4 The Methodology of Bioindication
4.1 Quantification and predictability
bioindicator studies. Although Auchenorrhyncha communities are considered good potential bioindicators (Duelli and Obrist, 2003; Nickel and Hildebrandt, 2003), the level of taxonomic knowledge of the group proves an obstacle to its use in many instances (Buchs, 2003a). By contrast, the spiders, mites and springtails are comparatively over-represented for their taxonomic diversity (Fig. 7.4). Mites and springtails are mostly used in agricultural and soil envi- ronments as indicators of habitat quality and contamination (Behan-Pelletier, 1999; Alvarez et al., 2001; Zaitsev and van Straalen, 2001; Ponge et al., 2003;
Geissen and Kampichler, 2004; Sousa et al., 2004), as are spiders (Gravesen, 2000; Wheater et al., 2000; Horvath et al., 2001; Gibb and Hochuli, 2002;
Woinarski et al., 2002; Cardoso et al., 2004a). Reasons for the frequency distri- bution of studies across taxa (Fig. 7.4) generally include the proportional spe- cies richness of the groups, but also the selection of taxonomically manageable or better-known groups, and taxa that are conspicuous, abundant and readily sampled or quantified (Brown, 1991; Buchs, 2003a).
includes the identification of a significant relationship between the putative bioindicator and the EP, as well as the extent to which the variability in the EP is explained by the relationship (Fig. 7.5). Statistical significance is a nec- essary but not sufficient condition for bioindicators, whereas the higher the explanatory power of the relationship (the stronger the relationship) the more useful the bioindicator is likely to be (Fig. 7.5). For example, in an evaluation of the potential of trap-nesting bees and wasps as ecological bioindicators of habitat quality, the relationship between species richness of trap-nesters and other groups of bees and wasps was both significant and strong (P < 0.001, r2 = 81.6%) (Tscharntke et al., 1998). However, although natural enemy species richness decreased significantly with increasing patch isolation (P = 0.003), the relationship was weak (r2 = 20%) (Tscharntke et al., 1998) and therefore not suitable as a basis for bioindication. This first stage in the development of a bioindicator, i.e. establishing the ‘relationship’ (Fig. 7.5), forms the basis of a large component of the literature on bioindication.
Second, robustness is the degree to which the relationship between the putative bioindicator and the EP remains constant within the spatial and tem- poral context of inference, i.e. of the bioindication objective (Fig. 7.5). This stage of bioindication development is related to the ‘output validation’ process
(i) Statistical significance (necessary condition)
(ii) Strength (the greater the strength of the relationship the more valuable
the bioindicator) Relationship
Spatial and temporal variability in measures of the relationship
within the context of the bioindication objective (the lower the variability, the more robust the bioindicator)
Robustness
The degree to which the first three levels of predictability hold when extrapolated to scenarios other than for which the bioindication
system was originally developed (e.g. the bioindication system may be applied
in different geographic areas or to indicate environmental change of a different form) Representivity
The degree to which the bioindicator represents the response of other taxa
to the same object of indication (the object of indication here may be a stressor, e.g. a pollutant or other form of disturbance, or the diversity or abundance
of other taxa)
Generality
Fig. 7.5. The predictability hierarchy in bioindication. As a bioindicator moves along the hierarchy of levels of predictability it gains value for biodiversity assessment and conservation management.
described by Bockstaller and Girardin (2003). It essentially involves the use of independent data to test the consistency or repeatability of bio indicator per- formance (i.e. to test the hypothesis) and to quantify the degree of confidence with which the bioindicator may be used (McGeoch et al., 2002). Thomson et al. (2005) provide an excellent example of establishing the robustness of biodiversity indication models. Models of bird and butterfly species richness were built from inventories conducted over an 8-year period, and then the performance of selected models were tested (using a more recent data-set collected over a shorter period in the same region) by comparing observed and predicted species richness values. Rösch et al. (2001) provide a further example by testing the repeatability of a moth assemblage as a bioindicator of habitat quality in urban areas. The study was repeated 3 years after the initial establishment of the relationship between moth species richness and habitat quality, using both the same and independent sampling sites. Although few such examples are to be found in the literature (but see, e.g. King et al., 1998;
Campbellet al., 2000; Fleishman et al., 2001; Hogg et al., 2001; Geissen and Kampichler, 2004), the existence of significant, strong and robust relationships are the minimum necessary criteria in most instances for a system to war- rant the ‘bioindicator’ tag. Bioindicators may be selected and applied without undergoing such rigorous assessment of their predictability. However, in the absence of such an assessment, no measured degree of confidence may be placed in the information provided by the bioindicator. In this case, the bio- indicator is used in the hope that it reflects some unmeas ured component of the environment. Indeed, this may be the only avenue possible under data- deficient scenarios, where measuring anything is better than measuring noth- ing in the hope that over time the purported bioindicator and its relationship with its environment will be better understood.
Thereafter, two additional criteria that the bioindicator may meet are representivity and generality (Fig. 7.5), and the requirement for them to do so is at least partly dependent on the bioindication category and objective. For example, the cadmium concentration in honeybees (Conti and Botre, 2001) is inclusive as an environmental bioindicator, and no relationship need exist between this bioindicator and heavy metal concentration in other taxa, i.e.
representivity is not a requirement. However, it would be valuable, for exam- ple, to assess how representative the positive relationship between restora- tive grazing and Lepidoptera abundance is of the abundance responses of other insect taxa (e.g. Poyry et al., 2005). Again, few studies have established the representivity of bioindicators (see Majer, 1983; Andersen et al., 2004;
Schulzeet al., 2004).
Finally, if a particular relationship between a bioindicator and a stressor is found to hold in a domain other than that for which it was first identified, e.g. in a different habitat, nature reserve, geographic region, then it may be said to have generality (achieving ‘universal laws’ or ‘predictive theory’ as outlined by Murray, 2000) (Fig. 7.5). The ‘GLOBENET’ initiative provides a unique example of an approach to establish generality in an ecological bioindication system. This initiative aims to assess and compare landscape changes across urban development gradients on a global scale, using a single
group of invertebrates, i.e. ground beetles, and common methodology in a similar landscape, i.e. urban mosaics (Niemelä et al., 2000b). The outcome of the programme to date has shown some success, with generality in the effect of urbanization on the composition of ground beetle assemblages found across several cities and continents (Ishitani et al., 2003). Approaches, such as GLOBENET, present underexploited, yet potentially powerful opportun- ities for developing general bioindicators. However, because few such case studies exist, it is not possible to estimate under which circumstances or how commonly generality is likely to be found. None the less, the criterion of generality is not a necessary requirement for the successful local application of a bioindication system.