Linking the spatial and genomic structure of adaptive potential for conservation management: a review.

We unified the recent literature with the goal to contribute to the discussion on how genetic diversity might best be conserved. We argue that this decision will be guided by how genomic variation is distributed among manageable populations (i.e. its spatial structure), the degree to which adaptive potential is best predicted by variation across the entire genome or the subset of that variation that is identified as putatively adaptive (i.e. its genomic structure), and whether we are managing species as single entities or as collections of diversifying lineages. The distribution of genetic variation and our ultimate goal will have practical implications for on-the-ground management. If adaptive variation is largely polygenic or responsive to change, its spatial structure might be broadly governed by the forces determining genome-wide variation (linked selection, drift, and gene flow), making measurement and prioritization straightforward. If we are managing species as single entities, then population-level prioritization schemes are possible so as to maximize future pooled genetic variation. We outline one such scheme based on the popular Shapley Value from cooperative game theory that considers the relative genetic contribution of a population to an unknown future collection of populations.

Capturing diversity: Split systems and circular approximations for conservation.

We investigated the implications of employing a circular approximation of split systems in the calculation of maximum diversity subsets of a set of taxa in a conservation biology context where diversity is measured using Split System Diversity (SSD). We conducted a comparative analysis between the maximum SSD score and the maximum SSD set(s) of size k, efficiently determined using a circular approximation, and the true results obtained through brute-force search based on the original data. Through experimentation on simulated datasets and SNP data across 50 Atlantic Salmon populations, our findings demonstrate that employing a circular approximation can lead to the generation of an incorrect max-SSD set(s). We built a graph-based split system whose circular approximation led to a max-SSD set of size k=4 that was less than the true max-SSD set by 17.6%. This discrepancy increased to 25% for k=11 when we used a hypergraph-based split system. The same comparison on the Atlantic salmon dataset revealed a mere 1% difference. However, noteworthy disparities emerged in the population composition between the two sets. These findings underscore the importance of assessing the suitability of circular approximations in conservation biology systems. Caution is advised when relying solely on circular approximations to determine sets of maximum diversity, and careful consideration of the data characteristics is crucial for accurate results in conservation biology applications.

A fast and scalable method for inferring phylogenetic networks from trees by aligning lineage taxon strings.

The reconstruction of phylogenetic networks is an important but challenging problem in phylogenetics and genome evolution, as the space of phylogenetic networks is vast and cannot be sampled well. One approach to the problem is to solve the minimum phylogenetic network problem, in which phylogenetic trees are first inferred, and then the smallest phylogenetic network that displays all the trees is computed. The approach takes advantage of the fact that the theory of phylogenetic trees is mature, and there are excellent tools available for inferring phylogenetic trees from a large number of biomolecular sequences. A tree-child network is a phylogenetic network satisfying the condition that every nonleaf node has at least one child that is of indegree one. Here, we develop a new method that infers the minimum tree-child network by aligning lineage taxon strings in the phylogenetic trees. This algorithmic innovation enables us to get around the limitations of the existing programs for phylogenetic network inference. Our new program, named ALTS, is fast enough to infer a tree-child network with a large number of reticulations for a set of up to 50 phylogenetic trees with 50 taxa that have only trivial common clusters in about a quarter of an hour on average.