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Testing different sorting techniques in macroinvertebrate samples from running waters
ELSEVIER
Limnologica 34,366-378 (2004) http://www.elsevier.de/limno
LIMNOLOGICA
Testing different sorting techniques in macroinvertebrate samples from running waters Peter Haase 1,*, Steffen Pauls 1, Andrea Sundermann 1, Armin Zenker 2
Senckenberg Research Institute and Natural History Museum, Department of Limnology and Conservation, Biebergemfind, Germany 2University of Hohenheim, Department of Zoology, Aquatic Ecology, Germany Received:March 30, 2004 • Accepted:July 12, 2004
Abstract With the aim of finding an efficient, standardised and practical protocol for sorting macroinvertebrate samples for water management practice, three different sorting techniques were tested: RIVPACS sorting, a modified AQEM/STAR (MAS) sorting protocol and a Live-sorting method. Based on the same AQEM/STAR sample to ensure comparable results, we compared RIVPACS and MAS sorting for 20 samples, and Live-sorting and MAS for a different set of 20 samples. Comparisons were based on both ecological and financial parameters relevant for the implementation of the EU Water Framework Directive in Germany. Parameters include recently developed multimetric assessment indices, their stream type specific core metric results, time effort and costs. While RIVPACS and MAS sorting produced similar results in terms of ecological assessment, time effort and costs, Live-sorting differed notably in all three respects. Live-sorting is the quickest and least expensive protocol, but shows higher variability than the other protocols. We discuss the differences and the level of standardisation for each of these methods. Key words: Stream assessment- sorting methods -macroinvertebrate sampling
Introduction In Germany a standardised method for investigating macroinvertebrates from streams is missing to date. To implement the EU Water Framework Directive (EUWFD) this is however of vital importance, because different protocols could lead to different assessment results (HAASE et al. 2004; HERINGet al. 2004a). Thus a LAWA 1financed project was initiated to develop a highly standardised method, which is also feasible in terms of time effort and costs.
The AQEM/STAR method (STARCONSORTIUM2003), which was recently developed, presents a highly standardised method and thus generally fulfils the required criteria. Unfortunately with this method a sample takes 12.6 hours to collect and treat (HAASE et al. 2004), a time effort not feasible for routine investigations. It was shown that 80% of the time to complete an AQEM/STAR sample lies in the sorting and determination of organisms (HAAsEet al. 2004). This large amount of time required is explained by the high number of individuals which are produced by the AQEM/STAR
LfinderarbeitsgemeinschaftWasser(LAWA),Contract04.02 *Corresponding author: PeterHaase,SenckenbergResearchInstitute and Natural History Museum, Departmentof Limnologyand Conservation, D-63599 BiebergemOnd,Germany;e-mail: [email protected] 0075-9511/04/34/04-366 $ 30.00/0
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Testing different macroinvertebrate sotting techniques method. Producing such high numbers of individuals is a general problem with many methods designed for generating macroinvertebrate data for stream assessment purposes (e.g. LORENZ et al. 2004; VINSON & HAWKINS 1996; CARTER & RESH 2001). Numerous studies have been aimed at reducing the number of individuals required to obtain stable assessment results (e.g. LORENZ et al. 2004; DOBERSTEIN et al. 2000; BARBOLrRet al. 1999; SOMERSet al. 1998; WALSH 1997). Completely sorting the individuals from a sample is thus coupled with a time effort too high for practical monitoring procedures (CARTER8~; RESH 2001). There are two general approaches that would reduce this effort: - A representative subsample is taken from the sample and sorted completely, o r - a smaller number of individuals from every taxon differentiated are sorted from the sample and their corresponding abundance in the sample estimated. With the aim of reducing the total number of individuals collected within an AQEM/STAR sample and thus the time required to sort and determine these organisms, we tested different sorting variants from each of these approaches. The first approach comprises lab-based sorting, for which samples are conserved in the field and sorted in the lab. Based on the defined AQEM/STAR subsample this sorting approach was tested using the AQEM/STAR method, the RIVPACS sorting protocol and the newly developed Modified AQEM/STAR method (MAS; HAASE et al. 2004). The MAS approach varies from the AQEM/STAR method in that it only considers larger organisms retained in a coarse fraction (> 2 ram). The second approach is usually applied directly in the field and has been used for many years for biological water quality monitoring investigations e.g. in Germany (c.f. BRAUKMANN 2000), Italy (c.f. BUFFAGNI et al. 2002), Latvia (unpublished protocol in STAR CONSORTIUM 2003). In these so-called "Live-sorting" methods the organisms are sorted and picked alive in the field. As mentioned above, the AQEM/STAR method is very time consuming. We assume that compared with the subsampling procedures we wish to test, this method will deliver the highest number of individuals and highest number of taxa because the entire subsample is sorted and all individuals are determined. We therefore assume that this method comes closest to truly describing the content of our subsample. For this reason, and because the focus of our comparison lies on determining the differences between the quicker protocols, we used the AQEM/STAR result as our guideline or 'reference' sample. In this paper we thus compare three different methods for sorting a sample in terms of their assessment results compared with AQEM/STAR and their usefulness in
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stream assessment applications. The three methods tested are Live-sorting according to BRAUKMANN(2000), MAS (HAASEet al. 2004), and RIVPACS (WRIGHTet al. 2000).
Material and Methods Sorting methods In the following we give a short summary of the sorting methods compared. For full details of the methods see the cited literature. • AQEM/STAR (STARCONSORTIUM2003) The AQEM/STAR method was developed in the course of two successive EU-funded projects, the aim of both being to develop and standardise macroinvertebrate assessment systems. The AQEM/STAR sorting protocol is done completely in the laboratory and first reduces the total sample material with a defined subsampling step. The subsample corresponds to 1/6 of the sample and at least 700 individuals sorted. If 1/6 of the samples contains <700 individuals, the subsample is increased until 700+ organisms are sorted. Al___!lindividuals of the subsample are picked and counted without magnification and then determined. The result is a taxa list giving the number of individuals extrapolated to whole sample. • MAN (HAASEet al. 2004) The MAS protocol is a modification of the AQEM/ STAR protocol. Before sorting, a coarse fraction (> 2 mm) is separated from the rest of the subsample using a sieve cascade. Only the coarse fraction is sorted completely without magnification. Here the criteria for the subsample are at least 1/6 of the sample and at least 350 individuals in the coarse fraction. Subsampling, sorting and counting is done in the lab without magnification. All picked individuals are determined. The result is a taxa list giving the number of individuals extrapolated to whole sample. • RIVPACS (WRIGHTet al. 1984; WRIGHTet al. 2000; STARCONSORTIUM2003) The RIVPACS protocol was developed in Great Britain and has since been adapted and used in several other countries (e.g. Australia: AusRivAS, SMITHet al. 1999; Canada: BAILEYet al. 1998, REYNOLDSONet al. 2001; New Zealand: JoY & DEATH 2003). The protocol followed for this comparison is given in STARCONSORTIUM (2003). In RIVPACS sorting, the operator selects a fraction of the sample which is completely sorted in the lab without magnification (1/2, 1/4 .... ). The unsorted rest of the sample is scanned for taxa which have not been found in the sorted fraction. Individuals from these taxa Limnologica (2004) 34,366-378
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are picked from the sample and retained in a separate vial for "extra" taxa. All individuals found in either a "fraction vial" or the "extras vial" are determined. The result is a taxa list giving the number of individuals extrapolated to whole sample. • Live-sorting (according to BRAUKMANN2000,
modified) The Live-sorting approach has been used for many years in several states in Germany. However there are many variations of this method both in terms of sampling and sorting. Testing all variants applied in Germany would not have been feasible in this study. We thus chose the protocol according to BRAUKMANN(2000), which is well described. In this protocol living individuals from each identifiable taxon in the sample are sorted and determined as far as possible in the field and their abundance in the sample estimated. The sample is transferred to a sorting tray, partly filled with water and thoroughly looked through. First all identifiable or differentiated taxa are noted in the field. Then their abundance in the whole sample is estimated in abundance classes. This is a modification of the method described by BRAUKMANN(2000), based on the fact abundance classes are used in most states of Germany and are therefore much more common then the semi-quantitative data used by BRAUKMANN(2000). The abundance classes are 1 -- 1 ind.; 2 = 2-20 ind.; 3 = 21-40 ind.; 4 = 41-80 ind.; 5 = 81-160 ind.; 6 = 161-320 ind.; 7 >320 ind. (ALF et al. 1992). Of all distinguishable taxa, individuals are taken to the lab for further determination. The number of individuals from each taxon taken to the lab for further determination depends both on the absolute abundance of that taxon in the sample, and more importantly on the expectation that a taxon in the field will yield numerous further taxa once determined in the lab. The result is a taxa list with abundance scores, rather than number of individuals.
Comparisonof sorting methods The comparison for sorting protocols was based on 40 samples taken according to the AQEM/STAR protocol. From these samples a subsample according to the criteria of the AQEM/STAR protocol was taken (1/6 of the sample and 700+ individuals). For 20 subsamples from 3 stream types the sorting protocol for RIVPACS and MAS were compared. Another 20 subsamples were used to compare the Livesorting and MAS sorting protocols. As a "reference" data set for each subsample, the entire subsample was sorted to generate the AQEM/STAR fraction. Thus the same subsample was always used for each of the three sorting protocols compared for each sample. This experimental design insured that differences obLimnologica (2004) 34,366-378
served were only due to different sorting methods and not based on sampling or subsampling heterogeneity. The following questions were addressed in the comparison: - How much time is needed for sorting? - How many individuals and taxa are produced? and - How do assessment results differ between sorting protocols? Time sheets were kept for all steps of the sorting procedure once the subsample was taken. In detail time was recorded for the sorting process and the determination of the actually picked individuals. Where it was necessary, times were added to complete the data for a certain protocol (e.g. AQEM/STAR fraction). Based on these time sheets, the personnel costs for each sorting method were calculated, taking into consideration that different tasks can be performed by differently qualified persons. Three categories of personnel were differentiated: student aids for simple tasks (~ 15/hour); technical staff for tasks requiring training (~ 30/hour); scientists for tasks requiring specialist skills (~ 50/hour). All determinations were done by the same person according to the taxonomic level defined in HAASE& SUNDERMANN(2004) to ensure comparable determination results. The taxa lists resulting from all protocols were compared regarding number of taxa and number of individuals determined. As ecological indicators, stream type specific core metric results were compared (HERING et al. 2004b). As comparative ecological assessment results we also calculated the stream-type specific multimetric index (MMI) values (BOHMER et al. 2004) for each of the sorting protocols for each sample. The differences between core metric and MMI values compared with AQEM/STAR were calculated as were the number of different classifications in ecological quality classes. Differences between MMI values for two protocols were tested for significance using Wilcoxon-Test. To test for significant differences between more than two protocols, Friedman ANOVA by ranks was calculated. If a significant difference was found using Friedmann ANOVA, Wilcoxon-Tests were calculated to show which of the protocols differ from each other. In this, the significance level was adjusted according to EMCEE (1997). Spearman's R was calculated in order to find out if two variables are correlated. All statistical analyses were done with the STATISTICA 6.1 software package (STATSOFT 2002). • Comparing RIVPACS and MAS
The subsample was first divided into a fine (_<2 mm) and coarse fraction (>2 ram) using an analytic sieve cascade to allow later compilation of results for all three tested protocols. RIVPACS sorting is performed with both fractions according to the protocol from STAR CONSOR-
Testing different macroinvertebrate sorting techniques TIUM (2003). The individuals from each fraction were counted and deposited separately into different containers. The remaining material from both fractions, which was not sorted with the RIVPACS protocol, was then completely sorted and the individuals put into separate containers for each fraction. Number of individuals was also counted. All material was then determined and recorded separately for each sorting method and the coarse and fine fraction. Besides the sorting times, all determination times were also recorded separately for each sorting method and the coarse and fine fractions. The RIVPACS result is generated by adding up the sorting and determination results from both the fine and coarse fraction sorted according to the RIVPACS protocol. Following the rules given in the RIVPACS protocol (STAR CONSORTIUM 2003), the number of individuals was extrapolated to the whole subsample. The MAS result is generated by compiling a taxa list from the entire coarse fraction (including the individuals from the RIVPACS sorting of the coarse fraction only). In a second step the results from the fine fraction (including the individuals from the RIVPACS sorting of the fine fraction) are added to the MAS taxa list, resulting in the AQEM/STAR result. In a final step the results from all three sorting protocols are extrapolated to the whole sample in correspondence to the subsample proportion. We compared a total of 20 subsamples from alpine streams (type 1), streams in the Pleistocene sediments of the alpine foothills (type 3), and small siliceous sandstone streams from the central highlands (type 5.1) (stream type numbers according to POTTGIESSER& SOMMERHAUSER2004). • Comparing Live-sorting and M A S The Live-sorting protocol requires that all of the sorting work is done in the field. For this comparison we therefore took the AQEM/STAR subsample (STAR CONSORTrCM2003) (----1/6 and 700+ individuals) in the field. The total sample was spread out in the subsampling grid and the fraction containing 700+ individuals estimated in the field. The subsample was taken using the subsampling grid according to the AQEM/STAR protocol (STARCONSORTIUM2003). Thus, by following the same procedure as is used in the lab, the subsample criterion of at least 1/6 of the total sample was met exactly. The criterion
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700+ individuals could only be estimated, and to make sure it was met, the subsample proportion was estimated generously. Live-sorting was done with the subsample. After the subsample was sorted, the remaining material from the same subsample was not discarded, but fixed and taken to the lab for further treatment. The individuals sorted alive in the field were determined in the lab and carefully returned into the subsample. Afterwards the same, complete subsample was separated into a coarse and fine fraction and each of these fractions sorted completely following the AQEM/STAR protocol. Live-sorting results are given in abundance classes. Some of the metrics used to calculate MMI values require number of individuals. Therefore relative proportions of individuals were estimated from abundance class data to delogarithmise abundance classes. The median number of individuals for each abundance class produces the best estimation, with the smallest systematic error [e.g. abundance class 3 was transformed into 30 individuals (ALF et al. 1992)]. The resulting number of individuals was then extrapolated to the whole sample in correspondence to the subsample proportion. The MAS and AQEM/STAR sorting result is generated in the same way as described for the RIVPACS and MAS comparison. The extrapolation and generation of MMI values follows the same procedure described above. We compared 20 samples from 5 stream types. In addition to those stream types included in the comparison above we also sampled large cobble/boulder bottom streams in lower-mountainous areas (type 9.2) and small streams from the Loess regions of the central lowlands (type 18) (stream type numbers according to POTTGIESSER~; SOMMERHAUSER2004).
Results Comparing RIVPACSand MAS Table 1 shows the sorting and determination times required and the number of individuals and taxa found with each of the sorting protocols in comparison to the AQEM/STAR sorting result.
Table 1. Sorting and determination effort required and number of taxa and individuals found by different sorting protocols. * = picked individuals, not extrapolated. N= 20
Sorting [h]
Taxa determination [h]
Average number of individuals*
Average number of taxa
Relative number of taxa [%]
RIVPACS MAS AQEM/STAR
2,3 3.0 9,1
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39 42 54
72 78 100
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When comparing the times needed for sorting and determining taxa, the RIVPACS protocol is the quickest. The MAS protocol requires a little more time, while the AQEM/STAR protocol takes nearly three times as long. The required time corresponds quite well with the number of individuals sorted. These are lowest for the RIVPACS protocol, somewhat higher for MAS and three or five times higher for AQEM/STAR than MAS and RIVPACS, respectively. In comparison to the total subsample (AQEM/STAR) the MAS sorting recovers 78% of the taxa, RIVPACS sorting 72%.
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For all 60 data sets (RIVPACS, MAS and AQEM/STAR results from 20 subsamples) results of each single core metric, required to calculate the stream type specific MMI, were calculated. The mean differences between the single core metric values for MAS and RIVPACS and those attained with the AQEM/STAR sorting are compared in Fig. 1. Using these core metric results, the multimetric index results (MMI values) were calculated for each of the sorting protocols for all 20 subsamples (Fig. 2).
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Limnologica (2004) 34,366-378
Testing different macroinvertebrate sorting techniques
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The mean deviation of MMI results of the RIVPACS sorting to AQEM/STAR is 0.00, with a standard deviation of _+0.04. For MAS, the mean deviation was ~).03. Here the standard deviation was +0.05 (Fig. 3). Significant differences between the three protocols were found (Friedmann ANOVA p < 0.00). After correcting the level of significance according to ENGEL(1997), Wilcoxon-Test results showed no significant difference between protocol pairs (AQEM/STAR/MAS p > 0.02; A Q E M / STAR/RIVPACS p = 1.0; RIVPACS/MAS p > 0.04). The MMI results for both the RIVPACS sorting protocol and MAS sorting correlate well (R > 0.90) with the AQEM/STAR MMI values (Fig. 4). For the 20 subsamples, RIVPACS sorting results in MMI values, which in 3 cases leads to different ecological classifications in comparison to AQEM/STAR, while the MAS sorting leads to 4 differing classifications based on the differences in MMI values. The differences are always one ecological quality class, which go both up and down in both sorting protocols (Fig. 2).
Comparing Live-sorting and MAS Table 2 shows the sorting and determination times required and the number of individuals and taxa found with each of the compared sorting protocols in comparison to the AQEM/STAR sorting result. Limnologica (2004) 34,366-378
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In the comparison of sorting and determination times the Live-sorting protocol is much quicker than the MAS method, taking only a quarter of the time. The AQEM/STAR method requires twice as tong as the MAS protocol and eight times longer than Live-sorting. These times correspond quite well with the number of individuals recovered by each of the sorting methods. They are much lower for Live-sorting than the MAS, and again much higher for the AQEM/STAR sorting protocol. In comparison to the completely sorted AQEM/STAR sample, Live-sorting recovers 48% of the taxa, while the MAS sorting recovers 78%. The individual stream type relevant core metric results were also calculated for this data set. Deviations of core metric results from the MAS and Live-sorting against the AQEM/STAR results are shown in Fig. 5. Again these core metric results were used to calculate the multimetric index assessment results (MMI values) for each of the sorting protocols for all 20 subsamples (Fig. 6). The mean deviation of the MMI results to the AQEM/STAR results is 0.01 for Live-sorting, while the standard deviation is quite high (_+0.11). For MAS the mean deviation is the same (0.01), while the standard deviation is distinctly lower (+0.04) (Fig. 7). No significant differences between the three protocols were found (Friedmann ANOVA p > 0.52). Live-sorting results do not correlate quite as well with AQEM/STAR MMI values (R = 0.86, p < 0.00) as they do for the MAS sorting (R = 0.97, p < 0.00) (Fig. 8). In 8 of 20 subsamples (40%) the differences in MMI results lead to different ecological quality classes of Live-sorting compared with AQEM/STAR. The MAS sorting protocol leads to a different ecological quality class once (5%). The deviations amount to one ecological class, which for Live-sorting go both up and down (Fig. 6). Table 3 summarises the main results from the direct comparison of the sorting methods (given in Table 1 and Table 2). Both the MAS and AQEM/STAR method are adversely affected by the direct comparison of methods using the same subsample, this is especially true for the number of individuals contained in a subsample where Live-sorting was performed in the field. Because of the experimental design (see above), these subsamples con-
Table 2. Sorting and determination effort required and number of taxa and individuals found by different sorting protocols. * = picked individuals, not extrapolated. N= 20
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Taxa deterruination [h]
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Average number of taxa
Relative number of taxa [%]
Live-sorting MAS AQEM/STAR
0.7 3.1 7.7
1.0 3.7 5.7
225 959 I891
28 45 58
48 78 100
Limnologica (2004) 34,366-378
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rained unusually high numbers of individuals and thus sorting and determination times of the MAS and AQEM/STAR protocols were higher. Therefore for the final comparison between the MAS and AQEM/STAR sorting protocols more independent data (HAASE et al. 2004) were included in the result table. A second advantage of the independent data set is, that it is much larger (N = 70) and thus gives more representative values. As the results in Table 3 show, results from the RIVPACS sorting comparison and the independent data set give similar results for the MAS and AQEM/STAR sorting protocols. Both laboratory based sorting protocols, MAS and RIVPACS, give similar results in all aspects. The RIVPACS sorting is a little bit less time consuming and delivers lower number of individuals and taxa. There is no or only a slight difference in mean deviation of MMI values for RIVPACS and MAS, respectively, from the AQEM/STAR MMI result, while the standard deviation is identical. In terms of scoring the "correct" ecological quality class, the differences are minimal (MAS 87%, RIVPACS 85%). A different picture results from the Live-sorting protocol: This method is much less time consuming than the two laboratory based methods MAS and RIVPACS. The mean deviation between AQEM/STAR and Live-sorting is also very low, while the spread of deviations is much
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0.8
MMI values AQEM/STARprotocol
Fig. 8. Correlation results between the MMI values obtained from AQEM/STAR sorting and either MAS sorting (left) or Live-sorting (right).
Table 3. Summary of results from the comparison of sorting protocols. * = picked individuals, not extrapolated; ** = reference value; *** = data from HAASEet al. (2004). AQEM/STAR (N= 70)*** Average time effort [h] (Sorting and determination) Average number of individuals* Average number of taxa Relative number of taxa [%] Mean deviation of MMI Standard deviation of MMI "Correct" ecological quality class [%]
MAS (N= 70)***
10.6
RIVPACS (N= 20)
5.1
1066 52 100"* 0"* 0'* 100"*
Live-sorting (N= 20)
4.3
469. 42 81 -0,01 ±0.04 87
1.7
385 39 72 0.00 ±0.04 85
225 28 48 0.01 ±0.11 60
Table 4. Time effort and costs of the different sorting methods. :: Stu (student assistant) =15 {/h; Tech (technical staff) = 30 ~/h; Sci (scientist) = 50 ~:/h; *: Sampling and subsampling always done according to the AQEM/STAR protocol.
Live-sorting
Sampling* Subsampling* Sorting Taxa determination Data entry
RIVPACS
Time [h]
Person~
0.75 0.5 0.7 1.0 0.5
Sci Stu Sci Sci Tech
Costs [~] 37.50 7.50 35.00 50.00 15.00
MAS
Time [h]
Person1
Costs [(~]
Time [h]
Person1
Costs[~]
0.75 0.5 2.3 2.0 0.5
Sci Stu Tech Sci Tech
37.50 7.50 69.00 100.00 15.00
0.75 0.5 2.6 2.5 0.5
Sci Stu Stu Sci Tech
37.50 7.50 39.00 125.00 15.00
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Total
3.45
145.00
6.05
229.00
6.85
224.00
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higher than in any of the other methods (Fig. 7: standard deviation _+0.11). There is a clear decline in both number of individuals and number of taxa. In our data set these differences in MMI values lead to a different ecological quality class score in 40% of the compared samples (Fig. 6). The total costs for a macroinvertebrate sample, which is sorted following the Live-sorting protocol come to 145.00. The corresponding values for the RIVPACS and MAS protocol are ~ 229.00 and ~ 224.00, respectively. Thereby the Live-sorting protocol is not only the fastest, but also by far the cheapest (Table 4).
Discussion With the chosen experimental design we were able to compare different sorting protocols based on exactly the same subsample. This procedure eliminates any natural or methodologically induced variability between data sets that are often a problem in comparisons of macroinvertebrate samples (NORRIS& GEORGES1993; NORRIS& GEOROES 1986). The sampling design did, however, require some minor changes to the normal protocols. For instance before RIVPACS sorting we separated a coarse and fine fraction and sorted them separately, a procedure not usually incorporated in RIVPACS (WRIOHT et al. 2000; WRICHTet al. 1984). We assume that dividing the fraction into a fine and coarse fraction facilitates the sorting process, because finding (smaller) organisms is presumably easier in a homogenous fraction. Thus the results generated here may slightly favour the RIVPACS method in terms of taxa recovered. In the Live-sorting comparison it was necessary to take a subsample in the field. In order to assure that both subsample criteria (at least 1/6 of the sample and at least 700 individuals) were fulfilled, we had to generously estimate the required subsample fraction to obtain enough individuals. This lead to higher numbers of individuals in the AQEM/STAR subsample than is usual. This would in turn lead to higher sorting and determination times for all sorting protocols than is usually required. This variation has no effect on the direct comparison of the AQEM/STAR, MAS and Live-sorting methods, because it affects all three methods equally. Because of these higher numbers of sorted individuals, we tried to attain an independent data set for Livesorting, MAS and AQEM/STAR for a final comparison of sorting results between all methods (cf. Table 3). Unfortunately we only had this data set for the AQEM/STAR and MAS sorting protocols. Live-sorting is negatively affected by higher numbers of individuals in terms of higher sorting and determination times. Nonetheless, this method was by far the fastest, so this negative effect can be ignored in the comparison. Limnologica (2004) 34,366-378
Based on our samples size, which is relatively small with an N of 20 for each comparison, we are only able to show tendencies. Differences between assessment results for single sorting methods are not significant. For our comparison of MAS and RIVPACS the differences are so small, that we assume, the methods would also present similar results, without showing significant differences with a larger sample size. For the comparison of MAS with Live-sorting, a larger data set might confirm the tendencies/trends (lower number of taxa, larger deviation in assessment result) we have made out with the available data. Despite these methodological complications we were able to show, that even based on the same subsample different sorting methods could lead to different assessment results. Due to sampling design, we can be sure that differences in our data resulted solely on sorting methods. Variability based on sample heterogeneity between subsample and an entire sample (LORENZet al. 2004; DOBERSTEINet al. 2000; RESH & JACKSON 1993), between subsamples, natural variability (e.g. RESH & ROSENBERG 1989; ILLn~S1982) or influences in the taxa lists resulting from different analysts involved in the determination process (c.f.e.g. STRmLINGet al. 2003) were omitted. From our data it seems essential that a standardised and uniformly used sorting protocol is adopted for ecological quality control of running waters, especially for implementation of the EU-WFD. For the development of a practical and easily applicable method for routine monitoring practice, not only the quality of the method but also the costs are important. Besides being the fastest, Live-sorting is also the cheapest of the compared sorting protocols, despite the fact that identifying the organisms in the field requires sufficient experience and can only be done by specialists. Another advantage of this method lies in only having to fix a small number of individuals for determination in the lab. On the other hand our results show that Live-sorting leads to higher levels of deviation in assessment results. In this respect, the standard deviation is a much stronger indicator of the quality of MMI results then the mean deviation of the MMI, since high positive and negative deviation were compensated in mean values. Concerning the quality of Live-sorting there are two other criteria, which should also be taken into consideration: - The expertise of the person sorting, and the weather conditions and illumination at the sampling site. -
The first factor causes distortions in the number of taxa recognised and consequently sorted and determined from a sample. A worker with long experience and specialist skills can differentiate and will thus pick more different taxa and individuals, than a less-skilled worker. These bias increases with highly mobile or rapidly mov-
Testing different macroinvertebrate sorting techniques ing organisms when sorting live samples (CARTER & RESH 2001). The second factor concerns the importance of illumination for sampling success. Different light intensities caused for example by weather conditions at the time of sampling (e.g. cloudy skies vs. sunshine) or the location of the sampling site (e.g. in a shady wood vs. an open meadow). One must also not underestimate the motivation a worker has to sort a sample in bad weather conditions, which in such a case is lower and limits sorting Success (CARTER • RESH 2001; RAWER-JOST2001). These factors alone lead to a high amount of variability within the Live-sorting procedure (c.f. CARTER & RESH 2001). This variability is intolerable considering the importance of the assessment results for the implementation of the EU-WFD. Another problem this method brings with it, is the difficulty in quality auditing such a procedure. The material sorted in the field is discarded. It is therefore no longer possible to detect whether all taxa were actually picked from the subsample and if the abundances were estimated more or less correctly. This factor could be eliminated if the whole sample was conserved after sorting and taken home. This would however not only cause higher auditing costs but more importantly would mean that a live sorting procedure could only be audited by sorting a conserved sample, whereby observed differences could be based on sorting method. Based on our results, the documented difficulties involved with variability within the method and limitations in standardising and auditing Live-sorting approaches, we do not feel confident, that such an approach would suffice to meet all requirements set out for implementing the EU-WFD. The differences in data quality and assessment results between the two laboratory methods are small. In the sampling process they also cost about the same. Therefore principally both methods seem equally suitable sorting procedures. There are however further aspects one must consider: - Compared to the MAS sorting protocol the RIVPACS sorting method is much more complex. It is therefore important to train personnel for this task (CLARKE et al. 2002). This in turn causes higher costs, which are not included in our calculation. - In RIVPACS sorting the worker must decide how much of the sample is sorted (1/4, 1/2 ...) and is allowed to change the sorting fraction during the sorting process. These aspects show that the RIVPACS sorting procedure could be more variable than the MAS method. - The person sorting a RIVPACS sample must have proficient taxonomic knowledge. He or she must, e.g. identify taxa, which have not been collected from the sorted fraction in the rest of the material. Technical
377
staff must therefore perform this task, while the MAS sorting can also be done by a student assistant. Different taxonomic skills may bias sorting results when using the RIVPACS sorting protocol (c.f. CLARKE 2000). Especially for the above mentioned higher amount of variability embedded in the RIVPACS method and the higher level of standardisation in the MAS protocol, we suggest using the MAS sorting method for macroinvertebrate studies, aimed at EU-WFD implementation in Germany. A detailed description of the method development and the protocol are given in HAASE et al. (2004).
Acknowledgements This study was supported by L~inderarbeitsgemeinschaft Wasser (Project number: LAWA 04.02) and by the European Union (Project: Standardisation of River Classifications, a project under the Fifth Framework Programme and the Key Action "Sustainable Management and Quality of Water" within the Energy, Environment and Sustainable Development Programme; contract No: EVK1-CT 2001-00089). We thank Eckart Coring for "live-sorting" samples from Lower-Saxony. Daniel Hering is thanked for helpful advice on the manuscript.
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BRAUKMANN, U. (2000): Hydrochemische und biologische Merkrnale regionaler Bachtypen in Baden-Wtirttemberg. Oberirdische Gew~isser, Gew~isser6kologie 56:1-501. BUFFAGNI,A., CROSA,G., HARPER,D. & KEMP,J. (2002): Using macroinvertebrate species assemblages to identify fiver channel habitat units: an application of the functional habitats concept to a large, unpolluted Italian fiver (River Ticino, northern Italy). Hydrobiologia (The Hague) 435: 213-225. CARTER,J. L. & RESH, V. H. (2001): After site selection and before data analysis: sampling, sorting, and laboratory procedures used in stream benthic macroinvertebrate monitoring programs by USA state agencies. J. N. Am. Benthol. Soc. 20: 658-682. CLARKE,R. T. (2000): Uncertainty in estimates of river quality based on RIVPACS. In: J. F. WRIGHT,D. W. SUTCEIFFE& M. T. FURSE(eds.), Assessing the biological quality of fresh waters: RIVPACS and other techniques, pp. 39-54. Freshwater Biological Association, Ambleside. CLARKE,R. T., FURSE,M. T., GUNN,R. J. M., WINDER,J. M. & WRIGHT,J. F. (2002): Sampling variation in macroinvertebrate data and implication for river quality indices. Freshwater Biology 47: 1735-1751. DOBERSTEIN,C., KArat, J. & CONQUZST,L. (2000): The effect of fixed-count subsampling on macroinvertebrate biomonitoting in small streams. Freshwater Biology 44: 355-371. ENGEL, J. (1997): Signifikante Schule der schlichten Statistik. Filander Verlag, Ftirth. HAASE, P., LOHSE, S., PAULS, S., SCHINDEHOTTE,K., SUNDERMANN, A., ROLAUFZS,P. & HERING,D. (2004): Assessing streams in Germany with benthic invertebrates: development of a practical standardised protocol for macroinvertebrate sampling and sorting. Limnologica 34: 349-365. HAASE, P. & SUNDERMANN,A. (2004): Standardisiemng der Erfassungs- und Auswertungsmethoden von Makrozoobenthosuntersuchungen in FlieBgew~issern. Gutachten der L~nderarbeitsgemeinschaft Wasser (LAWA) (http://www.fliessgewaesserbewertung.de/downloads/best_anhang3_taxaliste.pdf). HERING,D., MOOG, O., SAND1N,L. & VERDONSCHOT,P. F. M. (2004a): Overview and application of the AQEM assessment system. Hydrobiologia (The Hague) 516: 1-20. HERING,D., MEIER,C., RAWER-JOST,C., BISS, R., FELD, C. K., ZENKER, A., SUNDERMANN,A., LOHSE, S. & BOHMER, J. (2004b): Assessing streams in Germany with benthic invertebrates: selection of candidate metrics. Limnologica 34: 398-415. ILLIES,J. (1982): L~ingsprofil des Breitenbachs im Spiegel der Emergenz (Ins.: Ephemeroptera, Plecoptera, Trichoptera). Arch. Hydrobiol. 95: 157-168. JoY, M. K. & DEATH,R. G. (2003): Biological assessment of rivers in the Manawatu-Wanganui region of New Zealand using a predicative macroinvertebrate model. New Zealand Journal of Marine and Freshwater Research 37: 367-379. LORENZ, A., KIRCHNER,L. & HERING,D. (2004) 'Electronic subsampling' of macrobenthic samples: how many individuals are needed for a valid assessment result? Hydrobiologia (The Hague) 516: 299-312. NORRIS,R. & GEORGES,A. (1986): Design and analysis for assessment of water quality. In: DE DECKKER,P. & WILLIAMS, W. D. (eds.), Limnology in Australia, pp. 555-572. Kluwer, Dordrecht. Limnologica (2004) 34,366-378
NORRIS,R. & GEORGES,A. (1993): Analysis and interpretation of benthic marcoinvetrebrate surveys. In: ROSENBERG,D. & RESH, V. (eds.), Freshwater biomonitoring and benthic macroinvertebrates, pp. 234-287. Chapman Hall, New York. POTTGIESSER, T. & SOMMERHAUSER,M. (2004): Die Flieggew~issertypologie Deutschlands: System der Gewassertypen und Steckbriefe zu den Referenzbedingungen. In: STEINBERG,C., CALMANO,W., NILKEN, R.-D. & KLAPPER, H. (eds.), Handbuch Angewandte Limnologie. 19. Erg.Lfg. 7/04. VIII-2.1:1-16 + Anhang. ecomed Verlagsgesellschaft Landsberg. RAWER-JOOST, C. (2001): Eignung und Variabilit~it yon Verfahren zur 6kologischen Bewertung von Fliel3gew~issem im Mittelgebirge auf der Basis aut6kologischer Kenngr6Ben des Makrozoobenthos. PhD-thesis, University of Hohenheim. RESH, V. & JACKSON,J. (1993): Rapid assessment approaches to biomonitoring using benthic macroinvertebrates. In: ROSENBERG,D. & RESH, V. (eds.), Freshwater biomonitoring and benthic macroinvertebrates, pp. 195-233. Chapman Hall, New York. RESH, V. H. & ROSENBERG,D. M. (1989): Spatial-temporal variability and the study of aquatic insects. Canadian Entomologist 121: 941-963. REYNOLDSON,T. B., ROSENBERG,D. M. & RESH, V. H. (2001): Comparison of models predicting invertebrate assemblages for biomonitoring in the Fraser River catchment, British Columbia. Canadian Journal of Fisheries and Aquatic Sciences 58: 1395-1410. SMITH, M. J., KAY, W. R., EDWARD, D. H. D., PAPAS, P. J., RICHARDSON,K. S., SIMPSON,J. C., PINDER,A. M., GALE, D. J., HORWITZ,P. H. J., DAVIS,J. A., YUNG,F. H., NORRIS, R. H. & HALSE,S. A. (1999): AusRivAS: using macroinvertebrates to assess ecological condition of rivers in Western Australia. Freshwater Biology 41: 269-282. SOMERS,K. M., REID, R. A. & DAVID,S. M. (1998): Rapid biological assessments: how many animals are enough? Journal of the North American Benthological Society 17: 348-358. STARCONSORTIUM(2003): The AQEM sampling method to be applied in STAR. unpublished report, available from http://www.eu-star.at/tops/top_protocols.htm. STATSOFT,Inc. (2002): STATISTICA fiir Windows [SoftwareSystem fiir Datenanalyse] Version 6. www.statsoft.com. STRIBLING,J., MOULTON,S. R. & LESTER,G. T. (2003): Determining the quality of taxonomic data. J. N. Am. Benthol. Soc. 22: 621-631. VINSON, M. R. & HAWKINS,C. P. (1996): Effects of sampling area and subsampling procedure on comparisons of taxa richness among streams. J. N. Am. Benthol. Soc. 15: 392-399. WALSH, C. J. (1997): A multivariate method for determining optimal subsample size in the analysis of macroinvertebrate samples. Marine and Freshwater Research 48: 241-248. WRIGHT, J. F., MOSS, D., ARMITAGE,P. D. & FURSE, M. T. (1984): A preliminary classification of running-water sites in Great Britain based on macroinvertebrate species and the prediction of community type using environmental data. Freshwater Biology 14: 221-256. WRIGHT,J. F., SUTCLIFFE,D. W. & FURSE,M. T. (2000): Assessing the biological quality of fresh waters: RIVPACS and other techniques. Freshwater Biological Association, Ambleside.
Limnologica 34,366-378 (2004) http://www.elsevier.de/limno
LIMNOLOGICA
Testing different sorting techniques in macroinvertebrate samples from running waters Peter Haase 1,*, Steffen Pauls 1, Andrea Sundermann 1, Armin Zenker 2
Senckenberg Research Institute and Natural History Museum, Department of Limnology and Conservation, Biebergemfind, Germany 2University of Hohenheim, Department of Zoology, Aquatic Ecology, Germany Received:March 30, 2004 • Accepted:July 12, 2004
Abstract With the aim of finding an efficient, standardised and practical protocol for sorting macroinvertebrate samples for water management practice, three different sorting techniques were tested: RIVPACS sorting, a modified AQEM/STAR (MAS) sorting protocol and a Live-sorting method. Based on the same AQEM/STAR sample to ensure comparable results, we compared RIVPACS and MAS sorting for 20 samples, and Live-sorting and MAS for a different set of 20 samples. Comparisons were based on both ecological and financial parameters relevant for the implementation of the EU Water Framework Directive in Germany. Parameters include recently developed multimetric assessment indices, their stream type specific core metric results, time effort and costs. While RIVPACS and MAS sorting produced similar results in terms of ecological assessment, time effort and costs, Live-sorting differed notably in all three respects. Live-sorting is the quickest and least expensive protocol, but shows higher variability than the other protocols. We discuss the differences and the level of standardisation for each of these methods. Key words: Stream assessment- sorting methods -macroinvertebrate sampling
Introduction In Germany a standardised method for investigating macroinvertebrates from streams is missing to date. To implement the EU Water Framework Directive (EUWFD) this is however of vital importance, because different protocols could lead to different assessment results (HAASE et al. 2004; HERINGet al. 2004a). Thus a LAWA 1financed project was initiated to develop a highly standardised method, which is also feasible in terms of time effort and costs.
The AQEM/STAR method (STARCONSORTIUM2003), which was recently developed, presents a highly standardised method and thus generally fulfils the required criteria. Unfortunately with this method a sample takes 12.6 hours to collect and treat (HAASE et al. 2004), a time effort not feasible for routine investigations. It was shown that 80% of the time to complete an AQEM/STAR sample lies in the sorting and determination of organisms (HAAsEet al. 2004). This large amount of time required is explained by the high number of individuals which are produced by the AQEM/STAR
LfinderarbeitsgemeinschaftWasser(LAWA),Contract04.02 *Corresponding author: PeterHaase,SenckenbergResearchInstitute and Natural History Museum, Departmentof Limnologyand Conservation, D-63599 BiebergemOnd,Germany;e-mail: [email protected] 0075-9511/04/34/04-366 $ 30.00/0
Limnologica (2004) 34,366-378
Testing different macroinvertebrate sotting techniques method. Producing such high numbers of individuals is a general problem with many methods designed for generating macroinvertebrate data for stream assessment purposes (e.g. LORENZ et al. 2004; VINSON & HAWKINS 1996; CARTER & RESH 2001). Numerous studies have been aimed at reducing the number of individuals required to obtain stable assessment results (e.g. LORENZ et al. 2004; DOBERSTEIN et al. 2000; BARBOLrRet al. 1999; SOMERSet al. 1998; WALSH 1997). Completely sorting the individuals from a sample is thus coupled with a time effort too high for practical monitoring procedures (CARTER8~; RESH 2001). There are two general approaches that would reduce this effort: - A representative subsample is taken from the sample and sorted completely, o r - a smaller number of individuals from every taxon differentiated are sorted from the sample and their corresponding abundance in the sample estimated. With the aim of reducing the total number of individuals collected within an AQEM/STAR sample and thus the time required to sort and determine these organisms, we tested different sorting variants from each of these approaches. The first approach comprises lab-based sorting, for which samples are conserved in the field and sorted in the lab. Based on the defined AQEM/STAR subsample this sorting approach was tested using the AQEM/STAR method, the RIVPACS sorting protocol and the newly developed Modified AQEM/STAR method (MAS; HAASE et al. 2004). The MAS approach varies from the AQEM/STAR method in that it only considers larger organisms retained in a coarse fraction (> 2 ram). The second approach is usually applied directly in the field and has been used for many years for biological water quality monitoring investigations e.g. in Germany (c.f. BRAUKMANN 2000), Italy (c.f. BUFFAGNI et al. 2002), Latvia (unpublished protocol in STAR CONSORTIUM 2003). In these so-called "Live-sorting" methods the organisms are sorted and picked alive in the field. As mentioned above, the AQEM/STAR method is very time consuming. We assume that compared with the subsampling procedures we wish to test, this method will deliver the highest number of individuals and highest number of taxa because the entire subsample is sorted and all individuals are determined. We therefore assume that this method comes closest to truly describing the content of our subsample. For this reason, and because the focus of our comparison lies on determining the differences between the quicker protocols, we used the AQEM/STAR result as our guideline or 'reference' sample. In this paper we thus compare three different methods for sorting a sample in terms of their assessment results compared with AQEM/STAR and their usefulness in
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stream assessment applications. The three methods tested are Live-sorting according to BRAUKMANN(2000), MAS (HAASEet al. 2004), and RIVPACS (WRIGHTet al. 2000).
Material and Methods Sorting methods In the following we give a short summary of the sorting methods compared. For full details of the methods see the cited literature. • AQEM/STAR (STARCONSORTIUM2003) The AQEM/STAR method was developed in the course of two successive EU-funded projects, the aim of both being to develop and standardise macroinvertebrate assessment systems. The AQEM/STAR sorting protocol is done completely in the laboratory and first reduces the total sample material with a defined subsampling step. The subsample corresponds to 1/6 of the sample and at least 700 individuals sorted. If 1/6 of the samples contains <700 individuals, the subsample is increased until 700+ organisms are sorted. Al___!lindividuals of the subsample are picked and counted without magnification and then determined. The result is a taxa list giving the number of individuals extrapolated to whole sample. • MAN (HAASEet al. 2004) The MAS protocol is a modification of the AQEM/ STAR protocol. Before sorting, a coarse fraction (> 2 mm) is separated from the rest of the subsample using a sieve cascade. Only the coarse fraction is sorted completely without magnification. Here the criteria for the subsample are at least 1/6 of the sample and at least 350 individuals in the coarse fraction. Subsampling, sorting and counting is done in the lab without magnification. All picked individuals are determined. The result is a taxa list giving the number of individuals extrapolated to whole sample. • RIVPACS (WRIGHTet al. 1984; WRIGHTet al. 2000; STARCONSORTIUM2003) The RIVPACS protocol was developed in Great Britain and has since been adapted and used in several other countries (e.g. Australia: AusRivAS, SMITHet al. 1999; Canada: BAILEYet al. 1998, REYNOLDSONet al. 2001; New Zealand: JoY & DEATH 2003). The protocol followed for this comparison is given in STARCONSORTIUM (2003). In RIVPACS sorting, the operator selects a fraction of the sample which is completely sorted in the lab without magnification (1/2, 1/4 .... ). The unsorted rest of the sample is scanned for taxa which have not been found in the sorted fraction. Individuals from these taxa Limnologica (2004) 34,366-378
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are picked from the sample and retained in a separate vial for "extra" taxa. All individuals found in either a "fraction vial" or the "extras vial" are determined. The result is a taxa list giving the number of individuals extrapolated to whole sample. • Live-sorting (according to BRAUKMANN2000,
modified) The Live-sorting approach has been used for many years in several states in Germany. However there are many variations of this method both in terms of sampling and sorting. Testing all variants applied in Germany would not have been feasible in this study. We thus chose the protocol according to BRAUKMANN(2000), which is well described. In this protocol living individuals from each identifiable taxon in the sample are sorted and determined as far as possible in the field and their abundance in the sample estimated. The sample is transferred to a sorting tray, partly filled with water and thoroughly looked through. First all identifiable or differentiated taxa are noted in the field. Then their abundance in the whole sample is estimated in abundance classes. This is a modification of the method described by BRAUKMANN(2000), based on the fact abundance classes are used in most states of Germany and are therefore much more common then the semi-quantitative data used by BRAUKMANN(2000). The abundance classes are 1 -- 1 ind.; 2 = 2-20 ind.; 3 = 21-40 ind.; 4 = 41-80 ind.; 5 = 81-160 ind.; 6 = 161-320 ind.; 7 >320 ind. (ALF et al. 1992). Of all distinguishable taxa, individuals are taken to the lab for further determination. The number of individuals from each taxon taken to the lab for further determination depends both on the absolute abundance of that taxon in the sample, and more importantly on the expectation that a taxon in the field will yield numerous further taxa once determined in the lab. The result is a taxa list with abundance scores, rather than number of individuals.
Comparisonof sorting methods The comparison for sorting protocols was based on 40 samples taken according to the AQEM/STAR protocol. From these samples a subsample according to the criteria of the AQEM/STAR protocol was taken (1/6 of the sample and 700+ individuals). For 20 subsamples from 3 stream types the sorting protocol for RIVPACS and MAS were compared. Another 20 subsamples were used to compare the Livesorting and MAS sorting protocols. As a "reference" data set for each subsample, the entire subsample was sorted to generate the AQEM/STAR fraction. Thus the same subsample was always used for each of the three sorting protocols compared for each sample. This experimental design insured that differences obLimnologica (2004) 34,366-378
served were only due to different sorting methods and not based on sampling or subsampling heterogeneity. The following questions were addressed in the comparison: - How much time is needed for sorting? - How many individuals and taxa are produced? and - How do assessment results differ between sorting protocols? Time sheets were kept for all steps of the sorting procedure once the subsample was taken. In detail time was recorded for the sorting process and the determination of the actually picked individuals. Where it was necessary, times were added to complete the data for a certain protocol (e.g. AQEM/STAR fraction). Based on these time sheets, the personnel costs for each sorting method were calculated, taking into consideration that different tasks can be performed by differently qualified persons. Three categories of personnel were differentiated: student aids for simple tasks (~ 15/hour); technical staff for tasks requiring training (~ 30/hour); scientists for tasks requiring specialist skills (~ 50/hour). All determinations were done by the same person according to the taxonomic level defined in HAASE& SUNDERMANN(2004) to ensure comparable determination results. The taxa lists resulting from all protocols were compared regarding number of taxa and number of individuals determined. As ecological indicators, stream type specific core metric results were compared (HERING et al. 2004b). As comparative ecological assessment results we also calculated the stream-type specific multimetric index (MMI) values (BOHMER et al. 2004) for each of the sorting protocols for each sample. The differences between core metric and MMI values compared with AQEM/STAR were calculated as were the number of different classifications in ecological quality classes. Differences between MMI values for two protocols were tested for significance using Wilcoxon-Test. To test for significant differences between more than two protocols, Friedman ANOVA by ranks was calculated. If a significant difference was found using Friedmann ANOVA, Wilcoxon-Tests were calculated to show which of the protocols differ from each other. In this, the significance level was adjusted according to EMCEE (1997). Spearman's R was calculated in order to find out if two variables are correlated. All statistical analyses were done with the STATISTICA 6.1 software package (STATSOFT 2002). • Comparing RIVPACS and MAS
The subsample was first divided into a fine (_<2 mm) and coarse fraction (>2 ram) using an analytic sieve cascade to allow later compilation of results for all three tested protocols. RIVPACS sorting is performed with both fractions according to the protocol from STAR CONSOR-
Testing different macroinvertebrate sorting techniques TIUM (2003). The individuals from each fraction were counted and deposited separately into different containers. The remaining material from both fractions, which was not sorted with the RIVPACS protocol, was then completely sorted and the individuals put into separate containers for each fraction. Number of individuals was also counted. All material was then determined and recorded separately for each sorting method and the coarse and fine fraction. Besides the sorting times, all determination times were also recorded separately for each sorting method and the coarse and fine fractions. The RIVPACS result is generated by adding up the sorting and determination results from both the fine and coarse fraction sorted according to the RIVPACS protocol. Following the rules given in the RIVPACS protocol (STAR CONSORTIUM 2003), the number of individuals was extrapolated to the whole subsample. The MAS result is generated by compiling a taxa list from the entire coarse fraction (including the individuals from the RIVPACS sorting of the coarse fraction only). In a second step the results from the fine fraction (including the individuals from the RIVPACS sorting of the fine fraction) are added to the MAS taxa list, resulting in the AQEM/STAR result. In a final step the results from all three sorting protocols are extrapolated to the whole sample in correspondence to the subsample proportion. We compared a total of 20 subsamples from alpine streams (type 1), streams in the Pleistocene sediments of the alpine foothills (type 3), and small siliceous sandstone streams from the central highlands (type 5.1) (stream type numbers according to POTTGIESSER& SOMMERHAUSER2004). • Comparing Live-sorting and M A S The Live-sorting protocol requires that all of the sorting work is done in the field. For this comparison we therefore took the AQEM/STAR subsample (STAR CONSORTrCM2003) (----1/6 and 700+ individuals) in the field. The total sample was spread out in the subsampling grid and the fraction containing 700+ individuals estimated in the field. The subsample was taken using the subsampling grid according to the AQEM/STAR protocol (STARCONSORTIUM2003). Thus, by following the same procedure as is used in the lab, the subsample criterion of at least 1/6 of the total sample was met exactly. The criterion
369
700+ individuals could only be estimated, and to make sure it was met, the subsample proportion was estimated generously. Live-sorting was done with the subsample. After the subsample was sorted, the remaining material from the same subsample was not discarded, but fixed and taken to the lab for further treatment. The individuals sorted alive in the field were determined in the lab and carefully returned into the subsample. Afterwards the same, complete subsample was separated into a coarse and fine fraction and each of these fractions sorted completely following the AQEM/STAR protocol. Live-sorting results are given in abundance classes. Some of the metrics used to calculate MMI values require number of individuals. Therefore relative proportions of individuals were estimated from abundance class data to delogarithmise abundance classes. The median number of individuals for each abundance class produces the best estimation, with the smallest systematic error [e.g. abundance class 3 was transformed into 30 individuals (ALF et al. 1992)]. The resulting number of individuals was then extrapolated to the whole sample in correspondence to the subsample proportion. The MAS and AQEM/STAR sorting result is generated in the same way as described for the RIVPACS and MAS comparison. The extrapolation and generation of MMI values follows the same procedure described above. We compared 20 samples from 5 stream types. In addition to those stream types included in the comparison above we also sampled large cobble/boulder bottom streams in lower-mountainous areas (type 9.2) and small streams from the Loess regions of the central lowlands (type 18) (stream type numbers according to POTTGIESSER~; SOMMERHAUSER2004).
Results Comparing RIVPACSand MAS Table 1 shows the sorting and determination times required and the number of individuals and taxa found with each of the sorting protocols in comparison to the AQEM/STAR sorting result.
Table 1. Sorting and determination effort required and number of taxa and individuals found by different sorting protocols. * = picked individuals, not extrapolated. N= 20
Sorting [h]
Taxa determination [h]
Average number of individuals*
Average number of taxa
Relative number of taxa [%]
RIVPACS MAS AQEM/STAR
2,3 3.0 9,1
2.0 2.4 4,9
385 544 1411
39 42 54
72 78 100
Limnologica (2004) 34,366-378
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P. Haase et al.
When comparing the times needed for sorting and determining taxa, the RIVPACS protocol is the quickest. The MAS protocol requires a little more time, while the AQEM/STAR protocol takes nearly three times as long. The required time corresponds quite well with the number of individuals sorted. These are lowest for the RIVPACS protocol, somewhat higher for MAS and three or five times higher for AQEM/STAR than MAS and RIVPACS, respectively. In comparison to the total subsample (AQEM/STAR) the MAS sorting recovers 78% of the taxa, RIVPACS sorting 72%.
[]RIVPACS
•MAS
For all 60 data sets (RIVPACS, MAS and AQEM/STAR results from 20 subsamples) results of each single core metric, required to calculate the stream type specific MMI, were calculated. The mean differences between the single core metric values for MAS and RIVPACS and those attained with the AQEM/STAR sorting are compared in Fig. 1. Using these core metric results, the multimetric index results (MMI values) were calculated for each of the sorting protocols for all 20 subsamples (Fig. 2).
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Fig. 1. Mean deviation of the core metric results from the MAS and RIVPACS sorting from the AQEM/STAR results. The results P~ 0.4 are shown separately for each stream type. Alpine streams (Type 1), streams in the Pleistocene sediments of the alpine foothils E 0.2 (Type 3), small siliceous low mountain range sandstone streams (Type 5.1). 8 Description of "Core Metrics": Crustacea [%] = % Crustacea "6 0.0 g (individuals); Epirhithral [%] (ind) = % individuals with preferr~ ence EpirNthral (upper trout region); EPT [%] (abd) = % individ'~ -0.2 uals Ephemeroptera, Plecoptera, Trichoptera based on abunr,dance classes; Gatherers/Collectors [%] (ind) = % gatherers and :~-0.4 collectors (individuals); Lithal [%] = % individuals with preference lithal; Metarhithral [%] (ind) = % individuals with prefer-0.6 ence Metarhithral (lower-trout region); Pelal [%] (ind) = % individuals with preference pelal; Plecoptera [%] = % Plecoptera inn ~ d_ c dividuals; rheophil [%] (abd) -- % rheophile individuals based on abundance class; GFI type 14 = German Fauna Index type 14; c GFI type 9 = German Fauna Index type 9; GFI type 5 = German Fauna Index type 5; # Plecoptera = number of Plecoptera taxa.; Shredders [%] (ind) = % shredders (individuals); Rheoindex (ind) = Rheoindex according to Banning (individuals) (BANNING1998); RTI (ind) = Rhithron-Typie-lndex (individuals) (BIss et al. 2002); Diversity (SWl) = Diversity (Shannon-Wiener-Index); oligosap. Taxa [%] (abd) = proportion of oligosaprobic taxa [%] (abundance class); xenosap. Taxa [%] (ind) = proportion of xenosaprobic taxa [%] (individuals).
Limnologica (2004) 34,366-378
Testing different macroinvertebrate sorting techniques
371
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Fig. 2. Assessment results by sorting protocol. The figure shows the MMI values for the AQEM/STAR, MAS and RIVPACSsorting protocols for each sample.The resulting ecological quality class based on the MMI is inserted on the right.
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The mean deviation of MMI results of the RIVPACS sorting to AQEM/STAR is 0.00, with a standard deviation of _+0.04. For MAS, the mean deviation was ~).03. Here the standard deviation was +0.05 (Fig. 3). Significant differences between the three protocols were found (Friedmann ANOVA p < 0.00). After correcting the level of significance according to ENGEL(1997), Wilcoxon-Test results showed no significant difference between protocol pairs (AQEM/STAR/MAS p > 0.02; A Q E M / STAR/RIVPACS p = 1.0; RIVPACS/MAS p > 0.04). The MMI results for both the RIVPACS sorting protocol and MAS sorting correlate well (R > 0.90) with the AQEM/STAR MMI values (Fig. 4). For the 20 subsamples, RIVPACS sorting results in MMI values, which in 3 cases leads to different ecological classifications in comparison to AQEM/STAR, while the MAS sorting leads to 4 differing classifications based on the differences in MMI values. The differences are always one ecological quality class, which go both up and down in both sorting protocols (Fig. 2).
Comparing Live-sorting and MAS Table 2 shows the sorting and determination times required and the number of individuals and taxa found with each of the compared sorting protocols in comparison to the AQEM/STAR sorting result. Limnologica (2004) 34,366-378
372
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In the comparison of sorting and determination times the Live-sorting protocol is much quicker than the MAS method, taking only a quarter of the time. The AQEM/STAR method requires twice as tong as the MAS protocol and eight times longer than Live-sorting. These times correspond quite well with the number of individuals recovered by each of the sorting methods. They are much lower for Live-sorting than the MAS, and again much higher for the AQEM/STAR sorting protocol. In comparison to the completely sorted AQEM/STAR sample, Live-sorting recovers 48% of the taxa, while the MAS sorting recovers 78%. The individual stream type relevant core metric results were also calculated for this data set. Deviations of core metric results from the MAS and Live-sorting against the AQEM/STAR results are shown in Fig. 5. Again these core metric results were used to calculate the multimetric index assessment results (MMI values) for each of the sorting protocols for all 20 subsamples (Fig. 6). The mean deviation of the MMI results to the AQEM/STAR results is 0.01 for Live-sorting, while the standard deviation is quite high (_+0.11). For MAS the mean deviation is the same (0.01), while the standard deviation is distinctly lower (+0.04) (Fig. 7). No significant differences between the three protocols were found (Friedmann ANOVA p > 0.52). Live-sorting results do not correlate quite as well with AQEM/STAR MMI values (R = 0.86, p < 0.00) as they do for the MAS sorting (R = 0.97, p < 0.00) (Fig. 8). In 8 of 20 subsamples (40%) the differences in MMI results lead to different ecological quality classes of Live-sorting compared with AQEM/STAR. The MAS sorting protocol leads to a different ecological quality class once (5%). The deviations amount to one ecological class, which for Live-sorting go both up and down (Fig. 6). Table 3 summarises the main results from the direct comparison of the sorting methods (given in Table 1 and Table 2). Both the MAS and AQEM/STAR method are adversely affected by the direct comparison of methods using the same subsample, this is especially true for the number of individuals contained in a subsample where Live-sorting was performed in the field. Because of the experimental design (see above), these subsamples con-
Table 2. Sorting and determination effort required and number of taxa and individuals found by different sorting protocols. * = picked individuals, not extrapolated. N= 20
Sorting [h]
Taxa deterruination [h]
Average number of individuals*
Average number of taxa
Relative number of taxa [%]
Live-sorting MAS AQEM/STAR
0.7 3.1 7.7
1.0 3.7 5.7
225 959 I891
28 45 58
48 78 100
Limnologica (2004) 34,366-378
Testing different macroinvertebrate sorting techniques
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L i m n o l o g i c a (2004) 3 4 , 3 6 6 - 3 7 8
374
P. Haase et al. ecological quality class
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Fig. 6. Assessment results by sorting protocol. The figure shows the MMI values for the AQEM/STAR, MAS and Live-sorting sorting protocols for each sample.The resulting ecological quality class based on the MMI is inserted on the right.
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Fig. 7. Box-Whisker plots showing the deviation of MMI results obtained by either MAS or Live-sorting from the AQEM/STAR protocol results. Limnologica (2004) 34,366-378
rained unusually high numbers of individuals and thus sorting and determination times of the MAS and AQEM/STAR protocols were higher. Therefore for the final comparison between the MAS and AQEM/STAR sorting protocols more independent data (HAASE et al. 2004) were included in the result table. A second advantage of the independent data set is, that it is much larger (N = 70) and thus gives more representative values. As the results in Table 3 show, results from the RIVPACS sorting comparison and the independent data set give similar results for the MAS and AQEM/STAR sorting protocols. Both laboratory based sorting protocols, MAS and RIVPACS, give similar results in all aspects. The RIVPACS sorting is a little bit less time consuming and delivers lower number of individuals and taxa. There is no or only a slight difference in mean deviation of MMI values for RIVPACS and MAS, respectively, from the AQEM/STAR MMI result, while the standard deviation is identical. In terms of scoring the "correct" ecological quality class, the differences are minimal (MAS 87%, RIVPACS 85%). A different picture results from the Live-sorting protocol: This method is much less time consuming than the two laboratory based methods MAS and RIVPACS. The mean deviation between AQEM/STAR and Live-sorting is also very low, while the spread of deviations is much
Testing different macroinvertebrate sorting techniques
375
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Fig. 8. Correlation results between the MMI values obtained from AQEM/STAR sorting and either MAS sorting (left) or Live-sorting (right).
Table 3. Summary of results from the comparison of sorting protocols. * = picked individuals, not extrapolated; ** = reference value; *** = data from HAASEet al. (2004). AQEM/STAR (N= 70)*** Average time effort [h] (Sorting and determination) Average number of individuals* Average number of taxa Relative number of taxa [%] Mean deviation of MMI Standard deviation of MMI "Correct" ecological quality class [%]
MAS (N= 70)***
10.6
RIVPACS (N= 20)
5.1
1066 52 100"* 0"* 0'* 100"*
Live-sorting (N= 20)
4.3
469. 42 81 -0,01 ±0.04 87
1.7
385 39 72 0.00 ±0.04 85
225 28 48 0.01 ±0.11 60
Table 4. Time effort and costs of the different sorting methods. :: Stu (student assistant) =15 {/h; Tech (technical staff) = 30 ~/h; Sci (scientist) = 50 ~:/h; *: Sampling and subsampling always done according to the AQEM/STAR protocol.
Live-sorting
Sampling* Subsampling* Sorting Taxa determination Data entry
RIVPACS
Time [h]
Person~
0.75 0.5 0.7 1.0 0.5
Sci Stu Sci Sci Tech
Costs [~] 37.50 7.50 35.00 50.00 15.00
MAS
Time [h]
Person1
Costs [(~]
Time [h]
Person1
Costs[~]
0.75 0.5 2.3 2.0 0.5
Sci Stu Tech Sci Tech
37.50 7.50 69.00 100.00 15.00
0.75 0.5 2.6 2.5 0.5
Sci Stu Stu Sci Tech
37.50 7.50 39.00 125.00 15.00
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Total
3.45
145.00
6.05
229.00
6.85
224.00
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higher than in any of the other methods (Fig. 7: standard deviation _+0.11). There is a clear decline in both number of individuals and number of taxa. In our data set these differences in MMI values lead to a different ecological quality class score in 40% of the compared samples (Fig. 6). The total costs for a macroinvertebrate sample, which is sorted following the Live-sorting protocol come to 145.00. The corresponding values for the RIVPACS and MAS protocol are ~ 229.00 and ~ 224.00, respectively. Thereby the Live-sorting protocol is not only the fastest, but also by far the cheapest (Table 4).
Discussion With the chosen experimental design we were able to compare different sorting protocols based on exactly the same subsample. This procedure eliminates any natural or methodologically induced variability between data sets that are often a problem in comparisons of macroinvertebrate samples (NORRIS& GEORGES1993; NORRIS& GEOROES 1986). The sampling design did, however, require some minor changes to the normal protocols. For instance before RIVPACS sorting we separated a coarse and fine fraction and sorted them separately, a procedure not usually incorporated in RIVPACS (WRIOHT et al. 2000; WRICHTet al. 1984). We assume that dividing the fraction into a fine and coarse fraction facilitates the sorting process, because finding (smaller) organisms is presumably easier in a homogenous fraction. Thus the results generated here may slightly favour the RIVPACS method in terms of taxa recovered. In the Live-sorting comparison it was necessary to take a subsample in the field. In order to assure that both subsample criteria (at least 1/6 of the sample and at least 700 individuals) were fulfilled, we had to generously estimate the required subsample fraction to obtain enough individuals. This lead to higher numbers of individuals in the AQEM/STAR subsample than is usual. This would in turn lead to higher sorting and determination times for all sorting protocols than is usually required. This variation has no effect on the direct comparison of the AQEM/STAR, MAS and Live-sorting methods, because it affects all three methods equally. Because of these higher numbers of sorted individuals, we tried to attain an independent data set for Livesorting, MAS and AQEM/STAR for a final comparison of sorting results between all methods (cf. Table 3). Unfortunately we only had this data set for the AQEM/STAR and MAS sorting protocols. Live-sorting is negatively affected by higher numbers of individuals in terms of higher sorting and determination times. Nonetheless, this method was by far the fastest, so this negative effect can be ignored in the comparison. Limnologica (2004) 34,366-378
Based on our samples size, which is relatively small with an N of 20 for each comparison, we are only able to show tendencies. Differences between assessment results for single sorting methods are not significant. For our comparison of MAS and RIVPACS the differences are so small, that we assume, the methods would also present similar results, without showing significant differences with a larger sample size. For the comparison of MAS with Live-sorting, a larger data set might confirm the tendencies/trends (lower number of taxa, larger deviation in assessment result) we have made out with the available data. Despite these methodological complications we were able to show, that even based on the same subsample different sorting methods could lead to different assessment results. Due to sampling design, we can be sure that differences in our data resulted solely on sorting methods. Variability based on sample heterogeneity between subsample and an entire sample (LORENZet al. 2004; DOBERSTEINet al. 2000; RESH & JACKSON 1993), between subsamples, natural variability (e.g. RESH & ROSENBERG 1989; ILLn~S1982) or influences in the taxa lists resulting from different analysts involved in the determination process (c.f.e.g. STRmLINGet al. 2003) were omitted. From our data it seems essential that a standardised and uniformly used sorting protocol is adopted for ecological quality control of running waters, especially for implementation of the EU-WFD. For the development of a practical and easily applicable method for routine monitoring practice, not only the quality of the method but also the costs are important. Besides being the fastest, Live-sorting is also the cheapest of the compared sorting protocols, despite the fact that identifying the organisms in the field requires sufficient experience and can only be done by specialists. Another advantage of this method lies in only having to fix a small number of individuals for determination in the lab. On the other hand our results show that Live-sorting leads to higher levels of deviation in assessment results. In this respect, the standard deviation is a much stronger indicator of the quality of MMI results then the mean deviation of the MMI, since high positive and negative deviation were compensated in mean values. Concerning the quality of Live-sorting there are two other criteria, which should also be taken into consideration: - The expertise of the person sorting, and the weather conditions and illumination at the sampling site. -
The first factor causes distortions in the number of taxa recognised and consequently sorted and determined from a sample. A worker with long experience and specialist skills can differentiate and will thus pick more different taxa and individuals, than a less-skilled worker. These bias increases with highly mobile or rapidly mov-
Testing different macroinvertebrate sorting techniques ing organisms when sorting live samples (CARTER & RESH 2001). The second factor concerns the importance of illumination for sampling success. Different light intensities caused for example by weather conditions at the time of sampling (e.g. cloudy skies vs. sunshine) or the location of the sampling site (e.g. in a shady wood vs. an open meadow). One must also not underestimate the motivation a worker has to sort a sample in bad weather conditions, which in such a case is lower and limits sorting Success (CARTER • RESH 2001; RAWER-JOST2001). These factors alone lead to a high amount of variability within the Live-sorting procedure (c.f. CARTER & RESH 2001). This variability is intolerable considering the importance of the assessment results for the implementation of the EU-WFD. Another problem this method brings with it, is the difficulty in quality auditing such a procedure. The material sorted in the field is discarded. It is therefore no longer possible to detect whether all taxa were actually picked from the subsample and if the abundances were estimated more or less correctly. This factor could be eliminated if the whole sample was conserved after sorting and taken home. This would however not only cause higher auditing costs but more importantly would mean that a live sorting procedure could only be audited by sorting a conserved sample, whereby observed differences could be based on sorting method. Based on our results, the documented difficulties involved with variability within the method and limitations in standardising and auditing Live-sorting approaches, we do not feel confident, that such an approach would suffice to meet all requirements set out for implementing the EU-WFD. The differences in data quality and assessment results between the two laboratory methods are small. In the sampling process they also cost about the same. Therefore principally both methods seem equally suitable sorting procedures. There are however further aspects one must consider: - Compared to the MAS sorting protocol the RIVPACS sorting method is much more complex. It is therefore important to train personnel for this task (CLARKE et al. 2002). This in turn causes higher costs, which are not included in our calculation. - In RIVPACS sorting the worker must decide how much of the sample is sorted (1/4, 1/2 ...) and is allowed to change the sorting fraction during the sorting process. These aspects show that the RIVPACS sorting procedure could be more variable than the MAS method. - The person sorting a RIVPACS sample must have proficient taxonomic knowledge. He or she must, e.g. identify taxa, which have not been collected from the sorted fraction in the rest of the material. Technical
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staff must therefore perform this task, while the MAS sorting can also be done by a student assistant. Different taxonomic skills may bias sorting results when using the RIVPACS sorting protocol (c.f. CLARKE 2000). Especially for the above mentioned higher amount of variability embedded in the RIVPACS method and the higher level of standardisation in the MAS protocol, we suggest using the MAS sorting method for macroinvertebrate studies, aimed at EU-WFD implementation in Germany. A detailed description of the method development and the protocol are given in HAASE et al. (2004).
Acknowledgements This study was supported by L~inderarbeitsgemeinschaft Wasser (Project number: LAWA 04.02) and by the European Union (Project: Standardisation of River Classifications, a project under the Fifth Framework Programme and the Key Action "Sustainable Management and Quality of Water" within the Energy, Environment and Sustainable Development Programme; contract No: EVK1-CT 2001-00089). We thank Eckart Coring for "live-sorting" samples from Lower-Saxony. Daniel Hering is thanked for helpful advice on the manuscript.
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