The relationship among biodiversity, governance, wealth, and scientific capacity at a country level: Disaggregation and prioritization
- 379 Downloads
At a global level, the relationship between biodiversity importance and capacity to manage it is often assumed to be negative, without much differentiation among the more than 200 countries and territories of the world. We examine this relationship using a database including terrestrial biodiversity, wealth and governance indicators for most countries. From these, principal components analysis was used to construct aggregated indicators at global and regional scales. Wealth, governance, and scientific capacity represent different skills and abilities in relation to biodiversity importance. Our results show that the relationship between biodiversity and the different factors is not simple: in most regions wealth and capacity varies positively with biodiversity, while governance vary negatively with biodiversity. However, these trends, to a certain extent, are concentrated in certain groups of nations and outlier countries. We discuss our results in the context of collaboration and joint efforts among biodiversity-rich countries and foreign agencies.
KeywordsBiodiversity indicators Biodiversity assessment Decision makers Developing countries
We thank Dr. Barbara M. Thiers from the New York Botanical Garden for allowing us to use the Index Herbariorum data. Samy Gaiji of GBIF compiled the information about their huge database. Comments from Don Doering and Leonard Krishtalka helped to improve the manuscript. A.L.-N. was funded by a JRS Biodiversity Foundation grant (PS5183).
- Amano, T., and W. J. Sutherland. 2013. Four barriers to the global understanding of biodiversity conservation: Wealth, language, geographical location and security. Proceedings of the Royal Society B: Biological Sciences 280. doi: 10.1098/rspb.2012.2649.
- Fox, J., and S. Weisberg. 2011. An R companion to applied regression. Washington DC: Sage Publications.Google Scholar
- Groombridge, B., and M. Jenkins. 2002. World atlas of biodiversity. Berkeley, CA: University of California Press.Google Scholar
- Heywood, V. 1995. Global biodiversity assessment. Cambridge: Cambridge University Press.Google Scholar
- la Grange, A.M., N.J. le Roux, and S. Gardner-Lubbe. 2009. BiplotGUI: Interactive biplots in R. Journal of Statistical Software 30: 1–37.Google Scholar
- Ludwig, J., and M. Stafford Smith. 2005. Intrepreting and correcting cross-scale mismatches in resilience analysis: A procedure and examples from Australia’s rangelands. Ecology and Society 10: 20–26.Google Scholar
- Mittermeier, R., P. Robles-Gil, and C. Mittermeier. 1997. Megadiversidad: los países biológicamente más ricos del mundo. Mexico City: CEMEX.Google Scholar
- R Development Core Team. 2013. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna. Retrieved from http://www.R-project.org.
- Rosenzweig, M.L., J. Donoghue II, Y.M. Li, and C. Yuan. 2011. Estimating species density. In Biological diversity: Frontiers in measurment and assessment, ed. A.E. Magurran, and B.J. Mcgill, 276–288. Oxford: Oxford University Press.Google Scholar
- Stein, B., L. Kutner, and J.S. Adams. 2000. Precious heritage. The status of biodiversity in the United States. New York: The Nature Conservancy and Oxford University Press.Google Scholar
- Tancoigne, E., C. Bole, A. Sigogneau, and A. Dubois. 2011. Insights from Zootaxa on potential trends in zoological taxonomic activity. Frontiers in Zoology 8. DOI: Artn 5. doi: 10.1186/1742-9994-8-5.
- Wagner, C. S., I. Brahmakulam, B. Jackson, A. Wong, and T. Yoda, 2001. Science and technology collaboration: Building capability in developing countries. DTIC Document Report. (in Swedish, English summary).Google Scholar