Multiple modes in a coral species abundance distribution

Area of Science:

• Ecology
• Biodiversity Science
• Marine Biology

Background:

• Species abundance distributions (SADs) are fundamental to understanding biodiversity and community structure.
• SADs are sensitive to sample size, with small samples often yielding similar predictions from different models.
• Large sample sizes are crucial for accurately characterizing the relative abundances of rare species.

Purpose of the Study:

• To present and analyze the species abundance distribution from an exceptionally large sample of coral colonies (> 40,000) at a single site.
• To investigate the underlying community abundance distribution and identify potential causes for observed patterns.
• To assess the prevalence of multimodality in large ecological samples.

Main Methods:

• Collected and analyzed a dataset of over 40,000 coral colonies to generate a species abundance distribution.
• Examined the distribution on a logarithmic scale to identify modes.
• Employed four distinct model selection procedures to determine the number of modes in the underlying community abundance distribution.
• Investigated potential drivers, including habitat preferences and spatial aggregation.

Main Results:

• The species abundance distribution for the coral colonies was found to be multimodal on a logarithmic scale.
• Model selection procedures consistently indicated at least three modes in the underlying community abundance distribution.
• While not solely due to habitat preferences, spatial aggregation partially explained the observed multimodality.
• A review of published literature suggests multimodality may be a common characteristic of large ecological samples.

Conclusions:

• The study provides strong evidence for multimodality in a large coral reef community's species abundance distribution.
• Spatial aggregation is a contributing factor to the observed multimodality.
• The findings suggest that multimodal species abundance distributions may be a widespread phenomenon in large ecological datasets, challenging assumptions of simpler distribution models.