Multivariate Description

What Technique? Response variable(s)... Predictors(s) No Predictors(s) Yes... is one distribution summary regression models... are many indirect gradient analysis (PCA, CA, DCA, MDS) cluster analysis direct gradient analysis constrained cluster analysis discriminant analysis (CVA)

a) Rotate the Variable Space

Raw Data

Linear Regression

Two Regressions

Principal Components

Gulls Variables

Scree Plot

Output > gulls.pca2$loadings Loadings: Comp.1 Comp.2 Comp.3 Comp.4 Weight Wing Bill H.and.B > summary(gulls.pca2) Importance of components: Comp.1 Comp.2 Comp.3 Standard deviation Proportion of Variance Cumulative Proportion

Bi-Plot

Male or Female?

Linear Discriminant > gulls.lda <- lda(Sex ~ Wing + Weight + H.and.B + Bill, gulls) lda(Sex ~ Wing + Weight + H.and.B + Bill, data = gulls) Prior probabilities of groups: Group means: Wing Weight H.and.B Bill Coefficients of linear discriminants: LD1 Wing Weight H.and.B Bill

Discriminating

Relationship between PCA and LDA

CVA

b) Use Distance or Dissimilarity (Multi-Dimensional Scaling)

A Distance Matrix

Uses of Distances Distance/Dissimilarity can be used to:- Explore dimensionality in data (using PCO) As a basis for clustering/classification

UK Wet Deposition Network

Fitting Environmental Variables

A Map based on Measured Variables

Fitting Environmental Variables

c) Summarise by Weighted Averages

Species and Sites as Weighted Averages of each other SITES SPP Bel per Jun buf …42.. Jun art Air pra Ele pal Rum ace …23.. Vic lat Bra rut Ran fla Hyp rad Leo aut Pot pal Poa pra …4.. Cal cus Tri pra …2.. Tri rep Ant odo Sal rep Ach mil …2.. Poa tri …45.. Ely rep Sag pro Pla lan …5.. Agr sto Lol per …6.. Alo gen Bro hor …2..

Species and Sites as Weighted Averages of each other

Reciprocal Averaging - unimodal Site A B C D E F Species Prunus serotina Tilia americana Acer saccharum Quercus velutina Juglans nigra

Reciprocal Averaging - unimodal Site A B C D E F Species Score Species Iteration 1 Prunus serotina Tilia americana Acer saccharum Quercus velutina Juglans nigra Iteration Site Score

Reciprocal Averaging - unimodal Site A B C D E F Species Score Species Iteration 1 2 Prunus serotina Tilia americana Acer saccharum Quercus velutina Juglans nigra Iteration Site Score

Reciprocal Averaging - unimodal Site A B C D E F Species Score Species Iteration Prunus serotina Tilia americana Acer saccharum Quercus velutina Juglans nigra Iteration Site Score

Reciprocal Averaging - unimodal Site A B C D E F Species Score Species Iteration Prunus serotina Tilia americana Acer saccharum Quercus velutina Juglans nigra Iteration Site Score

Reordered Sites and Species Site A C E B D F Species Species Score Quercus velutina Prunus serotina Juglans nigra Tilia americana Acer saccharum Site Score

Managing Dimensionality (but not acronyms) PCA, CA, RDA, CCA, MDS, NMDS, DCA, DCCA, pRDA, pCCA

Type of Data Matrix species sites attributes species attributes sites attributes individuals desert macroph inverts uses watervar rain gulls

Ordination Techniques Linear methodsWeighted averaging unconstrainedPrincipal Components Analysis (PCA) Correspondence Analysis (CA) constrainedRedundancy Analysis (RDA) Canonical Correspondence Analysis (CCA)

Models of Species Response There are (at least) two models:- Linear - species increase or decrease along the environmental gradient Unimodal - species rise to a peak somewhere along the environmental gradient and then fall again

A Theoretical Model

Linear

Unimodal

Alpha and Beta Diversity alpha diversity is the diversity of a community (either measured in terms of a diversity index or species richness) beta diversity (also known as species turnover or differentiation diversity) is the rate of change in species composition from one community to another along gradients; gamma diversity is the diversity of a region or a landscape.

A Short Coenocline

A Long Coenocline

Inferring Gradients from Species (or Attribute) Data

Indirect Gradient Analysis Environmental gradients are inferred from species data alone Three methods: –Principal Component Analysis - linear model –Correspondence Analysis - unimodal model –Detrended CA - modified unimodal model

PCA - linear model

Terschelling Dune Data

PCA gradient - site plot

PCA gradient - site/species biplot standard nature biodynamic & hobby

Arches - Artifact or Feature?

The Arch Effect What is it? Why does it happen? What should we do about it?

From Alexandria to Suez

CA - with arch effect (sites)

CA - with arch effect (species)

Long Gradients ABCD

Gradient End Compression

CA - with arch effect (species)

CA - with arch effect (sites)

Detrending by Segments

DCA - modified unimodal

Making Effective Use of Environmental Variables

Approaches Use single responses in linear models of environmental variables Use axes of a multivariate dimension reduction technique as responses in linear models of environmental variables Constrain the multivariate dimension reduction into the factor space defined by the environmental variables

Unconstrained/Constrained Unconstrained ordination axes correspond to the directions of the greatest variability within the data set. Constrained ordination axes correspond to the directions of the greatest variability of the data set that can be explained by the environmental variables.

Direct Gradient Analysis Environmental gradients are constructed from the relationship between species environmental variables Three methods: –Redundancy Analysis - linear model –Canonical (or Constrained) Correspondence Analysis - unimodal model –Detrended CCA - modified unimodal model

Direct Gradient Analysis Basic PCA y ik = b 0k + b 1k x i + e ik –x i - the sample scores on the ordination axis –b 1k - the regression coefficients for each species (the species scores on the ordination axis) In RDA there is a further constraint on x i x i = c 1 z i1 + c 2 z i2 Making y ik = b 0k + b 1k c 1 z i1 + b 1k c 2 z i2 + e ik

Direct Gradient Analysis cca(species_data ~ e1 + e en + Condition(e5), data=environmental_data) cca(varespec ~ Al + P*(K + Baresoil) + Condition(pH), data=varechem)

Lake Nasser - Egypt

CCA - site/species joint plot

CCA - species/environment biplot

Removing the Effect of Nuisance Variables

Partial Analyses Remove the effect of covariates –variables that we can measure but which are of no interest –e.g. block effects, start values, etc. Carry out the gradient analysis on what is left of the variation after removing the effect of the covariates.

Cluster Analysis

Different types of data example Continuous data:height Categorical data ordered (nominal):growth rate very slow, slow, medium, fast, very fast not ordered:fruit colour yellow, green, purple, red, orange Binary data:fruit / no fruit

Similarity matrix We define a similarity between units – like the correlation between continuous variables. (also can be a dissimilarity or distance matrix) A similarity can be constructed as an average of the similarities between the units on each variable. (can use weighted average) This provides a way of combining different types of variables.

relevant for continuous variables: Euclidean city block or Manhattan Distance metrics A B A B (also many other variations)

Similarity coefficients for binary data simple matching count if both units 0 or both units 1 Jaccard count only if both units 1 (also many other variants) simple matching can be extended to categorical data 0,11,1 0,01,0 0,11,1 0,01,0

hierarchical divisive put everything together and split monothetic / polythetic agglomerative keep everything separate and join the most similar points (classical cluster analysis) non-hierarchical k-means clustering Clustering methods

Agglomerative hierarchical Single linkage or nearest neighbour finds the minimum spanning tree: shortest tree that connects all points chaining can be a problem

Agglomerative hierarchical Complete linkage or furthest neighbour compact clusters of approximately equal size. (makes compact groups even when none exist)

Agglomerative hierarchical Average linkage methods between single and complete linkage

Testing Significance in Ordination

Randomisation Tests

Randomisation Example Model: cca(formula = dune ~ Moisture + A1 + Management, data = dune.env) Df Chisq F N.Perm Pr(>F) Model < *** Residual Signif. codes: 0 *** ** 0.01 * 0.05