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。     

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.     

Analysis of chemometric models applied to Raman spectroscopy for monitoring key metabolites of cell culture

The Food and Drug Administration (FDA) initiative of Process Analytical Technology (PAT) encourages the monitoring of biopharmaceutical manufacturing processes by innovative solutions. Raman spectroscopy and the chemometric modeling tool partial least squares (PLS) have been applied to this aim for monitoring cell culture process variables. This study compares the chemometric modeling methods of Support Vector Machine radial (SVMr), Random Forests (RF), and Cubist to the commonly used linear PLS model for predicting cell culture components—glucose, lactate, and ammonia. This research is performed to assess whether the use of PLS as standard practice is justified for chemometric modeling of Raman spectroscopy and cell culture data. Model development data from five small-scale bioreactors (2 × 1 L and 3 × 5 L) using two Chinese hamster ovary (CHO) cell lines were used to predict against a manufacturing scale bioreactor (2,000 L). Analysis demonstrated that Cubist predictive models were better for average performance over PLS, SVMr, and RF for glucose, lactate, and ammonia. The root mean square error of prediction (RMSEP) of Cubist modeling was acceptable for the process concentration ranges of glucose (1.437 mM), lactate (2.0 mM), and ammonia (0.819 mM). Interpretation of variable importance (VI) results theorizes the potential advantages of Cubist modeling in avoiding interference of Raman spectral peaks. Predictors/Raman wavenumbers (cm⁻¹) of interest for individual variables are X1139–X1141 for glucose, X846–X849 for lactate, and X2941–X2943 for ammonia. These results demonstrate that other beneficial chemometric models are available for use in monitoring cell culture with Raman spectroscopy.     

A new biological method to control Anobium punctatum,by using the parasitoid wasp Spathius exarator

Biological pest control by means of beneficial organisms is for long part of agriculture and Integrated Pest Management (IPM). A new and efficient strategy to control the most common timber pest species in churches and museums, the furniture beetle Anobium punctatum (De Geer) (Coleoptera: Anobiidae), is based on the parasitoid wasp species Spathius exarator (Linnaeus) (Hymenoptera: Braconidae). Once this braconid wasp detects its host species beneath the surface, it pierces the wood with its ovipositor to lay one single egg onto the beetle larva. After hatching, the wasp larva feeds on the beetle larva thereby killing it. Afterwards, it pupates and emerges through a self-gnawed hole as an adult wasp. The tiny, 0.5-mm-wide exit hole can easily be distinguished from the 1- to 2-mm-wide exit hole of A. punctatum. Laboratory tests revealed that female wasps have an average life span of 85 days and produce a total of 24 offspring, when nutrition is provided. Between 2012 and 2019, braconid wasps were introduced into 54 different A. punctatum infested buildings. Treatment success was monitored by examining exit holes of new beetles and wasps thereby calculating corresponding parasitism rates. After the first year of treatment, parasitism rates were significantly higher with a mean value of 0.15 when compared to untreated objects with a natural parasitism and a mean value of 0.08 (n = 54). Following treatment of three objects over a period of eight years, parasitism rates continuously increased from 0.02 up to 0.31. In a church organ, which was treated during this period, the monitoring revealed a complete suppression of pest activity. These data prove that this biological method of pest control is an efficient, sustainable and non-toxic option to manage the common furniture beetle.     

Exploiting nonsystematic covariate monitoring to broaden the scope of evidence about the causal effects of adaptive treatment strategies

In studies based on electronic health records (EHR), the frequency of covariate monitoring can vary by covariate type, across patients, and over time, which can limit the generalizability of inferences about the effects of adaptive treatment strategies. In addition, monitoring is a health intervention in itself with costs and benefits, and stakeholders may be interested in the effect of monitoring when adopting adaptive treatment strategies. This paper demonstrates how to exploit nonsystematic covariate monitoring in EHR‐based studies to both improve the generalizability of causal inferences and to evaluate the health impact of monitoring when evaluating adaptive treatment strategies. Using a real world, EHR‐based, comparative effectiveness research (CER) study of patients with type II diabetes mellitus, we illustrate how the evaluation of joint dynamic treatment and static monitoring interventions can improve CER evidence and describe two alternate estimation approaches based on inverse probability weighting (IPW). First, we demonstrate the poor performance of the standard estimator of the effects of joint treatment‐monitoring interventions, due to a large decrease in data support and concerns over finite‐sample bias from near‐violations of the positivity assumption (PA) for the monitoring process. Second, we detail an alternate IPW estimator using a no direct effect assumption. We demonstrate that this estimator can improve efficiency but at the potential cost of increase in bias from violations of the PA for the treatment process.     

Can we manage fisheries with the inherent uncertainty from eDNA?

Environmental (e)DNA, as a general approach in aquatic systems, seeks to connect the presence of species' genetic material in the water and hence to infer the species' physical presence. However, fisheries managers face making decisions with risk and uncertainty when eDNA indicates a fish is present but traditional methods fail to capture the fish. In comparison with traditional methods such as nets, electrofishing and piscicides, eDNA approaches have more sources of underlying error that could give rise to false positives. This has resulted in some managers to question whether eDNA can be used to make management decisions because there is no fish in hand. As a relatively new approach, the methods and techniques have quickly evolved to improve confidence in eDNA. By evaluating an eDNA based research programmes through the pattern of the eDNA signal, assay design, experimental design, quality assurance and quality control checks, data analyses and concurrent search for fish using traditional gears, the evidence for fish presence can be evaluated to build confidence in the eDNA approach. The benefits for fisheries management from adopting an eDNA approach are numerous but include cost effectiveness, broader geographic coverage of habitat occupancy, early detection of invasive species, non-lethal stock assessments, exploration of previously inaccessible aquatic environments and discovery of new species hidden beneath the water's surface. At a time when global freshwater and marine fisheries are facing growing threats from over-harvest, pollution and climate change, we anticipate that growing confidence in eDNA will overcome the inherent uncertainty of not having a fish in hand and will empower the informed management actions necessary to protect and restore our fisheries.     

The use of an unoccupied aerial vehicle to survey shark species over sand and rocky-reef habitats in a marine protected area

Cabo Pulmo National Park was established in 1995 and has since seen a large increase in fish biomass. An unoccupied aerial vehicle (UAV) was used to survey shallow coastal habitat in which lemon sharks (Negaprion brevirostris), bull sharks (Carcharhinus leucas) and Pacific nurse sharks (Ginglymostoma unami) were recorded. Sharks were more common in the afternoon, potentially using warmer shallow areas to behaviourally thermoregulate. This study highlights UAV surveying to be a viable tool for species identification, a limitation of previous terrestrial surveys conducted in the area.