地被植物对广东常绿阔叶林土壤养分的指示作用

为了解地被植物群落对土壤养分的响应和指示作用,利用指示种分析(ISA)法研究了广东常绿阔叶林地被植物与土壤养分的关系。结果表明,速效氮(AN)、速效磷(AP)、速效钾(AK)和有机质(OM)的综合作用对地被植物分布有显著影响(P0.05),且以速效磷有机质速效钾速效氮。地被植物的组成与分布在不同AP和OM梯度中均有显著差异(P 0.05),但在不同AK和AN梯度中差异不显著。土壤AN≤270 mg/kg的指示种是广东蛇葡萄(Ampelopsis cantoniensis)和油点草(Tricyrtis macropoda),270～360 mg/kg的指示种是狗骨柴(Tricalysia dubia);AP≤2 mg/kg的指示种是华山姜(Alpinia chinensis);AK≤100mg/kg的指示种是赤楠蒲桃(Syzygiumbuxifolium),AK为100～150mg/kg的指示种是十字苔草(Carexcruciate),AK150 mg/kg的指示种是金钗凤尾蕨(Pterisfauriei);OM 0.8%的指示种是箬竹(Indocalamustessellatus),OM≤0.6%的指示种是华山姜(Alpinia chinensis)和蔓胡颓子(Elaeagnus glabra),OM为0.6%～0.8%的指示种是豆腐柴(Premna microphlla)。利用有效的地被植物调查方式来监测森林的土壤状况为森林经营管理及土壤健康评价带来方便。地被植物指示种对土壤养分的响应不但能为研究地的立地条件提供理论支撑,还能为该物种的人工栽培提供理论依据,这对于生物多样性保育、生境恢复的引种栽培有重要意义。     

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.     

Voice Pathologies Classification and Detection Using EMD-DWT Analysis Based on Higher Order Statistic Features

BackgroundThe voice is a prominent tool allowing people to communicate and to change information in their daily activities. However, any slight alteration in the voice production system may affect the voice quality. Over the last years, researchers in biomedical engineering field worked to develop a robust automatic system that may help clinicians to perform a preventive diagnosis in order to detect the voice pathologies in an early stage.MethodIn this context, pathological voice detection and classification method based on EMD-DWT analysis and Higher Order Statistics (HOS) features, is proposed. Also DWT coefficients features are extracted and tested. To carry out our experiments a wide subset of voice signal from normal subjects and subjects which suffer from the five most frequent pathologies in the Saarbrücken Voice Database (SVD), is selected. In The first step, we applied the Empirical Mode Decomposition (EMD) to the voice signal. Afterwards, among the obtained candidates of Intrinsic Mode Functions (IMFs), we choose the robust one based on temporal energy criterion. In the second step, the selected IMF was decomposed via the Discrete Wavelet Transform (DWT). As a result, two features vector includes six HOSs parameters, and a features vector includes six DWT features were formed from both approximation and detail coefficients. In order to classify the obtained data a support vector machine (SVM) is employed. After having trained the proposed system using the SVD database, the system was evaluated using voice signals of volunteer's subjects from the Neurological department of RABTA Hospital of Tunis.ResultsThe proposed method gives promising results in pathological voices detection. The accuracies reached 99.26% using HOS features and 93.1% using DWT features for SVD database. In the classification, an accuracy of 100% was reached for “Funktionelle Dysphonia vs. Rekrrensparese” based on HOS features. Nevertheless, using DWT features the accuracy achieved was 90.32% for “Hyperfunktionelle Dysphonia vs. Rekurrensparse”. Furthermore, in the validation the accuracies reached were 94.82%, 91.37% for HOS and DWT features, respectively. In the classification the highest accuracies reached were for classifying “Parkinson versus Paralysis” 94.44% and 88.87% based on HOS and DWT features, respectively.ConclusionHOS features show promising results in the automatic voice pathology detection and classification compared to DWT features. Thus, it can reliably be used as noninvasive tool to assist clinical evaluation for pathological voices identification.     

Species coexistence in resource-limited patterned ecosystems is facilitated by the interplay of spatial self-organisation and intraspecific competition

The exploration of mechanisms that enable species coexistence under competition for a sole limiting resource is widespread across ecology. Two examples of such facilitative processes are intraspecific competition and spatial self-organisation. These processes determine the outcome of competitive dynamics in many resource-limited patterned ecosystems, classical examples of which include dryland vegetation patterns, intertidal mussel beds and subalpine ribbon forests. Previous theoretical investigations have explained coexistence within patterned ecosystems by making strong assumptions on the differences between species (e.g. contrasting dispersal behaviours or different functional responses to resource availability). In this paper, I show that the interplay between the detrimental effects of intraspecific competition and the facilitative nature of self-organisation forms a coexistence mechanism that does not rely on species-specific assumptions and captures coexistence across a wide range of the environmental stress gradient. I use a theoretical model that captures the interactions of two generic consumer species with an explicitly modelled resource to show that coexistence relies on a balance between species' colonisation abilities and their local competitiveness, provided intraspecific competition is sufficiently strong. Crucially, the requirements on species' self-limitation for coexistence to occur differ on opposite ends of the resource input spectrum. For low resource levels, coexistence is facilitated by strong intraspecific dynamics of the species superior in its colonisation abilities, but for larger volumes of resource input, strong intraspecific competition of the locally superior species enables coexistence. Results presented in this paper also highlight the importance of hysteresis in understanding tipping points, in particular extinction events. Finally, the theoretical framework provides insights into spatial species distributions within single patches, supporting verbal hypotheses on coexistence of herbaceous and woody species in dryland vegetation patterns and suggesting potential empirical tests in the context of other patterned ecosystems.     

广州市主城区城市地形类型与风环境评价

对城市地形组合类型及其地形对风场影响的研究,有助于城市风场机理的认识与生态环境的优化。以高层建筑密集的广州市主城区为例,在确定城市宏观地形类型的基础上,基于最小成本路径（LCP）辨识了风道,并对风环境质量进行评价。主要结论有：（1）将城市地形简要概括划分为4个一级地形、11个二级地形类型。（2）以LCP路径与盛行风交角不超过22.5°的标准,筛选确定通风路径作为风道。根据LCP格网的密度与频次,结合不同风向下风速与风频,评价分析了风环境类型与空间分布。（3）根据自然与城市地形的配置,北风风环境质量远高于西北风和东风;东风由于风频与风速最低,因此通风条件较差。（4）珠江航道在三种风向下都是尺度最宽贯穿城市最好的风道;区域性的风道与风环境较好的地段集中在与主风向平行的主干道上,但以近似南北走向的居多;由于广州城市地形高度以珠江新城峰林为中心向外递减,以低地地形为主的主城区外围通风优于中心区域,特别是珠江新城峰林与网络状台地为主的老城区,通风环境较差。基于LCP的评价结果需要同其他方法相互验证,才能使其不断完善优化。     

基于MaxEnt模型的延河流域草本植物适生分布与功能性状分析

明确延河流域常见草本植物的潜在适生区分布,是植被恢复工作持续推进的基础。本研究收集了延河流域8种常见草本植物的地理分布信息和13个环境变量,采用MaxEnt和ArcGIS模拟了延河流域常见草本植物在当前气候下的潜在适生性分布,进而研究这8种不同草本植物适生性分布与功能性状变异特征之间的相关关系。研究结果显示：根据物种-性状排序图的分布格局判断,本研究选择的七个功能性状在植物所属科之间发生了明显趋异分化现象,在PC1右侧为禾本科植物,PC1左侧为菊科、豆科和唇形科植物。对物种适生性分布模拟结果表明,达乌里胡枝子在研究区内的适生性最高,百里香的适生性最低,表明达乌里胡枝子比其他常见草本物种更适合被选择为该流域的植被恢复的先锋物种。在功能性状变异特征相关性分析中,物种适生区大小与比叶面积变异系数呈显著正相关,与其他植物功能性状变异特征不显著。因此,比叶面积的变异系数更适合作为指示延河流域草本植物适生区大小的性状。     

基于MODIS-EVI的西南地区植被覆盖时空变化及驱动因素研究

基于MODIS-EVI和气象数据,利用最大值合成法、像元二分模型、趋势分析和相关分析等方法,探讨了西南地区2001-2015年植被覆盖时空变化特征及其对气候因子的响应,并分析了温度和降水对植被覆盖时空变化的驱动作用。结果表明：（1）2001-2015年,西南地区植被EVI以0.1%/a的变化率呈波动增加趋势,但空间异质性显著,呈现出从东南向西北逐渐递减的趋势;（2）西南地区以高和极高植被覆盖度为主,极低植被覆盖度区域约占研究区总面积的8.6%,植被覆盖度增加的区域集中分布在广西省北海-钦州、贵州省邵通-毕节-遵义、四川省广元-广安以及西藏那曲等地区,植被覆盖度呈减少趋势区域主要集中在西藏拉萨-阿里地区和四川成都-阿坝州-甘孜州等地区;（3）植被EVI与同期温度和降水相关性较好,均以正相关为主。在0.05显著水平下,受降水驱动的区域呈斑块状分布在西藏自治区和青海省交界处,以及云南和广西部分地区,约占研究区总面积的3.4%;受温度驱动的区域零星分布在各省、自治区,约占研究区总面积的1.6%;受温度和降水共同驱动的区域约占研究区总面积的7.2%,主要分布在西藏自治区的阿里地区北部,青海省的三江源地区以及四川和贵州两省交界处的小部分地区;西南地区大部分区域的植被EVI指数变化表现为非气候因素驱动。     

北京城市蝴蝶蜜源植物网络特征及重要蜜源植物识别

物种间相互作用网络研究能为物种多样性的保护、城市生态系统稳定性的维持提供指导。基于群落水平的城市蝴蝶蜜源植物互作网络的研究较少,对城市蝴蝶蜜源网络结构缺乏深入认识。研究在国内城市生态系统中构建蝴蝶蜜源网络,并探讨不同类型植物对网络特征的影响。2020年6-9月,在北京26个城市公园中记录访花蝴蝶和蜜源植物物种及互作频次,采用交互多样性（ID）、交互均匀性（IE）、专业化程度（H2''）定量化生态网络结构特征,采用Kruskal-Wallis秩和检验和变差分解分析不同生长型、起源、栽培方式的植物类型对网络结构的影响差异。采用物种的伙伴多样性（PD）和专业化程度（d''）识别重要蜜源植物。研究结果表明：（1）北京城市公园中22种蝴蝶与81种开花植物的交互作用,形成趋于泛化的生态网络结构;（2）不同生长型及不同起源的植物-蝴蝶网络的交互多样性及专业化程度有显著差异,草本及乡土植物对丰富网络中交互多样性和支持专业性更高的蝴蝶物种具重要作用,而植物的栽培方式对蜜源网络结构影响较小;（3）伙伴多样性高且专业化程度高的植物可被视为重要蜜源植物。基于蝴蝶多样性保护的目标,在城市生态系统中,绿色空间应注重构建乡土草本植物群落,优先选择重要蜜源植物。我们的发现印证了蝴蝶-蜜源植物生态网络方法作为联结生态研究和城市绿地实践管理的有效工具,能为城市生态系统中生物多样性保护提供科学策略,具有重要意义。