DETECTING BIOLOGICAL AND ENVIRONMENTAL CHANGES: DESIGN AND ANALYSIS OF MONITORING AND EXPERIMENTS (University of Bologna, 3-14 March 2008)

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Magdalen Chambers
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1 Dipartimento di Biologia Evoluzionistica Sperimentale Centro Interdipartimentale di Ricerca per le Scienze Ambientali in Ravenna INTERNATIONAL WINTER SCHOOL UNIVERSITY OF BOLOGNA DETECTING BIOLOGICAL AND ENVIRONMENTAL CHANGES: DESIGN AND ANALYSIS OF MONITORING AND EXPERIMENTS (University of Bologna, 3-14 March 2008) Monday 3 March 09:00-09:30 Introduction to the course and presentation of the participants A general introduction to experimental design (L. Benedetti-Cecchi) 09:30-10:30 Lecture: Logical and philosophical frameworks for the analysis of complex data. 10:30-10:45 Coffee break 10:45-13:15 Lecture: Sampling statistical populations (variables; frequency distributions; parameters and their estimates; precision and accuracy of sample estimates). 13:15-14:30 Lunch 14:30-16:00 Practical: Sampling populations: influence of variance and sample size on sample estimates 16:00-16:15 Coffee break 16:15-18:15 Lecture: Relationships among hypotheses, statistical models and data (estimation and hypothesis testing; linear statistical models; methods of estimation: OLS and ML; statistical hypothesis testing: general hints). Tuesday 4 March 09:00-10:00 Lecture: Experimental design (basic concepts: replication, randomisation, independence; choosing levels for predictor variables: fixed vs. random factors) 10:00-10:45 Lecture: Experimental design (relationships among predictor variables: hierarchical and factorial designs; extension to multifactorial designs). 11:00-13:00 Lecture: Obtaining the expected mean squares (EMS) for any complex design. 14:00-15:45 Lecture: Hierarchical designs (solution to spatial and temporal confounding; sampling at multiple scales in space and time). 16:00-17:00 Lecture: Factorial designs (understanding interactions among predictor variables; assessing the generality - or lack thereof - of processes: multifactorial experiments). 17:00-18:30 Practical: Assessing spatial and temporal variability: The analysis of patterns using simulated data sets.

2 Wednesday 5 March 09:00-11:00 Practical: Understanding natural processes (analysis and interpretation of real experiments). 11:00-11:15 Coffee break Multivariate Analyses using PRIMER v6 (Bob Clarke) 11:15-13:00 Lecture: Measures of resemblance (similarity/dissimilarity/distance) in multivariate structure, including pre-treatment options (standardisation, transformation, normalisation) and the effects of different coefficient choices 13:00-14:15 Lunch break 14:15-15:00 Lecture: Hierarchical clustering of samples (CLUSTER). 15:00-16:00 Practical: Introduction to PRIMER v6 routines and lab session on similarity options and CLUSTER. 16: Lecture: Ordination by Principal Components Analysis (PCA) Practical: Lab session on ordination by PCA or own data*. * You will be given real literature data sets to analyse in lab sessions, but you might prefer to use some or all of these to analyse your own data. Data should be in Excel sheets, with variables (e.g. species) as rows and samples as columns (or vice-versa), though 3-column text files (sample label, variable label, data value, eg from Access) can also be handled in v6. Data values should be numeric (use 1 and 0 for presence and absence data); non-numeric information on characteristics of each sample (factors) is placed below the Excel table, in the same columns, separated by a blank row. Thursday 6 March 09:00-10:00 Lecture: Ordination by non-metric Multi-Dimensional Scaling (MDS). 10:00-10:15 Coffee break 10:15-11:30 Practical: Lab session on MDS, or own data. 11:30-13:00 Lecture: Multivariate testing for differences between groups of samples (1- and 2- Way factor ANOSIM). break 14:00-14:30 Lecture: Determining variables which discriminate groups of samples (1- and 2- factor similarity percentages, SIMPER). 14:30-16:00 Practical: Lab session on 1- and 2-factor ANOSIM and SIMPER, or own data. 16:00-16:15 Coffee break 16:15-17:00 Lecture: Diversity measures (DIVERSE) and comments on sampling properties and multivariate treatment of multiple indices. Dominance plots and tests for differences between sets of curves (DOMDIS), particle-size distributions etc. 17:00-18:15 Practical: Lab session on DIVERSE, dominance plots and testing curves (DOMDIS), or own data.

3 Friday 7 March 09:00-10:45 Lecture: Linking potential drivers to an observed pattern, via bubble plots, the matching of multivariate structures (the BEST procedure), and linkage trees (LINKTREE, a non-parametric classification and regression tree approach). 11:00-12:00 Practical: Lab session on draftsman plots (to assess variable transforms), PCA, BEST and LINKTREE, or own data. 12:00-13:00 Lecture: Global hypothesis tests: of no agreement between two resemblance matrices (RELATE, a non-parametric Mantel test), comparing assemblage or environmental structure with linear or cyclic models in space and time; also of no evidence for a biota-environment link, allowing for the selection effects in finding an optimum match (the global BEST test). 13:00-14:15 Lunch 14:15-15:15 Practical: Lab session on RELATE (without/with replication) and the global BEST test. 15:15-15:45 Lecture: Testing in a 2-way layout (ANOSIM) with no replication, or own data. 16:00-16:45 Lecture: Stepwise form of the BEST routine, e.g. for species subsets determining overall assemblage pattern, 16:45-18:15 Practical: Lab session on 2-way ANOSIM with no replication and BEST, or own data. Monday 10 March 09:00-10:00 Lecture: Taxonomic (or phylogenetic/functional) diversity and distinctness; sampling properties and testing structures (TAXDTEST). 10:00-10:45 Practical: Lab session on DIVERSE and TAXDTEST, or own data. 11:00-11:45 Lecture: Second-stage analysis (2STAGE) to compare taxonomic levels and transformation or coefficient choices; also for a possible testing framework in some repeated measures designs. 11:45-13:00 Practical: Lab session on 2STAGE, or own data. 13:00-14:15 Lunch 14:15-15:00 Lecture: A global test for the presence of multivariate structure in a priori unstructured samples, using similarity profiles (the SIMPROF test). Also missing data estimation using the EM algorithm. 15:00-15:45 Practical: Lab session on SIMPROF tests in CLUSTER, or own data15:45-16:00 Coffee break 16:00-16:45 Lecture: Widening the scope of assemblage resemblance measures: dispersion weighting, taxonomic dissimilarities and adjusted coefficients for highly sparse data. 16:45-18:00 Practical: Any further questions and own data. Tuesday 11 March Analysing Multivariate Data in Response to Complex Experimental Designs (Marti J. Anderson)

4 09:00-10:00 Lecture: The nature of multivariate data and its properties. 10:00-10:45 Lecture: Permutational multivariate analysis of variance based on distances (PERMANOVA) 11:00-13:00 Lecture: A few more notes on dissimilarity measures and their properties; Permutational tests of dispersion (PERMDISP). 14:00-15:45 Practical: PCO, PERMANOVA and PERMDISP; interpreting multivariate interaction terms. 16:00-18:00 Practical: PCO, PERMANOVA and PERMDISP, continued. Wednesday 12 March 09:00-10:00 Lecture: Multi-factorial PERMANOVA and interpretations of results; multivariate pseudo variance components. 10:00-10:45 Practical: Multi-factorial PERMANOVA. 11:00-13:00 Practical: Multi-factorial PERMANOVA, continued. 14:00-15:45 Lecture: Multivariate multiple regression using permutation tests; model selection procedures. 16:00-18:00 Practical: DISTLM. Thursday 13 March 09:00-10:45 Lecture: Constrained and unconstrained ordination; redundancy analysis (RDA) and distance-based redundancy analysis (dbrda). 11:00-13:00 Practical: Constrained and unconstrained ordinations (PCA, RDA, PCO and dbrda). 14:00-15:45 Lecture: Canonical analysis of principal coordinates (CAP); canonical discriminant analysis (CDA); canonical correlation analysis (CCorA). 16:00-18:00 Practical: Canonical analysis; PCO and CAP. Friday 14 March 09:00-10:45 Lecture: Putting it all together a general strategy for multivariate analysis (ordination, tests of hypotheses, canonical ordination, identifying species responsible). 11:00-13:00 Practical: A general strategy for multivariate analysis (PERMANOVA, PCO, CAP, etc.).

5 14:00-15:45 Lecture: Multivariate BACI and beyond BACI. 16:00-18:00 Practical: Constructing tests for individual terms in PERMANOVA models, including contrasts, as for asymmetrical BACI designs, using DISTLM. Choosing correct permutational strategies, etc. Suggested Readings Anderson, M. J Permutation tests for univariate or multivariate analysis of variance and regression. Canadian Journal of Fisheries and Aquatic Sciences 58: Anderson, M. J A new method for non-parametric multivariate analysis of variance. Austral Ecology 26: Anderson, M. J. and Willis, T.J Canonical analysis of principal coordinates: a useful method of constrained ordination for ecology. Ecology 84: Benedetti-Cecchi, L Priority effects, taxonomic resolution, and the prediction of variable patterns of succession in littoral rock pools. Oecologia, Vol. 123, pp Benedetti-Cecchi, L Variability in abundance of algae and invertebrates at different spatial scales on rocky sea shores. Marine Ecology Progress Series, Vol. 209, pp Benedetti-Cecchi, L Beyond BACI: optimization of environmental sampling designs through monitoring and simulation. Ecological Applications, Vol. 11, pp Benedetti-Cecchi, L The importance of the variance around the mean effect size of ecological processes. Ecology, Vol. 84, pp Benedetti-Cecchi, L Increasing accuracy of causal inference in experimental analyses of biodiversity. Functional Ecology, 18: Clarke, K.R Non-parametric multivariate analyses of changes in community structure. Australian Journal of Ecology 18: Clarke, K.R Non-metric multivariate analysis in community-level ecotoxicology. Environmental Toxicology and Chemistry 18: Clarke, K.R., Ainsworth, M A method of linking multivariate community structure to environmental variables. Marine Ecology Progress Series 92: Clarke, K,R., Somerfield, P.J., Chapman, M.G. (2006). On resemblance measures for ecological studies, including taxonomic dissimilarities and a zero-adjusted Bray-Curtis coefficient for denuded assemblages. J Exp Mar Biol Ecol 330, Legendre, P. and Anderson, M. J Distance-based redundancy analysis: testing multispecies responses in multifactorial ecological experiments. Ecological Monographs 69: 1-24 McArdle, B. H. and Anderson, M. J Fitting multivariate models to community data: a comment on distance-based redundancy analysis. Ecology 82(1): Somerfield, P.J., Clarke, K.R. & Olsgard, F A comparison of the power of categorical and correlational tests applied to community ecology data from gradient studies. Journal of Animal Ecology 71: Underwood, A.J Techniques of analysis of variance in experimental marine biology and ecology. Oceanography and Marine Biology Annual Review 19: Underwood, A.J Experiments in ecology and management: their logics, functions and interpretations. Australian Journal of Ecology 15: Underwood, A.J Beyond BACI: Experimental designs for detecting human environmental impacts on temporal variations in natural populations. Australian Journal of Marine and Freshwater Research 42: Warwick, R.M., Clarke, K.R Practical measures of marine biodiversity based on relatedness of species. Oceanography and Marine Biology Annual Review 39:

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