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Bioindication in Norwegian rivers using non-diatomaceous benthic algae: The acidification index periphyton (AIP)
Ecological Indicators 9 (2009) 1206–1211
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Bioindication in Norwegian rivers using non-diatomaceous benthic algae: The acidification index periphyton (AIP) S. Schneider *, E.-A. Lindstrøm Norwegian Institute of Water Research, Gaustadalleen 21, 0349 Oslo, Norway
A R T I C L E I N F O
A B S T R A C T
Article history: Received 21 April 2008 Received in revised form 23 February 2009 Accepted 27 February 2009
Impacts of acidification on aquatic communities have been obvious in Scandinavia since the end of the 19th century and recent model simulations show that in Norway, acidification of surface waters will continue to be an issue in the coming decades. Here, we present a new index based on non-diatomaceous benthic algae (acidification index periphyton, AIP) that can be used to describe the mean annual acidity of Norwegian rivers. The AIP was applied to 608 samples from unlimed rivers all over Norway and values ranged from 5.35 to 7.28, thus covering a range from acid to neutral conditions. Application of the AIP to both limed sites and sites that formerly were acidified demonstrate that the algal community reacts with a several years delay to both river liming and natural recovery. The AIP is most sensitive between mean annual pH values of approximately 5.5 and 7.0 and can be especially useful in detecting the first signs of an acidification trend or the last steps of a recovery process. ß 2009 Elsevier Ltd. All rights reserved.
Keywords: Phytobenthos River Liming pH Acidification Index Norway
1. Introduction Acidification of rivers and lakes caused by sulfur and nitrogen emissions has a long history in Scandinavia (Henriksen, 1979; Hesthagen and Jonsson, 2002). A decline of salmon due to river acidification was in Norway first observed at the end of the 19th century, and emissions of acidifying gases in Europe peaked in the 1970s (Wright et al., 2005). In the early 1990s, Scandinavian lakes showed the first signs of recovery in response to lower levels of acid deposition, manifested by an increase in acid neutralizing capacity (ANC) and pH, and a decrease in inorganic Al3+ ion concentrations (Skjelkvale et al., 2007). While model simulations today predict that recovery will continue, they also predict that a small but significant deposition of sulfur and nitrogen will remain, even after 2010 (Wright et al., 2005). Particularly in the extremely sensitive areas of southern Norway, acid precipitation will continue to exceed the critical load of many surface waters (Wright et al., 2005). In addition, local acidification from e.g. nickel smelters still exists in Norway, though the SO2 emissions of the smelters have declined to approximately one third of the maximum level in the late 1970s (Lappalainen et al., 2007). Due to this continued, albeit reduced, acid deposition,
* Corresponding author. E-mail address: [email protected] (S. Schneider). 1470-160X/$ – see front matter ß 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.ecolind.2009.02.008
surface water acidification will remain an issue in Norway for the coming decades. As a measure to counteract the loss in Atlantic salmon stocks, many of the important salmon rivers in southern and southwestern Norway are limed, beginning in the 1980s or 1990s, leading to a recovery of both the salmon (Hesthagen and Larsen, 2003) and macroinvertebrate populations (Raddum and Fjellheim, 2003). The liming strategy primarily involves continuous liming with limestone powder from dispensers which are controlled by water flow, maintaining a pH of about 6.2–6.4 in spring and 6.0–6.2 during the rest of the year (Hesthagen and Larsen, 2003). Acidification is one of many factors that impact water fauna and flora. Chemical recovery, manifested by ANC, pH, and Al3+ ion concentrations, is an essential precondition for biological recovery. However, the biotic response to chemical recovery involves a multitude of chemical, physical, and biological interactions leading to e.g. lagged or threshold responses and thus is no straightforward consequence of changing water chemistry (Monteith et al., 2005). The focus on biological recovery is relevant within the context of the EU Water Framework Directive, where a biological monitoring system is needed that detects differences in fauna and flora compared to undisturbed reference conditions (EC, 2000). In other countries, benthic diatoms have successfully been used to infer surface water pH (Weckstro¨m et al., 1997; Kova´cs et al., 2006) and to monitor the response to reduced levels of
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acid deposition (Burns et al., 2008). In contrast to e.g. macroinvertebrates, which in Norway are often used for biomonitoring of acidification (Lien et al., 1991) grow benthic algae stationary at a river bottom and cannot escape short time acidification events by drifting with the current. Benthic algae are thus impacted in a different way by fluctuations in pH than macroinvertebrates. Here we present a new indicator system based on non-diatomaceous benthic algae to characterize mean annual river acidity in Norway.
2. Materials and methods In order to establish a quantitative relationship between algal communities and mean annual pH, 608 samples from 328 unlimed river sites throughout Norway were collected. 25 randomly picked samples were used for index validation, and species indicator values were calculated on a basis of 583 samples. In order to analyze the impact of river liming on the benthic algal community, 109 samples from 25 limed river sites were collected and analyzed together with 20 samples of the dataset of unlimed sites which were taken 1 year before the liming started. All samples were collected in the context of numerous projects between 1976 and 2006 and are stored within the periphyton database of the Norwegian Institute of Water Research (NIVA). Water chemistry samples were taken at the sampling sites between one and 24 times per year and the results are stored in the NIVA database. Site-specific, mean-annual water chemistry data for the 1 year previous to the benthic algae sampling were used to calculate indicator values and develop an acidification model. Macroscopic algae were surveyed according to the established method in Norway (Lindstrøm et al., 2004) along an approximately 10 m length of river bottom using an aquascope. At each river site, visible benthic algae were collected and stored separately in vials. Microscopic algae were collected from ten stones, with diameters ranging between 10 and 20 cm, taken from each sampling site. An area of about 8 8 cm from the upper side of each stone was brushed with a toothbrush to transfer the algae into a beaker containing approximately 1 l of river water and a subsample was taken. All samples were preserved with a few drops of formaldehyde. The preserved benthic algae samples were later examined under a microscope, and determined to species level, if possible. Presence of all benthic algae, i.e. algae that live attached to the river bottom or in close contact on or within patches of attached aquatic plants, was noted. Diatoms are not included, since their exact determination requires specific preparation procedures, such that in Norway not enough data on diatom species composition from river sites exist. In the present investigation, we calculated average pH-optima of 108 periphyton taxa that occurred at least twice in our dataset. This method was chosen because it is the adaptation of the weighted averaging method (Ter Braak and van Dam, 1989) to a dataset with presence-absence data, and weighted averaging usually is the most accurate method for quantifying species responses to environmental parameters (Ponader et al., 2007 and literature cited therein). Each indicator taxon occurred on average in 49 samples, individual values ranging between 2 and 223 occurrences. The indicator values (IV) represent the average pH at the sites where the species occur, and range from 5.13 for Batrachospermum keratophytum to 7.50 for Cladophora glomerata (Table 1). Taxa whose pH optima were broad, such as Mougeotia a, Mougeotiopsis calospora, and Oedogonium a were not included in the AIP, nor were incomprehensible taxa like ‘‘bright green Chaetophorales’’.
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Table 1 Indicator values (IV) for calculation of the AIP (acidification index periphyton). The genera Mougeotia, Oedogonium, Spirogyra, Zygnema, and Zygogonium can often only be determined to species level when they are taken into culture. In monitoring projects, there is often neither time nor money for culturing, thus these genera are usually subdivided by cell width, number and spiraling density of chloroplasts, form of end wall, and cell length/width ratio. m = mm; 1K = one chloroplast, 2K = two chloroplasts, 3K = three and more chloroplasts or chloroplasts very densely spiraled, L = lenticular end wall, R = reticulate end wall, l/b = cell length/width ratio. Taxon
IV
Cyanophyceae Calothrix braunii Calothrix fusca Calothrix gypsophila Calothrix ramenskii Capsosira brebissonii Chamaesiphon amethystinum Chamaesiphon confervicola Chamaesiphon fuscus Chamaesiphon incrustans Chamaesiphon minutus Chamaesiphon polymorphus Chamaesiphon rostafinskii Chamaesiphon subglobosus Chlorogloea microcystoides Clastidium rivulare Clastidium setigerum Coleodesmium sagarmathae Cyanophanon mirabile Gloeocapsopsis magma Hapalosiphon fontinalis Hapalosiphon hibernicus Heteroleibleinia kuetzingii Heteroleibleinia leptonema Homoeothrix batrachospermorum Homoeothrixjanthina Homoeothrix varians Hydrococcus rivularis Merismopedia glauca Merismopedia punctata Merismopedia tenuissima Nostoc parmeloides Nostoc sphaericum Nostoc verrucosum Oscillatoria amoena Phormidium autumnale Phormidium heteropolare Phormidium irriguum Phormidium limosum Phormidium nigrum Rhabdoderma lineare Rivularia biasolettiana Rivularia haematites Schizothrix lacustris Schizothrix lateritia Scytonema mirabile Scytonematopsis starmachii Siphonema polonicum Stigonema hormoides Stigonema mamillosum Stigonema minutum Stigonema multipartitum Stigonema ocellatum Stigonema ocellatum var. globosporum Tolypothrix distorta Tolypothrix penicillata Tolypothrix saviezii
6.90 6.98 7.18 7.18 5.19 6.97 7.05 6.91 7.33 6.79 7.02 6.45 6.52 6.89 7.01 7.09 6.26 6.71 5.71 5.25 5.25 7.17 7.03 7.18 7.12 6.94 6.97 5.42 5.86 5.76 7.22 7.31 7.25 6.86 7.17 6.80 7.38 7.10 7.09 5.28 7.20 7.03 6.36 6.86 5.65 5.48 5.30 5.19 6.25 5.46 5.25 5.38 5.47 7.17 6.97 7.31
Chrysophyceae Hydrurus foetidus
6.92
Phaeophyceae Heribaudiella fluviatilis
7.34
Rhodophyceae Audouinella hermannii Batrachospermum gelatinosum Batrachospermum keratophytum Lemanea condensata Lemanea fluviatilis
7.05 7.12 5.13 6.52 7.11
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Table 1 (Continued ) Taxon
IV
Lemanea fucina Chlorophyceae Aphanochaete repens Binuclearia tectorum Bulbochaete spp. Chaetophora elegans Cladophora glomerata Closterium tumidulum Coleochaete scutata Cosmarium reniforme Draparnaldia glomerata Klebsormidium montanum Klebsormidium rivulare Microspora abbreviata Microspora amoena Microspora loefgrenii (18–23 mm) Microspora palustris Microspora palustris var minor Mougeotiaa/b (10–18 mm) Mougeotia d (25–30 mm) Mougeotia e (30–40 mm) Oedogoniumb (13–18 mm) Oedogonium c (23–28 mm) Oedogonium d (29–32 mm) Oedogonium e (35–43 mm) Penium spp. Protoderma viride Schizochlamys gelitanosa Spirogyraa (20–42 m, lK, L) Spirogyra bl (16–20 mm, lK, L, l/b: 2–3) Spirogyra c1 (34–49 mm, 3K, L, l/b > 3, svart) Spirogyra lapponica (26 mm, lK, L, svart) Spirogyra majuscula Spirogyra sp1 (ll–20 mm, lK, R) Spirogyra sp2 (30–38 mm, 2K, R) Spondylosium planum Stigeoclonium tenue Teilingia excavata Teilingia granulata Tetraspora cylindrica Tetraspora gelatinosa Ulothrix subtilis Ulothrix zonata Zygnemab(22–25 mm) Zygnema c (30–40 mm) Zygogonium sp3 (16–20 mm)
6.85 7.14 5.57 6.43 7.36 7.50 6.55 7.14 7.28 7.09 5.56 6.02 6.50 7.18 5.57 5.60 5.66 5.57 6.98 7.16 6.92 7.09 7.27 7.27 5.65 6.73 6.48 7.01 7.21 7.23 7.07 7.34 7.03 7.22 7.15 7.19 6.45 7.02 7.38 7.18 7.19 7.26 6.99 7.04 5.40
Fig. 1. Correlation between AIP (acidification index periphyton) and mean annual pH within 1 year previous to sampling of benthic algae for 608 samples from unlimed rivers all over Norway (grey dots). The black rhombi denote 25 independent samples. The grey triangles denote six samples from Hedmark county collected in 2006 and four samples from Møre og Romsdal county collected in 2005. All samples were taken between July and November and at least three indicator taxa were present at each sampling site.
If one of the requirements is not met, the AIP must be denoted as ‘‘inconclusive’’. 4. Results 4.1. AIP model The AIP was applied to the 608 samples from unlimed rivers all over Norway (including 25 independent samples) and values ranged from 5.35 to 7.28 (Fig. 1). A nonlinear regression was applied to produce a fitting line between the AIP and the mean annual pH for the 1 year preceding benthic algae sampling, and 88% of the variability in AIP was explained by pH (R2 = 0.88). The AIP or mean pH can be obtained from the following inverted equations: 1:548 AIP ¼ 5:596 þ 1 þ expð16:923 2:753 pHÞ pH ¼
3. The acidification index periphyton (AIP) The AIP (acidification index periphyton), which can be used to estimate the average annual pH at a river site, is calculated as follows: Pn IV AIP ¼ i¼1 i ni AIP: acidification index periphyton; IVi: indicator value of species i (see Table 1); ni: number of indicator species. According to our present experience, the following criteria need to be met in order to reliably indicate the pH of a sampling site: the sample must be taken in summer or autumn (i.e. between July and November); a minimum of three indicative species (i.e. that have an AIP value according to Table 1) must be present at the sampling site; up to now, a reliable calculation of the AIP can only be carried out in Norway as no suitable data are available that would allow trials of the AIP from elsewhere as yet. In principle, an extension of the AIP to permit an implementation in other countries is possible and desirable.
1:548 16:923 lnðAIP5:596 1Þ 2:753
where: AIP: acidification index periphyton; pH: mean pH at the sampling site during 1 year before sampling; ln: natural logarithm; exp: exponent The AIP model was developed based on sites with a pH between approximately 4.5 and 8.5. Due to averaging, the range of the nonlinear regression model is shrunken to between 5.6 and 7.1. As a consequence, a valid calculation of pH from the AIP can only be done for sites with an AIP value between 5.6 and 7.1. 25 sites of our data set were not used for calculating taxon specific weighted averages and thus can be regarded as independent samples. All independent samples lie within the variability of the other samples and thus support the general applicability of the AIP in Norway (Fig. 1). The most recent samples in our dataset are from 2005 and 2006. Most of these samples lie on the right hand side of the regression model, indicating that the mean annual pH is slightly higher than was indicated by the AIP (Fig. 1). 4.2. Limed samples The AIP was applied to 109 samples from limed rivers in southern Norway and values ranged from 5.35 to 7.16 (Fig. 2). A
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Fig. 2. Nonlinear regression model for 109 samples from limed rivers and 20 samples from the same localities 1 year before the liming started. Unlimed samples were included for comparison.
nonlinear regression model was applied to produce a fitting line, and 34% of the variability in AIP of limed sites was explained by mean annual pH (R2 = 0.34). Since the 20 acidified sites 1 year before the liming started are a subsample of all heavily acidified sites, we assumed that the lower asymptote of the model for the limed sites must not differ from the one which was derived for the unlimed sites. In addition, we can think of no reason why the upper asymptote in AIP of limed sites should differ from the one derived from unlimed sites. Thus the model produced for the limed sites was constrained to the same asymptotes as the model for the unlimed sites, and follows the equation: 1:548 AIPlimed ¼ 5:596 þ 1 þ expð12:492 1:862 pHÞ AIPlimed: acidification index periphyton (limed samples); pH: mean pH at the sampling site during 1 year before sampling; exp: exponent At limed sites, the AIP tends to underestimate pH. In addition, AIP values at limed sites are more scattered around the correlation line than at unlimed sites (R2 of limed sites is 0.34 compared to 0.88 at unlimed sites). 5. Discussion 5.1. AIP at unlimed sites Desirable features in a bioindication tool are accuracy, broad applicability, and relatively straightforward use. To keep the AIP as simple as possible, it is based on presence-absence data. Utilization of semi-quantitative periphyton data provided no better correlation between the AIP and pH (unpublished data), likely due to the high variability of discharge that is a typical feature of most Norwegian rivers (Otnes and Ræstad, 1978). Floods can reduce the biomass of algae significantly, but have less impact on species composition (Pfister, 1993). We therefore expect the abundance of benthic algae to be mainly influenced by hydrologic conditions, while the species composition mainly reflects the water quality. Presence–absence data will probably be only marginally altered after floods and therefore be less dependent on recent hydrologic conditions, reflecting water quality, as is the intention of the AIP. The algal flora in Norwegian rivers is typically best developed by autumn (Lindstrøm et al., 2004). Therefore, benthic algae samples taken between December and June were excluded from the analyses. Additional exclusion of July samples from the dataset
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was found not to improve the correlation between AIP and mean annual pH (unpublished results). For bioindication of acidity in rivers in Norway, benthic algae sampling should therefore take place between July and November. The correlation between AIP and pH includes a circular argument since the same data set was used for calculating species indicator values. Data from 25 sites, however, were not used for calculating species indicator values. All independent samples lie within the variability of the other samples and thus support our assumption that the AIP is generally applicable in rivers in Norway (Fig. 1). Our dataset included different river types with respect to calcium and total organic carbon concentration from all over Norway. Therefore, according to our present knowledge, the AIP is applicable to all river types in Norway. The data used for calculating taxon specific indicator values where taken between 1976 and 2006 and thus include the most severe acidification phase of surface waters in Norway (Wright et al., 2005). The most recent data in our data set are data from Hedmark and Møre og Romsdal counties that were taken in 2005 and 2006 and thus are from a period where surface waters in Norway were in a process of recovery from the most severe acidification. All other data in our data set are from 2003 or earlier. Hedmark county received substantial acid precipitation in the 1980s and in parts also in the 1990s (Henriksen, 2002). In contrast, since 1985 the sites in Møre og Romsdal received very little acid precipitation that did not exceed the critical load (Henriksen, 2002). However, the critical load concept applied in Norway is based on damage to fish and macroinvertebrate populations (Lien et al., 1995; Henriksen and Posch, 2001). The dataset used by Lien et al. (1995) for detecting critical loads showed that both fish and macroinvertebrate damage largely occurred at a pH below 5.5. At a pH of 5.5, the AIP is already close to its lower asymptote (Fig. 1) and the benthic algae consist of a clearly acidic community. Thus acid deposition which does not exceed the critical load for fish and macroinvertebrates might nevertheless have an impact on benthic algae. Long-term monitoring data taken in Møre og Romsdal county show that both ANC and pH have slightly but continuously increased in surface waters since 1980 (SFT, 2007). We therefore conclude that the data from both Hedmark and Møre og Romsdal counties reflect a process of recovery from acidification. This is consistent with the fact that most of these samples lie on the right hand side of the regression model, indicating that the mean annual pH is slightly higher than was indicated by the AIP (Fig. 1). Since organisms need time to recolonize habitats, a lag phase is expected to occur in biological indicators that can take up to several decades (Snucins, 2003). River pH in Norway typically undergoes annual fluctuations, often with a minimum in spring due to snowmelt (DN, 2006). The mean annual pH at a sampling site, which was calculated from between one to 24 single measurements, therefore includes some uncertainty, which is reflected in the unexplained variability of the regression model. In addition, minimum pH or mean summer pH might play an even more important role for benthic algae than the mean annual pH. Unfortunately, the nature of the chemistry database used prohibits testing of such hypotheses. The Raddum index, which is based on macroinvertebrates (Lien et al., 1991), exists in two forms called ‘‘I’’ and ‘‘II’’ and is often used in Norway for bioindication of acidification in surface waters. The Raddum index I, which is based on presence–absence data of macroinvertebrate indicator taxa, indicates first signs of acidification at a pH below 5.5 (Lien et al., 1991). The relative abundance of sensitive stoneflies to the total abundance of stoneflies, which is used in the Raddum index II, indicates a drop of pH below 6 (Raddum, 1999). In contrast, the AIP presented here is most sensitive at a pH between 5.5 and 7 (see Fig. 1). We therefore conclude that the macroinvertebrate-based Raddum index in its
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two forms I and II is most useful in differentiating more severe acidification below a pH of 6, while the algal-based AIP is more useful in differentiating above a pH of 5.5. A combination of both indices might therefore be especially interesting for investigating acidification and recovery processes over time. Benthic algae should indicate the first signs of an acidification trend where the Raddum index has not yet reacted. During recovery from heavy acidification, however, the macroinvertebrates should be the first to respond, while the benthic algae can indicate the last steps back to pre-acidification conditions. The changes in individual, population, and community levels of fish and macroinvertebrates during acidification are mainly caused by elevated hydrogen, aluminum and cadmium concentrations (Herrmann et al., 1993; Herrmann, 2001). The factors that most affect benthic algae during acidification are less well known, though both pH and aluminum might play a role in the acidification effects to benthic algae (Genter and Amyot, 1994) as well as a decrease in grazing pressure (Planas, 1996). It is known, however, that different species of benthic algae have different abilities to use HCO3 as an inorganic carbon source for photosynthesis (Raven, 1992) and it is assumed that the bicarbonate loss and reduced free CO2 in acidic lentic waters influences benthic algae species composition (Planas, 1996). The portion of the non-linear model containing the inflection point, between pH 7 and 5.5, in the sigmoidal response of the AIP to mean pH coincides with a switch in the carbonic acid equilibrium from HCO3 to CO2 being the dominating carbon fraction (Kohl and Nicklisch, 1988). We therefore assume that inorganic carbon acquisition plays an important role in the switch between a neutral/alkaline and acidic benthic algal community that is reflected in the AIP. 5.2. Limed sites At limed sites, pH explained less of the variability in AIP than at unlimed sites (R2 of 0.34 for limed sites compared to 0.88 for unlimed sites). Thus we conclude that river liming can lead to a recovery of a benthic algal community, but that the reaction is less predictable than during natural recovery. In addition to the mean annual pH value, the response of the benthic algal community to river liming may be influenced by e.g. the time since liming started, episodic acidification not detected by the chemical monitoring that occurred during liming plant malfunctions, distance to the liming plant, or the distance to the nearest unacidified refuges from which acid sensitive species can recolonize the limed sites. Most limed samples have a lower AIP at a given pH than unlimed samples (Fig. 2), confirming the suggestion that a delayed reaction of the periphytic community towards an increase of pH by liming occurs. Since the objective of river liming in Norway is mainly to obtain water quality suitable for reproduction of salmon and other fish, the target is often set to a pH between 6.0 and 6.4 (DN, 2006). At this pH, the negative effects to fish (Sandøy, 2002) and macroinvertebrates (Fjellheim and Raddum, 2001) are supposed to be negligible. Since few data for limed sites with a pH above 6.6 exist, the model for limed sites is only reliable for a pH below 6.6. 6. Conclusions River benthic algae are a useful tool to detect processes of river acidification and recovery between a pH of approximately 5.5 and 7. Both natural recovery and liming have an impact on the benthic algal community, though the impact of river liming is less predictable than natural recovery. The algal community reacts with a delay of several years to both natural recovery and river liming.
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S. Schneider, E.-A. Lindstrøm / Ecological Indicators 9 (2009) 1206–1211 SFT (Statens forurensningstilsyn), 2007. Overva˚king av langtransportert forurenset luft og nedbør. A˚rsrapport – effekter 2006. SPFO rapport 1000/2007. Skjelkvale, B.L., Borg, H., Hindar, A., Wilander, A., 2007. Large scale patterns of chemical recovery in lakes in Norway and Sweden: importance of seasalt episodes and changes in dissolved organic carbon. Applied Geochemistry 22 (6), 1174–1180. Snucins, E., 2003. Recolonization of acid-damaged lakes by the benthic invertebrates Stenacron interpunctatum. Stenonema femoratum and Hyalella azteca. Ambio 32 (3), 225–229.
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Ter Braak, C.J., van Dam, H., 1989. Inferring pH from diatoms: a comparison of old and new calibration methods. Hydrobiologia 178, 209–223. Weckstro¨m, J., Korhola, A., Blom, T., 1997. Diatoms as quantitative indicators of pH and water temperature in subarctic Fennoscandian lakes. Hydrobiologia 347, 171–184. Wright, R.F., Larssen, T., Camarero, L., Cosby, B.J., Ferrier, R.C., Helliwell, R., Forsius, M., Jenkins, A., Kopacek, J., Majer, V., Moldan, F., Posch, M., Rogora, M., Schopp, W., 2005. Recovery of acidified European surface waters. Environmental Science & Technology 39 (3), 64A–72A.
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Ecological Indicators journal homepage: www.elsevier.com/locate/ecolind
Bioindication in Norwegian rivers using non-diatomaceous benthic algae: The acidification index periphyton (AIP) S. Schneider *, E.-A. Lindstrøm Norwegian Institute of Water Research, Gaustadalleen 21, 0349 Oslo, Norway
A R T I C L E I N F O
A B S T R A C T
Article history: Received 21 April 2008 Received in revised form 23 February 2009 Accepted 27 February 2009
Impacts of acidification on aquatic communities have been obvious in Scandinavia since the end of the 19th century and recent model simulations show that in Norway, acidification of surface waters will continue to be an issue in the coming decades. Here, we present a new index based on non-diatomaceous benthic algae (acidification index periphyton, AIP) that can be used to describe the mean annual acidity of Norwegian rivers. The AIP was applied to 608 samples from unlimed rivers all over Norway and values ranged from 5.35 to 7.28, thus covering a range from acid to neutral conditions. Application of the AIP to both limed sites and sites that formerly were acidified demonstrate that the algal community reacts with a several years delay to both river liming and natural recovery. The AIP is most sensitive between mean annual pH values of approximately 5.5 and 7.0 and can be especially useful in detecting the first signs of an acidification trend or the last steps of a recovery process. ß 2009 Elsevier Ltd. All rights reserved.
Keywords: Phytobenthos River Liming pH Acidification Index Norway
1. Introduction Acidification of rivers and lakes caused by sulfur and nitrogen emissions has a long history in Scandinavia (Henriksen, 1979; Hesthagen and Jonsson, 2002). A decline of salmon due to river acidification was in Norway first observed at the end of the 19th century, and emissions of acidifying gases in Europe peaked in the 1970s (Wright et al., 2005). In the early 1990s, Scandinavian lakes showed the first signs of recovery in response to lower levels of acid deposition, manifested by an increase in acid neutralizing capacity (ANC) and pH, and a decrease in inorganic Al3+ ion concentrations (Skjelkvale et al., 2007). While model simulations today predict that recovery will continue, they also predict that a small but significant deposition of sulfur and nitrogen will remain, even after 2010 (Wright et al., 2005). Particularly in the extremely sensitive areas of southern Norway, acid precipitation will continue to exceed the critical load of many surface waters (Wright et al., 2005). In addition, local acidification from e.g. nickel smelters still exists in Norway, though the SO2 emissions of the smelters have declined to approximately one third of the maximum level in the late 1970s (Lappalainen et al., 2007). Due to this continued, albeit reduced, acid deposition,
* Corresponding author. E-mail address: [email protected] (S. Schneider). 1470-160X/$ – see front matter ß 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.ecolind.2009.02.008
surface water acidification will remain an issue in Norway for the coming decades. As a measure to counteract the loss in Atlantic salmon stocks, many of the important salmon rivers in southern and southwestern Norway are limed, beginning in the 1980s or 1990s, leading to a recovery of both the salmon (Hesthagen and Larsen, 2003) and macroinvertebrate populations (Raddum and Fjellheim, 2003). The liming strategy primarily involves continuous liming with limestone powder from dispensers which are controlled by water flow, maintaining a pH of about 6.2–6.4 in spring and 6.0–6.2 during the rest of the year (Hesthagen and Larsen, 2003). Acidification is one of many factors that impact water fauna and flora. Chemical recovery, manifested by ANC, pH, and Al3+ ion concentrations, is an essential precondition for biological recovery. However, the biotic response to chemical recovery involves a multitude of chemical, physical, and biological interactions leading to e.g. lagged or threshold responses and thus is no straightforward consequence of changing water chemistry (Monteith et al., 2005). The focus on biological recovery is relevant within the context of the EU Water Framework Directive, where a biological monitoring system is needed that detects differences in fauna and flora compared to undisturbed reference conditions (EC, 2000). In other countries, benthic diatoms have successfully been used to infer surface water pH (Weckstro¨m et al., 1997; Kova´cs et al., 2006) and to monitor the response to reduced levels of
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acid deposition (Burns et al., 2008). In contrast to e.g. macroinvertebrates, which in Norway are often used for biomonitoring of acidification (Lien et al., 1991) grow benthic algae stationary at a river bottom and cannot escape short time acidification events by drifting with the current. Benthic algae are thus impacted in a different way by fluctuations in pH than macroinvertebrates. Here we present a new indicator system based on non-diatomaceous benthic algae to characterize mean annual river acidity in Norway.
2. Materials and methods In order to establish a quantitative relationship between algal communities and mean annual pH, 608 samples from 328 unlimed river sites throughout Norway were collected. 25 randomly picked samples were used for index validation, and species indicator values were calculated on a basis of 583 samples. In order to analyze the impact of river liming on the benthic algal community, 109 samples from 25 limed river sites were collected and analyzed together with 20 samples of the dataset of unlimed sites which were taken 1 year before the liming started. All samples were collected in the context of numerous projects between 1976 and 2006 and are stored within the periphyton database of the Norwegian Institute of Water Research (NIVA). Water chemistry samples were taken at the sampling sites between one and 24 times per year and the results are stored in the NIVA database. Site-specific, mean-annual water chemistry data for the 1 year previous to the benthic algae sampling were used to calculate indicator values and develop an acidification model. Macroscopic algae were surveyed according to the established method in Norway (Lindstrøm et al., 2004) along an approximately 10 m length of river bottom using an aquascope. At each river site, visible benthic algae were collected and stored separately in vials. Microscopic algae were collected from ten stones, with diameters ranging between 10 and 20 cm, taken from each sampling site. An area of about 8 8 cm from the upper side of each stone was brushed with a toothbrush to transfer the algae into a beaker containing approximately 1 l of river water and a subsample was taken. All samples were preserved with a few drops of formaldehyde. The preserved benthic algae samples were later examined under a microscope, and determined to species level, if possible. Presence of all benthic algae, i.e. algae that live attached to the river bottom or in close contact on or within patches of attached aquatic plants, was noted. Diatoms are not included, since their exact determination requires specific preparation procedures, such that in Norway not enough data on diatom species composition from river sites exist. In the present investigation, we calculated average pH-optima of 108 periphyton taxa that occurred at least twice in our dataset. This method was chosen because it is the adaptation of the weighted averaging method (Ter Braak and van Dam, 1989) to a dataset with presence-absence data, and weighted averaging usually is the most accurate method for quantifying species responses to environmental parameters (Ponader et al., 2007 and literature cited therein). Each indicator taxon occurred on average in 49 samples, individual values ranging between 2 and 223 occurrences. The indicator values (IV) represent the average pH at the sites where the species occur, and range from 5.13 for Batrachospermum keratophytum to 7.50 for Cladophora glomerata (Table 1). Taxa whose pH optima were broad, such as Mougeotia a, Mougeotiopsis calospora, and Oedogonium a were not included in the AIP, nor were incomprehensible taxa like ‘‘bright green Chaetophorales’’.
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Table 1 Indicator values (IV) for calculation of the AIP (acidification index periphyton). The genera Mougeotia, Oedogonium, Spirogyra, Zygnema, and Zygogonium can often only be determined to species level when they are taken into culture. In monitoring projects, there is often neither time nor money for culturing, thus these genera are usually subdivided by cell width, number and spiraling density of chloroplasts, form of end wall, and cell length/width ratio. m = mm; 1K = one chloroplast, 2K = two chloroplasts, 3K = three and more chloroplasts or chloroplasts very densely spiraled, L = lenticular end wall, R = reticulate end wall, l/b = cell length/width ratio. Taxon
IV
Cyanophyceae Calothrix braunii Calothrix fusca Calothrix gypsophila Calothrix ramenskii Capsosira brebissonii Chamaesiphon amethystinum Chamaesiphon confervicola Chamaesiphon fuscus Chamaesiphon incrustans Chamaesiphon minutus Chamaesiphon polymorphus Chamaesiphon rostafinskii Chamaesiphon subglobosus Chlorogloea microcystoides Clastidium rivulare Clastidium setigerum Coleodesmium sagarmathae Cyanophanon mirabile Gloeocapsopsis magma Hapalosiphon fontinalis Hapalosiphon hibernicus Heteroleibleinia kuetzingii Heteroleibleinia leptonema Homoeothrix batrachospermorum Homoeothrixjanthina Homoeothrix varians Hydrococcus rivularis Merismopedia glauca Merismopedia punctata Merismopedia tenuissima Nostoc parmeloides Nostoc sphaericum Nostoc verrucosum Oscillatoria amoena Phormidium autumnale Phormidium heteropolare Phormidium irriguum Phormidium limosum Phormidium nigrum Rhabdoderma lineare Rivularia biasolettiana Rivularia haematites Schizothrix lacustris Schizothrix lateritia Scytonema mirabile Scytonematopsis starmachii Siphonema polonicum Stigonema hormoides Stigonema mamillosum Stigonema minutum Stigonema multipartitum Stigonema ocellatum Stigonema ocellatum var. globosporum Tolypothrix distorta Tolypothrix penicillata Tolypothrix saviezii
6.90 6.98 7.18 7.18 5.19 6.97 7.05 6.91 7.33 6.79 7.02 6.45 6.52 6.89 7.01 7.09 6.26 6.71 5.71 5.25 5.25 7.17 7.03 7.18 7.12 6.94 6.97 5.42 5.86 5.76 7.22 7.31 7.25 6.86 7.17 6.80 7.38 7.10 7.09 5.28 7.20 7.03 6.36 6.86 5.65 5.48 5.30 5.19 6.25 5.46 5.25 5.38 5.47 7.17 6.97 7.31
Chrysophyceae Hydrurus foetidus
6.92
Phaeophyceae Heribaudiella fluviatilis
7.34
Rhodophyceae Audouinella hermannii Batrachospermum gelatinosum Batrachospermum keratophytum Lemanea condensata Lemanea fluviatilis
7.05 7.12 5.13 6.52 7.11
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Table 1 (Continued ) Taxon
IV
Lemanea fucina Chlorophyceae Aphanochaete repens Binuclearia tectorum Bulbochaete spp. Chaetophora elegans Cladophora glomerata Closterium tumidulum Coleochaete scutata Cosmarium reniforme Draparnaldia glomerata Klebsormidium montanum Klebsormidium rivulare Microspora abbreviata Microspora amoena Microspora loefgrenii (18–23 mm) Microspora palustris Microspora palustris var minor Mougeotiaa/b (10–18 mm) Mougeotia d (25–30 mm) Mougeotia e (30–40 mm) Oedogoniumb (13–18 mm) Oedogonium c (23–28 mm) Oedogonium d (29–32 mm) Oedogonium e (35–43 mm) Penium spp. Protoderma viride Schizochlamys gelitanosa Spirogyraa (20–42 m, lK, L) Spirogyra bl (16–20 mm, lK, L, l/b: 2–3) Spirogyra c1 (34–49 mm, 3K, L, l/b > 3, svart) Spirogyra lapponica (26 mm, lK, L, svart) Spirogyra majuscula Spirogyra sp1 (ll–20 mm, lK, R) Spirogyra sp2 (30–38 mm, 2K, R) Spondylosium planum Stigeoclonium tenue Teilingia excavata Teilingia granulata Tetraspora cylindrica Tetraspora gelatinosa Ulothrix subtilis Ulothrix zonata Zygnemab(22–25 mm) Zygnema c (30–40 mm) Zygogonium sp3 (16–20 mm)
6.85 7.14 5.57 6.43 7.36 7.50 6.55 7.14 7.28 7.09 5.56 6.02 6.50 7.18 5.57 5.60 5.66 5.57 6.98 7.16 6.92 7.09 7.27 7.27 5.65 6.73 6.48 7.01 7.21 7.23 7.07 7.34 7.03 7.22 7.15 7.19 6.45 7.02 7.38 7.18 7.19 7.26 6.99 7.04 5.40
Fig. 1. Correlation between AIP (acidification index periphyton) and mean annual pH within 1 year previous to sampling of benthic algae for 608 samples from unlimed rivers all over Norway (grey dots). The black rhombi denote 25 independent samples. The grey triangles denote six samples from Hedmark county collected in 2006 and four samples from Møre og Romsdal county collected in 2005. All samples were taken between July and November and at least three indicator taxa were present at each sampling site.
If one of the requirements is not met, the AIP must be denoted as ‘‘inconclusive’’. 4. Results 4.1. AIP model The AIP was applied to the 608 samples from unlimed rivers all over Norway (including 25 independent samples) and values ranged from 5.35 to 7.28 (Fig. 1). A nonlinear regression was applied to produce a fitting line between the AIP and the mean annual pH for the 1 year preceding benthic algae sampling, and 88% of the variability in AIP was explained by pH (R2 = 0.88). The AIP or mean pH can be obtained from the following inverted equations: 1:548 AIP ¼ 5:596 þ 1 þ expð16:923 2:753 pHÞ pH ¼
3. The acidification index periphyton (AIP) The AIP (acidification index periphyton), which can be used to estimate the average annual pH at a river site, is calculated as follows: Pn IV AIP ¼ i¼1 i ni AIP: acidification index periphyton; IVi: indicator value of species i (see Table 1); ni: number of indicator species. According to our present experience, the following criteria need to be met in order to reliably indicate the pH of a sampling site: the sample must be taken in summer or autumn (i.e. between July and November); a minimum of three indicative species (i.e. that have an AIP value according to Table 1) must be present at the sampling site; up to now, a reliable calculation of the AIP can only be carried out in Norway as no suitable data are available that would allow trials of the AIP from elsewhere as yet. In principle, an extension of the AIP to permit an implementation in other countries is possible and desirable.
1:548 16:923 lnðAIP5:596 1Þ 2:753
where: AIP: acidification index periphyton; pH: mean pH at the sampling site during 1 year before sampling; ln: natural logarithm; exp: exponent The AIP model was developed based on sites with a pH between approximately 4.5 and 8.5. Due to averaging, the range of the nonlinear regression model is shrunken to between 5.6 and 7.1. As a consequence, a valid calculation of pH from the AIP can only be done for sites with an AIP value between 5.6 and 7.1. 25 sites of our data set were not used for calculating taxon specific weighted averages and thus can be regarded as independent samples. All independent samples lie within the variability of the other samples and thus support the general applicability of the AIP in Norway (Fig. 1). The most recent samples in our dataset are from 2005 and 2006. Most of these samples lie on the right hand side of the regression model, indicating that the mean annual pH is slightly higher than was indicated by the AIP (Fig. 1). 4.2. Limed samples The AIP was applied to 109 samples from limed rivers in southern Norway and values ranged from 5.35 to 7.16 (Fig. 2). A
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Fig. 2. Nonlinear regression model for 109 samples from limed rivers and 20 samples from the same localities 1 year before the liming started. Unlimed samples were included for comparison.
nonlinear regression model was applied to produce a fitting line, and 34% of the variability in AIP of limed sites was explained by mean annual pH (R2 = 0.34). Since the 20 acidified sites 1 year before the liming started are a subsample of all heavily acidified sites, we assumed that the lower asymptote of the model for the limed sites must not differ from the one which was derived for the unlimed sites. In addition, we can think of no reason why the upper asymptote in AIP of limed sites should differ from the one derived from unlimed sites. Thus the model produced for the limed sites was constrained to the same asymptotes as the model for the unlimed sites, and follows the equation: 1:548 AIPlimed ¼ 5:596 þ 1 þ expð12:492 1:862 pHÞ AIPlimed: acidification index periphyton (limed samples); pH: mean pH at the sampling site during 1 year before sampling; exp: exponent At limed sites, the AIP tends to underestimate pH. In addition, AIP values at limed sites are more scattered around the correlation line than at unlimed sites (R2 of limed sites is 0.34 compared to 0.88 at unlimed sites). 5. Discussion 5.1. AIP at unlimed sites Desirable features in a bioindication tool are accuracy, broad applicability, and relatively straightforward use. To keep the AIP as simple as possible, it is based on presence-absence data. Utilization of semi-quantitative periphyton data provided no better correlation between the AIP and pH (unpublished data), likely due to the high variability of discharge that is a typical feature of most Norwegian rivers (Otnes and Ræstad, 1978). Floods can reduce the biomass of algae significantly, but have less impact on species composition (Pfister, 1993). We therefore expect the abundance of benthic algae to be mainly influenced by hydrologic conditions, while the species composition mainly reflects the water quality. Presence–absence data will probably be only marginally altered after floods and therefore be less dependent on recent hydrologic conditions, reflecting water quality, as is the intention of the AIP. The algal flora in Norwegian rivers is typically best developed by autumn (Lindstrøm et al., 2004). Therefore, benthic algae samples taken between December and June were excluded from the analyses. Additional exclusion of July samples from the dataset
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was found not to improve the correlation between AIP and mean annual pH (unpublished results). For bioindication of acidity in rivers in Norway, benthic algae sampling should therefore take place between July and November. The correlation between AIP and pH includes a circular argument since the same data set was used for calculating species indicator values. Data from 25 sites, however, were not used for calculating species indicator values. All independent samples lie within the variability of the other samples and thus support our assumption that the AIP is generally applicable in rivers in Norway (Fig. 1). Our dataset included different river types with respect to calcium and total organic carbon concentration from all over Norway. Therefore, according to our present knowledge, the AIP is applicable to all river types in Norway. The data used for calculating taxon specific indicator values where taken between 1976 and 2006 and thus include the most severe acidification phase of surface waters in Norway (Wright et al., 2005). The most recent data in our data set are data from Hedmark and Møre og Romsdal counties that were taken in 2005 and 2006 and thus are from a period where surface waters in Norway were in a process of recovery from the most severe acidification. All other data in our data set are from 2003 or earlier. Hedmark county received substantial acid precipitation in the 1980s and in parts also in the 1990s (Henriksen, 2002). In contrast, since 1985 the sites in Møre og Romsdal received very little acid precipitation that did not exceed the critical load (Henriksen, 2002). However, the critical load concept applied in Norway is based on damage to fish and macroinvertebrate populations (Lien et al., 1995; Henriksen and Posch, 2001). The dataset used by Lien et al. (1995) for detecting critical loads showed that both fish and macroinvertebrate damage largely occurred at a pH below 5.5. At a pH of 5.5, the AIP is already close to its lower asymptote (Fig. 1) and the benthic algae consist of a clearly acidic community. Thus acid deposition which does not exceed the critical load for fish and macroinvertebrates might nevertheless have an impact on benthic algae. Long-term monitoring data taken in Møre og Romsdal county show that both ANC and pH have slightly but continuously increased in surface waters since 1980 (SFT, 2007). We therefore conclude that the data from both Hedmark and Møre og Romsdal counties reflect a process of recovery from acidification. This is consistent with the fact that most of these samples lie on the right hand side of the regression model, indicating that the mean annual pH is slightly higher than was indicated by the AIP (Fig. 1). Since organisms need time to recolonize habitats, a lag phase is expected to occur in biological indicators that can take up to several decades (Snucins, 2003). River pH in Norway typically undergoes annual fluctuations, often with a minimum in spring due to snowmelt (DN, 2006). The mean annual pH at a sampling site, which was calculated from between one to 24 single measurements, therefore includes some uncertainty, which is reflected in the unexplained variability of the regression model. In addition, minimum pH or mean summer pH might play an even more important role for benthic algae than the mean annual pH. Unfortunately, the nature of the chemistry database used prohibits testing of such hypotheses. The Raddum index, which is based on macroinvertebrates (Lien et al., 1991), exists in two forms called ‘‘I’’ and ‘‘II’’ and is often used in Norway for bioindication of acidification in surface waters. The Raddum index I, which is based on presence–absence data of macroinvertebrate indicator taxa, indicates first signs of acidification at a pH below 5.5 (Lien et al., 1991). The relative abundance of sensitive stoneflies to the total abundance of stoneflies, which is used in the Raddum index II, indicates a drop of pH below 6 (Raddum, 1999). In contrast, the AIP presented here is most sensitive at a pH between 5.5 and 7 (see Fig. 1). We therefore conclude that the macroinvertebrate-based Raddum index in its
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two forms I and II is most useful in differentiating more severe acidification below a pH of 6, while the algal-based AIP is more useful in differentiating above a pH of 5.5. A combination of both indices might therefore be especially interesting for investigating acidification and recovery processes over time. Benthic algae should indicate the first signs of an acidification trend where the Raddum index has not yet reacted. During recovery from heavy acidification, however, the macroinvertebrates should be the first to respond, while the benthic algae can indicate the last steps back to pre-acidification conditions. The changes in individual, population, and community levels of fish and macroinvertebrates during acidification are mainly caused by elevated hydrogen, aluminum and cadmium concentrations (Herrmann et al., 1993; Herrmann, 2001). The factors that most affect benthic algae during acidification are less well known, though both pH and aluminum might play a role in the acidification effects to benthic algae (Genter and Amyot, 1994) as well as a decrease in grazing pressure (Planas, 1996). It is known, however, that different species of benthic algae have different abilities to use HCO3 as an inorganic carbon source for photosynthesis (Raven, 1992) and it is assumed that the bicarbonate loss and reduced free CO2 in acidic lentic waters influences benthic algae species composition (Planas, 1996). The portion of the non-linear model containing the inflection point, between pH 7 and 5.5, in the sigmoidal response of the AIP to mean pH coincides with a switch in the carbonic acid equilibrium from HCO3 to CO2 being the dominating carbon fraction (Kohl and Nicklisch, 1988). We therefore assume that inorganic carbon acquisition plays an important role in the switch between a neutral/alkaline and acidic benthic algal community that is reflected in the AIP. 5.2. Limed sites At limed sites, pH explained less of the variability in AIP than at unlimed sites (R2 of 0.34 for limed sites compared to 0.88 for unlimed sites). Thus we conclude that river liming can lead to a recovery of a benthic algal community, but that the reaction is less predictable than during natural recovery. In addition to the mean annual pH value, the response of the benthic algal community to river liming may be influenced by e.g. the time since liming started, episodic acidification not detected by the chemical monitoring that occurred during liming plant malfunctions, distance to the liming plant, or the distance to the nearest unacidified refuges from which acid sensitive species can recolonize the limed sites. Most limed samples have a lower AIP at a given pH than unlimed samples (Fig. 2), confirming the suggestion that a delayed reaction of the periphytic community towards an increase of pH by liming occurs. Since the objective of river liming in Norway is mainly to obtain water quality suitable for reproduction of salmon and other fish, the target is often set to a pH between 6.0 and 6.4 (DN, 2006). At this pH, the negative effects to fish (Sandøy, 2002) and macroinvertebrates (Fjellheim and Raddum, 2001) are supposed to be negligible. Since few data for limed sites with a pH above 6.6 exist, the model for limed sites is only reliable for a pH below 6.6. 6. Conclusions River benthic algae are a useful tool to detect processes of river acidification and recovery between a pH of approximately 5.5 and 7. Both natural recovery and liming have an impact on the benthic algal community, though the impact of river liming is less predictable than natural recovery. The algal community reacts with a delay of several years to both natural recovery and river liming.
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