The RAINFOR database: monitoring forest biomass and dynamics

Problem: Data from over 100 permanent sample plots which have been studied for 10–20 years need a suitable system for storage which allows simple data manipulation and retrieval for analysis. Methods: A relational database linking tree records, taxonomic nomenclature and corresponding environmental data has been built in MS Access as part of the RAINFOR project. Conclusion: The database allows flexible and long‐term use of a large amount of data: more than 100 tree plots across Amazonia, incorporating over 80 000 records of individual trees and over 300 000 total records of tree diameter measurements from successive censuses. The database is designed to enable linkages to existing soil, floristic or plant‐trait databases. This database will be a useful tool for exploring the impact of environmental factors on forest structure and dynamics at local to continental scales, and long term changes in forest ecology. As an early example of its potential, we explore the impact of different methodological assumptions on estimates of tropical forest biomass and carbon storage.     

Lessons learnt on the analysis of large sequence data in animal genomics

The ’omics revolution has made a large amount of sequence data available to researchers and the industry. This has had a profound impact in the field of bioinformatics, stimulating unprecedented advancements in this discipline. Mostly, this is usually looked at from the perspective of human ’omics, in particular human genomics. Plant and animal genomics, however, have also been deeply influenced by next‐generation sequencing technologies, with several genomics applications now popular among researchers and the breeding industry. Genomics tends to generate huge amounts of data, and genomic sequence data account for an increasing proportion of big data in biological sciences, due largely to decreasing sequencing and genotyping costs and to large‐scale sequencing and resequencing projects. The analysis of big data poses a challenge to scientists, as data gathering currently takes place at a faster pace than does data processing and analysis, and the associated computational burden is increasingly taxing, making even simple manipulation, visualization and transferring of data a cumbersome operation. The time consumed by the processing and analysing of huge data sets may be at the expense of data quality assessment and critical interpretation. Additionally, when analysing lots of data, something is likely to go awry—the software may crash or stop—and it can be very frustrating to track the error. We herein review the most relevant issues related to tackling these challenges and problems, from the perspective of animal genomics, and provide researchers that lack extensive computing experience with guidelines that will help when processing large genomic data sets.     

Using model‐data fusion to interpret past trends,and quantify uncertainties in future projections,of terrestrial ecosystem carbon cycling

Uncertainties in model projections of carbon cycling in terrestrial ecosystems stem from inaccurate parameterization of incorporated processes (endogenous uncertainties) and processes or drivers that are not accounted for by the model (exogenous uncertainties). Here, we assess endogenous and exogenous uncertainties using a model‐data fusion framework benchmarked with an artificial neural network (ANN). We used 18 years of eddy‐covariance carbon flux data from the Harvard forest, where ecosystem carbon uptake has doubled over the measurement period, along with 15 ancillary ecological data sets relative to the carbon cycle. We test the ability of combinations of diverse data to constrain projections of a process‐based carbon cycle model, both against the measured decadal trend and under future long‐term climate change. The use of high‐frequency eddy‐covariance data alone is shown to be insufficient to constrain model projections at the annual or longer time step. Future projections of carbon cycling under climate change in particular are shown to be highly dependent on the data used to constrain the model. Endogenous uncertainties in long‐term model projections of future carbon stocks and fluxes were greatly reduced by the use of aggregated flux budgets in conjunction with ancillary data sets. The data‐informed model, however, poorly reproduced interannual variability in net ecosystem carbon exchange and biomass increments and did not reproduce the long‐term trend. Furthermore, we use the model‐data fusion framework, and the ANN, to show that the long‐term doubling of the rate of carbon uptake at Harvard forest cannot be explained by meteorological drivers, and is driven by changes during the growing season. By integrating all available data with the model‐data fusion framework, we show that the observed trend can only be reproduced with temporal changes in model parameters. Together, the results show that exogenous uncertainty dominates uncertainty in future projections from a data‐informed process‐based model.     

Value of long‐term ecological studies

Long‐term ecological studies are critical for providing key insights in ecology, environmental change, natural resource management and biodiversity conservation. In this paper, we briefly discuss five key values of such studies. These are: (1) quantifying ecological responses to drivers of ecosystem change; (2) understanding complex ecosystem processes that occur over prolonged periods; (3) providing core ecological data that may be used to develop theoretical ecological models and to parameterize and validate simulation models; (4) acting as platforms for collaborative studies, thus promoting multidisciplinary research; and (5) providing data and understanding at scales relevant to management, and hence critically supporting evidence‐based policy, decision making and the management of ecosystems. We suggest that the ecological research community needs to put higher priority on communicating the benefits of long‐term ecological studies to resource managers, policy makers and the general public. Long‐term research will be especially important for tackling large‐scale emerging problems confronting humanity such as resource management for a rapidly increasing human population, mass species extinction, and climate change detection, mitigation and adaptation. While some ecologically relevant, long‐term data sets are now becoming more generally available, these are exceptions. This deficiency occurs because ecological studies can be difficult to maintain for long periods as they exceed the length of government administrations and funding cycles. We argue that the ecological research community will need to coordinate ongoing efforts in an open and collaborative way, to ensure that discoverable long‐term ecological studies do not become a long‐term deficiency. It is important to maintain publishing outlets for empirical field‐based ecology, while simultaneously developing new systems of recognition that reward ecologists for the use and collaborative sharing of their long‐term data sets. Funding schemes must be re‐crafted to emphasize collaborative partnerships between field‐based ecologists, theoreticians and modellers, and to provide financial support that is committed over commensurate time frames.     

Navigator: Tools for informal structure-activity relationship discovery

Navigator is a molecular database visualization system, designed to support exploratory data analysis and informal structure-activity relationship studies. In addition to the operations commonly found in chemical database systems, it provides new tools that facilitate substituent analysis and help elucidate the relationships among similar molecules and between related assays. Navigator's capabilities include two ways of displaying the relationships between analogs, mouse-sensitive charts of sets of molecules, mouse-sensitive plots of assay relationships, and access to a system for three-dimensional quantitative structure-activity relationship discovery. Navigator's mouse-based user interface provides a one-object/one-window paradigm that makes data manipulation easy even for inexperienced users. Navigator runs on Silicon Graphics workstations.     

The use of long‐term monitoring data for studies of planktonic diversity: a cautionary tale from two Swiss lakes

相似文献   

Integrated platform for manual and high‐throughput statistical validation of tandem mass spectra

As proteomic data sets increase in size and complexity, the necessity for database‐centric software systems able to organize, compare, and visualize all the proteomic experiments in a lab grows. We recently developed an integrated platform called high‐throughput autonomous proteomic pipeline (HTAPP) for the automated acquisition and processing of quantitative proteomic data, and integration of proteomic results with existing external protein information resources within a lab‐based relational database called PeptideDepot. Here, we introduce the peptide validation software component of this system, which combines relational database‐integrated electronic manual spectral annotation in Java with a new software tool in the R programming language for the generation of logistic regression spectral models from user‐supplied validated data sets and flexible application of these user‐generated models in automated proteomic workflows. This logistic regression spectral model uses both variables computed directly from SEQUEST output in addition to deterministic variables based on expert manual validation criteria of spectral quality. In the case of linear quadrupole ion trap (LTQ) or LTQ‐FTICR LC/MS data, our logistic spectral model outperformed both XCorr (242% more peptides identified on average) and the X!Tandem E‐value (87% more peptides identified on average) at a 1% false discovery rate estimated by decoy database approach.     

A quadratic correlation between long‐term mean group size and group density in a cooperatively breeding passerine

Both mean group size (MGS) and mean group density (MGD) are critical indices to characterize a population of cooperatively breeding birds. When a population reaches its carrying capacity, both long‐term MGS and long‐term MGD will remain relatively stable. However, there has been little study of how these two variables relate. The Masked laughingthrush Garrulax perspicillatus is a cooperatively breeding bird living in fragmented habitats. During 2010 and 2012‐2016, we used song playback to observe and confirm the group sizes and territory ranges of the birds and the data of bird presence to determine habitat suitability. By grouping the nearest territories according to their geographical coordinates, we divided the whole study area into 12 subareas and the whole population into 12 subpopulations. Then, we calculated both MGS and MGD for different time durations for each subpopulation. Finally, using MGD as independent variable and MGS as the dependent variable, we explored the correlations between MGS and MGD by fitting quadratic functions and modeling quadratic regression. Both MGS and MGD were averaged for different time durations and were cross‐related. Our results show that the MGS for more than 2 years significantly correlated with MGD for more than 3 years in a reverse parabolic shape, differing from that of short‐term effects. Our findings suggest that long‐term MGD is a better predictor of long‐term habitat quality and that long‐term MGS is determined by long‐term habitat quality in Masked Laughingthrushes. Based on above findings, we can infer that: (1) Long‐term habitat quality determines the long‐term MGS, but it sets no prerequisite for the status and source of group members; (2) Long‐term MGS in certain populations is adapted to the corresponding level of long‐term habitat quality, it facilitates us to predict the helper effects on current or future survival or reproduction in different situations. These findings and inferences are both helpful for us to understand the evolution of cooperative breeding.     

The portable UNIX programming system (PUPS) and CANTOR: a computational environment for dynamical representation and analysis of complex neurobiological data.

Many problems in analytical biology, such as the classification of organisms, the modelling of macromolecules, or the structural analysis of metabolic or neural networks, involve complex relational data. Here, we describe a software environment, the portable UNIX programming system (PUPS), which has been developed to allow efficient computational representation and analysis of such data. The system can also be used as a general development tool for database and classification applications. As the complexity of analytical biology problems may lead to computation times of several days or weeks even on powerful computer hardware, the PUPS environment gives support for persistent computations by providing mechanisms for dynamic interaction and homeostatic protection of processes. Biological objects and their interrelations are also represented in a homeostatic way in PUPS. Object relationships are maintained and updated by the objects themselves, thus providing a flexible, scalable and current data representation. Based on the PUPS environment, we have developed an optimization package, CANTOR, which can be applied to a wide range of relational data and which has been employed in different analyses of neuroanatomical connectivity. The CANTOR package makes use of the PUPS system features by modifying candidate arrangements of objects within the system's database. This restructuring is carried out via optimization algorithms that are based on user-defined cost functions, thus providing flexible and powerful tools for the structural analysis of the database content. The use of stochastic optimization also enables the CANTOR system to deal effectively with incomplete and inconsistent data. Prototypical forms of PUPS and CANTOR have been coded and used successfully in the analysis of anatomical and functional mammalian brain connectivity, involving complex and inconsistent experimental data. In addition, PUPS has been used for solving multivariate engineering optimization problems and to implement the digital identification system (DAISY), a system for the automated classification of biological objects. PUPS is implemented in ANSI-C under the POSIX.1 standard and is to a great extent architecture- and operating-system independent. The software is supported by systems libraries that allow multi-threading (the concurrent processing of several database operations), as well as the distribution of the dynamic data objects and library operations over clusters of computers. These attributes make the system easily scalable, and in principle allow the representation and analysis of arbitrarily large sets of relational data. PUPS and CANTOR are freely distributed (http://www.pups.org.uk) as open-source software under the GNU license agreement.     

生态环境移动数据采集系统研究与实现

针对快速、实时、有效采集并录入生态环境野外调查大样本量、多源数据的需求,充分应用移动GIS技术、移动智能终端、3G等现代信息技术优势,提出了基于ArcGIS for Mobile的移动数据采集方案,研究解决了包括系统运行机制、数据访问模式、移动数据库技术等面向全国生态环境野外调查的移动GIS关键技术,设计并研发野外调查移动数据采集系统。该系统采用C#和Java语言,以SQL Server 2008 为服务器端的数据库环境,在Microsoft Visual Studio 2008集成开发环境上实现设计开发,并在全国生态环境10年变化遥感调查与评估项目土地覆盖类型地面核查、生态系统参数野外观测等工作中予以应用。实践检验表明：该系统实现了野外调查数据的数字采集、智能校验、实时上传与有效管理,简化了填报程序,规范了填报内容,提高了工作效率,能够为生态环境相关的调查数据采集提供信息化支持。     

Input‐Output‐based Life Cycle Inventory

An input‐output‐based life cycle inventory (IO‐based LCI) is grounded on economic environmental input‐output analysis (IO analysis). It is a fast and low‐budget method for generating LCI data sets, and is used to close data gaps in life cycle assessment (LCA). Due to the fact that its methodological basis differs from that of process‐based inventory, its application in LCA is a matter of controversy. We developed a German IO‐based approach to derive IO‐based LCI data sets that is based on the German IO accounts and on the German environmental accounts, which provide data for the sector‐specific direct emissions of seven airborne compounds. The method to calculate German IO‐based LCI data sets for building products is explained in detail. The appropriateness of employing IO‐based LCI for German buildings is analyzed by using process‐based LCI data from the Swiss Ecoinvent database to validate the calculated IO‐based LCI data. The extent of the deviations between process‐based LCI and IO‐based LCI varies considerably for the airborne emissions we investigated. We carried out a systematic evaluation of the possible reasons for this deviation. This analysis shows that the sector‐specific effects (aggregation of sectors) and the quality of primary data for emissions from national inventory reporting (NIR) are the main reasons for the deviations. As a rule, IO‐based LCI data sets seem to underestimate specific emissions while overestimating sector‐specific aspects.     

sampbias,a method for quantifying geographic sampling biases in species distribution data

Geo‐referenced species occurrences from public databases have become essential to biodiversity research and conservation. However, geographical biases are widely recognized as a factor limiting the usefulness of such data for understanding species diversity and distribution. In particular, differences in sampling intensity across a landscape due to differences in human accessibility are ubiquitous but may differ in strength among taxonomic groups and data sets. Although several factors have been described to influence human access (such as presence of roads, rivers, airports and cities), quantifying their specific and combined effects on recorded occurrence data remains challenging. Here we present sampbias, an algorithm and software for quantifying the effect of accessibility biases in species occurrence data sets. sampbias uses a Bayesian approach to estimate how sampling rates vary as a function of proximity to one or multiple bias factors. The results are comparable among bias factors and data sets. We demonstrate the use of sampbias on a data set of mammal occurrences from the island of Borneo, showing a high biasing effect of cities and a moderate effect of roads and airports. sampbias is implemented as a well‐documented, open‐access and user‐friendly R package that we hope will become a standard tool for anyone working with species occurrences in ecology, evolution, conservation and related fields.     

流域尺度海量生态环境数据建库关键技术——以塔里木河流域为例

流域尺度海量生态环境数据库构建是生态环境精准化研究的基础。以塔里木河流域生态环境数据库构建为例,对流域尺度海量生态环境数据建库的无缝数据拼接、建库规范设计、要素代码设计、空间索引设计、特征展示表及一键入库设计等关键技术进行了探讨。针对流域跨带裂缝问题,从缝隙源出发,通过分离物理数据层和逻辑数据层并区分矢量数据和栅格数据,在统一的多尺度空间框架体系下实现了海量数据的跨带无缝拼接;数据库规范设计和要素代码设计是数据入库前的关键工作,针对流域实际,分别采用规范化英文字母和图形数据比例尺设置数据库命名规范和建立代码标准;在ArcSDE框架下,采用格网索引设计和多级金字塔结构分别构建矢量数据和栅格数据的空间索引,提高了数据的快速检索和浏览;通过建立特征展示表并提出"一键入库"策略,提高了系统响应及数据入库效率等。通过构建流域尺度海量生态环境数据库系统,实现了流域尺度多源、多类型、跨带海量生态环境数据的有效存储和管理,为流域一体化管理和生态环境研究提供了基础数据支撑。     

On the separation of net ecosystem exchange into assimilation and ecosystem respiration: review and improved algorithm

This paper discusses the advantages and disadvantages of the different methods that separate net ecosystem exchange (NEE) into its major components, gross ecosystem carbon uptake (GEP) and ecosystem respiration (R_eco). In particular, we analyse the effect of the extrapolation of night‐time values of ecosystem respiration into the daytime; this is usually done with a temperature response function that is derived from long‐term data sets. For this analysis, we used 16 one‐year‐long data sets of carbon dioxide exchange measurements from European and US‐American eddy covariance networks. These sites span from the boreal to Mediterranean climates, and include deciduous and evergreen forest, scrubland and crop ecosystems. We show that the temperature sensitivity of R_eco, derived from long‐term (annual) data sets, does not reflect the short‐term temperature sensitivity that is effective when extrapolating from night‐ to daytime. Specifically, in summer active ecosystems the long‐term temperature sensitivity exceeds the short‐term sensitivity. Thus, in those ecosystems, the application of a long‐term temperature sensitivity to the extrapolation of respiration from night to day leads to a systematic overestimation of ecosystem respiration from half‐hourly to annual time‐scales, which can reach >25% for an annual budget and which consequently affects estimates of GEP. Conversely, in summer passive (Mediterranean) ecosystems, the long‐term temperature sensitivity is lower than the short‐term temperature sensitivity resulting in underestimation of annual sums of respiration. We introduce a new generic algorithm that derives a short‐term temperature sensitivity of R_eco from eddy covariance data that applies this to the extrapolation from night‐ to daytime, and that further performs a filling of data gaps that exploits both, the covariance between fluxes and meteorological drivers and the temporal structure of the fluxes. While this algorithm should give less biased estimates of GEP and R_eco, we discuss the remaining biases and recommend that eddy covariance measurements are still backed by ancillary flux measurements that can reduce the uncertainties inherent in the eddy covariance data.     

Design patterns for wildlife‐related camera trap image analysis

This paper describes and explains design patterns for software that supports how analysts can efficiently inspect and classify camera trap images for wildlife‐related ecological attributes. Broadly speaking, a design pattern identifies a commonly occurring problem and a general reusable design approach to solve that problem. A developer can then use that design approach to create a specific software solution appropriate to the particular situation under consideration. In particular, design patterns for camera trap image analysis by wildlife biologists address solutions to commonly occurring problems they face while inspecting a large number of images and entering ecological data describing image attributes. We developed design patterns for image classification based on our understanding of biologists' needs that we acquired over 8 years during development and application of the freely available Timelapse image analysis system. For each design pattern presented, we describe the problem, a design approach that solves that problem, and a concrete example of how Timelapse addresses the design pattern. Our design patterns offer both general and specific solutions related to: maintaining data consistency, efficiencies in image inspection, methods for navigating between images, efficiencies in data entry including highly repetitious data entry, and sorting and filtering image into sequences, episodes, and subsets. These design patterns can inform the design of other camera trap systems and can help biologists assess how competing software products address their project‐specific needs along with determining an efficient workflow.     

metapop2: Re‐implementation of software for the analysis and management of subdivided populations using gene and allelic diversity

Management programmes often have to make decisions based on the analysis of the genetic properties and diversity of populations. Expected heterozygosity (or gene diversity) and population structure parameters are often used to make recommendations for conservation, such as avoidance of inbreeding or migration across subpopulations. Allelic diversity, however, can also provide complementary and useful information for conservation programmes, as it is highly sensitive to population bottlenecks, and is more related to long‐term selection response than heterozygosity. Here we present a completely revised and updated re‐implementation of the software metapop for the analysis of diversity in subdivided populations, as well as a tool for the management and dynamic estimation of optimal contributions in conservation programmes. This new update includes computation of allelic diversity for population analysis and management, as well as a simulation mode to forecast the consequences of taking different management strategies over time. Furthermore, the new implementation in C++ includes code optimization and improved memory usage, allowing for fast analysis of large data sets including single nucleotide polymorphism markers, as well as enhanced cross‐software and cross‐platform compatibility.     

FunRich: An open access standalone functional enrichment and interaction network analysis tool

As high‐throughput techniques including proteomics become more accessible to individual laboratories, there is an urgent need for a user‐friendly bioinformatics analysis system. Here, we describe FunRich, an open access, standalone functional enrichment and network analysis tool. FunRich is designed to be used by biologists with minimal or no support from computational and database experts. Using FunRich, users can perform functional enrichment analysis on background databases that are integrated from heterogeneous genomic and proteomic resources (>1.5 million annotations). Besides default human specific FunRich database, users can download data from the UniProt database, which currently supports 20 different taxonomies against which enrichment analysis can be performed. Moreover, the users can build their own custom databases and perform the enrichment analysis irrespective of organism. In addition to proteomics datasets, the custom database allows for the tool to be used for genomics, lipidomics and metabolomics datasets. Thus, FunRich allows for complete database customization and thereby permits for the tool to be exploited as a skeleton for enrichment analysis irrespective of the data type or organism used. FunRich ( http://www.funrich.org ) is user‐friendly and provides graphical representation (Venn, pie charts, bar graphs, column, heatmap and doughnuts) of the data with customizable font, scale and color (publication quality).     

Digitization of the Nissen–Riesen chimpanzee radiological growth series

Longitudinal morphological growth data of apes are incredibly difficult to obtain. Long life histories, combined with practical and ethical issues of obtaining such long‐term data have resulted in few longitudinal data sets in chimpanzees of known chronological ages. One classic, long‐term growth study of chimpanzees was that of Drs Nissen and Riesen initiated at the Yale Laboratories of Primate Biology in 1939. Through that study, whole‐body radiological images were taken on a regular basis from a “normative” group of chimpanzees from birth to adulthood. Here we have digitized the known remaining radiographs from that growth study, many of which are deteriorating, and uploaded the data set to the free, online database MorphoSource. The database comprises 3,568 X‐ray images of 15 of the 16 chimpanzee subjects in the normative group and 1 individual from an experimental group. Herein, we briefly review the historical context of this study and specific details of the data set.