Null model analysis of species associations using abundance data.

Area of Science:

• Ecology
• Community Ecology
• Quantitative Ecology

Background:

• Ecological community assembly is often studied using presence/absence matrices, focusing on negative species interactions.
• However, population-level processes drive interaction outcomes, suggesting abundance data may offer clearer insights into species segregation and aggregation patterns.

Purpose of the Study:

• To evaluate the performance of null model algorithms and covariation metrics for abundance matrices.
• To identify the most effective null model for detecting species segregation and aggregation patterns.

Main Methods:

• Benchmark tests were conducted using random and structured abundance matrices derived from a lognormal distribution.
• Fourteen null model algorithms and six covariation metrics were screened for Type I error rates and detection power.
• The best-performing null model was applied to 149 empirical abundance matrices and 36 interaction matrices.

Main Results:

• The optimal null model does not constrain species richness and uses marginal distributions for assigning individuals.
• Over 80% of tested matrices showed significant species segregation, consistent with previous presence/absence matrix analyses.
• Significant species aggregation was detected in plant data and interaction matrices (including plant-pollinator data).

Conclusions:

• Abundance matrices, when analyzed with appropriate null models, are powerful tools for quantifying species segregation and aggregation.
• The findings reinforce the prevalence of species segregation but also reveal significant aggregation in specific ecological contexts.
• This approach advances the understanding of community assembly rules by incorporating population-level abundance data.