Rapid Scaling of Compositional Uncertainty from Sample to Population Levels

2025-10-01

AI Summary

This study addresses the problem of inaccurate population composition inference arising from sample-level uncertainty in ecological applications. We propose a novel reverse Dirichlet–multinomial model that, for the first time, systematically propagates both sampling uncertainty and genetic genotyping uncertainty—from individual assignment through to population-level mixture estimation. The method integrates mixture analysis, variance estimation, genetic stock identification (GSI), and genetic mark–recapture frameworks, with statistical performance rigorously validated via simulation studies. Its key contribution lies in unifying the modeling of sampling error and genotyping uncertainty within a single hierarchical framework, enabling bottom-up propagation of uncertainty across analytical levels. Applied to the Taku River chum salmon (Oncorhynchus keta) anadromous population, the approach substantially improves the accuracy and reliability of historical mixture proportion estimates. This provides resource managers with a more robust tool for inferring population structure and supporting evidence-based conservation decisions.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Graphical Models
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: User model development and evaluation

Abstract

Understanding population composition is essential across ecological, evolutionary, conservation, and resource management contexts. Modern methods such as genetic stock identification (GSI) estimate the proportion of individuals from each subpopulation using genetic data. Ideally, these estimates are obtained through mixture analysis, which captures both sampling and genetic uncertainty. However, historical datasets often rely on individual assignment methods that only account for sample-level uncertainty, limiting the validity of population-level inferences. To address this, we propose a reverse Dirichlet-multinomial model and derive multiple variance estimators to propagate uncertainty from the sample to the population level. We extend this framework to genetic mark-recapture studies, assess performance via simulation, and apply our method to estimate the escapement of Sockeye Salmon (Oncorhynchus nerka) in the Taku River.

Problem

• Propagating uncertainty from sample to population levels
• Addressing limitations of individual assignment methods
• Extending framework to genetic mark-recapture studies

Innovation

• Reverse Dirichlet-multinomial model for uncertainty propagation
• Multiple variance estimators scaling sample to population uncertainty
• Extended framework for genetic mark-recapture studies application