Dormancy and germination: making every seed count in restoration

From 50 to 90% of wild plant species worldwide produce seeds that are dormant upon maturity, with specific dormancy traits driven by species' occurrence geography, growth form, and genetic factors. While dormancy is a beneficial adaptation for intact natural systems, it can limit plant recruitment in restoration scenarios because seeds may take several seasons to lose dormancy and consequently show low or erratic germination. During this time, seed predation, weed competition, soil erosion, and seed viability loss can lead to plant re‐establishment failure. Understanding and considering seed dormancy and germination traits in restoration planning are thus critical to ensuring effective seed management and seed use efficiency. There are five known dormancy classes (physiological, physical, combinational, morphological, and morphophysiological), each requiring specific cues to alleviate dormancy and enable germination. The dormancy status of a seed can be determined through a series of simple steps that account for initial seed quality and assess germination across a range of environmental conditions. In this article, we outline the steps of the dormancy classification process and the various corresponding methodologies for ex situ dormancy alleviation. We also highlight the importance of record‐keeping and reporting of seed accession information (e.g. geographic coordinates of the seed collection location, cleaning and quality information, storage conditions, and dormancy testing data) to ensure that these factors are adequately considered in restoration planning.     

放线菌分类地位的变迁及其系统学研究最新进展

放线菌是目前细菌域最大的谱系之一,门下物种繁多。放线菌作为人类生产和生活极为密切的微生物类群,其强大的代谢产物编码能力为人们所共识。放线菌系统学是以实现对放线菌进行分类、鉴定、命名为目标的基础学科,因此它是放线菌资源研究和开发利用的重要理论基础。近期,我们放线菌资源与利用团队对2009年版放线菌分类系统再次进行了更新和修订,并被国际权威分类学期刊IJSEM杂志接受发表,进一步巩固了我国在本领域的国际领先地位。重点对放线菌的概念演变、分类地位变迁,以及放线菌系统学研究的最新进展做全面概述,并对其未来发展方向进行了展望,以供国内从事本领域研究的同行参考。     

Quantified 123I-Thyroid Scan based classification of hyperthyroidism

The original thyroid scan (TS) was widely used to identify typical imaging patterns, suggesting the widely accepted main following clinical diagnoses: Grave's disease, Toxic adenoma, [hetero]-nodular goiters and thyroiditis. With the diffusion of sensitive TSH assays, considerable advances in the comprehension of the molecular mechanisms of hormonosynthesis, and new quantification possibilities especially using ¹²³I, the TS is a textbook of molecular imaging. The image can be finely quantified with, not only as regards the Uptake (¹²³IUp) and related parameters but also, the quantification of the spatial targeting leading to a Spatial Target Index (STI). Using this new molecular ¹²³I-TS, TSH values, and when required, correlation to Multiparametric Ultrasounds (MPUS), we generated a basic classification system of hyperthyroidism, with well-defined indexed criteria (C11-1 to C17-3), that allows reporting 24 distinct etiologies. Selected criteria involve TS and contrast patterns, precocious ¹²³IUp (p¹²³IUp), maximal TSH-dependent physiological Uptake, lobar concentration, Uptake and concentration ratios, STI, ^99mTc-MIBI TS and correlative MPUS. This approach allows to identify 4 subtypes of Graves’ disease, including hyperplastic, nodular and common GD variants entangled with Hashimoto's struma, 4 subtypes of Thyroid Functional Autonomy, including Disseminated Functional Autonomy, that cannot be diagnosed with other conventional procedures. Criteria C14-1 to C17-3 report on hyperthyroidism and iodine overload, factitia, main thyroiditis presentations and rare central or tumoral etiologies of hyperthyroidism. This classification, based on ¹²³I-TS molecular imaging, leads to unprecedented diagnostic finesse and paves the way for a personalized theranostic approach in thyroid pathology. Further development towards artificial intelligence networks is under study.     

中国植被分类系统修订方案

为了推动《中国植被志》研编工作, 该文回顾了中国植被分类系统的发展过程和主要阶段性成果, 提出了作为《中国植被志》研编技术框架组成部分的中国植被分类系统修订方案, 对各植被型组及各植被型进行了简单定义和描述, 并针对中国植被分类系统若干问题, 特别就中国植被分类系统总体框架、混交林的界定以及土壤在植被分类中的重要性等问题进行了讨论。1960年侯学煜在《中国的植被》中首次提出了中国植被分类的原则和系统, 1980年出版的《中国植被》制定了分类等级和划分依据等更加完善的系统, 之后《中国植被及其地理格局——中华人民共和国1:1 000 000植被图说明书》和《中国植物区系与植被地理》以及很多省区的植被专著对该系统进行过修订。2017年宋永昌在《植被生态学》中提出了一个分类等级单位调整的方案。本次提出的中国植被分类系统修订方案基本沿用《中国植被》的植被分类原则、分类单位及系统, 采用“植物群落学-生态学”分类原则, 主要以植物群落特征及其与环境的关系作为分类依据, 包含三级主要分类单位, 即植被型(高级单位)、群系(中级单位)和群丛(低级单位); 在三个主要分类单位之上分别增加辅助单位植被型组、群系组和群丛组, 在植被型和群系之下主要根据群落的生态差异和实际需要可再增加植被亚型或亚群系。修订方案包含了森林、灌丛、草本植被(草地)、荒漠、高山冻原与稀疏植被、沼泽与水生植被(湿地)、农业植被、城市植被和无植被地段9个植被型组, 划分为48个植被型(含30个自然植被型、12个农业植被型、5个城市植被型和无植被地段)。自然植被中有23个植被型进一步划分出了81个植被亚型。     

广州市湿地公园植物调查与分析

为更好地提高湿地公园的生态服务能力、加强湿地公园的物种保育功能并促进湿地公园建设和发展质量,对广州地区20个湿地公园植物进行了实地调查。结果表明,调查区域共有205种湿地植物;对其物种组成、生活型、生态习性、优势科属及应用频度等的分析表明,湿地公园的植物群落结构比较简单,主要植物种类同质化现象很严重,保护和珍稀植物种类应用较少等。基于此,推荐了55种本土植物作为广州市湿地公园进行生态改造和景观配置的候选物种,同时还提出了对未来湿地公园的建设和管理的若干建议。     

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.