Biodiversity Informatics最新文献

{"title":"ORGANIZATION OF BIODIVERSITY RESOURCES BASED ON THE PROCESS OF THEIR CREATION AND THE ROLE OF INDIVIDUAL ORGANISMS AS RESOURCE RELATIONSHIP NODES","authors":"S. Baskauf","doi":"10.17161/BI.V7I1.3664","DOIUrl":"https://doi.org/10.17161/BI.V7I1.3664","url":null,"abstract":"Abstract. - Kinds of occurrences (evidence of particular living organisms) can be grouped by common data and metadata characteristics that are determined by the way that the occurrence represents the organism. The creation of occurrence resources follows a pattern which can be used as the basis for organizing both the metadata associated with those resources and the relationships among the resources. The central feature of this organizational system is a resource representing the individual organism. This resource serves as a node which connects the organism's occurrences and any determinations of the organism's taxonomic identity. I specify a relatively small number of predicates which can define the important relationships among these resources and suggest which metadata properties should logically be associated with each kind of resource.","PeriodicalId":269455,"journal":{"name":"Biodiversity Informatics","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128866981","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"USING TAXONOMIC REVISION DATA TO ESTIMATE THE GLOBAL SPECIES RICHNESS AND CHARACTERISTICS OF UNDESCRIBED SPECIES OF DIVING BEETLES (COLEOPTERA: DYTISCIDAE)","authors":"V. Nilsson-Örtman, A. Nilsson","doi":"10.17161/BI.V7I1.3631","DOIUrl":"https://doi.org/10.17161/BI.V7I1.3631","url":null,"abstract":"Many methods used for estimating species richness are either difficult to use on poorly known taxa or require input data that are laborious and expensive to collect. In this paper we apply a method which takes advantage of the carefully conducted tests of how the described diversity compares to real species richness that are inherent in taxonomic revisions. We analyze the quantitative outcome from such revisions with respect to body size, zoogeographical region and phylogenetic relationship. The best fitting model is used to predict the diversity of unrevised groups if these would have been subject to as rigorous species level hypothesis-testing as the revised groups. The sensitivity of the predictive model to single observations is estimated by bootstrapping over resampled subsets of the original data. The Dytiscidae is with its 4080 described species (end of May 2009) the most diverse group of aquatic beetles and have a world-wide distribution. Extensive taxonomic work has been carried out on the family but still the number of described species increases exponentially in most zoogeographical regions making many commonly used methods of estimation difficult to apply. We provide independent species richness estimates of subsamples for which species richness estimates can be reached through extrapolation and compare these to the species richness estimates obtained through the method using revision data. We estimate there to be 5405 species of dytiscids, a 1.32-fold increase over the present number of described species. The undescribed diversity is likely to be biased towards species with small body size from tropical regions outside of Africa.","PeriodicalId":269455,"journal":{"name":"Biodiversity Informatics","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126246871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"EFFECTIVELY SEARCHING SPECIMEN AND OBSERVATION DATA WITH TOQE, THE THESAURUS OPTIMIZED QUERY EXPANDER","authors":"A. Güntsch, N. Hoffmann, P. Kelbert, W. Berendsohn","doi":"10.17161/BI.V6I1.1631","DOIUrl":"https://doi.org/10.17161/BI.V6I1.1631","url":null,"abstract":"Today’s specimen and observation data portals lack a flexible mechanism, able to link up thesaurus-enabled data sources such as taxonomic checklist databases and expand user queries to related terms, significantly enhancing result sets. The TOQE system (Thesaurus Optimized Query Expander) is a REST-like XML web-service implemented in Python and designed for this purpose. Acting as an interface between portals and thesauri, TOQE allows the implementation of specialized portal systems with a set of thesauri supporting its specific focus. It is both easy to use for portal programmers and easy to configure for thesaurus database holders who want to expose their system as a service for query expansions. Currently, TOQE is used in four specimen and observation data portals. The documentation is available from http://search.biocase.org/toqe/.","PeriodicalId":269455,"journal":{"name":"Biodiversity Informatics","volume":"137 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115990673","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"LOCALITY UNCERTAINTY AND THE DIFFERENTIAL PERFORMANCE OF FOUR COMMON NICHE-BASED MODELING TECHNIQUES","authors":"Miguel Fernández, Stanley D. Blum, S. Reichle, Q. Guo, Barbara A. Holzman, H. Hamilton","doi":"10.17161/BI.V6I1.3314","DOIUrl":"https://doi.org/10.17161/BI.V6I1.3314","url":null,"abstract":"We address a poorly understood aspect of ecological niche modeling: its sensitivity to different levels of geographic uncertainty in organism occurrence data. Our primary interest was to assess how accuracy degrades under increasing uncertainty, with performance measured indirectly through model consistency. We used Monte Carlo simulations and a similarity measure to assess model sensitivity across three variables: locality accuracy, niche modeling method, and species. Randomly generated data sets with known levels of locality uncertainty were compared to an original prediction using Fuzzy Kappa. Data sets where locality uncertainty is low were expected to produce similar distribution maps to the original. In contrast, data sets where locality uncertainty is high were expected to produce less similar maps. BIOCLIM, DOMAIN, Maxent and GARP were used to predict the distributions for 1200 simulated datasets (3 species x 4 buffer sizes x 100 randomized data sets). Thus, our experimental design produced a total of 4800 similarity measures, with each of the simulated distributions compared to the prediction of the original data set and corresponding modeling method. A general linear model (GLM) analysis was performed which enables us to simultaneously measure the effect of buffer size, modeling method, and species, as well as interactions among all variables. Our results show that modeling method has the largest effect on similarity scores and uniquely accounts for 40% of the total variance in the model. The second most important factor was buffer size, but it uniquely accounts for only 3% of the variation in the model. The newer and currently more popular methods, GARP and Maxent, were shown to produce more inconsistent predictions than the earlier and simpler methods, BIOCLIM and DOMAIN. Understanding the performance of different niche modeling methods under varying levels of geographic uncertainty is an important step toward more productive applications of historical biodiversity collections.","PeriodicalId":269455,"journal":{"name":"Biodiversity Informatics","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116728223","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ENVIRONMENTAL CORRELATION STRUCTURE AND ECOLOGICAL NICHE MODEL PROJECTIONS","authors":"A. Jiménez‐Valverde, Y. Nakazawa, A. Lira‐Noriega, A. Townsend Peterson","doi":"10.17161/BI.V6I1.1634","DOIUrl":"https://doi.org/10.17161/BI.V6I1.1634","url":null,"abstract":"The environmental causation of species' distributions depends on three general, interacting types of factors: the abiotic (or physical) environment, the biotic environment, and accessibility of areas across complex landscapes (Pulliam 2000; Soberón and Peterson 2005; Soberón 2007). Indirect variables, such as elevation, are those associated with the presence of species owing to correlation with the actual variables that directly and causally affect the fitness of the species, such as temperature or precipitation (Austin 2002). Put another way, variables can be arranged along a gradient of proximal to distal, regarding the immediacy of causality regarding the fitness of the species: indirect variables are always distal variables (Austin 2002). Contrary to proximal variables, distal variables are often easy measurable, and thus available in georeferenced databases (Fig. 1). Many researchers now attempt to reconstruct these environmental dimensions as ecological niche models (also termed \" bioclimatic envelopes, \" \" environmental niche models, \" or even \" species distribution models \"), using a variety of inferential approaches. Niche models have been used to predict geographic distributions of species (Guisan et al. 2006), anticipate distributions of unknown species (Raxworthy et al. 2003), estimate the invasive potential of species (Peterson 2003; Thuiller et al. 2005), and forecast climate change effects on species' distributions (Araújo et al. 2005). The predictive capacity of these approaches makes them particularly useful in applications involving \" transferring \" the niche model to make predictions regarding other landscapes or time periods (Araújo and Pearson 2005; Peterson et al. 2007). Such transferability applications, however, depend critically on the assumption that environmental variables relevant on one landscape or at one time will be relevant on another. Niche models are probably never based directly on genuinely proximate variables, but rather rely on more easily measurable variables that are inevitably less directly related to the population biology of the species in question. As such, the correlation structure among environmental variables becomes key (Morin and Lechowicz 2008): if correlation structures are stable and consistent across different landscapes and time periods, then niche models may be transferable to those other situations; if, on the other hand, correlation structures are not consistent among situations, then models may not be transferable, or at least not as fully or as readily. As correlation methods, niche modeling techniques simply select the set of variables that is best to explain the largest part of the variation in the dependent variable. Transferability exercises require the …","PeriodicalId":269455,"journal":{"name":"Biodiversity Informatics","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121844573","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"BRIDGING THE GAP BETWEEN TECHNOLOGY AND SCIENCE WITH EXAMPLES FROM ECOLOGY AND BIODIVERSITY","authors":"Laura Downey, D. Pennington","doi":"10.17161/BI.V6I1.1574","DOIUrl":"https://doi.org/10.17161/BI.V6I1.1574","url":null,"abstract":"Early informatics focused primarily on the application of technology and computer science to a specific domain; modern informatics has broadened to encompass human and knowledge dimensions. Application of technology is but one aspect of informatics. Understanding domain members’ issues, priorities, knowledge, abilities, interactions, tasks and work environments is another aspect, and one that directly impacts application success. Involving domain members in the design and development of technology in their domain is a key factor in bridging the gap between technology and science. This user-centered design (UCD) approach in informatics is presented via an ecoinformatics case study in three areas: collaboration, usability, and education and training.","PeriodicalId":269455,"journal":{"name":"Biodiversity Informatics","volume":"46 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114001465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AN EFFICIENT SEGMENTATION ALGORITHM FOR ENTITY INTERACTION","authors":"Eugene Ch’ng","doi":"10.17161/BI.V6I1.1633","DOIUrl":"https://doi.org/10.17161/BI.V6I1.1633","url":null,"abstract":"The inventorying of biological diversity and studies in biocomplexity require the management of large electronic datasets of organisms. While species inventory has adopted structured electronic databases for some time, the \u0000computer modelling of the functional interactions between biological entities at all levels of life is still in the stage of development. One of the challenges for this type of modelling is the biotic interactions that occur between large datasets of entities represented as computer algorithms. In real-time simulation that models the biotic interactions of large population datasets, the use of computational processing time could be extensive. One way of increasing the efficiency of such simulation is to partition the landscape so that entities need only traverse its local space for entities that falls within the interaction proximity. This article presents an efficient segmentation algorithm for biotic \u0000interactions for research related to the modelling and simulation of biological systems.","PeriodicalId":269455,"journal":{"name":"Biodiversity Informatics","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126822263","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DARWIN CORE BASED DATA STREAMLINING WITH DigiMus 2.0","authors":"A. Kakodkar, Sarika S. Kerkar, Neena Varghese, D. P. Kavlekar, C. T. Achuthankutty","doi":"10.17161/BI.V6I1.1626","DOIUrl":"https://doi.org/10.17161/BI.V6I1.1626","url":null,"abstract":"Cataloguing biological specimen is a important activity of biological museums world over. Software developed especially for this purpose have evolved overtime to achieve more accuracy in retrieving data from large and diverse datasets. Combining smaller datasets in to a larger information system requires uniformity of data based on a single data standard. In the developing world smaller datasets are maintained by individual researchers or small college and university groups. For standardizing data from such datasets, software needs to be developed, which require expertise and sufficient funds which are often unavailable. We present a simple open source web based tool developed using PHP to enable an individual with little or no knowledge of information systems or databases, to effectively streamline specimen data with data standard Darwin Core 1.2 ( DwC 1.2). Such data can then be shared and easily provided to larger datasets like Ocean Biogeographic Information Systems (OBIS) and Global Biodiversity Information Facility (GBIF). This tool can be accessed at http://www.niobioinformatics.in/digimus.php and its source code is freely available at http://www.niobioinformatics.in/digimus_source.php","PeriodicalId":269455,"journal":{"name":"Biodiversity Informatics","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127392229","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Converting Taxonomic Descriptions to New Digital Formats","authors":"Hong Cui","doi":"10.17161/BI.V5I0.46","DOIUrl":"https://doi.org/10.17161/BI.V5I0.46","url":null,"abstract":"Abstract.--The majority of taxonomic descriptions is currently in print format. The majority of digital descriptions are in formats such as DOC, HTML, or PDF and for human readers. These formats do not convey rich semantics in taxonomic descriptions for computer-aided process. Newer digital formats such as XML and RDF accommodate semantic annotations that allow computers to process the rich semantics on human's behalf, thus open up opportunities for a wide range of innovative usages of taxonomic descriptions, such as searching in more precise and flexible ways, integrating with gnomic and geographic information, generating taxonomic keys automatically, and text data mining and information visualization etc. This paper discusses the challenges in automated conversion of multiple collections of descriptions to XML format and reports an automated system, MARTT. MARTT is a machine-learning system that makes use of training examples to tag new descriptions into XML format. A number of utilities are implemented as solutions to the challenges. The utilities are used to reduce the effort for training example preparation, to facilitate the creation of a comprehensive schema, and to predict system performance on a new collection of descriptions. The system has been tested with several plant and alga taxonomic publications including Flora of China and Flora of North America.","PeriodicalId":269455,"journal":{"name":"Biodiversity Informatics","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133718095","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"More complex distribution models or more representative data","authors":"J. Lobo","doi":"10.17161/BI.V5I0.40","DOIUrl":"https://doi.org/10.17161/BI.V5I0.40","url":null,"abstract":"Distribution models for species are increasingly used to summarize species’ geography in conservation analyses. These models use increasingly sophisticated modeling techniques, but often lack detailed examination of the quality of the biological occurrence data on which they are based. I analyze the results of the best comparative study of the performance of different modeling techniques, which used pseudo-absence data selected at random. I provide an example of variation in model accuracy depending on the type of absence information used, showing that good model predictions depend most critically on better biological data.","PeriodicalId":269455,"journal":{"name":"Biodiversity Informatics","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132361560","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}