SDM is an outcome-oriented approach, meaning that objectives, rather than threats or problems, are the central focus. We define what we want, then identify the best way to achieve it, dealing with conflicts and barriers as they arise. For this approach to be effective, the set of objectives must be clearly specified and comprehensive (Failing and Gregory 2003; Game et al. 2013). Objectives should address the core purpose of the planning initiative as well as other values that may be affected by management actions. The aim of being inclusive is to permit potential conflicts to be dealt with within the SDM process. Not doing so risks failure at the time of implementation (which has been the fate of many conservation initiatives in the past).
It is not always possible to specify the complete list of objectives at the outset. Values that are secondarily impacted may not be known until later in the process, when management options have been fully specified. SDM handles this issue using an iterative approach. The process begins with an exploratory sketch that maps out the decision structure at a coarse level. The preliminary list of objectives is then refined as detail is added to the decision structure in successive iterations.
When selecting objectives, the desire for inclusiveness must be balanced against practicality. There are limits to how much detail can be accommodated. Secondary objectives should be weeded out if their presumed sensitivity to management actions is not validated at the assessment stage. Also, while some objectives may have subcomponents that need to be distinguished, there should be no double counting. The test is to ask if anything important would be overlooked if we were to substitute one sub-objective for another.
We must also take care to distinguish between ends and means. Ends are what we fundamentally want—our true objectives. Means are methods for achieving the ends, and they are captured in the SDM process as management alternatives. In practice, making this distinction can be challenging because ends and means exist in a hierarchy—the means described in one plan may serve as the ends of a lower-level plan. Much depends on how the decision is framed.
Another consideration is the level of detail used to describe the objectives. On the one hand, we want enough detail to ensure that the desired outcomes are clearly understood and not subject to alternative interpretations. On the other hand, getting too specific, through the use of detailed quantitative targets, can be counterproductive because it reduces decision-making flexibility (Martin et al. 2009). The aim is to express what we want without creating all-or-nothing scenarios. The possibility of compromise solutions needs to be left open, as more often than not, these will be the only workable solutions.
The relative weighting of objectives has been purposely omitted from our discussion so far. Although objectives do vary in their importance, formal weighting should not occur until the decision stage. Throughout the rest of the process, objectives should remain on a level playing field so that the search for optimal solutions is thorough and robust.
When it comes to specifying conservation objectives, the broad goal of maintaining biodiversity needs to be translated into ecologically meaningful working objectives relevant to the decision scope. In doing so, it is important to not lose sight of the conservation “big picture.” The individual outcomes sought within a given initiative should meaningfully contribute to the whole. If it is unclear how they do so, there is a problem. Trade-offs among conservation objectives and the effects of climate change present special challenges that must not be overlooked. These tasks require the technical expertise of conservation practitioners.
Conservation practitioners should also ensure that conservation objectives accurately reflect a “nature-first” perspective, uncontaminated by pre-emptive compromise (Tear et al. 2005). Even though such objectives will not always be achievable, they represent the appropriate starting point for deliberations. Clarity about what we really want enables decision makers to understand and reflect on the true cost of any compromises that may be required. This guards against the shifting baseline scenario, or “ratchet” effect, where the acceptance of a degraded state as a management norm leads to progressive declines over time (Pauly 1995).
Once objectives have been defined, suitable indicators for measuring them need to be identified so that the relative performance of the management alternatives can be assessed. Indicators are also used in monitoring programs and research projects (discussed below).
There are several basic characteristics that all good indicators share, regardless of the application (Duinker 2001; Tear et al. 2005). Good indicators are:
- Clear. The meaning of what is being measured is broadly understandable and not subject to alternative interpretations
- Reliable. The indicator accurately measures what it is intended to measure (i.e., free from bias) and repeated measurements generate the same results
- Practical. Cost and technical feasibility are not significant barriers
- Relevant. The indicator is sensitive to the processes of interest and can help discern important differences among management alternatives
An additional concern when selecting indicators is comprehensiveness. Like the proverbial blind men examining an elephant, we may be misled if we only see part of the full picture. It may take multiple distinct indicators to obtain a complete characterization of the entity we are measuring.
Developing a complete characterization can be challenging when working with amorphous management objectives (as many are). How are we to measure something comprehensively when its meaning is open to interpretation? The answer is that the selection of indicators is part of the interpretive process. The choices we make serve to crystallize the meaning of the objectives being measured within a given decision-making context. Thus, indicators are not only used to measure objectives, they also help us define objectives in practical terms.
Consider the selection of indicators for caribou recovery. Is it sufficient to view this objective through the lens of population size? Perhaps long-term viability is important as well. What about spatial distribution? Is it ok if all our caribou are in a zoo? If not, then perhaps we need some measure of distribution relative to the historical range. But then how do we account for climate change?
What this line of questioning illustrates is that indicator selection is actually an extension of the objective-setting process. It includes both technical and subjective elements that together give concrete, practical meaning to our objectives.
The desire for comprehensiveness must be balanced against tractability. A long list of indicators may help to characterize an objective, but it seriously complicates the task of assessing management alternatives, particularly when multiple objectives are involved. There is a limit to how much information can be accommodated before overload occurs. Thus, restraint is necessary; we should “count what counts,” and not more. Each indicator should represent a distinct and important aspect of the objective, without redundancy.
Lastly, indicators are used in decision-making applications to predict the future state of objectives under alternative management approaches. Predicted performance provides the basis for deciding which approach is best. This adds another dimension to the selection process. Not only should indicators be technically sound and complete, their response to management actions should be understood so that meaningful predictions can be made.
As might be expected, it can be difficult to find indicators that meet all of the above criteria. It is often necessary to make do with indirect measures. And for some objectives, especially those associated with cultural or esthetic values, quantitative measurement may not be possible. For example, it may be difficult to obtain a meaningful numerical measure of the quality of recreational experiences. In these cases, a qualitative indicator using a constructed scale can be used (e.g., very good, good, average, bad, very bad). As a rule, it is better to make the best of a rough estimate than to omit an objective from consideration. This ensures that the decision-making process is inclusive and robust. For such indicators, it is important to include clear descriptions of each level so that there is a common understanding of their meaning.
Conservation practitioners support this stage of the decision-making process by providing the ecological expertise needed for selecting appropriate indicators for biodiversity objectives. This includes knowing what options are available, in the form of direct and indirect measures. It also includes the ability to judge which indicators will be most effective in terms of reliability, practicality, and accurately representing the desired biodiversity outcomes.
In higher-level planning initiatives, there may be considerable pressure to simplify the conservation dimension of the decision. It falls to conservation practitioners to ensure that the central concerns of conservation are not lost in the process. Proposals to capture biodiversity objectives using broad indicators of environmental health or ecosystem services are inappropriate. These measures are not proxies for biodiversity (see Chapter 4). Nor is it acceptable to focus exclusively on a single species because it has a high public profile or is the centre of a legal dispute. Finally, biodiversity indicators should not be based on species richness or simplistic summary measures of overall biodiversity status (Devictor and Robert 2009). Such composite measures tend to mask the elements of biodiversity that are of primary conservation concern (see Box 7.2, Chapter 7). Instead, indicators should focus on the components of biodiversity that are sensitive to human disturbance and require protection.