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Análisis del Uso de Datos de Colecciones de Museo en la Evaluación de la Biodiversidad
Author(s) -
Ponder W. F.,
Carter G. A.,
Flemons P.,
Chapman R. R.
Publication year - 2001
Publication title -
conservation biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.2
H-Index - 222
eISSN - 1523-1739
pISSN - 0888-8892
DOI - 10.1046/j.1523-1739.2001.015003648.x
Subject(s) - taxon , data collection , biodiversity , range (aeronautics) , sampling (signal processing) , geography , computer science , cluster analysis , biological data , robustness (evolution) , data mining , data science , ecology , statistics , machine learning , mathematics , biology , biochemistry , materials science , genetics , filter (signal processing) , gene , composite material , computer vision
Natural‐history collections in museums contain data critical to decisions in biodiversity conservation. Collectively, these specimen‐based data describe the distributions of known taxa in time and space. As the most comprehensive, reliable source of knowledge for most described species, these records are potentially available to answer a wide range of conservation and research questions. Nevertheless, these data have shortcomings, notably geographic gaps, resulting mainly from the ad hoc nature of collecting effort. This problem has been frequently cited but rarely addressed in a systematic manner. We have developed a methodology to evaluate museum collection data, in particular the reliability of distributional data for narrow‐range taxa. We included only those taxa for which there were an appropriate number of records, expert verification of identifications, and acceptable locality accuracy. First, we compared the available data for the taxon of interest to the “background data,” comprised of records for those organisms likely to be captured by the same methods or by the same collectors as the taxon of interest. The “adequacy”of background sampling effort was assessed through calculation of statistics describing the separation, density, and clustering of points, and through generation of a sampling density contour surface. Geographical information systems (GIS) technology was then used to model predicted distributions of species based on abiotic (e.g., climatic and geological) data. The robustness of these predicted distributions can be tested iteratively or by bootstrapping. Together, these methods provide an objective means to assess the likelihood of the distributions obtained from museum collection records representing true distributions. Potentially, they could be used to evaluate any point data to be collated in species maps, biodiversity assessment, or similar applications requiring distributional information.

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