Fundam. Appl. Limnol. Vol. 180/2, 91–100Article Stuttgart, March 2012
Performance of profundal macroinvertebrate assessment in boreal lakes depends on lake depth Papers collated based on and as part of the EU funded project WISER (funded by the European Union under the 7th Framework Programme, contract No. 226273)
Jussi Jyväsjärvi1*, Jukka Aroviita2 and Heikki Hämäläinen1 With 6 figures and 1 table Abstract: Profundal macroinvertebrates (PMI) are commonly used in assessment and monitoring of lakes. Tradi-
tional PMI community typologies and indices have been developed and tested for large and deep lakes with welldefined pelagic and profundal zones, whereas small lakes have received less attention; despite their large number. We evaluated the performance of PMI assemblage assessments in boreal lakes with data from 255 Finnish lake basins, which were divided into four lake mean depth categories (shallow, intermediate, deep and very deep). We first described natural community variation with DCA ordination and then, within each depth category using ANOSIM, compared community composition between minimally disturbed reference (REF) basins and basins impacted mainly by nutrient enrichment (IMP). We assessed the status of PMI assemblages using three metrics (Benthic Quality Index, Percent Model Affinity and Shannon diversity) that aimed to meet the normative criteria set by the EU Water Framework Directive. Judging by conventional criteria, there was a consistent improvement in bioassessment performance of PMI with lake depth. Assemblages in shallow REF and IMP sites were similar and measures of ecological status showed neither difference between REF and IMP sites nor response to nutrient pollution. In deep and very deep IMP lakes the communities differed significantly from those in REF lakes and community and metric variation was more strongly related to phosphorus concentration. Our results suggest that PMI assemblages of deeper boreal lakes respond predictably to anthropogenic nutrient enrichment, whereas assemblages of shallow lakes are either ‘naturally eutrophic’ and thus resistant to change or unpredictably variable making it difficult to detect any impacts of anthropogenic nutrient enrichment on communities. Future studies are thus needed to evaluate the performance of littoral macroinvertebrate assemblages for shallow lake bioassessment.
Key words: bioassessment, ecological status, reference condition approach, shallow lakes, Water Framework Di-
rective.
Introduction Profundal macroinvertebrates (PMI) are widely used in ecological assessment of lakes. The approach has a long history in limnological research as the earliest lake classification schemes were often based on
profundal chironomids indicative of trophic status (Brinkhurst 1974). These early works, like the more recent literature based on contemporary (e.g. Gerstmeier 1989, Kansanen et al. 1990, Bazzanti et al. 2012 (this issue), Pilotto et al. 2012 (this issue)) and paleolimnological (Meriläinen et al. 2003, Hynynen et al. 2004)
Authors’ addresses:
University of Jyväskylä, Department of Biological and Environmental Science, P. O. Box 35, FIN-40014 Jyväskylä University, Finland. 2 Finnish Environment Institute, Freshwater Centre, Monitoring and Assessment, P. O. Box 413, FI-90014 Oulu University, Finland. * Corresponding author;
[email protected] 1
© 2012 E. Schweizerbart’sche Verlagsbuchhandlung, Stuttgart, Germany DOI: 10.1127/1863-9135/2012/0205 eschweizerbart_XXX
www.schweizerbart.de 1863-9135/12/0205 $ 2.50
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data have documented predictable patterns in PMI communities. However, those works were primarily based on data from large and deep stratified lakes with distinctive pelagic and profundal zones, whereas small and shallow lakes have received much less attention despite their abundance in the boreal region (Simola & Arvola 2005). There is thus an obvious need to evaluate procedures for their PMI assessments, particularly given the demands set by modern water legislatives like the European Union Water Framework Directive (WFD), that emphasize lake status assessment predominantly by biological properties (European Commission 2000). The unique characteristics of shallow lakes, such as lack of stratification and the associated phenomena (Scheffer 1998) seem to generate heterogeneous and unstable profundal macroinvertebrate assemblages. For instance, Johnson (1998) measured high spatiotemporal variation in profundal assemblages of 16 small and shallow Swedish lakes. Similarly, Hämäläinen et al. (2003) found high inter-annual variation and low stability and persistence of PMI compared to sublittoral fauna in a shallow near-pristine lake in eastern Finland. For ecological assessment that uses the natural status as a baseline, the high community variation in both space and time is a major concern (Reynoldson et al. 1997, Bailey et al. 2004). Recent studies have indeed recognized difficulties in assessing the status of profundal fauna in small boreal lakes and therefore have suggested replacing or supplementing PMI by littoral macroinvertebrates (Alahuhta et al. 2009) or chironomid pupal exuviae (Raunio et al. 2007).
In this study we investigated the effect of lake depth on biological metrics indicative of the quality status of the PMI fauna. We compared the degree of natural variability of PMI assemblages and their sensitivity to detect human impact among groups of shallow, intermediate, deep and very deep boreal lakes. Multivariate analyses were used to examine the compositional differences between groups of minimally disturbed reference lakes and eutrophication impacted lakes. The status of PMIs was measured using three community metrics corresponding to WFD criteria.
Material and methods Data We used previously published data on macroinvertebrate communities and environmental variables from 255 Finnish lake basins (Fig. 1). The data are from multiple sources, including the HERTTA database of SYKE (Finnish Environment Institute), monitoring reports, theses and our own observations. Macroinvertebrates were collected once from the deepest point of each lake basin in September–October between 1989 and 2008 using standard methods (3–8 Ekman grab replicates, A = 250–300 cm2 replicate–1, 500 µm sieve). All macroinvertebrates were sorted quantitatively from the ethanol-preserved samples, identified to the lowest possible taxon and counted. Taxa that were mainly littoral and occurred in only one site were removed from the data (see Jyväsjärvi et al. 2009). We pooled the replicates to one sample for each basin and converted the counts to densities (ind. m– 2). Environmental data for each macroinvertebrate sampling site were compiled from the HERTTA database or determined by us (Table 1). The data consist of geographic (Altitude [metres above sea level, m.a.s.l.], Longitude and Latitude), water
Table 1. Mean values of geographic, morphometric and water chemistry variables for minimally disturbed reference sites (REF) and sites impacted by human activity (IMP) in each depth category.
Shallow Altitude (m.a.s.l.) Area (km2) Latitude Longitude Mean depth (m) Maximum depth (m) Colour (mg Pt L–1) TP (μg L–1) TN (μg L–1) Cond (μS L–1) Chl-a (μg L–1) Hypolimnetic temperature (°C) Dissolved oxygen (mg L–1)
Intermediate
Deep
Very deep
REF (N = 25)
IMP (N = 58)
REF (N = 30)
IMP (N = 30)
REF (N = 32)
IMP (N = 28)
REF (N = 27)
IMP (N = 25)
142.2 3.1 62.8 27.2 3.2 8.7 78.7 16.5 418.7 3.0 9.9 13.4 3.2
104.5 9.9 62.4 27.1 2.9 7.7 108.4 43.4 734.9 6.7 18.8 14.6 4.9
114.0 9.2 62.1 26.7 5.0 15.7 51.1 10.5 355.5 4.0 5.6 9.0 4.0
104.5 15.9 62.2 26.8 5.0 15.5 95.3 27.9 608.6 5.6 13.5 11.3 3.2
109.6 50.7 62.3 27.5 7.3 28.0 37.4 7.8 348.0 4.8 3.9 7.9 5.3
83.6 175.7 62.5 26.8 8.2 31.7 52.3 21.1 659.1 6.1 7.5 9.4 4.7
87.3 437.7 62.1 27.7 12.6 50.6 28.9 6.5 398.2 4.8 3.6 7.1 7.9
79.7 493.9 62.1 26.7 13.3 42.5 51.3 15.6 557.0 6.1 7.2 8.2 6.9
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Fig. 1. Location of the 255 study basins in Finland.
quality (Colour [mg Pt L–1], total phosphorus [TP], total nitrogen [TN], and chlorophyll-a [Chl-a] concentrations [µg L–1], conductivity [Cond, mS m–1], hypolimnetic dissolved oxygen concentration [DO, mg L–1], and water temperature [°C]), and morphometric (area [km2], mean depth [m], and sampling depth [m]) variables. Water colour, Chl-a TP, TN and Cond were measured from the epilimnion (–1 m from the lake surface) whereas DO and temperature were measured from hypolimnion (+1 m from the lake bottom) according to the Finnish standards. All water quality parameter values are based either on a single measurement or are mean values of two to four measurements from the summer stagnation period (August) of the year of macroinvertebrate sampling
depth, the primary natural environmental driver of PMI fauna (Jyväsjärvi et al. 2009), as the sole predictor to divide the REF basins objectively into biologically meaningful depth categories. We used a distance-based MRT with a ten-fold cross-validation (Jyväsjärvi et al. 2011) to divide REF sites into groups with minimised biological impurity using Bray-Curtis distances (Bray & Curtis 1957) calculated from the species abundance data as the response. MRT successfully divided REF data into four categories with a balanced number of REF sites: ‘shallow’ lakes with mean depth 4.3 – 5.8 m, ‘deep’ lakes with mean depth > 5.8 – 9.8 m and ‘very deep’ lakes (mean depth > 9.8 m) (Table 1; Fig. 2).
Assessment metrics
Grouping of the study sites We used 114 lake basins as reference (hereafter REF) sites with minimal human influence. The basins were identified by the personnel of the Finnish environmental administration according to the criteria suggested by the EU REFCOND guidance document (European Commission 2003) and using all available data on anthropogenic pressures. The remaining 141 sites subject to a range of anthropogenic disturbances, mainly nutrient enrichment, were assigned to impacted (IMP) sites’ group. To address our study question, we first constructed a multivariate regression tree (MRT, De’ath 2002) using lake mean
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The WFD annex V includes the following characteristics to be included into invertebrate assessment protocol in lakes: the deviation of i) the ‘ratio of disturbance-sensitive taxa to insensitive taxa’, ii) ‘level of diversity’ and iii) ‘taxonomic composition and abundance’ from undisturbed type-specific reference conditions (European Commission 2000). Based on preliminary evaluation of the various PMI assessment metrics (Tolonen et al. 2005) we selected three best performing metrics, each principally corresponding to one of the preceding structural features, to assess the status of PMI fauna and used the pooled macroinvertebrate samples in all calculations.
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Fig. 2. Multivariate regression tree (MRT) showing the mean depth threshold value for each node and the numbers of REF sites in
each terminal leaf.
First, to measure the ratio of disturbance sensitive taxa to insensitive taxa, we used Benthic Quality Index (BQI) (Wiederholm 1980), that has been commonly used in Europe (Gerst meier 1989, Kansanen et al. 1990, Johnson 1998, Verbruggen et al. 2011). The index is based on relative abundances of seven chironomid species or genera, which are scored by integers from 1 (eutrophic species) to 5 (oligotrophic) according to their perceived position along a nutrient richness gradient. We calculated BQI for each site as the abundance weighted average of taxon scores ki: BQI =
7
∑ i =0
ni × ki N
where ki is an integer from 1 (preference of eutrophy) to 5 (oligotrophy) for each indicator taxon i, ni is the numerical abundance of taxon i in a sample and N is the sum of ni. The included indicator taxa with their corresponding scores are: Chironomus plumosus L. (k = 1), Chironomus anthracinus (Zett.) (2), Sergentia coracina (Zett.) (3), Stictochironomus rosenschoeldi (Zett.) (3), Micropsectra spp. (4), Paracladopelma spp. (4) and Heterotrissocladius subpilosus (Kieffer) (5). Note that according to Wiederholm (1980), if none of the indicator species is found in a sample, the BQI value will be zero, and this indicates the worst or toxic conditions. However, as the index in this case is indefinite or may stem from sampling error (Jyväsjärvi et al. 2010), we excluded 31 basins with no indicator species from the analyses (11, 9, 2 and 9 in shallow, intermediate, deep and very deep lakes, respectively). Second, to measure level of diversity, we used the Shannon index (H') (Shannon & Weaver 1949) calculated for each site as: s n n H´= −∑ i log 2 i N N i =1 where ni is the number of individuals of species i in a sample, N is the total number of individuals in a sample and s is the total number of taxa in a sample.
Third, to measure the taxonomic composition and abundance, we used Percent Model Affinity (PMA; Novak & Bode 1992). PMA compares the observed taxonomic composition (relative abundances of taxa) in a site to the taxonomic composition of a reference (model) assemblage. We calculated PMA separately for each site as: PMA = 100 − 0.5∑ |ai − bi | i
where ai is the average percentage of individuals of a taxon i in samples from REF sites belonging to the corresponding depth category and bi is the observed percentage of individuals of the same taxon in a sample from study site belonging to the corresponding depth category. If a taxon was observed only in IMP sites, ai was set to 0. For each metric and site, we estimated the deviation from reference conditions as the ratio of observed (O) value to the expected (E) value (O/E; equivalent to Ecological Quality Ratio demanded by the WFD). For BQI, site-specific E values were derived with a regression model of Jyväsjärvi et al. (2010). For Shannon H' and PMA, E values were derived as depth category specific mean values among REF sites.
Data analyses To illustrate the natural variation of PMI assemblages we parameterized variability of species composition among REF sites using Detrended Correspondence Analysis (DCA; Hill & Gauch 1980). DCA is an ordination technique based on reciprocal averaging and it ordinates both the species and samples simultaneously. DCA was performed on the entire species data with log-transformed (log10(x +1)) abundance data without downweighting the rare taxa using the decorana function of the vegan package (Oksanen et al. 2010) for R program (R Development Core Team 2008). To relate natural compositional variability to environmental gradients, we calculated correlations between environmental variables and axis scores.
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Fig. 3. Two-dimensional DCA ordination plots displaying both 114 REF sites and 88 taxa. Direction and length of the arrows in
left panel denote the correlations between assemblage and environmental variables (only those with p value
