The conservation of the genetic integrity of Apis mellifera subspecies has emerged as an important objective. In 2019, the Emilia-Romagna region became the first Italian regional authority to issue a law specifically addressing the protection of the native Apis mellifera ligustica subspecies. In this study we analysed a highly informative portion of the mitochondrial DNA (mtDNA), widely used for assessing genetic diversity of honey bee populations. By analysing 1143 honey bees sampled after the introduction of this law, we provided a snapshot of the distribution of mtDNA haplotypes in this region. The two most frequent mtDNA haplotypes were C1 (characteristic of A. m. ligustica) and C2 (characteristic of A. m. carnica), reported in 86.5% and 11.0% of the analysed bees, respectively. About 1.3% and 1.1% of the analysed bees carried mtDNA haplotypes of the A and M lineages (haplotypes A1a, A1e, A4, A26, A65 and two novel ones, A2w and A6a; M3, M3a, M4 and M79). Continued genetic monitoring will be important to assess the impact of this regional law over the coming years. Based on the obtained results, we recommend a more stringent policy to prevent the erosion of the genetic integrity of the native subspecies A. m. ligustica.

Small populations of imperiled species are susceptible to the negative consequences of skewed sex-ratios. In imperiled species with environmental sex determination such as sea turtles, examining sex ratios across a range of environments and population abundance levels can provide insight into factors that influence population resilience, which can then be the foci of management plans for these species. Breeding sex ratios (the ratio of actively breeding males to females during a reproductive season; BSRs) extrapolated from genetic parentage analyses are a common approach for enumerating sex ratios in sea turtles. Such analyses also allow for the characterization of multiple paternity within sea turtle clutches, which should reflect BSRs and breeding behaviors. We characterized the first BSR for a breeding assemblage of loggerhead sea turtles (Caretta caretta) belonging to the temperate, low-abundance Northern Gulf of Mexico Recovery Unit using genotypes of 16 microsatellite loci from nesting females and hatchlings. Unlike prior studies at both more-tropical and more-temperate, and higher-abundance, Recovery Units in this region, we found a balanced BSR of 1.3:1 males:female and a low incidence (~17%) of multiple paternity. This suggests that there are relatively few males breeding at this assemblage and within this Recovery Unit. Beaches in this region are expected to produce substantial numbers of male hatchlings based on sand temperature data. The relative dearth of mature males may then be due to hydrologic disturbances that disproportionately affect the fitness and survival of male hatchlings, or due to demographic stochasticity. More work is needed to study the factors that might influence male hatchling production and fitness in this region, particularly as climate change is predicted to lead to feminization in global sea turtle populations. Our work demonstrates the broad utility of characterizing BSRs and other sex ratios across a range of populations in imperiled, environmentally sensitive species.

Luculia yunnanensis is a vulnerable species endemic to Yunnan Province, Southwestern China, which has high ornamental value. Its wild population has not been fully protected and utilized for a long time, which is not conducive to the long-term stable development of this species. Genetic diversity assessment is the basis and prerequisite for the conservation of rare species. In this study, 21 phenotypic traits and 17 highly polymorphic EST-SSR markers were used to analyze the genetic diversity and genetic structure of 164 individuals from six L. yunnanensis populations. The coefficient of variation of 21 phenotypic traits ranged from 11.76% to 52.58% (mean=21.72%), and the coefficient of variation of 18 traits was less than 30%. The average values of Ne, I, Ho and He were 1.710, 0.619, 0.384, and 0.352, respectively. The genetic diversity of LLO (Ho = 0.476 and He = 0.426) and LCM (Ho = 0.424 and He = 0.381) populations in Lushui County was highest. The GDX populations (Ho = 0.335 and He = 0.269) isolated by Gaoligong Mountain had the lowest genetic diversity. The AMOVA results showed that 13.04% of the genetic variation was among populations and 86.96% was within populations. The average phenotypic differentiation coefficient of phenotypic traits among populations was 18.69%. The results of phenotypic and genetic variation analysis were consistent, indicating that the most of variation exists within population. Genetic structure, UPGMA clustering and PCA analysis results showed that the populations of L. yunnanensis had obvious geographical divisions, and the populations distributed in the southern region and distributed in the northern region of the Nujiang River clustered into one group respectively. Combining the results of phenotypic and molecular markers, we recommend that give priority to the protection of LLO, LCM and GDX population, in order to ensure the sustainable utilization of L. yunnanensis germplasm resources.

Long-term genetic studies of wild populations are very scarce, but are essential for connecting ecological and population genetics models, and for understanding the dynamics of biodiversity. We present a study of a wild wheat population sampled over a 36-year period at high spatial resolution. We genotyped 832 individuals from regular sampling along transects during the course of the experiment. Genotypes were clustered into ecological microhabitats over scales of tens of metres, and this clustering was remarkably stable over the 36 generations of the study. Simulations show that it is difficult to determine whether this spatial and temporal stability reflects extremely limited dispersal or fine-scale local adaptation to ecological parameters. Using a common-garden experiment, we showed that the genotypes found in distinct microhabitats differ phenotypically. Our results provide a rare insight into the population genetics of a natural population over a long monitoring period.

The Cape Verde palm tree, Phoenix atlantica, holds significant ecological and cultural importance within the Cape Verde archipelago. However, its genetic distinctiveness has been questioned due to its close relationship and morphological similarity to the date palm (Phoenix dactylifera). In this study, we used an expanded sample set, 18 simple sequence repeat (SSR) markers, and a plastid minisatellite to characterize P. atlantica in Cape Verde and investigate its relationship with other Phoenix species. Our findings identify genetic markers that differentiate the P. atlantica genetic pool, including a unique fixed allele. We also provide evidence of the recent divergence of P. atlantica from Northern African date palm populations, suggesting a relatively recent colonization of Cape Verde by palm trees. Additionally, we characterized the genetic composition of palm tree populations across three Cape Verde islands, concluding that wild samples from certain populations in Boavista and Sal are best suited for establishing a seed and/or germplasm bank for replantation efforts, representing a crucial step for the conservation of Cape Verde\'s natural heritage. Overall, our results enhance the understanding of the historical trajectories and genetic characterization of palm trees in Africa, offering valuable insights for conservation strategies.

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The extinction risk of the giant panda has been demoted from \"endangered\" to \"vulnerable\" on the International Union for Conservation of Nature Red List, but its habitat is more fragmented than ever before, resulting in 33 isolated giant panda populations according to the fourth national survey released by the Chinese government. Further comprehensive investigations of the genetic background and in-depth assessments of the conservation status of wild populations are still necessary and urgently needed. Here, we sequenced the genomes of 612 giant pandas with an average depth of ~26× and generated a high-resolution map of genomic variation with more than 20 million variants covering wild individuals from six mountain ranges and captive representatives in China. We identified distinct genetic clusters within the Minshan population by performing a fine-grained genetic structure. The estimation of inbreeding and genetic load associated with historical population dynamics suggested that future conservation efforts should pay special attention to the Qinling and Liangshan populations. Releasing captive individuals with a genetic background similar to the recipient population appears to be an advantageous genetic rescue strategy for recovering the wild giant panda populations, as this approach introduces fewer deleterious mutations into the wild population than mating with differentiated lineages. These findings emphasize the superiority of large-scale population genomics to provide precise guidelines for future conservation of the giant panda.

The Changle goose (CLG), a Chinese indigenous breed, is celebrated for its adaptability, rapid growth, and premium meat quality. Despite its agricultural value, the exploration of its genomic attributes has been scant. Our study entailed whole-genome resequencing of 303 geese across CLG and five other Chinese breeds, revealing distinct genetic diversity metrics. We discovered significant migration events from Xingguo gray goose to CLG and minor gene flow between them. We identified genomic regions through selective sweep analysis, correlating with CLG\'s unique traits. An elevated inbreeding coefficient in CLG, alongside reduced heterozygosity and rare single nucleotide polymorphisms (RSNPs), suggests a narrowed genetic diversity. Genomic regions related to reproduction, meat quality, and growth were identified, with the GATA3 gene showing strong selection signals for meat quality. A non-synonymous mutation in the Sloc2a1 gene, which is associated with reproductive traits in the CLG, exhibited significant differences in allelic frequency. The roles of CD82, CDH8, and PRKAB1 in growth and development, alongside FABP4, FAF1, ESR1, and AKAP12 in reproduction, were highlighted. Additionally, Cdkal1 and Mfsd14a may influence meat quality. This comprehensive genetic analysis underpins the unique genetic makeup of CLG, providing a basis for its conservation and informed breeding strategies.

A fundamental goal in population genetics is to understand how variation is arrayed over natural landscapes. From first principles we know that common features such as heterogeneous population densities and barriers to dispersal should shape genetic variation over space, however there are few tools currently available that can deal with these ubiquitous complexities. Geographically referenced single nucleotide polymorphism (SNP) data are increasingly accessible, presenting an opportunity to study genetic variation across geographic space in myriad species. We present a new inference method that uses geo-referenced SNPs and a deep neural network to estimate spatially heterogeneous maps of population density and dispersal rate. Our neural network trains on simulated input and output pairings, where the input consists of genotypes and sampling locations generated from a continuous space population genetic simulator, and the output is a map of the true demographic parameters. We benchmark our tool against existing methods and discuss qualitative differences between the different approaches; in particular, our program is unique because it infers the magnitude of both dispersal and density as well as their variation over the landscape, and it does so using SNP data. Similar methods are constrained to estimating relative migration rates, or require identity-by-descent blocks as input. We applied our tool to empirical data from North American grey wolves, for which it estimated mostly reasonable demographic parameters, but was affected by incomplete spatial sampling. Genetic based methods like ours complement other, direct methods for estimating past and present demography, and we believe will serve as valuable tools for applications in conservation, ecology and evolutionary biology. An open source software package implementing our method is available from https://github.com/kr-colab/mapNN.

Linking genetic diversity to extinction is a common goal in genomic studies. Recently, a debate has arisen regarding the importance of genetic variation in conservation as some studies have failed to find associations between genome-wide genetic diversity and extinction risk. However, only rarely are genetic diversity and fitness measured together in the wild, and typically demographic history and environment are ignored. It is therefore difficult to infer whether a lack of an association is real or obscured by confounding factors. To address these shortcomings, we analyzed genetic data from 7,501 individuals with extinction data from 279 meadows and mortality of 1,742 larval nests in a butterfly metapopulation. We found a strong negative association between genetic diversity and extinction when considering only heterozygosity in models. However, this association disappeared when accounting for ecological covariates, suggesting a confounding between demography and genetics and a more complex role for heterozygosity in extinction risk. Modeling interactions between heterozygosity and demographic variables revealed that associations between extinction and heterozygosity were context-dependent. For example, extinction declined with increasing heterozygosity in large, but not currently small populations, although negative associations between heterozygosity, extinction, and mortality were detected in small populations with a recent history of decline. We conclude that low genetic diversity is an important predictor of extinction, predicting >25% increase in extinction beyond ecological factors in certain contexts. These results highlight that inferences about the importance of genetic diversity for population viability should not rely on genomic data alone but require investments in obtaining demographic and environmental data from natural populations.