Trait probability density (TPD): measuring functional diversity across scales based on TPD with R

Area of Science:

• Ecology
• Biodiversity Science
• Trait-based Ecology

Background:

• Functional diversity (FD) is crucial for understanding ecological processes, including global change impacts and restoration.
• Current methods for estimating FD are often limited by spatial scale and do not account for intraspecific trait variability (ITV).

Purpose of the Study:

• To introduce a unifying framework using trait probability density (TPD) functions to estimate and partition FD across multiple scales.
• To incorporate intraspecific trait variability (ITV) into FD calculations.
• To provide probabilistic implementations of key FD metrics.

Main Methods:

• Estimation of TPD functions at various spatial scales.
• Probabilistic implementation of FD components: functional richness, evenness, divergence, redundancy, and rarity.
• Decomposition of beta FD into nested and unique components.

Main Results:

• The TPD approach reconciles existing FD methods within a single framework.
• FD can be seamlessly partitioned across scales, from individuals to global levels.
• ITV is explicitly accounted for in FD estimations.

Conclusions:

• The TPD framework offers a unified and expanded approach to functional ecology analyses.
• This method captures the probabilistic and multidimensional nature of FD.
• The TPD R package facilitates comparative studies across diverse regions and case studies.