Genomic information can aid in the establishment of sustainable management plans for commercially exploited marine fishes, aiding in the long-term conservation of these resources. The southern African hakes (Merluccius capensis and M. paradoxus) are commercially valuable demersal fishes with similar distribution ranges but exhibiting contrasting life histories. Using a comparative framework based on Pool-Seq genome-wide SNP data, we investigated whether the evolutionary processes that shaped extant patterns of diversity and divergence are shared among these two congeneric fishes, or unique to each one. Our findings revealed that M. capensis and M. paradoxus show similar levels of genome-wide diversity, despite different census sizes and life-history features. In addition, M. capensis shows three highly structured geographic populations across the Benguela Current region (one in the northern Benguela and two in the southern Benguela), with no consistent genome-environment associations detected. In contrast, although population structure and outlier analyses suggested panmixia for M. paradoxus, reconstruction of its demographic history suggested the presence of an Atlantic-Indian Ocean subtle substructuring pattern. Therefore, it appears that M. paradoxus might be composed by two highly connected populations, one in the Atlantic and one in the southwest Indian Ocean. The reported similar low levels of genomic diversity, as well as newly discovered genetically distinct populations in both hake species can thus assist in informing and improving conservation and management plans for the commercially important southern African Merluccius.

Genomic data and machine learning approaches have gained interest due to their potential to identify adaptive genetic variation across populations and to assess species vulnerability to climate change. By identifying gene-environment associations for putatively adaptive loci, these approaches project changes to adaptive genetic composition as a function of future climate change (genetic offsets), which are interpreted as measuring the future maladaptation of populations due to climate change. In principle, higher genetic offsets relate to increased population vulnerability and therefore can be used to set priorities for conservation and management. However, it is not clear how sensitive these metrics are to the intensity of population and individual sampling. Here, we use five genomic datasets with varying numbers of SNPs (NSNPs  = 7006-1,398,773), sampled populations (Npop  = 23-47) and individuals (Nind  = 185-595) to evaluate the estimation sensitivity of genetic offsets to varying degrees of sampling intensity. We found that genetic offsets are sensitive to the number of populations being sampled, especially with less than 10 populations and when genetic structure is high. We also found that the number of individuals sampled per population had small effects on the estimation of genetic offsets, with more robust results when five or more individuals are sampled. Finally, uncertainty associated with the use of different future climate scenarios slightly increased estimation uncertainty in the genetic offsets. Our results suggest that sampling efforts should focus on increasing the number of populations, rather than the number of individuals per populations, and that multiple future climate scenarios should be evaluated to ascertain estimation sensitivity.

Haplotype networks can be used to demonstrate the genealogical relationships of DNA sequences within species, and thus are widely applied in population genetics, molecular ecology, epidemiology and evolutionary studies. However, existing programs become computationally infeasible as the sample size increases. Here, we present fastHaN, an efficient and scalable program suitable for constructing haplotype networks for large samples. On a data set with the haplotype length of 30 kb, the Median Joining Network (MJN) algorithm implemented by fastHaN is 3000 times faster than PopART and 70 times faster than NETWORK in single-threaded mode. The implementation of the Templeton-Crandall-Sing (TCS) algorithm is 100 times faster than PopART and 5800 times faster than the TCS software. Moreover, fastHaN also enables multi-threaded mode with scalability. The source code is freely available on https://github.com/ChenHuaLab/fastHaN/. A web-based version is also available on https://ngdc.cncb.ac.cn/haplotype/.

A high-quality reference genome can be a valuable resource for threatened species by providing a foundation to assess their evolutionary potential to adapt to future pressures such as environmental change. We assembled the genome of a female hihi (Notiomysits cincta), a threatened passerine bird endemic to Aotearoa New Zealand. The assembled genome is 1.06 Gb, and is of high quality and highly contiguous, with a contig N50 of 7.0 Mb, estimated QV of 44 and a BUSCO completeness of 96.8%. A male assembly of comparable quality was generated in parallel. A population linkage map was used to scaffold the autosomal contigs into chromosomes. Female and male sequence coverage and comparative genomics analyses were used to identify Z-, and W-linked contigs. In total, 94.6% of the assembly length was assigned to putative nuclear chromosome scaffolds. Native DNA methylation was highly correlated between sexes, with the W chromosome contigs more highly methylated than autosomal chromosomes and Z contigs. 43 differentially methylated regions were identified, and these may represent interesting candidates for the establishment or maintenance of sex differences. By generating a high-quality reference assembly of the heterogametic sex, we have created a resource that enables characterization of genome-wide diversity and facilitates the investigation of female-specific evolutionary processes. The reference genomes will form the basis for fine-scale assessment of the impacts of low genetic diversity and inbreeding on the adaptive potential of the species and will therefore enable tailored and informed conservation management of this threatened taonga (treasured) species.