SAD

SAD samples feasible sets of species abundance distributions to evaluate how total abundance (N) and species richness (S) constrain observable ecological patterns.

Key Features:

• Efficient Sampling Algorithms: Implements advanced algorithms that accelerate sampling from the feasible set of species abundance distributions by several orders of magnitude.
• Feasible Set Exploration: Explores the feasible set comprising all SADs that share the same total abundance (N) and species richness (S).
• Constraint-Based Framework: Uses a constraint-based framework to assess whether empirical patterns, including the prevalence of hollow-curve SADs, are typical within the space of possible distributions.
• Inclusion of Zero-Values: Accounts for zero-values representing species absences in analyses of discrete abundance distributions.
• Software Implementations: Provides implementations in Python and R for executing the sampling algorithms.

Scientific Applications:

• Community-structure analysis: Investigates how species richness (S) and total abundance (N) shape community-level abundance patterns.
• Typicality assessment of empirical SADs: Assesses whether observed species abundance distributions are typical or exceptional within the feasible set.
• Constraint-based ecological inference: Applies constraint-based comparisons to gain insights into ecological dynamics and the origins of common SAD shapes.

Methodology:

Random samples are generated from the feasible set of SADs defined by total abundance (N) and species richness (S) using efficient sampling algorithms capable of handling ecologically realistic combinations across extensive datasets.