Ordinaatiomenetelmät

Ordinaatiomenetelmät, often translated as ordination methods, are statistical techniques used in ecology and other fields to analyze and visualize the relationships between a set of samples and the variables measured within them. The primary goal is to represent complex, high-dimensional data in a lower-dimensional space, typically a two-dimensional plot, while preserving as much of the original variation as possible. This allows researchers to identify patterns, gradients, and groupings that might not be apparent in the raw data.

Common ordination methods include Principal Component Analysis (PCA), Principal Coordinates Analysis (PCoA, also known as Multidimensional Scaling or MDS), Correspondence Analysis (CA), and Non-metric Multidimensional Scaling (NMDS). PCA is a linear method that finds orthogonal axes (principal components) that capture the maximum variance in the data. PCoA is similar to PCA but can be used with various distance or dissimilarity measures. CA is specifically designed for analyzing contingency tables, often used with species occurrence data. NMDS is a non-linear method that aims to represent the rank order of dissimilarities between samples, making it robust to the underlying distribution of the data.

The output of ordination is typically a scatter plot where each point represents a sample. The position of the points in this ordination space reflects their similarity or dissimilarity based on the measured variables. Environmental variables can often be overlaid on the plot to help interpret the observed patterns, suggesting environmental factors that might be driving the observed variations in the samples. Ordination methods are widely used for exploring community composition, understanding species distributions along environmental gradients, and assessing the impact of disturbances.