Developing the multitrait concept for functional diversity: lessons from a system rich in functions but poor in...

Area of Science:

• Marine Ecology
• Benthic Macrofauna Ecology
• Functional Trait Diversity

Background:

• Functional diversity indices are commonly used but rarely empirically evaluated, especially with non-continuous traits.
• Existing studies often overlook the utility of categorical traits for assessing ecosystem function.
• There's a need for empirical quantification of functional diversity using a comprehensive trait-based approach.

Purpose of the Study:

• To empirically quantify functional trait richness, redundancy, and variability in benthic macrofauna using categorical traits.
• To assess the impact of sampling effort on understanding functional properties of meta-assemblages.
• To explore the relationship between species richness and trait richness, and trait packaging within species.

Main Methods:

• Compiled extensive functional knowledge for benthic macrofauna, utilizing 25 biological traits and 102 modalities.
• Quantified functional diversity metrics (richness, redundancy, variability) across large spatial scales (>1000 km) and long temporal scales (10 years).
• Examined the effect of sampling effort and evaluated species richness-trait richness relationships.

Main Results:

• Functional trait diversity varied significantly between areas with different salinity, anthropogenic stress, and species richness.
• Fewer samples are required to characterize functional structure compared to taxonomic structure, highlighting trait-based advantages.
• Empirical trait-based analysis revealed large-scale ecological differences and complexities in assemblage structure and function.

Conclusions:

• Categorical trait analysis provides a powerful and efficient tool for understanding benthic ecosystem function, management, and conservation.
• Empirical application of categorical traits requires understanding species-trait relationships, covariation, and abundance patterns.
• Trait-based approaches offer valuable insights into ecological complexities and can identify general ecological patterns.