Evaluation of Systematic Position of Helicoprorodontids and Chaeneids (Ciliophora,Litostomatea): An Attempt to Break Long Branches in 18S rRNA Gene Phylogenies

Phylogenetic position of some free‐living litostomatean taxa has not been correctly determined because of long‐branch artifacts in 18S rRNA gene trees. The main aim of this study was to test the effectiveness of various masking algorithms, tree‐building techniques, binarization of DNA data as well as combining morphological and molecular data to eliminate long‐branch attraction of two problematic groups, helicoprorodontids and chaeneids. Guidance and SlowFaster masking in a combination with PhyloBayesian tree construction erased the artifactual positions of helicoprorodontids and chaeneids. On the other hand, binarization of DNA sequences and the strategy of combining morphological and molecular data eliminated only the artifactual position of chaeneids but not that of helicoprorodontids which were still being attracted by out‐group taxa. According to statistical tree topology tests and comparative morphological studies, helicoprorodontids are classified as a distinct order while chaeneids are considered to be fast evolving members of the order Lacrymariida. The high body contractility, “cephalization” of the anterior body end, and helicalization of the anterior portion of some or all somatic ciliary rows indicate relatedness of helicoprorodontids, chaeneids, and lacrymariids. On the other hand, the dorsal brush separated from the circumoral kinety by dense ciliary files supports kinships of chaeneids, lacrymariids, and didiniids.     

Robustness of the biotic indicators used for classification of ecological status of lotic water bodies: A testing method when the data series are short

Successful implementation of the Water Framework Directive and achieving its objective of good ecological status of all water bodies depend on the power of the set of monitoring indicators to capture the change in the ecological status of aquatic systems. In this context, testing the robustness and sensitivity of ecological indicators currently used for assessing the status of lotic water bodies is instrumental for the adaptation and further development of assessment methods. This is also a prerequisite for an effective, context-based monitoring system and for improving the quality of the decision making for water bodies. This is particularly challenging in regions where the sets of indicators are under development, the data series are relatively short and data which addresses the individual error sources are lacking. Here we show that hierarchical clusters and ordination analysis provide appropriate tools with which the validity of the ecological status of water bodies set up based on biological multimetric monitoring indices in a small water basin could be tested. We hypothesize that robust and informative monitoring methods classify all water bodies belonging to a single ordination grouping in the same quality class (high, good, moderate, poor or bad). In our case study multimetric biological indicators failed to discriminate between the good and moderate ecological status. Community structure as well as water conductivity and nitrate load were primarily responsible for the observed difference between ordination groupings. Inconsistencies shown in our case study are likely to be induced by insufficient refinement of monitoring schemes and by the constraints existing in the data series and available metadata. We show that multiplication of indicators leads to discrepant interpretation and problematic application. Proposed ordination analysis proves to be a simple and useful tool to detect such discrepancies and support further progress in indicator development. Integrated and longer data and metadata series are needed to refine context-based monitoring methods.     

Phylogenetic signals in thermal traits remain stronger in the tropics if we can believe published physiological data. A reply to McKechnie et al., “Data quality problems undermine analyses of endotherm upper critical temperatures”

Due to concerns about data quality, McKechnie, Coe, Gerson, and Wolf ( 2016 ) questioned the conclusions of our study (Khaliq et al., 2015 ) published in this journal. Here, we argue that most of the questioned data points are in fact useful for macrophysiological analyses, mostly because the vast majority of data are explicitly reported in the peer‐reviewed physiological literature. Furthermore, we show that our conclusions remain largely robust irrespective of the data inclusion criterion. While we think that constructive debates about the adequate use of primary data in meta‐studies as well as more transparency in data inclusion criteria are indeed useful, we also emphasize that data suitability should be evaluated in the light of the scope and scale of the study in which they are used. We hope that this discussion will not discourage the exchange between disciplines such as biogeography and physiology, as this integration is needed to address some of the most urgent scientific challenges.     

神农架川金丝猴栖息地优势乔木树种遥感识别及其分布特征

针对神农架川金丝猴生境基础研究中乔木树种大范围分布数据难以获取问题,尝试利用多源多时相遥感数据结合专家知识分层次实现树种识别。首先采用冬季Landsat8/OLI数据根据物侯特性分层提取常绿、落叶林的地域范围;进而依据夏季Worldview-2高分遥感影像的实地乔木样本的光谱特征分层次完成常绿树种(巴山冷杉、华山松、青$、刺叶栎)和落叶树种(红桦、日本落叶松、米心水青冈、漆树、锐齿槲栎、椅杨)的识别;并通过实地植被样方及专家知识通过高程数据完成分类结果的修正;最后结合GIS对主要优势树种的地形及地域分布特征进行了空间分析。实验精度表明常绿林中巴山冷杉、华山松、刺叶栎、虫害华山松整体精度较高,落叶林中红桦、漆树等识别精度相对较高,部分树种如椅杨、锐齿槲栎识别精度较低;总体上常绿树种的精度要优于落叶树种。从植物地理学、遥感、GIS三者相结合的角度,将多源、多时相遥感数据与物种物候特性、专家知识进行有效整合,提出了一种乔木树种识别的方法(1)提供了复杂山地环境的主要乔木优势种识别途径,且具有通用性;(2)完成了物种物候特性与遥感数据特性的整合利用,有效降低数据成本费用;(3)配合地面样方及专家知识修正结果,避免了过分依赖光谱特征引起的误判。这将为神农架川金丝猴栖息地保护与恢复提供更精确的数据依据。     

Concomitant Information in Competing Risk Analysis

A competing risk model, accommodating both Type I censoring and random withdrawals, is expanded to incorporate concomitant information by allowing the parameters of the underlying distributions to be a linear function of two covariates. The model is developed for two competing risks, one following a Weibull distribution and the other a Rayleigh distribution, and random withdrawals following a Weibull distribution. A method is developed for testing the equality of the coefficients for a given covariate for each of the competing risks using MLE'.     

Multiattribute evaluation of regional cotton variety trials

Summary The Australian Cotton Cultivar Trials (ACCT) are designed to investigate various cotton [Gossypium hirsutum (L.)] lines in several locations in New South Wales and Queensland each year. If these lines are to be assessed by the simultaneous use of yield and lint quality data, then a multivariate technique applicable to three-way data is desirable. Two such techniques, the mixture maximum likelihood method of clustering and three-mode principal component analysis, are described and used to analyze these data. Applied together, the methods enhance each other's usefulness in interpreting the information on the line response patterns across the locations. The methods provide a good integration of the responses across environments of the entries for the different attributes in the trials. For instance, using yield as the sole criterion, the excellence of the namcala and coker group for quality is overlooked. The analyses point to a decision in favor of either high yields of moderate to good quality lint or moderate yield but superior lint quality. The decisions indicated by the methods confirmed the selections made by the plant breeders. The procedures provide a less subjective, relatively easy to apply and interpret analytical method of describing the patterns of performance and associations in complex multiattribute and multilocation trials. This should lead to more efficient selection among lines in such trials.     

To share or not to share: Incentivizing data sharing in life science communities

Most scientists recognize the importance of sharing data online in an open fashion. Nonetheless, many studies have documented the concerns that accompany data sharing activities, including loss of credit or IP, misuse and the time needed to curate interoperable data. To this end, discussions around data sharing often identify incentives that could potentially ameliorate these disincentivising concerns. Nonetheless, current Open Data discussions often rely on evidence‐based studies to identify the disincentives to overcome. This results in highly specific and directed interventions. In contrast, this paper offers a different interpretation of these concerns. To do so, it makes use of the Thomas Theorem which suggests that: “If men define situations as real, they are real in their consequences”. Using empirical evidence from sub‐Saharan African (bio)chemistry laboratories, this paper illustrates how individual perceptions of research environments – whether associated with evidence or not – are highly influential in shaping data sharing practices. It concludes with the suggestion that discussion on incentivising data sharing amongst scientific communities need to take a broader set of concerns into account and offer a more creative approach to ameliorating environmental disincentives.     

基于物候特征的盐渍化信息数据挖掘研究

盐渍化是影响植被和作物长势的重要因素,精确反演盐渍化的时空分布信息至关重要。基于MOD13A1-NDVI数据反演生长季开始日期(SOS)、生长季结束日期(EOS)、生长季长度(LEN)等物候参数和计算出能高精度反演盐渍化空间分布的多种植被指数、盐分指数、地形指数、干旱指数等参数后作为BP-ANN人工神经网络的输入因子来反演盐渍化信息,同时按照植被类型和地貌类型进行分区来反演盐渍化信息,以探讨盐渍化受植被和地貌类型的影响。主要结论如下:(1)盐渍化的形成受多种因素的影响,与物候参数大多呈非线性关系,不能单纯的以某拟合公式来进行表达,需要借助人工神经网络超强的非线性拟合能力来反演盐渍化信息。(2)通过深入挖掘植被物候信息,在融入物候参数后的反演精度显著提高。可决系数R2从0.68(非物候参数)增加到0.79(包括物候参数),但是需要加入地形、影像数据和土壤水分等方面的信息来更加精确的反演盐渍化信息。生物累积量指标LSI(Large seasonal integral)和SSI(Small seasonal integral)能够很好的表征盐渍化的信息。(3)划分植被类型后的盐渍化提取精度进一步提高,可决系数R~2达到了0.88。(4)以地貌特征作为类型分区后,反演结果的R~2达到了0.85,精度较高,比以植被类型作为分区的精度略小。高程较低区域的盐渍化现象普遍较重,盐渍化程度受到地形和地貌因素的影响显著。(5)农用地区域多为非盐渍化和轻度盐渍化地,稀疏植被区多为重盐渍化地。研究区的非盐渍化和轻盐渍化地、中盐渍化地和重度盐渍化地比例分别为53.42%,13.71%,32.87%。以上的研究结果提出了一种融合物候信息和非物候参数来反演盐渍化信息的方法,进行深入的协同植被物候监测盐渍化信息方面的数据挖掘,在融入了物候参数后,盐渍化的预测精度显著提高。