potential for the future development of monitoring programmes involv- ing insects, as the few highly successful existing schemes demonstrate.
However, successful biodiversity assessment and monitoring must necessar- ily lie in the employment of all available information, tools and approaches, including abiotic information, and not only selected biodiversity indicators (Bonn et al., 2002; Faith, 2003; Bonn and Gaston, 2005). Comprehensive, good quality species data will always remain most valuable for assessing bio diversity, as well as setting and monitoring conservation targets (Brooks et al., 2004).
systematics. Within the insect bioindicator literature there are ~0.14 reviews per paper, which is 20-fold greater than other fields. Therefore, there is appar- ently a higher ratio of debate to empirical support in insect bioindication than may be expected.
Available evidence suggests that the success of bioindication is dependent on several factors, including the scale at which bioindication is undertaken.
Bioindication may be conducted in the context of ecological or earth-system processes that operate from fine scale, short-term events to global, broadscale pro- cesses and evolutionary timescales (see Fig. 7.2 in McGeoch, 1998). Patterns are well known to be scale-dependent and often more predictable at broad than at fine scales (Wiens, 1989; McGeoch, 1998; Huston, 1999; Hamer and Hill, 2000).
Other determinants of the success or otherwise of bioindication include: (i) the category of bioindication (environmental bioindicators have generally been more successful than ecological or biodiversity indicators) (McGeoch, 1998); (ii) the level of organization involved (e.g. heavy metal concentrations in animal tissue is more predictable than community structure) (Noss, 1990;
Lawton, 1999; Murray, 2000); (iii) the environment in which bioindication is conducted (it is generally acknowledged, for example, that aquatic systems tend to be less complex and variable than terrestrial systems) (Steele, 1991);
(iv) the method used (as demonstrated earlier for biodiversity indicators);
and (v) the taxon considered (as a consequence of the diversity, knowledge of and responsiveness of different taxonomic groups) (Landries et al., 1988;
Holloway and Stork, 1991). A priori information on taxon responsiveness may, however, be unreliable. For example, the four dominant dung and car- rion beetles in a montane cloud forest and shade coffee plantation landscape in Mexico differed significantly between habitat types (Arellano et al., 2005), whereas in a rainforest–agroforestry system in Indonesia, the five dominant dung beetle species were unresponsive to habitat type (Shahabuddin et al., 2005). However, there remain too few comprehensive case studies in the field, and generalizations regarding the nature of successful bioindicators are in many cases premature.
Finally, bioindication is an applied science and as such the adoption of bioindicators by end-users, i.e. conservation planners, land managers and policy makers, is the ultimate measure of its success. Increasing attention is being paid to the need to make bioindicators policy-relevant (Nicholson and Fryer, 2002; Failing and Gregory, 2003; Niemi and McDonald, 2004).
Indeed, the last of Bockstaller and Girardin’s (2003) three indicator valid- ation stages is ‘end-use validation’, or establishing the usefulness of an indicator as a benchmark for decision making. Some of the challenges asso- ciated with the translation of bioindication science into policy are reflected by the following statements: ‘we cannot simply talk about monitoring birds and butterflies to most policy-makers – it has little or no chance of working’
(Watson, 2005); ‘the practices we call “mistakes” make good sense from the standpoint of doing careful science but can lead to trouble, and surpris- ing failures in the implementation of biodiversity initiatives’ (Failing and Gregory, 2003) versus a bioindicator will never ‘be a freeze-dried, talking bug on a stick, i.e. the simple, standardized and easily applied measuring
rod, asked for by regulatory authorities’ (van Straalen, 1998), and admin- istrations are ‘spoiled by the easy handling of abiotic indicators’ (Buchs, 2003a).
A recent review of the criteria used by global conservation organiza- tions to select, prioritize and monitor conservation areas, reflects the need to develop effective bioindicators (Gordon et al., 2005). Criteria that have been used to assess the ‘usefulness’ of scientific assessments, such as bioindicators, include: (i) the demand for simple, user-friendly, cost-effective protocols that may be applied by non-specialists; (ii) outputs that are readily interpretable and easy to communicate; (iii) technically accurate results; (iv) quantification of the uncertainties involved; (v) provision of summary indicators or indi- ces; (vi) presentation of alternative viewpoints and involvement of experts from a range of stakeholder groups; and (vii) a process that is transparent and incorporates institutional, local and indigenous knowledge (Andersen, 1999; Failing and Gregory, 2003; Loh et al., 2005; Watson, 2005). Clearly some of these criteria fall within the ambit of the science of bioindication, whereas others are relevant to policy indicators that are often composite, encompass- ing several indicators, including economic, social and sustainable develop- ment indicators, and may or may not incorporate bioindicators (Table 7.1) (Bellaet al., 1994; Levy et al., 2000; Muller et al., 2000; Soberon et al., 2000;
Burger and Gochfeld, 2001; Osinski et al., 2003). In the latter case, decision mak- ing generally involves several interested parties, and trade-offs between multiple, complex alternatives (Failing and Gregory, 2003). The information provided by bioindicators will thus form only a part of the information on which decisions are based when applying policy indicators (although see, e.g. Gregory et al., 2005; Loh et al., 2005). None the less, Watson (2005) urges that assessments of biodiversity change must proceed to illustrate the impact of such change on issues that people care about, such as livelihoods, health, security and well-being. In a system using low-mobility butterflies (amongst other) as indicators of conservation value, Noe et al. (2005) show how the goals and ideas of organic farmers on the conservation of wildlife quality differ from those of biologists. However, the involvement of these organic farmers in the bioindication process positively influenced their perception of biodiversity (Noe et al., 2005).
In the context of bioindication (as defined in this chapter), the challenge to scientists is to thus help bridge the science–policy divide by develop- ing bioindicators that optimize feasibility, cost, information content, rele- vance and simplicity of application and interpretation (Rempel et al., 2004).
However, the range of utilitarian demands for bioindicators to comply with policy and management requirements is not possible, or even desirable, in all instances. There is inevitably a trade-off between efficiency of application and the power of the bioindicator to reflect systems and changing processes.
As a consequence, a change in the philosophy of end-users may be necessary (Lindenmayer, 1999; Buchs, 2003a). Reliability and validity cannot be sacri- ficed for convenience, and the exclusion of bioindicators from indicator sets is preferable to the inclusion of simple, inadequately validated or poorly understood indicators.