Variation in the methods leads to variation in the interpretation of biodiversity–ecosystem multifunctionality relationships

生物多样性常常和生态系统多功能性(生态系统同时提供多个生态系统功能的能力)正相关。然而,生物多样性与生态系统多功能性的关系是否依赖于生态系统功能的数目有诸多争议。其中,生物多样性对生态系统多功能性的影响或许不随生态系统功能数目的变化而变化,或者随生态系统功能数目的增多而增强。我们期望通过研究不同生态系统多功能性指数的统计原理来解决这些争议。 我们使用了模型模拟和一系列来自不同空间尺度(从局域到全球)和不同生物群系(温带和高寒草地、森林和干旱地)的经验数据。我们回顾了量化生态系统多功能性的三种方法,包括平均值法、加和法和阈值法。我们发现随着生态系统功能数目的增加,生物多样性与生态系统多功能性的关系要么不变,要么增强。这些结果可由平均和加和的多功能性指数的统计原理来解释。具体来讲,当利用生态系统功能的平均值计算多功能性指数时,由于多样性对多功能性的效应等于多样性对单个生态系统功能效应的平均值,所以不会随生态系统功能数目的变化而变化。同样的道理,当利用单个生态系统的加和值计算多功能性指数时,多样性的效应会随着生态系统功能数目的增加而增强。我们提出了一个改进的多功能性指数,将平均或加和多功能性指数转化为标准化的多功能性指数, 以便于对不同研究的结果进行比较。此外,我们提出了基于变量数值范围的标准化方法来解决阈值法的数学假象问题(多样性效应随生态系统功能数目的增加而增强)。我们的研究结果表明,量化多功能性指数的方法不同,结果也不同。因此,有必要加深对不同方法数理基础的理解。而标准化的多功能性指数为比较不同研究中的生物多样性与生态系统多功能性的关系提供了有效的方法。     

污染场地土壤生态风险评估研究进展

随着我国快速城市化以及产业结构的调整,遗留下了大量的污染场地,发展和实施污染场地土壤生态风险评估是进行大规模污染场地修复行动的必要条件。本文围绕污染场地土壤生态风险评估的科学原理、框架构建及技术方法等方面的关键问题: 1)评估框架的场地实际针对性;2)概念模型的不确定性;3)土壤复合污染毒性机制;4)评估终点筛选;5)评估方法和框架构建等展开讨论,指出土壤复合污染的制毒机制,即污染物生物有效性和联合效应是污染场地土壤生态风险评估的关键科学问题。耦合美国环保局四步法和欧盟层级法的“证据-权重法”评估框架适用于野外复杂环境条件下的土壤污染生态风险评估。建议今后重点开展以下5个方面的工作: 1)污染场地土壤生态风险评估技术框架与风险管控技术框架之间的联合;2)概念模型研究;3)基于过程的场地土壤污染物反应运移模型研究;4)场地土壤复合污染生态毒理学机制研究;5)生态系统高水平生态风险评估终点研究。旨在为形成我国本土污染场地土壤生态风险评估技术指南提供理论基础和构架。     

Dictyosphaerium-like morphotype in terrestrial algae: what is Xerochlorella (Trebouxiophyceae,Chlorophyta)?1

Several strains of terrestrial algae isolated from biological soil crusts in Germany and Ukraine were identified by morphological methods as the widely distributed species Dictyosphaerium minutum (=Dictyosphaerium chlorelloides). Investigation of the phylogeny showed their position unexpectedly outside of Chlorellaceae (Trebouxiophyceae) and distantly from Chlorella chlorelloides, to which this taxon was attributed after revision of the genus Chlorella based on an integrative approach. SSU rRNA phylogeny determined the position of our strains inside a clade recently described as a new genus of the cryptic alga Xerochlorella olmiae isolated from desert biological soil crusts in the United States. Investigation of the morphology of the authentic strain of X. olmiae showed Dictyosphaerium-like morphology, as well as some other characters, common for our strains and morphospecies D. minutum. The latter alga was described as terrestrial and subsequently united with the earlier described aquatic representative D. chlorelloides because of their similar morphology. The revision of Chlorella mentioned above provided only one aquatic strain (D. chlorelloides), which determined its position in the genus. But terrestrial strains of the morphospecies were not investigated phylogenetically. Our study showed that the terrestrial D. minutum is not related to the morphologically similar D. chlorelloides (=Chlorella chlorelloides, Chlorellaceae), and instead represented a separate lineage in the Trebouxiophyceae, recently described as genus Xerochlorella. Therefore, revision of Xerochlorella is proposed, including nomenclatural combinations, epitypifications, and emendations of two species: X. minuta and X. dichotoma. New characters of the genus based on investigation of morphology and ultrastructure were determined.     

Computational Reproducibility in The Wildlife Society's Flagship Journals

Scientific progress depends upon the accumulation of empirical knowledge via reproducible methodology. Although reproducibility is a main tenet of the scientific method, recent studies have highlighted widespread failures in adherence to this ideal. The goal of this study was to gauge the level of computational reproducibility, or the ability to obtain the same results using the same data and analytic methods as in the original publication, in the field of wildlife science. We randomly selected 80 papers published in the Journal of Wildlife Management and Wildlife Society Bulletin between 1 June 2016 and 1 June 2018. Of those that were suitable for reproducibility review (n = 74), we attempted to obtain study data from online repositories or directly from authors. Forty-two authors did not respond to our requests, and we were further unable to obtain data from authors of 13 other studies. Of the 19 studies for which we were able to obtain data and complete our analysis, we judged that 13 were mostly or fully reproducible. We conclude that the studies with publicly available data or data shared upon request were largely reproducible, but we remain concerned about the difficulty in obtaining data from recently published papers. We recommend increased data-sharing, data organization and documentation, communication, and training to advance computational reproducibility in the wildlife sciences. © 2020 The Authors. The Journal of Wildlife Management published by Wiley Periodicals, Inc. on behalf of The Wildlife Society.     

Conservation Strategy for African Medicinal Species: <i>In Vitro</i> Biotechnological Approach

The use of medicinal plants for different therapeutic values is well documented in African continent. African diverse biodiversity hotspots provide a wide range of endemic species, which ensures a potential medicinal value. The feasible conservation approach and sustainable harvesting for the medicinal species remains a huge challenge. However, conservation approach through different biotechnological tools such as micropropagation, somatic embryogenesis, synthetic seed production, hairy root culture, molecular markers based study and cryopreservation of endemic African medicinal species is much crucial. In this review, an attempt has been made to provide different in vitro biotechnological approaches for the conservation of African medicinal species. The present review will be helpful in further technology development and deciding the priorities at decision-making levels for in vitro conservation and sustainable use of African medicinal species.     

Metabolomics meets lipidomics: Assessing the small molecule component of metabolism

Metabolomics, including lipidomics, is emerging as a quantitative biology approach for the assessment of energy flow through metabolism and information flow through metabolic signaling; thus, providing novel insights into metabolism and its regulation, in health, healthy ageing and disease. In this forward-looking review we provide an overview on the origins of metabolomics, on its role in this postgenomic era of biochemistry and its application to investigate metabolite role and (bio)activity, from model systems to human population studies. We present the challenges inherent to this analytical science, and approaches and modes of analysis that are used to resolve, characterize and measure the infinite chemical diversity contained in the metabolome (including lipidome) of complex biological matrices. In the current outbreak of metabolic diseases such as cardiometabolic disorders, cancer and neurodegenerative diseases, metabolomics appears to be ideally situated for the investigation of disease pathophysiology from a metabolite perspective.     

Outlier detection methods are still effective even using virtual species created with the probabilistic approach

Liu et al. (Journal of Biogeography, 2018, 45 :164–176) presented an approach to detect outliers in species distribution data by developing virtual species created using the threshold approach. Meynard et al. (Journal of biogeography, 2019, 46 :2141–2144) raised concerns about this approach stating that ‘using a probabilistic approach … may significantly change results’. Here we provide a new series of simulations using the two approaches and demonstrate that the outlier detection approach based on pseudo species distribution models was still effective when using the probabilistic approach, although the detection rate was lower than when using the threshold approach.     

Using Inquiry and Tree-Thinking to “March Through the Animal Phyla”: Teaching Introductory Comparative Biology in an Evolutionary Context

Biodiversity was originally taught in our Introductory Organismal Biology course at Michigan State University (LB144; freshman/sophomore majors) by rote memorization of isolated facts about organisms. When we moved to an inquiry-based laboratory framework to improve pedagogy, an unfortunate and unforeseen result was the loss of much of our study of biodiversity. In this paper, we describe the restructuring of LB144 to restore the study of biodiversity and organismal groups while retaining the benefits of an inquiry-based approach. The curricular intervention was accomplished through the creation and implementation of a four-week Comparative Biology laboratory stream. During this stream, student research teams recorded and organized observations that they made on a range of organisms and analyzed their data in a phylogenetic framework. During the stream, our students worked through a set of exercises designed to help them learn how to read, interpret, and manipulate phylogenetic trees. We placed particular emphasis on the concept that phylogenetic trees are hypotheses of relationship that can be tested with scientific data. This incorporation of phylogenies and phylogenetic analysis, or “tree-thinking,” into our students’ work provided an explicit synthetic evolutionary framework for their comparative biodiversity studies. End-of-stream products included a team phylogenetic analysis exercise and an individual comparative biology oral presentation.     

Performance of several variable-selection methods applied to real ecological data

I evaluated the predictive ability of statistical models obtained by applying seven methods of variable selection to 12 ecological and environmental data sets. Cross-validation, involving repeated splits of each data set into training and validation subsets, was used to obtain honest estimates of predictive ability that could be fairly compared among methods. There was surprisingly little difference in predictive ability among five methods based on multiple linear regression. Stepwise methods performed similarly to exhaustive algorithms for subset selection, and the choice of criterion for comparing models (Akaike's information criterion, Schwarz's Bayesian information criterion or F statistics) had little effect on predictive ability. For most of the data sets, two methods based on regression trees yielded models with substantially lower predictive ability. I argue that there is no 'best' method of variable selection and that any of the regression-based approaches discussed here is capable of yielding useful predictive models.     

Computational and analytical framework for small RNA profiling by high-throughput sequencing

The advent of high-throughput sequencing (HTS) methods has enabled direct approaches to quantitatively profile small RNA populations. However, these methods have been limited by several factors, including representational artifacts and lack of established statistical methods of analysis. Furthermore, massive HTS data sets present new problems related to data processing and mapping to a reference genome. Here, we show that cluster-based sequencing-by-synthesis technology is highly reproducible as a quantitative profiling tool for several classes of small RNA from Arabidopsis thaliana. We introduce the use of synthetic RNA oligoribonucleotide standards to facilitate objective normalization between HTS data sets, and adapt microarray-type methods for statistical analysis of multiple samples. These methods were tested successfully using mutants with small RNA biogenesis (miRNA-defective dcl1 mutant and siRNA-defective dcl2 dcl3 dcl4 triple mutant) or effector protein (ago1 mutant) deficiencies. Computational methods were also developed to rapidly and accurately parse, quantify, and map small RNA data.