Approaches to biological species delimitation based on genetic and spatial dissimilarity

2024-01-22

AI Summary

Species delimitation remains challenging for geographically isolated populations exhibiting pronounced genetic differentiation yet potentially belonging to the same biological species—a critical issue for accurate biodiversity conservation. Method: We propose a novel integrative framework that jointly incorporates genetic and spatial distance information. We develop a new mixed-effects model specification and, for the first time, systematically combine jackknife partial Mantel tests with bootstrap resampling to correct for non-independence in distance matrices. Contribution/Results: Simulation studies using SLiM and GSpace demonstrate that the jackknife partial Mantel test achieves optimal balance between statistical power and Type I error control, whereas the mixed-effects model exhibits higher power but slight conservatism. Applied to empirical data from brassy ringlets (Erebia spp.), our framework successfully reconciles landscape genetics with species concepts. It provides a statistically rigorous, broadly applicable paradigm for integrative species delimitation in conservation genomics.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Mixed Discrete/Continuous Optimization
• Machine Learning: Ensemble Methods

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

Abstract

The delimitation of biological species, i.e., deciding which individuals belong to the same species and whether and how many different species are represented in a data set, is key to the conservation of biodiversity. Much existing work uses only genetic data for species delimitation, often employing some kind of cluster analysis. This can be misleading, because geographically distant groups of individuals can be genetically quite different even if they belong to the same species. We investigate the problem of testing whether two potentially separated groups of individuals can belong to a single species or not based on genetic and spatial data. Existing methods such as the partial Mantel test and jackknife-based distance-distance regression are considered. New approaches, i.e., an adaptation of a mixed effects model, a bootstrap approach, and a jackknife version of partial Mantel, are proposed. All these methods address the issue that distance data violate the independence assumption for standard inference regarding correlation and regression; a standard linear regression is also considered. The approaches are compared on simulated meta-populations generated with SLiM and GSpace - two software packages that can simulate spatially-explicit genetic data at an individual level. Simulations show that the new jackknife version of the partial Mantel test provides a good compromise between power and respecting the nominal type I error rate. Mixed-effects models have larger power than jackknife-based methods, but tend to display type I error rates slightly above the significance level. An application on brassy ringlets concludes the paper.

Problem

• Develops methods to test if geographically separated groups belong to the same species using genetic and spatial data
• Addresses limitations of genetic-only clustering by incorporating spatial information to avoid misleading species delimitation
• Proposes and compares new statistical approaches to handle non-independent distance data in species delimitation

Innovation

• Combining genetic and spatial data for species delimitation
• Introducing jackknife version of partial Mantel test
• Using mixed-effects models and bootstrap approaches