Defining high-level objectives

In the present context, high-level objectives are about what we want from or for biodiversity and ecosystem services in general. These could be complicated general requirement like "develop a well-balanced reserve network for the whole country", or "facilitate movement of species in response to expected climate change". Alternatively, they could be relatively simple such as "find two new bird protection areas of a size larger than 5000 hectares each". For operational processes, high-level objectives are set by someone who has legitimate power for making decisions in the society, such as elected authorities, usually guided by processes involving relevant stakeholders. In most cases, someone other than those running the spatial planning analyses thus sets this means high-level objectives.

High-level objectives are usually not directly useful for operational use and must be translated into an operational (Zonation) project structure and workflow (see Chapter 3). In other words, one must design an analysis that answers the high-level objectives in question. Designing such an analysis involves much work: you must acquire sufficient data, involve the relevant stakeholders, understand and possibly include societal constraints in the analysis and so on. In general, high-level objectives will have major implications both for setting data requirements and for the defining the extent to which involvement will be required from stakeholder groups. While a scientifically motivated minor project can be implemented amongst a small group of scientists, large projects that lead to e.g. land use restrictions will generally require mandatory involvement with stakeholders (landowners, zoning authorities, environmental administration, environmental NGOs, etc.) from the outset of project. A number of well-described examples of projects implementing engagement with stakeholders can be found in the systematic conservation planning literature.

Goals should determine the data you need. For example, if the goal is to develop a national protected area network, then data required will need to cover biodiversity as broadly as possible. This in turn implies that significant effort will need to be expended in data preparation. Most likely, it will turn out that only partial data is available within the time available, and the question will frequently arise: are my data adequate? Fortunately the answer to this question is only rarely “no”: outputs of Zonation analyses can be usually be utilized to a limited extent even if the available input data are incomplete. The provision here is that the answer is only a partial answer, applying only to those biodiversity components that are covered by data either directly or indirectly via surrogacy relationships. This reflects a general reality that while prioritization analyses, including those from Zonation, are frequently used to support land use decision-making, only rarely are these based on data that are complete and unbiased. This because the alternative course, of postponing decision making until all relevant data are available, is equally unpalatable, leading inevitably to the making of ‘ad-hoc’ decisions in the absence of more robust, evidence-based decision making frameworks.

We emphasize that co-learning is one major outcome of implementing a spatial conservation planning project with a stakeholder group. Usually everybody involved learns something useful. Scientists may – and generally probably should – learn about stakeholder values and the availability and quality of data. Stakeholders might learn about the fundamental principles of biodiversity conservation and ecologically informed land use planning. Both may gain new understanding and appreciation about the state of the environment, about pressing conservation needs and the costs of actions, etc. In most cases, high-level objectives specified at the outset of the planning project are progressively clarified and made more explicit through a consultative process. In addition, perspectives and objectives may need to be updated as the project progresses, which may in turn lead to new analytical and data needs. The nature of the process is unavoidably somewhat iterative.