Data Mining for Global Trends in Mountain Biodiversity

1st Edition

Eva M. Spehn, Christian Korner

CRC Press
Published June 21, 2017
Reference - 200 Pages - 63 B/W Illustrations
ISBN 9781138112636 - CAT# K35193

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Thanks to advances in electronic archiving of biodiversity data and the digitization of climate and other geophysical data, a new era in biogeography, functional ecology, and evolutionary ecology has begun. In Data Mining for Global Trends in Mountain Biodiversity, Christian Korner, Eva M. Spehn, and a team of experts from the Global Mountain Biodiversity Assessment of DIVERSITAS explore two of the hottest subjects in science and technology: biodiversity and data mining. They demonstrate how to harness the scientific power of biological databases for furthering ecological and evolutionary theory.

Expert contributors address two aspects of the Global Mountain Biodiversity Assessment. They cover how to link biodiversity data with geophysical data and how to use biodiversity data to substantiate evolutionary and ecological theory. The text provides different methodological approaches and examples of successful mining of geo-referenced data in mountain regions on various scales. It includes:


  • Elevational and latitudinal gradients in plant diversity
  • E-mining trends in diversity of Lepidoptera, beetles, and birds
  • Niche modeling to explain past trends and predict future trends in mountain biodiversity
  • Sharing biodiversity data with the Global Biodiversity Information Facility

Using electronic databases opens ways to manage biodiversity in a sustainable fashion, test evolutionary and ecological theories, and measure the impact of climate change on various species and its effect on conservation efforts. The information and examples presented in this book can stimulate the creative use of archive data to answer old questions with new tools, and advance knowledge and understanding of mountain biodiversity worldwide. The book highlights the benefits of and the continuing need for an increase in the amount and quality of georeferenced data provided online in order to meet the challenges of global change.


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