Evolutionary Applications最新文献

Large epidemiological impacts resulting from disease vector control interventions are typically associated with significant disruption of vector populations. While vector density is a frequently measured response, impacts on demography and connectivity are suspected but rarely quantified. We analysed low-coverage whole-genome sequence data of 893 Anopheles gambiae mosquitoes collected between 2014 and 2015 during a cluster-randomized control trial (cRCT) in Burkina Faso to compare a pyrethroid-only net (ITN) with a pyrethroid-pyriproxyfen (ITN-PPF) net. Despite reductions of clinical malaria by 12% and vector density by 22% in the ITN-PPF arm, we found no significant changes in An. gambiae population genetic structure or diversity. We found remarkably low population differentiation and a lack of discernible clustering by treatment, time, or space. Nucleotide diversity and inbreeding coefficient remained stable between treatments, and genome-wide scans showed no putative signatures of selection between trial arms. These results show that ITN-PPF did not alter An. gambiae genetic structure, possibly due to large, vagile populations in West Africa. More widely, this is the first evidence that epidemiologically meaningful reductions in vector density may not impact genetic diversity or connectivity and challenges what constitutes adequate vector control in large populations.

Phytoplankton respond to their environment through genetic adaptation and plasticity to maintain fitness. This poses challenges when growing microalgae for industrial applications because, inherently, outdoor mass cultivation may lead to adaptations that alter desirable phenotypic traits and environmental niches. Here, we used common garden experiments to quantify the plasticity and adaptive responses to seasonal and geographic climate variation of Nannochloropsis, a microalga commonly used in biotechnology. An initially monoclonal strain was grown outdoors across four locations in Hawaii, Texas, California, and New Mexico. Following 17 and 22 months of cultivation outdoors, we collected samples during winter and summer, respectively, and we compared strains' growth from the four sites across temperature and light gradients in the laboratory. Despite hundreds of generations of exposure to divergent climates, with ~20°C and three-fold differences in daily light intensity, strains showed only minor differences in performance. Thermal performance varied more among seasons than sites, whereas light performance varied with both season and site. Our study indicates that Nannochloropsis exhibits broad plasticity in response to light and temperature, which may inhibit genetic adaptation in space or time. Highly variable field conditions, with daily and seasonal climate fluctuations, may favor plasticity and prevent the rapid adaptation often seen in laboratory studies of microorganisms in constant environments.

As global temperatures rise and become more variable, the capacity of domestic species to adapt, while maintaining production efficiency, is becoming a pressing concern. In this context, genotype-by-environment (GxE) interactions pose a significant challenge for selective breeding, as traits that perform well in one environment may not in another. These interactions complicate the design of breeding programmes that aim to ensure long-term resilience while optimising short-term productivity. Genomic Offsets—a metric that can quantify the mismatch between current and future genotype–environment associations, predicting potential genetic mismatch to environmental change—may offer a promising solution. In this perspective piece, we explore potential applications of genomic offsets in agriculture and aquaculture, including their use as tools for risk assessment, selective breeding and cryopreservation. We discuss how genomic offsets can overcome hurdles posed by GxE interactions, addressing practical considerations such as data requirements and methodological frameworks, and needed validation efforts. By predicting genetic mismatches and guiding the selection of individuals best suited for changing environmental conditions, our proposed Adaptive Breeding Framework may help breeders proactively enhance the resilience of farmed populations.

The brown planthopper (BPH, Nilaparvata lugens) is a major rice pest in Asia, causing significant yield losses. As BPH cannot overwinter in temperate regions, East Asian populations are wind-borne migratory insects originating from tropical regions. The identification of precise migratory patterns is essential for forecasting BPH outbreaks and implementing effective pest management strategies. Despite extensive studies using meteorological data, field population observations, and whole-genome analyses, the BPH migratory pathways to East Asia remain unclear. To address this question, we conducted population genomics analyses using 454 BPH individuals densely collected from China, Korea, and Vietnam between 2017 and 2022. We showed that BPH migration into East Asia exhibits substantial annual variation and involves genomically distinct overwintering origins. Principal component analysis revealed two major groups with whole-genome differentiation. This separation was confirmed by statistically significant F_ST estimates, suggesting migration pathways involving at least two overwintering populations. Ancestry coefficient analysis further confirmed the complexity of the ancestry of East Asian BPH. These results demonstrate the complex migratory dynamics of East Asian BPH populations, possibly with the influence of differential selective pressures among overwintering origins. Given the heterogeneity of migratory pathways to East Asia, we argue for temporally and geographically dense field monitoring with the incorporation of genetic information to enhance early warning and BPH management strategies.

Intraspecific genetic diversity across a species' geographic range is relevant to adaptive potential and long-term population persistence, and identifying genetically distinct groups within species can direct management decisions focused on conserving species-level genetic diversity. Comparative phylogeography using whole genome techniques allows for investigation of whether co-distributed species exhibit shared spatial genetic differentiation at fine spatial scales, thereby facilitating a comparative approach to both landscape and conservation genetics. By sequencing over 900 low-coverage whole genomes, we evaluated the concordance of genetic structure and diversity from 12 co-occurring species of migratory birds whose breeding ranges span adjacent North American ecogeographic regions: the vast boreal forest belt and the temperate montane Appalachian forests. We detected concordant phylogeographic patterns in 11 of 12 species wherein populations from the southern Appalachians were genetically distinct from boreal belt populations. Our results reveal that small populations persisting in the southern Appalachian Mountains consistently harbor genetic diversity that is subtly distinct from much larger, widespread boreal populations of the same species. However, in most species, levels of standing genetic diversity were not significantly different between Appalachian and boreal populations despite the drastic difference in geographic extent of these populations. We found no evidence for shared signatures of selection across the genome, suggesting that the concordance of spatial genetic structure across species emerges from species-specific patterns of molecular divergence across the genome rather than parallel patterns of selection. Conservation of the Appalachian ecosystem would likely support maintenance of distinct genetic diversity in several migratory avian species with widespread distributions.

The western chimpanzee (Pan troglodytes verus) is a Critically Endangered taxon. In Guinea-Bissau (GB), the subspecies is increasingly threatened, but there is a lack of understanding regarding the degree of genetic threat faced by populations. This hinders the development of targeted conservation strategies and the prioritization of efforts by national agencies. In this study, we use microsatellite data from four parks located in southern GB and five whole-genome sequences to estimate the effective population size (N_e) and infer the recent and ancient demographic history of populations using different methods. We also aim to integrate the different N_e estimates to improve our understanding of the evolutionary history and current demography of this great ape and to discuss the strengths and limitations of each estimator and their complementarity in informing conservation decisions. Results from the PSMC method suggest a large ancestral N_e, likely due to ancient structure over the whole subspecies distribution until approximately 10,000–15,000 years ago. After that, a change in connectivity, a real decrease in size, or a combination of both occurred, which reduced the then still large ancestral population to a smaller size (MSVAR: ~10,000 decreasing to 1,000–6,000 breeding individuals), possibly indicating a fragmentation into coastal and inland subpopulations. In the most recent past, contemporary N_e is close to 500 (GONE: 395–583, NeEstimator: 107–549), suggesting a high risk of extinction. The populations located at the coastal parks may have been small or isolated for several generations and are at higher risk, whereas the ones located inland exhibit higher long-term N_e and can be considered a stronghold for chimpanzee conservation. Through combining different types of molecular markers and analytical methodologies, we tried to overcome the limitations of obtaining high-quality DNA samples from wild threatened populations and estimated N_e at different temporal and spatial scales, which is crucial information to make informed conservation decisions at local and regional scales.

Hybridization, the interbreeding of distinct genotypes, drives evolutionary processes like speciation and adaptation, potentially via phenotypic transgression, where hybrids exhibit novel traits. In crop breeding, research has largely focused on optimizing heterosis to enhance hybrid performance, particularly for traits such as biomass. It is only recently that the ecological implications of hybridization have been considered, highlighting hybridization as a biotic interaction occurring within populations and communities. This shift raises fundamental questions about whether hybrid performance shows consistent patterns across individual and population scales, particularly regarding predictions based on parental genetic distance. Here, we address this question by examining Arabidopsis thaliana F2 hybrids across a wide range of genetic distances, to compare hybrid performance at individual and stand levels. Our results reveal scale-dependent patterns: individual performance peaks at intermediate parental genetic distances, while stand-level performance increases with genetic divergence, particularly in hybrids between relict and non-relict lineages. These results underscore the importance of scale when evaluating hybrid performance, as plant–plant interactions at the group level can alter the collective outcomes of individual performance. Finally, this framework underscores the importance of integrating individual and population perspectives to better understand the outcomes and potential applications of hybridization.

Advances in gene-editing technologies offer opportunities to improve disease management in aquaculture. Gene-editing applications for farmed Atlantic salmon (Salmo salar) include harnessing innate parasite resistance to protect against salmon lice (Lepeophtheirus salmonis). The potential for salmon lice to counter-adapt to changes in the host should be considered. However, salmon farms are highly connected through louse transmission, and so it is important to gauge the impact of new technologies over large scales. Exploring the epidemiology and evolution of lice across a farm network is possible using metapopulation models. Here, we expand upon an eco-evolutionary model to simulate the stocking of theoretical gene-edited Atlantic salmon that rejected lice to a similar degree as the more resistant coho salmon (Oncorhynchus kisutch). Model outputs suggested that such louse resistance would be highly effective at controlling outbreaks and reducing the need for additional delousing treatments. Lice were controlled more efficiently when gene edits were prioritized at key farms in the louse dispersal network. In scenarios where gene edits selected for adaptive traits in the louse population, however, lice rapidly evolved counter-resistance, leading to a significant reduction in treatment efficacy. When highly connected farms were left as refugia (not stocked with edited salmon), the rate of adaptation was slowed, thus extending the effectiveness of gene edits through time. The refuge effect was further enhanced if there were fitness trade-offs to counter-resistance in lice. We note that the long-term benefits of the refugia approach—to individual farms and to the wider industry—must be balanced with the costs in the short term, especially for the refuge farms. Careful planning of how to distribute new technologies can maximize efficiency and help safeguard them against parasite evolution. Spatial eco-evolutionary models are powerful tools for scenario testing that assist with decision making.

Scots pine (Pinus sylvestris L.) is characterized by considerable intraspecific adaptive variability in response to environmental stress factors due to its wide geographical range. Adaptability is key for forestry, promising resilience against upcoming Europe's climate-driven droughts. We studied three provenances of pedigreed Scots pine seedlings from distinct upland and lowland habitats in the Czech Republic. A water deficit was induced in 2-year-old, potted seedlings in a greenhouse. Their physiological responses to drought were investigated at the beginning of growing season during the development of new shoots, and after subsequent summer rewatering. (1) We analyzed several physiological traits to assess their effectiveness in detecting treatment effects: steady-state quantum yield of PSII (QY Lss), maximum quantum yield of PSII (QY max), steady-state non-photochemical quenching (NPQ Lss), needle chlorophyll fluorescence ratio (SFR_R), and needle temperature normalized to ambient temperature (∆T), using a high-throughput phenotyping unit. The divergence in SFR_R, QY max, QY Lss, NPQ Lss, and ΔT suggests that drought stress significantly impacts photosynthetic efficiency and heat dissipation, with recovery occurring after rewatering. (2) We detected differences within and among provenances utilizing a single nucleotide polymorphism genotyping array and linear mixed models integrating estimated genomic relationships to investigate genetic variation in needle functional traits in time. Throughout the experiment, heritability (h² ) varied widely among traits—with QY max and QY Lss showing the greatest variability (from 0 to 0.37), NPQ Lss exhibiting a narrower range aside from two outlier peaks, and SFR_R and ∆T displaying lower variability and lower h² values (0–0.24). The photosynthesis-related traits (QY max, QY Lss) showed the highest genetic variation, underscoring their potential for early-age phenotyping and selection of drought-tolerant genotypes. These findings address practical problems in forest management, particularly in light of changing weather patterns and climate variability, and provide a foundation for advanced optically based, early-age phenotyping to enhance forest resilience.

Salmon lice (Lepeophtheirus salmonis) pose a major challenge to the sustainability of salmon aquaculture due to their capacity to rapidly evolve resistance to parasite control methods. As the effectiveness of chemical treatments has declined, the industry has increasingly relied on preventive strategies to limit initial infections. One such approach is depth-based farming, where fish are held deeper in the water column using submerged cages. These systems reduce exposure to lice, which typically concentrate near the surface. However, there is growing concern that such practices may inadvertently select for lice that are better adapted to deeper swimming, potentially enabling resistance to depth-based interventions. In this study, we investigated whether vertical swimming behaviour in salmon lice larvae is influenced by the depth at which their parents were collected. We sampled 122 adult female lice carrying egg strings from commercial salmon farms using either standard cages (0–20 m) or submerged cages (20–40 m). The first-generation larvae were reared under controlled conditions, and the vertical positioning of 11,291 copepodid larvae was tested in pressure columns simulating a depth of 10 m. Our results revealed a significant interaction between larval depth distribution and the cage type from which the parental lice were sourced (χ² = 278.85, df = 1, p < 0.001). Larvae from standard cages showed a greater tendency to ascend (35% vs. 23%) and were less likely to sink (19% vs. 27%) compared to larvae from submerged cages. These findings suggest that vertical swimming behaviour may be heritable, with submerged cages potentially selecting for deeper-dwelling lice over time. This study provides the first evidence that the depth preference of salmon lice larvae may be influenced by their parents' environment. Understanding this behavioural inheritance is crucial for evaluating the long-term sustainability of submerged cage systems and for developing lice management strategies that anticipate evolutionary responses.