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 diversityE-mining trends in diversity of Lepidoptera, beetles, and birdsNiche modeling to explain past trends and predict future trends in mountain biodiversitySharing 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.
Publisher: Taylor & Francis Ltd
Number of pages: 200
Weight: 367 g
"Overall, the book provides a rich resource of valuable information and stimulation for those who are willing to dig into the detail of the individual chapters, As a whole, it demonstrates well how data mining techniques ran complement, but not necessarily replace, expensive experiments, thus furthering ecological and evolutionary theory."
-- Spehn. Mountain Research and Development (MRD). August 2010, Vol 30, No. 3
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