Freshwater Science, 2012, 31(2):657–667 ’ 2012 by The Society for Freshwater Science DOI: 10.1899/10-060.1 Published online: 8 May 2012
Responses of spring macroinvertebrate and bryophyte communities to habitat modification: community composition, species richness, and red-listed species Jari Ilmonen1,6, Heikki Mykra¨2,7, Risto Virtanen3,8, Lauri Paasivirta4,9, AND Timo Muotka5,10 1
Finnish Environment Institute, P.O. Box 140, FI-00251 Helsinki, Finland Finnish Environment Institute, Freshwater Centre, P.O. Box 413, FI-90014 University of Oulu, Finland 3 University of Oulu, Department of Biology, P.O. Box 3000, FI-90014 University of Oulu, Finland 4 Ruuhikoskenkatu 17 B 5, FI-24240 Salo, Finland 5 University of Oulu, Department of Biology, P.O. Box 3000, FI-90014 University of Oulu, and Finnish Environment Institute, Natural Environment Centre, Finland
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Abstract. Like all headwater systems, springs have been increasingly modified by multiple land uses, but the effects of these modifications on species diversity and community composition are poorly understood. We assessed the consequences of forestry-related disturbance (mainly draining) on benthic macroinvertebrate and bryophyte diversity and community composition in boreal springs. We used predictive modeling (BEnthic Assessment of SedimenT [BEAST]), indicator species analysis, and descriptive statistics on data from 55 near-pristine reference springs and 20 modified (test) springs spanning 3 ecoregions (hemiboreal to middle boreal) in Finland. Invertebrate and bryophyte communities were relatively similar between the reference and test springs. BEAST identified deviation from the reference condition in 9 and 10 sites based on benthic macroinvertebrates and bryophytes, respectively. These sites were identified mostly as possibly different from reference, with the exception of a few seriously degraded springs with low levels of groundwater flow. Indicator species for both reference-condition and test springs included spring-preferring and ubiquitous taxa. Bryophyte richness was lower in test than in reference springs, but no significant differences were detected for macroinvertebrate richness. Red-listed species were more common in reference than in test springs. Our results suggest that, despite only moderate effects on community composition, anthropogenic disturbance impoverishes spring fauna and flora and causes a decline of spring-preferring red-listed species. Restoration will be necessary to preserve biodiversity in springs, but benign methods should be used to avoid further disturbance to biota, particularly red-listed spring specialists. Key words: springs, disturbance, benthic macroinvertebrates, bryophytes, diversity, red-listed taxa, predictive modeling, restoration.
Freshwater ecosystems are globally threatened because of pollution, flow regulation, intensive land use, and overexploitation of freshwater resources (Abell 2002, Baron et al. 2002). Awareness of the degradation of freshwater environments has led to improved legislation, such as the European Water Framework Directive (WFD) (2000/60/EC), and an
increasing demand for effective bioassessment. Considerable effort has been directed at developing bioassessment protocols for surface waters, but management and conservation guidelines for springs are poorly developed or nonexistent (Sada et al. 2005, Barquı´n and Scarsbrook 2008). Spring bioassessment is extremely rare, and Keleher and Rader (2008) were the first investigators to apply techniques similar to surface-water bioassessment to springs and associated wetlands. Springs depend on continuous discharge of groundwater, and they form groundwater–surface water and aquatic–terrestrial ecotones. Thus, they are
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E-mail addresses:
[email protected] [email protected] 8
[email protected] 9
[email protected] 10
[email protected] 7
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important components of riverine landscape biodiversity (Ward and Tockner 2001). Springs that merge into groundwater-influenced wetlands are a key ecological attribute of boreal mire ecosystems (Eurola et al. 1984). Springs also have been identified as biodiversity hotspots in forested landscapes and are included among the 13 woodland key habitats designated in the national Forest Act of Finland (see Pyka¨la¨ 2007). However, spring habitats have suffered from human disturbance across the world, and the unique biota of springs is rapidly becoming threatened (Fensham and Price 2004, Heino et al. 2005, Sada et al. 2005, Cantonati et al. 2006, Barquı´n and Scarsbrook 2008). Springs are stream sources and, thus, are unaffected by upstream events. In headwater streams, forest and peatland drainage can affect macroinvertebrate richness and community composition via increased load of inorganic material (e.g., sand) and impaired water quality (Vuori and Joensuu 1996, Vuori et al. 1998). However, the level and quality of ground waters in large aquifers are little affected by forestry (Rusanen et al. 2004). Thus, if spring hydrology is not heavily disturbed and the aquifer is uncontaminated, changes caused by landscape modification, such as ditching, are mostly local by nature (Keleher and Rader 2008). However, physical disturbance from landuse pressures in the vicinity of a spring can, at least temporarily, impair spring water quality (Ilmonen et al. 2006). Our aims were to assess: 1) whether benthic macroinvertebrate and bryophyte communities in anthropogenically modified springs differ from those in unmodified, near-pristine springs; and 2) whether bioassessments based on these 2 groups of organisms produce similar results. We used ordination-based predictive modeling, BEnthic Assessment of SedimenT (BEAST), on abundance data to assess whether modified (test) site assemblages differed from their reference-condition counterparts (Reynoldson et al. 1997, 2001). In addition, we assessed the effect of spring habitat modification on the abundance and richness of bryophytes and macroinvertebrates, with particular emphasis on red-listed species, and identified indicator taxa for the reference and modified springs. Methods Field protocol We extracted data for our study from a larger data set of biological communities in springs across Finland (Ilmonen et al. 2009, Virtanen et al. 2009), with some additional sampling done in 2009. Anthropogenic disturbance is severe in the southern ½ of
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TABLE 1. Medians and ranges of environmental variables at reference and test sites. Total spring area and the area of the pools, helocrenes, and minerogenic and organogenic brooks were classified as 1: ,10 m2, 2: 10–99 m2, 3: 100– 999 m2, 4: 1000–9999 m2, 5: §10,000 m2. Spring naturalness was classified as 0: destroyed, 1: severely altered, 2: moderately altered, 3: pristine or near-pristine.
Variable
Reference sites
Test sites
Mean annual air 3 (0–5) 3 (1–4) temperature (uC) Spring area 2 (1–5) 2 (1–3) Pool area 0 (0–3) 1 (0–3) Helocrene area 1 (0–5) 0 (0–2) Area of minerogenic brooks 1 (0–2) 0 (0–2) Area of organogenic brooks 1 (0–2) 1 (0–2) Naturalness 2 (2–3) 1 (0–1) Altitude (m asl) 115 (35–225) 145 (80–235) pH 6.4 (6.3–8.2) 6.2 (5.5–6.9) Conductivity (mS/m) 4.5 (4.2–22.2) 3.8 (1.7–21.6)
Finland, whereas springs in the northern boreal ecoregion are mostly pristine (Ilmonen et al. 2008, 2009). Therefore, we restricted our study to the southern ½ of the country, spanning from the hemiboreal to middle boreal ecoregion (lat 60–65uN). We used pristine or near-pristine springs (n = 55) as reference sites and 20 severely modified springs as test sites. Disturbance at the test sites was mainly caused by draining of wetlands for forestry purposes, which had occurred mainly 20 to 25 y prior to sampling. Draining activity in Finland decreased dramatically by the late 1980s (Lindholm and Heikkila¨ 2006), and since the mid 1990s, draining in the vicinity of pristine or near-pristine springs has been prohibited by environmental legislation. We estimated the naturalness of each spring in the field on a scale of 0 to 3, following Heino et al. (2005): 0 = completely destroyed, e.g., groundwater outflow at the bottom of a ditch, no other spring habitat available; 1 = severely altered, e.g., ditching in the immediate neighborhood of a spring; 2 = minor disturbance, e.g., minor structures for water extraction or logging or ditching ,100 m from the spring; and 3 = pristine or near-pristine spring. All test sites were scored into status classes 0 or 1 and reference sites into classes 2 or 3 (Table 1). We also used a classification based on logarithmic scale (1: ,10 m2, 2: 10–99 m2, 3: 100–999 m2, 4: 1000–9999 m2, 5: §10,000 m2) to estimate the total spring area and the areas of different habitat types, i.e., spring pools, helocrenes (seeps including mossy, muddy, or sandy substrate), and spring brooks (with minerogenic or organogenic substrate). We measured altitude from
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topographic maps (scale 1:20,000) to the nearest 5 m. We estimated the mean annual air temperature at the location of a spring by overlaying the study sites on a map of long-term (1961–1990) mean annual temperatures of Finland (Tikkanen 2006). We assigned the mean annual temperature of each locality to the closest full-degree isopleth. We measured springwater pH and conductivity in situ with a portable device (WTW pH/Cond 340i; WTW, Weilheim, Germany). We sampled benthic macroinvertebrates in late spring to early summer (May to early June) with a D-frame hand net (20 cm wide, 0.5-mm mesh size) by sweeping submerged substrates or by pressing mossy or muddy substrates and collecting loose material into the net. Because of the large variation in spring size, we related total sampling time to spring area (area 1: 2 min, 2: 5 min, 3: 10 min, 4: 20 min, 5: 30 min). We sampled all habitat types (spring pools, helocrenes, spring brooks with minerogenic or organogenic substrate) within the first 20 m of the spring source. We identified macroinvertebrates to species level whenever feasible (Ilmonen et al. 2009). We sampled bryophytes at 1- or 2-m intervals (depending on spring size) from the point of discharge along the main course of the flow in 0.5 3 0.5 m quadrats. The number of plots was usually 6, but in some of the smallest springs, we could sample only 1 or 2 plots. We assessed % cover of each bryophyte species for each plot and used the mean cover of each species in predictive modeling. In addition, we conducted a 10to-15-min qualitative sweep-up search at each site to approximate total bryophyte richness. Nomenclature follows Fauna Europaea (Fauna Europaea version 2.0; available online at: http://www.faunaeur.org) for macroinvertebrates and Ulvinen et al. (2002) for bryophytes. Numerical analyses Predictive modeling involves 3 steps: 1) classification of reference sites into groups based on their biotic assemblages; 2) development of a predictive model for the assemblage groups, and assigning test sites to their respective reference-site groups based on environmental variables; and 3) assessing the biological quality of the test sites by comparing them with their respective reference-site groups (Reynoldson et al. 2001, Van Sickle et al. 2006). First, we divided the reference sites into 4 groups, separately for macroinvertebrates and bryophytes, using the first 2 divisions of Two-Way Indicator Species Analysis (TWINSPAN) on untransformed abundance data, and with pseudospecies cut levels of 0, 2, 5, 10, and 20. We then used stepwise
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discriminant function analysis (DFA) with forward selection to identify the best subset of predictor variables for the reference groups, using the dfa.step function provided by Van Sickle et al. (2006) for the R statistical environment (version 2.9.2; R Development Core Team, Vienna, Austria). The function was terminated when all predictors had nominal p values , 0.05, and all excluded variables had p . 0.05, based on partial F-statistics of Wilks’ lambda (see Van Sickle et al. 2006). The stepwise DFA analysis also produced cross-validated classification results, which we used to assess performance of the predictive model. We also assessed model performance by comparing within-group Bray–Curtis distances of the TWINSPAN groups with those of the ungrouped reference sites. We excluded variables potentially affected by anthropogenic disturbance (total spring area, area of helocrenes and pools) from the modeling stage. Thus, the candidate variables for DFA were: northern and eastern coordinates, mean air temperature, altitude (log[x]-transformed), area of minerogenic and organogenic brooks, and water pH and conductivity (log[x]transformed). Next, we predicted test sites to their respective reference-site groups based on environmental variables and the chosen DFA model, separately for macroinvertebrates and bryophytes. We used the biotic groupings and predicted probabilities for test sites to belong to each reference-site group to assess the difference between test and reference sites with BEAST predictive modeling. We first ran nonmetric multidimensional scaling (NMDS) ordinations for each reference-site group using log(x + 1)-transformed macroinvertebrate and arcsine!(x)-transformed bryophyte data. We found the best 3-dimensional ordination solution (i.e., one with the lowest stress value) for each group by running 100 analyses with random starting configurations and Bray–Curtis distance as the dissimilarity measure. Stress values of the reference-site ordinations ranged from 4.6 to 11.3 for macroinvertebrates and from 4.4 to 14.3 for bryophytes. Second, we used NMDS in a predictive mode (NMS scores function) to calculate ordination scores for the test sites. In this method, a calibration data set and calibration scores are used to predict scores for new sites (McCune and Grace 2002). Scores for new sites are calculated using a set of variables overlapping with the calibration data (species in the case of community data), and the model uses an iterative search to find the best fit for each of the new sites, one at a time, based on the variables (species). Adding the new sites does not affect the original ordination. We used the site 3 species matrix of the respective reference group and
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FIG. 1. Assessment of test sites with the BEnthic Assessment of SedimenT (BEAST) confidence ellipses showing the null model of macroinvertebrates for nonmetric multidimensional scaling (NMDS) ordination axes 2 vs 1 (A) and 3 vs 1 (B) of all test sites (numbers 1–20) plotted over the reference sites (open circles). Confidence ellipses, including 90, 99, and 99.9% of the reference sites and the respective evaluation bands (1: equal to reference condition, 2: possibly different, 3: different, 4: very different from reference). Test sites were assessed by their worst position in the ordination. Six sites were different (sites 1, 2, 4, 17, 18, 19) and 1 site (14) very different from reference.
the scores of the reference-group ordination as calibration data sets to predict scores for the test sites assigned to that reference-site group. In addition, we applied a null model by predicting coordinates for all test sites over all reference sites, i.e., without the grouping and DFA steps. Null models are a novelty in the River InVertebrate Prediction and Classification System (RIVPACS) methodology (Van Sickle et al. 2005, Aroviita et al. 2009) and have not yet been applied in BEAST assessments. However, we consider a null model useful for assessing model performance and for assessing the biological quality of the test sites independently of the error sources related to grouping (Reynoldson et al. 2001, Van Sickle et al. 2005). We ran NMDS for the reference sites and the predictivemode NMDS in PC-ORD (version 5.0; MjM Software, Gleneden Beach, Oregon). We then plotted ordinations of the reference groups with the data.ellipse function of the car package (Fox 2009) in R based on the NMDS ordination scores. The function superimposes normal-probability contours over a scatterplot of the data at specified confidence levels. The most commonly used confidence intervals in BEAST are 90, 99, and 99.9%, which produce 4 assessment bands with different probabilities of assemblage change (Reynoldson et al. 1997, 2001). Placement of a test site within the innermost band 1
(within 90% of the reference sites’ normal-probability distribution) indicates condition equivalent to reference, whereas band 2 (90–99%) indicates that assemblage structure is possibly different, band 3 (99– 99.9%) indicates different, and band 4 (.99.9%) indicates very different from reference. We used the coordinates obtained by NMDS scores to plot test sites on 3 dimensions to assess the assemblage dissimilarity between test and reference sites. When a test site bordered 2 bands, we attributed it to the higher band. We attributed each test site to the worst position on the plots across all dimensions, i.e., according to the greatest difference from the reference (see Fig. 1A, B for an example). We used descriptive box plots and Welch 2-sample t-tests to compare the diversity and abundance of macroinvertebrates and bryophytes between reference and test sites. Abundance and taxon richness were assessed separately for all taxa and for crenophilous (spring-preferring) taxa. We identified crenophilous taxa following Eurola et al. (1984) and Ulvinen et al. (2002) for bryophytes and Ilmonen et al. (2009) for macroinvertebrates. We considered the observed richness of bryophytes comparable across sites, but the number of macroinvertebrate taxa was rarefied to the lowest number of individuals recorded using the rarefy function of the vegan package
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TABLE 2. Assessment of test sites by BEnthic Assessment of SedimenT (BEAST) models based on benthic macroinvertebrate and bryophyte data, showing results for predictive and null models. Assessment: 1 = equal to reference, 2 = possibly different, 3 = different, and 4 = very different from reference. Invertebrates Test site 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Bryophytes
Predictive
Null
Predictive
Null
1 1 1 2 2 1 2 1 2 1 1 1 2 3 1 1 2 3 2 1
2 2 1 2 1 1 1 1 1 1 1 1 1 4 1 1 2 2 2 1
1 1 3 3 2 1 2 2 1 1 1 1 1 3 1 1 2 3 3 2
2 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 2 1 1 2
(Oksanen et al. 2010) in R statistical environment. We also used Fisher’s exact test to compare the occurrence of red-listed macroinvertebrate and bryophyte species (categories near threatened [NT] to critically endangered [CR] in the national conservation status assessment; Rassi et al. 2001) between reference and test sites. We transformed abundances of macroinvertebrates (count of individuals, log[x + 1]) and bryophytes (% cover, !x) to approximate normality. For illustrative purposes, we ran a 3-dimensional NMDS ordination, with Bray–Curtis distance as the dissimilarity measure, on the whole data set (reference sites + test sites) for both macroinvertebrates and bryophytes. We used the metaMDS function of the vegan package (Oksanen et al. 2010) in the R statistical environment. This function automatically performs several analyses to find the best solution with the lowest stress value. We log(x)-transformed abundance data (macroinvertebrates) and Wisconsin-doublestandardized and !(x)-transformed the mean cover data (bryophytes) prior to NMDS. We identified indicator species for the reference and test sites with Indicator Species Analysis (IndVal) (Dufreˆne and Legendre 1997). The indicator value (IV) varies between 0 and 100, attaining its maximum value when all individuals of a species occur in a single
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group of sites and in all sites of that group. We tested significance of the IVs of each species with a Monte Carlo test with 4999 permutations. Prior to predictive modeling, we excluded the most sporadic taxa (singletons for macroinvertebrates, species occurring in just 1 spring with ,1% cover for bryophytes), resulting in 146 and 42 taxa for macroinvertebrates and bryophytes, respectively. However, we used the complete data set for assessing species richness and the occurrence of red-listed species (176 and 50 taxa, respectively). For bryophytes, we also included species recorded in qualitative samples. We did predictive modeling and descriptive statistics in the R statistical environment and used PC-ORD for classification, ordination, and indicator species analysis. Results The final DFA model for the 4 macroinvertebrate groups included northern coordinate, minerogenic brooks, and water pH as predictors, and yielded an overall cross-validated classification success of 64%, ranging from 30 to 89% across groups. Within-group distances of the TWINSPAN groups ranged from 0.52 to 0.57 and were slightly lower than the mean amongsite distance in the ungrouped reference data (0.63). The final bryophyte model included eastern coordinate, minerogenic brooks, and pH as predictors, and yielded an overall cross-validated classification success of 51%. The model failed to predict any sites correctly in 1 of the site groups, where the moss species Fontinalis antipyretica Hedw. was dominant, but in other groups the classification success ranged from 50 to 64%. Within-group dissimilarities ranged from 0.48 to 0.73 and were lower than the average distance in the ungrouped reference data (0.82). BEAST identified deviation from the reference condition in 9 and 10 sites based on benthic macroinvertebrates and bryophytes, respectively (Table 2). However, most of these sites were identified as possibly different (within band 2). Only 2 and 5 sites, respectively, were identified as different (within band 3). The bryophyte model was diluted by a failure to predict springs dominated by F. antipyretica from their environmental characteristics because 2 of the possibly different sites (5 and 17) and 1 of the different sites (19) were dominated by this species, and would not have been different if compared with the reference group 1 characterized by the same species. The null models comparing all test sites with all reference sites identified less deviation from the reference condition than did the predictive models, a result implying that grouping did improve the model. However, 1 test site (14) was identified as very different from all reference
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FIG. 2. Box-and-whisker plots for abundance of all (A, B) and crenophilous (C, D) macroinvertebrate (A, C) and bryophyte (B, D) taxa in the reference and test springs. Heavy lines show medians, ends of boxes show quartiles, and whiskers show ranges.
sites based on the macroinvertebrate null model. Some discrepancy was found in sites identified as different from reference condition according to macroinvertebrates and according to bryophytes. Four sites were identified as possibly different by one but not the other group. However, 5 test sites were identified as either possibly different or different by both models, and 2 sites (14 and 18) were identified as different by both macroinvertebrate and bryophyte predictive models. Total abundance of macroinvertebrates and bryophytes and the abundance of crenophilous taxa showed large variation across reference and test sites. Values ranged from 23 to .3000 individuals for macroinvertebrates (Fig. 2A) and from 0 to 100% cover for bryophytes (Fig. 2B). For macroinvertebrates, the difference in total abundance between test and reference sites bordered at significance, with higher abundances in reference than in test sites (t = 1.97, df = 42, p = 0.055). Total bryophyte cover did not differ between reference and test sites (t = 0.33, df = 28, p = 0.75), nor did the abundances of crenophilous macroinvertebrates or bryophytes (both p . 0.35; Fig. 2C, D). Taxon richness also showed considerable variation between the reference and test sites for macroinvertebrates (9–51 taxa, respectively) and bryophytes (1–18 taxa). Rarefied macroinvertebrate richness did not differ between the reference and test sites, regardless of whether all (t = 20.65, df
= 37) or only crenophilous taxa (t = 20.48, df = 34) were considered (both p . 0.5; Fig. 3A, C). In contrast, bryophyte richness was significantly higher in the reference sites, regardless of whether all (t = 5.10, df = 61) or only crenophilous taxa (t = 4.40, df = 49, both p , 0.001; Fig. 3B, D) were considered. Redlisted macroinvertebrates were recorded in 17 reference and 1 test site (odds ratio 8.3, p = 0.03). Redlisted bryophytes were more common in the reference (22 sites) than in test (1 site) sites (odds ratio 12.4, p = 0.004). Final stress value in the 3-dimensional NMDS ordination was 18.2 for macroinvertebrates and 18.4 for bryophytes. The key environmental variables across all springs and for both organism groups were geographical coordinates, water pH, and spring habitat structure, particularly the amount of minerogenic spring brooks. However, reference and test springs were not well separated along these environmental gradients (Fig. 4A, B). Ten significant (p , 0.05) macroinvertebrate indicators were identified for the reference sites (Table 3). Five of the macroinvertebrate indicators were crenophilous species with indicator values ranging from 23.6 to 67.6. Of these taxa, only the caddisfly Crunoecia irrorata (Curtis) was confined to reference sites (recorded in 13 [24%] springs). Only 1 significant bryophyte indicator, the crenophilous Rhizomnium
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FIG. 3. Box-and-whisker plots for taxon richness of all (A, B) and of crenophilous (C, D) macroinvertebrate (A, C) and bryophyte (B, D) taxa in the reference and test springs. Heavy lines show medians, ends of boxes show quartiles, and whiskers show ranges.
FIG. 4. Nonmetric multidimensional scaling (NMDS) ordination of the macroinvertebrate (A) and bryophyte (B) data from reference and test sites. The linear fit of environmental variables significant in the predictive models is shown by the length and angle of the arrows relative to the axes. East and north refer to geographic coordinates. Min.br. = minerogenic brooks.
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TABLE 3. Significant (p , 0.05) indicator taxa and their indicator values (IV) for the reference and test springs. Cr = crenophilous (spring-preferring) taxon. * indicates p = 0.052. Taxon Reference sites (n = 55) Macroinvertebrates Nemurella pictetii Klapa´lek Asellus aquaticus (L.) Leuctra nigra (Olivier) Micropsectra atrofasciata -agg.a Dixa submaculata -agg.a Thienemanniella acuticornis (Kieffer) Brillia bifida (Kieffer) Syndiamesa spp. Baetis rhodani (Pictet) Crunoecia irrorata (Curtis) Bryophytes Rhizomnium magnifolium (Horik.) T.J.Kop. Test sites (n = 20) Macroinvertebrates Psectrocladius limbatellus -agg.a Micropsectra junci (Meigen) Diamesa incallida (Walker) Stempellinella brevis (Edwards) Thienemanniella clavicornis -agg.a s. Cranston 1982 Xenopelopia nigricans (Goetghebuer) Glyphotaelius pellucidus (Retzius) Bryophytes Warnstorfia exannulata (W.Gu¨mbel) Loeske
IV
67.6 66.3 59.5 49.9 42.9 34.5 34.5 28.6 27.3 23.6
Cr Cr Cr Cr Cr
23.6 Cr
46.3 28.6 28.2 19.1 18.9
Cr Cr Cr Cr
15.0 13.8 29.6* Cr
a
Larval type known to include several closely related species
magnifolium (Horik.) T.J.Kop., (p = 0.046, IV = 23.6) was identified for the reference sites (recorded in 13 [24%] of the reference and none of the test sites). Four of the 7 macroinvertebrate taxa indicating test sites were crenophilous, but they were all relatively rare (recorded in 5–17 springs) with weak indicator values (18.9–28.6) (Table 3). Of bryophytes, the crenophilous bryophyte Warnstorfia exannulata (W.Gu¨mbel) Loeske (recorded in 20 reference and 20 test sites) had the highest, yet nonsignificant (IV = 29.6, p = 0.052) indicator value for the modified sites. Even significant (p , 0.05) indicator values for the modified sites were rather weak (13.8–46.3), a result implying that no macroinvertebrate or bryophyte taxa showed strong preference for these sites. Discussion The main finding of our study was that, according to predictive modeling, benthic macroinvertebrate and bryophyte communities in most of the test sites did not differ noticeably from the reference-site
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communities, although all test sites were severely modified by anthropogenic disturbance. Half of the sites were identified as impaired by the BEAST model using either macroinvertebrates or bryophytes, but most of these sites were assessed as possibly different from the reference condition. Only 2 (macroinvertebrates) and 5 (bryophytes; including 1 misclassified spring dominated by F. antipyretica) of the test sites were identified as different from the reference condition, 2 of these by both organism groups. The 2 test sites identified as different by both groups were characterized by low discharge (,,1 L/s), whereas many springs equally disturbed but with a stronger groundwater flow harbored macroinvertebrate and bryophyte assemblages similar to the reference condition. Therefore, it might be tempting to conclude that spring biota has not suffered severely from habitat disturbance and that springs with high groundwater flow are resistant to anthropogenic disturbance of habitat structure. We used snapshot surveys of near-pristine reference sites to assess the condition of the test sites, which is a commonly used approach in bioassessment (e.g., Reynoldson et al. 2001, Van Sickle et al. 2006, Aroviita et al. 2009). However, this approach has inherent limitations deriving from natural spatial and temporal variation of biological communities (White and Walker 1997, Mykra¨ et al. 2008b). In boreal springs, natural compositional variation of benthic macroinvertebrates and bryophytes is high because of wide variation in the physicochemical characteristics of springs (Ilmonen et al. 2009, Virtanen et al. 2009). Such a high degree of natural variation makes detection of anthropogenic alteration of biotic assemblages challenging. Assemblages in modified habitats may resemble natural assemblages if some habitatspecific taxa are present, even though many other taxa may have disappeared. Our results do suggest that some of the most common crenophilous species (e.g., the stonefly Nemurella pictetii Klapa´lek and the bryophyte W. exannulata) are highly tolerant of habitat disturbance and will survive as long as continuous groundwater flow is secured. The amount of groundwater discharge to a spring is certainly a key factor affecting the spring biota (e.g., Hoffsten and Malmqvist 2000, Von Fumetti et al. 2006), and sufficient and continuous groundwater flow acts as a buffer against physical disturbance in springs (see also Keleher and Rader 2008). Undocumented historical events could still affect extant reference sites, further obstructing the detection of human effects (White and Walker 1997). For example, Keleher and Rader (2008) found no response to moderate livestock grazing, probably because the
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springs have already been modified by the historical grazing pressure of large ungulates. In our study, only 27% of reference sites were within strictly protected areas, and many of the springs have been susceptible to historical disturbance by forestry (the ghost of land use past; Harding et al. 1998). Thus, even the reference-condition springs may have been affected to some degree by landscape-level disturbances. One of the cornerstones of BEAST modeling is that the classification success of the model needs to be fairly high because the test sites are assigned to their respective reference-site groups using the DFA model (Reynoldson et al. 2001, Van Sickle et al. 2006). In our study, 1 distinct group of springs, characterized by the moss F. antipyretica, could not be predicted by the DFA model, leading to misclassification of some of the test sites. Nevertheless, the overall classification success was within the range typical of surface-water bioassessment (e.g., Reynoldson et al. 2001, Van Sickle et al. 2006, Aroviita et al. 2009), and only slightly lower than the classification success of artesian springs (Keleher and Rader 2008). Predictive modeling also clearly improved the assessment, judged by the greater sensitivity and lower within-group dissimilarities relative to the ungrouped null models. Overall, BEAST is a rather conservative method. Typically, ,50% or slightly more of test sites are identified as differing from reference, mostly as possibly different (Reynoldson et al. 2001, Feio et al. 2007a, b). More rarely is a majority of test sites identified as different or very different from the reference (Moreno et al. 2009). Furthermore, our results show that responses of different groups of organisms to disturbance may differ, and therefore, bioassessment will be more sensitive if multiple groups, rather than a single group of organisms, are considered (Feio et al. 2007a, Mykra¨ et al. 2008a). In fact, the requirement for an integrated approach to freshwater assessment is deeply rooted in the EU Water Framework Directive (2000/60/EC), which demands that ecological classification of water bodies be based on several biological-quality elements (European Communities 2000). Community-level differences between the reference and test springs were relatively weak, but our results nevertheless imply that modification does affect spring biota. First, spring bryophyte flora has become impoverished as a result of anthropogenic stress, as indicated by lower bryophyte richness, especially richness of specialist taxa, in modified springs (see also Heino et al. 2005). Second, both red-listed macroinvertebrates and bryophytes were more common in reference than in test sites, a result indicating
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the loss of the most sensitive taxa at some of the test sites. Keleher and Rader (2008) found that sensitive taxa were more abundant in reference springs and tolerant taxa in impacted springs. Is predictive modeling an appropriate tool for spring bioassessment? Predictive models have been used mostly in river bioassessment but have been applied recently to wetlands (Davis et al. 2006) and springs (Keleher and Rader 2008). Predictive models generally emphasize common species, whereas rare taxa are typically excluded, or given less weight, because their relationships with environmental factors cannot be easily defined because of sampling limitations (Marchant 2002). However, in our case, threatened species provided a much stronger signal of spring deterioration than did abundant species. With one exception (a singleton coleopteran classified as vulnerable [VU]), these taxa were included in predictive modeling, but because of the logical structure of the model, they had very little effect on model outcome. Therefore, in addition to predictive modeling, indicators of ecosystem health that include rare species (e.g., species richness and occurrence of spring specialists) are needed in spring bioassessment (see also Keleher and Rader 2008). The red-listed macroinvertebrates and bryophytes in our study were species that favor or require springs as their habitat, and thus, they indicate a long-term temporal persistence of a spring (Hoffsten and Malmqvist 2000). However, we concur with Marchant (2002) in that bioassessment and conservation are largely separate issues, and bioassessment may, in most cases, be successful even if no extra weight is given to rare and threatened species. In conclusion, large-scale disturbance of boreal springs seems to have had a negative effect at the species level, causing a regional decrease of springspecialist taxa. Considering the large extent of the disturbance, (.90% of springs in southern Finland are degraded; Ilmonen et al. 2008), this disturbance poses a serious threat to spring biodiversity. Therefore, restoration of spring habitats seems necessary to stop the negative trend. Sites modified by forestry-related land use showed few consistent differences from near-pristine reference sites in terms of community composition, a result implying potential for self-repair of spring communities over a time scale of a few decades. Furthermore, the fact that some of the modified springs contained red-listed spring specialists sets strong restrictions on spring restoration (Ilmonen et al. 2006). A need for restoration of spring habitats does exist, but restoration should be carried out using very benign methods designed to restore the hydrology of a spring rather than the original,
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usually unknown, physical state of the habitat. In any case, our results emphasize that a prerestoration survey for the presence of spring specialist and redlisted species is always needed before any restoration measures are implemented. Acknowledgements The work was carried out as part of the GENESIS project on groundwater systems (www.thegene sisproject.eu) financed by the European Community 7th Framework Programme (contract number 226536). Collection of field data was funded by the Finnish Ministry of Environment under the research programme of Deficiently Known and Threatened Forest Species (PUTTE) and by the Southwest Finland Regional Environment Centre, Forest and Park Service, Finnish Association for Nature Conservation, Entomological Society of Finland, Ladnapuoldsa Scientific Association, and Academy of Finland. We also acknowledge the constructive comments by Daniel Spitale and an anonymous referee. Literature Cited ABELL, R. 2002. Conservation biology for the biodiversity crisis: a freshwater follow-up. Conservation Biology 16: 1435–1437. AROVIITA, J., H. MYKRA¨, T. MUOTKA, AND H. HA¨MA¨LA¨INEN. 2009. Influence of geographical extent on typology- and model-based assessments of taxonomic completeness of river macroinvertebrates. Freshwater Biology 54: 1774–1787. BARON, J. S., N. L. POFF, P. L. ANGERMEIER, C. N. DAHM, P. H. GLEICK, N. G. HAIRSTON, R. B. JACKSON, C. A. JOHNSTON, B. D. RICHTER, AND A. D. STEINMAN. 2002. Meeting ecological and societal needs for freshwater. Ecological Applications 12:1247–1260. BARQUI´N, J., AND M. SCARSBROOK. 2008. Management and conservation strategies for coldwater springs. Aquatic Conservation: Marine and Freshwater Ecosystems 18: 580–591. CANTONATI, M., R. GERECKE, AND E. BERTUZZI. 2006. Springs of the Alps – sensitive ecosystems to environmental change: from biodiversity assessments to long-term studies. Hydrobiologia 562:59–96. DAVIS, J., P. HORWITZ, R. NORRIS, B. CHESSMAN, M. MCGUIRE, AND B. SOMMER. 2006. Are river bioassessment methods using macroinvertebrates applicable to wetlands? Hydrobiologia 572:115–128. DUFREˆNE, M., AND P. LEGENDRE. 1997. Species assemblages and indicator species: the need for a flexible asymmetrical approach. Ecological Monographs 67:345–366. EUROLA, S., S. HICKS, AND E. KAAKINEN. 1984. Key to Finnish mire types. Pages 11–117 in P. D. Moore (editor). European mires. Academic Press, London, UK.
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