Co-evolution, policy drivers, and opportunities
In an ideal world, scientific information alone would direct the development of policy. In the real world, however, multiple variables come into play in the development of policy, including not only scientific information but also economics, legal matters, resources, politics and political will, and timing, among others (May 2002). The integration of sometimes competing variables is required to form coherent policies when, or indeed, if, a policy window of opportunity (Kingdon 1984) becomes available. For example, there is little point having scientific evidence if there is no political will to follow through by making politically difficult decisions. Therefore, it is argued that true integration across many drivers will require a common purpose—something not readily embraced by those who place themselves on either side of the science-policy divide (May 2002). Once consensus is achieved then the objectives of a policy would be to eliminate or reduce the pressure or risk to an acceptable level.
Theoretically, the absolute impact of humans on the global environment is closely related to population and per capita resource utilization. This first was put into mathematical terms by Ehrlich and Holdren (1971) as:
I = PAT (Equation 3.1)
Where I = Human impact, P = human population, A = affluence (amount of material required by each individual), and T = Technology (related to the stage in technological development).
The carrying capacity of the system is strongly related to the effective functionality of the biota as these pressures increase. The IPAT equation has aroused con?siderable debate (Chertow 2001) and has been criticized for its simplicity, but it had been widely adapted and employed. Human population is increasing (« 6.8 billion currently, reaching a predicted level of 9 billion by 2050), but also the average amount of resource that each human uses (A) is predicted to rise at a much greater rate (Watson 2005) . This will be reflected in functional 'demands' (ecosystem services) from the system. This can be interpreted in terms of the co-evolutionary model where the drive to decrease impact stimulates both research for that purpose and concern over biodiversity threats. Offshore renewables present a current example. Clean power is needed and relevant guidance is rapidly being put in place to minimize the impact (W 7). The mitigation of environmental impact by management control can also be described mathematically:
I = PAT(l-E) (Equation 3.2)
Where E = effectiveness of a management approach, expressed as a value between 0 and 1 (0 = no effect, 1 = 100% effective).
E may increase with time as scientists respond to policy pressure and provide appropriate informa- tion—Ruijgrok's co-evolution (Ruijgrok et al. 1999). However, there can be a considerable lag before science is translated into policy, and this is sometimes in the order of decades rather than years. However, it is rarely necessary that impact be reduced to zero, but rather to an acceptable level which can be sustained by the system without deleterious effects. This emphasizes system resilience (Gibbs 2009), or as the more recently formulated 'ecosystem vulnerability' (De Lange et al. 2010). For marine systems, the OVI = Oil Vulnerability Index (King and Sanger 1979, cited in De Lange et al. 2010) and the VME = vulnerability of marine ecosystems (Halpern et al. 2007), are examples of the latter. The overall goal is to ensure that management control is in place and effective before systems become damaged and unsustainable. Like the original IPAT, this derivation is simplistic but emphasizes the role that policy can play in moderating impact.