Forecasting China’s primary energy demand based on an improved AI model

An improved energy demand forecasting model is built based on the autoregressive distributed lag (ARDL) bounds testing approach and an adaptive genetic algorithm (AGA) to obtain credible energy demand forecasting results. The ARDL bounds analysis is first employed to select the appropriate input variables of the energy demand model. After the existence of a cointegration relationship in the model is confirmed, the AGA is then employed to optimize the coefficients of both linear and quadratic forms with gross domestic product, economic structure, urbanization, and technological progress as the input variables. On the basis of historical annual data from 1985 to 2015, the simulation results indicate that the proposed model has greater accuracy and reliability than conventional optimization methods. The predicted results of the proposed model also demonstrate that China will demand approximately 4.9, 5.6, and 6.1 billion standard tons of coal equivalent in 2020, 2025, and 2030, respectively.     

Using environmental and geographic data to optimize ex situ collections and preserve evolutionary potential

Maintenance of biodiversity through seed banks and botanical gardens, where the wealth of species’ genetic variation may be preserved ex situ, is a major goal of conservation. However, challenges can persist in optimizing ex situ collections if trade-offs exist among cost, effort, and conserving species evolutionary potential, particularly when genetic data are not available. We evaluated the genetic consequences of population preservation informed by geographic (isolation by distance [IBD]) and environmental (isolation by environment [IBE]) distance for ex situ collections for which population provenance is available. We used 19 genetic and genomic data sets from 15 plant species to assess the proportion of population genetic differentiation explained by geographic and environmental factors and to simulate ex situ collections prioritizing source populations based on pairwise geographic distance, environmental distance, or both. Specifically, we tested the impact prioritizing sampling based on these distances may have on the capture of neutral, functional, or putatively adaptive genetic diversity and differentiation. Individually, IBD and IBE explained limited population genetic differences across all 3 genetic marker classes (IBD, 10–16%; IBE, 1–5.5%). Together, they explained a substantial proportion of population genetic differences for functional (45%) and adaptive (71%) variation. Simulated ex situ collections revealed that inclusion of IBD, IBE, or both increased allelic diversity and genetic differentiation captured among populations, particularly for loci that may be important for adaptation. Thus, prioritizing population collections based on environmental and geographic distance data can optimize genetic variation captured ex situ. For the vast majority of plant species for which there is no genetic information, these data are invaluable to conservation because they can guide preservation of genetic variation needed to maintain evolutionary potential within collections.     

Incorporating putatively neutral and adaptive genomic data into marine conservation planning

The availability of genomic data for an increasing number of species makes it possible to incorporate evolutionary processes into conservation plans. Recent studies show how genetic data can inform spatial conservation prioritization (SCP), but they focus on metrics of diversity and distinctness derived primarily from neutral genetic data sets. Identifying adaptive genetic markers can provide important information regarding the capacity for populations to adapt to environmental change. Yet, the effect of including metrics based on adaptive genomic data into SCP in comparison to more widely used neutral genetic metrics has not been explored. We used existing genomic data on a commercially exploited species, the giant California sea cucumber (Parastichopus californicus), to perform SCP for the coastal region of British Columbia (BC), Canada. Using a RAD-seq data set for 717 P. californicus individuals across 24 sampling locations, we identified putatively adaptive (i.e., candidate) single nucleotide polymorphisms (SNPs) based on genotype–environment associations with seafloor temperature. We calculated various metrics for both neutral and candidate SNPs and compared SCP outcomes with independent metrics and combinations of metrics. Priority areas varied depending on whether neutral or candidate SNPs were used and on the specific metric used. For example, targeting sites with a high frequency of warm-temperature-associated alleles to support persistence under future warming prioritized areas in the southern coastal region. In contrast, targeting sites with high expected heterozygosity at candidate loci to support persistence under future environmental uncertainty prioritized areas in the north. When combining metrics, all scenarios generated intermediate solutions, protecting sites that span latitudinal and thermal gradients. Our results demonstrate that distinguishing between neutral and adaptive markers can affect conservation solutions and emphasize the importance of defining objectives when choosing among various genomic metrics for SCP.     

农地整治权属调整中农户认知对行为响应的作用机制--基于改进TPB及多群组SEM

农地整治权属调整是实现农业规模化和现代化的重要手段,而农户有效参与是推动权属调整的内在动力,研究农地整治权属调整中农户认知对其行为响应的作用机制,有助于高效引导农户参与权属调整,并为制定农地整治权属调整的政策提供科学依据。基于改进计划行为理论和湖北省11个县(市、区)1044份农户抽样调查数据,采用多群组结构方程模型探究了农地整治权属调整中不同类型农户认知对其行为响应的作用机制。结果表明:农地整治权属调整中农户行为逻辑符合改进计划行为理论,农户的行为态度、主观规范、知觉行为控制交互影响行为意愿,进而转换为行为响应,政府支持在行为意愿和行为响应之间起到部分中介作用。总体上,农户行为响应是"自发性""诱发性"和"约束性"三重行动逻辑的结合,且"自发性"占主导地位。多群组结构方程估计结果表明,纯农型与兼业Ⅰ型农户的行为响应受到"自发性""诱发性"和"约束性"三重影响,兼业Ⅱ型农户受到"自发性"和"诱发性"双重影响,非农型农户仅受到"诱发性"单一影响。因此,为引导农户积极参与农地整治权属调整,应该分别针对各类农户相应地提高其参与的"自发性"和"诱发性",降低"约束性",充分发挥农村社会经济组织的作用,将四种不同类型农户的利益需求统筹考虑,降低权属调整过程中利益协调难度。     

万山汞矿区稻田土壤甲基汞的分布特征及其影响因素分析

运用等温气相色谱冷原子荧光技术(GC-CVAFS)对贵州万山汞矿区主要河流范围内稻田土壤甲基汞(MeHg)等含量进行了测定,并从区域层面对土壤甲基汞(MeHg)的分布特征及影响因素进行了研究。结果表明:万山汞矿区稻田土壤MeHg和总汞(THg)含量范围分别为0.72~6.70ng/g和0.49~188.00μg/g,甲基化率范围为0.002%~0.470%;在水平空间分布上,6个检测区域的土壤MeHg含量均随着远离汞矿核心区而降低,但是不同区域之间的降低变化程度不尽相同。通过对稻田土壤SiO2、Al2O3、Fe2O3、TS、TP、TN、有机质、pH等土壤性质与土壤MeHg以及甲基化率进行相关性分析发现,MeHg与THg、TS、TP、TN、有机质存在显著的正相关关系,与SiO2表现出显著性负相关,表明土壤甲基汞不但和总汞含量有关,还受到土壤其它理化因子,尤其是一些营养因子所控制。     

回采工作面瓦斯涌出量遗传投影寻踪回归预测

为快速、准确预测回采工作面瓦斯涌出量,基于投影降维思想,建立一种遗传算法(GA)投影寻踪回归预测方法。选取煤层瓦斯原始含量、埋藏深度、煤层厚度、煤层倾角、工作面长度、推进速度、采出率、临近层瓦斯含量、临近层厚度、临近层层间距、岩层岩性、开采深度作为评价因子,对某矿15个学习样本进行训练,建立GA投影寻踪回归预测模型。利用该矿3个实测样本对模型进行检验,并与主成分分析和BP神经网络方法结果进行对比。研究表明:利用GA投影寻踪回归预测回采工作面瓦斯涌出量,平均误差为3.43%,最大误差为5.7%,精度优于其他2种方法。     

AHP与GA-SVM耦合模型在滑坡预警中的应用*

为了减少滑坡造成的损失，提高滑坡预测的准确性，通过搭建灾害模拟平台获得滑坡的实验数据，在获得多组模拟实验数据后，分析各变量的特性。首先，通过层次分析(Analytic Hierarchy Process，AHP)算法，对滑坡进行危险度划分；然后，通过支持向量机(Support Vector Machine，SVM)建立模型，遗传算法(Genetic Algorithm,GA)再优化SVM参数，提出1种层次分析法与GA-SVM相耦合的模型。研究结果表明:AHP方法划分后的数据，通过GA与SVM结合建立的模型精度较好，实验预测结果与实际结果较为吻合，与单一SVM相比，精度更高，结果更好，更加适用于多变量的复杂非线性滑坡预警。     

The ethics of genetic engineering and gene drives in conservation

The ethical issues associated with using genetic engineering and gene drives in conservation are typically described as consisting of risk assessment and management, public engagement and acceptance, opportunity costs, risk and benefit distributions, and oversight. These are important, but the ethical concerns extend beyond them because the use of genetic engineering has the potential to significantly alter the practices, concepts, and value commitments of conservation. I sought to elucidate the broader set of ethical issues connected with a potential genetic engineering turn in conservation and provide an approach to ethical analysis of novel conservation technologies. The primary rationales offered in support of using genetic engineering and gene drives in conservation are efficiency and necessity for achieving conservation goals. The instrumentalist ethical perspective associated with these rationales involves assessing novel technologies as a means to accomplish desired ends. For powerful emerging technologies the instrumentalist perspective needs to be complemented by a form-of-life perspective frequently applied in the philosophy of technology. The form-of-life perspective involves considering how novel technologies restructure the activities into which they are introduced. When the form-of-life perspective is applied to creative genetic engineering in conservation, it brings into focus a set of ethical issues, such as those associated with power, meaning, relationships, and values, that are not captured by the instrumentalist perspective. It also illuminates why the use of gene drives in conservation is so ethically and philosophically interesting.     

Effect of biogeographic history on population vulnerability in European amphibians

The genetic diversity of populations, which contributes greatly to their adaptive potential, is negatively affected by anthropogenic habitat fragmentation and destruction. However, continental‐scale losses of genetic diversity also resulted from the population expansions that followed the end of the last glaciation, an element that is rarely considered in a conservation context. We addressed this issue in a meta‐analysis in which we compared the spatial patterns of vulnerability of 18 widespread European amphibians in light of phylogeographic histories (glacial refugia and postglacial routes) and anthropogenic disturbances. Conservation statuses significantly worsened with distances from refugia, particularly in the context of industrial agriculture; human population density also had a negative effect. These findings suggest that features associated with the loss of genetic diversity in post‐glacial amphibian populations (such as enhanced fixation load or depressed adaptive potential) may increase their susceptibility to current threats (e.g., habitat fragmentation and pesticide use). We propose that the phylogeographic status of populations (i.e., refugial vs. post‐glacial) should be considered in conservation assessments for regional and national red lists.     

Assessing the components of adaptive capacity to improve conservation and management efforts under global change

Natural‐resource managers and other conservation practitioners are under unprecedented pressure to categorize and quantify the vulnerability of natural systems based on assessment of the exposure, sensitivity, and adaptive capacity of species to climate change. Despite the urgent need for these assessments, neither the theoretical basis of adaptive capacity nor the practical issues underlying its quantification has been articulated in a manner that is directly applicable to natural‐resource management. Both are critical for researchers, managers, and other conservation practitioners to develop reliable strategies for assessing adaptive capacity. Drawing from principles of classical and contemporary research and examples from terrestrial, marine, plant, and animal systems, we examined broadly the theory behind the concept of adaptive capacity. We then considered how interdisciplinary, trait‐ and triage‐based approaches encompassing the oft‐overlooked interactions among components of adaptive capacity can be used to identify species and populations likely to have higher (or lower) adaptive capacity. We identified the challenges and value of such endeavors and argue for a concerted interdisciplinary research approach that combines ecology, ecological genetics, and eco‐physiology to reflect the interacting components of adaptive capacity. We aimed to provide a basis for constructive discussion between natural‐resource managers and researchers, discussions urgently needed to identify research directions that will deliver answers to real‐world questions facing resource managers, other conservation practitioners, and policy makers. Directing research to both seek general patterns and identify ways to facilitate adaptive capacity of key species and populations within species, will enable conservation ecologists and resource managers to maximize returns on research and management investment and arrive at novel and dynamic management and policy decisions.