logistic regression) or an identity link function (multiple regression) (Quinn and Keough 2002).”
MethodResultTime for some a priori thinking about post hoc testing
Ruxton, G., & Beauchamp, G. (2008) · Behavioral Ecology · doi:10.1093/beheco/arn020 · Cited by 617 · see citing works
The sensitive hare: sublethal effects of predator stress on reproduction in snowshoe hares
Sheriff, M. J., Krebs, C. J., & Boonstra, R. (2009) · Journal of Animal Ecology · doi:10.1111/j.1365-2656.2009.01552.x · Cited by 546 · see citing works
Nature of Phosphorus Limitation in the Ultraoligotrophic Eastern Mediterranean
Thingstad, T. F., Krom, M. D., Mantoura, R. F. C., Flaten, G. A. F., Groom, S., Herut, B., Kress, N., Law, C. S., Pasternak, A. F., Pitta, P., et al. (2005) · Science · doi:10.1126/science.1112632 · Cited by 544 · see citing works
Kong, L., Zhu, J., Han, W., Jiang, X., Xu, M., Zhao, Y., Dong, Q., Pang, Z., Guan, Q., Gao, L., et al. (2010) · Acta Diabetologica · doi:10.1007/s00592-010-0226-0 · Cited by 529 · see citing works
“This method was used to find the linear combinations of variables that maximize the probability of correctly assigning observations to their pre-determined groups [30].”
MethodAlien flora of Europe: species diversity, temporal trends, geographical patterns and research needs.
L, P. W., Başnou, C., Herbarium, K., & Moro, A. D. (2008) · Preslia · Cited by 519 · see citing works
Evidence of threshold temperatures for xylogenesis in conifers at high altitudes
Rossi, S., Deslauriers, A., Anfodillo, T., & Carraro, V. (2006) · Oecologia · doi:10.1007/s00442-006-0625-7 · Cited by 520 · see citing works
“where px is the probability of xylogenesis being active, xj is the temperature on a given day j, b0 and b1 are the intercept and slope of the logit regression (Quinn and Keough 2002).”
BackgroundMethodMüller, J. C., & Bütler, R. (2010) · European Journal of Forest Research · doi:10.1007/s10342-010-0400-5 · Cited by 513 · see citing works
“Three applications are possible: (a) using univariate t-tests for continuous variables, which have to be adjusted in multiple testings; (b) using a simultaneous GLM and a selection using the P-values, although selection control is then missing; and (c) using the AIC criterion, which selects the variables with the best…”
Personality traits and dispersal tendency in the invasive mosquitofish ( Gambusia affinis )
Côté, J., Fogarty, S., Weinersmith, K. L., et al. (2010) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.2009.2128 · Cited by 513 · see citing works
Joint drumming: Social context facilitates synchronization in preschool children
Kirschner, S., & Tomasello, M. (2009) · Journal of Experimental Child Psychology · doi:10.1016/j.jecp.2008.07.005 · Cited by 494 · see citing works
“Therefore, we calculated the median absolute deviation (described in Quinn & Keough, 2002) of all interresponse intervals from a particular trial.”
MethodLandscape-level thresholds of habitat cover for woodland-dependent birds
Radford, J. Q., Bennett, A. F., & Cheers, G. (2005) · Biological Conservation · doi:10.1016/j.biocon.2005.01.039 · Cited by 506 · see citing works
Ecological opportunity and sexual selection together predict adaptive radiation
Wagner, C. E., Harmon, L. J., & D, O. S. C. (2012) · Nature · doi:10.1038/nature11144 · Cited by 500 · see citing works
Co‐existing grass species have distinctive arbuscular mycorrhizal communities
Vandenkoornhuyse, P., Ridgway, K. P., Watson, I. J., FITTER, A. H., & Young, J. P. W. (2003) · Molecular Ecology · doi:10.1046/j.1365-294x.2003.01967.x · Cited by 499 · see citing works
Clusella‐Trullas, S., Blackburn, T. M., & Chown, S. L. (2011) · The American Naturalist · doi:10.1086/660021 · Cited by 495 · see citing works
“We then used low AIC values, low collinearity, and high tolerance levels between variables (Quinn and Keough 2002) to guide variable selection for multiple predictor models.”
MethodRecurrent jellyfish blooms are a consequence of global oscillations
Condon, R. H., Duarte, C. M., Pitt, K. A., Robinson, K. L., Lucas, C. H., Sutherland, K. R., Mianzan, H. W., Bøgeberg, M., Purcell, J. E., Decker, M. B., et al. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1210920110 · Cited by 489 · see citing works
“The datasets encompassed multiple metrics of abundance across different sampling frequencies (Table S1); therefore, each dataset was standardized to an annual mean of 0 and SD of 1 to create a jellyfish index, allowing comparisons among diverse metrics (21).”
MethodElsdon, B. K. W. T. S. (2008) · CRC Press eBooks · doi:10.1201/9781420065756-9 · Cited by 474 · see citing works
Egg size and offspring quality: a meta‐analysis in birds
Krist, M. (2011) · Biological Reviews · doi:10.1111/j.1469-185x.2010.00166.x · Cited by 472 · see citing works
“Models that include predictors with variance inflation factors (VIFs) less than 10 (Quinn & Keough, 2002) or 5 (Zuur, Ieno & Smith, 2007) are usually considered to give acceptable results although also more stringent criteria have been suggested (Graham, 2003).”
BackgroundSpatial complementarity in tree crowns explains overyielding in species mixtures
Williams, L., Paquette, A., Cavender-Bares, J. M., Messier, C., & Reich, P. B. (2017) · Nature Ecology & Evolution · doi:10.1038/s41559-016-0063 · Cited by 461 · see citing works
Effect of Human Disturbance on Bee Communities in a Forested Ecosystem
Winfree, R., Griswold, T., & Kremen, C. (2006) · Conservation Biology · doi:10.1111/j.1523-1739.2006.00574.x · Cited by 461 · see citing works
“It then positions the sites graphically in two or more dimensions so as to maximize the rank correlation between the pairwise intersite distances calculated by the ecological measure and the pairwise intersite distances in the graphical ordination (McCune & Grace 2002; Quinn & Keough 2002).”
MethodThe flora of German cities is naturally species rich
Kühn, I., Brandl, R., & Klotz, S. (2004) · Cited by 460 · see citing works
Effects of Microplastic Fibers and Drought on Plant Communities
Lozano, Y. M., & Rillig, M. C. (2020) · Environmental Science & Technology · doi:10.1021/acs.est.0c01051 · Cited by 458 · see citing works
Plastic debris ingestion by marine catfish: An unexpected fisheries impact
Possatto, F. E., Barletta, M., da Costa, M. F., do Sul, J. A. I., & Dantas, D. V. (2011) · Marine Pollution Bulletin · doi:10.1016/j.marpolbul.2011.01.036 · Cited by 453 · see citing works
“When significant differences between species or phase factors were detected, a post hoc Bonferroni test was applied to identify the sources of the difference (Quinn and Keough, 2002).”
MethodDistribution patterns of microplastics within the plankton of a tropical estuary
Lima, A. R. A., da Costa, M. F., & Barletta, M. (2014) · Environmental Research · doi:10.1016/j.envres.2014.03.031 · Cited by 451 · see citing works
“05) was used whenever significant differences were detected (Quinn and Keough, 2002).”
MethodBoyd, P. W., Collins, S., Dupont, S., Fabricius, K., Gattuso, J., Havenhand, J. N., Hutchins, D. A., Riebesell, U., Rintoul, M. S., Vichi, M., et al. (2018) · Global Change Biology · doi:10.1111/gcb.14102 · Cited by 442 · see citing works
Which function describes the species–area relationship best? A review and empirical evaluation
Dengler, J. (2009) · Journal of Biogeography · doi:10.1111/j.1365-2699.2008.02038.x · Cited by 446 · see citing works
“However, they did not penalize for the extra parameter T1, which is necessary when comparing the fit of regression models of varying complexity (e.g. Quinn & Keough, 2002).”
BackgroundMethodResultInfluentialStepwise Model Fitting and Statistical Inference: Turning Noise into Signal Pollution
Mundry, R., & Nunn, C. L. (2008) · The American Naturalist · doi:10.1086/593303 · Cited by 440 · see citing works
“Multiple regression requires a large number of cases compared to the number of predictor variables (Tabachnick and Fidell 2001; Quinn and Keough 2002; Field 2005).”
BackgroundMethodInfluentialThe hydrodynamics of eel swimming
Tytell, E. D., & LAUDER, G. V. (2004) · Journal of Experimental Biology · doi:10.1242/jeb.00968 · Cited by 437 · see citing works
Habitat influences on urban avian assemblages
Evans, K. L., Newson, S. E., & Gaston, K. J. (2008) · Ibis · doi:10.1111/j.1474-919x.2008.00898.x · Cited by 430 · see citing works
Tolerance for inequity may increase with social closeness in chimpanzees
Brosnan, S. F., Schiff, H. C., & de Waal, F. Β. Μ. (2005) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.2004.2947 · Cited by 401 · see citing works
“Methods followed Quinn & Keough (2002) and tests were performed using SAS v. 8.2.”
MethodInfluentialNested by design: model fitting and interpretation in a mixed model era
Schielzeth, H., & Nakagawa, S. (2013) · Methods in Ecology and Evolution · doi:10.1111/j.2041-210x.2012.00251.x · Cited by 394 · see citing works
“Multiple categorical variables can be either nested or crossed depending on the experimental design employed during data collection (Scheiner & Gurevitch 2001; Quinn & Keough 2002; Ryan 2007; Hinkelmann & Kempthorne 2008; Kirk 2009).”
BackgroundMethodInfluentialSample size determination: A practical guide for health researchers
Althubaiti, A. (2022) · Journal of General and Family Medicine · doi:10.1002/jgf2.600 · Cited by 481 · see citing works
Ecology of butterflies in Europe
(2010) · Choice Reviews Online · doi:10.5860/choice.47-5648 · Cited by 399 · see citing works
Chamizo, S., Cantón, Y., Miralles, I., et al. (2012) · Soil Biology and Biochemistry · doi:10.1016/j.soilbio.2012.02.017 · Cited by 389 · see citing works
“When the factors or their interaction were significant for the dependent variables, planned orthogonal contrasts (see Quinn and Keough, 2002) were performed to test the significance of our a priori hypothesis about the horizontal (crust type or development) and vertical (layers) trends of the…”
MethodExpDes: An R Package for ANOVA and Experimental Designs
Ferreira, E. B., Cavalcanti, P. P., & Nogueira, D. A. (2014) · Applied Mathematics · doi:10.4236/am.2014.519280 · Cited by 387 · see citing works
PHYSICAL AND BIOTIC DRIVERS OF PLANT DISTRIBUTION ACROSS ESTUARINE SALINITY GRADIENTS
Crain, C. M., Silliman, B. R., Bertness, S. L., et al. (2004) · Ecology · doi:10.1890/03-0745 · Cited by 385 · see citing works
Can we mitigate animal–vehicle accidents using predictive models?
Malo, J. E., Suárez, F., & DÍEZ, A. (2004) · Journal of Applied Ecology · doi:10.1111/j.0021-8901.2004.00929.x · Cited by 381 · see citing works
Life‐history correlates of plant invasiveness at regional and continental scales
Hamilton, M. A., Murray, B. R., Cadotte, M. W., Hose, G. C., Baker, A. C., Harris, C. J., & Licari, D. (2005) · Ecology Letters · doi:10.1111/j.1461-0248.2005.00809.x · Cited by 377 · see citing works
“Standardized slopes of the regressions were used to compare increase in abundance (i.e. rate of spread) between spatial scales (Quinn & Keough 2002).”
MethodEvans, K. L., Chamberlain, D., Hatchwell, B. J., Gregory, R. D., & Gaston, K. J. (2010) · Global Change Biology · doi:10.1111/j.1365-2486.2010.02247.x · Cited by 377 · see citing works
“For both sets of models, the tolerance values of all predictors (analysis of all species, range 0.61–0.82; analysis of all predictors, range 0.29–0.74) were consistently above the threshold (0.1) at which colinearity becomes a major concern (Quinn & Keough, 2002).”
BackgroundInfluentialKozak, M., & Piepho, H. (2017) · Journal of Agronomy and Crop Science · doi:10.1111/jac.12220 · Cited by 377 · see citing works
Energy reserves and metabolism as indicators of coral recovery from bleaching
Rodrigues, L. J., & Grottoli, A. G. (2007) · Limnology and Oceanography · doi:10.4319/lo.2007.52.5.1874 · Cited by 368 · see citing works
Guo, S., Popp, J., & Bocklitz, T. (2021) · Nature Protocols · doi:10.1038/s41596-021-00620-3 · Cited by 361 · see citing works
Knowing when to draw the line: designing more informative ecological experiments
Cottingham, K. L., Lennon, J. T., & Brown, B. L. (2005) · Frontiers in Ecology and the Environment · doi:10.1890/1540-9295(2005)003[0145:kwtdtl]2.0.co;2 · Cited by 360 · see citing works
Freckleton, R. P. (2010) · Behavioral Ecology and Sociobiology · doi:10.1007/s00265-010-1045-6 · Cited by 358 · see citing works
Rodent foraging is affected by indirect, but not by direct, cues of predation risk
Orrock, J. L. (2004) · Behavioral Ecology · doi:10.1093/beheco/arh031 · Cited by 352 · see citing works
“This approach is conservative because power increases if a reduction in seed removal of the same magnitude occurs in response to more than one urine type (Quinn and Keough, 2002).”
BackgroundHigher-order interactions capture unexplained complexity in diverse communities
Mayfield, M. M., & Stouffer, D. B. (2017) · Nature Ecology & Evolution · doi:10.1038/s41559-016-0062 · Cited by 342 · see citing works
Factors affecting carcass use by a guild of scavengers in European temperate woodland
Selva, N., Jędrzejewska, B., Jędrzejewski, W., & Wajrak, A. (2005) · Canadian Journal of Zoology · doi:10.1139/z05-158 · Cited by 343 · see citing works
“We used log-linear models (Quinn and Keough 2002; Manly et al. 2002) to analyse the effects of carcass characteristics (origin, species, and location) on the presence of the main scavengers at carcasses.”
BackgroundMethodHeikkinen, R. K., Luoto, M., Virkkala, R., & Rainio, K. (2004) · Journal of Applied Ecology · doi:10.1111/j.0021-8901.2004.00938.x · Cited by 353 · see citing works
Costamagna, A. C., & Landis, D. A. (2006) · Ecological Applications · doi:10.1890/1051-0761(2006)016[1619:petcos]2.0.co;2 · Cited by 350 · see citing works
“Significant interactions in the ANOVA models were further explored via slicing by main effects (Quinn and Keough 2002), and means were separated using least squares means difference (LSMD, SAS Institute 2001).”
MethodBrisson, J., & Chazarenc, F. (2008) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2008.05.047 · Cited by 353 · see citing works
“Statistical differences based on well-replicated units in an appropriate experimental design increase confidence that they are due to differences in plant species and not by chance alone or some confounding factor (Quinn and Keough, 2002).”
BackgroundGeneration lengths of the world's birds and their implications for extinction risk
Bird, J. P., Martin, R. W., Akçakaya, H. R., Gilroy, J. J., Burfield, I. J., Garnett, S. T., Symes, A., Taylor, J., Sekercioglu, C. H., & Butchart, S. H. M. (2020) · Conservation Biology · doi:10.1111/cobi.13486 · Cited by 358 · see citing works
“For S, we applied a logit transformation to normalize the response, whereas F and L were log transformed and modeled as Gaussian responses (Quinn & Keough 2002).”
MethodWild bees along an urban gradient: winners and losers
Banaszak‐Cibicka, W., & Żmihorski, M. (2011) · Journal of Insect Conservation · doi:10.1007/s10841-011-9419-2 · Cited by 346 · see citing works
“Available ecological traits for all the species (n = 104) were used to explain species-specific preferences or avoidance of urbanization with the help of a generalized linear model (GLM) with binomial error distribution and logit link function (Quinn and Keough 2002) implemented in SPSS 16.”
MethodInfluentialSwaisgood, R. R., & Shepherdson, D. J. (2005) · Zoo Biology · doi:10.1002/zoo.20066 · Cited by 349 · see citing works
Chown, S. L., Jumbam, K. R., Sørensen, J. G., & Terblanche, J. S. (2008) · Functional Ecology · doi:10.1111/j.1365-2435.2008.01481.x · Cited by 329 · see citing works
Matthaei, C. D., Piggott, J. J., & Townsend, C. R. (2010) · Journal of Applied Ecology · doi:10.1111/j.1365-2664.2010.01809.x · Cited by 333 · see citing works
Comparative Gut Microbiota of 59 Neotropical Bird Species
Hird, S. M., Sánchez, C., Carstens, B. C., & Brumfield, R. T. (2015) · Frontiers in Microbiology · doi:10.3389/fmicb.2015.01403 · Cited by 334 · see citing works
Capture, swallowing, and egestion of microplastics by a planktivorous juvenile fish
Ory, N. C., Gallardo, C., Lenz, M., & Thiel, M. (2018) · Environmental Pollution · doi:10.1016/j.envpol.2018.04.093 · Cited by 334 · see citing works
Discriminant function analysis with nonindependent data: consequences and an alternative
Mundry, R., & Sommer, C. (2007) · Animal Behaviour · doi:10.1016/j.anbehav.2006.12.028 · Cited by 325 · see citing works
Soil Chemical and Physical Properties That Differentiate Urban Land‐Use and Cover Types
Pouyat, R., Yesilonis, I. D., Russell-Anelli, J. M., et al. (2007) · Soil Science Society of America Journal · doi:10.2136/sssaj2006.0164 · Cited by 331 · see citing works
“Moreover, the multivariate approach reduces Type I errors that occur in multiple univariate tests (Quinn and Keough, 2002).”
Ahrends, A., Burgess, N. D., Milledge, S. A. H., Bulling, M. T., Fisher, B., Smart, J. C. R., Clarke, G. P., Mhoro, B. E., & Lewis, S. L. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0914471107 · Cited by 328 · see citing works
“We therefore reduced the predictor set for each dependent variable to the strongest uncorrelated set according to the predictive power of variables in univariate tests (53).”
MethodSpecies diversity can drive speciation
Emerson, B. C., & Kolm, N. (2005) · Nature · doi:10.1038/nature03450 · Cited by 328 · see citing works
Elevation alters ecosystem properties across temperate treelines globally
Mayor, J. R., Sanders, N. J., Classen, A. T., Bardgett, R. D., Clément, J.-C., Fajardo, A., Lavorel, S., Sundqvist, M. K., Bahn, M., Chisholm, C., et al. (2017) · Nature · doi:10.1038/nature21027 · Cited by 325 · see citing works
- Authors
- Quinn, G. P., & Keough, M. J.
- Affiliations
- Monash University (AU)
- The University of Melbourne (AU)
- Venue
- Cambridge University Press eBooks
- Publisher
- Cambridge University Press
- Published
- 2002-03-21
- OpenAlex
- W1489838709
- Cited by
- 10,674
- References
- 0
- Citations by year
- 2026: 200 · 2025: 218 · 2024: 239 · 2023: 250 · 2022: 275 · 2021: 394 · 2020: 448 · 2019: 430 · 2018: 446 · 2017: 473 · 2016: 528 · 2015: 625 · 2014: 763 · 2013: 719 · 2012: 675
- FWCI
- 50.52
- Type
- book
- Language
- en
- Open access
- closed
- Locations
- Cambridge University Press eBooks (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
- Social Sciences › Decision Sciences › Management Science and Operations Research › Optimal Experimental Design Methods
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Keywords
- experimental design, statistical analysis, biology, hypothesis testing, Bayesian analysis, linear models, generalized linear models, logistic regression, analysis of variance, repeated measures, log-linear models, multivariate analysis, classification, ordination, exploratory data analysis, assumption checking
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