Valuation of Biodiversity Benefits (OECD)

Several problems however exist, rendering category assessment quite difficult to apply inpolicy making. While the classification of a species in a category relies on an objective evaluation, theactual definitions of these categories rely on a subjective view. In practice, the large number ofavailable criteria (e.g. α , β and γ criterion) used for evaluation in some way already reflect thedifficulties that exist in determining value. Furthermore, the information provided by the list of speciesunder the category labelled as “threat” is not necessarily sufficient to determine priorities forconservation action Indeed, if Red Data Books are elected as the ecological method to establishbiodiversity priorities, it is likely that choices would be made based on the likelihood of speciesextinction rather than on the (differences) level of among the gene flow between the survival speciesand the species with higher probability of extinction. Finally, given the scientific understanding ofpopulation and ecosystems, it is possible to develop alternative and more complete indicatorsinvolving the use of numerous other criteria concerning conservation action.An early example of a multi-criteria rating is the method proposed by Randwell (1969). Themethod was used to evaluate coastal habitats and combines the use of eight criteria into a single score,the Comparative Biological Value Index (CBVI). Each of these criteria are rated according to the scaleshown in Table 8.2 and the final score is obtained by summing up the scores for all the nine criteria:CBVI = Ph + O + D + G + S + P + E + CThe maximum potential value is 28 and the minimum value is 7. The higher the CBVI valuethe greater the requirement for site protection. Since Randwell, the use of indices constitutes a popularpractice in ecological valuation and management (see Spellerberg 1992 for an extensive review ofCBVI assessment of landscape and urban habitats). However, this valuation approach relies on inputcriteria that require some subjective valuation. As such they may not be helpful to the decision-makingprocess as originally intended.Recently, computer-based systems have been used to develop a general, integratedframework where one can simulate natural/management changes and assess the respectiveconservation evaluations. One specific application of this valuation approach is to study speciespopulation dynamics and estimate a minimum viable population, defined as the smallest populationwhich has an acceptable probability of persisting over a given time period (Soulé 1987). 52 This in turnallows for the calculation of the minimum dynamic area, i.e. the geographical area of suitable habitatrequired to support the minimum viable population. Recent published studies on the leadbeaster’spossum (Lindenmayer et al. 1993), the eastern barred bandicoot (Lacy and Clark 1990), the Scottishwild boar, and the giant panda (Zhou and Pan 1997) confirm the use of the population viabilityanalysis in policy decision circles and help conservation decisions. Another example of the applicationof a computer system to ecological evaluation is the System for Evaluating Rivers for Conservation(SERCON) recently undertaken by the Scottish Natural Heritage, the nature conservation agency inScotland (Boon et al. 1997). The overall objective was to predict the impact of different developmentscenarios on river ecological conservation value as well as to provide a simple way of communicatingsuch results to planners, developers and policy-makers.Ecological valuation based on computer modelling presents important advantages. First, itencourages greater rigour in data evaluation since it permits the introduction of elements ofsubjectivity within a transparent and consistent framework. Second, it allows for a direct comparisonof alternative conservation policies, independently of the number of criteria involved and respectiveattributes. For example, it permits the comparison of a conservation policy involving a criterion with52Several general models have been developed for this task, e.g. VORTEX (Lacy 1993) and METAPOP(Akcakaya 1994), and these can be used to consider the complex interactions between demographic,environmental and genetic influences on a population.159