Investigating Reasons for Differences in the Results of Environmental,Physical, and Hybrid Input‐Output Models

It is vital to find reasons for differences in the results of environmental input‐output (EIO), physical input‐output (PIO), and hybrid input‐output (HIO) models for industrial and environmental policy analysis. Using EIO, PIO, and HIO models, China's industrial metabolism is calculated. Four reasons were found to account for differences in the results of analysis using EIO, PIO, and HIO models: the manner in which they deal with residential consumption, service sectors, and waste recycling, and the assumption of unique sector prices. The HIO model, which treats residential consumption as sectors of the intermediate delivery matrix, is preferred to the EIO and PIO models for analyzing industrial and environmental policies. Moreover, waste recycling in five sectors—agriculture; the manufacture of paper, printing, and articles for culture, education, and sports activities; the manufacture of nonmetallic mineral products; smelting and pressing of metals; and construction—should be comprehensively considered when using the HIO model to study problems related to these five sectors. Improvements in the EIO, PIO, and HIO models and future work are also discussed.     

Design of a data model for developing laboratory information management and analysis systems for protein production

Data management has emerged as one of the central issues in the high-throughput processes of taking a protein target sequence through to a protein sample. To simplify this task, and following extensive consultation with the international structural genomics community, we describe here a model of the data related to protein production. The model is suitable for both large and small facilities for use in tracking samples, experiments, and results through the many procedures involved. The model is described in Unified Modeling Language (UML). In addition, we present relational database schemas derived from the UML. These relational schemas are already in use in a number of data management projects.     

Tabular strategies for metadata in ecology,evolution, and the environmental sciences

Data support knowledge development and theory advances in ecology and evolution. We are increasingly reusing data within our teams and projects and through the global, openly archived datasets of others. Metadata can be challenging to write and interpret, but it is always crucial for reuse. The value metadata cannot be overstated—even as a relatively independent research object because it describes the work that has been done in a structured format. We advance a new perspective and classify methods for metadata curation and development with tables. Tables with templates can be effectively used to capture all components of an experiment or project in a single, easy‐to‐read file familiar to most scientists. If coupled with the R programming language, metadata from tables can then be rapidly and reproducibly converted to publication formats including extensible markup language files suitable for data repositories. Tables can also be used to summarize existing metadata and store metadata across many datasets. A case study is provided and the added benefits of tables for metadata, a priori, are developed to ensure a more streamlined publishing process for many data repositories used in ecology, evolution, and the environmental sciences. In ecology and evolution, researchers are often highly tabular thinkers from experimental data collection in the lab and/or field, and representations of metadata as a table will provide novel research and reuse insights.     

生态学中的点格局研究概况及其在国内的应用

点格局分析是研究生态学格局的工具之一,近年来在生态学中的应用越来越多。为深入了解点格局分析方法在国内的研究与应用情况,以所总结的研究进展、一般步骤和基本要点为背景,分析评述了1996—2015年期间以点格局为主题的国内中文核心期刊文献。结果表明,在国内生态学格局研究中,应用研究占据主导地位,研究对象广泛,包括以树木为主的乔、灌、草等不同生活型的植物,甚至包括景观;基础研究,包括概括性统计量、零模型与点过程模型等方面,以及专用软件工具包的开发等研究薄弱。在应用中存在一定问题,主要表现为:概括性统计量使用单一,且以Ripley的K-函数及其变形为主;零模型(或点过程模型)是科学问题的统计表达,但是有一半以上的研究未明确给出零模型。建议在未来应用研究中重视多种统计量的组合使用和原假设的建立,在探讨热带、亚热带森林等具有复杂空间结构系统的多样性格局时,考虑对象的不同世代和系统的不同垂直层次,并加强多变量或三维概括性统计量的开发、点格局分析方法与动态过程模型的结合研究等工作。     

Chronological change of resource metabolism and decarbonization patterns in Pakistan: Perspectives from a typical developing country

With economic growth in many developing countries, not all are making similar progress with regard to material and environmental efficiencies. This study examines material use and CO₂ emission patterns and intensities from 1971 to 2015 in a typical developing country, Pakistan, and investigates national‐level and multi‐country‐level efficiency improvements using data envelopment analysis. The results are used to derive key policy insights for a sustainable economic transition with higher resource and carbon efficiencies. Results show that material intensity has reduced by 39.1% while CO₂ intensity has risen by 21.5% in the country. Pakistan, when compared with its top 10 export countries, was relatively more material and CO₂ intensive. National‐level efficiency was found to be low in most of the periods due to material/energy intensive agriculture and industries, low value‐added exports, etc. Insights from the national‐level efficiency analysis indicate that surging CO₂ intensities have started to decline since 2010 and the economy has greatly stabilized. Multi‐country analysis revealed that the efficiency gap between Pakistan and its developed export countries (such as the United Kingdom and France) has widened during the study period. Insights from the multi‐country analysis suggest that the economic growth and industrialization improves material and environmental efficiencies to some extent, yet these improvements are not equally distributed among all countries. As a way forward, integrated policies on sustainable resource consumption, carbon mitigation, and economic growth are necessary for accruing higher benefits from rising global trade and resource connectedness.     

Meat,dairy, and more: Analysis of material,energy, and greenhouse gas flows of the meat and dairy supply chains in the EU28 for 2016

To decarbonize the European Union, protein consumption must transition to diets low in meat and dairy which will drastically change the material and energy flows in current meat and dairy supply chains. To understand the impacts on current flows, a baseline is required. Although recent studies have improved the scope of reported greenhouse gas (GHG) emissions, no quantitative overview exists including intermediate and final product flows. To address this knowledge gap, we structured the meat and dairy supply chains into a connected set of transformation nodes and distribution nodes. The former are processes transforming inputs into outputs, whereas the latter distribute the outputs to other processes using them as inputs. Currently, livestock play a central role in agriculture and other industries through the consumption of 271 Mt fodder crops, 108 Mt grain, 85 Mt grazed biomass, 49 Mt oil meal, and 16 Mt feed by‐products. This feed is transformed into 64 Mt dairy and 35 Mt meat which ensures that the EU28 is a net exporter of meat and dairy while providing 25 Mt of by‐products. This production also leads to 435 Mt CO₂‐eq. with the main contribution from beef cattle (35%), dairy cattle (32%), and swine (20%). Thus, the lower GHG intensities of dairy products compared to meat do not imply a low contribution to the total emissions. By mapping the material, energy, and GHG emission flows, we have created a baseline suitable for identifying potential supply chain changes and their related GHG increase or decrease resulting from the protein transition.     

宏观生态系统科学整合研究的多学科知识融合及其技术途径

综合认识大尺度的宏观生态系统结构功能、空间变异和动态演变的过程机理和模式机制,实现对生态系统变化及其对人类福祉影响的定量模拟、科学评估和预测预警,服务生态系统的利用保护及调控管理,是当代宏观生态系统科学的重要发展方向,正在孕育并形成大尺度的宏观生态系统科学整合生态学(IEMES)研究新领域.本研究通过对宏观生态系统科学...     

An extended general location model for causal inferences from data subject to noncompliance and missing values

Noncompliance is a common problem in experiments involving randomized assignment of treatments, and standard analyses based on intention-to-treat or treatment received have limitations. An attractive alternative is to estimate the Complier-Average Causal Effect (CACE), which is the average treatment effect for the subpopulation of subjects who would comply under either treatment (Angrist, Imbens, and Rubin, 1996, Journal of American Statistical Association 91, 444-472). We propose an extended general location model to estimate the CACE from data with noncompliance and missing data in the outcome and in baseline covariates. Models for both continuous and categorical outcomes and ignorable and latent ignorable (Frangakis and Rubin, 1999, Biometrika 86, 365-379) missing-data mechanisms are developed. Inferences for the models are based on the EM algorithm and Bayesian MCMC methods. We present results from simulations that investigate sensitivity to model assumptions and the influence of missing-data mechanism. We also apply the method to the data from a job search intervention for unemployed workers.     

结构方程模型及其在生态学中的应用

基于多变量统计方法同时研究自然系统内多个因子之间的相互关系, 是阐释复杂的自然系统的一个重要手段。相比传统的多变量统计法, 结构方程模型基于研究者的先验知识预先设定系统内因子间的依赖关系, 不仅能够判别各因子之间的关系强度(路径系数), 还能对整体模型进行拟合和判断, 从而能更全面地了解自然系统。由于结构方程模型只在近年才被应用到生态学的数据分析中, 因此该文试图对其作一简略介绍, 包括结构方程模型的定义和变量类型, 结合事例研究展现结构方程模型分析的一般步骤、在生态学中的应用以及相关软件的介绍等。望能为相关研究人员提供直观的认识, 加强结构方程模型在生态学数据分析中的应用。     

A novel method for identifying behavioural changes in animal movement data

A goal of animal movement analysis is to reveal behavioural mechanisms by which organisms utilize complex and variable environments. Statistical analysis of movement data is complicated by the fact that the data are multidimensional, autocorrelated and often marked by error and irregular measurement intervals or gappiness. Furthermore, movement data reflect behaviours that are themselves heterogeneous. Here, we model movement data as a subsampling of a continuous stochastic processes, and introduce the behavioural change point analysis (BCPA), a likelihood-based method that allows for the identification of significant structural changes. The BCPA is robust to gappiness and measurement error, computationally efficient, easy to implement and reveals structure that is otherwise difficult to discern. We apply the analysis to a GPS movement track of a northern fur seal ( Callorhinus ursinus ), revealing an unexpectedly complex diurnal behavioural profile, and demonstrate its robustness to the greater errors associated with the ARGOS tracking system. By informing empirical interpretation of movement data, we suggest that the BCPA can eventually motivate the development of mechanistic behavioural models.     

Data Acquisition for Applying Physical Input‐Output Tables in Chinese Cities

The physical input‐output table (PIOT) is a useful tool for analyzing the environmental sustainability of cities. Taking Chinese statistical sources as an example in this study, we discuss data acquisition methods for applying the PIOT to cities. We propose several methods and present a case study of Suzhou City to illustrate the proposed methods. These methods can provide foundations for constructing the PIOT of cities in other countries.     

A checklist for choosing between R packages in ecology and evolution

1. The open source and free programming language R is a phenomenal mechanism to address a multiplicity of challenges in ecology and evolution. It is also a complex ecosystem because of the diversity of solutions available to the analyst.
2. Packages for R enhance and specialize the capacity to explore both niche data/experiments and more common needs. However, the paradox of choice or how we select between many seemingly similar options can be overwhelming and lead to different potential outcomes.
3. There is extensive choice in ecology and evolution between packages for both fundamental statistics and for more specialized domain‐level analyses.
4. Here, we provide a checklist to inform these decisions based on the principles of resilience, need, and integration with scientific workflows for evidence.
5. It is important to explore choices in any analytical coding environment—not just R—for solutions to challenges in ecology and evolution, and document this process because it advances reproducible science, promotes a deeper understand of the scientific evidence, and ensures that the outcomes are correct, representative, and robust.

     

Data representativeness in LCA: A framework for the systematic assessment of data quality relative to technology characteristics

A shortcoming in current data quality assessment schemes is that the data quality information is not used systematically to identify the critical data in a life cycle inventory (LCI) model. In addition, existing criteria employed to evaluate representativeness lack relevance to the specific context of a study. A novel framework is proposed herein for the evaluation of the representativeness of LCI data, including an analysis of the importance of the data and a modification of quality criteria based on unit process characteristics. Temporal characteristics are analyzed by identifying the technology shift, because data generated before this time are considered outdated. Geographical and technological characteristics are analyzed by defining a “related area” and a “related technology,” which is done by identifying a number of relevant geographical and technical factors, and then comparing the collected data with these factors. The framework was illustrated in a case study on household waste incineration in Denmark. The results demonstrated the applicability of the method in practice, and they provided data quality criteria unique to waste incineration unit processes, for example, different time intervals to evaluate temporal representativeness. However, the proposed method is time demanding, and thus sector‐level characteristic analyses are feasible instead of the user having to do the analyses.     

A general probabilistic model for group independent component analysis and its estimation methods

Independent component analysis (ICA) has become an important tool for analyzing data from functional magnetic resonance imaging (fMRI) studies. ICA has been successfully applied to single-subject fMRI data. The extension of ICA to group inferences in neuroimaging studies, however, is challenging due to the unavailability of a prespecified group design matrix and the uncertainty in between-subjects variability in fMRI data. We present a general probabilistic ICA (PICA) model that can accommodate varying group structures of multisubject spatiotemporal processes. An advantage of the proposed model is that it can flexibly model various types of group structures in different underlying neural source signals and under different experimental conditions in fMRI studies. A maximum likelihood (ML) method is used for estimating this general group ICA model. We propose two expectation-maximization (EM) algorithms to obtain the ML estimates. The first method is an exact EM algorithm, which provides an exact E-step and an explicit noniterative M-step. The second method is a variational approximation EM algorithm, which is computationally more efficient than the exact EM. In simulation studies, we first compare the performance of the proposed general group PICA model and the existing probabilistic group ICA approach. We then compare the two proposed EM algorithms and show the variational approximation EM achieves comparable accuracy to the exact EM with significantly less computation time. An fMRI data example is used to illustrate application of the proposed methods.     

A new physical accounting model for material flows in urban systems with application to the Stockholm Royal Seaport District

Sustainable urbanization requires streamlining of resource management in urban systems which in turn requires understanding of urban metabolism (UM). Even though various methods have been applied for UM analysis, to date there is no standardized method for comprehensive accounting of material flows in urban systems. Moreover, the accounting of material flows is rarely implemented with a bottom‐up approach that can provide a thorough analysis of UM. This article presents the Urban Accounting Model (UAM) which aims to allow comprehensive accounting of urban material flows based on a bottom‐up approach. The model comprises two interlinked sub‐models. The first was developed by integrating a new physical input output table (PIOT) framework for urban systems into a three‐dimensional structure. The second comprises a set of physical accounts for systematic accounting of material flows of each economic sector in the system in order to support the compilation of the PIOTs. The functions of the UAM were explored through its application to two urban neighborhoods in the Stockholm Royal Seaport district. The application highlighted that the UAM can describe the physical interactions between the urban system and the environment or other socioeconomic systems, and capture the intersectoral flows within the system. Moreover, its accounts provide information that allow an in‐depth analysis of the metabolism of specific sectors. Overall, the UAM can function as a useful tool for UM analysis as it systematizes data collection and at the same time depicts the physical reality of the urban system.     

HMSS2: An advanced tool for the analysis of sulphur metabolism,including organosulphur compound transformation,in genome and metagenome assemblies

The global sulphur cycle has implications for human health, climate change, biogeochemistry and bioremediation. The organosulphur compounds that participate in this cycle not only represent a vast reservoir of sulphur but are also used by prokaryotes as sources of energy and/or carbon. Closely linked to the inorganic sulphur cycle, it involves the interaction of prokaryotes, eukaryotes and chemical processes. However, ecological and evolutionary studies of the conversion of organic sulphur compounds are hampered by the poor conservation of the relevant pathways and their variation even within strains of the same species. In addition, several proteins involved in the conversion of sulphonated compounds are related to proteins involved in sulphur dissimilation or turnover of other compounds. Therefore, the enzymes involved in the metabolism of organic sulphur compounds are usually not correctly annotated in public databases. To address this challenge, we have developed HMSS2, a profiled Hidden Markov Model-based tool for rapid annotation and synteny analysis of organic and inorganic sulphur cycle proteins in prokaryotic genomes. Compared to its previous version (HMS-S-S), HMSS2 includes several new features. HMM-based annotation is now supported by nonhomology criteria and covers the metabolic pathways of important organosulphur compounds, including dimethylsulphoniopropionate, taurine, isethionate, and sulphoquinovose. In addition, the calculation speed has been increased by a factor of four and the available output formats have been extended to include iTol compatible data sets, and customized sequence FASTA files.     

Open science and meta‐analysis allow for rapid advances in ecology: A response to Menegotto et al. (2019)

Menegotto and colleagues’ (2019) commentary on our paper (Kinlock et al., 2018) does not negate our findings, but by recategorizing and reanalysing a portion of our data set, advances our knowledge of the latitudinal diversity gradients (LDGs) in marine ecosystems, particularly emphasizing different findings for benthic LDGs as a result of the recategorization of the data. Furthermore, we see the contribution by Menegotto et al. (2019) as highlighting the importance of scientific transparency; we believe that this insight into the nature of LDGs in marine systems would have been delayed, if not unobtainable, had we not provided fully transparent methods and complete data in our paper.