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Environmental variation within a species' range can create contrasting selective pressures, leading to divergent selection and novel adaptations. The conservation value of populations inhabiting environmentally marginal areas remains in debate and is closely related to the adaptive potential in changing environments. Strong selection caused by stressful conditions may generate novel adaptations, conferring these populations distinct evolutionary potential and high conservation value under climate change. On the other hand, environmentally marginal populations may be genetically depauperate, with little potential for new adaptations to emerge. Here, we explored the use of ecological niche models (ENMs) linked with common garden experiments to predict and test for genetically determined phenotypic differentiation related to contrasting environmental conditions. To do so, we built an ENM for the alpine plant Silene ciliata in central Spain and conducted common garden experiments, assessing flowering phenology changes and differences in leaf cell resistance to extreme temperatures. The suitability patterns and response curves of the ENM led to the predictions that: (1) the environmentally marginal populations experiencing less snowpack and higher minimum temperatures would have delayed flowering to avoid risks of late‐spring frosts and (2) those with higher minimum temperatures and greater potential evapotranspiration would show enhanced cell resistance to high temperatures to deal with physiological stress related to desiccation and heat. The common garden experiments revealed the expected genetically based phenotypic differentiation in flowering phenology. In contrast, they did not show the expected differentiation for cell resistance, but these latter experiments had high variance and hence lower statistical power. The results highlight ENMs as useful tools to identify contrasting putative selective pressures across species ranges. Linking ENMs with common garden experiments provides a theoretically justified and practical way to study adaptive processes, including insights regarding the conservation value of populations inhabiting environmentally marginal areas under ongoing climate change. [ABSTRACT FROM AUTHOR]

Species distribution models are generic empirical techniques that have a number of applications. One of these applications is to determine which environmental conditions are most important for a species. The calculation of this variable importance depends on a number of assumptions, including that the observations that are used to estimate the models are independent of each other. Spatial autocorrelation, which is a common feature most environmental factors confounds this assumption. Besides, many species distribution models are trained using a number of explanatory variables that have different levels of spatial autocorrelation. In this study we quantified the effects of differences in spatial autocorrelation in explanatory variables and the type of species responses to environmental gradients on variable importance estimations in species distribution models. We simulated data for both environmental predictors and species, so that we were in control of the true contribution of every variable in the model and the importance that could be estimated after fitting the models. We found that spatial autocorrelation in the predictors inflated the variable importance estimates, but only when the response of species to the environmental gradients is linear. This inflation effect was larger when the environmental preferences of species coincided with the dominant environmental conditions in a study site. Additionally we find that unimodal responses to the predictors yield systematically a higher variable importance compared to linear responses. We conclude that the type of response to environmental conditions and the relative levels of spatial autocorrelation in the environmental variables cause most bias in relative variable importance estimations. In this way, this study helps to clarify in a systematic and controlled approach how to make proper inferences about variable importance in species distribution models. [ABSTRACT FROM AUTHOR]

In this paper, a novel fault localization method combining frequency response analysis (FRA) and evidential reasoning process is proposed. FRA test is conducted at first so that we can obtain corresponding responses of several monitoring nodes on the winding. Traditional FRA interpretation methods simply compares those response curves, while in our work, evidential reasoning method is adopted to present beliefs about the fault locations. Extracted features from monitoring sensors are mapped into deviation matrix by which possibility assignments of each fault location is computed. So we finally obtain fault distribution probabilities. According to test calculation results, proposed method can give fault probabilities of each section of transformer winding, which correctly indicate the fault position. [ABSTRACT FROM AUTHOR]

Understanding invasive species distributions and potential invasions often requires broad‐scale information on the environmental tolerances of the species. Further, resource managers are often faced with knowing these broad‐scale relationships as well as nuanced environmental factors related to their landscape that influence where an invasive species occurs and potentially could occur. Using invasive buffelgrass (Cenchrus ciliaris), we developed global models and local models for Saguaro National Park, Arizona, USA, based on location records and literature on physiological tolerances to environmental factors to investigate whether environmental relationships of a species at a global scale are also important at local scales. In addition to correlative models with five commonly used algorithms, we also developed a model using a priori user‐defined relationships between occurrence and environmental characteristics based on a literature review. All correlative models at both scales performed well based on statistical evaluations. The user‐defined curves closely matched those produced by the correlative models, indicating that the correlative models may be capturing mechanisms driving the distribution of buffelgrass. Given climate projections for the region, both global and local models indicate that conditions at Saguaro National Park may become more suitable for buffelgrass. Combining global and local data with correlative models and physiological information provided a holistic approach to forecasting invasive species distributions. [ABSTRACT FROM AUTHOR]

Aim We examined whether species occurrences are primarily limited by physiological tolerance in the abiotically more stressful end of climatic gradients (the asymmetric abiotic stress limitation (AASL) hypothesis) and the geographical predictions of this hypothesis: abiotic stress mainly determines upper-latitudinal and upper-altitudinal species range limits, and the importance of abiotic stress for these range limits increases the further northwards and upwards a species occurs. Location Europe and the Swiss Alps. Methods The AASL hypothesis predicts that species have skewed responses to climatic gradients, with a steep decline towards the more stressful conditions. Based on presence–absence data we examined the shape of plant species responses (measured as probability of occurrence) along three climatic gradients across latitudes in Europe (1577 species) and altitudes in the Swiss Alps (284 species) using Huisman–Olff–Fresco, generalized linear and generalized additive models. Results We found that almost half of the species from Europe and one-third from the Swiss Alps showed responses consistent with the predictions of the AASL hypothesis. Cold temperatures and a short growing season seemed to determine the upper-latitudinal and upper-altitudinal range limits of up to one-third of the species, while drought provided an important constraint at lower-latitudinal range limits for up to one-fifth of the species. We found a biome-dependent influence of abiotic stress and no clear support for abiotic stress as a stronger upper range-limit determinant for species with higher latitudinal and altitudinal distributions. However, the overall influence of climate as a range-limit determinant increased with latitude. Main conclusions Our results support the AASL hypothesis for almost half of the studied species, and suggest that temperature-related stress controls the upper-latitudinal and upper-altitudinal range limits of a large proportion of these species, while other factors including drought stress may be important at the lower range limits. [ABSTRACT FROM AUTHOR]

Predicted no-effect concentrations (PNECs), which represent the concentration of a substance below which an unacceptable effect most likely will not occur, are widely used for risk assessment and in environmental policy and regulation. They are typically based on single-species laboratory toxicity tests; often, a single test result for the most sensitive endpoints drives the derivation of a PNEC. In the present study, the authors provide a case study emphasizing the importance of determining the reliability of those most sensitive endpoints. Five 21-d Daphnia magna toxicity tests conducted using the same procedures by 2 laboratories gave 20% inhibitory concentration responses to a specific ionic composition of total dissolved solids that varied from 684 mg/L to more than 1510 mg/L. The concentration-response curve was shallow; thus, these differences could have been attributable to chance alone. The authors strongly recommend that the most sensitive endpoints that determine PNECs not be based on a single toxicity test result but rather on the geometric mean of at least 3 test results to adequately assess and bound test variability, especially when the concentration-response curve is shallow. Environ Toxicol Chem 2015;34:1088-1090. © 2015 SETAC [ABSTRACT FROM AUTHOR]