An international biodiversity observation year

The International Geophysical Year (IGY), which took place between July 1957 and December 1958, helped us to rethink the world. At a time when there was a major paradigm shift in our understanding of the physical world, the international collaboration of the IGY helped to reset the discipline. The International Biodiversity Observation Year (IBOY) is now occurring at a time when our dependence on, and understanding of, biodiversity is being acknowledged as a paradigm shift in our present view of the world. Although the benefits of IGY were initially intellectual with practical effects remaining unknown until many years later, the benefits of greater knowledge of biodiversity will support efforts towards sustainability and affect the quality of life, both now and in the future. By providing the framework for international collaborations between scientists involved in every aspect of life on Earth, IBOY has the potential to redefine our current understanding of biodiversity in a manner similar to how IGY helped redefine the geophysical world.     

BioFTF: An R package for biodiversity assessment with the functional data analysis approach

This paper presents the R package BioFTF, which is a tool for statistical biodiversity assessment in the functional data analysis framework. Diversity is a key topic in many research fields; however, in the literature, it is demonstrated that the existing indices do not capture the different aspects of this concept. Thus, a main drawback is that different indicators may lead to different orderings among communities according to their biodiversity. A possible method to evaluate biodiversity consists in using diversity profiles that are curves depending on a specific parameter. In this setting, it is possible to adopt some functional instruments proposed in the literature, such as the first and second derivatives, the curvature, the radius of curvature and the arc length. Specifically, the derivatives and the curvature (or the radius of curvature) highlight any peculiar behaviour of the profiles, whereas the arc length helps in ranking curves, given the richness. Because these instruments do not solve the issue of ranking communities with different numbers of species, we propose an important methodological contribution that introduces the surface area. Indeed, this tool is a scalar measure that reflects the information provided by the biodiversity profile and allows for ordering communities with different richness. However, this approach requires mathematical skills that the average user may not have; thus, our idea is to provide a user-friendly tool for both non-statistician and statistician practitioners to measure biodiversity in a functional context.     

基于环境DNA-宏条形码技术的底栖动物监测及水质评价研究进展

底栖动物是淡水生态系统中物种多样性最高的类群,也是应用最广泛的水质监测指示生物之一。传统的底栖动物监测以形态学为基础,耗时费力,无法满足流域尺度大规模监测的需求。环境DNA-宏条形码技术是一种新兴的生物监测方法,其与传统方法相比优势在于采样方法简单、低成本、高灵敏度,不受生物样本和环境状况的影响,不依赖分类专家和鉴定资料,能够快速准确地对多个类群进行大规模、高通量的物种鉴定。然而,在实际应用中该方法的效果受诸多因素的影响,不同的方法、流程往往会产生差异较大的结果。鉴于此,着重分析总结了应用环境DNA-宏条形码技术监测底栖动物的关键影响因素,包括样品采集与处理流程、分子标记选择、引物设计、PCR偏好性、参考数据库的完整性及相应的优化。并基于此探讨了提高环境DNA-宏条形码技术在底栖动物监测效率和准确率的途径,以期为底栖动物环境DNA-宏条形码监测方案的制定提供可靠的参考。最后对该技术在底栖动物监测和水质评价中的最新发展方向进行了展望。     

A cloud-based toolbox for the versatile environmental annotation of biodiversity data

A vast range of research applications in biodiversity sciences requires integrating primary species, genetic, or ecosystem data with other environmental data. This integration requires a consideration of the spatial and temporal scale appropriate for the data and processes in question. But a versatile and scale flexible environmental annotation of biodiversity data remains constrained by technical hurdles. Existing tools have streamlined the intersection of occurrence records with gridded environmental data but have remained limited in their ability to address a range of spatial and temporal grains, especially for large datasets. We present the Spatiotemporal Observation Annotation Tool (STOAT), a cloud-based toolbox for flexible biodiversity–environment annotations. STOAT is optimized for large biodiversity datasets and allows user-specified spatial and temporal resolution and buffering in support of environmental characterizations that account for the uncertainty and scale of data and of relevant processes. The tool offers these services for a growing set of near global, remotely sensed, or modeled environmental data, including Landsat, MODIS, EarthEnv, and CHELSA. STOAT includes a user-friendly, web-based dashboard that provides tools for annotation task management and result visualization, linked to Map of Life, and a dedicated R package (rstoat) for programmatic access. We demonstrate STOAT functionality with several examples that illustrate phenological variation and spatial and temporal scale dependence of environmental characteristics of birds at a continental scale. We expect STOAT to facilitate broader exploration and assessment of the scale dependence of observations and processes in ecology.

In ecology and evolution, processes, data collection, and inference or prediction usually occur at different scales in space and time. This study introduces a cloud-based toolbox for the flexible fusion of biodiversity records with remotely sensed and other environmental information that supports an assessment and accounting of such scale dependencies.     

An Entropy-based gene selection method for cancer classification using microarray data

Background  

Accurate diagnosis of cancer subtypes remains a challenging problem. Building classifiers based on gene expression data is a promising approach; yet the selection of non-redundant but relevant genes is difficult.     

An approach for classification of highly imbalanced data using weighting and undersampling

Real-world datasets commonly have issues with data imbalance. There are several approaches such as weighting, sub-sampling, and data modeling for handling these data. Learning in the presence of data imbalances presents a great challenge to machine learning. Techniques such as support-vector machines have excellent performance for balanced data, but may fail when applied to imbalanced datasets. In this paper, we propose a new undersampling technique for selecting instances from the majority class. The performance of this approach was evaluated in the context of several real biological imbalanced data. The ratios of negative to positive samples vary from ~9:1 to ~100:1. Useful classifiers have high sensitivity and specificity. Our results demonstrate that the proposed selection technique improves the sensitivity compared to weighted support-vector machine and available results in the literature for the same datasets.     

Monitoring endangered freshwater biodiversity using environmental DNA

Freshwater ecosystems are among the most endangered habitats on Earth, with thousands of animal species known to be threatened or already extinct. Reliable monitoring of threatened organisms is crucial for data‐driven conservation actions but remains a challenge owing to nonstandardized methods that depend on practical and taxonomic expertise, which is rapidly declining. Here, we show that a diversity of rare and threatened freshwater animals—representing amphibians, fish, mammals, insects and crustaceans—can be detected and quantified based on DNA obtained directly from small water samples of lakes, ponds and streams. We successfully validate our findings in a controlled mesocosm experiment and show that DNA becomes undetectable within 2 weeks after removal of animals, indicating that DNA traces are near contemporary with presence of the species. We further demonstrate that entire faunas of amphibians and fish can be detected by high‐throughput sequencing of DNA extracted from pond water. Our findings underpin the ubiquitous nature of DNA traces in the environment and establish environmental DNA as a tool for monitoring rare and threatened species across a wide range of taxonomic groups.     

Status and distribution of mangrove forests of the world using earth observation satellite data

Aim Our scientific understanding of the extent and distribution of mangrove forests of the world is inadequate. The available global mangrove databases, compiled using disparate geospatial data sources and national statistics, need to be improved. Here, we mapped the status and distributions of global mangroves using recently available Global Land Survey (GLS) data and the Landsat archive. Methods We interpreted approximately 1000 Landsat scenes using hybrid supervised and unsupervised digital image classification techniques. Each image was normalized for variation in solar angle and earth–sun distance by converting the digital number values to the top‐of‐the‐atmosphere reflectance. Ground truth data and existing maps and databases were used to select training samples and also for iterative labelling. Results were validated using existing GIS data and the published literature to map ‘true mangroves’. Results The total area of mangroves in the year 2000 was 137,760 km² in 118 countries and territories in the tropical and subtropical regions of the world. Approximately 75% of world's mangroves are found in just 15 countries, and only 6.9% are protected under the existing protected areas network (IUCN I‐IV). Our study confirms earlier findings that the biogeographic distribution of mangroves is generally confined to the tropical and subtropical regions and the largest percentage of mangroves is found between 5° N and 5° S latitude. Main conclusions We report that the remaining area of mangrove forest in the world is less than previously thought. Our estimate is 12.3% smaller than the most recent estimate by the Food and Agriculture Organization (FAO) of the United Nations. We present the most comprehensive, globally consistent and highest resolution (30 m) global mangrove database ever created. We developed and used better mapping techniques and data sources and mapped mangroves with better spatial and thematic details than previous studies.     

Towards next-generation biodiversity assessment using DNA metabarcoding

Virtually all empirical ecological studies require species identification during data collection. DNA metabarcoding refers to the automated identification of multiple species from a single bulk sample containing entire organisms or from a single environmental sample containing degraded DNA (soil, water, faeces, etc.). It can be implemented for both modern and ancient environmental samples. The availability of next-generation sequencing platforms and the ecologists' need for high-throughput taxon identification have facilitated the emergence of DNA metabarcoding. The potential power of DNA metabarcoding as it is implemented today is limited mainly by its dependency on PCR and by the considerable investment needed to build comprehensive taxonomic reference libraries. Further developments associated with the impressive progress in DNA sequencing will eliminate the currently required DNA amplification step, and comprehensive taxonomic reference libraries composed of whole organellar genomes and repetitive ribosomal nuclear DNA can be built based on the well-curated DNA extract collections maintained by standardized barcoding initiatives. The near-term future of DNA metabarcoding has an enormous potential to boost data acquisition in biodiversity research.     

An upgraded national biodiversity risk assessment index

The setting of priorities for international conservation assistance is important due to limited available financial resources. A recent study constructed a national biodiversity risk assessment index (NABRAI) in order to prioritise nations for conservation assistance. The present study aimed to upgrade the original index in order to address computational and weighting inconsistencies. The results of the upgraded index corresponded relatively well with those of the original model. We feel this study goes a step further towards strengthening the methodologies for biodiversity risk assessment. However, due to the absence of theoretical constructs for biodiversity risk assessment and the considerable disagreement between the various models of biodiversity risk, we recognise a need for a more sophisticated understanding of national biodiversity risk before these models can be used to identify global conservation priorities with any degree of confidence.     

Testing multiple substrates for terrestrial biodiversity monitoring using environmental DNA metabarcoding

Biological surveys based on visual identification of the biota are challenging, expensive and time consuming, yet crucial for effective biomonitoring. DNA metabarcoding is a rapidly developing technology that can also facilitate biological surveys. This method involves the use of next generation sequencing technology to determine the community composition of a sample. However, it is uncertain as to what biological substrate should be the primary focus of metabarcoding surveys. This study aims to test multiple sample substrates (soil, scat, plant material and bulk arthropods) to determine what organisms can be detected from each and where they overlap. Samples (n = 200) were collected in the Pilbara (hot desert climate) and Swan Coastal Plain (hot Mediterranean climate) regions of Western Australia. Soil samples yielded little plant or animal DNA, especially in the Pilbara, probably due to conditions not conducive to long‐term preservation. In contrast, scat samples contained the highest overall diversity with 131 plant, vertebrate and invertebrate families detected. Invertebrate and plant sequences were detected in the plant (86 families), pitfall (127 families) and vane trap (126 families) samples. In total, 278 families were recovered from the survey, 217 in the Swan Coastal Plain and 156 in the Pilbara. Aside from soil, 22%–43% of the families detected were unique to the particular substrate, and community composition varied significantly between substrates. These results demonstrate the importance of selecting appropriate metabarcoding substrates when undertaking terrestrial surveys. If the aim is to broadly capture all biota then multiple substrates will be required.     

Species‐level biodiversity assessment using marine environmental DNA metabarcoding requires protocol optimization and standardization

DNA extraction from environmental samples (environmental DNA; eDNA) for metabarcoding‐based biodiversity studies is gaining popularity as a noninvasive, time‐efficient, and cost‐effective monitoring tool. The potential benefits are promising for marine conservation, as the marine biome is frequently under‐surveyed due to its inaccessibility and the consequent high costs involved. With increasing numbers of eDNA‐related publications have come a wide array of capture and extraction methods. Without visual species confirmation, inconsistent use of laboratory protocols hinders comparability between studies because the efficiency of target DNA isolation may vary. We determined an optimal protocol (capture and extraction) for marine eDNA research based on total DNA yield measurements by comparing commonly employed methods of seawater filtering and DNA isolation. We compared metabarcoding results of both targeted (small taxonomic group with species‐level assignment) and universal (broad taxonomic group with genus/family‐level assignment) approaches obtained from replicates treated with the optimal and a low‐performance capture and extraction protocol to determine the impact of protocol choice and DNA yield on biodiversity detection. Filtration through cellulose‐nitrate membranes and extraction with Qiagen's DNeasy Blood & Tissue Kit outperformed other combinations of capture and extraction methods, showing a ninefold improvement in DNA yield over the poorest performing methods. Use of optimized protocols resulted in a significant increase in OTU and species richness for targeted metabarcoding assays. However, changing protocols made little difference to the OTU and taxon richness obtained using universal metabarcoding assays. Our results demonstrate an increased risk of false‐negative species detection for targeted eDNA approaches when protocols with poor DNA isolation efficacy are employed. Appropriate optimization is therefore essential for eDNA monitoring to remain a powerful, efficient, and relatively cheap method for biodiversity assessments. For seawater, we advocate filtration through cellulose‐nitrate membranes and extraction with Qiagen's DNeasy Blood & Tissue Kit or phenol‐chloroform‐isoamyl for successful implementation of eDNA multi‐marker metabarcoding surveys.     

Place prioritization for biodiversity conservation using probabilistic surrogate distribution data

We analyse optimal and heuristic place prioritization algorithms for biodiversity conservation area network design which can use probabilistic data on the distribution of surrogates for biodiversity. We show how an Expected Surrogate Set Covering Problem (ESSCP) and a Maximal Expected Surrogate Covering Problem (MESCP) can be linearized for computationally efficient solution. For the ESSCP, we study the performance of two optimization software packages (XPRESS and CPLEX) and five heuristic algorithms based on traditional measures of complementarity and rarity as well as the Shannon and Simpson indices of α‐diversity which are being used in this context for the first time. On small artificial data sets the optimal place prioritization algorithms often produced more economical solutions than the heuristic algorithms, though not always ones guaranteed to be optimal. However, with large data sets, the optimal algorithms often required long computation times and produced no better results than heuristic ones. Thus there is generally little reason to prefer optimal to heuristic algorithms with probabilistic data sets.     

An integrated species distribution modelling framework for heterogeneous biodiversity data

Most knowledge about species and habitats is in-homogeneously distributed, with biases existing in space, time and taxonomic and functional knowledge. Yet, controversially the total amount of biodiversity data has never been greater. A key challenge is thus how to make effective use of the various sources of biodiversity data in an integrated manner. Particularly for widely used modelling approaches, such as species distribution models (SDMs), the need for integration is urgent, if spatial and temporal predictions are to be accurate enough in addressing global challenges.Here, I present a modelling framework that brings together several ideas and methodological advances for creating integrated species distribution models (iSDM). The ibis.iSDM R-package is a set of modular convenience functions that allows the integration of different data sources, such as presence-only, community survey, expert ranges or species habitat preferences, in a single model or ensemble of models. Further it supports convenient parameter transformations and tuning, data preparation helpers and allows the creation of spatial-temporal projections and scenarios. Ecological constraints such as projection limits, dispersal, connectivity or adaptability can be added in a modular fashion thus helping to prevent unrealistic estimates of species distribution changes.The ibis.iSDM R-package makes use of a series of methodological advances and is aimed to be a vehicle for creating more realistic and constrained spatial predictions. Besides providing convenience functions for a range of different statistical models as well as an increasing number of wrappers for mechanistic modules, ibis.iSDM also introduces several innovative concepts such as sequential or weighted integration, or thresholding by prediction uncertainty. The overall framework will be continued to be improved and further functionalities be added.     

An assessment of the use of volunteers for terrestrial invertebrate biodiversity surveys

Species’ distributions, assemblage patterns and the processes influencing these are poorly understood, and urgently require study. Use of volunteers to collect data is becoming increasingly common in biodiversity research. We assess the effectiveness of volunteers sampling terrestrial savanna invertebrates in comparison to experienced researchers, and examine the potential contribution of volunteers to terrestrial invertebrate surveys. There were relatively few differences in the diversity sampled by 54 Earthwatch Institute volunteers when compared to expert researchers. The major difference was in the results from the less spatially constrained method, where experience (microhabitat selection) most affected results, and experienced researchers performed better both quantitatively (more species sampled) and qualitatively (more unique and rare species). For the more constrained and less subjective methods, our training enabled the volunteers to quickly equal the experienced experts. Volunteers’ experience in invertebrate research influenced both the researchers’ perceptions of volunteers’ capacity and the actual performance of the volunteers. This suggests that appropriate training for the methods used can help to improve volunteers’ success with the sampling. We demonstrated that volunteers collect valid data; for the most part they sample invertebrates as effectively as a trained researcher, and that using volunteers has enormous direct benefits in terms of volume of work accomplished. For invertebrate studies using volunteers, we recommend that the subjectivity of the method be minimised, that experience is compensated for by increasing volunteer effort (two volunteers = one researcher), and that there is close management of volunteers in the field to ensure ongoing data quality. Volunteers provide a valuable resource to researchers carrying out biodiversity surveys, but using volunteers to carry out a scientifically sound project is not an easy option, and should only be implemented when volunteers would make a meaningful contribution and enable an otherwise impossible project.     

Efficient rescue of threatened biodiversity data using reBiND workflows

Abstract

Biodiversity data generated in the context of research projects often lack a strategy for long-term preservation and availability, and are therefore at risk of becoming outdated and finally lost. The reBiND project aims to develop an efficient and well-documented workflow for rescuing such data sets. The workflow consists of phases for data transformation into contemporary standards, data validation, storage in a native XML database, and data publishing in international biodiversity networks. It has been developed and tested using the example of collection and observational data but is flexible enough to be transferred to other data types and domains.     

Aquatic biodiversity assessment for the lazy

The world is covered in DNA. In any ecosystem, extracellular DNA fragments can be found that once formed the genomes of a variety of micro‐ and macroorganisms. A few years ago, it was proposed to use this environmental DNA (eDNA) as a source of information on local vertebrate biodiversity (Ficetola et al. 2008 ; Taberlet et al. 2012 ). This idea offered an elegant solution to take up the gauntlet of rapidly increasing monitoring needs. Coupled with barcoding efforts, it promised to be cost‐efficient in many respects, for example man‐hours and taxonomic expertise. Ecologists and conservation biologists with an interest in aquatic ecosystems have enthusiastically adopted and pioneered this new method, producing dozens of eDNA studies. Most of these studies have, however, focused on a single or a few aquatic species. In this issue of Molecular Ecology, Valentini et al. ( 2016 ) move the field a step further by demonstrating that metabarcoding approaches – which simultaneously target large groups of organisms such as amphibians or fish – can match and sometimes even outperform other inventory methods.     

Tree-based disease classification using protein data

A reliable and precise classification of diseases is essential for successful diagnosis and treatment. Using mass spectrometry from clinical specimens, scientists may find the protein variations among disease and use this information to improve diagnosis. In this paper, we propose a novel procedure to classify disease status based on the protein data from mass spectrometry. Our new tree-based algorithm consists of three steps: projection, selection and classification tree. The projection step aims to project all observations from specimens into the same bases so that the projected data have fixed coordinates. Thus, for each specimen, we obtain a large vector of 'coefficients' on the same basis. The purpose of the selection step is data reduction by condensing the large vector from the projection step into a much lower order of informative vector. Finally, using these reduced vectors, we apply recursive partitioning to construct an informative classification tree. This method has been successfully applied to protein data, provided by the Department of Radiology and Chemistry at Duke University.