Genetic constraints on in-situ reflectance spectral variation in bermudagrass populations across Hainan Island

Island populations provide an ideal natural experiment with which to study the forces driving population evolution. Seventeen populations of Dacrydium pectinatum de Laubenfels on Hainan Island, China, were sampled throughout its distribution range and then assessed using inter-simple sequence repeats (ISSR) markers. Population genetic parameters were estimated by Bayesian approaches as well as conventional methods. Genetic bottleneck signatures were further dissected by performing three heterozygosity excess tests and the mode-shift indicator test. Compared with other coniferous species, a relatively high level of genetic variation and a low degree of differentiation was revealed in D. pectinatum. In addition, severe bottlenecks were identified at local, regional as well as range-wide scale. Ecological and life-history traits were suggested to play major roles in the shaping of the genetic variation pattern. In particular, long life span could have exerted a lagging effect on both the genetic variation and differentiation of extant populations. Our findings may contribute to improving management practices for the restoration of D. pectinatum.

Cognitive deficits are common across many neurodevelopmental and psychiatric conditions, including those studied in the current set of PGC-CNV papers. How changes in regional gene expression across the cerebral cortex influence cognitive ability remains unknown. Population variation in gene dosage—which significantly impacts gene expression—represents a unique paradigm to address this question. We developed a cerebral-cortex gene-set burden analysis (CC-GSBA) to associate a trait with genomic deletions and duplications that disrupt genes with similar expression profiles across 180 cortical regions. We performed CC-GSBA across 180 cortical regions to test associations with cognitive ability in 260,000 individuals from general population cohorts. Most cortical gene sets were associated with a decrease in cognitive ability when deleted or duplicated, and this novel approach revealed opposing cortical patterns for the effect sizes of deletions and duplications. These cortical patterns of effect sizes followed the cortical gradient previously characterized at the molecular, cellular, and functional levels. We show that genes with preferential expression in sensorimotor regions demonstrated the largest effect on cognition when deleted. At the opposing end of the cortical gradient, genes with preferential expression in multimodal association regions affected cognition the most when duplicated. These two gene dosage cortical patterns could not be explained by particular cell types, developmental epochs, or genetic constraints, highlighting the fact that the macroscopic network organization of the cerebral cortex is key to understanding the effects of gene dosage on cognitive traits.

Populations subject to severe stress may be rescued by natural selection, but its operation is restricted by ecological and genetic constraints. The cost of natural selection expresses the limited capacity of a population to sustain the load of mortality or sterility required for effective selection. Genostasis expresses the lack of variation that prevents many populations from adapting to stress. While the role of relative fitness in adaptation is well understood, evolutionary rescue emphasizes the need to recognize explicitly the importance of absolute fitness. Permanent adaptation requires a range of genetic variation in absolute fitness that is broad enough to provide a few extreme types capable of sustained growth under a stress that would cause extinction if they were not present. This principle implies that population size is an important determinant of rescue. The overall number of individuals exposed to selection will be greater when the population declines gradually under a constant stress, or is progressively challenged by gradually increasing stress. In gradually deteriorating environments, survival at lethal stress may be procured by prior adaptation to sublethal stress through genetic correlation. Neither the standing genetic variation of small populations nor the mutation supply of large populations, however, may be sufficient to provide evolutionary rescue for most populations.

Ecological epigenetics is gaining importance within the field of Molecular Ecology, because of its novel evolutionary implications. Linking population ecology to the variation in epigenetic profiles can help explain the effect of environmental conditions on phenotypic differences among populations. While epigenetic changes have largely been investigated through the examination of DNA methylation under laboratory conditions, there is a limited understanding of the extent of DNA methylation variation in wild populations. Assuming that epigenetic variation is important in nature, the conditions experienced by different conspecific populations should result in levels of DNA methylation that are independent of their genetic differentiation. To test this, we investigated levels of DNA methylation among populations of the sandhopper Talorchestia capensis that show phenotypic (physiological) differences in their response to environmental conditions, at the same time evaluating their genetic relationships. Given the high levels of inter-individual physiological variation observed within populations, we further hypothesised that inter-individual differences in methylation would be high. Levels of genetic and epigenetic variation were assessed within and among populations from five localities using the methylation sensitive amplified polymorphism technique. Population differentiation was higher for epigenetics than genetics, with no clear geographical pattern or any relation to biogeography. Likewise, individuals showed greater variability in their epigenetic than their genetic profiles. Four out of five populations showed significant negative relationships between epigenetic and genetic diversity. These results show uncoupling between epigenetic and genetic variation and suggest that: (1) epigenetics are more responsive to local, site-specific environmental conditions than genetics and (2) individual differences in epigenetic profiles drive phenotypic variation within (and most likely among) natural populations. Within populations, epigenetics could offer a level of phenotypic flexibility beyond genetic constraint that allows rapid responses to variable or unpredictable environments, potentially compensating for low genetic variability.

The oriental fruit fly, Bactrocera dorsalis, is a serious pest of fruits and vegetables in South‐east Asia, and, because of quarantine restrictions, impedes international trade and economic development in the region. Revealing genetic variation in oriental fruit fly populations will provide a better understanding of the colonization process and facilitate the quarantine and management of this species. The genetic structure in 15 populations of oriental fruit fly from southern China, Laos and Myanmar in South‐east Asia was examined with a 640‐bp sequence of the mitochondrial cytochrome oxidase subunit I (COI) gene. The highest levels of genetic diversity were found in Laos and Myanmar. Low to medium levels of genetic differentiation (FST ≤ 0.134) were observed among populations. Pooled populations from mainland China differed from those in Laos and Myanmar (FST = 0.024). Genetic structure across the region did not follow the isolation‐by‐distance model. The high genetic diversity observed in Laos and Myanmar supports the South‐east Asian origin of B. dorsalis. High genetic diversity and significant differentiation between some populations within mainland China indicate B. dorsalis populations have been established in the region for an extended period of time. High levels of genetic diversity observed among the five populations from Hainan Island and similarity between the Island and Chinese mainland populations indicate that B. dorsalis was introduced to Hainan from the mainland and has been on the island for many years. High genetic diversity in the recently established population in Shanghai (Pudong) suggests multiple introductions or a larger number of founders.

To conserve and manage wetland resources, it is important to map wetlands and monitor their changes. However, wetland mapping is difficult because of spectral confusion with other landcover classes and spectral variability among different types of wetlands. This paper demonstrated a multi-source based approach for improving the accuracy of wetland mapping. According to the formation of wetland, variables derived from multi-source image date, and other ancillary GIS layers were first integrated to create the possible region for wetland (PRW). And then classification was conducted within the PRW to map wetlands. PRW could greatly reduce the amount of other land cover classes that participated in the classification, therefore reducing both the difficulty of wetland mapping and the commission error rate. In this study, we chose the test area in Dongzhai Harbor, Hainan Island, China, where the main wetland type is mangrove. The mapping was based on LANDSAT TM/ETM+ imagery combined with DEM data, and the ancillary GIS data included available water, soil and vegetation layers. The thematic accuracy of the mapping was assessed using high- resolution images from Google Earth and local wetlands databases. Classification of the 2001 Landsat ETM+ scene alone resulted in consumer's accuracy of 65% and Kappa coefficients of 0.69, whereas the multi-source based approach with the same training samples resulted in an accuracy of 86% and 0.80. The developed method is portable, relatively easy to implement, and should be applicable in other landcover classes and over larger extents.

Geographical gradients in selection can shape different genetic architectures in natural populations, reflecting potential genetic constraints for adaptive evolution under climate change. Investigation of natural pH/pCO2 variation in upwelling regions reveals different spatio-temporal patterns of natural selection, generating genetic and phenotypic clines in populations, and potentially leading to local adaptation, relevant to understanding effects of ocean acidification (OA). Strong directional selection, associated with intense and continuous upwellings, may have depleted genetic variation in populations within these upwelling regions, favouring increased tolerances to low pH but with an associated cost in other traits. In contrast, diversifying or weak directional selection in populations with seasonal upwellings or outside major upwelling regions may have resulted in higher genetic variances and the lack of genetic correlations among traits. Testing this hypothesis in geographical regions with similar environmental conditions to those predicted under climate change will build insights into how selection may act in the future and how populations may respond to stressors such as OA.

The additive genetic variance-covariance matrix (G) summarizes the multivariate genetic relationships among a set of traits. The geometry of G describes the distribution of multivariate genetic variance, and generates genetic constraints that bias the direction of evolution. Determining if and how the multivariate genetic variance evolves has been limited by a number of analytical challenges in comparing G-matrices. Current methods for the comparison of G typically share several drawbacks: metrics that lack a direct relationship to evolutionary theory, the inability to be applied in conjunction with complex experimental designs, difficulties with determining statistical confidence in inferred differences and an inherently pair-wise focus. Here, we present a cohesive and general analytical framework for the comparative analysis of G that addresses these issues, and that incorporates and extends current methods with a strong geometrical basis. We describe the application of random skewers, common subspace analysis, the 4th-order genetic covariance tensor and the decomposition of the multivariate breeders equation, all within a Bayesian framework. We illustrate these methods using data from an artificial selection experiment on eight traits in Drosophila serrata, where a multi-generational pedigree was available to estimate G in each of six populations. One method, the tensor, elegantly captures all of the variation in genetic variance among populations, and allows the identification of the trait combinations that differ most in genetic variance. The tensor approach is likely to be the most generally applicable method to the comparison of G-matrices from any sampling or experimental design.

A classic example of phenotypic plasticity in plants is the set of traits that change in response to shade. There is widespread evidence that plants in low light conditions often avoid shade by growing taller or by increasing their photosynthetic efficiency, i.e. the shade avoidance syndrome. Whether this plasticity might evolve in response to natural selection depends upon the presence of its standing genetic variation in wild populations. There is limited evidence for heritable standing variation in the plastic response of plants to shade. In this study, we used an experimental common garden approach to investigate this plastic response in snapdragon plants (Antirrhinum majus L.) originating from four natural populations from the Mediterranean region. Our results showed that individual plants reacted strongly to the presence of shade by growing longer shoots, longer internodes, and increasing their specific leaf area in these four populations. Our results also revealed genetic variation for the plastic response within these populations, as well as little genetic constraints to its evolution. Our findings imply that natural populations of A. majus harbour standing genetic variation for phenotypic plasticity in response to shade, providing them the potential to evolve in response to selection.

Gut microbiome is considered as a critical role in host digestion and metabolic homeostasis. Nevertheless, the lack of knowledge concerning how the host-associated gut microbiome underpins the host metabolic capability and regulates digestive functions hinders the exploration of gut microbiome variation in diverse geographic population. In the present study, we selected the black Amur bream (Megalobrama terminalis) that inhabits southern China drainage with multiple geographic populations and relatively high digestive plasticity as a candidate to explore the potential effects of genetic variation and environmental discrepancy on fish gut microbiome. Here, high-throughput 16S rRNA gene sequencing was utilized to decipher the distinct composition and diversity of the entire gut microbiota in wild M. terminalis distributed throughout southern China. The results indicated that mainland (MY and XR) populations exhibited a higher alpha diversity than that of the Hainan Island (WS) population. Moreover, a clear taxon shift influenced by water temperature, salinity (SA), and gonadosomatic index (GSI) in the course of seasonal variation was observed in the gut bacterial community. Furthermore, geographic isolation and seasonal variation significantly impacted amino acid, lipid, and carbohydrate metabolism of the fish gut microbiome. Specifically, each geographic population that displayed its own unique regulation pattern of gut microbiome was recognized as a specific digestion strategy to enhance adaptive capability in the resident environment. Consequently, this discovery suggested that long-term geographic isolation leads to variant environmental factors and genotypes, which made a synergetic effect on the diversity of the gut microbiome in wild M. terminalis. In addition, the findings provide effective information for further exploring ecological fitness countermeasures in the fish population.

Island endemic species have a much higher risk of extinction than those of mainland. The phoenix damselfly (Pseudolestes mirabilis Kirby, 1900), the single representative of the family Pseudolestidae (Insecta: Odonata) which is endemic to Hainan Island, is facing a danger of extinction along with the acceleration of urbanization. To investigate population genetics and further evaluate the conservational importance, the mitochondrial gene COI of 126 individuals from 11 populations of the phoenix damselfly were sequenced and analyzed. This is the first comprehensive population ecological study for this species. The results recovered low genetic diversity and weak phylogeographic structure. Multiple lines of evidence including neutrality test, mismatch distribution analysis, phylogenetic topologies, and Bayesian skyline plot supported a population expansion just after the Last Glacial Maximum. Statistics of genetic diversity, gene flow, and potential habitats reconstruction suggested that the refugia constricted to the south central areas of the island. Meanwhile, the small population size and low genetic variation in some peripheral populations also implied a niche reduction. Increasing human activity and severe environment destruction may be the main reasons causing a recent decline of the population and increasing the risk of extinction. Therefore, urgent conservational efforts must be implemented to ensure the long-term survival of P. mirabilis. The present research has provided a way to prioritize and assess management strategies of this charismatic species.

Adaptive Phenotypic Plasticity: Target or By-Product of Selection in a Variable Environment?

Frequency shift or intensity shift? The origin of spectral changes in vibrational spectra

The use of near infrared reflectance spectroscopy (NIRS) on undried samples of grass silage to predict chemical composition and digestibility parameters

Electroencephalography (EEG) is crucial for diagnosing neurological disorders, but model generalization is often hampered by domain shifts arising from variations in hardware, patient populations, recording protocols, and annotation standards. We propose a domain adaptation model that uses EEG-specific properties, integrating the Mean Teacher Model (MTM), Maximum Mean Discrepancy (MMD), Topology Loss, Frequency Regularization, and Attractor Loss to align heterogeneous datasets.Experiments using CHB-MIT as the source and TUH Seizure / MDD datasets as targets demonstrate significant improvements in classification performance (F1 score) and domain alignment (Jaccard coefficient). The model successfully preserves critical neurophysiological markers, capturing 20Hz epilepsy-related spikes and parietal alpha suppression associated with depression. It also highlights spatial contributions from key channels like P8, constructing a latent space that effectively retains epileptic and depressive EEG features.