新疆吉木萨尔县蝴蝶群落多样性

蝴蝶作为指示生物, 被广泛地应用于生物多样性监测及环境质量评估。探究新疆吉木萨尔县蝴蝶群落多样性, 可为当地蝴蝶多样性的保护及环境监测提供基础数据。本研究采用样线法在新疆吉木萨尔县选取山前荒漠、农田、山地草原、山地森林、亚高山草甸5种不同的生境类型, 对蝴蝶种类和群落多样性进行调查。共记录蝴蝶4,401号, 隶属于7科26属38种。其中蛱蝶科有9属12种, 为优势科; 粉蝶科的个体数最多, 占比55.01%; 绢蝶科、凤蝶科和弄蝶科的种类数和个体数最少, 均为单科种, 是该地区的稀有类群。对不同生境蝴蝶群落多样性和相似度分析比较的结果显示: 5种生境中多样性指数从高到低依次为亚高山草甸、山地森林、山地草原、农田及山前荒漠, 其中山地森林和亚高山草甸的相似性系数较高, 达到0.77, 山前荒漠和山地草原的相似性系数最低, 为0.37。蝴蝶物种数及多样性指数随海拔的增加呈上升趋势。蝴蝶群落随月份发生变化, 蝴蝶种类和数量在5月发生、7月达到峰值。蝶类个体数在3年内呈下降趋势。研究结果表明, 蝴蝶物种的组成和多样性与生境类型具有密切联系, 保护生态环境, 维持该地区植物群落的多样性、降低人为干扰程度是保护蝶类多样性的关键。     

基于实地监测的城市林木调控PM2.5能力研究

随着城市的不断扩张,PM_(2.5)污染凸显,引起广泛关注。研究表明,城市林木为城市环境提供了重要的生态保障,在调控、缓解、降低城市PM_(2.5)污染危害等方面发挥极其重要的作用,通过筛选树种、优化配置结构、提高林木质量等方面进行城市林木前瞻性布局。然而,结合前期研究基础如何进一步深入研究并研究突破城市林木调控PM_(2.5)污染机制与机理,实现调控PM_(2.5)效应的最大化、最优化,依然是一个亟待解决的难题,这迫切需要在多尺度、多维度进行调控PM_(2.5)效应研究,并在不同尺度、不同维度进一步进行结合、延伸。对基于实地监测的城市林木调控PM_(2.5)能力研究现状相关文献进行归纳总结,并从林木单位叶面积与形态特征、配置结构特征、气象条件以及其他因素等方面归纳城市林木调控PM_(2.5)机制,同时从城市林木调控PM_(2.5)效应的时间变化特征、水平距离和垂直变化特征、内外变化特征等方面总结城市林木调控PM_(2.5)时空特征。最终提出研究城市林木调控PM_(2.5)效应目前存在的主要问题以及未来研究展望。     

植被综合生态质量时空变化动态监测评价模型

为了能掌握全国植被综合生态质量的高低及其时空变化,构建了既能反映植被生产力又能反映植被覆盖度的植被综合生态质量指数,建立了植被综合生态质量指数年际对比和多年变化趋势评价模型。利用构建的指数和评价模型,以2017年作为监测评价的当年,以2000—2017年作为评价的多年时段,对全国植被综合生态质量时空变化进行了监测评价。结果表明：（1）2017年全国大部地区植被综合生态质量指数高于2000—2016年多年平均值,生态质量偏好;2017年福建、广西、海南、广东、云南植被综合生态质量位居全国前五位,构建的植被综合生态质量指数及其年际对比模型可以定量反映全国植被综合生态质量的空间差异和年际差异。（2）全国有90.7%的区域2000—2017年植被综合生态质量指数呈提高趋势,东北地区西部、内蒙古东部、华北大部、西北地区东部、西南地区东部、华南西部等地生态质量指数提升明显,构建的植被综合生态质量指数多年变化趋势评价模型可以定量反映植被生态质量的多年变化趋势和幅度。（3）南方大部地区2000—2017年平均年植被综合生态质量指数在50.0以上,北方大部地区在50.0以下;我国中东部大部地区在20.0以上,西部大部地区在20.0以下,表明南方大部地区年植被生态质量好于北方、中东大部好于西部。可见,构建的植被综合生态质量指数及其年际对比和多年变化趋势评价模型,能够监测评价当年和多年全国植被综合生态质量的时空变化,可为掌握全国植被生态质量动态提供模型和方法。     

2000-2015年祁连山南坡生态系统服务价值时空变化

祁连山是我国西部重要的生态安全屏障和生物多样性保护优先区域,其中南坡地区是水源涵养服务核心区,但目前对该区域生态系统服务时空分布及动态变化仍缺乏深入认识。利用遥感技术和生态系统服务理论,综合多源数据产品,借鉴生态系统服务计算方法,对包括土壤保持等7个生态系统服务类型展开功能及价值核算,对祁连山南坡土壤保持等7个生态系统服务类型2000-2015年间的时空分异及动态展开监测和评估,研究表明：（1）近16年来祁连山南坡生态系统服务平均单位面积价值为76.27万元/km²,总价值为658.74亿元,且调节气候、水源涵养和调节空气质量服务构成了研究区生态系统服务的主体。（2）高寒草甸和高寒草原的生态系统服务总价值最高,而森林和灌丛的单位面积服务价值最高;天峻县和祁连县的生态系统服务总价值最高,而海晏县和刚察县的单位面积服务价值最高。（3）近16年来祁连山南坡生态系统服务价值呈增长趋势,其中调节气候和调节空气质量服务价值的增速最快,且增长区域主要集中在研究区北部祁连县和天峻县境内。（4）研究区东部山区、南部环湖区以及中部湿地区能够提供多项复合生态服务功能,应作为生态保护和管理的重点。研究结果能为祁连山生态红线制定及国家公园建设提供理论依据和技术支撑。     

多学科协作诊治模式下的营养干预在老年骨折患者围术期的应用

目的研究多学科协作诊治模式(MDT)下的营养干预对围术期老年骨折患者的干预效果。方法选取新疆维吾尔自治区人民医院骨科2018年9月至2019年9月收治的42例行常规治疗的围术期老年患者作为非MDT组,选取同期43例实施MDT模式并给予营养干预的围术期老年患者作为MDT组,比较两组患者术后基本情况、并发症发生情况及恢复情况。结果术后MDT组患者总蛋白、白蛋白水平均高于非MDT组,NRS2002评分、CRP、IL-6水平均低于非MDT组,差异均有统计学意义(t=-3.679 6、-4.138 8、9.073 3、-6.669 5、-4.500 2,P=0.000 4、0.000 1、0.000 1、0.000 1、0.000 1)。MDT组患者住院时间少于非MDT组,差异有统计学意义(髋关节置换术:t=2.154 8,P=0.034 1;膝关节置换术:t=2.491 9,P=0.018 9)。MDT组患者肠道功能恢复情况明显优于非MDT组,差异有统计学意义(χ~2=10.512 1,P=0.001 2)。结论 MDT模式下的营养干预能够有效促进老年骨折患者的术后康复,有利于预防并发症的发生。     

Raman-based dynamic feeding strategies using real-time glucose concentration monitoring system during adalimumab producing CHO cell cultivation

The use of Process Analytical Technology tools coupled with chemometrics has been shown great potential for better understanding and control of mammalian cell cultivations through real-time process monitoring. In-line Raman spectroscopy was utilized to determine the glucose concentration of the complex bioreactor culture medium ensuring real-time information for our process control system. This work demonstrates a simple and fast method to achieve a robust partial least squares calibration model under laboratory conditions in an early phase of the development utilizing shake flask and bioreactor cultures. Two types of dynamic feeding strategies were accomplished where the multi-component feed medium additions were controlled manually and automatically based on the Raman monitored glucose concentration. The impact of these dynamic feedings was also investigated and compared to the traditional bolus feeding strategy on cellular metabolism, cell growth, productivity, and binding activity of the antibody product. Both manual and automated dynamic feeding strategies were successfully applied to maintain the glucose concentration within a narrower and lower concentration range. Thus, besides glucose, the glutamate was also limited at low level leading to reduced production of inhibitory metabolites, such as lactate and ammonia. Consequently, these feeding control strategies enabled to provide beneficial cultivation environment for the cells. In both experiments, higher cell growth and prolonged viable cell cultivation were achieved which in turn led to increased antibody product concentration compared to the reference bolus feeding cultivation.     

Three critical factors affecting automated image species recognition performance for camera traps

Ecological camera traps are increasingly used by wildlife biologists to unobtrusively monitor an ecosystems animal population. However, manual inspection of the images produced is expensive, laborious, and time‐consuming. The success of deep learning systems using camera trap images has been previously explored in preliminary stages. These studies, however, are lacking in their practicality. They are primarily focused on extremely large datasets, often millions of images, and there is little to no focus on performance when tasked with species identification in new locations not seen during training. Our goal was to test the capabilities of deep learning systems trained on camera trap images using modestly sized training data, compare performance when considering unseen background locations, and quantify the gradient of lower bound performance to provide a guideline of data requirements in correspondence to performance expectations. We use a dataset provided by Parks Canada containing 47,279 images collected from 36 unique geographic locations across multiple environments. Images represent 55 animal species and human activity with high‐class imbalance. We trained, tested, and compared the capabilities of six deep learning computer vision networks using transfer learning and image augmentation: DenseNet201, Inception‐ResNet‐V3, InceptionV3, NASNetMobile, MobileNetV2, and Xception. We compare overall performance on “trained” locations where DenseNet201 performed best with 95.6% top‐1 accuracy showing promise for deep learning methods for smaller scale research efforts. Using trained locations, classifications with <500 images had low and highly variable recall of 0.750 ± 0.329, while classifications with over 1,000 images had a high and stable recall of 0.971 ± 0.0137. Models tasked with classifying species from untrained locations were less accurate, with DenseNet201 performing best with 68.7% top‐1 accuracy. Finally, we provide an open repository where ecologists can insert their image data to train and test custom species detection models for their desired ecological domain.     

Acoustic localization of terrestrial wildlife: Current practices and future opportunities

Autonomous acoustic recorders are an increasingly popular method for low‐disturbance, large‐scale monitoring of sound‐producing animals, such as birds, anurans, bats, and other mammals. A specialized use of autonomous recording units (ARUs) is acoustic localization, in which a vocalizing animal is located spatially, usually by quantifying the time delay of arrival of its sound at an array of time‐synchronized microphones. To describe trends in the literature, identify considerations for field biologists who wish to use these systems, and suggest advancements that will improve the field of acoustic localization, we comprehensively review published applications of wildlife localization in terrestrial environments. We describe the wide variety of methods used to complete the five steps of acoustic localization: (1) define the research question, (2) obtain or build a time‐synchronizing microphone array, (3) deploy the array to record sounds in the field, (4) process recordings captured in the field, and (5) determine animal location using position estimation algorithms. We find eight general purposes in ecology and animal behavior for localization systems: assessing individual animals' positions or movements, localizing multiple individuals simultaneously to study their interactions, determining animals' individual identities, quantifying sound amplitude or directionality, selecting subsets of sounds for further acoustic analysis, calculating species abundance, inferring territory boundaries or habitat use, and separating animal sounds from background noise to improve species classification. We find that the labor‐intensive steps of processing recordings and estimating animal positions have not yet been automated. In the near future, we expect that increased availability of recording hardware, development of automated and open‐source localization software, and improvement of automated sound classification algorithms will broaden the use of acoustic localization. With these three advances, ecologists will be better able to embrace acoustic localization, enabling low‐disturbance, large‐scale collection of animal position data.     

基于植保大数据的病虫害移动智能采集新设备

为提高农作物重大病虫害发生信息自动化、智能化采集能力,全面提升监测预警水平,笔者基于大数据、人工智能和深度学习技术,研发了一款农作物病虫害移动智能采集设备——智宝,主要实现了3个方面的功能:一是病虫害发生信息自动采集上报.通过该产品进行人工拍照,可实现对田间农作物重大病虫害发生图像、发生位置、发生数量、微环境因子等数据的实时采集和上报.二是自动识别计数.基于植保大数据与人工智能技术,通过构建病虫害自动识别系统,可实现重大病虫害精准识别与分析,只要拍摄照片,即可快速、精确地识别病虫害种类,并自动计数、上报到指定的测报系统.三是自动分析判别分级.针对拍摄采集上报的重大病虫害发生信息,系统可在自动识别和计数的基础上,进一步对病虫害发生严重程度进行智能判别分级,甚至根据相关预测模型,对病虫害的发生趋势进行辅助分析预测,提出预测建议.通过2016—2019年组织多地植保机构进行试验改进,该技术产品日趋成熟,有望在未来的农作物病虫害发生信息采集和预测预报工作中推广使用.