ROMANIA PHYTOBENTHOS LAKES

Method name

National (Romanian) Assessment Method for Natural Lakes Ecological Status based on Phytobenthos (Diatoms)

Acronym

RO-AMLP

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Template for reporting on Intercalibration of new or revised ecological assessment methods according to finalised

Intercalibration results (Gap 2)

Scientific development and confirmation of the national method for ecological status assessment of waterbodies

(rivers, lakes) based on diatoms (phytobenthos), and completion of the intercalibration exercise.

1: lakesMartyn Kelly,

Bowburn Consultancy, 11 Monteigne Drive, Bowburn, Durham DH6 5QB

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Contents1. Introduction...................................................................................................................................4

2. Description of the national assessment method............................................................................4

3. National data set............................................................................................................................4

4. Typology........................................................................................................................................5

5. Pressure-impact relationships.......................................................................................................7

6. Reference/benchmark conditions................................................................................................12

7. Boundary setting procedure........................................................................................................16

8. Checking of WFD compliance and feasibility...............................................................................17

8.1 Compliance checking.................................................................................................................17

8.2 Feasibility checking....................................................................................................................18

9. IC procedure................................................................................................................................19

9.1 ROLN08 - “upland” lakes............................................................................................................19

9.2 “lowland lakes”..........................................................................................................................20

10. Normalisation of boundaries ......................................................................................................30

11. Conclusions .................................................................................................................................31

12. References ................................................................................................................................... 33

Appendix 1: Rationale for excluding ROLN06T from intercalibration..................................................34

A1. Introduction...............................................................................................................................34

A2. Descriptive statistics..................................................................................................................34

Conductivity.....................................................................................................................................34

Magnesium......................................................................................................................................35

Total hardness.................................................................................................................................37

Total dissolved solids (TDS)..............................................................................................................38

A3. Cluster analysis..........................................................................................................................39

A4. Classification analysis................................................................................................................42

A5. Composition of diatom assemblages from therapeutic lakes....................................................45

Appendix 2: Procedure for computation of ecological status for Romanian lakes using phytobenthos ............................................................................................................................................................. 47

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1. Introduction

Intercalibration of phytobenthos-based methods of ecological assessment of lakes was completed as part of the second phase of intecalibration. The exercise was conducted across all GIGs, with 11 Member States participating. Romania was not part of this exercise; however, sufficient data have now been accumulated to allow options for status assessment using phytobenthos to be considered.

This report describes the Romanian phytobenthos assessment system for lakes and shows how this is compliant with the WFD Normative Definitions and that the class boundaries correspond with those agreed by the completed intercalibration exercise. However, the report goes on to show that lowland lakes in Romania behave atypically, when compared to lakes in other countries that have already been intercalibrated. There are two (possibly linked) reasons: factors other than nutrients (BOD in particular) appears to be having a strong influence on the diatom assemblages in these lakes and a number of taxa that are abundant in Romanian lakes are not represented in Rott’s Trophic Index, the intercalibration metric. Although it is not possible to intercalibrate the method, we believe that it gives an accurate picture of the state of Romanian lakes and will use it for ecological assessment in order to fulfil our obligations under the WFD. Work will continue to develop a practical approach to assessing lake status using phytobenthos.

2. Description of the national assessment method

The national assessment method for phytobenthos uses diatoms as proxies for the entire phytobenthos community. Samples are collected two or three times a year from submerged stems of emergent plants or hard substrates, by brushing with a toothbrush. These are then digested using hydrogen peroxide and permanent slides are prepared. These are analysed in the laboratory and at least 300 diatoms (usually at least 400) are identified to species level and the number of each species counted. The main identification literature used is Krammer and Lange-Bertalot (1986-2004).

At the start of this exercise, no national metric had been proposed; section 5 of this report evaluates common diatom metrics used elsewhere in Europe for their suitability for use in Romania, and recommends the use of Rott’s trophieindex (TI: Rott et al. 1999) as an appropriate measure for lake ecological assessment in this country.

3. National data set

Diatom count data and associated environmental information are available from 367 samples representing 59 lakes in Romania. These data were collected between 2010 and 2013 and are summarised in Table 1.

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Table 1. Summary of data available for Romanian lake phytobenthos intercalibration exercise. See Table 2 for a description of lake types

Type

Number of samples

All Benchmark / reference

Lakes Samples Lakes SamplesROLN01 22 127 4 16ROLN02 10 10 1 9ROLN03 1 8 1 8ROLN04 3 20 3 20ROLN05 5 44 1 8ROLN06T 6 40 0 0ROLN07 3 12 1 4ROLN08 8 28 1 9ROLNCAPMLA01 1 8 0 0Total 59 297 12 74

4. Typology

The current Romanian typology is summarised in Table 2. The phytobenthos intercalibration exercise was a cross-GIG exercise, in which lakes were divided into three “supertypes”: “low alkalinity”, “moderate alkalinity” and “high alkalinity”. This is possible because benthic diatoms are sampled from the shallow littoral zone of lakes and, therefore, major factors structuring other communities within lakes are less relevant as the focus is on a single habitat. This enabled methods from across the EU to be compared.

All Romanian lake types with the exception of ROLN08 fitted the criteria for the “high alkalinity” supertype. ROLN08 covered a range of alkalinity values, but most corresponded to the “moderate alkalinity” supertype. ROLN06T, on the other hand, has very high alkalinity and conductivity, associated with mineral rich springs, and this is distinguished as a separate type. As both water chemistry and the diatom assemblage show brackish influences (see Appendix 1), this type is not included in the intercalibration. ROLN09 (shallow, silicious, temporary lakes) has also been omitted as there are no similar lakes amongst those currently intercalibrated against which they can be compared.

In practice, ROLN01, ROLN02, ROLN03, ROLN04 and ROLN05 can all be considered as variants of a basic “lowland high alkalinity” type for the purpose of this intercalibration. In addition, ROLN07, representing a transitional type between the “lowland” and “highland” lakes has been included with this lowland type on the basis of the relatively high alkalinity of the lakes in this type and the general habitat of the lake littoral.

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Table 2. The Romanian national lake typology.

Typology General description Ecoregion Altitude (m) Average depth (m)

ROLN01Lowland, very shallow, silicious, very small, small and medium size.

12, 16 <200 <3

ROLN02Lowland, very shallow, calcareous, small, medium and very large size.

12 <200 <3

ROLN03 Lowland, very shallow, calcareous, very large size. 12 <200 <3

ROLN04Lowland, very shallow and shallow, peat, small, medium and large size.

12 <200 <3

ROLN05Lowland, shallow, silicious/calcareous, medium size.

12 <200 3-15

ROLN06TLowland, very shallow and shallow, silicious/ calcareous.(therapeutic).

12 <200 <3

ROLN07Hills and tableland, very shallow and shallow, silicious, very small size.

10, 16 200-800 < 15

ROLN08Highland, very shallow and shallow, silicious, very small size.

10 >800 <-15

ROLN09

Lowland - temporary lakes, very shallow, silicious, small and medium size - Not validated.

12 <200 <3

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Fig. 1. Range of alkalinity values associated with Romanian national lake types, based on data collected between 2010 and 2013. Horizontal lines show limits between types and supertypes: blue line: 0.2 meq L-1 (threshold between low and moderate alkalinity supertypes); red line: 1 meq L-1 (threshold between moderate and high alkalinity supertypes). Note that the scale on the Y axis is logarithmic.

5. Pressure-impact relationships

The phytobenthos intercalibration exercise addresses the effect of eutrophication on benthic algal assemblages. Although, in theory, any nutrient may be limiting, in practice attention focuses on the role of phosphorus. Preliminary analyses (Figs 1 & 2) indicated that total nitrogen is unlikely to be the limiting nutrient except in a few already eutrophic instances in Romania. Lakes with high values of TN tend to have elevated TP as well, so it should be possible to use TP to indicate the primary “stress” gradients within the dataset.

As most of the lakes in Romania are shallow, additional consideration needs to be given to the nature of “pressure-impact” relationships in situations where cause-effect relationships are known to be non-linear (Moss, 2010). The status of the phytobenthos needs to be considered alongside that of other BQEs in order to understand the potential impacts of both “top down” and “bottom up” influences.

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Fig. 2. Relationship between Total Phosphorus (TP) and Total Nitrogen (TN) in Romanian lakes expressed as concentrations (left) and as the ratio between the two (right). Red line shows TN:TP = 16. All axes are shown logarithmically transformed.

Preliminary analyses established the performance of various widely-used diatom metrics available in the Omnidia package (version 5.3; Lecointe et al., 1993: Fig. 3). Note that the version of the TDI in this package is not the same as the metric currently used by UK and Ireland for assessing ecological status based on lake phytobenthos composition (Bennion et al., 2014). Linear regressions between the diatom metric and log10 TP and log10 Alkalinity indicate the ability of the metric to capture the pressure gradient, and the scale of interference from type / geological factors (Table 3). In all cases, both “pressure” and “type” were significant, though R2 was very low in the case of Sladacek’s index (INDSLA). The greatest contribution of alkalinity was seen in the case of the IBD, a “general degradation“ rather than a specific “eutrophication” index. The IPS, Rott’s TI and Rott’s SI all performed similarly with respect to both parameters.

The choice of Rott’s TI (Rott et al., 1999) as the basis for ecological status assessments in Romania is made for the following reasons:

Although Rott’s TI was not designed as a metric for assessing lakes, it performed well when applied to datasets from standing waters and was chosen as the “Intercalibration Common Metric” for the cross-GIG phytoplankton intercalibration exercise (Kelly et al., 2014);

Rott’s TI was, moreover, designed as a “trophic”, rather than a “saprobic” or “general degradation” metric, as is the case for the IPS and Rott’s SI. It should, in theory be most sensitive over the range where phosphorus exerts a causal effect on the phytobenthos, rather than merely correlate with high phosphorus concentrations at a part of the gradient where other factors (e.g. low oxygen concentration) are exerting a stronger effect on community composition;

As Rott’s TI was used as the Intercalibration Common Metric, the intercalibration process will be more straightforward, as there will be no additional errors introduced by regressing the national metric against the ICM in order to convert Romanian data to the common scale.

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However, as the work continued, limitations of the TI when applied to Romanian lakes became apparent. This will be discussed in more detail below (see 9.2) but the outcome was that lowland lakes in Romania are also subject to relatively high levels of organic pollution (manifest as high BOD levels, in particular) which complicates a comparison with data from other Member States where the primary stress gradient is due to inorganic nutrients. The possibility of using the IPS as an alternative national metric were explored; however, any benefits of using the IPS are cancelled out as this metric has to be converted back to TI in order to compare the boundary positions with those of other MS.

Table 3. Regression parameters between diatom index values and TP alone and in combination with alkalinity. Both TP and alkalinity were significant in all cases; “difference” indicates the increase in R2 through addition of Alkalinity to the equation.

Index TP TP + Alkalinity

R2 Slope Significance R2 Difference

IBD 0.223 -3.223 *** 0.341 0.118

TDI_20 0.152 -2.509 *** 0.173 0.021

IPS 0.242 -3.038 *** 0.286 0.044

Rott TI 0.221 -2.816 *** 0.270 0.049

Rott SI 0.263 -2.534 *** 0.297 0.034

EPI-D 0.203 -2.430 *** 0.239 0.036

INDSLA 0.039 -1.162 *** 0.054 0.015

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Fig. 3. Scatterplots showing the relationship between candidate diatom metrics and total phosphorus (TP, left) and alkalinity (right). Both X axes are displayed on a logarithmic scale.

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Fig. 3 (continued)

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6. Reference/benchmark conditions

Table 4 summarizes the criteria used to select reference sites. Although many lakes corresponding to ROLN08 have low levels of human impact, only one can be defined as a ”reference lake” following all the criteria listed in Table 4 (Lake Bucura, ROLN08). Other lakes may be added in the near future, once screening is complete. ”Benchmark” lakes, representing least disturbed conditions for this type, identified during the development of the macrophyte method.

The following criteria were used to identify benchmarks for lakes corresponding to E-C1:

no major point sources in catchment, complete zonation of the macrophytes in the littoral zone,

no (or insignificant) artificial modifications of the shore line, no mass recreation (camping, swimming, rowing) low/moderate fishing (fish standing stock <50 kg ha–1) Based on TP, TN, COD values and intensity of fishing a combined stressor was developed.

The stressor ranges from 0–4. Lakes considered as alternative benchmark sites have a combined stressor value <1.5. This means that: Fishing is low (fish stock <50 kg ha–1) Vegetation period mean TP <115 µg l-1

Vegetation period mean TN <1550 µg l-1

Eleven lakes fulfilled these alternative benchmark criteria, representing all lowland lake types except ROLN06T.

Benchmark sites from lowland lakes had significantly lower values of TP and TN than non-benchmark sites, but also had lower values of variables reflecting geological type (alkalinity and conductivity) (Fig. 4). There was no significant difference in TI or IPS values between benchmark and non-benchmark sites for lowland lakes (Fig. 5). These benchmark lakes were not used to set class boundaries; however, they were used to validate the boundaries, with an expectation that the average condition of these lakes should be ”good ecological status”.

A single ”true” reference site is available for ROLN08 at present although, as Figs 6 and 7 show, neither this nor the single ”benchmark” site for this type are typical for the type as a whole. The reference site has water that is softer than most of the other lakes, whilst the water at the benchmark site is harder. There is no difference in nutrient concentrations, but note that, overall, concentrations of nutrients are much lower in ROLN08 than in the lowland lakes. The reference site has lower Ti than other reference sites used in the moderate alkalinity intercalibration (Fig. 8); however, this may also reflect the relatively low alkalinity of this site. Lacu Rosu is a barrier lake in an area of volcanic rocks in the Eastern Caparthians which fulfils the type criteria for ROLN08 in all respects, even if it is atypical for the ”moderate alkalinity” subtype.

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Table 4. Criteria for establishing reference conditions of Romanian lakes.

I. Assessment criteria high ecological state for biological quality elements and supporting hydromorphological elements

and good ecological state for supporting physico-chemical elements.

II. Land use, agriculture, forestry influences of urbanization, land use and forestry must be reduced as much as possible; land use: > 85% natural (eg. “natural” forests, wetlands, marshes, meadows, pastures); without intensive crops (including vines), in the surroundings; ≤5% urbanization and peri-urban areas in the surroundings.

III. Pollution sources reduced impact of wastewater from scattered households = that can not be connected to a

centralized wastewater collection system (ie <10 inhabitants/km2) in the whole basin; values of specific synthetic pollutants must be below detection limit for the most advanced

analytical techniques that are available; the values for specific non-synthetic pollutants must be at most equal to the natural background value;

no direct discharges of treated or untreated wastewater.

IV. Hydromorphological alterations the level of direct morphological alteration (eg bank structures, river profiles and lateral

connectivity) must be reduced; only water abstractions that result in insignificant reduction of the flows that have very limited

effects on the quality elements are allowed; only flow regularizations that result in insignificant reduction of the flow that have very limited

effects on the quality elements are allowed; absence/ minor influence of artificial barriers upstream of the section; absence/ minor influence of artificial barriers downstream of the section; absence of cross artificial structures that can reduce natural water flow speed; absence of dams on short sectors (to protect against flooding).

V. Riparian vegetation riparian vegetation must be consistent with the type and geographical location of the river.

VI. Biological pressures Introduction of alien species

compatible with a minor alteration of indigenous biota by introduction of plant or animal species; without alterations caused by invasive plant or animal species.

Fisheries and aquaculture fishing operations should allow conservation of the structure, the productivity, the function and

the diversity of the ecosystem (including habitat and ecologically related dependent species) which the fishery exploitation depends on;

non indigenous fish stocks should not significantly affect the ecosystem structure and function; without bio-manipulation.

VII. Other pressures Recreational uses

no use of reference sections for recreational purposes (no intensive camping, swimming, boats, sailing).

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Fig. 4. Variation in values of key chemical variables (a: alkalinity; b: conductivity; c: total phosphorus (TP); and, d: total nitrogen (TN)) in benchmark and other lakes for all Romanian lowland lake types (excluding soda lakes). Results of Wilcoxon test for significance of differences: alkalinity: P < 0.001; conductivity: P < 0.001; TP: P < 0.01; TN: P < 0.001.

Fig. 5. Variation in values of TI and IPS between benchmark and other lakes for all Romanian lowland lake types (excluding soda lakes). Result of Wilcoxon test: Not significant in either case.

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Fig. 6. Variation in values of key chemical variables (a: alkalinity; b: conductivity; c: total phosphorus (TP); and, d: total nitrogen (TN)) in benchmark and other lakes for ROLN08 (highland lakes). Results of Kruskal-Wallis tests for significance of differences: alkalinity: P < 0.01; conductivity: P < 0.01; TP: Not significant; TN: not significant.

Fig. 7. Variation in values of TI (left) and IPS (right) between reference, benchmark and other lakes for ROLN08 (highland lakes). Result of Kruskal-Wallis test: Not significant in either case.

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Fig. 8. Comparison between TI values for the ROLN08 reference site (“RO”) and other reference sites used in intercalibration of the “moderate alkalinity” supertype (“GIG”). Result of Wilcoxon test: P < 0.01.

7. Boundary setting procedure

The issue of establishing status class boundaries for Romanian lakes is complicated by the complete absence of reference sites for the “lowland” types, and a single, possibly atypical, reference site for ROLN08, the “upland” type. For this reason, the average of reference values used elsewhere in the cross-GIG exercise have been used as the denominator in EQR calculations. These are: 1.88 for lowland lakes and 1.38 for upland lakes, expressed as TI. These values equate to IPS values of 15.8 and 18.0 respectively (Fig. 9). These values were used to derive boundaries between status classes (Table 5). These are presented for both TI and IPS although, for reasons explained above, the IPS cannot be intercalibrated either.

Average high/good and good/moderate boundaries from the phytobenthos intercalibration exercise, are then used to provide boundaries for Romania, again to overcome the problems in identifying a realistic set of reference and benchmark sites from which type-specific boundaries for Romania itself can be developed. Boundaries between the remaining classes are computed by division of the remaining scale.

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Table 5. Status class boundaries for “lowland” and “upland” lakes in Romania

Boundary “lowland lakes” “upland lakes”

TI as EQR IPS value TI as EQR IPS value

High / good 0.965 15.2 0.849 15.3

Good / moderate 0.790 12.5 0.588 10.6

Moderate / poor 0.530 8.4 0.392 7.6

Poor / bad 0.260 4.11 0.196 3.5

Fig. 9. Relationship between TI and IPS for Romanian lake dataset

8. Checking of WFD compliance and feasibility

8.1 Compliance checking

Table 6. Outcome of compliance checking for Romanian phytobenthos assessment system for lakes.

Compliance criterion Conclusions

1. Ecological status classified by one of five classes:, high, good, moderate, poor, bad.

Suggested approach for RO is to adopt the ICM (ROTT_TI) as national metric, along with median positions of intercalibrated boundaries. Lower class boundaries will be obtained by division of the remaining scale.

2. High, good and moderate status set in line with WFD’s normative definitions

See above. RO boundaries will be median of other national boundaries set according to normative definitions.

3. All relevant parameters Exercise intercalibrates one component of BQE “Macrophytes

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Compliance criterion Conclusions

indicative of BQE are covered and Phytobenthos”. In line with other MS, phytobenthos abundance has not been included in assessments.

4. Assessment is adapted to intercalibration common types ...

Lake phytobenthos intercalibration grouped common types into three “supertypes”. Most RO lakes fit into “high alkalinity” supertype; ROLN08 has been assigned to the “moderate alkalinity” supertype. ROLN06T is associated with very high conductivity water and has been omitted from the intercalibration exercise.

5. The water body is assessed against type-specific near-natural reference conditions

No. Only one reference site is available at present.

Continuous benchmarking was performed in the cross-GIG phytobenthos intercalibration exercise, so no alternative benchmark criteria have been defined. The only option is to use the average values for all reference sites in the X-GIG supertypes as the denominator for RO lakes.

6. Assessment results expressed as EQR

Yes.

7. Sampling procedures allow for representative information about water body quality / ecological status in space and time

Two or three samples per year are collected from the margins at a central portion (i.e. approximately mid-way between inflow and outlet).

8. All data relevant for assessing the biological parameters specified in the normative definitions are covered by the sampling procedure

See response to point 3.

9. Selected taxonomic level achieves adequate confidence and precision in classification

Yes: diatoms are identified to species level.

8.2 Feasibility checking

This has largely been considered in previous sections:

Typology - All RO types have been assigned to the appropriate “supertype”.

Pressure and assessment concept - RO shares the same concepts with other Member States whose phytobenthos methods have already been intercalibrated; however, high levels of BOD (see below) may confound comparisons against lakes where diatom assemblages are presumed to respond primarily to inorganic nutrients.

Collection of intercalibration dataset - see section 3 (“National data set”) and Table 7.

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Benchmark standardisation - this was not carried out. The main phytobenthos intercalibration exercise used continuous benchmarking and it is not possible to add one extra MS to this without repeating the whole process.

Table 7. Data acceptance criteria used for the data quality control and data acceptance checking (after Kelly et al., 2014)

Criterion Data acceptance checking RO compliance?

Data requirements (obligatory and optional)

Obligatory: littoral diatom samples and TP, collected according to criteria below

Optional: other water chemistry

Yes

Sampling and analytical methodology

All sampling and analysis is based on EN13946 and EN14407

Yes

Level of taxonomic precision Species level identification ; data provided with Omnidia (four-letter) codes

Yes

Minimum number of sites/samples per intercalibration type

Not specified. Varies between types See Section 3

Coverage of status gradient Sufficient coverage of all relevant quality classes per type

See note 1

Note 1: ROLN08 has limited coverage below good status; this is a characteristic of this lake type, whose representatives are located far from centres of population and are not subject to significant anthropogenic pressure; “lowland” lake types, on the other hand, have representatives spanning the entire status gradient.

9. IC procedure

The approach adopted (i.e. using the ICM as the national metric and the average of intercalibrated boundaries precludes the need for formal intercalibration. This section focuses on the performance of the proposed system, when applied to Romanian lakes.

9.1 ROLN08 – “upland” lakes

Fig. 10 shows ROLN08 samples overlain onto a graph of MA lake samples from currently intercalibrated methods. When the average reference value for the GIG is used to calculate the EQR, most Romanian samples fit the main trend of the data, but are clustered at “high status”. When the average of samples from the sole ROLN08 reference lake is used as the denominator, the Romanian samples do not stand above the main trend. In both cases, a consequence of the short gradient is that a linear regression between the ICM and TP is not significant although supporting evidence suggests that these boundaries are reflecting the true status of these lakes.

In order to validate these boundaries (set without direct reference to Romanian data), samples from “reference” and “benchmark” lakes were plotted against the high and good status boundaries (Fig.

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11). Both reference and benchmark samples were largely classified as “high status” (using the GIG average), with some samples from the sole benchmark site within “good status”. However, when just the Romanian data are used to calibrate the EQR, there is a clear distinction between reference (high status) and benchmark (good status) samples (Fig. 10, right. Interpretation is complicated because the sole Romanian reference site is, itself, chemically distinct from other ROLN08 lakes (Fig. 6). The first option was that used for the lake phytobenthos intercalibration, so shows Romanian data on a common scale with other Member States and is, perhaps, to be preferred for this exercise.

9.2 “lowland lakes”

Fig. 12 shows all lowland lake types overlain onto a graph of HA lakes from currently intercalibrated methods. This pan-European data shows a strong relationship between EQR and TP (r2 = 0.407) and there is also a statistically significant relationship between EQRs from Romania data and TP. However, the strength of the relationship, measured as r2, is low (0.036), with the data showing a V-shaped pattern with a lower “arm” below the trend of the pan-European data and the upper “arm” above it. The two “arms” join as the water becomes more eutrophic. A consequence of this is that a large number of lowland lakes are at less than good status, despite relatively low TP concentrations. This effect cannot be attributed to different behaviour between types; rather most well-represented types seem to consist of a large number of samples with low EQR values and a few with higher values (Figs 13 and 14). ROLN07 straddles a wide range of EQR values, but this type is represented by just three lakes and 12 samples. Samples from benchmark lakes, too, are generally classified as less than good status, with the exception of ROLN07 (Fig. 15). Although there are significant differences in both TN and TP concentrations in ecological status classes (Fig. 16), comparisons with macrophytes and phytoplankton (Fig. 17) shows that phytobenthos is relatively insensitive when compared to these other BQEs.

Fig. 10. The intercalibration dataset for moderate alkalinity lakes (red circles) with ROLN08 data superimposed (blue circles) with EQR denominator calculated using average of reference sites for all MS in the intercalibration exercise (left) and just Romanian data (right). Horizontal lines are the average position of high/good (blue) and good/moderate (green) boundaries for participating

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countries. Regression statistics: intercalibration data: F = 44.84, P < 0.001, r2 = 0.20; ROLN08 data: F = 2.15, not significant, r2 = 0.05.

Fig. 11. Boxplot showing the distribution of samples from “reference”, “benchmark” and “other” lakes with EQR denominator calculated using average of reference sites for all MS in the intercalibration exercise (left) and just Romanian data (right). Horizontal lines are the proposed positions of Romanian high/good (blue) and good/moderate (green) boundaries.

Fig. 12. The intercalibration dataset for high alkalinity lakes (red circles) plus Romanian “lowland” data (blue circles). Horizontal lines are the average position of high/good (blue) and good/moderate (green) boundaries for participating countries. Regression statistics: intercalibration data: F = 338.4, P < 0.001, r2 = 0.41; ROLN08 data: F = 11.6, P < 0.001, r2 = 0.04.

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Having ruled out lake type as an explanation for the unexpected behaviour of Romanian lowland lakes, a further series of analyses were performed using Non-metric Multidimensional Scaling (NMDS) in the Vegan package (Okensanen et al. 2006) in R (R Development Core Team, 2006). An ordination of all samples from lowland lakes had high stress (0.291), meaning that the original dissimilarities were not well preserved in the reduced number of dimensions, and that interpretation should proceed with caution. However, the Envfit function (Zelený & Schaffers, 2012) within Vegan indicates that several of the environmental variables have significant effects on the final ordination (Fig. 18; Table 8). In particular, BOD has a particularly strong relationship (stronger than that for TP) whilst NO3-N also has a strong effect. Interestingly, the effect of NO3-N is distinct from those of both BOD and TP whilst conductivity and alkalinity have impacts that are distinct from both of these.

The strong effect of BOD on the structure of the ordination suggests that the lowland lakes may be responding to organic pollution, as well as nutrients. Fig. 19 shows how two variables often associated with organic pollution are distributed within the lowland lake dataset: values of NH4-N are generally quite low, with the median at a point where NH4-N is unlikely to have a major effect on the ecology (Fig. 19, left). Values BOD, however, are high, with a large number of sites > 10 mg L -1 O2, suggesting a high potential for direct effects on the biota. Strong intercorrelations within the dataset (e.g. TP and BOD: Spearman’s rank correlation = 0.461 ***) suggest that it is not possible to interpret the relationship between TP and diatom metrics without also considering the confounding effect of BOD. It is possible that a combination of polluted inflow water, and stirring of sediments by bottom-feeding fish contribute to the high BOD of these lakes.

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Fig. 13. Response of Romanian lowland lake diatom samples to TP, considered by lake type. Open circles: whole lowland lake dataset; red circles: single type.

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Fig. 14. Distribution of EQR values in lowland lakes considered by type. Horizontal lines indicate position of high/good (blue) and good/moderate (green) status boundaries.

Fig. 15. Distribution of EQR values from lowland benchmark lakes, considered by type. Horizontal lines indicate position of high/good (blue) and good/moderate (green) status boundaries. Note that ROLNCAPMLA01 does not have any benchmark lakes.

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Fig. 16. Relationship between ecological status class (determined by phytobenthos) and values of four environmental variables: alkalinity (top left, Kruskal Wallis test: not significant), TP (top right, P < 0.01); TN (bottom left, P < 0.05); BOD5 (bottom right, not significant).

Fig. 17. Comparison between average EQRs for phytobenthos and phytoplankton (a.) and macrophytes (b.) for lowland lakes in Romania. Note that there is a positive correlation (Pearson’s correlation coefficient, r = 0.41, P < 0.01) between average values of macrophyte and phytoplankton EQRs)

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Table 8. Results of Envfit analyses on Romanian lowland lake dataset. NMDS1 and NMDS2 give the vector co-ordinates for each environmental variable; r2 and Pr indicate the strength of the relationship and its significance.

Variable Transformation NMDS1 NMDS2 r2 Pr(>r)

Alkalinity Log10 0.90051 0.43483 0.0754 0.001 ***

pH 0.22262 0.97491 0.0558 0.001 ***

PO4-P Log10 0.58784 -0.80898 0.0002 0.971

TP Log10 0.82482 -0.5654 0.1115 0.001 ***

NH4-N Log10 -0.9785 0.20626 0.0033 0.63

NO2-N Log10 -0.31125 0.95033 0.0048 0.516

NO3-N Log10 -0.65085 0.75921 0.1255 0.001 ***

DO conc. 0.84185 0.53971 0.031 0.011 *

BOD Log10 0.99977 -0.0215 0.3125 0.001 ***

Conductivity Log10 0.9478 0.31887 0.1239 0.001 ***

Fig. 18. NMDS ordination plot of diatom assemblages in Romanian lowland lakes, overlain with vectors for the principal environmental variables.

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Fig. 19. Histograms showing distribution of NH4-N (left) and 5-day BOD values in the lowland lakes datasets.

A final possibility is that the TI, itself, is not as effective as it could be for analyses of Romanian lakes. Fig. 20 shows the distribution of indicator taxa used in the TI, as a proportion of the total and Table 9 lists these taxa. Of particular note is the relatively large number of records where taxa which lack a sensitivity value (i.e. nominally set to “0.0”) constitute more than a quarter (i.e. 25%) of all the diatoms, and even a small number where these taxa constitute > 50% of the total.

Although the TI proved to be a useful “common metric” in the lake phytobenthos intercalibration exercise, it is important to remember that it was originally designed for rivers, not lakes, and has not been updated since the original version in 1999, despite many developments in taxonomy and changes in nomenclature since that date. Although diatoms in the shallow littoral zone of lakes are subject to similar pressures to those in streams (Cantonati & Lowe, 2014), the unique nature of Romanian lowland lakes may have pushed this metric beyond the point where it can be safely used. As the TI is the intercalibration metric, this problem will remain even if a different metric were selected as the national metric.

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Fig. 20. Distribution of taxa, grouped by TI sensitivity values (“TIs”), in the Romanian lake dataset

Table 9. List of taxa with sensitivity scores for the TI that are either frequent (i.e. in > 10% of samples) or abundant (i.e. form at least 10% of the total in at least one sample) or both in the Romanian lake dataset. The list includes taxa from all Romanian lake types.

Taxon Frequency Maximum RAAchnanthes brevipes Agardh 1.9 36.0Achnanthes longipes Agardh 2.5 50.3Achnanthes microcephala (Kutzing) Grun. 0.8 24.1Amphiprora alata Kutzing 6.6 24.0Amphora coffeaeformis (Agardh) Kutzing 10.4 42.6Aulacoseira granulata (Ehr.) Simonsen 3.0 29.2Ctenophora pulchella (Ralfs ex Kutz.) Williams et Round 11.5 84.0Cyclotella atomus Hustedt 5.2 19.0Cyclotella comta (Ehr.)Kutzing 5.5 15.7Cyclotella species 2.5 24.8

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Taxon Frequency Maximum RACylindrotheca closterium Reimann & Lewin 0.5 17.0Cymbella lanceolata (Agardh ?)Agardh 27.0 22.4Cymbella ventricosa Agardh 39.3 47.2Diatoma elongatum (Lyngbye) Agardh 21.9 65.8Diploneis sp.. 0.3 11.2Encyonema ventricosum (Agardh) Grunow 4.6 13.8Eunotia acus Metzeltin & Lange-Bertalot 0.8 20.2Eunotia serra Ehrenberg.var.diadema (Ehr.) Patrick 0.3 15.3Fragilaria crotonensis Kitton 6.3 13.3Fragilaria intermedia Grunow 10.4 40.6Fragilaria vaucheriae (Kutzing) Petersen 1.9 16.6Gomphocymbella species 1.4 11.4Gomphoneis olivacea(Hornemann)Dawson ex Ross & Sims 0.5 19.4Gomphonema angustatum (Kutzing) Rabenhorst 4.4 11.1Gomphonema constrictum Ehrenberg 7.7 23.6Gomphonema longiceps Ehr. 6.8 16.1Gomphonema staurophorum (Pantocsek) Cleve-Euler 2.7 11.7Hantzschia amphioxys var.vivax Grunow in Cl. & Grun. 0.3 14.0Mastogloia smithii Thwaites 9.6 36.0Melosira granulata (Ehr.) Ralfs 10.4 42.9Navicula gracilis Ehrenberg 8.5 16.4Navicula sp. 4.4 100.0Navicymbula pusilla Krammer var pusilla 10.1 93.8Nitzschia actinastroides (Lemm.) Van Goor 0.3 13.5Nitzschia austriaca Hustedt 3.8 71.0Nitzschia closterium (Ehr.) W.Smith 4.1 27.9Nitzschia constricta (Gregory) Grunow 7.7 14.5Nitzschia holsatica Hustedt 4.1 45.8Nitzschia nana Grunow in Van Heurck 2.2 24.7Nitzschia paradoxa (Gmelin) Grun 0.5 16.1Nitzschia romana Grunow 9.6 36.4Nitzschia species 2.7 23.2Nitzschia vitrea Norman 1.6 12.0Planothidium cyclophorum(Heiden) Van de Vijver 0.3 19.8Rhopalodia gibberula (Ehrenberg) O.Muller 2.2 22.0Rhopalodia musculus (Kutzing) O.Muller 1.9 16.7Stephanodiscus astraea(Ehrenberg)Grunow 7.1 43.7Surirella species 0.5 10.3Synedra berolinensis Lemmermann 0.3 11.4Synedra tabulata (Agardh)Kutzing var. tabulata 6.3 46.3Synedra vaucheriae Kutz. 4.6 17.2

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10. Normalisation of boundaries

For reporting ecological status, it is convenient to adjust the EQR scale so that boundaries between status classes occur at EQR = 0.2, 0.4. 0.6 and 0.8. The procedure for doing this consists of three steps:

1. Express the original (i.e. untransformed) EQR as a proportion of the class width:

EQRas proportion=( EQR−lower class limitrawUpper class limitraw−lower class limit raw )

where: EQR is the value to be normalised; “lower class limitraw” is the lowest limit (as EQR) of the class to which the sample belongs, and “upper class limitraw” is the highest EQR value of the class to which the sample belongs.

2. Adjust this value to the width of the normalised class (i.e. 0.2 of the total EQR scale):

EQRas normalised class width=EQRas proportion×0.2

3. Calculate normalised EQR by adding this value to the lower class limit of the normalised class:

Normalisd EQR=EQRasnormalised classwidth+lower classlimit normalised

Example: an RO01 sample with EQR = 0.55 would fit into the “poor status” class (EQR 0.29-0.59). The normalised EQR for this would be as follows:

EQRnormalised=0.55−0.290.59−0.29

×0.2+0.2

EQRnormalised=0.87×0.2+0.2

EQRnormalised=0.37

For high status, the highest recorded EQR is used as an upper anchor. It is possible that future EQRs will exceed this value and, if this happens, the final normalised EQR should be rounded down to 1.0. Table 10 provides information to help with this calculation.

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Table 10. Equations for normalising EQRs.

Status “raw” EQR Normalised EQR

ROLN01, ROLN02, ROLN03, ROLN04, ROLN05, ROLN07

High 0.965 ((EQR-0.965)/(1.6-0.965))*0.2+0.8

Good 0.79 and < 0.965 ((EQR-0.79)/(0.965-0.79)) * 0.2 + 0.6

Moderate 0.53 and < 0.79 ((EQR-0.53)/(0.79-0.53)) * 0.2 + 0.4

Poor 0.26 and < 0.53 ((EQR-0.26)/(0.53-0.26)) * 0.2 + 0.2

Bad < 0.26 (EQR/0.26) * 0.2

ROLN08

High 0.849 ((EQR-0.849)/(1.31-0.849))*0.2+0.8

Good 0.588 and < 0.849 ((EQR-0.588)/(0.849-0.588)) * 0.2 + 0.6

Moderate 0.392 and < 0.588 ((EQR-0.392)/(0.588-0.392)) * 0.2 + 0.4

Poor 0.196 and < 0.392 ((EQR-0.196)/(0.392-0.196)) * 0.2 + 0.2

Bad < 0.196 (EQR/0.196) * 0.2

11. Conclusions

This report used the principles developed from the cross-GIG lake phytobenthos intercalibration process as the basis for an operational system for evaluating ecological status in Romanian lakes. The approach adopted for phytobenthos intercalibration, using established metrics as intercalibration common metrics, means that the intercalibration metric itself should provide a robust means of assessing ecological status for those Member States considering the adoption of new methods.

The method was tested on all Romanian lake types with the exception of soda / therapeutic lakes, (ROLN06T) where the predominant stressor gradient is a natural salinity gradient, rather than eutrophication, and temporary lakes (ROLN09), which have not been intercalibrated in any other Member States.

A further lake type, ROLN08, has a short pressure gradient which means that the dataset does not fulfill all of the criteria required for formal intercalibration. However, the Romanian data fits within the trend established by the phytobenthos intercalibration process and the boundaries proposed will provide benchmarks against which changes in the diatom assemblage can be judged.

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Finally, particular problems were encountered using this approach in lowland lakes. In particular, many Romanian lakes had a lower ecological status at a given TP value than expected from previous analyses of European lake phytobenthos. Further analysis of the dataset suggested that this was due to confounding effects of other variables, in particular BOD. The high values of BOD may be due to a combination of polluted inflow water and to the stocking of these lakes with bottom-feeding fish, which will remobilise organic matter and nutrients. There were also some concerns about the performance of the TI in these lakes although, in general, the boundaries proposed seem to reflect ecological status.

Overall, however, the assumption in the lake phytobenthos intercalibration exercise was that diatom assemblages were responding, primarily, to inorganic nutrients. The evidence from this study is that lowland lakes in Romania are subject to multiple pressures, and it is not possible to make a “like-for-like” comparison with data from other countries. At this stage, therefore, Romania will not proceed with intercalibration; work will continue to develop an effective national metric that meets the particular assessment needs of Romanian standing waters. The method should be reported to ECOSTAT but not formally intercalibrated, citing interferences from other pressures as the primary reason why a comparison with other Member States cannot be performed. However, the thresholds proposed in this report appear to set a precautionary boundaries that can be used in the interim whilst further development is undertaken.

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12. References

Bennion, H., Kelly, M.G., Juggins, S., Yallop, M.L., Burgess, A., Jamieson, J. & Krokowski, J. (2014). As -sessment of ecological status in UK lakes using benthic diatoms. Freshwater Science 33: 639-654.

Cantonati, M., & Lowe, R.L. (2014). Lake benthic algae: toward an understanding of their ecology. Freshwater Science 33:475–486.

Kelly, M.G., Bennett, C., Coste, M., Delgado, C., Delmas, F., Denys, L., Ector. L., Fauville, C., Ferreol, M., Golub, M., Jarlman, A., Kahlert, M., Lucey, J., Ní Chatháin, B., Pardo, I., Pfister, P., Picinska-Faltynowicz, J., Schranz, C., Schaumburg, J., Tison, J., van Dam, H. & Vilbaste, S. (2008). A comparison of national approaches to setting ecological status boundaries in phytobenthos assessment for the European Water Framework Directive: results of an intercalibration exercise . Hydrobiologia 695 109-124.

Kelly, M., Urbanic, G., Acs, E. Bennion, H., Bertrin, V., Burgess, A., Denys, L., Gottschalk, S., Kahlert, M., Karjalainen, S.-M., Kennedy, B., Kosi, G., Marchetto, A., Morin, S., Picinska-Fałtynowicz, J., Poikane, S., Rosebery, J. Schoenfelder, I., Schoenfelder, J., Varbiro, G.(2014). Comparing aspirations: intercalibration of ecological status concepts across European lakes for littoral diatoms. Hydrobiologia 734: 125-141.

Krammer, K. & Lange-Bertalot, H. (1986). Die Süsswasserflora von Mitteleuropa 2: Bacillariophyceae. 1 Teil: Naviculaceae. 876pp. Stuttgart: Gustav Fischer-Verlag.

Krammer, K. & Lange-Bertalot, H. (1988). Die Süsswasserflora von Mitteleuropa 2: Bacillariophyceae. 2 Teil: Bacillariaceae, Epithemiaceae, Surrielaceae. 596pp. Stuttgart: Gustav Fischer-Verlag.

Krammer, K. & Lange-Bertalot, H. (1991a). Die Süsswasserflora von Mitteleuropa 2: Bacillariophyceae. 3 Teil: Centrales, Fragilariaceae, Eunotiaceae. 576pp. Stuttgart: Gustav Fischer-Verlag.

Krammer, K. & Lange-Bertalot, H. (2004). Die Süsswasserflora von Mitteleuropa 2: Bacillariophyceae. 4 Teil: Achnanthaceae, Kritische Erganzungen zu Navicula (Lineolatae) und Gomphonema. Gesamtliteraturverzeichnis Teil. 436pp. Stuttgart: Gustav Fischer-Verlag.

Lecointe, C., Coste, M. & Prygiel, J. (1993). "Omnidia": software for taxonomy, calculation of diatom indices and inventories management. Hydrobiologia 269/270: 509-513.

Oksanen J., Kindt, R., Legendre, P. & O'Hara R.B. (2006) Vegan: community ecology package version 1.8-5. http://cran.r-project.org/

R Development Core Team (2006) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org.

Rott, E., Pipp, E., Pfister, P., van Dam, H., Ortler, K., Binder, N. & Pall, K. (1999). Indikationslisten für Aufwuchsalgen in Österreichischen Fliessgewassern. Teil 2: Trophieindikation. 248pp. Bundesministerium fuer Land- und Forstwirtschaft, Wien, Austria.

Zelený D. & Schaffers A.P. (2012): Too good to be true: pitfalls of using mean Ellenberg indicator values in vegetation analyses. Journal of Vegetation Science, 23: 419–431.

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Appendix 1: Rationale for excluding ROLN06T from intercalibration

National Institute of Research and Development for Biological Sciences, Bucharest

and

Romanian Waters National Administration | ANAR

A1. Introduction

ROLN06T represents soda / therapeutic lakes, associated with very high alkalinity and conductivity values, and has been excluded from the analyses described above due to their distinctive character (see also distinctive Bulgăreanu, 1993, 1996).

In this appendix, the characteristics of these therapeutic lakes are described in relation to other lowland lakes in Romania. A dataset composed of 47 samples from six therapeutic lakes and 45 observations from other lowland lakes was analyzed.

A2. Descriptive statistics

The following 11 variables have been investigated: conductivity, alkalinity, pH, dissolved oxygen, BOD5, calcium, magnesium, total hardness, suspended solids, TDS and chlorophyll a. Differences between variables values between the two lake categories (e.g. ROLN06T and other lakes (ROLN01, ROLN09, ROLN05, ROLNPM01, ROLNPM02, ROLN02, ROLN03, ROLN04)) were tested for significance using Wilcoxon Rank Test.

In this section the results of the following four variables will be presented: Conductivity, Magnesium, Total hardness and Total Dissolved Solids (TDS), since these are the ones which best discriminated between the two types of lake.

Conductivity

A summary of the values of the variable conductivity, taking into account the two type categories, is given in the following table (Table A1) (all values in µS/cm):

Table A1:

Variable Lake type Mean Median SD IQR Min Max

Conductivity other types 2236.25 876.26 3150.38 2397.44 16.66 15588.33Conductivity ROLN06T 73164.11 70885.44 52698.00 91698.35 20854.58 132750.00

There is a large difference (the Wilcoxon Rank Test p-value is 2.46e-07 hence the null hypothesis is rejected in favor of the alternative of different distributions) in conductivity values the therapeutic lakes (of type ROLN06T) and the lakes of other types from the lowland region. The next figures illustrate this characteristic:

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Figure A1

Figure A2 shows how conductivity differentiates between the two lake types.

Figure A2

Magnesium

The following table (Table A2) presents the mean, median, standard deviation, IQR (Inter-Quartile Range) and minimum and maximum values for the Magnesium variable (all values in mg/L):

Table A2:

Variable Lake type MeanMedia

n SD IQR Min Max

Magnesium other types 90.39 46.77 101.94 94.01 1.11 396.07Magnesium ROLN06T 1343.99 965.46 1074.37 1616.42 307.95 2839.87

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The average value of Magnesium for therapeutic lakes is almost 15 times greater than values for other lakes. This difference was confirmed when tested for significance using Wilcoxon Rank Test (with a p-value of 0.000164).

These differences in the distribution of the Magnesium variable can also be seen in the following boxplot and scatter plot:

Figure A3

Separation of lakes using magnesium is not as pronounced as for conductivity but is still at an acceptable level (see Figure A4), thus the variable Magnesium will be an important factor in the cluster analysis.

Figure A4

Total hardness

Descriptive statistics for total hardness are given below (Table 3) (all values in mg/L CaCO3):

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Table A3:

Variable Lake type Mean Median SD IQR Min Max

Total hardness other types 423.00 288.64 395.16 383.76 2.15 1652.57Total hardness ROLN06T 6497.1

53002.57 6504.90 8028.9

91544.07 16860.63

The mean and median values of total hardness are about ten times greater for therapeutic lakes compared with other freshwater lakes. Differences in the distributions of total hardness are illustrated in Figure A5 and were tested for significance using Wilcoxon Rank Test (p-value of 0.000119).

Figure A5

Figure A6 presents the number of observations for each value of the total hardness variable by lake type. As for magnesium, total hardness has a good potential for separating the lakes.

Figure A6

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Total dissolved solids (TDS)

Summary statistics for TDS are shown in Table A4 (all values in mg/l).

Table A4:

Variable Lake type Mean Median SD IQR Min Max

Total dissolved solids

other types

1583.40 517.51 2441.13 1607.12 11.03 11183.66

Total dissolved solids

ROLN06T 53228.59 48045.93 37342.86 62149.59 18471.48 101087.12

There are significant differences (the Wilcoxon Rank Test p-value is 0.000104 showing that the distributions are not the same) between the values of the variable in the two lake categories investigated. These differences are also illustrated by the following figures:

Figure A7

The next figure shows that the variable Total Dissolved Solids separates very well the lakes so it will represent a potential factor of classification in the forthcoming analysis.

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Figure A8

A3. Cluster analysis

The last section highlighted four variables with a high potential for discriminating between the two categories (therapeutic lakes as lakes of type ROLN06T and lakes of other types).

Hierarchical agglomerative clustering methods build a hierarchy of clusters. Essentially the method starts with each observation (lakes) in its own cluster and one by one, joins the clusters together, at each step choosing to merge the two clusters which are closest. There are several methods used for finding the distance between a cluster and an observation (such that the clusters can be joined together) but in this analysis the Ward method is employed, since this method determines clusters of more equal sizes (useful when other methods find clusters with only a few observations).

For the distance between the observations the Canberra distance is selected since it performs its own standardization and it is not so sensible at high values, as other distances are:

dC ((x1 ,…,xn) ,( y1,…, yn))=∑i=1

n

¿ x i− y i∨¿

¿ x i∨+¿ y i∨¿¿¿

The result of a hierarchical clustering method is a tree-like structure, known as a dendrogram, with the branches of similar observations closer together in the tree.

The following figures show the dendogram and the corresponding cluster plot obtained from the hierarchical clustering method for the analyzed data set:

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Figure A9

Figure A10

A group of 8 lakes was identified in which all 6 therapeutic lakes observations were found grouped. The 8 lakes identified are: Ciulnita, Balta Amara, Sarat Movila Miresii, Fundata, Amara, Balta Alba, Sarat-Braila, Techirghiol sarat.

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Next table (Table A5) shows the median values of the four variables used in the analysis taking into account the type of the lake:

Table A5:

Lake type Conductivity Magnesium Total hardness Total dissolved solids

other types

1492.70 54.92 335.90 865.21

ROLN06T 28117.08 626.81 2278.76 21053.42

To see how well an observation (lake) belongs to the cluster identified by the hierarchical algorithm, the following silhouette plot was generated. A silhouette plot (or analysis) is a means of evaluating the statistical strength of the clusters Each vertical bar in the plot (the silhouette width) measures how well the observation belongs to the cluster to which it has been assigned (in our case the two predefined clusters). This measure (the silhouette width) is calculated by comparing (and normalizing) the closeness of a lake to other lakes inside its own cluster (other types and ROLN06T) with that for lakes outside the cluster (the closeness is computed by averaging the dissimilarity measures, which are based on the distance measure of the chosen clustering method – here Canberra distance). Silhouette widths can be interpreted as follows:

If the silhouette width of an observation has a large value (close to 1) than the observation is well clustered (it belongs to the group to which it was assigned)

If the silhouette width of an observation has a value near 0 than the observation lies between two clusters (i.e. the observation has a similar value for the measure of closeness for within and outside the cluster observations)

If the silhouette width of an observation has a negative value than the observation has been probably wrongly assigned to the cluster (the average dissimilarity between the observation and the observations within the cluster is greater than the average dissimilarity between the observation and all other observations).

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Figure A11

Two observations have a negative silhouette which means that these were wrongly assigned to the cluster of therapeutic lakes. These two observations are Balta Amara and Ciulnita, neither of which is a therapeutic lake.

A4. Classification analysis

The previous section showed that when clustering methods are applied to a data set, the values of the variables show if there is any grouping structure in the data. An interesting problem will be to see if a new observation belongs to one of the groups identified naturally in the cluster analysis step. The statistical methods used to predict which of several non-overlapping groups the new observation belongs to are called classification methods.

In the next subsection one such method is presented, namely the random forests method.

It should be mentioned that the initial set of observations was divided in two subsets: a first subset of observations that will serve as a training set for the model and a second subset that will be used as a test set. The training data set is formed of 45 lakes from the lowland region (here all the therapeutic lakes were included) and the test data set (8 observations) contains lake observations from hilly and alpine areas.

Random forests is a method for classification (or regression) based on the construction of set of decision trees (in our case 1000) from the training data set and returning the class of the new observations in the test set by choosing the most frequent class of the individual trees. Each decision tree is built using a random subset of observations and variables from the training data set (that is, a random subset of rows and columns).

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The rationale behind the method is that applied on a collection of individually imperfect models (or trees), then by averaging the results of all these models one can find a better model from their combination than any of the individual parts.

Due to the high number of trees grown, this method can be computationally expensive.

The following figure shows the “out-of-the-bag” error rate (“OOB”) based on the number of trees generated for each category and in total. The Random forests method involves bootstrap sampling of the input data. In this sampling, about 37% of the data is not used for training and can be used for testing. These are called the “out of bag” samples and the error estimated on these is the “out of bag error” (the proportion of how many of the left out samples are not correctly classified). The OOB error is a means for assessing the accuracy of the Random forest method (there is some technical discussion about this but, in practice, the OOB error overestimates the true error) by saying that the accuracy is 1-OOB error, that is if the error is small then the classifier has done a good job. In our case, if we populate the forest with more than 450 trees we observe that the OOB error is close to 0 and the algorithm performs very well (has a high accuracy).

Figure A12

One can see that after approximatively 450 decision trees, the error rate does not fluctuate anymore. The next plot gives us the order of the importance for the variables used in our model:

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Figure A13

The variables that contribute most to the classification process (those that have a higher value of mean decrease Gini) are: Total dissolved solids and Conductivity, as we expected from the exploratory phase. The values for the average decrease in Gini index and the average decrease of accuracy (a measure of how bad the model performs without a variable) for each variable are given in the table (Table A6) below:

Table A6:

VariableMean Decrease

Accuracy Mean Decrease Gini

Total dissolved solids 0.09 3.56Conductivity 0.08 3.38

Total hardness 0.03 1.89Magnesium 0.01 1.05

It is observed that the classification error is 0%, but due to the small amount of data used the model may be overfitted.

To compute the prediction error, a test set formed of lake observations from the hilly and alpine regions (a total of 8 observations) was used. The prediction is given in the following table (Table A7) for each observation:

Table A7:Lake name True lake type Predicted lake type

Bodi Mogosa other types other typesStiucilor other types other types

Buhaescu other types other typesLala other types other types

Lacu Rosu other types other typesIt is worth mentioning that 3 out of the 8 observations contained missing values and could not be included in the computation. The prediction error observed is 0%.

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A5. Composition of diatom assemblages from therapeutic lakes

Having established that these therapeutic lakes have a distinctive charcter, the structure of diatom assemblages was further investigated using Non-metric MultiDimensional Scaling, NMDS.

Preliminary NMDS analysis produced an ordination with an acceptable level of stress (0.153) after three attempts. The first two axes of this ordination were then correlated with conductivity, nutrients and biological metrics (Table A8). Both axes were more strongly correlated with conductivity than with nutrients or with biological properties, suggesting that this variable (as a proxy for osmotic stress) exerts a strong influence on the assemblage. TN is, in addition, strongly correlated with Conductivity. As the phytobenthos intercalibration deals only with eutrophication pressure, it would be inappropriate to include this lake type in subsequent analyses. There is a relationship between axis 2 and Rott’s TI (the intercalibration metric) but it is not possible to determine whether this is causal or a result of autocorrelations within the dataset.

Table A9 lists taxa recorded at a maximum relative abundance of 20% or greater, along with broad indications of habitat. Nomenclatural differences mean that it was not always possible to match a species to descriptions in recent literature but, notwithstanding this matter, eight taxa (31%) are rarely encountered in freshwaters and a further five are freshwater species known to extend into brackish conditions. Again, this suggests that conductivity / salinity / osmotic stress is a major factor shaping the diatom assemblages in this type.

Table A8: Pearson correlation coefficients for the relationship between the first two axes of NMDS analysis of diatom assemblage data from ROLN06T samples with chemical and biological properties of the lakes. N.S. = not significant; * = P 0.05; ** = P 0.01, > 0.001; *** = P 0.001.

Variable Axis 1 Axis 2

Conductivity 0.521 *** 0.470 ***

TP 0.349 * 0.411 **

TN 0.407 ** 0.415 **

Rott’s TI 0.205 N.S. 0.425 **

“Diversité” (from Omnidia)` 0.077 N.S. 0.436 **

Shannon-Wiener diversity 0.068 N.S. 0.322 *

Number of taxa 0.486 ** 0.418 ** Table A9: List of diatom species recorded at a maximum relative abundance of 20% or more during analyses of samples from ROLN06T lakes between 2010 and 2014. Number of records indicates the constancy of each taxon within the type (max: 40) and comments on habitat are derived from Hartley et al. (1996) and Hofmann et al. (2011). Habitat types are: F (freshwater), B (brackish), M (marine) and ? (unknown, possibly indicating nomenclatural issues).

Species name Number of records

Maximum Relative Abundance Comment / Habitat

Achnanthes brevipes 7 36.0 B

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Achnanthes longipes 8 50.3 M

Amphora coffeaeformis 20 42.6 M,B

Amphora ovalis 6 24.7 F

Campylodiscus noricus 3 44.0 F?

Cocconeis placentula 10 35.7 F,B

Craticula halophila 13 36.3 F,B

Ctenophora pulchella 14 84.0 F,B,(M)

Cyclotella meneghiniana 10 28.2 F,B

Cymbella laevis 8 75.9 F

Fragilaria capucina 8 24.7 F

Fragilaria intermedia 10 27.3 ?

Navicella pusilla 14 93.8 F

Navicula cryptocephala 8 21.4 F

Navicula trivialis 5 20.1 F

Nitzschia austriaca 9 71.0 B

Nitzschia capitellata 3 35.4 F

Nitzschia closterium 6 27.9 B,M

Nitzschia hantzschiana 2 55.5 F

Nitzschia holsatica 2 28.4 F?

Nitzschia palea 11 35.4 F

Nitzschia pusilla 5 31.7 F

Nitzschia sigma 2 26.4 B

Rhoicosphenia curvata 6 26.4 F,B,(M)

Synedra tabulata 13 46.3 B?

Tryblionella hungarica 15 33.5 B

References

Bulgăreanu, V.-A.C. (1993). The protection and management of saline lakes of therapeutic value in Romania. International Journal of Salt Lake Research 2: 165-171.

Bulgăreanu, V.-A.C. (1996). Protection and management of anthroposaline lakes in Romania. Lakes and Rivers: Research and Management 2: 211-229.

Hartley, B., Barber, H.G. & Carter, J.R. (1996). An Atlas of British Diatoms. (edited by P.A. Sims) 601pp. Biopress, Bristol.

Hofmann, G., Werum, M. & Lange-Bertalot, H. (2011). Diatomeen im Süßwasser-Benthos von Mitteleuropa. A.R.G. Gantner Verlag K.G., Rugell.

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Appendix 2: Procedure for computation of ecological status for Romanian lakes using phytobenthos

Sampling

1. Samples should be collected two or three times a year from submerged stems of emergent plants or hard substrates.

2. Appropriate location details for each site must be recorded, in order for the site to be assigned to an appropriate type for subsequent assessment of ecological status.

3. Samples are digested in the laboratory using hydrogen peroxide and permanent slides are prepared.

4. Samples are analysed in the laboratory using microscopes equipped with x1000 magnification. At least 400 diatoms are identified to species level and the number of each species counted. The main identification literature used is Krammer and Lange-Bertalot (1986-2004).

5. Data are entered into Omndia version 5.3.

Calculation of indices

1. Use Omnidia to compute Rott’s TI for each sample.2. Expected values should be assigned to each sample depending on the stream type, using

Table B1. 3. Calculate EQRTI as observed EQR / expected EQR using Rott’s TI values (note that Omnidia

output for Rott’s TI is on a 1-20 scale and needs to be divided by 5 to be on the original scale used by Rott et al. (1999).

Determination of ecological status

1. Convert EQR value to corresponding ecological status class for the type using Table B2. Table B3 gives equations for converting “raw” EQRs to a normalised scale (i.e. with status class boundaries placed at equal distances) for reporting purposes.

Table B1: Reference values for Rott’s TI used for calculation of ecological status assessment in Romanian lakes.

Lake type Expected values Note

ROLN01, ROLN02, ROLN03, ROLN04, ROLN05, ROLN07 (“lowland lakes”)

1.88

ROLN06T No method currently available

ROLN08 (“upland lakes”) 1.38

ROLN09 No method currently available

Table B2: Status class boundaries for Romanian lakes .

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“raw” EQR “normalised” EQR (for reporting)

Boundary “lowland lakes” “upland lakes”

High > 0.965 > 0.849 > 0.8

Good > 0.79 0.965 > 0.588 0.849 > 0.6 0.8

Moderate > 0.53 0.79 > 0.392 0.588 > 0.4 0.6

Poor > 0.26 0.53 > 0.196 0.392 > 0.2 0.4

Bad 0.26 0.196 < 0.2

Table B3: Equations for normalising EQRs.

Status “raw” EQR Normalised EQR

ROLN01, ROLN02, ROLN03, ROLN04, ROLN05, ROLN07

High 0.965 ((EQR-0.965)/(1.6-0.965)) * 0.2 + 0.8

Good 0.79 and < 0.965 ((EQR-0.79)/(0.965-0.79)) * 0.2 + 0.6

Moderate 0.53 and < 0.79 ((EQR-0.53)/(0.79-0.53)) * 0.2 + 0.4

Poor 0.26 and < 0.53 ((EQR-0.26)/(0.53-0.26)) * 0.2 + 0.2

Bad < 0.26 (EQR/0.26) * 0.2

ROLN08

High 0.849 ((EQR-0.849)/(1.31-0.849)) * 0.2 + 0.8

Good 0.588 and < 0.849 ((EQR-0.588)/(0.849-0.588)) * 0.2 + 0.6

Moderate 0.392 and < 0.588 ((EQR-0.392)/(0.588-0.392)) * 0.2 + 0.4

Poor 0.196 and < 0.392 ((EQR-0.196)/(0.392-0.196)) * 0.2 + 0.2

Bad < 0.196 (EQR/0.196) * 0.2

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