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CHAPTER 17 Bray-Curtis (Polar) Ordination PowerPoint PPT Presentation

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CHAPTER 17 Bray-Curtis (Polar) Ordination. Tables, Figures, and Equations. From: McCune, B. & J. B. Grace. 2002. Analysis of Ecological Communities . MjM Software Design, Gleneden Beach, Oregon

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CHAPTER 17 Bray-Curtis (Polar) Ordination

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Bray-Curtis (Polar) Ordination

Tables, Figures, and Equations

From: McCune, B. & J. B. Grace. 2002. Analysis of Ecological Communities.MjM Software Design, Gleneden Beach, Oregon

  • Table 17.1. Development and implementation of the most important refinements of Bray-Curtis ordination (from McCune & Beals 1993).

How it works

1. Select a distance measure (usually Sørensen distance) and calculate a matrix of distances (D) between all pairs of N points.

2. Calculate sum of squares of distances for later use in calculating variance represented by each axis.

3. Select two points, A and B, as reference points for first axis.

4. Calculate position (xgi) of each point i on the axis g. Point i is projected onto axis g between two reference points A and B (Fig. 17.1). The equation for projection onto the axis is:

Eqn. 1

The basis for the above equation can be seen as follows. By definition,

Eqn. 2

By the law of cosines,

Eqn. 3

Then substitute cos(A) from Equation 2 into Equation 3.

5. Calculate residual distances Rgih (Fig. 17.2) between points i and h where f indexes the g preceding axes.

6. Calculate variance represented by axis k as a percentage of the original variance (Vk%). The residual sum of squares has the same form as the original sum of squares and represents the amount of variation from the original distance matrix that remains.

7. Substitute the matrix R for matrix D to construct successive axes.

8. Repeat steps 3, 4, 5, and 6 for successive axes (generally 2-3 axes total).

Figure 17.3. Example of the geometry of variance-regression endpoint selection in a two-dimensional species space.

  • Table 17.2. Basis for the regression used in the variance-regression technique. Distances are tabulated between each point i and the first endpoint D1i and between each point and the trial second endpoint D2i*.

Figure 17.4. Using Bray-Curtis ordination with subjective endpoints to map changes in species composition through time, relative to reference conditions (points A and B). Arrows trace the movement of individual SUs in the ordination space.

Figure 17.5. Use of Bray-Curtis ordination to describe an outlier (arrow). Radiating lines are species vectors. The alignment of Sp3 and Sp6 with Axis 1 suggests their contribution to the unusual nature of the outlier.


  • Table 17.3. Comparison of Euclidean and city-block methods for calculating ordination scores and residual distances in Bray-Curtis ordination.

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