一种基于数码相机图像和群落冠层结构调查的草地地上生物量估算方法

草地地上生物量是影响其生态系统功能最重要的因素之一, 也是草地生态学研究中不可或缺的监测指标。草地地上生物量监测多采用收割法进行, 但这种破坏性取样方法会对研究区域带来巨大干扰, 尤其是面积较小的长期定位监测或者控制实验研究样地, 从而使得地上生物量监测的频次受到很大限制。因此, 通过获取某些原位易测变量, 建立地上生物量的估算方法具有重要意义。该研究依托内蒙古典型草地刈割控制实验平台, 通过数码照片获取不同土地利用方式下的植被覆盖度, 并对样方内的叶面积指数、植被高度、物种多样性等参数进行了测定, 最后利用一元回归模型、逐步回归模型和随机森林模型对地上生物量进行估算。结果表明, 植被覆盖度、叶面积指数、植被平均高度、植被最大高度和物种丰富度是影响地上生物量的主要驱动因素。通过构建适宜于本地的逐步回归模型, 可以实现草地地上生物量的准确预测。在该研究区域中, 预测模型的决定系数(R²) = 0.91, 均方根误差(RMSE) = 35.60 g·m^-2。该研究提供了一种快速、准确且非破坏性测定草地地上生物量的方法, 可作为传统收割法的有效补充。     

全程无缝隙护理模式在护理质量管理中的应用

目的 探讨全程无缝隙护理模式对肿瘤科护理工作质量的影响。方法 2013年1月-12月我院肿瘤科采用无缝隙护理模式的患者120例为观察组,2012年1月-12月采用常规护理的120例肿瘤科患者为对照组,对比两组患者的心理舒适度、患者对护理工作的满意度。结果 观察组患者的安全感、满足感、尊重感所占比例均高于对照组,差异均有统计学意义（P＜0.05）。观察组患者对护士的工作能力、关爱与沟通、服务热情主动、病区管理、健康教育满意度均高于对照组,差异均有统计学意义（P＜0.05）。结论 全程无缝隙护理模式应用于肿瘤科护理,能够有效改善患者不良情绪,提高护理工作质量及患者满意度。     

基于BP神经网络的流域生态恢复度计算——以福建长汀朱溪小流域为例

以福建省长汀县朱溪小流域为研究对象,通过野外调查、室内分析以及遥感影像提取相结合的方法获取数据。利用Matlab7.0软件建立BP神经网络生态恢复模型,定量评价退化生态系统的恢复程度。选择土壤理化性质(有机质、全N、全P、全K、容重和p H)、植被结构(植被盖度)、物种多样性指数(Shannon-Wiener指数)和热环境(地表温度)等4个方面的9个指标建立退化生态系统评价体系,并作为生态恢复模型的输入层数据,生态恢复度作为输出层数据。使用Matlab7.0进行数据预处理、样本训练、样本检验并建立生态恢复模型。利用建立的生态恢复模型对整个朱溪小流域生态恢复度进行定量评价。结果表明,生态恢复模型预测结果与流域生态恢复的实际情况基本吻合,利用BP神经网络模型定量评价退化生态系统的恢复程度具有可行性。朱溪小流域内生态恢复程度极低的区域面积仅占0.94%,95.48%区域为中等恢复程度,说明生态保护措施已初见成效;生态恢复程度高的区域面积仅占3.62%,意味着未来仍需加强治理和保护工作。     

L929细胞镉中毒模型的建立

以L929细胞为研究对象,建立镉中毒体外模型,研究镉中毒对成纤维细胞生长发育的影响.本研究使用含有终浓度为100、50、25和5μmol·L-1氯化镉的DMEM培养基培养L929细胞,培养后0、6、12、24、36、48 h时分别观察细胞形态学变化,并应用MTT(四甲基偶氮唑盐)比色法来测定细胞的活性,研究结果表明,与正常组比较,当镉浓度大于50 μmol·L-1时,细胞形态损伤严重,细胞活性明显下降(p＜0.01);当镉浓度为5 μmol·L-1时,细胞未出现明显损伤,细胞活性无统计学差异(p＞0.05);时间效应观察结果显示,当镉作用36 h以上,细胞出现严重损伤,较多细胞悬浮于细胞培养基中,细胞活性明显下降(p＜0.01).结果表明,镉的终浓度为25 μmol·L-1,与L929细胞共同作用24 h为建立镉中毒体外模型的最佳条件.     

Diet composition of two larval headwater stream salamanders and spatial distribution of prey

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Water use and yield of bioenergy poplar in future climates: modelling the interactive effects of elevated atmospheric CO2 and climate on productivity and water use

Vegetation exerts large control on global biogeochemical cycles through the processes of photosynthesis and transpiration that exchange CO₂ and water between the land and the atmosphere. Increasing atmospheric CO₂ concentrations exert direct effects on vegetation through enhanced photosynthesis and reduced stomatal conductance, and indirect effects through changes in climatic variables that drive these processes. How these direct and indirect CO₂ impacts interact with each other to affect plant productivity and water use has not been explicitly analysed and remains unclear, yet is important to fully understand the response of the global carbon cycle to future climate change. Here, we use a set of factorial modelling experiments to quantify the direct and indirect impacts of atmospheric CO₂ and their interaction on yield and water use in bioenergy short rotation coppice poplar, in addition to quantifying the impact of other environmental drivers such as soil type. We use the JULES land‐surface model forced with a ten‐member ensemble of projected climate change for 2100 with atmospheric CO₂ concentrations representative of the A1B emissions scenario. We show that the simulated response of plant productivity to future climate change was nonadditive in JULES, however this nonadditivity was not apparent for plant transpiration. The responses of both growth and transpiration under all experimental scenarios were highly variable between sites, highlighting the complexity of interactions between direct physiological CO₂ effects and indirect climate effects. As a result, no general pattern explaining the response of bioenergy poplar water use and yield to future climate change could be discerned across sites. This study suggests attempts to infer future climate change impacts on the land biosphere from studies that force with either the direct or indirect CO₂ effects in isolation from each other may lead to incorrect conclusions in terms of both the direction and magnitude of plant response to future climate change.     

The neglected tool in the Bayesian ecologist's shed: a case study testing informative priors' effect on model accuracy

Despite benefits for precision, ecologists rarely use informative priors. One reason that ecologists may prefer vague priors is the perception that informative priors reduce accuracy. To date, no ecological study has empirically evaluated data‐derived informative priors' effects on precision and accuracy. To determine the impacts of priors, we evaluated mortality models for tree species using data from a forest dynamics plot in Thailand. Half the models used vague priors, and the remaining half had informative priors. We found precision was greater when using informative priors, but effects on accuracy were more variable. In some cases, prior information improved accuracy, while in others, it was reduced. On average, models with informative priors were no more or less accurate than models without. Our analyses provide a detailed case study on the simultaneous effect of prior information on precision and accuracy and demonstrate that when priors are specified appropriately, they lead to greater precision without systematically reducing model accuracy.     

Are we underestimating the genetic variances of dimorphic traits?

Populations often contain discrete classes or morphs (e.g., sexual dimorphisms, wing dimorphisms, trophic dimorphisms) characterized by distinct patterns of trait expression. In quantitative genetic analyses, the different morphs can be considered as different environments within which traits are expressed. Genetic variances and covariances can then be estimated independently for each morph or in a combined analysis. In the latter case, morphs can be considered as separate environments in a bivariate analysis or entered as fixed effects in a univariate analysis. Although a common approach, we demonstrate that the latter produces downwardly biased estimates of additive genetic variance and heritability unless the quantitative genetic architecture of the traits concerned is perfectly correlated between the morphs. This result is derived for four widely used quantitative genetic variance partitioning methods. Given that theory predicts the evolution of genotype‐by‐environment (morph) interactions as a consequence of selection favoring different trait combinations in each morph, we argue that perfect correlations between the genetic architectures of the different morphs are unlikely. A sampling of the recent literature indicates that the majority of researchers studying traits expressed in different morphs recognize this and do estimate morph‐specific quantitative genetic architecture. However, ca. 16% of the studies in our sample utilized only univariate, fixed‐effects models. We caution against this approach and recommend that it be used only if supported by evidence that the genetic architectures of the different morphs do not differ.