is calculated from relative, rather than absolute differences in the frequency of occurrence of a species across habitats. As a result, if the abundance of a species changes in a similar direction across environmental states of interest this may not affect a change in its fidelity value. Furthermore, the logistic nature of the relationship between fidelity and abundance (as well as the fact that abundance is logarithmic in the relationship) means that a substantial abundance change (over 1 order of magnitude) may not result in any change in fidelity (Fig. 7.7b). Properties such as these make the IndVal method a par- ticularly effective tool for ecological bioindication. More widespread appli- cation of the method is, however, necessary to establish the generality of its properties.
as indicators has also yielded mixed results (e.g. Araujo et al., 2001; Ricketts et al., 2002; Ekschmitt et al., 2003; Faith, 2003; Lassau and Hochuli, 2004; Bonn and Gaston, 2005; Dobson, 2005). None the less, particular approaches have proved more promising than others. For example, within-taxon surrogates generally perform better than across-taxon surrogates (Fleishman et al., 2000, 2001), lower-taxonomic levels (e.g. genera) tend to predict species richness bet- ter than higher taxonomic levels (albeit scale-dependent) (e.g. Balmford et al., 2000; Grelle, 2002; La Ferla et al., 2002; Cardoso et al., 2004b) and relationships show a tendency to be stronger at course than fine scales (Grand et al., 2004;
Sarkaret al., 2005).
Despite the growing acceptance that different aspects of biodiversity (tax- onomic, biogeographic and threat status) are often weakly correlated (Orme et al., 2005), a novel recent approach does appear particularly promising. This involves the use of presence–absence data for selected species to model assem- blage species richness (Mac Nally and Fleishman, 2002). The method was devel- oped by modelling the species richness of butterflies in the central Great Basin (USA) as a function of the occurrence of a subset of selected, putative indicator species (Mac Nally and Fleishman, 2002, 2004). Widespread and rare species were excluded from the initial putative indicator species set on the basis that neither group is likely to serve as an effective indicator of spatial variation in biodiversity (see also rationale in McGeoch et al., 2002). The best set of indica- tor species (explanatory variables) was then determined using a combination of information criterion and analysis of deviance approaches (Mc Cullagh and Nelder, 1989; Hooper et al., 2002). A subset of four to five (<10%) of the butterfly species was found to explain a significant proportion of the variation in species richness (77–88%) (Mac Nally and Fleishman, 2002). Not only was the explan- atory power of the models strong, but they also proved robust when tested using a formal validation process, including a test of the models using a spa- tially and temporally independent data-set (Mac Nally and Fleishman, 2004).
Interestingly, the emergent indicator species represented the full range of flight phenologies in the assemblage, and also encompassed diverse (both taxonomic and growth form) larval host plants (Mac Nally and Fleishman, 2002). The approach worked equally well for birds (Fleishman et al., 2005). Furthermore, a model incorporating the occurrence patterns of six butterfly species predicted 82% of the deviance in combined butterfly and bird species richness (over 130 species) (Fleishman et al., 2005). Finally, these models are also particularly valu- able because they were developed and tested at a scale at which conservation management decisions take place (McGeoch, 1998; Mac Nally and Fleishman, 2004). The species-occupancy modelling approach thus certainly warrants con- tinued exploration, in different geographic regions and with different taxa, to establish its generality.
5.2 Monitoring
Biodiversity indicators are used not only to estimate broadscale biodiver- sity, but also to monitor biodiversity change over time and assess progress
towards conservation targets (Fig. 7.1). For example, the biodiversity targets of the CBD 2010 include a detailed understanding of the rates of biodiversity change by 2010 (UNEP, 2003). However, options for achieving such under- standing within the time frame are limited, and must necessarily rely heavily on both historical information and on the use of selected taxa, or biodiversity indicators, to represent wholescale biodiversity. A comprehensive discussion of the approaches and steps to achieving this target is presented in the recent discussion meeting issue Beyond Extinction Rates: Monitoring Wild Nature for the 2010 Target (Buckland et al., 2005). As pointed out by Dobson (2005), to achieve the 2010 target, regular sampling of the selected species is required, preferably using a globally comparable method, and at least three data points are necessary for each species, community and location.
This raises at least two issues relevant to the application of biodiversity indicators, and the use of insects in monitoring schemes. First, a dichotomy immediately arises between those regions with long-term data-sets, where monitoring systems are already in place, and those without (Brown, 1991;
Revenga et al., 2005; Thomas, 2005). Second, the taxa used will be biased towards those for which most data are available, and in several regions are thus less likely to include insects than, e.g. birds and plants (Dobson, 2005;
Gregory et al., 2005; Loh et al., 2005; Lughadha et al., 2005; Thomas, 2005).
Therefore, assessment for the 2010 targets will largely depend on existing data and current programmes, such as the four complementary schemes for assessing changes in butterfly biodiversity in the UK, i.e. Red Data Books on species conservation status, multiscale atlases and mapping schemes to moni- tor changes in species distributions, transects that generate population time series data and occasional surveys that quantify population characteristics of selected species across their range (Thomas, 2005). Other well-developed monit oring schemes for insects include those for freshwater macroinverte- brates (Dallas and Day, 1993; Wright et al., 1993; Revenga et al., 2005; Thomas, 2005); see also Conrad et al. (Chapter 9, this volume). Importantly, the exten- sive data that have been generated by monitoring schemes hold signifi- cant potential for developing methodologies and testing bioindicators (e.g.
Bucklandet al., 2005; Thomas, 2005).
In regions without extensive, good-quality baseline data or established survey schemes, options for 2010 are more limited. Here, unvalidated bio- diversity indicators and the use of any available data, as well as the estab- lishment of new survey schemes will be required. Indeed, although few scientifically rigorous systems exist for monitoring changes in insect bio- diversity, and those that do are neither geographically or taxonomically rep- resentative, or in most cases situated in high biodiversity regions, substantial experience exists to support the development of such schemes elsewhere.
Thomas (2005) is of the view that this is not only possible, but that monitor- ing programmes similar to the extensive, robust system in place for butter- flies in the UK, should be developed and tested for Odonata, as well as certain groups of Diptera and Hymenoptera, albeit with a view beyond 2010.
Therefore, while options to achieve the 2010 targets are limited, given good leadership and financial support (Thomas, 2005), there is enormous
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).