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961.
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Alexandre Roulin 《Die Naturwissenschaften》2009,96(3):375-382
Identifying the factors that mediate covariation between an ornament and other phenotypic attributes is important to determine
the signaling function of ornaments. Sign and magnitude of a covariation may vary across environments if the expression of
the ornament or of its linked genes regulating correlated phenotypes is condition-dependent. I investigated in the barn owl
Tyto alba whether sign and magnitude of covariation between body mass and two heritable melanin-based plumage ornaments change with
food supply, along the reproductive cycle and from the morning to the evening. Using a dataset of 1,848 measurements of body
mass in 336 breeding females, I found that females displaying large black spots were heavier than conspecifics with smaller
spots in the afternoon (i.e., a long time after the last feeding) but not in the morning (i.e., a short time after the last
feeding). This is consistent with the recently proposed hypothesis that eumelanin-based ornaments are associated with the
ability to maintain energy balance between food intake and energy expenditure. Thus, covariation between melanin-based coloration
and body mass can be detected only under specific conditions potentially explaining why it has been reported in only ten out
of 28 vertebrate species. The proposition that ornamented individuals achieve a higher fitness than drab conspecifics only
in specific environments should be tested for other ornaments. 相似文献
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随着经济的发展,建设项目选址不合理导致的大气环境问题日益凸显。文章分析了建设项目选址的大气环境适宜性问题,提出了大气环境适宜性的计算方法,并以济南市某建设项目为例进行了具体应用。该研究可用于修正历史上形成的项目不合理选址,也可用于引导新建项目合理选址,利于大气环境持续利用和经济持续发展。 相似文献
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以"村容整洁"为核心的村庄绿化是新农村生态建设重要内容。绿化树种选择应优先选择乡土树种,兼顾经济、环境效益与景观的统一。 相似文献
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Many different models can be built to explain the distributions of species. Often there is no single model that is clearly better than the alternatives, and this leads to uncertainty over which environmental factors are limiting species’ distributions. We investigated the support for different environmental factors by determining the drop in model performance when selected predictors were excluded from the model building process. We used a paired t-test over 37 plant species so that an environmental factor was only deemed significant if it consistently improved the results for multiple species. Geology and winter minimum temperatures were found to be the environmental factors with the most support, with a significant drop in model performance when either of these factors was excluded. However, there was less support for summer maximum temperature, as other environmental factors could combine to produce similar model performance. Our method of evaluating environmental factors using multiple species will not be capable of detecting predictors that are only important for one or two species, but it is difficult to distinguish these from spurious correlations. The strength of the method is that it increases inference for factors that consistently affect the distributions of many species. We discourage the assessment of models against predefined benchmarks, such as an area under the curve (AUC) of more than 0.7, as many alternative models for the same species produce similar results. Therefore, the benchmarks do not provide any indication of how the performance of the selected model compares to alternative models, and they provide weak inference to accept any selected model. 相似文献
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