Comparison of clustering and ordination methods implemented to the full and partial data of benthic macroinvertebrate communities in streams and channels

Nijboer, R.C.; Park, Young‐Seuk; Lek, Sovan; Verdonschot, P.F.M.

doi:10.1007/3-540-26894-4_16

Cited by 2 publications

(1 citation statement)

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“…For example, Cao, Williams & Williams (1998) showed that rare species are important, and Nijboer & Schmidt‐Kloiber (2004) recently found that excluding taxa with low abundances and/or with small distribution ranges resulted in higher classification error. Moreover, Nijboer et al. (2005) showed that developing a typology using indicator taxa only resulted in a different classification than if all taxa were used.…”

Section: Discussionmentioning

confidence: 99%

The use of indicator taxa as representatives of communities in bioassessment

Nijboer¹,

Verdonschot²,

Werf³

2005

Freshwater Biology

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1. Sampling and processing of benthic macroinvertebrate samples is time consuming and expensive. Although a number of cost-cutting options exist, a frequently asked question is how representative a subset of data is of the whole community, in particular in areas where habitat diversity is high (like Dutch surface water habitats). 2. Weighted averaging was used to reassign 650 samples to a typology of 40 community types, testing the representativeness of different subsets of data: (i) four different types of data (presence/absence, raw, 2 log-and ln-transformed abundance), (ii) three subsets of 'indicator' taxa (taxa with indicator weights 4-12, 7-12, and 10-12) and (iii) single taxonomic groups (n ¼ 14) by determining the classification error. 3. 2 log-and ln-transformed abundances resulted in the lowest classification error, whilst the use of qualitative data resulted in a reduction of 10% of the samples assigned to their original community type compared to the use of ln-transformed abundance data. 4. Samples from community types with a high number of unique indicator taxa had the lowest classification error, and classification error increased as similarity among community types increased. Using a subset of indicator taxa resulted in a maximum increase of the classification error of 15% when only taxa with an indicator weight 10-12 were included (error ¼ 49.1%). 5. Use of single taxonomic groups resulted in high classification error, the lowest classification error was found using Trichoptera (68%), and was related to the frequency of the taxonomic group among samples and the indicator weights of the taxa. 6. Our findings that the use of qualitative data, subsets of indicator taxa or single taxonomic groups resulted in high classification error implies low taxonomic redundancy, and supports the use of all taxa in characterising a macroinvertebrate community, in particular in areas where habitat diversity is high.

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Section: Discussionmentioning

confidence: 99%