Abstract
Cambodia forms part of the Indo-Burma hotspot. Its extent of biodiversity, however, is subject to considerable uncertainty, as there has been little systematic collection of flora and fauna. During the Khmer Rouge regime institutions were banned, academics were prosecuted and written documentation systematically destroyed. Compared with neighbouring countries Cambodia has a low population density and relatively large natural areas that are still intact. However, deforestation is expanding rapidly and a significant but un-estimated area of forest has been degraded by development of agro-industries, encroachment, illegal logging, over-harvesting and forest fire as well as the use of chemicals during war. The purpose of the paper is to: (i) apply reserve selection methods to design more robust conservation networks when knowledge of species occurrence is incomplete and habitat is threatened, and (ii) evaluate the usefulness of systematic conservation planning in a developing country where data are limited and institutions for implementation are weak. This study investigates the performance of four non-probabilistic strategies: (i) a so-called ‘rule of thumb', (ii) hotspot, (iii) minimum cost representation, and (iv) maximum coverage; and one probabilistic design strategy, i.e. maximum expected coverage. The maximum expected coverage approach is between 15% and 24% more efficient than the non-probabilistic strategies. Finally, the relevance of such tools to real-world conservation planning in Cambodia is investigated. By incorporating experts in the generation of data, running the models and setting up premises, they acknowledge that it is possible to contribute to more systematic conservation planning in developing countries.
Original language | English |
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Journal | Biological Conservation |
Volume | 137 |
Issue number | 2 |
Pages (from-to) | 223-236 |
Number of pages | 14 |
ISSN | 0006-3207 |
DOIs | |
Publication status | Published - 2007 |
Keywords
- Former LIFE faculty
- Cambodia; Tree conservation; Reserve selection; Uncertainty; Conflict assessment