Borana rangeland of southern Ethiopia: Estimating biomass production and carrying capacity using field and remote sensing data

Assessing rangeland productivity is critical to reduce ecological degradation and promote sustainable livestock management. Here, we estimated biomass productivity and carrying capacity dynamics in the Borana rangeland of southern Ethiopia by using field-based data and remote sensing data (i.e., normalized difference vegetation index (NDVI)). Data was collected from both rainy and dry seasons when biomass production was high and low respectively. Results of linear regression showed that both biomass production (R²_adj = 0.672) and NDVI value (R²_adj = 0.471) were significantly decreased from 1990 to 2019. Field data and NDVI values for mean annual biomass showed a significant linear relationship. The model accuracy in the annual relationship between the observed and predicted biomass values was strong (R²_adj = 0.986) but with high standard error, indicating that the observed biomass production in the rangeland area was not in good condition as compared with the predicted one. This study suggests that, using NDVI data and field-based data in combined way has high potential to estimate rangeland biomass and carrying capacity dynamics at extensively grazed arid and semi-arid rangelands. And to use for estimating stoking rates and predicting future management techniques for decision making.     

2000-2010年黄河流域植被覆盖的时空变化

黄河流域位于干旱、半干旱和半湿润地区,生态环境脆弱,近年来,在气候变化和人类活动影响下,植被覆盖状况发生了变化。因此需要对黄河流域植被覆盖的变化进行监测,进而掌握流域植被的动态变化特征。在此背景下,利用2000-2010年的250 m分辨率的MOD13Q1数据来研究黄河流域植被覆盖区域的NDVI时空变化特征。采用Theil-Sen Median趋势分析和Mann-Kendall检验来研究NDVI的变化趋势特征,通过对Theil-Sen Median趋势分析和Mann-Kendall检验的结果和Hurst指数的结果的叠加,来研究NDVI的可持续特征。研究表明：1）从空间分布上看,黄河流域NDVI呈现出西部和东南部高,北部低的特征;2）从时间变化特征上看,2000-2010年植被覆盖区域年均NDVI均值在0.3-0.4之间波动,其中2000-2004年NDVI波动较大,但自2005年以来NDVI呈现快速增长的趋势;3）从变化趋势上看,2000-2010年黄河流域植被改善的区域远远大于退化的区域,改善的区域占植被覆盖区域总面积的62.9%,退化的区域占27.7%,9.4%的区域NDVI稳定不变;4）从可持续性来看,86.0%的植被覆盖区域NDVI呈现正向可持续性,即NDVI的可持续性较强;由变化趋势与Hurst指数的耦合信息得出,持续改善的面积占植被覆盖区域总面积的53.7%,持续稳定不变的区域占7.8%,持续退化的区域占24.5%,另外14.0%的区域未来变化趋势无法确定,持续退化和未来变化趋势无法确定区域的植被变化状况需要研究人员继续关注。     

The tick Ixodes ricinus: distribution and climate preferences in the western Palaearctic

In this study, multivariate spatial clustering on monthly normalized difference vegetation index (NDVI) maps is used to classify ecological regions over the western Palaearctic. This classification is then used to delineate the distribution and climate preferences of populations (clades) of the tick Ixodes ricinus L. (Acari: Ixodidae) from a geographically extensive dataset of tick records and a gridded 2.5-km resolution climate dataset. Using monthly layers of the NDVI, regions of similar ecological attributes were defined and nine populations with significant differences in critical climate parameters (P< 0.005) were detected. Grouping of tick records according to other categories, such as political divisions, a 4 degrees x 4 degrees grid overlying the study area, or the CORINE) and USGS) vegetation classification schemes did not provided significantly separated populations (P = 0.094-0.304). Factor analysis and hierarchical tree clustering provided an ecological overview of these tick clades: two Mediterranean and one Scandinavian (western) clades are clearly separated from a node that includes clades of different parts of central Europe and the British Isles, with contrasting affinities between the different clades. The capture records of these ecologically separated clades produce a clear bias when bioclimate envelope modelling is applied to the mapping of habitat suitability for the tick in the western Palaearctic. The best-performing methods (Cohen's kappa = 0.834-0.912) use partial models developed with data from each ecoregion, which are then overlapped over the region of study. It is concluded that the use of ecologically derived ecoregions is an objective step in assessing the presence of ecologically different clades, and provides a guide in the development of data partitioning for habitat suitability modelling.     

基于多源数据的三江源区生态工程建设前后草地动态变化及驱动因素研究

基于2000-2015年的NDVI、NPP和GPP多源数据,并结合人类干扰强度等级的构建,采用线性回归分析和地理加权回归分析方法,对比分析了在不同干扰等级下三江源区生态工程实施前5年和后10年草地的动态变化及其驱动因素。结果表明：（1）人类干扰强度和NDVI、NPP、GPP在空间上具有显著的区域差异,均表现为从西北向东南增加的趋势,且2006-2010年和2011-2015年草地NDVI、NPP和GPP值明显高于2000-2004年;（2）与2000-2004年相比,2006-2010年草地NDVI、NPP和GPP值的草地退化面积均减少,而2011-2015年退化面积又均增加,表明生态工程建设后的5年较前5年草地退化状况得到改善,然而在下一个5年草地又出现退化趋势;（3）在各人类干扰等级下生态工程实施后的5年内70%以上的草地处于改善状态,均出现退化面积比例显著减少,改善面积比例显著增加的趋势,而在2011-2015后5年草地退化面积比例增加,表明生态工程的实施促进了草地生态系统的改善,尤其在重度人类干扰区,草地由2000-2004年的52.71%退化区域到2006-2010年84.95%的改善区域。（4）影响草地动态变化的主要因素为距离道路的距离、距离居民点的距离、人口密度、干旱、温度和坡度等,各影响因素分布具有明显的空间异质性。研究结果将有助于为三江源区草地生态系统恢复、管理和战略制定提供科学指导和决策依据。     

Pheno-climatic profiles of vegetation based on multitemporal analysis of satellite data

Satellite Remote Sensing offers numerous advantages: study of large areas in a short time, study of areas with not easy accessibility, synoptic observation of territory, multitemporal observations of the same area, monitoring land modifications and change detection studies. The effectiveness of using satellite images for studying and mapping vegetation and land use has been stressed since the early 1980s. The photosynthetically active vegetation presents a very characteristic spectral response. In fact, leaves absorb red radiation (RED) in order to do photosynthetic process and reflect almost completely near infrared (NIR) wavelengths. The most diffused index for quantifying photosynthetically active biomass is the NDVI (Normalized Difference Vegetation Index): NDVI = (NIR-RED)/(NIR+RED). The NDVI is calculated, for each pixel of the images analysed, through an appropriate software. Low values of NDVI correspond to scarcely vegetated areas, while high values indicate densely vegetated ones. In order to distinguish among vegetation typologies we need some images of the same territory, well distributed during the year, showing seasonal variations of vegetation photosynthetic activity. Then it will be e.g. very easy distinguish between evergreen species (with NDVI almost steady during the year) and deciduous ones. Several types of sensors aboard some satellites allow different investigations to be done. AVHRR sensor on NOAA and TM sensor on Landsat are among the best known sensors available. They have different characteristics as for spectral resolution (number of spectral bands), spatial resolution (size of each elementary cell) and temporal resolution (the period of the satellite passes on the same territory). Vegetation phenology (including biomass and photosynthetic activity) heavily depends on climatic factors. The most important are: solar radiance, with an annual cycle and maximum at summer solstice; air temperature, (depending on solar radiance) with an annual cycle and maximum more than one month later; water availability, which is strongly dependent on rainfalls; in the Mediterranean area they can have an annual cycle (maximum during winter) or a six-monthly one (maxima near the equinoxes). Having a set of multitemporal satellite data (e.g. 12 monthly NOAA-AVHRR images) we can use a mathematical model able to discriminate annual and six-monthly cycles. Through Fourier analysis, the mathematical model calculate, for each pixel of the image, the parameters of the annual NDVI profile and create a synthetic image (pheno-climatic map), in which the values of the three RGB components (Red, Green, Blue ) are proportional to the integral of the NDVI profile for the following three periods: B=Nov-Feb G=Mar-Jun R=Jul-Oct. A similarly analysis is possible with Landsat satellite data, which have a higher spatial resolution, given that some shrewdness are taken. In fact, it is necessary to select satellite images according to the presence of cloud cover, which is--over the Italian peninsula--quite common during the March-April and October-November intervals. The purpose of carrying out pheno-climatic maps can be accomplished using 6 Landsat-TM images well-distributed during a year, every two months, even if the images have been taken during different years.     

基于TM影像的景观空间自相关分析——以北京昌平区为例

格局与尺度之间的关系是景观生态学的核心研究内容。景观格局发生在不同的尺度 ,而尺度又影响格局的研究 ,因而 ,在景观生态学研究中应用多种量化研究方法于一系列尺度来确定和特征化空间格局研究 ,并探求空间格局与生态学功能和生态学过程之间的关系是非常必要的。以北京昌平区为例 ,从 TM影像中选取 5个具有突出自然和社会经济背景差异的景观 ,即林地景观、农田景观、都市边缘景观、卫星城景观和灌丛景观为研究对象 ,基于归一化植被指数 (N DVI) ,采用常用空间自相关指数 ,即 Moran的 I系数和 Geary的 c系数进行一系列的空间自相关分析 ,旨在阐明 :变化的空间粒度如何影响空间分析 ?以及空间分析如何响应划区效应 ?此外 ,基于 N DVI和数字高程模型 (DEM)也探讨了对于不同的数据类型 ,格局的尺度依赖性如何变化。研究结果表明 :空间粒度的变化对于景观分析有着显著的影响 ,随着空间粒度的增加 ,空间自相关均呈下降趋势 ;不同景观类型对于空间粒度的变化有着不同的响应 ,人为干扰较多的景观具有较低的空间自相关 ,但对空间粒度的变化表现出较强的敏感性 ;对于不同的数据类型 ,格局分析对空间粒度变化的响应是不同的     

A mesoscale analysis of floristic patterns in the south-west Finnish Archipelago

Aim To identify the ecological gradients based on the flora on a mesoscale in an archipelago. To interpret the results of the ordination and the classification of a grid cell‐based botanical data set, with several environmental and geographical attributes. To compare the mesoscale distribution patterns of vascular plants with patterns previously observed on an island scale, and to develop a floristic zonation of the study area. Location The south‐west Finnish Archipelago. Methods Vascular plant species‐lists from over 1500 localities were assigned to 5 × 5 km grid cells. The grid cell‐based floristic data were subjected to both unconstrained [detrended correspondence analysis (DCA)] and constrained [detrended canonical correspondence analysis (DCCA)] gradient analyses. The results of DCA were interpreted with calculated weighted averages of Ellenberg's indicator values for vascular plants, the number of occurring taxa and indices for the strength of human influence and the occurrence of limestone. The results of DCCA were interpreted with geographical attributes of the grid cells and the occurrence of limestone. The grid cells were clustered using two‐way indicator species analysis (twinspan ). Results Both the unconstrained and the constrained ordinations gave consistent and interpretable results. The main ecological gradient runs from the grid cells containing species‐rich islands with high human impact to grid cells containing species‐poor islands with low human impact. This gradient also represents the continuum from areas with large islands near the mainland, to the outermost areas at the edge of the open sea. The secondary gradient was shown to be a gradient of soil reaction. twinspan gave a clustering primarily based on the location of the grid cells on an inner–outer archipelago gradient, but the occurrence of limestone also influenced the classification. The archipelago was divided into five non‐homogeneous areas based on the twinspan clusters. The detected gradients correspond well with the gradients detected in a similar island‐level analysis. Main conclusions The two major ecological gradients in the study area seem to be robust, which is indicated by the similar results obtained both on an island and on a mesoscale. A shift from local and regional processes to broader geographical gradients probably starts to occur at the applied scale. The distribution patterns are strongly affected by the inner–outer archipelago gradient and the occurrence of limestone.     

Fine scale waterbody data improve prediction of waterbird occurrence despite coarse species data

While modelling habitat suitability and species distribution, ecologists must deal with issues related to the spatial resolution of species occurrence and environmental data. Indeed, given that the spatial resolution of species and environmental datasets range from centimeters to hundreds of kilometers, it underlines the importance of choosing the optimal combination of resolutions to achieve the highest possible modelling prediction accuracy. We evaluated how the spatial resolution of land cover/waterbody datasets (meters to 1 km) affect waterbird habitat suitability models based on atlas data (grid cell of 12 × 11 km). We hypothesized that the area, perimeter and number of waterbodies computed from high resolution datasets would explain distributions of waterbirds better because coarse resolution datasets omit small waterbodies affecting species occurrence. Specifically, we investigated which spatial resolution of waterbodies better explain the distribution of seven waterbirds nesting on ponds/lakes with areas ranging from 0.1 ha to hundreds of hectares. Our results show that the area and perimeter of waterbodies derived from high resolution datasets (raster data with 30 m resolution, vector data corresponding with map scale 1:10 000) explain the distribution of the waterbirds better than those calculated using less accurate datasets despite the coarse grain of the species data. Taking into account the spatial extent (global vs regional) of the datasets, we found the Global Inland Waterbody Dataset to be the most suitable for modelling distribution of waterbirds. In general, we recommend using land cover data of a resolution sufficient to capture the smallest patches of the habitat suitable for a given species’ presence for both fine and coarse grain habitat suitability and distribution modelling.     

Global patterns of fern species diversity: An evaluation of fern data in GBIF

Despite that several studies have shown that data derived from species lists generated from distribution occurrence records in the Global Biodiversity Information Facility (GBIF) are not appropriate for those ecological and biogeographic studies that require high sampling completeness, because species lists derived from GBIF are generally very incomplete, Suissa et al. (2021) generated fern species lists based on data with GBIF for 100 km × 100 km grid cells across the world, and used the data to determine fern diversity hotspots and species richness–climate relationships. We conduct an evaluation on the completeness of fern species lists derived from GBIF at the grid–cell scale and at a larger spatial scale, and determine whether fern data derived from GBIF are appropriate for studies on the relations of species composition and richness with climatic variables. We show that species sampling completeness of GBIF is low (<40%) for most of the grid cells examined, and such low sampling completeness can substantially bias the investigation of geographic and ecological patterns of species diversity and the identification of diversity hotspots. We conclude that fern species lists derived from GBIF are generally very incomplete across a wide range of spatial scales, and are not appropriate for studies that require data derived from species lists in high completeness. We present a map showing global patterns of fern species diversity based on complete or nearly complete regional fern species lists.     

Past distribution of epiphyllous liverworts in China: The usability of historical data

Epiphyllous liverworts form a special group of bryophytes that primarily grow on the leaves of understory vascular plants in tropical and subtropical evergreen broadleaf forests. Being sensitive to moisture and temperature changes, epiphyllous liverworts are often considered to be good indicators of climate change and forest degradation. However, they are a poorly collected and taxonomically complicated group, with an only partly identified distribution pattern. In this study, we built four models based on 24 environmental variables at four different spatial resolutions (i.e., 1 km, 5 km, 10 km, and 15 km) to predict the past distribution of epiphyllous liverworts in China, using Maxent model and 63 historical location records (i.e., presence‐only data). Both area under the curve of the receiver operating characteristic (AUC) and true skill statistic (TSS) methods are used to assess the model performance. Results showed that the model with the predictors at a 15‐km resolution achieved the highest predictive accuracy (AUC=0.946; TSS=0.880), although there was no statistically significant difference between the four models (p > 0.05). The most significant environmental variables included aridity, annual precipitation, precipitation of wettest month, precipitation of wettest quarter, and precipitation of warmest quarter, annual mean NDVI, and minimum NDVI. The predicted suitable areas for epiphyllous liverworts were mainly located in the south of Yangtze River and seldom exceed 35°N, which were consistent with the museum and herbarium records, as well as the historical records in scientific literatures. Our study further demonstrated the value of historical data to ecological and evolutionary studies.     

Solubilization of the active vasoactive intestinal peptide receptor from human colonic adenocarcinoma cells

The non-ionic detergent n-octyl-beta-D-glucopyranoside was used to solubilize the VIP (vasoactive intestinal peptide) receptor from human colonic adenocarcinoma cell line HT29-D4. The binding of monoiodinated 125I-VIP to the solubilized receptor was specific, time-dependent, and reversible. Scatchard analysis of data obtained from competitive displacement of monoiodinated 125I-VIP by native VIP suggested the presence of two classes of VIP binding sites with Kd values of 0.32 and 46.7 nM. The binding capacities of these two classes were 1.7 x 10(10) and 30.2 x 10(10) sites/mg of proteins, respectively. The solubilized receptor retained the specificity of the human VIP receptor towards the peptides of the VIP/secretin/glucagon family. The order of potency in inhibiting monoiodinated 125I-VIP binding was VIP (IC50 = 1.0 x 10(-9) M) much greater than peptide histidine methionine amide (IC50 = 10(-7) M) greater than growth hormone-releasing factor (IC50 = 3 x 10(-7) M) greater than secretin (IC50 greater than 10(-6) M); glucagon had no effect on VIP binding. The reducing agent dithiothreitol inhibited in a dose-dependent manner the binding of 125I-VIP. Covalent cross-linking experiments between the solubilized receptor and 125I-VIP showed that after sodium dodecyl sulfate-polyacrylamide gel electrophoresis and autoradiography two major and one minor polypeptides of Mr 67,000, 72,000, and 83,000 were specifically labeled. When analyzed by gel filtration, the n-octyl-beta-D-glucopyranoside-solubilized 125I-VIP-receptor complex was resolved into two major peaks with molecular mass in the range of 60-70 and 270-300 kDa. Thus, the soluble form of the VIP receptor was probably a multimeric complex in which disulfide bonds may play an important role to hold the receptor in an active configuration.     

Implications of location specific data and their usefulness in conservation planning: an example from Indian Himalayan Region (IHR)

The present study evaluated the usefulness of collection of location specific data for assessing patterns of species diversity and endemism based on a dataset for the 10 temperate flowering plant families from the Indian Himalaya Region. Analysis was based on 818 grid cells (15 × 15′) representing throughout the Indian Himalayan Region (IHR). Based on the existing information on diverse aspects of the selected plant families four indices, i.e., species richness, weighted endemism, 1–4 cell endemism and corrected weighted endemism were developed and mapped for selected plant families. Analysis revealed that endemism is significantly (P < 0.001) correlated with species richness (1–4 cell endemic index: r = 0.85; weighted endemism; r = 0.58). On the basis of four different indices, potential areas have been identified for conservation. Data of each indices have been overlaid to each other for identification and prioritization of endemic rich areas in the Indian Himalayan Region.     

Modeling the distribution of Schistosoma mansoni and host snails in Uganda using satellite sensor data and Geographical Information Systems

The potential value of MODIS satellite sensor data on Normalized Difference Vegetation Index (NDVI) and land surface temperatures (LST) for describing the distribution of the Schistosoma mansoni-"Biomphalaria pfeifferi"/Biomphalaria sudanica parasite-snail system in inland Uganda, were tested by developing annual and seasonal composite models, and iteratively analysing for their relationship with parasite and snail distribution. The dry season composite model predicted an endemic area that produced the best fit with the distribution of schools with > or =5% prevalence. NDVI values of 151-174, day temperatures of 26-36 degrees C, and night temperatures of 15-20 degrees C were used as criteria for the prediction model. Using the same approach with host snail data indicated that most of Uganda is suitable "B. pfeifferi"/B. sudanica habitat, except for possibly the north-eastern region of the country. The parasite, however, appears to be restricted in its distribution in both the north-eastern and the south-western regions of Uganda. The absence of disease in the south-west can not be attributed to the absence of snail hosts. Results suggest a combination of satellite sensor data on temperature and standard climate data on precipitation, as the best ecological determinants of the S. mansoni-"B. pfeifferi"/B. sudanica system. Satellite composite models and logistic regression analysis, suggest low night time temperature as one of the significant factors inhibiting S. mansoni transmission in the south-western highland areas of Uganda. The developed models are, however, unique, representing species-specific ecologic preferences of the S. mansoni-"B. Pfeifferi"/B. sudanica system in inland Uganda. Further validation studies are needed to test the value of the model in other countries in East Africa.     

北京城区道路系统路网空间特征及其与LST和NDVI的相关性

城市道路系统是人类活动最为频繁的场所之一,研究路网空间特征与相关环境指标的关系,可为定量分析城市建设过程对城市生态环境的影响以及为城市基础设施规划管理提供方法。对北京市五环内各级道路抽样调查及面向对象的高分辨率遥感影像识别,建立道路空间信息数据库,运用TM影像反演获得地表温度(LST)及归一化植被指数(NDVI),研究4种不同分析网格下城市路网特征与两者的空间相关性。结果表明,在较大分析网格下(4×4),路网空间指标与LST mean及NDVI mean相关性显著;中等网格下,除LST与NDVI的平均值外,路网空间指标还可与两者最小值建立相关性;在较小窗口下(32×32),除平均值与最小值,还可建立特定道路指标与LST max及NDVI max的联系;各路网指标中,单位面积道路总长与LST及NDVI相关性最强;加权道路结点数在8×8和32×32网格分析中,与NDVI的相关性高于该指标未加权值,而结点指标均不适合与LST max建立联系;在各分析网格下,4项道路指标均与NDVI mean呈极显著负相关,表明道路绿地建设规模还不足以对NDVI的强度及分布产生较大影响。     

玛纳斯河流域盐渍化灌区生态环境遥感监测研究

土壤盐渍化已成为全球性问题,给生态环境及农业生产带来严重的威胁。为了快速、准确评价土壤盐渍化给区域生态坏境带来的影响,该文提出了新的完全基于遥感数据的遥感生态指数(SSEI,Soil Salinization Ecology Index)来监测玛纳斯河流域盐渍化灌区生态环境变化。该指数利用主成分分析的方法耦合与土壤盐渍化相关的土壤盐度、地表反照率、植被覆盖度和土壤湿度四大地表参数,指数构建是数据本身性质所决定,不同于以往遥感与非遥感指数加权叠加易受人为影响。研究结果表明:耦合与盐渍化信息相关的各遥感指数得到的生态指数,能够对土壤盐渍化影响区域的生态环境进行快速、定量、客观的监测。将该指数应用到新疆玛纳斯河流域灌区,结果表明在近26年优和良等级生态环境面积增加了12.89%,这说明灌区生态环境有所改善。该研究对土壤盐渍化监测与评价具有一定参考意义。     

Analysis of bioclimatic time series and their neural network-based classification to characterise drought risk patterns in South Italy

A new approach to characterise geographical areas with a drought risk index (DRI) is suggested, by applying an artificial neural network (ANN) classifier to bioclimatic time series for which operational temporal units (OtUs) are defined. A climatic database, corresponding to a grid of 8 km x 8 km cells covering the Italian peninsula, was considered. Each cell is described by the time series of seven variables recorded from 1989 to 2000. Sixteen cells were selected according to land cover homogeneity and completeness of the time series data. The periodic components of the time series were calculated by means of the fast Fourier transform (FFT) method. Temporal units corresponding to the period of the sinusoidal functions most related to the data were used as OtUs. The ANN for each OtU calculates a DRI value ranging between -1 and 1. The value is interpretable as the proximity of the OtUs to one of two situations corresponding to minimum and maximum drought risk, respectively. The former set (DRI = -1) is represented by an ideal OtU with minimum values of temperatures and evapo-transpiration, and maximum values of rainfall, normalised difference vegetation index (NDVI) and soil water content. The second set (DRI = 1) is represented by the reciprocal OtU to the former one. The classification of the cells based on DRI time profiles showed that, at the scale used in this work, DRI has no dependence on land cover class, but is related to the location of the cells. The methodology was integrated with GIS (geographic information system) software, and used to show the geographic pattern of DRI in a given area at different periods.     

BEEM-Static: Accurate inference of ecological interactions from cross-sectional microbiome data

The structure and function of diverse microbial communities is underpinned by ecological interactions that remain uncharacterized. With rapid adoption of next-generation sequencing for studying microbiomes, data-driven inference of microbial interactions based on abundance correlations is widely used, but with the drawback that ecological interpretations may not be possible. Leveraging cross-sectional microbiome datasets for unravelling ecological structure in a scalable manner thus remains an open problem. We present an expectation-maximization algorithm (BEEM-Static) that can be applied to cross-sectional datasets to infer interaction networks based on an ecological model (generalized Lotka-Volterra). The method exhibits robustness to violations in model assumptions by using statistical filters to identify and remove corresponding samples. Benchmarking against 10 state-of-the-art correlation based methods showed that BEEM-Static can infer presence and directionality of ecological interactions even with relative abundance data (AUC-ROC>0.85), a task that other methods struggle with (AUC-ROC<0.63). In addition, BEEM-Static can tolerate a high fraction of samples (up to 40%) being not at steady state or coming from an alternate model. Applying BEEM-Static to a large public dataset of human gut microbiomes (n = 4,617) identified multiple stable equilibria that better reflect ecological enterotypes with distinct carrying capacities and interactions for key species.ConclusionBEEM-Static provides new opportunities for mining ecologically interpretable interactions and systems insights from the growing corpus of microbiome data.     

Crystallization and preliminary crystallographic studies of an antitumour lectin from the edible mushroom Agrocybe aegerita

An antitumour lectin named AAL has been purified from the fruiting body of edible mushroom Agrocybe aegerita. In addition to having a distinct bioactivity, AAL shows strong inhibition effects on human and mouse tumour cells. It has been shown that AAL exerts its antitumour effects via apoptosis-induction. AAL and AAL-lactose complex have been crystallized and their diffraction data were collected with resolution of 2.6 A and 3.0 A, respectively. Both crystals belong to space group P 6122 with unit cell parameters a = 123.98 A, b = 123.98 A, c = 56.86 A, alpha= beta=90 degrees, gamma = 120 degrees and a = 123.69 A, b = 123.69 A, c = 56.64 A, alpha = beta = 90 degrees, gamma = 120 degrees, respectively.     

Correlation of in vitro and in vivo models for the oral absorption of peptide drugs

The aim of this study was to evaluate two in vitro models, Caco-2 monolayer and rat intestinal mucosa, regarding their linear correlation with in vivo bioavailability data of therapeutic peptide drugs after oral administration in rat and human. Furthermore the impact of molecular mass (Mm) of the according peptides on their permeability was evaluated. Transport experiments with commercially available water soluble peptide drugs were conducted using Caco-2 cell monolayer grown on transwell filter membranes and with freshly excised rat intestinal mucosa mounted in Using type chambers. Apparent permeability coefficients (P (app)) were calculated and compared with in vivo data derived from the literature. It was shown that, besides a few exceptions, the Mm of peptides linearly correlates with permeability across rat intestinal mucosa (R (2) = 0.86; y = -196.22x + 1354.24), with rat oral bioavailability (R (2) = 0.64; y = -401.90x + 1268.86) as well as with human oral bioavailability (R (2) = 0.91; y = -359.43x + 1103.83). Furthermore it was shown that P (app) values of investigated hydrophilic peptides across Caco-2 monolayer displayed lower permeability than across rat intestinal mucosa. A correlation between P (app) values across rat intestinal mucosa and in vivo oral bioavailability in human (R (2) = 0.98; y = 2.11x + 0.34) attests the rat in vitro model to be a very useful prediction model for human oral bioavailability of hydrophilic peptide drugs. Presented correlations encourage the use of the rat in vitro model for the prediction of human oral bioavailabilities of hydrophilic peptide drugs.     

Human-induced changes in US biogenic volatile organic compound emissions: evidence from long-term forest inventory data

Volatile organic compounds (VOCs) emitted by woody vegetation influence global climate forcing and the formation of tropospheric ozone. We use data from over 250 000 re‐surveyed forest plots in the eastern US to estimate emission rates for the two most important biogenic VOCs (isoprene and monoterpenes) in the 1980s and 1990s, and then compare these estimates to give a decadal change in emission rate. Over much of the region, particularly the southeast, we estimate that there were large changes in biogenic VOC emissions: half of the grid cells (1°× 1°) had decadal changes in emission rate outside the range ?2.3% to +16.8% for isoprene, and outside the range 0.2–17.1% for monoterpenes. For an average grid cell the estimated decadal change in heatwave biogenic VOC emissions (usually an increase) was three times greater than the decadal change in heatwave anthropogenic VOC emissions (usually a decrease, caused by legislation). Leaf‐area increases in forests, caused by anthropogenic disturbance, were the most important process increasing biogenic VOC emissions. However, in the southeast, which had the largest estimated changes, there were substantial effects of ecological succession (which decreased monoterpene emissions and had location‐specific effects on isoprene emissions), harvesting (which decreased monoterpene emissions and increased isoprene emissions) and plantation management (which increased isoprene emissions, and decreased monoterpene emissions in some states but increased monoterpene emissions in others). In any given region, changes in a very few tree species caused most of the changes in emissions: the rapid changes in the southeast were caused almost entirely by increases in sweetgum (Liquidambar styraciflua) and a few pine species. Therefore, in these regions, a more detailed ecological understanding of just a few species could greatly improve our understanding of the relationship between natural ecological processes, forest management, and biogenic VOC emissions.