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# Basic Concepts for Ordination - PowerPoint PPT Presentation

Basic Concepts for Ordination. Tanya, Nick, Caroline . What is ordination?. Puts information in order of importance to the researcher There are two types of ordination Direct Ordination Indirect Ordination . Direct Ordination.

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## PowerPoint Slideshow about 'Basic Concepts for Ordination' - tate

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### Basic Concepts for Ordination

Tanya, Nick, Caroline

• Puts information in order of importance to the researcher

• There are two types of ordination

• Direct Ordination

• Indirect Ordination

• Places information in order with respect to a pre-defined environmental measure

• Time (Generation)

• Distance

• Elevation

• Abstract – tries to make a meaningful summary of the patterns underlying the data

• Creates graphs or diagrams that show the relationships among data points

• Data space

• Multidimensional mathematic space where each variable represents a dimension

• Regression makes one variable dependent on the others

• Indirect Ordination treats all variables as equals

• Indirect Ordination works well for co-correlated data whereas regression does not

• In raw data axes correspond to some measurement made by the researcher

• All axes are equally important

• In ordinated data the numbers on the axes are ordination scores

• Axes produced ordination are in descending order of importance

• Ordination scores – abstract way of measuring ordinated data

• Has no relation to raw data

• Points that are close together are similar and contain similar measurements, while points that are far apart are very different and contain different measurements

• Choosing variables is subjective

• Excluding variables should be robust

• Repeat ordination several times

• Typical to restrict to one type of variable

• Ex. Given biological data or chemical data or climate data etc.

• Can be done by hand without a computer

• Simplest of all indirect ordinations

• Rectangular matrix of data is created

• Matrix is converted into a square matrix that quantifies differences between samples

• Two samples are chosen as the end points and are used to construct a scale diagram

• Second set of samples is chosen to construct another axis

• Process is repeated

• Being subjective and arbitrary

• Many permutations to select endpoints and distance indices

• Many techniques possible to describe the same data set – this gives 40 different possible permutations

• Sensitive to outliers

• Geometry may fail to work

• Not a simple calculation – amount of work goes with the square of the number of samples

• Essentially this matrix is made up of numbers (dissimilarity indices) that represent the difference between pairs of samples

• Dissimilarity index between a sample and itself is zero

• For different types of data, there are different formulas for calculating the dissimilarity indices

• Once we have the dissimilarities between all samples have been calculated, two samples need to be chosen as the end-points

• the simplest way to choose the endpoints is to choose the two points that are most dissimilar (have the largest dissimilarity index – close to 1 being the most dissimilar)

• First you have to construct the first ordination axis with the endpoints

• Then you have to draw a circle with the radius representing the distance between the first endpoint and the point your are plotting and repeat the process with the second endpoint

• Where the two circles intersect is where your point is located