Perspectives in Ecology and Conservation ( IF 4.7 ) Pub Date : 2021-04-30 , DOI: 10.1016/j.pecon.2021.03.006 Pierre Chassé , Cécile Blatrix , Nathalie Frascaria-Lacoste
Protected areas are one of the main policy instruments used by policymakers to tackle the current biodiversity crisis. While numerous studies highlight the inability of such areas to protect the full range of biodiversity, the procedures by which protected areas are created nevertheless remain understudied. A better understanding of the related policy processes is necessary to overcome the “research-implementation gap” and, hopefully, decrease biodiversity loss. This article seeks to fill this blind spot in conservation by conducting interdisciplinary research at the crossroads of ecology and policy studies. We applied mixed methods (i.e. quantitative and qualitative analysis) to the historical archives of national nature reserve (NNR) projects to identify the weight of scientific statements and other factors involved in the decision-making process. Our results reveal a two-step process. Scientific opinion about NNR projects operates as the primary filter. Then, another triage is made under social, political and economic interests. Such situation challenges the idea that more evidence would lead to better conservation. In our opinion, the key issue is to determine the ways to improve the success of NNR projects rather than improving data and algorithms. In this sense, we call for the implementation of an “informed opportunism” approach and suggest some leads to favor its practical application. This research highlights the importance of interdisciplinary research to reach conservation goals.
中文翻译:
确定法国保护区的位置:“科学利益”重要吗?
保护区是决策者用来应对当前生物多样性危机的主要政策工具之一。虽然许多研究强调这些地区无法保护所有生物多样性,但创建保护区的程序仍然没有得到充分研究。需要更好地了解相关政策过程,以克服“研究与实施差距”,并有望减少生物多样性的丧失。本文试图通过在生态学和政策研究的十字路口进行跨学科研究来填补保护领域的这一盲点。我们应用了混合方法(即 定量和定性分析)到国家自然保护区 (NNR) 项目的历史档案,以确定科学陈述的权重和决策过程中涉及的其他因素。我们的结果揭示了一个两步过程。关于 NNR 项目的科学观点是主要过滤器。然后,根据社会、政治和经济利益进行另一次分类。这种情况挑战了更多证据将导致更好保护的想法。在我们看来,关键问题是确定提高 NNR 项目成功率的方法,而不是改进数据和算法。从这个意义上说,我们呼吁实施“知情机会主义”方法,并提出一些有利于其实际应用的线索。