Journal Article

Pattern detection in null model analysis

Ulrich, Werner, Gotelli, Nicholas J.

2013

Overview

Null model analysis has been a popular tool for detecting pattern in binary presence-absence matrices, and previous tests have identified algorithms and metrics that have good statistical properties. However, the behavior of different metrics is often correlated, making it difficult to distinguish different patterns. We compared the performance of a suite of null models and metrics that have been proposed to measure patterns of segregation, aggregation, nestedness, coherence, and species turnover. We found that any matrix with segregated species pairs can be re-ordered to highlight aggregated pairs. As a consequence, the same null model can identify a single matrix as being simultaneously aggregated, segregated or nested. These results cast doubt on previous conclusions of matrix-wide species segregation based on the C-score and the fixed-fixed algorithm. Similarly, we found that recently proposed classification schemes based on patterns of coherence, nestedness, and segregation and aggregation cannot be uniquely distinguished using proposed metrics and null model algorithms. It may be necessary to use a combination of different metrics and to decompose matrix-wide patterns into those of individual pairs of species or pairs of sites to pinpoint the sources of non-randomness.

• Publisher: Blackwell Publishing,Blackwell Publishing Ltd,Blackwell
• Subject: Algorithms / Animal and plant ecology / Animal, plant and microbial ecology / Binary system / Biological and medical sciences / classification / community ecology / Editor's Choice / Fundamental and applied biological sciences. Psychology / General aspects / Matrix