Best Practice Guide: Comments and Responses (alphabetical order by Member State) – 2017Martyn Kelly, Geoff Phillips, Heliana Teixeira, Fuensanta Salas, Sandra Poikane, September 2017

MS Contributor, MS, Date

Comment Response

AT Karin Deutsch, Georg Wolfram 23.06.2017

Nutrient standard Guidance/Draft: Generally we like the document very much, it gives a clear and comprehensible overview about the topic, the way how to use the toolkit and the very essential questions regarding use and interpretation of the results. Some additional smaller comments of Georg Wolfram and I are marked in the guidance :

- Fig. 2.1. Position of the arrows??- Box 4: sometimes named type 1 and 2, sometimes type I and II- 3.1. Overview : Boxes 1 and 4 are too small for the text; Box 5: It

might help if the last three boxes are labelled with A, B and C, as will follow on the next pages

- 3.2. sometimes R², sometimes r²- 4.1., 1st para, last sentence : incomplete sentence- 4.1.1. 4th para: Does this make sense? Since intercalibrated methods

are the basis, all methods should have EQR values.- 4.2.3. – 2nd para: I wondered why you use 0.36 as a threshold. It is

explained on the next page in the box. Maybe the explanation would better be place directly in the text, the first time this R² is mentioned.

- 4.2.4. “Average of the median nutrient concentration of water bodies of adjacent biological classes” - To be precise: the diagrams show the avg of the percentiles, which are expressed as log P, but not the avg of the non-logarithmic nutrient concentrations

- Fig 4.5. - Are all national boundary values mentioned in Tab. 4-6 in the figure? E.g. Broad type 10 includes 8 national values, but in the figure only 3 are visible – or does it mean that some countries have the same values?

- 5.4. Multivariate analyses : Chapter number wron – correct is 5.3- Some comments to Appendix (see file 14)

These comments were received after the latest version had been distributed. Corrections will be incorporated into the next version.

Fig. 2.1: These have been changed

Standardised on Type I / Type II

These issues should be resolved now

Standardised throughout

Section 4 comments: text has changed substantially in the latest version and it is not easy to track back to each individual comment. I think we can assume substantial issues will have been noticed by others (which is why I cannot find them in the latest version!).

4.1.1. There are situations when metrics that are not EQRs may be appropriate (see 2.4.1)

4.2.3. We will move this from the box to the main body

4.2.4. We will make this change

Fig. 4.5: Yes this is because some countries use the same value for different types

1

DE1

Ursula Riedmüller, Andrew Dolman, Germany, 02.05.2017.

We read the guidance and made some comments (see File 1)In general it all seems quite good.

We have dealt with the in-text comments. Most have been accommodated; some of the statistical issues may need further discussion between Geoff, Andrew and others at the workshop.

Chapter 2.2. Section starting: “The important point is that, given the uncertainty ...”Comment: Good important section. This is something that I did not fully consider before. However: it might be impossible to know how a particular country will use / interpret boundaries before they are actually implemented.

Thank you. We agree with the point you make, but the issue of how MS regulate is beyond our brief. We have raised the issue here and leave it for others to consider the implications.

Chapter 2.4.1 Comment: I haven’t yet looked at the details of what is done here. But in general, detecting / testing for (non)linearity is a tricky process. In particular it suffers from sample size problems. With a small sample you can easily mis-diagnose.

Response: hopefully, the procedures in the toolkit will address these concerns.

Chapter 2.4.1 Comment: It is also a problem that EQR cannot go below 0. Working with ratios is in my opinion a big mistake if fitting ordinary regression models.

Response: We agree that working with a ratio as the dependent variable is not ideal, however the EQR is the fundamental quality unit of the WFD and is thus the natural variable to work with. It also allows data from different countries and potentially types to be combined. For the majority of the data the theoretical bounding at 0 is unlikely to be a problem as few values will be close to 0, or probably less than 0.2. This is not the case for the bounding at 1.0 which is why we raise the issue in the text. We do suggest in the text that raw metrics could be preferable, but point out the difficulties of combining datasets.

Chapter 2.4.1 Comment: Some examples of the problem could be good here. Response: If we have sufficient time we will include an example of working with a raw metric, although the principal of applying a regression remain the same.

2

2.4.1 Comment: The choice of type ii or type i regression also doesn’t matter much if there is a good range of data either side of the status boundary.

Response: To an extent we agree with this point. Different slopes will be generated by type i & type ii regressions depending on the correlation of the data. The effect these will have on the boundary will depend on how far the mean value of the data is from the boundary of interest. A “good range of data either side” would imply that the mean is close to the boundary. We have added some text to reflect this point.

2.4.2 Comment: Also, the more extreme the quantile (further from 0.5), the less power to estimate the parameters of the line (slope and intercept) and so more uncertainty. Not good to use very high or low quantiles like 0.05, 0.95 (5%, 95%) – maybe no higher / lower than 25%, 75%? Unless lots of data.

Response: good point. A sentence to this effect has been added.

2.4.3 Comment: No mention here of the minimisation of mis-match method, which I rather like. It is robust to non-linearity and outliers, and compromises well with management concerns. It also assumes equal proportional error in the response and predictor – like some type ii regression methods.

Response: We agree with this comment and have added a couple of sentences to this section to describe this approach.

2.4.4 Comment: The “AVERAGE” function is used in the Excel sheet, which is the mean. Although with 2 values the mean and median are the same – maybe using median here is confusing.

Response: Agree, we will have edited the text

Fig. 4-1 Comment: This figure would be better with log transformed axes, certainly for TN, maybe for EQR also

Response: Suggestion accepted and figure replaced.

3.3 Comment: I think the four classes rule is too strict. H, G, M is enough for the GM boundary in my experience, even with moderate < 0.6 R2. I think later in the document this rule is relaxed.

Response: We agree, although it is important that data cover a wide range of moderate and have modified the text to reflect this.

3.3 Comment: I’m flogging my own dead horse here, but standard multiple regression approaches don’t work well when there are two alternatively limiting nutrients in the same data set. i.e. when limitation is “Liebig like”.

Response: This is an issue that needs further discussion and we suggest it is a topic for the workshop. An option would be to remove the multiple regression approach and replace with

3

a section on identification of limiting nutrients and the selection of the appropriate predictor nutrient, however am not yet convinced that is the correct approach. This is probably one of the “big” issues that has been identified and needs further work, although I am not clear exactly how that is achieved.

4.1.1 Comment: Could perhaps use bootstrap for this. (for estimating uncertainty)

Response: agree in principle, but beyond the scope of the present project. Added a comment saying that bootstrapping could be applied in the future. (A boot strap method is included in R, although it needs more testing.)

Box 4 and 2.4.4 Comment: Which methods? The categorical methods really don’t work if there are few waterbodies in each category, or missing categories?

Response: sentence added to 2.4.4

8.2.1.2 Comment: The fact that this maximises the agreement between the biological and supporting element thresholds is the strongest argument for me.

Response: Thank you: We are not certain that a type II regression does maximise agreement, although intuitively is does feel a correct statement

8.2.1.2 Comment: We could try bootstrapping Response: text suggesting this as an option for the future has been included.

8.2.3 Comment: Quantile regression is suggested here, but in the box below it refers to the lower uncertainty estimate from a standard regression

Response: We have modified the text

8.2.6.Comment 1: Not sure if this is what is meant, but low N high P can be a result of high denitrification rates in shallow waterbodies, low import of N from wooded catchments, and high internal P loading from historically accumulated P.Comment 2: An alternative approach here would be to set P and N targets, on the understanding that only 1 needs to be met. i.e. assuming something close to Liebig limitation.For a set of lakes where there is a good statistical relationship with N, but not P, the N target would be estimated as in section 4. The P target taken from similar lakes. Would not matter if the P target was exceeded as long as N target met. However:

Response: Useful comments. This is a topic that may need to be discussed at the next workshop

4

a) I’m not sure the WFD can accommodate this at the moment

b) for an N target to be accepted, there needs to be a belief that N can limit phytoplankton productivity, and this is generally not accepted – although I think it is true for shallow lakes and rivers where high denitrification prevent accumulation of fixed N.

DE2 Martin Halle, Germany 31 May 2017

DE short description of the German method to determine stream/river type specific thresholds between the “high” and the “good” as well as between the “good” and the “moderate ecological status/potential” for the general physico-chemical parameters of the WFD having a supporting function for the biological assessing of water bodies File 9

Thanks. Some elements of this work are included : method is included page 23, logistic regression is included in the tool-kit. More in detail will be discussed at the Berlin`s workshop

ES1 Jorge Ureta Maesu, Spain, 27.04.2017

1. Limitations (p 7)Comment: Space-time gap between nutrients and their impact on BQEs (as for example for Phytoplankton within coastal waters) are not usually take into account.Comment: For coastal waters, WB have no clear limits.Comment: Several administrations involved (Mediterranean Spanish Coast involves 5 regional governments).

Point 1 has been included;

2. Ecological Key principles (p 7)Rationale: Nutrients are related to bad water quality (low EEQRs) and negatively influence organisms (BQEs).Reminder: For the majority of BQEs, high nutrients imply low metric values; but for BQE Phytoplankton, high nutrients imply a high metric values. That is why The EQR for BQE phytoplankton is inverted calculated.

Point 2: The EQR normally refers to the value after transformations such as this so no change is needed;

3. Chemical Key principles (p 12) Point 3: this point is already made in the section;

5

Reminder: TP is bound into phytoplankton cells, thus can not be validated against Chlorophyll-a (they are not independent variables; see 2.4.2 Type I or Type II regression p13).

4. Results of analyses of GIG data sets in TRW and CW (p 45)As we will test the tool kit (see section B) this information could be included in the tables of this section, as ongoing.This paragraph could be also modified: (p 46) In this sense, it is anticipated that further work will be carried out during 2017 to extend the coverage to CW in the Black Sea, to estuaries (in TRW category), and coastal data (CW category) in the NEAGIG, and possibly to additional CW in the MED, in closer collaboration with MS experts and testing the use of alternative approaches.

Although we have not yet included MS toolkit testing results in new datasets in TRW and CW, it is still planned to do this (along with WFD IC datasets results from IC not yet included at this stage). We expect to discuss the best way to integrate the MS testing results in the BPG and how to present them, but in some cases we need additional information on metadata regarding the data used for allowing a proper contextualisation of the results obtained.For the moment, in the latest version of the Best Practice Guidance draft, section 4 suffered some changes and we have added the following comment:"This section will be revised and completed after final discussions with experts at the Berlin Workshop in September 2017."

5. Other approaches (p 51)FAN and FLU indexes alternative approach could be included (information below)6. Validation (p 56)Regarding TP see comment from point 3Comment: Validation with land independent evidence could be considered, such as LUSI (information below)

Point 5-6: these sound interesting and we will gladly include more information if it can be sent;

7. A chapter could be included (maybe in the introduction) regarding what are nutrients and which of them are more or less suitable to support GES (for example TP vs PO4).

Point 7: we do already discuss this in 2.3

ES2 Alejandra Puig Infante, Spain, 29 May 2017

Find attached our comments to ECOSTAT nutrient work File8. I hope they will be useful to the work made by the experts.

Thanks. Your points have been noted.

ES3 Eva Flo Arcas, Spain, 31.05. 2017

I send you the document where FAN and FLU method to assess coastal water physicochemical state is proposed as an alternative approach to establish nutrient concentrations boundaries (File 12). The document includes the description of the method, its validation against Chlorophyll-a concentration

Thanks. This proposed alternative seems very interesting and we would like to have it presented for discussion in the Berlin workshop 2017. For the moment it was left out of the alternative approaches section as it follows a different principle from the remaining, which use an alternative BQE to derive nutrient

6

(µg/l), as a proxy of phytoplankton biomass, and the results regarding nutrient concentrations boundaries based on this methodology. This methodology and the results of the document are not yet published in peer-reviewed scientific literature but I am currently finishing the manuscript.

boundaries. But this will be revisited after the Berlin meeting.

FI Marko Järvinen

Attached is a publication describing the use of Bayesian tools in setting the targets for Finnish coastal waters. File 10

Thanks. We’ve added a reference to this.

FR Marina Le Loarer, France, 2.06. 2017

General Comments :- A French collective expertise on eutrophication will be published in mid-September 2017. It would be interesting to take into account the general remarks of this expertise.- Do not forget the problem of international basins for which a coherent common approach is essential (same indicator for comparison along the border).- In the cocktail of pressures, do not to forget the role of hydrology which can be crucial for some primary producers (phytoplankton in particular) and a possible role of calcium in the phosphorus availability.

Comments on the guide :1.2 Limitations

« Th rate at which different components of the biota respond to change may well vary between groups of organisms » : It is necessary to establish thresholds in relation to the same biological compartment (primary producers seem the most suitable) to allow a comparison between Member States.

This will be available too late to be incorporated

The problem of international basins, and the need for international co-operation is already mentioned several times (e.g. 1.1, para. 4, .2.4.1, Box 4)

We agree with the general point but our remit extends only to helping Member States establish thresholds, not to the issue of comparisons between countries. A general principle of using the “most sensitive” component of the biota is stressed (Box 1 and 4.3.1) and this, in conjunction with 1OAO and the recommendation to use intercalibrated metrics, should ensure consistency between countries.

7

2.1 EcologicalTotal absence in the document of the use of a link between drivers and nutrients. Data on land use in watersheds have been collected for some intercalibration exercises. The definition of thresholds could also be based on the relationship between land use and nutrients;

2.2 Regulatory“In rivers, many more factors other than nutrients (hydromorphological, pesticides, riverine woodlands, micropolluants …) influence biological status, particularly when BQEs such as invertebrates are considered. In these cases, relationships between nutrient concentration and biological status have a very high uncertainty”: This argues in favor of the use of primary production indicators.

Figure 2.1: there are mistakes in the legend:a) best fit lineb) upper confidence linec) lower confidence line

2.3 Chemical: “It is, however, important to recognise that differences in approaches to sampling, analysis and averaging of data may complicate comparisons between Member States”: this important point is little developed in this guide.

2.3. Chemical It is necessary to specify that it would be essential to normalize the nutrient concentrations with the salinity in coastal and transitional waters in order to allow a real comparison between the water bodies.It is also necessary to specify that, in some MS, the nutrient samples are not collected at the same season as the biological samples (temporal difference: winter/spring-summer)). The sampling temporal frequency may be also very different between MS and may influenced the results.

4. Descriptions of procedure:4.2.1. Stepwise procedure/Assemble a data setidem: It is not possible to answer the second recommendation because all the countries do not have an adapted sampling strategy (temporal difference in

Not strictly true (e.g. 2.1) but, more importantly, this is not part of our remit, which is to establish usable relationships between pressure and biota. The role of land-use in setting thresholds is one that will be discussed at the Berlin workshop.

We agree with the point but are not convinced that this extra detail is needed at this point. The points raised are all covered elsewhere in the document.

These have all been corrected.

2.3 & 4.2.1 - We agree that this point is not developed. Again, the primary purpose is to help countries develop thresholds rather than to compare them. In any case, the need to consider the influence of the datasets characteristics (namely period of sampling along with other methodological constraints) have now been addressed in the initial sections of the BPG. Although it is not the role of this work to dwell deeply into MS data specificities or determine the type of data that should be used, but rather provide MS with minimum common methodological approaches and procedures that they are able to adapt and apply to their very different situations.

8

sample collection).4.2.3. Fit linear regression models; step 3Why the model R2 value > 0.36 is considered good to make predictions?

Table 4.3:“possible range”: The ranges are extremely wideThere is a high variability of ranges within broad types. How to make a choice when the range of values is extremely wide due to the difference of the systems?

Table 4.4:Table 4.4 shows quartile values for types containing 3 national threshold values. It is aberrant; We need a minimum of values to have quartiles.This table and the graphs that follow arise the question of the nature of the metric used by each Member State (percentile, mean, median and sampling period).

Table 4.10 and 4.11:for coastal water results are on going.

6. Validation:To validate the thresholds, it might be interesting to consider the possibility of using land use (or other measures drivers) “More generally, a useful control would be to check whether the standards derived from biological EQR correspond to ecological community thresholds, i.e. breakpoints in the response of some taxa to nutrient gradients. All species inventories available may be used to detect such thresholds (e.g. Sundermann et al 2015, Roubeix et al 2016) whose locations in nutrient gradients can be compared with previously determined class boundaries.” References to add in the guidance :- Roubeix, V., Danis, P.-A., Feret, T. & Baudoin, J.-M. (2016). Identification of

Answered in the Guidance

Again, the guidance is primarily for use within Member States, with international collaboration recommended only when this is not possible. Your concern is recognized but it is not one that can be solved easily.This is explained in 2.4.2. There will also be some extra text in 4.2.3This is an issue that should be raised at the Berlin workshop. The Broad Typology has limitations and needs to be applied very carefully.

We will edit the table to remove quartiles where N <=3. We will need to check whether the countries that used more extreme summary metrics were excluded from this summary table. The difference between mean and median is not sufficient to be of concern, in Phillips & Pitt we halved upper percentile values to bring them approximately in line with mean and median

Yes, CW & TRW IC datasets were not yet fully analysed and will probably be after the Berlin workshop.

This will be discussed at the Berlin workshop.

This point has already been made, but the Roubeix et al. reference has been added

There will also be a session at the workshop dedicated to discuss further this topic.

9

ecological thresholds from variations in phytoplankton communities among lakes: contribution to the definition of environmental standards. Environmental Monitoring Assessment 188:246.- Sundermann, A., Leps, M., Leisner, S., & Haase, P. (2015). Taxon-specific physico-chemical change points for stream benthic invertebrates. Ecological Indicators, 57, 314-323,

IE1 Gary Free, 5 April 17

IE Comment sent on 5 April: This looks good. I’d be a bit wary of including the percentage approach “50% greater than the baseline state” in the Baltic example as its probably better to use more ecological criteria then semi-arbitrary % values. Maybe it could be edited to remove this reference to 50%? For the river predictions based on alkalinity and altitude – the MEI type approach -personally I never liked this as a small % of limestone in a catchment can influence alkalinity but not necessarily be of relevance for nutrient levels. Also as the section alludes to it can be influenced by lime additions and quite a high % can be lost from the uplands – see http://www.sciencedirect.com/science/article/pii/037837749501174H. For the validation part - it seems that you are suggesting that if a good relationships can’t be found with an EQR and nutrients then a standard could be suggested by some of the validation approaches? I think this is probably ok in the absence of good EQR v Nutrient relationships but it loses some of the comparability aspects at EU level. This would be a pity but I think where very poor relationships are found – it may be useful to set nutrient boundaries at points of ecological change such as the loss or gain of ‘high-profile’ taxa.ReferenceFree, G., Tierney, D., Little, R., Kelly, F., Kennedy, B., Plant, C., Trodd, W., Wynne, C., Caroni, R., Byrne, C. 2016. Lake ecological assessment metrics in Ireland: relationships with phosphorus and typology parameters and the implications for setting nutrient standards. Biology and Environment: Proceedings of the Royal Irish Academy 116B, no. 3: 191-204.

ResponseThanks for all these comments. A diplomatic comment has been added to the “50% greater than baseline ..” criterion. We do not disagree with your comments on 5.4 but the thresholds produced at for high level planning purposes and a different approach would be needed to determine catchment-specific interventions. Validation is a topic that requires more discussion and will be the subject of a workshop at the Berlin meeting.

IE2 Gary Free, Ireland, 9 May 17

Task 1. To comment on the draft Best Practice Guide Overall I found the guidance very well put together. The analysis was easy enough to follow. I tried it on our national datasets and found broadly the same results to that I had obtained previously (Free et al., 2016).The main comments would be that 1) I welcome the additional emphasis being placed on the validation step from the previous version.2) I would still be a little wary of the emphasis on the premise that the EQR data

Thanks for these positive comments.

2.We note the comment, but our view remains

10

is more variable than nutrient data thereby limiting the use of OLS regression (detailed comments provided on 18/10/16). I would say OLS is ok and it produces more stringent and possibly more correct nutrient boundaries.

3) The Excel template is of an excellent standard and makes it very easy for MS to apply the approach. I can’t imagine MSs having an issue with pasting four columns of data to achieve so many analysis results.

4) I am a little wary of the use of section 4.3 looking at ranges of GIG data sets and existing MS boundaries. I think if MS follow the guide you have produced it will yield more robust and comparable estimates that will better serve as a reference for comparison. At least there should be the provision to update this section following implementation of the guidance by MS.

that the variability of the annual mean nutrient and biological metrics can be similar. An OLS regression will always generate a shallower slope, the effect on the boundary however will depend on whether the mean of the data are greater or less than the boundary in question.

4.We note this comment and following the Berlin workshop we could agree to modify the guidance on the use of these values

Some minor comments:1) From the Commission’s perspective it would be good to have an additional tab at the end of the workbook entitled ‘reporting tab’. At the moment a separate workbook is supplied with MS having to paste in results. If they were populated directly it might reduce errors, save time etc. It should be easy to do just linking the result cells in the workbook.

Response: We agree this would have been useful for the testing exercise, however this format was only to assist collate results from testing and we are not aware what format might be useful. We can provide this in the future if needed

2) I think the default in the workbook to combine poor and bad is not ideal and it would be better if it was left to the WFD default of all 5 classes.

3) The r scripts are very useful. I tested them and they worked fine.

4) For using N and P multiple regression models to estimate boundaries. I think this is useful but I wonder if the values estimated are suitable for boundaries. In many ways N may be easier to estimate than P and better represent underlying pressure gradients (less variable, better resolution analytically). It would be worthwhile to validate this approach against univariate models and lake-specific time-series.

5) The log transformation is probably not too relevant in the categorical tab of the Excel workbook as it won’t influence percentiles.

Splitting below moderate does not have any influence on the categorical methods as it is only the distribution of data in Moderate class that can influence the boundary values for Good

The issue of how N and P interact remains an unresolved issue. In the new tool kit we introduce the use of Co Plots which may help split data sets so that univariate models can be used

5) see also comment from AT. Will be relevant if we are taking average of two percentiles?

6) In the Excel workbook the SMA results appeared to be closer to the R version of RMA rather than the Excel version of the RMA.

Response: The table produced by the R script for linear regression outputs the parameters for the OLS model 1, the RMA model 4 and the inverted

11

OLS model 2. The explanation p100 of the Guidance document incorrectly reported it output models 1, 3 and 2 and the output did not contain the model numbers. We have corrected the Guidance document and included the model numbers in the output from R. The values have been checked and both Excel and R produce the same values using the example data set. Thank you for spotting this issue.

7) We had some large datasets > 500 so we had to do the categorical analysis and minimisation of mismatch manually. I am not sure if this will affect too many MS?

8) The points about the possibility of combining types to increase n and representation across the gradient are made in the report. They could be emphasised a bit more though as it is quite important. In our approach for lakes (Free et al., 2016) we combined all data and then looked for the influence of type factors retrospectively using modelling. This is also not ideal but I think all MS will face this compromise. 9) It would be good to add in a space for units in the Excel reporting template. I was forgetting to put them in so maybe a dedicated column would prevent this.

Task 3. To provide examples of alternative approaches and validation examples by end of May 2017We have provided an example of the decline in Charophytes for the guidance, thank you for including it.

7) If it is a common problem, we’ll insert an extra section on “the problems of having too much data”!

8) Happy to discuss how this might be emphasized during the Berlin workshop

9) There is a space for units in the data sheet and the latest version also has a method of adding axis labels which can contain units

Done.

IT Aldo Marchetto, Italy, 18.04.2017

I think that the guide is very clear. My big concern is about the possibility to do this work without a chemical intercalibration of phosphorus measurement. This was a concern raised by the German colleagues in Berlin, which I fully share. TP measurement at the ppb level is tricky, and many labs are simply not aware of the difficulties. (…)

2.3 has been modified to cover this point. A further point is that the detection limit must be appropriate to the water body under investigation.

LV Ilga Kokorite, Latvia, 29.04.2017

Thank you for the nicely written Best Practice Guide! I think that it is very helpful when establishing nutrient boundary values. It is easy to follow these guidelines.In the Best Practice Guide book you were also asking for some more ideas on the validation of results. In the Guide you have mentioned an example with Arctic char that needs waters with good quality. In Latvia, we have a similar approach. Our bigger lakes and several stretches of medium and large rivers are designated as the waterbodies that are important either for salmonid or cyprinid fishes.

Thanks for these comments. We have included the use of salmonids in lakes and rivers in Table 6.1. We agree that saprobity levels should be correlated with nutrient concentrations, but this may be a more appropriate test for lower status classes, rather than for H/G and G/M which are the focus of this report.

12

Water quality criteria for these lakes and rivers are set so that they support life of respective fish type. Water quality criteria are more stringent for salmonid fish waters than for cyprinid ones. Chemical water quality parameters related to eutrophication are nitrites (NO2-), ammonia (NH3), O2, biochemical oxygen demand. It can be mention that high ammonia concentrations are toxic for fish (In eutrophic lakes with high NH4+ are converted to NH3). So, N standard for good quality waters should support healthy life for fishes. (…)

MT Angela Bartolo, Malta, 09.06. 2017

Comments on the Best Practice Guide:(i) Further guidance should be compiled in relation to the use of nutrient boundaries, once these are established, for the assessment of ecological status. For example a clearer understanding is required in relation to the following two statements:a. Page 6: ‘high concentrations of inorganic nutrients are a major factor contributing to the failure of many water bodies to achieve GES’b. Page 8: ‘users need to remember that nutrient standards are intended to support good ecological status but the ultimate measure of status is the condition of the biology, not the chemistry’(ii) Within this context, Malta also points out that the flexibility in applying the thresholds as described by the two strategies outlined on page 11 is considered key to enable effective use of nutrient boundaries once established.

Thanks for these thoughts. The role of this document is to support the EU’s Eutrophication Strategy, and this document contains a lot of the context and guidance that you are looking for. Response: Sections 1 and 2, taken as a whole, deal with your concerns. The idea is to help MS via “best practice” rather than to enforce a uniform approach. But this is also a topic that will be addressed in the Berlin workshop, and will be reflected in the final version of the BPG.

Comment: (iii) MT agrees with the statement on pg 6 ‘the links between these nutrients and ecosystem functioning are complex. This creates uncertainty in relationships between biology and nutrients and, in turn, creates difficulties in setting realistic targets for inorganic nutrient concentrations that would enable GES to be achieved’. We particularly highlight difficulties in demonstrating causal relationships. Due to the data limitations, we can never be certain that nutrients are the only pressure causing failures in ecological status.

Response: We are attempting to include in the BPG new methodological approaches, and also extending the alternative approaches section in this new version of the BPG, to allow MS to deal with e.g. uncertainty in relationships between biology and nutrients; difficulties in demonstrating causal relationships; existence of other pressure(s) causing failures in ecological status.

(iv) With respect to statement on page 12 (the prevailing assumption is that phosphorous is the limiting nutrient in freshwaters whilst nitrogen limits in marine and coastal waters), We suggest - subject to confirmation by experts – that for coastal waters in the Mediterranean, phosphorous is the limiting nutrient;

This has been changed. We will be cautious to acknowledge that indeed in some CW systems, such as in some parts of the Mediterranean, phosphorus can be the limiting nutrient. There will be new Figures in this BPG updated version

13

with examples from CW showing this.

PL Agniezska Kolada, Poland, 30.04.17

Firstly, I should say that we very much support the approach (es) presented in the Guide. Actually, we followed exactly the same approach when establishing standards for Polish waters in 2012 (report on verification of the type-specific boundary values for ecological status assessment of waters based on physicochemical parameters performed by ADASA and IOŚ) and when searching for the thresholds for nutrient load from the catchments. Both reports are in Polish, I am sorry. Therein, we firstly searched for regressions and once they appeared rather poor, we used quartile method for establishing boundary values. In this context, methods presented in the Guidance seem for us much obvious and intuitive to be applied in such works. Referring to the Guide itself, one of the most crucial statement that should be emphasised more and that people should be much aware of is included in Box 2.1. The primarily condition for using the procedures presented in the Guide is that the current boundary values for EQRs are NOT established based on nutrient concentrations. Otherwise, it would be circularity [to derive standards for nutrients based on the standards for EQRs derived from EQR-nutrient relationship]. Then they are so, one of the solution would be to go back to raw data (raw metrics) instead of EQRs that you also recommend in the Guide. You may remember that I presented some results on that during SIL in Geneva in 2015. Although Polish standards for BQEs are NOT derived from nutrients, analysis of such primal relationships usually supports understanding of the phenomena. In the Guide, the regression of R>0.6 is considered satisfactory to use regression for setting standards. In fact, such regression is hardly achievable, though not impossible, in biological studies, particularly for TP. As stated in the guide, log-transformation usually helps but rarely solves the problem. In our study we used to use TSIs (trophic state index by Carlson 1977 and Kratzer & Brezonik 1981) instead of raw or log-transformed data. This approach is indeed rather old-fashion but much improve the relationships as nutrients recalculated to TSIs much approach normal distribution. This may be one of the ‘alternative’ approach. (…)

Thanks for these useful comments. The importance of avoiding circularity has been stressed in 2.1. We also agree that use of raw metrics has some advantages (see 2.4.1)

SE1 Karin Wesslander, Sweden, 30.04.2017

Overall, I find the report thorough and it captures many parts.I miss, somewhere in the introduction of the report, information about that the described method was mainly developed for freshwater and that there are few applications in coastal waters. It feels like the report is directed mainly to freshwater. (I guess the reason is due to few coastal examples but this could

Thanks for these thoughts. You are right that the work, at present, has a bias towards freshwater, but we are trying to amend this. The offending sentence in 4.2.1 has been deleted and text in 2.3 has been amended to incorporate your

14

maybe be written)Regarding data for the method: Winter nutrients are often assessed in coastal waters. BQE as chlorophyll and biomass are assessed during spring/ summer. It is not specified in the manual how to handle if the nutrient is assessed during another season as inorganic winter nutrients. In Sweden we assess winter nutrients (inorganic and totals) and summer nutrients (only totals).p17 chapter 3.1: Some boxes don’t show all text, they are too small.p22 chapter 4.2.1: It says that “If data from several years or multiple stations in a water body are available these should be averaged to provide a single water body mean value to avoid issues with spatial and temporal autocorrelation in regression analysis.” I do not understand this sentence. This produces “one” value…. should we not work with timeseries? Do you mean that the dataset should consist of one value per water body in the water type? So if the water type consist of 5 water bodies we should only have 5 samples in the dataset?This is all for now but maybe more comments will come when I start produce the dataset and testing the tool.

comments. 3.1 is being extensively rewritten, so the problem you identify here should be solved.

SE2

Lars Sonesten, Sweden, 03 May 17

Guide commented (File2)1.1 Comment: Well, this is a bit like the hen and the egg. If a MS has established a GES considerably higher than other/neighbouring states, the MP may reach (their) GES although it might differ considerably from what other MS regard/define as GES. In a comparison, it may differ from the situation in other MS, but as the responsibility is on the MS it is hard to say it’s wrong. The MS has reached “their own GES”, although it differs from others definition (if you understand what I am trying to say). If you want to be sure that all MS reach the same GES, the methods need to be harmonised, and you can’t let the definitions be set by individual MS.

Response: We have tried to separate the issue of GES (covered by IC) and supporting element standards and also to avoid producing a prescriptive document. In theory, if a MS has an intercalibrated GES boundary, then the methods here should help them achieve a meaningful nutrient threshold. The idea is to help MS via “best practice” rather than to enforce a uniform approach. I think that the section, taken as a whole, deals with your concerns.

Fig. 2-1; Fig. 2-2 Comment: It’s a bit hard to distinguish the different lines. Is it really necessary with all the details/observations? Slightly ticker lines would at least be preferable.

Response: Thank you. We think that these figures are important as they illustrate an important concept. The problem with using thicker lines is that it will make the diagram more difficult to interpret.

2.4.1 Comment: Another option might be PLS, very suitable for multiple collinear explanatory variables (i.e. in place of type)

Response: Thank you for this suggestion, we are not familiar with PLS, but this is a

15

technique that could be explored in any further work.

2.4.1 Comment: Smith actually only refer to Jolicoeur 1990 Response: Thank you. We have made this clear by using a secondary citation.

3.1 Comment: The whole chapter is a bit on its own as the explanations comes in chapter 4. Consider to move this part into its proper context

The latest BPG draft version already addressed this

3.1 Comment: Some boxes don’t show all text, they are too small. Response: Corrected but this will be checked for the final version.

3.2 Comment: Why not a short description so the reader do not need to try to find the definition?Why writing “high r2”? Better to specify what you already have defined to avoid different interpretationsI would prefer to have the explaining/additional text in-between the boxes either in a slightly smaller font or in italics to make it more visible that it is something else and I would try to make it clearer to which box the explanation actually refer to (might be clearer with a smaller font as it then will not fill the whole space between some boxesIn the last box: Does section B mean approach B? If so, please use the same wording

The latest BPG draft version already addressed this

4.1.1 Comment: I do not get this, as it is a bit contradictory. Higher nutrient concentrations in comparison to what? To the observations in the G class, yes, but if you are talking about the observation within the M class, these observations has comparatively lower concentrations.

Response: reworded (Geoff: can you check what I have written?) Yes, we have changed the wording to make it easier to follow? Actually I am starting to wonder if these categorical approaches are ever appropriate unless there is no continuous variable.

4.2.1 Comment: It says that “If data from several years or multiple stations in a water body are available these should be averaged to provide a single water body mean value to avoid issues with spatial and temporal autocorrelation in regression analysis.” I do not understand this sentence. This produces “one” value…. should we not work with time series? Do you mean that the dataset should consist of one value per water body in the water type? So if the water type consist of 5 water bodies we should only have 5 samples in the dataset?

Response: I could not find this text in the revised version so it may have been dealt with already. See last paragraph in 2.4.1.

16

4.2.3 Comment (on r2 = 0.36 threshold) But it’s not good enough to be a linear relationship as then the criteria has been stated to be 0.6!? Or is it so that you mix type I and type II regression without properly indicate which method you actually use?I have asked a statistician about this 0.36 and she could not see any obvious cut-off limit from her point of view. How well is this Smith/Jolicoeur cited on this matter? As far as we understand both these articles are dealing with the special case RMA, and we can’t see any general recommendation to use an r cut-off at 0.6

The issue here is that for conventional OLS a noisy relationship will give a very low slope which can be tested to be significantly different from zero. Type II regression however does not allow for this and thus we take the advice from Smith that using the parameters from a type II regression model when r2 is < 0.36 is potentially unreliable. We also stress that a variety of methods should be compared, including categorical approaches which do not depend on a statistical model

4.2.3 Comment: Do you have any reference to this estimate of variance around the “best of fit” value?I asked the same statistician about this procedure and she was as confused as I am. The 50% of residuals are around the estimated line, and is it directly projectable to the x-axis? Assume that it all is type II regression, and consequently the percentiles are orthogonal to the estimated regression line.NOTE! It must be very vital that the residuals are normally distributed in this context?

Response: We acknowledge that this approach is not a standard statistical procedure, however we think that it does provide lines which illustrate the upper and lower bounds of the data, they were not intended to provide an estimate of “best fit”. It may be that they should not be used to directly project to the x axis, although we feel that this does give some indication of the potential maximum range of the predicted values and thus potentially expected boundary values. In the document we refer to these as the “Possible Range”.

4.2.4 Comment: Defined as the mean of 43-53? The Type II regression gave 49 Response: The correct value is the Type II regression value of 49, mistake caused by slightly different version of the example data set

Fig. 4-1 Comment: I am not really sure what you intend to highlight with this figure as it is quite complicated to understand without any further explanation

Response: A more detailed explanation and additional graphs were added for clarity.

5.5 Comment: This is definitely not a precautionary way to do it, as the retention will not be the same over time (assumes some kind of steady-state) i.e. during the transition of a lake that inevitably will become more nutrient rich, and eventually make turn into a P source (internal loading)

Response: for ALS

7 Comment: If I remember correctly, the charophytes are extremely efficient to take up P from the water, which may be a confounding factor as these lakes are poor in P in the water, but may have a lot of P in the sediments (also co-

Response: We agree that this is a complex issue. The co-precipitation, in particular, can act as a “buffer” to limited enrichment but the

17

precipitation with CaCO3 increase the P in the sediments). charophyte community crashes quickly once this capacity is exceeded. In this passage we are trying to establish the principle of the use of charophytes as a means of validation, so are trying to avoid excessive detail.

8.1 Comment: Is this supposed to be the same as the “most likely” value given in the Step 3 box which gives 49 µg/l or is it the median/mean of 43-53? A bit confusing, but maybe it ought to be the same or how is the “most likely” value estimated?

Response: Correct value is 49.

8.2.1.3 Comment: hence? Response: We have modified the sentence, but retained “hence”

8.2.1.3Refers to 4.1.1. Comment: Please, see my concern/questions on page 26.

Response: our response to the earlier comment should have dealt with this..

Table 4-6 Comment: This database has to be available for each MS to check the data taken into account

Response: This is referring to CTRW data, and all datasets used in this exercise were made available with the CTRW report Teixeira and Salas, 2016

SI Natasa Dolinar, Slovenia, 28.04.2017

Overall I find the report very detailed and informative. (…) At the beginning of the process of data preparation I found the Guide somewhat ambiguous on how to treat samples and sites data. (…)I’m also a little reserved on how much averaging should be made, I recommend that only yearly nutrient (and biological) data is averaged and this only on single stations and not multiple stations together (…)

We’re glad to hear that you thought the manual was useful. We have modified the text in 2.4.1; however, it is not possible to provide an unambiguous answer and this is a topic where each situation needs to be discussed with an experienced statistician.

SK1 Emilia Elexova, Slovakia, 30.03.2017

In Best practice guide on pages 130-132 there are missing the identification marks for Slovakia even if SK is „visible” there.

This was a problem with the legend to the diagram in the appendix, Slovakian data were shown but not listed in the legend due to problems with R.

SK2 Elena Rajczykova, Slovakia, 04.05.2017

We have carefully studied Best Practice Guide on establishing nutrient concentrations to support good ecological status. The Best practice guide is sufficiently detailed in explanation of the conditions, when and how it is appropriate to use individual statistical procedures. A valuable part for its usage is a "road map" and a description of the procedures with illustrative explanatory examples. We have no critical comments on the prepared manual. We would like to ask you, do you prefer the median/average values of the nutrient

ResponseThanks for your encouraging comments. We have added some text in 2.3 which should help to answer this question (although our answer, in fact, takes the form of another question)

18

concentrations measured during the vegetation period or during the whole calendar year for classification schemes preparation?According to our opinion, the prepared manual will help verify the national classification schemes for nutrients.Note: Annex 2, Part A2.3.1.3 Figure 6 lacks the indication of SK data.

Best Practice Guide: Comments and Responses (alphabetical order by Member State) - 2016

MS Contributor, MS, Date

Comment Response

AT Georg Wolfram, Austria, 27.09.16

1. What is not fully clear to me: To whom shall this guidance be addressed, and what is the final goal? Most, if not all, countries have already set national boundaries for phosphorus and nitrogen. Are the MS expected to modify or change the boundaries?

Response: the purpose of the document is to help and advice (1.1). There is no obligation to modify or change boundaries but an opportunity to check these. There are, in fact, a number of situations where boundaries have not been set (particularly in TRAC) and this is designed to support MS in that position.

2. In my eyes, the regulatory standards and how they are used (chapter 2.4) is a key point. However, as long as there is no harmonized approach on this issue, a comparison of boundaries will always remain critical.

Response: We agree, and we discuss this in detail. Developing a harmonized approach on this is, however, beyond our remit.

3. In the AlpGIG we first have agreed on TP boundaries, then derived boundaries for phytoplankton metrics such as chlorophyll-a from TP. The chlorophyll-a concentration and/or the total biovolume was then used to set boundaries for the trophic index. This was often criticized and the biological assessment misunderstood as a complicated tool to measure nutrients. Now, this guidance goes one step beyond and suggests a method how to set nutrient boundaries using the biology. This may be simplified, but we will probably face this discussion. Just wanted to mention it …

Response: this is an example of the circular reasoning that we fear that some approaches to intercalibration have introduced (see section 2.1). It is certainly something that needs further discussion. The critical issue may be the strength of the evidence on which the initial TP boundaries were set. The guidance here emphasizes the need for independent validation in such cases.

4. There are lakes and rivers, where the biology is only weakly correlated with nutrient concentrations. The EC lakes are mentioned in chapter 8.3.6, the river Danube is another example. The guidance doesn’t go very much

Response: we recognize that this is an issue. So far we have not been able to produce generic guidance on handling situations such as this. It may be that each situation needs

19

into depth on this issue, but I think it would be worth to extend this section, e.g. the suggestion of using a nutrient combination rule, or even a combination of different ‘physico-chemical quality elements’ such as salinity (chloride) and nutrients (-> IC exercise XGIG phytoplankton). Maybe, it is not only relevant for EC lakes and the Danube, but also for other types, especially among rivers.

a different approach …

5. When establishing a relationship between biology and chemistry, the eutrophication history should be considered (e.g. internal loading). Maybe a comment on this can be added. I assume this is especially related to the relationship between nutrients in the water column and BQE bound to the sediment (invertebrates, maybe also macrophytes)

Response: a sentence has been added to 2.3.

6. Finally, I have read the Alp Lakes example with great interest (Table 4-1 on page 26 and Appendix A1). It would be interesting to add and compare the boundaries used during the IC exercise as well as the current national boundaries. Just in brief: If I remember correctly, in the AlpGIG we used 8 µg/L for L-AL3 and 12 µg/L for L-AL4 as boundaries for defining reference sites in these two Alpine IC lake types. Your suggestion is 5-8 µg/L as H/G boundary for L-AL3 and 9-14 µg/L for L-AL4. And in AT, the national guidance gives H/M boundaries of 6-10 µg/L for L-AL4 and 10-16 µg/L for L-AL3 lakes. Nice match.

No response needed

FR1 Nolwenn Bougon28.10.16 (file6)

2.3 Comment: RSP (reactive soluble phosphorus) should be used to approach the phosphorus bioavailable instead of TP. TP overestimates the supply that is available to the biota. If we considered annual mean of phosphorus: 90% of phosphorus turn into TP in 10% of time, and most of the time this happens out of the periods of plant production; so when there is no effect on biology.

An other way could be to use TP flow (for rivers) as a proxy; but this involves that we know which weighting we should apply to which systems.

Response: we do not disagree. The purpose of this section is to point out the complexities associated with measuring nutrients. Our opinion is that, within and between countries, errors associated with estimation of the pressure are likely to be almost as large as errors in estimation of the response. However, we also recognize that many countries have established practices and are unlikely to change. Recognising this issue is important, even if solving it is probably impossible.

3 Comment: Can you explain? I am not sure to understand. Response: should be covered in section 7.

4.2.3 Comment: This part is not clear for me; can you explain it differently? Explained

20

4.2.4 Comment: Can a median value be superior than a 75th quartile? Response: The selection of the most appropriate categorical method is a value judgement. The 75th quartile of the higher class is relatively precautionary as it takes no account of the range of nutrient in the lower class. Averaging the adjacent class median values, or averaging the adjacent quartiles does. The choice is dependent on what the boundary value will be used for. Feedback from the consultation shows that there is no agreement regarding which approach is most appropriate, although most favour the minimisation of mis-match method.

4.3 Comment: Can you explain? I am not sure to understand. Response: reworded.

4.4 Comment: This part need to be detailed. More explanation of the graphs need to be added.

Response: In this BPG version, this type of graphs is explained in detail in the section that introduces the toolkit analyses. In a previous BPG draft, these were only meant to be illustrative examples for Coastal and Transitional Waters, but since they were taken from the Teixeira and Salas report (2016) and they introduce confusion at this stage, we removed them from this section. Also to keep it more in line with what was presented for the Freshwater section.

FR2 Nolwenn Bougon28.10.16 (file13)

FRANCE - General comments:

1 - Concerning “outliers”

The status of « outliers » should be more described. All the points used in the analyses are real and measured data. Indeed, they are numerous but they do exist, and they belong to a same data set. They do not fit with the linear regression but I would not take the liberty of reporting important group of points as outliers.

We require an objective justification of the status of the outliers: to explain and to set out arguments in order to confirm the scientific objectivity and

Response:

1-Outliers: The BPG and the toolkit guidelines point out the statistical methods more robust to the presence of outliers, and those methods where their presence might pose a problem to their application. In the latter case it is also highlighted the need to be clear whenever outliers are removed and ways to identify and report those outliers are provided. In the case outliers are not accounted for in the analysis those points are still plotted for clarity. Ultimately, it will be the experts’ choice

21

to limit the practical use of the proposed linear regressions (To what type(s) of systems can the methods proposed be applied or not?).

2 - Concerning “Validation »

The process of validation should be developed. The nutrient thresholds are defined by statics analysis and their ecological meaning need to be confirmed (e.g. by confrontation with values from the literature). The nutrient thresholds should be combined with a confidence index (considering for example the p-values, the consistency with the literature, the number data used for the analysis …).

3 - Concerning “datasets used for the exercise for coastal (CW) and transitional (TRW) waters”

We have some questions about the informations presented in the table 4.3-1 “ Summary of datasets available” and the table 4.3-2 “List of datasets available for analysis”; for example: in our database, the nutrient parameters are expressed in µmol/L and not in µg/L…

So will it be possible to us back the datasets (at least our database) you used for the analysis to check the differents points we are questioning?

4 - Concerning “standardisation” of DIN concentrations by the salinity”

It seems that the concentrations of DIN are not standardised by the salinity.

This standardisation is essential to compare DIN concentration from different salinities. If the data are not standardised, the results of the analysis have no meaning.

5 - Concerning “coastal waters results”

Can you tell us when the results will be available?

6 - Concerning “Chla data used to estimate the EQR”

The Chla data used to estimate the EQR should be more detailled: year, productive period seasonal period as nutrients…?

7 - Concerning “the results” - The results should be discussed with the different experts of each MS. An exchange between the statistician and the different experts of each MS should be planned.

whether to remove them or to select analyses that can accommodate the outliers in their datasets.

2 - Validation: we are developing this topic. The issue of a confidence index is interesting and could be discussed further at the workshop in Berlin.

3- CW & TRW datasets - the data set used is the final common data set used for the IC exercise. Units were changed by IC experts leads in order to have the same units for all MS involved in the IC exercise.

Yes, datasets are available for consultation as well as all the analyses run so far.

4- In the final IC common data set used and provided by the experts leads, the DIN was already standarised, but we will check it again.

5 & 7- The case-study and results included in the guide are only examples to illustrate the tool kit methodology and allow the inclusion of adequate methods for Cw&TRW as possible. But ok, we can discuss during the meeting some results, and after the workshop discussions on the use of the boundaries we will consider if reviewing and including the analysis/results for the whole IC datasets in CW & TRW.

6-ok, we will include these details, but most likely as an revised version of the separate CTRW report or as annex to the BPG, to be decided yet.

22

IE1 Gary Free18 Oct 2016

Please find attached some comments on the latest guidance document. 2 files (File 3 and File 4)Overall I found it very well put together.The analysis was easy enough to follow. I tried it on a national dataset for macrophytes and found the same results to that I had obtained previously. The main comments would be that1) I would like to see the validation part of the standards to the fore a bit more

Response: validation section is a work in progress and will be discussed more at the Berlin workshop;

2) It would be good to test if EQR data is really more variable than nutrient data thereby limiting the use of OLS regression. I would say OLS is ok and it produces more stringent and possibly more correct nutrient boundaries.

Included in the current BPG version

IE2 Gary Free18 Oct 2016File 3

Overall I think the guidance is very useful and it is clear that substantial amount of thought and work has gone into its preparation. I found it easy to follow and was able to implement the instructions in Excel and R using the scripts from the toolkit folder on a national dataset. The values achieved were that same as those I had found previously following the earlier draft of the guidance.

The report could benefit for being more formally separated into a 1) general principals document 2) proposed analysis trajectory document for member states to follow. This was mentioned in the mail but just to make the distinction between a polished glossy document and then to have the analysis trajectory as the more open plethora of possibilities.

I have put comments and suggested changes in the track changes word document also attached. Some additional comments for consideration are below:

Done

These will have been incorporated during the editing process.

23

I think the focus on the three regression approaches may not be justified. The basis for this is that the EQR is very variable and is therefore not suitable as an x variable to predict nutrient concentrations. I have previously examined the coefficient of variation (CV) for nutrients and BQEs. When considering, for example, TP and a phytoplankton EQR the TP is 5 times as variable! (see Table below). It may be preferable to recommend just using TP as a dependent variable or testing the assumption that an EQR is more variable than the nutrient parameter. There is a potential to simplify things and just use TP as a dependent variable in all cases.

After step 5 p25, I would like to see a formal validation step inserted here to ensure that the boundary is compatible with the definitions of Good status. For example a plot of key taxa associated with good status or for lakes, the lack of significant abundance of cyanobacteria. Or a graph of macrophyte abundance vs the TP gradient to ensure that a diverse and abundant macrophyte community, compatible with good status, is demonstrated to be present at the proposed TP boundaries.

P 25 Ranges of nutrient standards associated with broad typesIt would be nice to include these in an appendix in easy to look up reference tables. For the CB types I am worried that such levels might not correspond to preserving GES for other BQEs. I think before proposing this range that the MS can look up, at least one MS should produce a graph demonstrating that communities of other BQEs are in GES at these nutrient levels. It is dangerous to propose such levels otherwise.

The suggestion that all methods should be used and the results compared is a good one. Even if MS want to just check that their EQS are within the ranges proposed it would be preferable that this exercise is performed anyway.It is great that there is clear guidance that data span a long gradient (p7). I found this to be the most important factor. 

Included in the current BPG version

For discussion at the workshop. Whether it is appropriate for a “Best Practice Guide” to endorse a “formal validation step” may be questioned by some, I suspect. We have done all we can to encourage validation.

For consideration at the workshop

Thanks. No response needed.

IE3 File 4 (Guide with

2.4.1 Comment: I would question if you have site specific reference EQR value then why not a site specific nutrient standard? Difficult to attempt except for lakes with palaeolimnology.

Response: the only MS that uses site-specific expected values is, to our knowledge, UK, and they do also have site-specific P standards.

24

comments) 2.4.1 Comment: Also the normalisation of the EQR is likely to influence

the slope. I tested this in a few instances and found that the change before and after normalisation was not significant for IE lake data, but it may be in other cases

.Response: We agree with this point and have added text to highlight the issue

2.4.1 Comment: I think this may not be correct. I have examined the CV for nutrients and BQEs and when considering for example TP and a phytoplankton EQR the TP is 5 times as variable! Table inserted into comments. It may be preferable to recommend just using TP as dependent variable or testing the assumption that an EQR is more variable than the nutrient. There is potential to simplify things and just use TP as dependent variable in all cases (the RMA, if only applied when the r2 is >0.6 will give the same results anyway).

Response: we have inserted “often” into the text to deal with this point.

3 Comment: So Is it formally proposed that if a MS boundary is within this range then no further action is required? This would be simple but not without risk given the range of standards for some types e.g. CB lakes. It would be best to point MS to proceeded to validation with separate data set (last point below). Maybe put in a separate box below to deal with this if it is proposed.

Response: the following has been added to the first bullet point:The broad range of thresholds obtained for some types means that agreement with Table 4.3 should not be accepted uncritically and the nutrient threshold should be validated following procedures in section 7. Ideally, an independent data set (i.e. different to that used to develop the threshold) should be used for this exercise.

4.2 Comment: not sure if this is the stepwise procedure starting here or if it is an overview and worked example?

I think this section aimed to provide a simple guide to the analysis, without the need to understand the detail. Perhaps we need to change the heading, in my document the heading was “Stepwise Procedure”, no mention of overview.

4.2.7.2 Comment: I would like to see a formal validation step inserted here to ensure that the boundary is compatible with the definitions of Good status. – like a plot of key taxa associated with good status for example for lakes the lack of significant abundance of cyanobacteria - or a graph of macrophyte abundance vs the TP gradient to ensure that a diverse and abundant macrophyte compatible with good status is demonstrated to be present at the proposed TP boundaries.

Response: we have emphasised the need for a validation step at several points in the manual (most recently 4.2.6).

IT Aldo I think that the elaboration is very interesting and that the procedure are Response:

25

Marchetto 28.09. 16

statistically correct.However, for what concern the overall design of the elaboration and of the guide, I found a general problem that I believe should be addressed in a sort of introduction to the use of the guide.My concern is about the fact that, if I understood the procedure correctly, in the guide N and P are treated separately, as there was no interaction between the ecological effects of their levels.In my opinion it should be added a chapter in which to state that the first action to be performed should be the identification of the limiting element on the basis of the Redfield ratio. A second step would be the application of the procedure, for the limiting nutrient, or for both if the reduction of the limiting one would cause the other to became (co-) limiting.Otherwise MS may spend considerable effort and money in trying to reduce the non-limiting element with no ecological effect.For example here in lake Maggiore (but the same happens in large part of Europe), the limiting element is P, at a level of about 10-15 µg/L as Total P, while N is around 0.9 mg/L (as Total N), As N:P is largely above the Redfield ratio there is no reason to try to reduce N levels to the values indicated by the N:Chl relationships, which is only valid when N is limiting.Furthermore, this would be impossible, as here N mainly comes from atmospheric deposition due to air pollution in the Po plain, a factor which is not included in the logic of the WFD. If this is of interest, I can develop the topic with more examples.

Many thanks for these thoughts. We agree that the guidance is presently weak on this topic and would like to take up your offer of help. Perhaps a presentation at the Berlin workshop would help us to focus our minds on what needs to be done?

This version of the guidance draws now attention to the need of checking for the effect limiting nutrient.

LV Ilga Kokorite 11 Oct 16

See comments to the Guide in File 5To my opinion this Guidance and Toolkit are very good job. It was possible to run statistical tests and get results in both Excel and R when data from the example files were used. I also replaced data in Excel toolkit with a small data set from Latvian large rivers. It worked without any error messages.It was a good idea to include a chapter on the use of nutrient boundary values. Points summarized there might be valuable during discussions about which values (more stringent or relaxed) should be used for setting the class boundaries.The aim and possible content of chapter on marine waters was not clear for me. Will it provide some "acceptable range" of boundary values for common types (as in case of lakes broad or IC types), so each country could compare its result - is it acceptable or not? Now it looks to me that

Comments included

Regarding the marine waters, the initial aim was to allow testing the toolkit in these systems in order to better adapt proposed methods or identify additional approaches needed to deal with CW and TRW constraints. In the Berlin workshop 2017 there will be room to discuss whether tables with some "acceptable range" of boundary values for common types are needed/desirable by MS and how would they be used.

26

the chapter on marine waters will contain a lot of examples from countries where data sets are available.Some minor comments are inserted in the attached guidance file.

LV

Comments on the Guide Roadmap A Comment: Could “high r2” be replaced by r2 >0.6? Response: done.

4.2.2 Comment: is step 2c missing? This is now solved in the current draft.

Comment: some boundary values are in black and some – in grey. Explanation is needed.

Response: The values in black were I think originally intended to highlight the maximum and minimum predicted values. However, this is not needed so we have made all values black

UK Mike Best11 Oct 16

1. In the best practice guide the marine chapter is very incomplete

2. The report also has not worked up much information from the North East Atlantic (NEA) region. This is a bit strange as in the Coastal Phytoplankton Intercalibration process – we had 11 Countries, 10 Coastal Typologies and 24 regions and we got an intercalibration bewtween winter DIN and 90%ile Chlorophyll. It wasn’t a straight forward link but there were several environmental factors involved: DIN (42%) of turbidy (about 30%), flushing (about 30%) and region explain 90%ile Chl. These factors are not considered in the guide or report. [We discussed this at Berlin last year]. The transitional waters are even more complex

3. The R templates use parameters that we don’t use in our DIN tool. Currently we have no need to have a DIN EQR (it is a supporting parameter and the EQR is found in Phytoplankton and Opportunistic Macroalgae). In UK coastal waters P is not considered a significant parameter (it is rarely numerically limiting). We think the spreadsheets (and R) are really only suitable for freshwater. Some of the graphical methods in the report could be used on nutrients alone

4. We tend to use units of micro moles (um) rather than ug/l.

Response:For Geoff, Fuen

1 & 2. Regarding the marine chapter, the initial aim was to allow testing the toolkit in these systems in order to better adapt proposed methods or identify additional approaches needed to deal with CW and TRW constraints, which are now better addressed in the current draft. Now that we have the final IC results in CWTW NEA GIG phytoplankton, we aim at having the marine chapter complete with all datasets analysed, which will be finalized after the Berlim workshop. We agree with Mike that in the case of coastal and transitional waters there are other important factors to have into account, and we will have room to discuss this at the workshop in time for the final additions to the marine chapter.

3- The R templates, as well as the excel spreadsheets can be adapted to the nutrient in use by the MS. The ones provided are intended to be templates and examples of application, which can be used with other nutrients and/or units according to each case particular needs.

27

4- Units. We have used the IC common data set provided by expert’s leads. In this common data set the units are ug/l. In any case we have made adjustments in order to allow the use of the templates and R scripts with any unit, with little modifications by the MS experts. In some CW & TRW datasets used as illustrative examples we had the need to harmonize some units to allow comparison across different regions. But in the CTRW separate report the original reported units in the IC datasets are used for each MS results.

28