Abstract

Market and policy failures are leading to an overexploitation and degradation of ecosystems, thereby constituting one of the primary reasons for species loss. In response to this problem, governments are fostering conservation activities that go beyond the creation of protected areas. This is done by ecosystem management approaches, which aim to address biodiversity loss by targeting species habitats and stressors. In this context, market-based instruments (MBIs) that are economic incentives and disincentives, such as agri-environmental schemes or taxes, are used to encourage conservation activities and internalize the cost of biodiversity use. MBIs have the advantage of addressing the core of the problem, namely that biodiversity is currently traded as a free good. They provide a more cost-efficient way for reaching conservation objectives in contrast to traditional top-down Command-And-Control regulations, such as laws. Nonetheless, the outcome of these instruments is currently difficult to predict and how different MBIs can be combined to gain the best possible conservation outcome remains unknown. Moreover, it is not always clear how different levels of management in forestry and agriculture actually impact habitat structure and thus species diversity. Thus, there is a pressing need to measure and predict management outcomes and success for effective conservation policy. During my PhD, I will test how different MBIs conserve avifauna in agricultural and forest landscapes. I intend to understand how (1) woodland management grants can maximize outcomes for bird conservation and simultaneously benefit local communities in the UK; (2) increases in monetized government investments into subsidies for biodiversity will actually lead to an increase in biodiversity conservation; (3) ways in which different MBIs can be combined to better promote biodiversity conservation. My approach will be to collect field data in an existing woodland experiment setup by the Forestry Commission England and the RSPB , mine government databases for economic and biodiversity data, and draw upon remote-sensing technology to measure management outcomes.