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Ordination (序列化)






Mount Usu / Sarobetsu post-mined peatland
From left: Crater basin in 1986 and 2006. Cottongrass / Daylily

The apparent complexity of techniques for analyzing vegetation data (Kent & Coker 1992)
Plant community data are multivariate in nature

= Crude data matrix

Aims of multivariate analysis

1. Summarizing plant community data
2. Defining environmental gradient ↓

Multivariate analysis: to decrease complicated information content by a few components that summarizes the information
ordination

Reduction of many species/variables into a few components

[indirect ordination, direct ordination, cluster]
[Advanced course in environmental conservation (textbooks), statistics]

索引
Data reduction
Classification: Phytosociological approach, Cluster analysis (TWINSPAN)
Ordination: direct gradient analysis (CCA), Indirect gradient analysis (PCA,DCA)
History
1901 Pearson: developed PCA as a regression
1927 Spearman: applied factor analysis (to psychology)
1930 Ramensky: introduces the term 'ordnung (German)' into ecology
1954 Goodall DW: introduced PCA into ecology and proposed the term 'ordination'
1970 Whittaker RH (ホイッタカー): developd gradient analysis
1971 Gabriel KR: developd biplot graphical display
1973 Hill MO: re-invented correspondence analysis and introduced CA (as reciprocal averaging) into ecology
1986 ter Braak C: invented CCA

Ordination (序列化)


ordinatio (L)
≡ multidimensional scaling, component analysis and latent-structure analysis
Ordination (gradient analysis) is one of the popular multivariate analyses

an analytical method of ordering samples (plots) and/or species along actual or presumed gradients

Normal analysis
= R-analysis (R分析): Stand or quadrat ordination
R analysis
Ordination diagram: scatter plot of the eigenvector; used both for biplots and joint plots.
Biplot: an ordination diagram of two kinds of entities, e.g., species and environmental variables, which has particular rules of interpretation because it is based on a bilinear model. Interpretation proceeds by projecting points on directions defined by arrows in the biplot.
Joint plot: an ordination diagram of two kinds of entities based on a weighted averaging method. Ordination axis: eigenvector, latent variable, theoretical explanatory variable.
Inverse analysis
or transposed analysis
= Q-analysis (Q分析: Species or environmental-factors ordination R analysis
Species score (種スコア): eigenvector coefficient; loading in PCA, center of species curve in CA and DCA.
Sample score (サンプルスコア/プロットスコア): value of eigenvector in a sample.

Indirect ordination (間接環境勾配分析)


To synthesize species or environmental data and to produce an ordination of quadrats based on environmental or species variables alone.
Indirect gradient analysis (indirect ordination): internal analysis, "factor analysis", unconstrained ordination, unconstrained multidimensional scaling, possibly followed post-hoc by an regression analysis on external variables

Indirect ordination (modified after Kent & Coker 1992)
Method Note Application
Bray and Curtis (polar ordination) (PO) Originally calculated and drawn using compass construction Widely used between 1960 and 1970. Now superseded by more sophisticated techniques
Principal component analysis (PCA) Relatively complex, requiring computing facilities for calculation Widely used from 1966-present. However, now not recommended due to distortion ('horseshoe') effects
Reciprocal averaging/ correspondence analysis (RA/CA) Simple calculation for one axis. Requires computer for full analysis Used extensively from 1973-1985. Now replaced by DCA
Detrended correspondence analysis (DCA) 'Improved' version of RA/CA. Requires computer program DECORANA (or R) for analysis Widely used 1980-present
Multi-dimensional scaling (MDS) requiring computing facilities NMDS is becoming popular
CA Family Fig. 1. Algorithms for (A) correspondence analysis, (B) detrended correspondence analysis, and (C) canonical correspondence analysis, diagrammed as flowcharts. LC scores are the linear combination site scores, and WA scores are the weighted averaging site scores. (Palmer 1993)

Table. Classification of gradient analysis techniques by type of problem, response model and method of estimation. The techniques listed under “linear/least-squares” and “unimodal/weighted averaging” can be carried out with CANOCO.

RESPONSE MODEL: linear unimodal

Method of estimation:least-squaremaximum likelihoodweighted averaging
Type of problem:
RegressionMultiple regressionGaussian regressionweighted averaging of site scores (WA)
Calibrationlinear calibration; "inverse regression"Gaussian calibrationweighted averaging of species scores (WA)
OrdinationPrincipal component analysis (PCA)Gaussian ordinationcorrespondence analysis (CA)5); detrended correspondence analysis (DCA)
Constrained ordination1)Redundancy analysis (RDA)4)Gaussian canonical ordinationCorrespondence analysis (CCA), detrended CCA
Partial ordination2)Partial components analysispartial Gaussian ordinationpartial correspondence analysis; partial DCA
Partial constrained ordination3)Partial redundancy analysispartial Gaussian canonical ordinationpartial canonical correspondence analysis; partial detrended DCA
  1. constrained multivariate regression = canonical ordination
  2. ordination after regression on covariables = constrained partial multivariate regression
  3. constrained ordination after regression on covariables = constrained partial multivariate regression.
  4. "reduced-rank" correlation = "PCA of y with respect to x"
  5. multiple correspondence analysis = dual scaling = homogeneity analysis

Polar ordination, PO (極座標分析)


Procedure
1. make similarity matrix
2. Set up two samples that are the lowest similarity on the two poles
3. Calculate scores of the other samples as follows

polar ordination

AZ = l = 1.0 - 0.2, KA = d1 = 0.4, KZ = d2 = 0.6
Kscore x = x = √(d12 - y2)
x2 = d12 - y2
(l - x)2 = d22 - y2
Here, when delete y, x is evaluated as: x = (l2 + d12 - d22)/2l

Eigenvalue
x:y = kind of contribution rate on axis AZ
Measure how much variation in the species data is explained by the particular axis and, hence, by the environmental variables.

Principal component analysis, PCA (主成分分析)


PCA: linear response model ↔ CA: unimodal response model
Originally defined for data with multi-normal distributions, thus the data should be normalized.
Deviations from normality do not necessarily bias the results, however, one should be careful of the descriptors and try to ensure they are not skewed or have outliers.
Four versions of PCA
  1. un-standardized un-centered PCA
  2. un-standardized centered PCA
  3. standardized un-centered PCA
  4. standardized centered PCA (normally we use this)
An extension of fitting straight lines and planes by least-square regression
Procedure: Two-way weighted summation algorithm
PCA
a. Iteration process
1. Take arbitrary initial site scores (xi), not all equal to zero.
2. Calculate new species scores (bk) by weighted summation of the site scores (Eq 5.8).
3. Calculate new site scores (xi) by weighted summation of the species scores (Eq 5.9).
4. For the first axis go to step 5. For second and higher axes, make the site scores (xi) uncorrelated with the previous axes by the orthogonalization procedure described below.
5. Standardize the site scores (xi). See below for the standardization procedure.
6. Stop on convergence, i.e., when the new site scores are sufficiently close to the site scores of the previous cycle of the iteration; ELSE go to step 2.
PCA b. Orthogonalization procedure
4.1. Denote the site scores of the previous axis by fi and the trial scores of the present axis by xi.
4.2. Calculate v = Σi=1nxifi.
4.3. Calculate xi, new = xi, old - vfi.
4.4. Repeat Steps 4.1-4.3 for all previous axes.
c. Standardization procedure
5.1. Calculate the sum of squares of the site scores s2 = Σi=1nx2.
5.2. Calculate xi, new = xi, old/s
Note that, upon convergence, s equals the eigenvalue.

Weighted average (加重平均)


Weighted averaging method: method based on a unimodal response model (= unimodal trace line) of which the optimum (mode, ideal point) is estimated by weighted averaging. Ex. correspondence analysis.
Procedure
  1. sample the vegetation
  2. omit indifferent species (usually common species)
  3. spectrum approach
    Frequency species (J) in attribute category (K) (e.g., moisture 1 → 6)
    F = {sum of species (i)j, k}/{total of all cover (ΣiΣjCijk)} × 100, sum of species (i)j,k = Cijk
  4. indicator index Π ... weighting factor analysis
    weight = Zj ... proposed many methods of weighting
    Πk = Σ(each species weight)/Σ(all species weight) × 10
Table. Site ordination by weighted average.
_______________Site
Species __Weight__1__2___3
__1_______100___5__1___0
__2________80___3__3___0
__3________50___2__4___1
__4________30___0__0___3
__5_________0__10__0___5
Total ___________10__9___9
Weighted average 84.0 30.0 15.6
Ex. Site 1: (100 × 5 + 300 × 80 + 50 × 2 + 30 × 0 + 0 × 0)/10

(Curtis & McIntosh 1951)

Klinka weighted average method
Moisture score_1 (dry)____2____3_____4_____5__6 (wet)__Total
Cover_________36.81_14.32___8.94__0.28__0.16__0.32___60.83
Weight________36.8__28.6___26.8___1.1___0.8___1.9____96.0

Soil moisture indicator index = Weight/Cover × 10 = 15.8

Arch or horseshoe effect (アーク効果と馬蹄効果)


= hump
PCA → horseshoe
CA = arch

Hump can be seen in any PCA and CA

The appearance of a projected data swarm as a curve ("arch" or "horseshoe") when the data were obtained from sampling unit
How to remove hump: Do DCA or drop environmental variables highly correlated with the arch to remove the arch. Dropping variables is effective. CANOCO has an improved polynomial detrending technique
hump

Detrended correspondence analysis, DCA (傾向化除去対応分析)


Reciprocal averaging, RA

= correspondence analysis (Hill 1973)
CA is an extension of the method of weighted averaging (Whittaker 1967)
→ Species commonly show bell-shaped response curves with respect to environmental gradients DCA
Figure 5.3. Artificial example of unimodal response curves of five species (A-E) with respect to standardized variables, showing different degrees of separation of the species curves, a. real environmental factor, e.g., moisture. b: first axis of CA. c: first axis of CA folded in this middle and the response curves of the species lowered by a factor of about 2. Sites are shown as dots at y = 1 if species D is absent.
Procedure
a: iteration process (反復過程)
1. Take arbitrary, but unequal, initial site scores (xi)
2. Calculate new species scores (uk) by weighted averaging of the site scores (Eq. 5.1).
3. Calculate new site scores (xi) by weighted averaging of the species scores (Eq 5.2).
4. For the first axis go to step 5. For second and higher axes, make the site scores (xi) uncorrelated with the previous axes by the orthogonaliztion procedure described below.
5. Standardize the site scores (xi). See below for the standardization procedure.
6. Stop on convergence, i.e., when the new site scores are sufficiently close to the site scores of the previous cycle of the iteration; ELSE go to step 2.
b: orthogonalization procedure (直交化過程)
4.1. Denote site scores of the previous axis by fi and the trial scores of the present axis by xi.
4.2. Calculate v = Σi=1ny+ixifi/y++,

where y+i = Σi=1myki, and y++ = Σi=1ny+i

4.3. Calculate xi, new = xi, old - vfi.
4.4. Repeat Steps 4.1-4.3 for all previous axes
c: Standardization procedure (標準化過程)
5.1. Calculate the centroid, z, of site scores (xi). z = Σi=1ny+ixi/y++
5.2. Calculate the dispersion of the site scores s2 = Σi=1ny+i(xi - z)2/y++
5.3. Calculate xi, new = (xi, old - z)/s
Note that, upon convergence, s equals the eigenvalue.
Ex. Ecological Statistics Package

Detrended correspondence analysis, DCA (Hill 1979)

dca
Fig. Method of detrending by segments (simplified). The closed circles indicate site scores before detrending; the open circles are site scores after detrending. The closed circles are obtained by subtracting, within each of five segments, the mean of the trial scores of the second axis (Hill & Gauch 1980).

Partial ordination


Partial constrained ordinations

partial CCA, RDA, etc

pollution effects × seasonal effects (→ covariables)

Eliminate (partial out) effect of covariables. Relate residual variation to pollution variables
Replace environmental variables by their residuals obtained by regressing each pollution variable on the covariables

Direct ordination


Direct gradient analysis (direct ordination): external analysis, canonical ordination (including DCCA), ordination constrained by external variables, constrained multivariate regression, reduced-rank regression.

Canonical Correspondence Analysis (CCA) (正準化対応分析)


CCA
(Nishimura et al. 2009)

Canonical ordination(正準序列化, 適訳無): An ordination in which the axes are constrained to be linear combinations of environmental variables. Designed to detect patterns of variation in the species that can be best explained by the observed environmental variables. Differs from indirect ordination because it incorporates a correlation and regression between floristic data and environmental factors within the ordination analysis.
Direct ordination (modified after Kent & Coker 1992)
(Detrended) canonical correspondence analysis ([D]CCA)

Not strictly indirect ordination since it is a revised version of DCA with ordination axes constrained by multiple regression with environmental factors. Use CANOCO / CANOPLOT / CANODRAW (Micro$oft Windows version available) for analysis (ter Braak et al. 2002)
Becoming widely used

CCA is the canonical form of correspondence analysis (CA). As in CA, this is an iterative approach with scores which eventually stabilize. CCA uses multiple regression to select the linear combination of environmental variables that maximizes the dispersion of the species scores, i.e., CCA chooses the best weights for the environmental variables for maximum species dispersion. Maximum dispersion is the same as explaining most of the variation in the species score on each axis. Further axes are linear combinations of environmental variables that maximize dispersion of species scores but uncorrelated with previous axes. CCA does not give sites maximum dispersion because sites are restricted to be a linear combination of environmental variables.
CCA is widely used technique for direct gradient ordination (ter Braak & Smilauer 2002), assuming that species have a unimodal distribution along environmental gradients.
DCCA is the detrended form of CCA, as well as relationship between DCA and CA.
CCA (Canonical correspondence analysis) (ter Braak 1987, 1988)
Gaussian curve (ガウス曲線, 正常分配曲線): curve expressing erro Distribution - The simplest model for a unimodal species response curve (see explorations in coenospace). It has only three parameters, and the equation is:

y = A·exp(-(x-B)2/C),

where A is the maximum height of the curve, B is the modal location of the curve, and C is a measure of the breadth of the curve (often called niche breadth, tolerance, or standard deviation). The curve is bell-shaped. The difference between a Gaussian Curve and a Normal Distribution is that the latter is a statistical distribution, and hence the area under the curve is constrained to be one, and the y-axis represents frequency.

Terminology
Canonical axis: an ordination axis that is constrained to be a linear combination of environmental variables
Canonical coefficients: parameters of the final regression = the best weights
Linear method: method based on a linear model, e.g., linear regression, multiple regression, principal components analysis, redundancy analysis
Partial CCA
All the same precautions apply as with CCA
Output will be nearly identical with the inclusion of the variance accounted for by the partial variable.

Total inertial in the ordination - same as CA
Inertia partitioned out - comparison of the model with and without partial variable
Inertia constrained by variables in model
Unconstrained inertia
There is no significance test for the partial variable(s), just the % variance accounted for
Will work similarly with RDA

Packages handling CCA
  • CANOCO / PC-Ord: Commercial computer programs used for various ordination, e.g., canonical correspondence analysis, detrended correspondence analysis, and principal component analysis.
  • vegan (R library): Free program, used for ordination.

Cluster analysis (クラスター分析)


= clustering, classification

A. Hiearchical cluster analysis (階層クラスター分析)

1. Aglomerative strategy
Nearest neighbor method (single-linkage method)
Mountford average-linkage method
Farthest neighbor method (complete-linkage method)
2. Divisive strategy
Phytosociology - not recommended in general

Nomenclature: Weber HE, Moravec J & Theurillat J-P. 2000. International code of phytosociological nomenclature. 3rd edition. Journal of Vegetation Science 11: 739-768

TWINSPAN (two-way indicator species analysis) (Hill 1979)

B. Non-hiearchical cluster analysis (非階層クラスター分析)

TWINSPAN


       SITE
       11  1
       219807631245
sp. 9  211-3-------  0000
sp. 5  53415-------  0001
sp. 3  452113------  0010
sp.11  3254-5------  0011
sp. 8  -1---53-----  01
sp. 2  -5414545-424  100
sp. 1  231--3221411  101
sp. 6  ----22-23411  1100
sp.10  --142-135553  1101
sp. 7  ----------34  1110
sp.12  ----------54  1110
sp. 4  -----151----  1111
       000000111111
       000001001111
       00011 010011
       00101   0101
       01

Table. Output of TWINSPAN

flow
Fig. Flowchart of TWINSPAN (RA = reciprocal averaging). Compare to the ordination flowchart (see above).

Phytosociology (植物社会学)


= vegetation taxonomy, or syntaxonomy
A system for classifying plant communities by means of the tabulation of data collected by quadrats

coverage (被度)
nomenclature (命名規約)

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