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Species of the mustelid subfamily Guloninae inhabit diverse habitats on multiple continents, and occupy a variety of ecological niches. They differ in feeding ecologies, reproductive strategies and morphological adaptations. To identify candidate loci associated with adaptations to their respective environments, we generated a de novo assembly of the tayra (Eira barbara), the earliest diverging species in the subfamily, and compared this with the genomes available for the wolverine (Gulo gulo) and the sable (Martes zibellina). Our comparative genomic analyses included searching for signs of positive selection, examining changes in gene family sizes and searching for species-specific structural variants. Among candidate loci associated with phenotypic traits, we observed many related to diet, body condition and reproduction. For example, for the tayra, which has an atypical gulonine reproductive strategy of aseasonal breeding, we observed species-specific changes in many pregnancy-related genes. For the wolverine, a circumpolar hypercarnivore that must cope with seasonal food scarcity, we observed many changes in genes associated with diet and body condition. All types of genomic variation examined (single nucleotide polymorphisms, gene family expansions, structural variants) contributed substantially to the identification of candidate loci. This argues strongly for consideration of variation other than single nucleotide polymorphisms in comparative genomics studies aiming to identify loci of adaptive significance.
The study of perceptual flexibility in speech depends on a variety of tasks that feature a large degree of variability between participants. Of critical interest is whether measures are consistent within an individual or across stimulus contexts. This is particularly key for individual difference designs that are deployed to examine the neural basis or clinical consequences of perceptual flexibility. In the present set of experiments, we assess the split-half reliability and construct validity of five measures of perceptual flexibility: three of learning in a native language context (e.g., understanding someone with a foreign accent) and two of learning in a non-native context (e.g., learning to categorize non-native speech sounds). We find that most of these tasks show an appreciable level of split-half reliability, although construct validity was sometimes weak. This provides good evidence for reliability for these tasks, while highlighting possible upper limits on expected effect sizes involving each measure.
In-depth understanding of the potential implications of climate change is required to guide decision- and policy-makers when developing adaptation strategies and designing infrastructure suitable for future conditions. Impact models that translate potential future climate conditions into variables of interest are needed to create the causal connection between a changing climate and its impact for different sectors. Recent surveys suggest that the primary strategy for validating such models (and hence for justifying their use) heavily relies on assessing the accuracy of model simulations by comparing them against historical observations. We argue that such a comparison is necessary and valuable, but not sufficient to achieve a comprehensive evaluation of climate change impact models. We believe that a complementary, largely observation-independent, step of model evaluation is needed to ensure more transparency of model behavior and greater robustness of scenario-based analyses. This step should address the following four questions: (1) Do modeled dominant process controls match our system perception? (2) Is my model's sensitivity to changing forcing as expected? (3) Do modeled decision levers show adequate influence? (4) Can we attribute uncertainty sources throughout the projection horizon? We believe that global sensitivity analysis, with its ability to investigate a model's response to joint variations of multiple inputs in a structured way, offers a coherent approach to address all four questions comprehensively. Such additional model evaluation would strengthen stakeholder confidence in model projections and, therefore, into the adaptation strategies derived with the help of impact models. This article is categorized under: Climate Models and Modeling > Knowledge Generation with Models Assessing Impacts of Climate Change > Evaluating Future Impacts of Climate Change