Forest Fragmentation Reduces Seed Dispersal of Duckeodendron cestroides, a Central Amazon Endemic

Fragmentation that alters mutualistic relationships between plants and frugivorous animals may reduce the seed dispersal of trees. We examined the effects of forest fragmentation on the distributions of seeds and seedlings of a Central Amazon endemic tree, Duckeodendron cestroides . In the dry seasons of 2002–2004, seeds and first-year seedlings were counted within wedge-shaped transects centered around Duckeodendron adults in fragments and nearby continuous forests at the Biological Dynamics of Forest Fragmentation Project. Analyses showed that fragmentation reduced seed dispersal quantity and quality. The percent and distance of dispersed seeds were both twice as great in continuous forest as in fragments. The distances of each tree's five furthest dispersed seeds were three times greater in continuous forest than fragments. Over the 3-yr study, 20 times more seeds were dispersed more than 10 m from parent crowns in continuous forest than fragments. A regression analysis showed more dispersed seeds at all distances in continuous forest than fragments. Dispersal differences were strong in 2002 and 2004, years of large fruit crops, but weak or absent in 2003, when fruit production was low. As distance from parent crowns increased, the number of seedlings declined more rapidly in fragments than continuous forest. Distance-dependent mortality between the seed and seedling stages appeared to be more important in continuous forest than fragments. This research provides ample, indirect evidence demonstrating that forest fragmentation can result in the breakdown of a seed dispersal mutualism, potentially jeopardizing the reproduction of a rare, tropical tree.     

VPMCD: variable interaction modeling approach for class discrimination in biological systems

Data classification algorithms applied for class prediction in computational biology literature are data specific and have shown varying degrees of performance. Different classes cannot be distinguished solely based on interclass distances or decision boundaries. We propose that inter-relations among the features be exploited for separating observations into specific classes. A new variable predictive model based class discrimination (VPMCD) method is described here. Three well established and proven data sets of varying statistical and biological significance are utilized as benchmark. The performance of the new method is compared with advanced classification algorithms. The new method performs better during different tests and shows higher stability and robustness. The VPMCD is observed to be a potentially strong classification approach and can be effectively extended to other data mining applications involving biological systems.     

Some aspects of forest soil and litter ecology in the Dawyck Cryptogamic Sanctuary with a particular reference to fungi

Here we report on ecology and biodiversity of fungi in a unique mycological sanctuary in Britain, where data on species composition have been collected since 1994. To complement the biodiversity data by the information on the fungal ecological interactions and their role in the overall ecosystem functioning, soil properties and the composition of forest litter and field layer, bacterial population numbers and fungal biomass (in terms of ergosterol) were measured in 8 plots covered with different vegetations (beech, birch, birch-oak-beech, grass) over a May–Aug. period, and the results were analysed by correlation analysis and stepwise regression modelling together with data on protozoa and nematodes available from parallel research. The results highlighted the complexity of factors influencing temporal dynamics and spatial variability of fungal biomass in soil and forest litter. Most of the registered interactions appeared to be transient, and this should be taken into account while interpreting environmental observations. Interpretation of the specific relationships is given and implications for further research and overall ecosystem functioning are discussed.     

Productivity,obesity, and human capital: Panel data evidence

The consequences of obesity for adults and children are well documented in the extant literature. We use panel data of 105 countries from 1990 to 2019 to estimate the effect of obesity on economic performance. We predict obesity using lagged values of child obesity as instruments. Predicted obesity has a negative and significant effect on productivity. This effect is independent of the effect of human capital and other macroeconomic determinants of economic performance. There is only weak evidence that this effect operates through the deterioration of human capital formation caused by childhood obesity.     

海州湾春季短蛸的栖息分布特征及其与环境因子的关系

近年来,我国近海多种重要渔业资源处于不同程度的衰退状态,而短蛸具有生命周期短、生长迅速的特点,在我国近海经济渔获产量中占重要地位。然而,有关短蛸的栖息分布特征及其与环境因子的关系尚缺乏研究,不利于更好地保护和利用其资源。本研究根据2011年和2013—2017年春季海州湾的渔业资源和环境因子调查数据,采用随机森林模型、人工神经网络模型和广义提升回归模型3种机器学习方法分析了短蛸的栖息分布特征及其与环境因子的关系。结果表明: 随机森林模型的拟合效果和预测能力在3种模型中优势较大,选择该模型进行分析表明,底层水温、水深和底层盐度对短蛸的栖息分布有较大影响。短蛸的相对资源密度随底层水温、水深和底层盐度的增加均呈先上升后下降趋势。根据FVCOM模型模拟的环境数据,应用随机森林模型预测了短蛸在海州湾海域的栖息分布,发现短蛸主要分布在34.5°—35.8° N、119.7°—121° E之间的海域。     

一种基于数码相机图像和群落冠层结构调查的草地地上生物量估算方法

草地地上生物量是影响其生态系统功能最重要的因素之一, 也是草地生态学研究中不可或缺的监测指标。草地地上生物量监测多采用收割法进行, 但这种破坏性取样方法会对研究区域带来巨大干扰, 尤其是面积较小的长期定位监测或者控制实验研究样地, 从而使得地上生物量监测的频次受到很大限制。因此, 通过获取某些原位易测变量, 建立地上生物量的估算方法具有重要意义。该研究依托内蒙古典型草地刈割控制实验平台, 通过数码照片获取不同土地利用方式下的植被覆盖度, 并对样方内的叶面积指数、植被高度、物种多样性等参数进行了测定, 最后利用一元回归模型、逐步回归模型和随机森林模型对地上生物量进行估算。结果表明, 植被覆盖度、叶面积指数、植被平均高度、植被最大高度和物种丰富度是影响地上生物量的主要驱动因素。通过构建适宜于本地的逐步回归模型, 可以实现草地地上生物量的准确预测。在该研究区域中, 预测模型的决定系数(R²) = 0.91, 均方根误差(RMSE) = 35.60 g·m^-2。该研究提供了一种快速、准确且非破坏性测定草地地上生物量的方法, 可作为传统收割法的有效补充。     

An effective method for accurate nymphal-stage delimitation of the cicada Hyalessa fuscata

Subterranean nymphal development in cicadas presents challenges to researchers in accurately estimating the number of their developmental stages, although such information is crucial to understanding and predicting their population dynamics. While most studies have relied on head width as an attribute for life-stage determination to date, such character in cicadas can be highly variable and thus differentiation solely based on such morphology is prone to subjectivity in practice. Here, we propose a reliable method for instar estimation that is applicable to Hyalessa fuscata nymphs. We first obtained morphometrics of nymphs in all stages. Second, we computed logarithm-transformation and principal component analysis to extract a transformed variable that captures most of the variance of morphological characteristics. Third, k-means were computed to divide the dataset into distinct clusters assuming four-, five- and six life-stage scenarios for the best interferences of life stages. Finally, simple linear regression analysis was conducted to compare and select the best fit model. Our result shows that five nymphal stages best fit for H. fuscata nymphs. This method is expected to provide an easy-to-handle ecological tool for the study of life history of cicadas as well as other insects that have long life cycles and multiple developmental stages.     

Managing for multiple species: greater sage-grouse and sagebrush songbirds

Human activity has altered 33–50% of Earth's surface, including temperate grasslands and sagebrush rangelands, resulting in a loss of biodiversity. By promoting habitat for sensitive or wide-ranging species, less exigent species may be protected in an umbrella effect. The greater sage-grouse (Centrocercus urophasianus; sage-grouse) has been proposed as an umbrella for other sagebrush-obligate species because it has an extensive range that overlaps with many other species, it is sensitive to anthropogenic activity, it requires resources over large landscapes, and its habitat needs are known. The efficacy of the umbrella concept, however, is often assumed and rarely tested. Therefore, we surveyed sage-grouse pellet occurrence and sagebrush-associated songbird abundance in northwest Colorado, USA, to determine the amount of habitat overlap between sage-grouse and 4 songbirds (Brewer's sparrow [Spizella breweri], sage thrasher [Oreoscoptes montanus], sagebrush sparrow [Artemisiospiza nevadensis]), and green-tailed towhee [Pipilo chlorurus]). During May and June 2013–2015, we conducted standard point count breeding surveys for songbirds and counted sage-grouse pellets within 300 10-m radius plots. We modeled songbird abundance and sage-grouse pellet occurrence with multi-scaled environmental features, such as sagebrush cover and bare ground. To evaluate sage-grouse as an umbrella for sagebrush-associated passerines, we determined the correlation between probability of sage-grouse pellet occurrence and model-predicted songbird densities per sampling plot. We then classified the sage-grouse probability of occurrence as high (probability >0.5) and low (probability ≤0.5) and mapped model-predicted surfaces for each species in our study area. We determined average songbird density in areas of high and low probability of sage-grouse occurrence. Sagebrush cover at intermediate scales was an important predictor for all species, and ground cover was important for all species except sage thrashers. Areas with a higher probability of sage-grouse occurrence also contained higher densities of Brewer's sparrows, green-tailed towhees, and sage thrashers, but predicted sagebrush sparrow densities were lower in these areas. In northwest Colorado, sage-grouse may be an effective umbrella for Brewer's sparrows, green-tailed towhees, and sage thrashers, but sage-grouse habitat does not appear to capture areas that support high sagebrush sparrow densities. A multi-species focus may be the best management and conservation strategy for several species of concern, especially those with conflicting habitat requirements. © The Wildlife Society, 2019     

Prediction analysis for microbiome sequencing data

One goal of human microbiome studies is to relate host traits with human microbiome compositions. The analysis of microbial community sequencing data presents great statistical challenges, especially when the samples have different library sizes and the data are overdispersed with many zeros. To address these challenges, we introduce a new statistical framework, called predictive analysis in metagenomics via inverse regression (PAMIR), to analyze microbiome sequencing data. Within this framework, an inverse regression model is developed for overdispersed microbiota counts given the trait, and then a prediction rule is constructed by taking advantage of the dimension‐reduction structure in the model. An efficient Monte Carlo expectation‐maximization algorithm is proposed for maximum likelihood estimation. The method is further generalized to accommodate other types of covariates. We demonstrate the advantages of PAMIR through simulations and two real data examples.     

Prediction of platinum-resistance patients of gastric cancer using bioinformatics

Lack of guidelines for personalized chemotherapy treatment after surgery has caused gastric cancer (GC) patients' unnecessary exposure to toxicity and the financial burden of chemotherapy treatments. In our study, we aimed to identify potential biomarkers to predict GC patients' susceptibility to platinum-based on Gene Expression Omnibus (GEO) data sets. A total of 603 differentially expressed genes (DEGs) were identified between platinum-resistant cell lines and platinum-sensitive cell lines based on the Cancer Cell Line Encyclopedia (CCLE) data sets. A total of 253 patients who had accepted radical gastrectomy were recruited, of which 97 received platinum-based chemotherapy and 156 were untreated. Three biomarkers (BRMS1, ND6, SRXN1) were then selected by univariate and multivariate Cox regression analysis to establish the predictive models using nomogram. Then this model was further validated through the GEO data set (GSE62254) which showed that this model could precisely predict the disease-free survival and overall survival of patients treated with platinum-based chemotherapy after surgery compared with untreated GC patients (P < 0.0001). This predictive model might provide helpful messages about the patients' susceptibility to platinum to guide personalized chemotherapy.