Effective population size (Ne) is a pivotal evolutionary parameter with crucial implications in conservation practice and policy. Genetic methods to estimate Ne have been preferred over demographic methods because they rely on genetic data rather than time‐consuming ecological monitoring. Methods based on linkage disequilibrium (LD), in particular, have become popular in conservation as they require a single sampling and provide estimates that refer to recent generations. A software program based on the LD method, GONE, looks particularly promising to estimate contemporary and recent‐historical Ne (up to 200 generations in the past). Genomic datasets from non‐model species, especially plants, may present some constraints to the use of GONE, as linkage maps and reference genomes are seldom available, and SNP genotyping is usually based on reduced‐representation methods. In this study, we use empirical datasets from four plant species to explore the limitations of plant genomic datasets when estimating Ne using the algorithm implemented in GONE, in addition to exploring some typical biological limitations that may affect Ne estimation using the LD method, such as the occurrence of population structure. We show how accuracy and precision of Ne estimates potentially change with the following factors: occurrence of missing data, limited number of SNPs/individuals sampled, and lack of information about the location of SNPs on chromosomes, with the latter producing a significant bias, previously unexplored with empirical data. We finally compare the Ne estimates obtained with GONE for the last generations with the contemporary Ne estimates obtained with the programs currentNe and NeEstimator.

As biodiversity loss outpaces recovery, conservationists are increasingly turning to novel tools for preventing extinction, including cloning and invitro gametogenesis of biobanked cells. However, restoration of populations can be hindered by low genetic diversity and deleterious genetic load. The persistence of the northern white rhino (Ceratotherium simum cottoni) now depends on the cryopreserved cells of 12 individuals. These banked genomes have higher genetic diversity than southern white rhinos (C. s. simum), a sister subspecies that successfully recovered from a severe bottleneck, but the potential impact of genetic load is unknown. We estimated how demographic history has shaped genome-wide genetic load in nine northern and 13 southern white rhinos. The bottleneck left southern white rhinos with more fixed and homozygous deleterious alleles and longer runs of homozygosity, whereas northern white rhinos retained more deleterious alleles masked in heterozygosity. To gauge the impact of genetic load on the fitness of a northern white rhino population restored from biobanked cells, we simulated recovery using fitness of southern white rhinos as a benchmark for a viable population. Unlike traditional restoration, cell-derived founders can be reintroduced in subsequent generations to boost lost genetic diversity and relieve inbreeding. In simulations with repeated reintroduction of founders into a restored population, the fitness cost of genetic load remained lower than that borne by southern white rhinos. Without reintroductions, rapid growth of the restored population (>20-30% per generation) would be needed to maintain comparable fitness. Our results suggest that inbreeding depression from genetic load is not necessarily a barrier to recovery of the northern white rhino and demonstrate how restoration from biobanked cells relieves some constraints of conventional restoration from a limited founder pool. Established conservation methods that protect healthy populations will remain paramount, but emerging technologies hold promise to bolster these tools to combat the extinction crisis.

Evolutionary Applications is a fully peer reviewed open access journal.It publishes papers that utilize concepts from evolutionary biology to address biological questions of health, social and economic relevance.Papers are expected to employ evolutionary concepts or methods to make contributions to areas such as (but not limited to): medicine, agriculture, forestry, exploitation and management (fisheries and wildlife), aquaculture, conservation biology, environmental sciences (including climate change and invasion biology), microbiology, and toxicology.All taxonomic groups are covered from microbes, fungi, plants and animals.In order to better serve the community, we also now strongly encourage submissions of papers making use of modern molecular and genetic methods (population and functional genomics, transcriptomics, proteomics, epigenetics, quantitative genetics, association and linkage mapping) to address important questions in any of these disciplines and in an applied evolutionary framework.Theoretical, empirical, synthesis or perspective papers are welcome.

Molecular mechanisms driving the escalation of pyrethroid resistance in the major malaria mosquitoes of Central Africa remain largely uncharacterized, hindering effective management strategies. Here, resistance intensity and the molecular mechanisms driving it were investigated in a population of Anopheles coluzzii from northern Cameroon. High levels of pyrethroid and organochloride resistance were observed in An. coluzzii population, with no mortality for 1× permethrin; only 11% and 33% mortalities for 5× and 10× permethrin diagnostic concentrations, and <2% mortalities for deltamethrin and DDT, respectively. Moderate bendiocarb resistance (88% mortality) and full susceptibility to malathion were observed. Synergist bioassays with piperonyl butoxide recovered permethrin susceptibility, with mortalities increasing to 53.39%, and 87.30% for 5× and 10× permethrin, respectively, implicating P450 monooxygenases. Synergist bioassays with diethyl maleate (DEM) recovered permethrin and DDT susceptibilities (mortalities increasing to 34.75% and 14.88%, respectively), implicating glutathione S‐transferases. RNA‐seq‐based genome‐wide transcriptional analyses supported by quantitative PCR identified glutathione S‐transferase, GSTe2 (RNA‐seqFC = 2.93 and qRT‐PCRFC = 8.4, p < 0.0043) and CYP450, CYP6Z2 (RNA‐seqFC = 2.39 and qRT‐PCRFC = 11.7, p < 0.0177) as the most overexpressed detoxification genes in the pyrethroid‐resistant mosquitoes, compared to mosquitoes of the susceptible Ngousso colony. Other overexpressed genes include P450s, CYP6M2 (FC = 1.68, p < 0.0114), CYP4G16 (FC = 2.02, p < 0.0005), and CYP4G17 (FC = 1.86, p < 0.0276). While high frequency of the 1014F kdr mutation (50%) and low frequencies of 1014S (6.61%) and 1575Y (10.29%) were observed, no ace‐1 mutation was detected in bendiocarb‐resistant populations, suggesting the preeminent role of metabolic mechanism. Overexpression of metabolic resistance genes (including GSTe2 and CYP6Z2 known to confer resistance to multiple insecticides) in An. coluzzii from the Sudan Savannah of Cameroon highlights the need for alternative management strategies to reduce malaria burden in northern Cameroon.

Genomic selection (GS) has great potential to increase genetic gain in poultry breeding. However, the performance of genomic prediction in duck growth and breast morphological (BM) traits remains largely unknown. The objective of this study was to evaluate the benefits of genomic prediction for duck growth and BM traits using methods such as GBLUP, single-step GBLUP, Bayesian models, and different marker densities. This study collected phenotypic data for 14 growth and BM traits in a crossbreed population of 1893 Pekin duck × mallard, which included 941 genotyped ducks. The estimation of genetic parameters indicated high heritabilities for body weight (0.54-0.72), whereas moderate-to-high heritabilities for average daily gain (0.21-0.57) traits. The heritabilities of BM traits ranged from low to moderate (0.18-0.39). The prediction ability of GS on growth and BM traits increased by 7.6% on average compared to the pedigree-based BLUP method. The single-step GBLUP outperformed GBLUP in most traits with an average of 0.3% higher reliability in our study. Most of the Bayesian models had better performance on predictive reliability, except for BayesR. BayesN emerged as the top-performing model for genomic prediction of both growth and BM traits, exhibiting an average increase in reliability of 3.0% compared to GBLUP. The permutation studies revealed that 50 K markers had achieved ideal prediction reliability, while 3 K markers still achieved 90.8% predictive capability would further reduce the cost for duck growth and BM traits. This study provides promising evidence for the application of GS in improving duck growth and BM traits. Our findings offer some useful strategies for optimizing the predictive ability of GS in growth and BM traits and provide theoretical foundations for designing a low-density panel in ducks.

With the discovery of a major effect region (GREB1L, ROCK1) for adult migration timing in genomes of both Chinook Salmon and Steelhead, several subsequent studies have investigated the effect size and distribution of early and late migration alleles among populations in the Columbia River. Here, we synthesize the results of these studies for the major lineages of Chinook Salmon and Steelhead that include highly distinct groups in the interior Columbia River that exhibit atypical life histories from most coastal lineage populations of these two species. Whole‐genome studies with high marker density have provided extensive insight into SNPs most associated with adult migration timing, and suites of markers for each species have been genotyped in large numbers of individuals to further validate phenotypic effects. For Steelhead, the largest phenotypic effect sizes have been observed in the coastal lineage (36% of variation for passage timing at Bonneville Dam; 43% of variation for tributary arrival timing) compared to the inland lineage (7.5% of variation for passage timing at Bonneville Dam; 8.4% of variation for tributary arrival timing) that overwinter in freshwater prior to spawning. For Chinook Salmon, large effect sizes have been observed in all three lineages for multiple adult migration phenotypes (Coastal lineage: percentage of variation of 27.9% for passage timing at Bonneville Dam, 28.7% for arrival timing for spawning; Interior ocean type: percentage of variation of 47.6% for passage timing at Bonneville Dam, 39.6% for tributary arrival timing, 77.9% for arrival timing for spawning; Interior stream type: percentage of variation of 35.3% for passage at Bonneville Dam, 9.8% for tributary arrival timing, 4.7% for arrival timing for spawning). Together, these results have extended our understanding of genetic variation associated with life history diversity in distinct populations of the Columbia River, however, much research remains necessary to determine the causal mechanism for this major effect region on migration timing in these species.

The genetic composition of founding populations is likely to play a key role in determining invasion success. Individual genotypes may differ in habitat preference and environmental tolerance, so their ability to colonize novel environments can be highly variable. Despite the importance of genetic variation on invasion success, its influence on the potential distribution of invaders is rarely investigated. Here, we integrate population genomics and ecological niche models (ENMs) into a single framework to predict the distribution of globally invasive common ragweed (Ambrosia artemisiifolia) in Australia. We identified three genetic clusters for ragweed and used these to construct cluster-specific ENMs and characterize within-species niche differentiation. The potential range of ragweed in Australia depended on the genetic composition and continent of origin of the introduced population. Invaders originating from warmer, wetter climates had a broader potential distribution than those from cooler, drier ones. By quantifying this change, we identified source populations most likely to expand the ragweed distribution. As prevention remains the most effective method of invasive species management, our work provides a valuable way of ranking the threat posed by different populations to better inform management decisions.

Age‐at‐maturity and iteroparity are two life history variations of steelhead trout (Oncorhynchus mykiss) that are believed to increase population resilience and stability. While repeat‐spawning individuals are thought to have historically made up a substantial portion of the reproductive population in the Columbia River and the majority of females still attempt outmigration as kelts, return rates of repeat‐spawner are low throughout the basin and below 1% for the furthest migrating stocks. Notably, outmigrating adults exhibit variation in rematuration phenology, displaying either “consecutive” (reproduce immediately the following season) or “skip” (delay spawning for future seasons) spawning patterns. Here, we use low coverage whole genome sequencing of consecutive versus skip spawning female Columbia River steelhead from two populations to test for genomic differences between these two iteroparous phenotypes. We identified genomic regions on several chromosomes which were associated with the phenology of iteroparity, including a region on chromosome 25 containing two genes, estradiol receptor beta (ERβ) and glycoprotein hormone beta‐5 (GPHB5), which, in mammals, are estrogen‐sensitive and expressed in reproductive tissues. Allele frequencies in this ERβ/GPHB5 region differed among female steelhead of different age at maturity, but not males. These genes also shared an island of linkage disequilibrium with the SIX6 gene, 600Kbp away on the same chromosome, a region of known association with age‐at‐maturity. These observations contribute to growing evidence that age‐at‐maturity and the phenology of iteroparity are determined by overlapping physiological processes and genetic pathways.