Knowledge for Development

Relevant publications


Training manual on spatial analysis of plant diversity and distribution

By: Scheldeman, Xavier and van Zonneveld, Maarten. Bioversity International. 2010.This training manual is intended for scientists and students who work with biodiversity data and are interested in developing skills to effectively carry out spatial analysis based on (free) GIS applications with a focus on diversity and ecological analyses. These analyses offer a better understanding of spatial patterns of plant diversity and distribution, helping to improve conservation efforts. The training manual focuses on plants of interest for improving livelihoods (e.g. crops, trees and crop wild relatives) and/or those which are endangered.Spatial analyses of interspecific and intraspecific diversity are explained using different types of data: species presence morphological characterization data molecular data. Although this training focuses on plant diversity, many of the types of analyses described can also be applied for other organisms such as animals and fungi.http://www.bioversityinternational.org/training/training_materials/gis_manual.html

3/05/2011


Why forests are important for global poverty alleviation: a spatial explanation

By Sunderlin, W.D.; Dewi, S.; Puntodewo, A.; Muller, D.; Angelsen, A.; Epprecht, M. 2008. Forests have been declared important for the well-being of the poor because of the kinds of goods and services that they provide. We asked whether forests are important for the poor not only because of the kinds of goods and services they provide, but also because they tend to be located where the poor are. We conducted a spatial analysis to ascertain the degree of spatial association between poverty and forests in seven countries: Brazil, Honduras, Malawi, Mozambique, Uganda, Indonesia, and Vietnam. For most of these countries, there was a significant positive correlation between high natural forest cover and high poverty rate (the percentage of the population that is poor) and between high forest cover and low poverty density (the number of poor per unit area). We explain the findings and discuss policy implications and topics for future research.

10/02/2009