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.     

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.     

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。     

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.     

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.     

西藏自治区措勤县日阿布洗铜多金属矿床地质特征及成因类型

西藏自治区日阿布洗铜多金属矿位于冈底斯山脉北缘斑岩型铜矿成矿带,含矿斑岩体为晚喜山期花岗斑岩,具全岩矿化特征,斑岩体与构造蚀变岩带形成矿床。研究表明,地表和浅部出露含矿斑岩体只是岩枝,矿区剥蚀程度低,主斑岩体尚未出露,深部找矿潜力巨大。经深部钻孔工程及地表槽探验证,矿体品位和厚度沿走向和倾向变化系数较小,伴生组份较多,并对其资源量进行了初步估算。喜山晚期第一阶段花岗斑岩侵入体是主要成矿物质来源,矿床成因类型应为斑岩型铜矿床。     

西藏自治区措勤县德能铜多金属矿床地质特征及成因分析

通过对西藏自治区措勤县德能铜多金属矿的研究发现,矿区内共6条达到工业品位的铜矿体,并伴生有铅、锌、金、银等多种有益组分,成矿地质条件较好。矿体主要赋存于燕山期石英闪长玢岩岩体外接触带及构造破碎带中,其中Cu4矿体规模最大,经深部钻孔工程及地表槽探验证,矿体品位和厚度沿走向和倾向变化系数较小。燕山晚期第一阶段第一次石英闪长玢岩侵入体是主要成矿物质来源,后期北西西向断裂构造是主要导矿和容矿构造。     

基于形态滤波遗传算法对目标红外图像特征检测

将形态滤波和自适应遗传算法结合起来,提出了一个用于目标检测的形态滤波器参数优化遗传算法。通过设计了形态滤波的遗传算法,寻求最佳结构元,以实现对目标的自适应检测。作为2个特殊环境中的应用检测实例,分别对固体推进器尾喷焰红外图像和复杂背景下的弱小目标进行检测。实验结果表明该方法能够更好地适应环境变化,更大程度地提高目标信号检测的信号噪声（SNR）比,有效地检测出复杂背景下的目标。     

基于遗传-支持向量机和遗传-径向基神经网络的有机物正辛醇-水分配系数QSPR研究

基于遗传算法(GA)的因子筛选和支持向量机(SVM)的非线性回归,提出了1种改进的有机物定量结构-性质相关(QSPR)建模方法--遗传-支持向量机(GA-SVM),并将其用于38种食品工业常用有机物正辛醇-水分配系数(Kow)的QSPR建模.结果显示,QSPR模型选取了分子量、Hansen极性、沸点、含氧率和含氢率5种参数;模型的预测值与实测值间的误差平方和(SSE)、均方差(RMSE)和决定系数(R2)分别为0.048、0,036和0.999,表明模型具有较强的预测能力;同时,交叉验证的结果(SSE=0.295,RMSE=0.089,R2=0.995)也表明,模型具有良好的稳健性,因此,GA-SVM算法适用于对有机物正辛醇-水分配系数的QSPR建模.此外,将基于GA-SVM的QSPR模型分别与基于遗传-径向基神经网络(GA-RBFNN)和基于线性算法的模型进行了比较,结果表明,应用GA-SVM建立的QSPR模型无论从稳健性还是预测能力上都优于应用其它2种算法建立的模型,因此,GA-SVM算法比GA-RBFNN和线性算法更适合于对有机物正辛醇-水分配系数进行QSPR建模.     

中国遗传资源优先保护等级评价标准的构建

确定科学合理的生物遗传资源优先保护等级,是当前保护生物学研究的一个核心问题。文章通过分析国内外遗传资源优先保护等级评价研究进展,针对遗传资源的特点,对遗传资源与物种关系、遗传资源多样性、境外种群影响、遗传资源价值、潜在利用价值、人工繁育技术、特有情况等方面进行了探讨并提出相应建议,初步确定了遗传资源优先保护等级的评价标准,以期能为生物遗传资源的有效保护和科学管理提供参考依据。