A web-based GIS tool for exploring the world's biodiversity: The Global Biodiversity Information Facility Mapping and Analysis Portal Application (GBIF-MAPA)

Legacy biodiversity data from natural history and survey collections are rapidly becoming available in a common format over the Internet. Over 110 million records are already being served from the Global Biodiversity Information Facility (GBIF). However, our ability to use this information effectively for ecological research, management and conservation lags behind. A solution is a web-based Geographic Information System for enabling visualization and analysis of this rapidly expanding data resource. In this paper we detail a case study system, GBIF Mapping and Analysis Portal Application (MAPA), developed for deployment at distributed database portals. Building such a system requires overcoming a series of technical and research challenges. These challenges include: assuring fast speed of access to the vast amounts of data available through these distributed biodiversity databases; developing open standards based access to suitable environmental data layers for analyzing biodiversity distribution; building suitably flexible and intuitive map interfaces for refining the scope and criteria of an analysis; and building appropriate web-services based analysis tools that are of primary importance to the ecological community and make manifest the value of online biodiversity GBIF data. After discussing how we overcome these challenges, we provide case studies showing two examples of the use of GBIF-MAPA analysis tools.     

GBIF information is not enough: national database improves the inventory completeness of Amazonian epiphytes

Distribution data sharing in global databases (e.g. GBIF) allowed the knowledge synthesis in several biodiversity areas. However, their Wallacean shortfalls still reduce our capacity to understand distribution patterns. Including exclusive records from other databases, such as national ones (e.g. SpeciesLink), could mitigate these shortfall problems, but it remains not evaluated. Therefore, we assessed whether (i) the inventory completeness, (ii) taxonomic contribution and (iii) spatial biases could be improved when integrating both global and national biodiversity databases. Using Amazonian epiphytes as a model, we compared the available taxonomic information spatially between GBIF and SpeciesLink databases using a species contribution index. We obtained the inventory completeness from sources using species accumulation curves and assessed their spatial biases by constructing spatial autoregressive models. We found that both databases have a high amount of exclusive records (GBIF: 36.7%; SpeciesLink: 21.7%) and species (17.8%). Amazonia had a small epiphyte inventory completeness, but it was improved when we analyzed both databases together. Individually, both database records were biased to sites with higher altitude, population and herbarium density. Together, river density appeared as a new predictor, probably due to the higher species contribution of SpeciesLink along them. Our findings provide strong evidence that using both global and national databases increase the overall biodiversity knowledge and reduce inventory gaps, but spatial biases may persist. Therefore, we highlight the importance of aggregating more than one database to understand biodiversity patterns, to address conservation decisions and direct shortfalls more efficiently in future studies.

     

Are We Filling the Data Void? An Assessment of the Amount and Extent of Plant Collection Records and Census Data Available for Tropical South America

Large-scale studies are needed to increase our understanding of how large-scale conservation threats, such as climate change and deforestation, are impacting diverse tropical ecosystems. These types of studies rely fundamentally on access to extensive and representative datasets (i.e., “big data”). In this study, I asses the availability of plant species occurrence records through the Global Biodiversity Information Facility (GBIF) and the distribution of networked vegetation census plots in tropical South America. I analyze how the amount of available data has changed through time and the consequent changes in taxonomic, spatial, habitat, and climatic representativeness. I show that there are large and growing amounts of data available for tropical South America. Specifically, there are almost 2,000,000 unique geo-referenced collection records representing more than 50,000 species of plants in tropical South America and over 1,500 census plots. However, there is still a gaping “data void” such that many species and many habitats remain so poorly represented in either of the databases as to be functionally invisible for most studies. It is important that we support efforts to increase the availability of data, and the representativeness of these data, so that we can better predict and mitigate the impacts of anthropogenic disturbances.     

Global patterns of fern species diversity: An evaluation of fern data in GBIF

Despite that several studies have shown that data derived from species lists generated from distribution occurrence records in the Global Biodiversity Information Facility (GBIF) are not appropriate for those ecological and biogeographic studies that require high sampling completeness, because species lists derived from GBIF are generally very incomplete, Suissa et al. (2021) generated fern species lists based on data with GBIF for 100 km × 100 km grid cells across the world, and used the data to determine fern diversity hotspots and species richness–climate relationships. We conduct an evaluation on the completeness of fern species lists derived from GBIF at the grid–cell scale and at a larger spatial scale, and determine whether fern data derived from GBIF are appropriate for studies on the relations of species composition and richness with climatic variables. We show that species sampling completeness of GBIF is low (<40%) for most of the grid cells examined, and such low sampling completeness can substantially bias the investigation of geographic and ecological patterns of species diversity and the identification of diversity hotspots. We conclude that fern species lists derived from GBIF are generally very incomplete across a wide range of spatial scales, and are not appropriate for studies that require data derived from species lists in high completeness. We present a map showing global patterns of fern species diversity based on complete or nearly complete regional fern species lists.     

Filling in the GAPS: evaluating completeness and coverage of open‐access biodiversity databases in the United States

Primary biodiversity data constitute observations of particular species at given points in time and space. Open‐access electronic databases provide unprecedented access to these data, but their usefulness in characterizing species distributions and patterns in biodiversity depend on how complete species inventories are at a given survey location and how uniformly distributed survey locations are along dimensions of time, space, and environment. Our aim was to compare completeness and coverage among three open‐access databases representing ten taxonomic groups (amphibians, birds, freshwater bivalves, crayfish, freshwater fish, fungi, insects, mammals, plants, and reptiles) in the contiguous United States. We compiled occurrence records from the Global Biodiversity Information Facility (GBIF), the North American Breeding Bird Survey (BBS), and federally administered fish surveys (FFS). We aggregated occurrence records by 0.1° × 0.1° grid cells and computed three completeness metrics to classify each grid cell as well‐surveyed or not. Next, we compared frequency distributions of surveyed grid cells to background environmental conditions in a GIS and performed Kolmogorov–Smirnov tests to quantify coverage through time, along two spatial gradients, and along eight environmental gradients. The three databases contributed >13.6 million reliable occurrence records distributed among >190,000 grid cells. The percent of well‐surveyed grid cells was substantially lower for GBIF (5.2%) than for systematic surveys (BBS and FFS; 82.5%). Still, the large number of GBIF occurrence records produced at least 250 well‐surveyed grid cells for six of nine taxonomic groups. Coverages of systematic surveys were less biased across spatial and environmental dimensions but were more biased in temporal coverage compared to GBIF data. GBIF coverages also varied among taxonomic groups, consistent with commonly recognized geographic, environmental, and institutional sampling biases. This comprehensive assessment of biodiversity data across the contiguous United States provides a prioritization scheme to fill in the gaps by contributing existing occurrence records to the public domain and planning future surveys.     

The contribution of small collections to species distribution modelling: A case study from Fuireneae (Cyperaceae)

The recent and rapid digitization of biodiversity data from natural history collection (NHC) archives has enriched collections based data repositories; this data continues to inform studies of species' geographic distributions. Here we investigate the relative impact of plant data from small natural history collections (collections with < 100,000 specimens) on species distributional models in an effort to document the potential of data from small NHCs to contribute to and inform biodiversity research. We modelled suitable habitat of five test case species from Fuireneae (Cyperaceae) in the United States using specimen records available via the Global Biodiversity Information Facility and that of data ready to mobilize from two regional small herbaria. Data were partitioned into three datasets based on their source: 1) collections-based records from large NHCs accessed GBIF, 2) collections-based records from small NHCs accessed from GBIF, and 3) collections-based records from two small regional herbaria not yet mobilized to GBIF. We extracted and evaluated the ecological niche represented for each of the three datasets by applying dataset occurrences to 14 environmental factors, and we modelled habitat suitability using Maxent to compare the represented distribution of the environmental values among the datasets. Our analyses indicate that the data from small NHCs contributed unique information in both geographic and environmental space. When data from small collections were combined with data from large collections, species models of the ecological niche resulted in more refined predictions of habitat suitability, indicating that small collections can contribute unique occurrence data which enhance species distribution models by bridging geographic collection gaps and shifting modelled predictions of suitable habitat. Inclusion of specimen records from small collections in ongoing digitization efforts is essential for generating informed models of a species' niche and distribution.     

Monitoring records of plant species in the Hakone region of Fuji-Hakone-Izu National Park, Japan, 2001–2010

The monitoring of species occurrences is a crucial aspect of biodiversity conservation, and regional volunteerism can serve as a powerful tool in such endeavors. The Fuji-Hakone-Izu National Park in the Hakone region of Kanagawa Prefecture, Japan, boasts a volunteer association of approximately 100 members. These volunteers have monitored plant species occurrences from 2001 to the present along several hiking trails in the region. In this paper, I present the annual observation records of plant occurrences in Hakone from 2001 to 2010. This data set includes 1,071 species of plants from 151 families. Scientific names follow the Y List, and this data set includes several threatened plant species. Data files are formatted based on the Darwin Core and Darwin Core Archives, which are defined by the Biodiversity Information Standards (BIS) or Biodiversity Information Standards Taxonomic Databases Working Group (TDWG). Data files filled on required and some additional item on Darwin Core. The data set can download from the author’s personal Web site as of July 2012. These data will soon be published for the Global Biodiversity Information Facility (GBIF) through GBIF Japan. All users can then access the data from the GBIF portal site.     

台湾生物多样性资料整合之经验与策略

台湾生物多样性数据库之整合从2001年开始, 是因为数位典藏计划、生物多样性推动方案, 及台湾加入全球生物多样性信息网络(Global Biodiversity Information Facility, GBIF), 均在这一年启动。2002年“中研院”开始建置台湾物种名录数据库(TaiBNET), GBIF之台湾入口网TaiBIF则是在2004年时建置, 用来整合台湾生物多样性的资料并与国际接轨。所采用之方法及格式均依循GBIF所发展的交换标准, 一来可以整合台湾的数据, 二来可及时与国际交换数据。虽然TaiBNET及TaiBIF已突破智慧财产权(知识产权)的障碍, 可搜集整合数位典藏各子计划逐年累积的资料, 但跨部门间及非数位典藏计划所产生的数据, 仍因各单位及个人的本位主义而难以整合分享, 特别是生态分布原始数据。因此2008年在“中研院”成立了跨主管部门的委员会, 制订可行之资料搜集、整合与公开的政策, 并要求各主管部门在委办合约中纳入。无人否认数据库整合的重要, 但在现行对研究人员考评制度下, 研究人员大多不愿投入数据库建置的学术服务工作, 所获得的人力与经费亦日益短缺而难以永续经营。亟需相关部门的重视与支持。TaiBIF在过去6年来的推动成果虽未臻理想, 但所获的经验和心得仍有可供外界参考与借镜之处。     

Analysing the content of the European Ocean Biogeographic Information System (EurOBIS): available data, limitations, prospects and a look at the future

The European Ocean Biogeographic Information System—EurOBIS—is an integrated data system developed by the Flanders Marine Institute (VLIZ) for the EU Network of Excellence “Marine Biodiversity and Ecosystem Functioning” (MarBEF) in 2004. Its principle aims are to centralise the largely scattered biogeographical data on marine species collected by European institutions and to make these quality-controlled data freely available and easily accessible. It is in essence a distributed system in which individual datasets go through a series of quality control procedures before they are integrated into one large consolidated database. EurOBIS is freely available online at , where marine biogeographical data—with a focus on taxonomy, temporal and spatial distribution—can be consulted and downloaded for analyses. Over the last 6 years, EurOBIS has collected 228 datasets contributed by more than 75 institutes, representing over 13.6 million distribution records of which almost 12.5 million records are species level identifications. It is now the largest online searchable public source of European marine biological data, holding biogeographical information on 26,801 species and 9,221 genera. EurOBIS acts as the European node of OBIS, the Ocean Biogeographic Information System of the Census of Marine Life (CoML). EurOBIS shares its data with OBIS, which in its turn shares its content with the Global Biodiversity Information Facility (GBIF). This article describes the status of the European Ocean Biogeographic Information System, identifies data gaps, possible applications, uses and limitations. It also formulates a strategy for the growth and improvement of the system and wants to appeal for more contributions.     

Influence of different data cleaning solutions of point‐occurrence records on downstream macroecological diversity models

Digital point‐occurrence records from the Global Biodiversity Information Facility (GBIF) and other data providers enable a wide range of research in macroecology and biogeography. However, data errors may hamper immediate use. Manual data cleaning is time‐consuming and often unfeasible, given that the databases may contain thousands or millions of records. Automated data cleaning pipelines are therefore of high importance. Taking North American Ephedra as a model, we examined how different data cleaning pipelines (using, e.g., the GBIF web application, and four different R packages) affect downstream species distribution models (SDMs). We also assessed how data differed from expert data. From 13,889 North American Ephedra observations in GBIF, the pipelines removed 31.7% to 62.7% false positives, invalid coordinates, and duplicates, leading to datasets between 9484 (GBIF application) and 5196 records (manual‐guided filtering). The expert data consisted of 704 records, comparable to data from field studies. Although differences in the absolute numbers of records were relatively large, species richness models based on stacked SDMs (S‐SDM) from pipeline and expert data were strongly correlated (mean Pearson''s r across the pipelines: .9986, vs. the expert data: .9173). Our results suggest that all R package‐based pipelines reliably identified invalid coordinates. In contrast, the GBIF‐filtered data still contained both spatial and taxonomic errors. Major drawbacks emerge from the fact that no pipeline fully discovered misidentified specimens without the assistance of taxonomic expert knowledge. We conclude that application‐filtered GBIF data will still need additional review to achieve higher spatial data quality. Achieving high‐quality taxonomic data will require extra effort, probably by thoroughly analyzing the data for misidentified taxa, supported by experts.     

A specialist’s audit of aggregated occurrence records

Occurrence records for named, native Australian millipedes from the Global Biodiversity Information Facility (GBIF) and the Atlas of Living Australia (ALA) were compared with the same records from the Millipedes of Australia (MoA) website, compiled independently by the author. The comparison revealed some previously unnoticed errors in MoA, and a much larger number of errors and other problems in the aggregated datasets. Errors have been corrected in MoA and in some data providers’ databases, but will remain in GBIF and ALA until data providers have supplied updates to these aggregators. An audit by a specialist volunteer, as reported here, is not a common occurrence. It is suggested that aggregators should do more, or more effective, data checking and should query data providers when possible errors are detected, rather than simply disclaim responsibility for aggregated content.     

From GenBank to GBIF: Phylogeny-Based Predictive Niche Modeling Tests Accuracy of Taxonomic Identifications in Large Occurrence Data Repositories

Accuracy of taxonomic identifications is crucial to data quality in online repositories of species occurrence data, such as the Global Biodiversity Information Facility (GBIF), which have accumulated several hundred million records over the past 15 years. These data serve as basis for large scale analyses of macroecological and biogeographic patterns and to document environmental changes over time. However, taxonomic identifications are often unreliable, especially for non-vascular plants and fungi including lichens, which may lack critical revisions of voucher specimens. Due to the scale of the problem, restudy of millions of collections is unrealistic and other strategies are needed. Here we propose to use verified, georeferenced occurrence data of a given species to apply predictive niche modeling that can then be used to evaluate unverified occurrences of that species. Selecting the charismatic lichen fungus, Usnea longissima, as a case study, we used georeferenced occurrence records based on sequenced specimens to model its predicted niche. Our results suggest that the target species is largely restricted to a narrow range of boreal and temperate forest in the Northern Hemisphere and that occurrence records in GBIF from tropical regions and the Southern Hemisphere do not represent this taxon, a prediction tested by comparison with taxonomic revisions of Usnea for these regions. As a novel approach, we employed Principal Component Analysis on the environmental grid data used for predictive modeling to visualize potential ecogeographical barriers for the target species; we found that tropical regions conform a strong barrier, explaining why potential niches in the Southern Hemisphere were not colonized by Usnea longissima and instead by morphologically similar species. This approach is an example of how data from two of the most important biodiversity repositories, GenBank and GBIF, can be effectively combined to remotely address the problem of inaccuracy of taxonomic identifications in occurrence data repositories and to provide a filtering mechanism which can considerably reduce the number of voucher specimens that need critical revision, in this case from 4,672 to about 100.     

A Tenebrionid beetle’s dataset (Coleoptera,Tenebrionidae) from Peninsula Valdés (Chubut,?Argentina)

The Natural Protected Area Peninsula Valdés, located in Northeastern Patagonia, is one of the largest conservation units of arid lands in Argentina. Although this area has been in the UNESCO World Heritage List since 1999, it has been continually exposed to sheep grazing and cattle farming for more than a century which have had a negative impact on the local environment. Our aim is to describe the first dataset of tenebrionid beetle species living in Peninsula Valdés and their relationship to sheep grazing. The dataset contains 118 records on 11 species and 198 adult individuals collected. Beetles were collected using pitfall traps in the two major environmental units of Peninsula Valdés, taking into account grazing intensities over a three year time frame from 2005–2007. The Data quality was enhanced following the best practices suggested in the literature during the digitalization and geo-referencing processes. Moreover, identification of specimens and current accurate spelling of scientific names were reviewed. Finally, post-validation processes using DarwinTest software were applied. Specimens have been deposited at Entomological Collection of the Centro Nacional Patagónico (CENPAT-CONICET). The dataset is part of the database of this collection and has been published on the internet through GBIF Integrated Publishing Toolkit (IPT) (http://data.gbif.org/datasets/resource/14669/). Furthermore, it is the first dataset for tenebrionid beetles of arid Patagonia available in GBIF database, and it is the first one based on a previously designed and standardized sampling to assess the interaction between these beetles and grazing in the area. The main purposes of this dataset are to ensure accessibility to data associated with Tenebrionidae specimens from Peninsula Valdés (Chubut, Argentina), also to contribute to GBIF with primary data about Patagonian tenebrionids and finally, to promote the Entomological Collection of Centro Nacional Patagónico (CENPAT-CONICET) and its associated biodiversity data. For these reasons, we believe that this information will certainly be useful for future faunistic, ecological, conservational and biogeographical studies.     

Patterns,biases and prospects in the distribution and diversity of Neotropical snakes

Motivation

We generated a novel database of Neotropical snakes (one of the world's richest herpetofauna) combining the most comprehensive, manually compiled distribution dataset with publicly available data. We assess, for the first time, the diversity patterns for all Neotropical snakes as well as sampling density and sampling biases.

Main types of variables contained

We compiled three databases of species occurrences: a dataset downloaded from the Global Biodiversity Information Facility (GBIF), a verified dataset built through taxonomic work and specialized literature, and a combined dataset comprising a cleaned version of the GBIF dataset merged with the verified dataset.

Spatial location and grain

Neotropics, Behrmann projection equivalent to 1° × 1°.

Time period

Specimens housed in museums during the last 150 years.

Major taxa studied

Squamata: Serpentes.

Software format

Geographical information system (GIS).

Results

The combined dataset provides the most comprehensive distribution database for Neotropical snakes to date. It contains 147,515 records for 886 species across 12 families, representing 74% of all species of snakes, spanning 27 countries in the Americas. Species richness and phylogenetic diversity show overall similar patterns. Amazonia is the least sampled Neotropical region, whereas most well‐sampled sites are located near large universities and scientific collections. We provide a list and updated maps of geographical distribution of all snake species surveyed.

Main conclusions

The biodiversity metrics of Neotropical snakes reflect patterns previously documented for other vertebrates, suggesting that similar factors may determine the diversity of both ectothermic and endothermic animals. We suggest conservation strategies for high‐diversity areas and sampling efforts be directed towards Amazonia and poorly known species.     

Software identification tools for biodiversity and biosecurity monitoring

The taxonomic crisis, associated with declining taxonomic expertise and resources, poses serious problems for biodiversity and biosecurity monitoring and management. While there has been a dramatic increase in the amount of taxonomic information available on the Web, resulting from such global initiatives as Species 2000, All Species and the Global Biodiversity Information Facility (GBIF), the lack of identification services has meant this information is often not accessible to many potential users. Apart from the immediate problems raised by the inability of ecologists, conservation managers, plant health inspectors and other practitioners to identify specimens of concern, this in turn means they are also unable to access the increasing wealth of information about particular organisms that is potentially available to them.     

海洋生物多样性信息资源

海洋生物多样性甚高, 但却饱受人为的破坏及干扰。目前全球最大的含点位数据的在线开放性数据库是海洋生物地理信息系统(OBIS), 共约12万种3,700万笔资料; 另一个较大的数据库世界海洋生物物种登录(WoRMS)已收集全球22万种海洋生物之物种分类信息。除此之外, 以海洋生物为主的单一类群的数据库只有鱼库(FishBase)、藻库(AlgaeBase)及世界六放珊瑚(Hexacorallians of the World)3个。跨类群及跨陆海域的全球性物种数据库则甚多, 如网络生命大百科(EOL)、全球生物物种名录(CoL)、整合分类信息系统(ITIS)、维基物种(Wikispecies)、ETI生物信息(ETI Bioinformatics)、生命条形码(BOL)、基因库(GenBank)、生物多样性历史文献图书馆(BHL)、海洋生物库(SeaLifeBase); 海洋物种鉴定入口网(Marine Species Identification Portal)、FAO渔业及水产养殖概要(FAO Fisheries and Aquaculture Fact Sheets)等可查询以分类或物种解说为主的数据库。全球生物多样性信息网络(GBIF)、发现生命(Discover Life)、水生物图库(AquaMaps)等则是以生态分布数据为主, 且可作地理分布图并提供下载功能, 甚至于可以改变水温、盐度等环境因子的参数值, 利用既定的模式作参数改变后之物种分布预测。谷歌地球(Google Earth)及国家地理(National Geographic)网站中的海洋子网页, 以及珊瑚礁库(ReefBase)等官方机构或非政府组织之网站, 则大多以海洋保育的教育倡导为主, 所提供的信息及素材可谓包罗万象, 令人目不暇给。更令用户感到方便的是上述许多网站或数据库彼此间均已可交互链接及查询。另外, 属于搜索引擎的谷歌图片(Google Images)与谷歌学术(Google Scholar)透过海洋生物数据库所提供的直接链接, 在充实物种生态图片与学术论文上亦发挥极大帮助, 让用户获得丰富多样的信息。为了保育之目的, 生物多样性数据库除了整合与公开分享外, 还应鼓励并推荐大家来使用。本文乃举Rainer Froese在巴黎演讲之内容为例, 介绍如何使用海洋生物多样性之数据来预测气候变迁对鱼类分布的影响。最后就中国大陆与台湾目前海洋生物多样性数据库的现况、两岸的合作及如何与国际接轨作介绍。     

Harvestmen of the BOS Arthropod Collection of the University of Oviedo (Spain) (Arachnida,Opiliones)

There are significant gaps in accessible knowledge about the distribution and phenology of Iberian harvestmen (Arachnida: Opiliones). Harvestmen accessible datasets in Iberian Peninsula are unknown, an only two other datasets available in GBIF are composed exclusively of harvestmen records. Moreover, only a few harvestmen data from Iberian Peninsula are available in GBIF network (or in any network that allows public retrieval or use these data). This paper describes the data associated with the Opiliones kept in the BOS Arthropod Collection of the University of Oviedo, Spain (hosted in the Department of Biología de Organismos y Sistemas), filling some of those gaps. The specimens were mainly collected from the northern third of the Iberian Peninsula. The earliest specimen deposited in the collection, dating back to the early 20^th century, belongs to the P. Franganillo Collection. The dataset documents the collection of 16,455 specimens, preserved in 3,772 vials. Approximately 38% of the specimens belong to the family Sclerosomatidae, and 26% to Phalangidae; six other families with fewer specimens are also included. Data quality control was incorporated at several steps of digitisation process to facilitate reuse and improve accuracy. The complete dataset is also provided in Darwin Core Archive format, allowing public retrieval, use and combination with other biological, biodiversity of geographical variables datasets.     

How sensitive are climatic niche inferences to distribution data sampling? A comparison of Biota of North America Program (BONAP) and Global Biodiversity Information Facility (GBIF) datasets

Climatic niche modeling is widely used in modern macroecology and evolutionary biology to model species' distributions and ecological niches. Frequently, Global Biodiversity Information Facility (GBIF) distribution data are used as raw data for such models. Unfortunately, the accuracy of resulting niche estimates is difficult to assess, and GBIF users continue to call for a better understanding of GBIF data precision and uncertainty. Our research evaluates how GBIF data perform in comparison with curated, county-level species distributions from the Biota of North America Program (BONAP; www.bonap.org) to estimate the climatic niche for Carex (Cyperaceae), one of the largest angiosperm genera in the temperate zone. In particular, we investigate the complementary strengths and weaknesses of the two datasets in the context of climatic niche estimation, namely (1) the incomplete sampling coverage of species distributions of GBIF data, and (2) elevation bias in climatic niche estimates resulting from the coarse resolution of the BONAP distribution dataset. To do so, we quantified climatic niches for 388 North American Carex species and calculated the distance of GBIF and BONAP climatic niche estimates in principal component space. We found little to no relationship between differences in climatic niche estimates and sampling coverage metrics, suggesting that sparse sampling coverage of GBIF data may have negligible average effects on mean climate estimates at the species level. However, elevation had a significant effect on differences in niche estimates, suggesting that using county-level distribution data—in our study, represented by BONAP—may introduce a bias in estimated climatic niche. To investigate if any such bias is phylogenetically structured, we examined the relationship between GBIF and BONAP climatic niche tip states using Phylogenetic Generalized Least Squares Regression (PGLS), estimating phylogenetic signal in the residuals of the regression by estimating Pagel's λ simultaneously with other regression parameters. Estimates were tightly correlated, with little to no phylogenetic signal—low λ—in the regression residuals, suggesting that any potential bias is more or less independent of phylogeny for the sedges of North America. Based on our results, we recommend a hybrid GBIF and BONAP data approach for the best understanding of species' climatic niche, relying on BONAP to fill in distributions primarily where collection records may be sparse. Our findings provide needed context and perspective on the implications of alternative distribution datasets for climatic niche estimation, especially in a macroevolutionary context.     

B-HIT - A Tool for Harvesting and Indexing Biodiversity Data

With the rapidly growing number of data publishers, the process of harvesting and indexing information to offer advanced search and discovery becomes a critical bottleneck in globally distributed primary biodiversity data infrastructures. The Global Biodiversity Information Facility (GBIF) implemented a Harvesting and Indexing Toolkit (HIT), which largely automates data harvesting activities for hundreds of collection and observational data providers. The team of the Botanic Garden and Botanical Museum Berlin-Dahlem has extended this well-established system with a range of additional functions, including improved processing of multiple taxon identifications, the ability to represent associations between specimen and observation units, new data quality control and new reporting capabilities. The open source software B-HIT can be freely installed and used for setting up thematic networks serving the demands of particular user groups.