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1 Applying MCI indices in different freshwater environments

1.1 Hard- and soft-bottomed streams

Traditionally, freshwater ecologists have favoured wadeable, hard-bottomed or stony streams for biological monitoring programmes. Such streams often are more visually appealing and support communities dominated by mayflies, stoneflies and caddisflies, which are not only more sensitive to pollution but also are more exciting or attractive to many ecologists than the snails, worms and chironomids that dominate soft-bottomed stream habitats. Furthermore, sampling macroinvertebrates from stony streams is easier, with well-known and well-proven sampling methodologies (even before the publication of standard methods - Stark et al 2001).

For these reasons, soft-bottomed streams have been a neglected habitat, despite (or perhaps because of) their proximity to centres of population and their consequential pollution. The macroinvertebrates that inhabit soft-bottomed streams generally are more tolerant of enrichment and (especially) sedimentation effects, and so are less sensitive indicators for monitoring disturbance. This could also explain why ecologists have avoided undertaking biomonitoring programmes in soft-bottomed streams.

Lack of standard methods is no longer a reason to ignore biomonitoring in soft-bottomed streams. Stark et al (2001) have provided standard sampling, sample processing, and quality control procedures for macroinvertebrate communities in hard- and (for the first time in New Zealand) soft-bottomed streams, and Stark and Maxted (2004, 2007) have developed versions of the MCI specifically for soft-bottomed streams. Auckland's SoE monitoring network is dominated by soft-bottomed streams, with 45 of 62 sites sampled in 2005, and there are likely to be soft-bottomed streams in most, if not all, other regions of New Zealand.

Clearly, a soft-bottomed stream is not simply a consequence of underlying geology, but will also depend on stream slope, land use and other factors. Interrogation of the River Environment Classification for the various factors that might determine the nature of stream substrates could enable the prevalence of soft-bottomed streams to be estimated, but this is beyond the scope of this review. The bottom line is that soft-bottomed streams are likely to comprise a significant proportion (perhaps 20−40%) of New Zealand's streams and rivers, so the continuing development of methods for their bioassessment is worthwhile. Ultimately, whether or not you have a soft-bottomed or a hard-bottomed stream is a decision that requires local knowledge, and is best made when standing on the stream bank!

Hard- or soft-bottomed MCI?

Now that there are hard- and soft-bottomed versions of the MCI available, which indices should you use? In most cases, the MCI tolerance values should be used on samples collected using the hard-bottomed sampling protocols (C1 or C3: Stark et al 2001) and the MCI-sb tolerance values used on samples collected using the soft-bottomed protocols (C2 or C4: Stark et al 2001). However, there may be exceptions due to the aims of the investigation. For example, if the stream of interest is a hard-bottomed stream inundated with fine sediment and there are no riffles to sample using the hard-bottomed protocols, then the soft-bottomed sampling protocol C2 would minimise the filling of the net with fine sediment, which would otherwise cause a processing nightmare. If the objective is to assess the degree of disturbance relative to its potential as a hard-bottomed stream, then the data collected using the soft-bottomed protocol might be more accurately assessed using the hard-bottomed tolerance values for MCI or QMCI calculations. The key is to consider the project objectives when selecting the index to use, and to recognise that rules should not take the place of common sense.

1.2 Wadeable versus non-wadeable waterways

All variants of the MCI have been developed using data from wadeable streams. Data are limited, due to sampling difficulties, so there has been no formal evaluation of the performance of these indices for large, non-wadeable rivers. There is no reason why biotic indices such as the MCI cannot be applied to, or developed for, non-wadeable rivers. The major difficulty is obtaining representative samples and then calibrating the interpretation of the index values.

An analysis of River Environment Classification data indicates that nearly 89% of New Zealand's mapped streams are 1st to 3rd order. Most of these are likely to be wadeable. Higher-order (i.e. 4-8) streams and rivers are not necessarily unable to be sampled using methods developed for wadeable streams (Stark et al 2001). Large braided rivers in Canterbury, for example, such as the lower Waitaki, have smaller braids or shallow margins along major braids that are accessible. Other large rivers, such as the lower Waikato River, may be of similar stream order but certainly are not wadeable.

1.3 Other freshwater habitats

Although macroinvertebrate biotic indices have not been developed in New Zealand for assessing wetland or lake health, given suitable data sets there is no reason why they could not be developed, just as they have been for wetlands in Western Australia (Chessman et al 2002) or lakes in France (Verneaux et al 2004).

Use of the MCI for other freshwater habitats

The existing versions of the MCI and MCI-sb should not be used to assess the environmental health of wetlands or lakes because they have not been calibrated or evaluated for these habitats. This may seem like an unnecessary caution given that the existing indices were developed for stony- and soft-bottomed streams, but we have seen the MCI used to assess the health of lake margins, which is not recommended. There is, of course, no reason why macroinvertebrate biotic indices could not be developed for other freshwater habitat types.

1.4 Incorporating the MCI-sb into existing biotic monitoring programmes

The MCI-sb is calculated in exactly the same way as the hard-bottomed MCI except for the different list of tolerance values that are used (see Table 1). Consequently, the MCI-sb can easily be integrated into existing monitoring programmes once it has been determined that the sites in question are soft-bottomed sites. It is a simple matter to recalculate MCI-sb values for existing data. Use of the MCI-sb would have no effect on the integrity of existing time series data, although if trends testing has already been undertaken these would need to be re-calculated using the MCI-sb values. The quality thresholds (e.g. excellent, good, fair, poor) developed for the hard-bottomed indices (Stark 1998) were found to be applicable to the soft-bottomed indices (Stark and Maxted 2007), making it easy to incorporate the new index scores into existing monitoring programmes.

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