Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6292708 | Ecological Indicators | 2017 | 12 Pages |
Abstract
Freshwater macroinvertebrates have been extensively used as environmental indicators and are the most prevalent biological group used in aquatic bioassessment in the European Water Framework Directive (WFD 2000/60/EEC), usually through several popular indices, as the Biological Monitoring Working Party (BMWP). Many of these indices are based on taxa richness, i.e. the number of taxa present in a given area, as the simplest and most common measure of biodiversity. Given the importance to the WFD of the ecological status assessment by macroinvertebrates and the consequences thereof, sampling requires careful consideration and evaluation of the associated uncertainty. In this work, carried out in a Mediterranean river, we show that after 20 sample “kicks” it was possible to estimate the true taxa richness using Clench nonlinear asymptotic models (CM). However, cumulative curves of taxa extracted with kick sampling underestimated the true number of theoretical taxa (A). In order to achieve an acceptable error a very large sample size was required, always >20 kicks. According to these criteria, sampling was clearly inefficient in most localities. The minimum effort required to achieve a significant and acceptable level of taxa richness, for 90% of A, should be between 25 and 71 kicks and for 95% of A, 52-150 kicks. Both satisfactory percentages represent a mean difference of 2 (range 0-6) and 3 (range 1-8) taxa actually not being captured from the total predicted for each locality, for 90 and 95% of the CM asymptote, respectively. This study shows that by using the 20 kicks methodology it is possible to achieve reliable true macroinvertebrate richness estimates, but the establishment of the community composition, i.e. the full taxa making up any index score, will be inaccurate to an unknown degree.
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Ecology, Evolution, Behavior and Systematics
Authors
A. Ramos-Merchante, J. Prenda,