Nature ecology & evolution最新文献

Evolutionary change requires genetic variation, and a reigning paradigm in biology is that rates of microevolution can be predicted from estimates of available genetic variation within populations. However, the accuracy of such predictions should decay on longer evolutionary timescales, as the influence of genetic constraints diminishes. Here we show that intrinsic developmental variability and standing genetic variation in wing shape in two distantly related flies, Drosophila melanogaster and Sepsis punctum, are aligned and predict deep divergence in the dipteran phylogeny, spanning >900 taxa and 185 million years. This alignment cannot be easily explained by constraint hypotheses unless most of the quantified standing genetic variation is associated with deleterious side effects and is effectively unusable for evolution. However, phenotyping of 71 genetic lines of S. punctum revealed no covariation between wing shape and fitness, lending no support to this hypothesis. We also find little evidence for genetic constraints on the pace of wing shape evolution along the dipteran phylogeny. Instead, correlational selection related to allometric scaling, simultaneously shaping developmental variability and deep divergence in fly wings, emerges as a potential explanation for the observed alignment. This suggests that pervasive natural selection has the potential to shape developmental architectures of some morphological characters such that their intrinsic variability predicts their long-term evolution.

Horseshoe bats are natural hosts of zoonotic viruses, yet the genetic basis of their antiviral immunity is poorly understood. Here we generated two new chromosomal-level genome assemblies for horseshoe bat species (Rhinolophus) and three close relatives, and show that, during their diversification, horseshoe bats underwent extensive chromosomal rearrangements and gene expansions linked to segmental duplications. These expansions have generated new adaptive variations in type I interferons and the interferon-stimulated gene ANXA2R, which potentially enhance antiviral states, as suggested by our functional assays. Genome-wide selection screens, including of candidate introgressed regions, uncover numerous putative molecular adaptations linked to immunity, including in viral receptors. By expanding taxon coverage to ten horseshoe bat species, we identify new variants of the SARS-CoV-2 receptor ACE2, and report convergent functionally important residues that could explain wider patterns of susceptibility across mammals. We conclude that horseshoe bats have numerous signatures of adaptation, including some potentially related to immune response to viruses, in genomic regions with diverse and multiscale mutational changes.

European countries have committed to ambitious upscaling of privately funded nature conservation. We review the status and drivers of biodiversity finance in Europe. By implementing semistructured interviews with 25 biodiversity finance key informants and three focus groups across Europe, we explore opportunities and challenges for upscaling private investment in nature. Opportunities arise from macroeconomic and regulatory changes, along with various technological and financial innovations and growing professional experience. However, persistent barriers to upscaling include the ongoing lack of highly profitable investment opportunities and the multitude of risks facing investors, including political, ecological and reputational risks influencing supply and demand of investment opportunities. Public policy plays the foundational role in creating and hindering these mechanisms. Public policy can create nature markets and investment opportunities, meanwhile agricultural subsidies and poor coordination between public funding sources undermine the supply of return-seeking investment opportunities. Investors demand derisking investments from uncertainties; in part caused by political uncertainty. These markets require profound state intervention to enable upscaling whilst achieving positive ecological outcomes; private investment will probably not upscale without major public policy change and public investment.

Correction to: Nature Ecology & Evolution https://doi.org/10.1038/s41559-024-02338-3, published online 15 February 2024.

The emergence of generative artificial intelligence (AI) models specializing in the generation of new data with the statistical patterns and properties of the data upon which the models were trained has profoundly influenced a range of academic disciplines, industry and public discourse. Combined with the vast amounts of diverse data now available to ecologists, from genetic sequences to remotely sensed animal tracks, generative AI presents enormous potential applications within ecology. Here we draw upon a range of fields to discuss unique potential applications in which generative AI could accelerate the field of ecology, including augmenting data-scarce datasets, extending observations of ecological patterns and increasing the accessibility of ecological data. We also highlight key challenges, risks and considerations when using generative AI within ecology, such as privacy risks, model biases and environmental effects. Ultimately, the future of generative AI in ecology lies in the development of robust interdisciplinary collaborations between ecologists and computer scientists. Such partnerships will be important for embedding ecological knowledge within AI, leading to more ecologically meaningful and relevant models. This will be critical for leveraging the power of generative AI to drive ecological insights into species across the globe.

Sex chromosomes are a fundamental aspect of sex-biased biology, but the extent to which homologous X–Y gene pairs (‘the gametologs’) contribute to sex-biased phenotypes remains hotly debated. Although these genes tend to exhibit large sex differences in expression throughout the body (XX females can express both X members, and XY males can express one X and one Y member), there is conflicting evidence regarding the degree of functional divergence between the X and Y members. Here we develop and apply co-expression fingerprint analysis to characterize functional divergence between the X and Y members of 17 gametolog gene pairs across >40 human tissues. Gametolog pairs exhibit functional divergence between the sexes that is driven by divergence between the X versus Y members (assayed in males), and this within-pair divergence is greatest among pairs with evolutionarily distant X and Y members. These patterns reflect that X versus Y gametologs show coordinated patterns of asymmetric coupling with large sets of autosomal genes, which are enriched for functional pathways and gene sets implicated in sex-biased biology and disease. Our findings suggest that the X versus Y gametologs have diverged in function and prioritize specific gametolog pairs for future targeted experimental studies.