Home

PDF (WISERBUGS Software User manual)

1. Number of each named top and bottom level Multi metric rule available tations ma hierarchal grouping of metrics 5 8 6 B ei D E F G HT T J K 3 M N G P a 1_ WISERBUGS 2 WISER Bioassessment Uncertainty Software 3 Fite specifiying sampling variation and other errors 4 ecological status class limits for metrics 5 and the Multi metric rules for each metric group 6 Multi metrio rule types Specification of Metric rule type for each Bottom level Metric group 7 1 Worst class Default if no type or zero specified Top level and each bottom level Metric o 1 8 2 Average class rounded better Overall 93 3 Average class rounded worse Top MG1 TaxaiDiversity z 10 4 Median class rounded better Level MG2 Eutrophication 2 M 5 Median class rounded worse Metrio MG3 Acidification 2 A amp Average metric values to create Multi Metric Index group Met i 6 5 Number of simulations required max 10000 10000 ee m Indes is Uncertainty in observed 0 value Pita Required Hierarchy of metric groups kiet hd Sarees lars Sortinglidentification conan E Top level group Bottom level group a or blank No
2. Page 20 47 WISER didi EAS mn Deliverable D6 1 3 WISERBUGS WISER Bioassessment Uncertainty Guidance Software tool for assessing confidence of WFD ecological status class Column C Base assessment on Observed values 1 or EOR vales 2 Enter a 1 if the ecological status class for this metric is to based on its observed values e g as for the Saprobic index in the example below Enter a 2 if the ecological status class for this metric is to based on its derived EQR value whereby the observed value is normalised by its expected RC value and range e g as for the Average score per Taxon in the example below Column D Index is integer Enter a 1 if the metric can only take integer values This ensures the simulated observed values and the confidence limits are also integers e g as for DFSI in the example below 4 1 3 Ecological status class limits Columns E H The program complies with the WFD in using five ecological status classes high good moderate poor bad For each individual metric or MMI to be used the User must provide the ecological status class limits for the high good moderate and poor classes Columns E F G and H If the metric is to be used in its EQR form the limits must be for the EQR e g as for the Average score per Taxon in the example below ots v pe Metric
3. In this example the overall class is based on the rule type 1 Worst case the default which means it is the worst status class of all of the top level groups and metrics In the example below this means taking the worst class of the classes based on the top level groups MGI Taxa Diversity MG2 Eutrophication MG3 Acidification and the group MG4 which is the class derived from a MMI Cells N9 N 6 can be used to specify the relative weights to be assigned to each top level group if an averaging rule codes 2 or 3 is being used to derive the overall class for a site In the example below the relative weights for the top level groups 1 4 are given in Cells N9 N 2 as 1 3 2 and 1 respectively Note these weights are not actually used if the overall rule type is Worst case Wiz Me gx C D 6 Specification of Metric rule type for each 7 Top level and each bottom level Metric 0 1 Top Level MG2 Eutrophication 2 f T T T T T T Metric 6 Page 28 47 WISER d d i EZA S mn Deliverable D6 1 3 WISERBUGS WISER Bioassessment Uncertainty Guidance Software tool for assessing confidence of WFD ecological status class 4 1 7 Specifying the hierarchal grouping of the individual metrics and their weights Columns N O P and Q Column N is used to specify the top level grouping each metric Column P is optionally used to specify the bottom level grouping each metric By default each m
4. Software tool for assessing confidence of WFD ecological status class 4 1 6 Specifying the metric rule type for each top and bottom level group of metrics Cells block E8 N16 of the Metric Specification file is used to specify the multi metric rule to be used for combining the metrics in each top and bottom level group of metrics Optional relative weights for top level groups can also be specified in Cells N9 N16 The User must give a name for each top level group of metrics in cells D9 D16 In the example below top level group 1 has been named MG1 Taxa Diversity in Cell D9 and the rule for combining classes of metrics in this top level group 1 is coded 2 in cell E9 i e take the Average class rounded better as defined in cell AS and explained above Similarly top level group 2 has been named MG2 Eutrophication and also uses rule type 2 as does top level group 3 named MG3 Acidification Top level group 4 simply labelled MG4 in Cell D 2 is specified in Cell E72 as using rule code 6 which indicates from Cell A12 that the EQR values of metrics assigned to this group should be averaged to derive a new multi metric index MMI The name of the actual MMI as opposed to its group name is specified as a new row amongst the individual metrics see Section 4 1 7 for further details Cell E8 specifies the type of rule to used to combine the top level group metrics into the overall status class for the site
5. FMETSPEC WISERBUGS_TestMetricsSpec11 xls FOBS WISERBUGS_TestObs11 xls FEONE WISERBUGS _TestEZero11 xls FEZERO WISERBUGS TestEOne 1 xls FCORR WISERBUGS TestCorr1 1 xls with some sampling correlations WISERBUGS TestCorrNulll1 xls with zero sampling correlations FOUT WISERBUGS_TestOutl 1 xls Page 45 47 WISER d fd i FEATS Deliverable D6 1 3 WISERBUGS WISER Bioassessment Uncertainty Guidance Software tool for assessing confidence of WFD ecological status class Blank input files for modification by the User File Code name Example file FMETSPEC WISERBUGS BlankMetricsSpec11 xls FCORR WISERBUGS BlankCorr1 1 xls The contents layout and format of each files is detailed in Section 4 1 4 5 It is highly recommended that the User leave the supplied example input files unchanged A User Metric Specification file FMETSPEC should be built up from the Blank version supplied or from one of the User s existing Metric Specification files This is because the layout but not the User defined parameters of the spreadsheet and in particular the rectangular block of cells Al Q20 is assumed by the program to be fixed and should not be altered or the program will probably crash The details on the individual metrics to be used should be added in rows 21 onwards Page 46 47 WISER Fdd i ZAT ne Deliverable D6 1 3 WISERBUGS WISER Bio
6. Whenever the RC metric values are based on a set of sampled sites they will be prone to estimation error For example in UK RIVPACS the expected ie RC values of the standardly used metrics Number of Families and Average score per taxon are both based on generally large sample weighted averages of RIVPACS reference sites the error SD in the E values were estimated only from the variability due to errors in measuring or estimating the RIVPACS environmental model predictor variables estimated using multiple personnel and given for illustration in Colum M above If a stream type specific target value is used for E and estimated from say the mean metric value for a sample of m Reference Condition sites then the error SD for the E value for sites in that stream type could be estimated as the standard error SE of the mean i e the SD of the m values divided by Vm In the STAR project most partners used a stream type specific approach to selecting RC sites and estimating metric values Page 24 47 WISER didi EA Sa em Deliverable D6 1 3 WISERBUGS WISER Bioassessment Uncertainty Guidance Software tool for assessing confidence of WFD ecological status class 4 1 5 7 Metric groups metric weights amp Multi metric rules for combining metrics Program WISERBUGS has been designed to be as flexible as possible in allowing the User to specify and assess a wide range of rules for combining information from individ
7. Zelinka amp Marvan 11 Observed EO E1 EQR St Dev Lower 2 5 Upper 2 5 Status high good moderate poor bad 12 U2311073 Clun at Marlow 2 008 9 9 2 008 0 122 1771 2 248 mod 0 5 47 51 6 0 9 0 13 U2311163 Cole at Small Heath 2485 9 9 2485 0 118 2 255 2 719 poor 0 0 56 TEE 16 7 14 U2311173 Darwen at Cann Bridge 2 775 9 9 2775 0 119 2 539 3 01 bad 0 0 0 Ta 92 8 15 U2310803 Omore at Bridgend 2 025 9 9 2 025 0 119 1 794 2 256 mod 0 3 42 1 56 6 1 1 0 16 U2310823 Rhymney at Bedwas 2 358 9 9 2 358 0 12 2 119 2 594 poor 0 0 2 31 2 66 5 2 2 17 U2310873 Tame at Stockport 2493 9 9 2 493 0 121 2253 2 729 poor 0 0 55 76 2 18 3 The second example shown below is for the metric Average score per Taxon This metric usually referred to as ASPT is used here in its EQR form a A AY AZ BA BB BC BD BE BE BG BH BI Bul BK 9 SiteVaterbody Identifier 10 Average score per Taxon Aisi Observed E0 E1 EQR StDev Lower2 5 Upper2 5 Status high good moderate poor bad 42 U2311073 Clun at Marlow 65 0 623 1043 0 042 0 961 1 125 high 100 0 0 0 0 13 U2311163 Cole at Small Heath 4 062 0 596 0682 0 043 0 598 0 766 mod 0 1 2 59 2 39 4 0 2 _14 U2311173 Darwen at Cann Bridge 3 875 O 676 0573 0 038 05 0 648 poor 0 0 0 5 62 9 36 7 15 U2310803 Omore at Bridgend 6 188 0 627 O987 0 042 0 904 1 07 high 99 1 0 9 0 0 0 16 U2310823 Rhymney at Bedwas 5 412 0 658 0822 0 039 0 745 0 9 good 45 82 13 5 0 0 17 U2310873
8. as the FEONE file 4 3 1 Options for the form and layout of the FEZERO and FEONE files One of the three options 1 2 or 3 MUST be specified in Cell AZ or the FEZERO and FEONE files Cell AJ 1 Individual values of Eo or E supplied for each site waterbody of each metric can be the same or different Page 33 47 WISER didi EA Sa em Deliverable D6 1 3 WISERBUGS WISER Bioassessment Uncertainty Guidance Software tool for assessing confidence of WFD ecological status class The layout for an option 1 FEONE file is exactly the same as for the Observed metrics values file detailed in Section 4 2 namely as a matrix of sites waterbodies in columns and metrics in rows A G 1 1 U2311073 U2311163 U2311173 U2310803 U2310823 i 2 Example with 3 Number of Taxa 71 39 29 35 36 4 Saprobic Index Zelinka amp Marvan 2 9 2 9 2 9 2 9 2 9 Cell Al 1 5 Average score per Taxon 6 23 5 96 6 76 6 27 6 58 6 Diversity Shannon VViener Index 3 5 3 5 3 5 3 5 3 5 7_ EPT Taxa 18 18 18 16 16 8 Number of Families 32 5 28 9 32 1 27 6 31 8 Cell A7 2 Same value of Egor E for a particular metric to be used for all sites waterbodies Metric names must be given in Column A as for the option 1 and the single value of Eo or E for each metric must be given in Column B No samples names are needed in row 1 or read because the Eo or E parameters are the same for all site waterbodys DI
9. beet cans anda aaa eaedunitagens niece vaaenteceil cn Geanodlaveds xacasns eGanetavetes 3 Te OVVIE W ieoten a ENEE T E E E EA EEE aE 5 De NUNS HALT ATOM P AO E E E E 11 3 R nimg the prograMesrsspsr r eiae Ees a EA EOT EEEE EEEE EE EE EESE ENEE 13 3 1 First TON osoiteta EE ETE ER T ETE se 14 3 2 Changing the working directory and your input output file names cece 14 3 3 Stages of a program TUN iren nia a E E E ETT 14 4 Input files details sesser eeseo erneten erea asra E eE rS O EEE EOE E EERE AEE OSEE T aT raien it 17 4 1 Metric specification file File code name FMETSPEC ecceceseeseeeeeeeeeeteenneeaee 19 4 1 1 Metric names Column A cos cccscssnccsscutssccuessintsacennisansesnascacenaadaacevanddanessaiuadssbadsstensiats 20 4 1 2 Metrics used Observed values or EQR Columns B D cccccceeseeesseeesneees 20 4 1 3 Ecological status class limits Columns E A ccceeceeceeseceteceteeeeeeeeeeeeseenseeeees 21 4 1 4 Estimates of uncertainty components Columns M cccceecccecceesseetseesteeeeeeeees 22 4 1 5 7 Metric groups metric weights amp Multi metric rules for combining metrics 25 4 1 5 Codes for multi metric rule types for combining metrics or their classes 25 4 1 6 Specifying the metric rule type for each top and bottom level group of metrics 28 4 1 7 Specifying the hierarchal grouping of the individual metrics and their weights Columns N O P an
10. choice of high quality sites or metric values to represent the biological Reference Conditions for the site waterbody The approach to assessing uncertainty in program WISERBUGS is simply to estimate the range or variability of estimates of ecological status that could have been obtained using the chosen sampling methods and protocols Because the true status class of a site waterbody is not known the approach does not try to estimate Type I or Type IJ errors but merely to quantify the inherent variability in the methods used to estimate site waterbody ecological quality The approach cannot assess whether the metrics used in the bioassessment are good indicators of true ecological quality but merely whether they give repeatable results External practical experience with using particular metrics or multi metric assessments systems must be used to judge their usefulness and reliability to detect the range of biological conditions Thus the program only assesses aspects of precision rather than accuracy The error assessment software must of necessity be based on the best available estimates of the various sources of variation and errors in observed metric values and EQRs as provided by the User from the WISER or STAR projects or elsewhere Sources of variation for which no estimates are currently available are ignored in the error assessment program and effectively treated as zero In such cases the software system wi
11. level group in each top level group and in the overall site waterbody bioassessment Section 4 1 7 describes how individual metrics are optionally assigned to top or bottom level metric groups and optionally assigned different weights in calculating group averages 4 1 5 Codes for multi metric rule types for combining metrics or their classes Cells A6 12 specify the codes for the a x z N en Multi metric rule types 1 Worst class Default if no type or zero specified 2 Average class rounded better 9 3 Average class rounded worse 10 4 Median class rounded better tule types available s 8 Rules 1 5 are for combining status classes 11 5 Median class rounded worse 12 6 Average metric values to create Multi Metric Index of the metrics within a group Rule 6 creates a Multi metric index MMI Page 25 47 WISER didi DS em Deliverable D6 1 3 WISERBUGS WISER Bioassessment Uncertainty Guidance Software tool for assessing confidence of WFD ecological status class Rule 1 Worst class take the worst of the classes of the individual members of the group Rules 2 and 3 are both based on the average of the numerical classes of the members of the group rounded to the nearest class where high 5 good 4 moderate mod 3 poor 2 bad 1 Thus high good poor 5 4 2 3 11 3 3 67 which is rounded to 4 good wh
12. probability patterns in graphically plots in studies involving assessing changes in probability of each status class in relation to gradual change in EQR metric values along the quality gradient e g varying EQR from zero to one To ensure that exactly the same uncertainty results in terms of confidence limits and probabilities of assignment to each status class in repeated runs of the program on the same input data leave or set the random number seed in cell B 6 to a positive integer This is recommended and is the initial default Setting the random number seed to 1 generates a different set of simulations every run Page 31 47 WISER didi Se em Deliverable D6 1 3 WISERBUGS WISER Bioassessment Uncertainty Guidance Software tool for assessing confidence of WFD ecological status class 4 2 Observed values of each metric for each site waterbody File code name FOBS The observed values of each metric for each site waterbody are stored in a User specified EXCEL spreadsheet file called an Observed metrics values file in a standard layout with the site waterbody names in columns and the metrics as rows as shown in the example below A B IS D E 1 Metric U2311073 Cole at Small Heath U2311173 U2310803 Omore at Bridgend 2 3 Abundance ind m 7323 2904 8493 1670 4 4 5 Number of Taxa 71 39 29 35 6 7 Saprobic Index Zelinka amp Marvan 2 008 2 485 2 449 2 025 8 9 Average score per Taxon 6 500 4 062 3 875 6 188 1
13. similar status classes for any particular site A large part of their correlation is often due to the fact that they are responding similarly across one or more stressor gradients Depending on whether high or low values of each metric indicate high quality two metrics may be either positively or negatively correlated For example the Saprobic Index and ASPT might be expected to be negatively correlated In uncertainty assessments based on simulations of sampling variation in each metric the concern is more subtle A particular site waterbody at a point period in time has a certain biological quality Replicate samples from the same site at one point period differ only because of sampling sub sampling and sample processing variation collectively referred in WISERBUGS as sampling variation Metrics which measure similar components of the biological composition such as giving similar relative weights to taxa are likely to give correlated values between replicate samples within any one site Multi metric indices MMI and status classes based on several correlated indices are affected by all such correlations As an simple example if two metrics are very highly correlated and their status classes are equivalent then in any one sample both metrics are likely to indicate the same class Therefore a MMI is roughly equivalent to either one of the metrics and a multi metric class will be the same as the class based on any one metric Simulations whic
14. to the data entry mistake which should then be corrected and the program re run In particular the name of each site waterbody is written to the WISERBUGS LOG file as it is processed Therefore if some of the sites waterbodies have been processed the last site waterbody name listed should indicate where the problem has occurred Note Because of the generality and flexibility for User input and analysis provided in program WISERBUGS it is very difficult to be able to anticipate all possible type of typing and other errors related to the User s input files Therefore the program will probably occasionally fail without providing any or clear information on the reason The User should always check the log file WISERBUGS LOG for any clues If required for later reference the log file WISERBUGS LOG for a particular run could be saved to a separate directory as it is overwritten in each run If program WISERBUGS completes ok then the uncertainty analysis results for all of the samples are written to an Output EXCEL file as described in Section 5 2 Page 39 47 WISER didi EAS em Deliverable D6 1 3 WISERBUGS WISER Bioassessment Uncertainty Guidance Software tool for assessing confidence of WFD ecological status class 5 2 Output EXCEL file of ecological status classes and uncertainty assessment Rows 1 6 of the Output EXCEL file give the names of each of the input files used in the analysis The results f
15. v fe A B ic D E IF G R 1 2 l Example with ox 3 Number of Taxa 71 re 4 Saprobic Index Zelinka amp Marvan 2 9 Cell Al _ 2 5 Average score per Taxon 6 23 6 Diversity Shannon VWiener Index a5 7 EPT Taxa 18 8 Number of Families 32 5 Cell A7 3 Same null value for Eo or E to be used for all metrics for all sites waterbodies null value for Eo 0 null value for E 1 With option 3 set in Cell AZ of the FEZERO file no other cell in the FEZERO file needs to be filled or is read by WISERBUGS Similarly for the FEONE file Sample names in the FEZERO and FEONE files Sample names are only required for option 1 i e when Cell A7 1 Sample names in the FEZERO and FEONE files must be EXACTLY THE SAME and in EXACTLY THE SAME ORDER as in the Observed metrics values file see Section 4 2 If there any differences or mis matches then program WISERBUGS issues an error message in the program log file WISERBUGS LOG and warns the User that the program failed Page 34 47 WISER F7 i ri Deliverable D6 1 3 WISERBUGS WISER Bioassessment Uncertainty Guidance Software tool for assessing confidence of WFD ecological status class Metric names in the FEZERO and FEONE files Metric names are required in Column A for options 1 and 2 of the FEZERO and FEONE files i e when their Cell A is set to 1 or 2 The metric names must includ
16. 0 11 DSFI 6 5 Not Calculated 9 12 13 IBE Agem 11 4 6 8 9 14 This layout is exactly the same as that output from the AQEM STAR metric calculation program AQEMrap so that this output can if required be used as the file of observed metrics files for input to program WISERBUGS 4 2 1 Site Waterbody names Site or Waterbody names can be any strings of up to 100 characters providing they are valid as strings within EXCEL cells 4 2 2 Metric names The names of the metrics must includes all of those specified for use in the site waterbody bioassessment by the Metric Specification file see Section 4 1 but it can include other currently unused metrics The name for a particular metric must be EXACTLY THE SAME in both files If the name of any of the metrics specified for use is not found in the Observed metrics values file then program WISERBUGS issues an error message in the program log file WISERBUGS LOG and warns the User that the program failed 4 2 3 Observed metric values Observed sample values of the metrics to be used need to be numeric Page 32 47 WISER didi EA Sa en Deliverable D6 1 3 WISERBUGS WISER Bioassessment Uncertainty Guidance Software tool for assessing confidence of WFD ecological status class 4 2 4 Missing observed metric value indicator The missing value indicator is 9 This should be used where the value of a particular metric cannot be or has not been calculated for a
17. Collaborative Project large scale integrating project Grant Agreement 226273 Theme 6 Environment including Climate Change Duration March 1 2009 February 29 2012 Sey OROGRAMME O WISER r4 d i ol Water bodies in Europe Integrative Systems to assess Ecological status and Recovery DELIVERABLE Deliverable D6 1 3 WISERBUGS WISER Bioassessment Uncertainty Guidance Software tool for assessing confidence of WFD ecological status class User Manual and software Release 1 1 Sept 2010 Lead contractor Bournemouth University BourneU Contributors Ralph Clarke Due date of deliverable Month 34 Actual submission date Month 19 Project co funded by the European Commission within the Seventh Framework Programme 2007 2013 Dissemination Level PU PP RE CO Public X Restricted to other programme participants including the Commission Services Restricted to a group specified by the consortium including the Commission Services Confidential only for members of the consortium including the Commission Services WISER Wahid ve Deliverable D6 1 3 WISERBUGS WISER Bioassessment Uncertainty Guidance Software tool for assessing confidence of WFD ecological status class Page 2 47 WISER didi DS em Deliverable D6 1 3 WISERBUGS WISER Bioassessment Uncertainty Guidance Software tool for assessing confidence of WFD ecological status class Content COMUCNL ssh Acedasitavecastetetsnalansaitl adaiancdasede
18. ER didi DS mn Deliverable D6 1 3 WISERBUGS WISER Bioassessment Uncertainty Guidance Software tool for assessing confidence of WFD ecological status class The main menu window is where the User specifies all of the input and output files to be used in the analysis The layout and content of the individual input files are described in section 4 and the output file is explained in Section 5 3 1 First run On the first run of the software the default working directory is the start up directory where the software is stored e g C Program Files WISERBUGS and all the input and output files are those supplied with the software package On your first run of program WISERBUGS it is recommended that you simply run the program with the initial default working directory and all of the supplied input and output files using the instructions below This will help you familiarise yourself with the way the program works and the type of output you will obtain To set up your own analyses on your own data using your own metrics multi metric indices and rules you must first read Section 4 3 2 Changing the working directory and your input output file names e On start up the path of the last used default working directory and the file names and paths of the last used input and output files are displayed e The default working directory for all of your input and output files can be changed in order to work from any directory of your choice Simp
19. ISERBUGS WISER Bioassessment Uncertainty Guidance Software tool for assessing confidence of WFD ecological status class In this example the overall status class for macroinvertebrates is then combined with the classes of three other top level group metrics namely fish metric EFI group 2 macrophytes MTR group 3 and habitats HMI group 4 to derive the overall status class for the site waterbody based on multiple BQEs Obviously to derive an uncertainty analysis for this overall bioassessment requires estimates of the sampling and other sources of variation and error for each metric of each BQE involved 4 1 8 Number of simulations cells B15 and B16 The number of simulations of observed ry B 14 and EQR values to be used to assess the 15 Number of simulations required max 100000 100000 16 Integer random number seed 1 uses current time 234 17 uncertainty is specified in cell B S The maximum permitted number of simulations is 100000 A minimum of 10000 simulations is recommended and this is the default Using 10000 simulations would ensure that the estimates of the percentage probability of belonging to each status class would vary by at most 2 in at least 95 of repeated runs of the program using different random number seeds and the low percentage probabilities in unlikely status classes for a water body would vary by very much less Using the maximum simulations would give the smoothest
20. Tame at Stockport 32 0 693 0462 0 036 0 39 0 534 bad 0 0 0 0 3 99 7 aa In WISERBUGS all EQR values are derived from the formula in equation 1 repeated here for convenience O E EQR i equation 1 0 By setting the Eo values to zero and the E values to the RIVPACS type model expected value under Reference Conditions the EQR values become RIVPACS type O E ratios of the observed O to expected E values of metrics or biotic indices e g see Clarke et al 1996 Wright Sutcliffe amp Furse 2000 Clarke Wright amp Furse 2003 In the example above for the second site waterbody the observed ASPT is 4 062 the expected E1 values is 5 96 give an EQR O E of 0 682 The 95 confidence for the EQR were 0 598 0 766 placing it in the moderate with a probability of 59 2 However there is also a 39 4 that the site waterbody could be classed as poor status Page 42 47 WISER 4141 AS Deliverable D6 1 3 WISERBUGS WISER Bioassessment Uncertainty Guidance Software tool for assessing confidence of WFD ecological status class All EQR values are constrained to be greater than or equal to zero by resetting any negative values to zero However unless a metric EQR is being used in a multi metric index MMI EQR values greater than one are left unchanged This is to permit compatibility with the widely used RIVPACS type O E ratios Multi metric index MMI confidence limits status class and probabilities of
21. Waterbody Identifier Multi metric status First Overall then for each top level metric group MG Overall Status high good moderat poor bad U2311073 Clun at Marlow good 0 57 1 42 9 0 0 U2311163 Cole at Small Heath poor 0 0 12 8 84 8 2 4 U2311173 Darwen at Cann Bridge poor 0 0 0 9 98 9 0 2 U2310803 Omore at Bridgend good 0 3 74 4 25 4 0 0 U2310823 Rhymney at Bedwas mod 0 1 4 96 1 2 4 0 U2310873 Tame at Stockport poor 0 0 0 1 93 8 6 1 Metric group status class and probabilities of class membership Exactly the same information on status class and class probabilities is then given for each of the top level metric groups see start of statistics for top level group MGI Taxa Diversity in columns I J of the example above The status class information is also calculated but not currently output for each bottom level metric group if they are defined However all of the hierarchical multi metric rules are carried to build up to an overall status class for both the observed data and for every simulation This is how WISERBUGS obtains confidence limits and probabilities of class at all levels of the hierarchical grouping of metrics Individual metric confidence limits status class and probabilities of class membership To the right of the observed status class and probabilities of class for the overall site waterbody assessment and for each top level metric group the output gives information for each individual metric used in the assessme
22. ainty Guidance Software tool for assessing confidence of WFD ecological status class The EQR could be a RIVPACS type O E ratio where E is set a RIVPACS model based site specific expected value and Ep is set to zero When several EQRs are used to create a Multi Metric Index MMI by averaging their values each EQR is forced into the range 0 1 by setting any EQR values greater than 1 to 1 If EQRs are used then the User can provide an estimate of the error SD for the Reference Condition values E1 of each metric for the group of sites or water bodies to be assessed The same User specified Metric Specification File must also give the ecological status class High to Poor limits for each metric or EQR Program WISERBUGS allows the User to specify a wide range of rules for combining individual metrics into multi metric indices MMI or for combining individual metric classes into a metric group class and thus in deriving an overall site waterbody assessment For example it can cope with combining status classes for macro invertebrate metrics designed to measure one type of stress e g eutrophication diversity or acidification and then combine using the same or a different rule the classes from these individual stress types into an overall class for macro invertebrates and then combine using the same or a different rule the overall class for macroinvertebrates with that for one or more other biological quality elements
23. assessment Uncertainty Guidance Software tool for assessing confidence of WFD ecological status class APPENDIX 2 Example metric uncertainty standard deviations Example estimates of the replicate sampling standard deviation SD of a range of macroinvertebrate metrics derived from the STAR replicated sampling programme All SD based on untransformed metric values Metric Sampling Replicate method sampling SD AQEM STAR 0 384 ASPT RIVPACS 0 236 Saprobic Index AQEM STAR 0 075 Zelinka amp Marvan RIVPACS 0 048 Diversity AQEM STAR 0 253 Shannon Weiner RIVPACS 0 180 Total number AQEM STAR 3 35 of Families RIVPACS 4 23 Number of AQEM STAR 1 53 EPT Taxa RIVPACS 2 14 Page 47 47
24. class membership Finally if any multi metric indices were involved estimates are provided of their MMI values together with their confidence limits status classes and probabilities of class membership for each site waterbody Reporting status class for a site waterbody with missing values for one or more metrics In the example above the third site waterbody U2311173 Darwen at Carn Bridge has an overall status class of poor The symbol is used to indicate that one or more metrics required by the User to be in the site waterbody assessments had missing values for this site waterbody This could be because it was just not derived or more likely that the taxonomic resolution or taxonomic detail and or the taxa present were not sufficient to calculate a meaningful value for the metric In such cases the output file gives missing values 9 for the statistics of any metric with missing values The status class of missing metrics is set to none Any MMI values are based on the average EQR values of the remaining metrics involved in the MMI Each status class which is derived from metrics with one or more missing values is given a at the end of the class name to indicate it is based on incomplete information Page 43 47 WISER didi EA Sa em Deliverable D6 1 3 WISERBUGS WISER Bioassessment Uncertainty Guidance Software tool for assessing confidence of WFD ecological status class HELP If you have a
25. cs labelled Index A Index B and Index C the row named by the User Multi metric Index based on Indices A B and C in Column A is also given a 4 in Column N to identify it with the three metrics whilst the 6 in Column B identifies this row as being a derived MMI as described above Page 29 47 WISER didi EA Sa em Deliverable D6 1 3 WISERBUGS WISER Bioassessment Uncertainty Guidance Software tool for assessing confidence of WFD ecological status class Weights in Column O and Q In this example the MMI is calculated as the average of the EQR values from the three metrics Index A Index B and Index C However twice as much weight is given to Index B as the other two metrics as specified by the relative weights 1 2 and 1 specified in Column O above The same relative weights 1 2 1 could equally have been specified as 0 25 0 50 and 0 25 or 30 60 30 In general with m metrics in a group and weight w assigned to metric i with EQR Xj the weighted average value for the MMI is MMI wX w2X2 WmXm W1 W2 Wm The same weighting approach can be used to calculate the average of the status classes of the individual metrics in a group For example with three metrics of class high 5 good 4 and poor 2 and with respective weights 1 1 and 3 then the weighted average class for the metric group is group class 1 x hi
26. d O Jesion aa ATA OEE E AOE E E E 29 4 1 8 Number of simulations cells BIS and BIO wo ccccccccccsccesssccsscecesscecssscecssscecssseeeseees 31 4 2 Observed values of each metric for each site waterbody File code name FOBS 32 4 2 1 Site Wat erbody NAMES cu seripsctuessnnceibunveswassnencsusinysaneeiecccssiumeivciansndbucncscqutisninceasics 32 4 2 2 Meti names 5 5 icaassssivusciapacewaadnadecan ys nea ea an EEEE EAEE EEE EEEE ERARA EEO 32 4 2 3 Opserved metrie Valts scuidccandxnuxesoniinnsacaianvasannesveatainnetiawtmcaasrnenssulnndinesmnnastuountbes 32 4 2 4 Missing observed metric value indicator sssssenseeseesseeseesresseeserssressessrssressesse 33 4 3 EQR metric parameters Eg and E File code names FEZERO amp FEONE 33 4 3 1 Options for the form and layout of the FEZERO and FEONE files 33 4 3 2 Methods for setting the Reference Condition E value of a metric c cece 35 4 4 Correlations between metrics in sampling variation File code name FCORR 36 4 5 Direct entry of multi metric index MMI values and their uncertainty 37 Page 3 47 WISER didi EAS em Deliverable D6 1 3 WISERBUGS WISER Bioassessment Uncertainty Guidance Software tool for assessing confidence of WFD ecological status class 5 O tp t file details seserinis eei Ea OEN EEE EEEE EERE ERES 39 5 1 Log file detailing analysis progress and any problems fil
27. d by the monitoring programmes shall be given in the monitoring Plan Thus water body monitoring and management organisations need to have some understanding and estimates of the confidence to which an individual water body can be assigned to an ecological status class In addition the WFD requires that the Ecological Status of surface waters of Member States are maintained or improved However because of the uncertainties associated with biological monitoring waterbodies may appear to change Ecological Status over time when in reality this is only an artefact due to the uncertainty resulting from the whole bioassessment process and sampling procedures A core part of the WISER project was to collect standardised field sample and survey information on phytoplankton aquatic macrophytes macroinvertebrates fish and aquatic habitats at each of a wide range of lake transitional and coastal water body sites across Europe One important reason for this was to improve understanding and provide estimates of the sampling uncertainty replicate sub sample spatial and temporal associated with specific sampling surveying methods individual metrics and multi metric classification rules Page 5 47 WISER d fd i FEAT Deliverable D6 1 3 WISERBUGS WISER Bioassessment Uncertainty Guidance Software tool for assessing confidence of WFD ecological status class The WISERBUGS software program has been written to provide a general means of using sim
28. di EE em Deliverable D6 1 3 WISERBUGS WISER Bioassessment Uncertainty Guidance Software tool for assessing confidence of WFD ecological status class 3 Running the program To run the program from the Desktop Select the Start menu Programs WISERBUGS WISERBUGS The WISERBUGS program icon can also be copied to the desktop in the usual way for more immediate access The program main menu window is displayed as follows 15 x WISER WISERBUGS BU 14 46 Fa SS N WISER Bioassessment Uncertainty Guidance Software aisem Release 1 1 September 2010 Default Working C AWISERBUGS Edit Directory Metric Specification File C wISERBUGS WISERBUGS_TestMetricsS pect 1 xls Observed Mori values C WISERBUGS WISERBUGS_Test0bs11 xls Edit Ne Metric standardisation C WISERBUGS WISERBUGS_TestEZero11 xls Edi EZERO E01 File Metric standardisation C WISERBUGS WISERBUGS_TestEOne11 xls Edi EONE E1 File it Metric sampling C AWISERBUGS WISERBUGS_TestCorrl1 xls Edi Correlation File it Results output File C WwISERBUGS WISERBUGS_TestOutl1 xls Edit Log output File C WISERBUGS WISERBUGS LOG Edit The Log file contains information on any detected Press to cary out the uncertainty EXCEL OUTPUT FILE OF errors with the program s Press to Quit program analysis and store results in the Heels MUS DHE ified Output fil Bid a Lae always ere pam ai RUNNING THIS PRORAM Page 13 47 WIS
29. e WISERBUGS LOG 39 5 2 Output EXCEL file of ecological status classes and uncertainty assessment 40 HELP EEEE E E ETR EE A E E R 44 REFERENCES nenesinin eee a E E N a 44 APPENDIX 1 Example input and output files supplied with program WISERBUGS 45 APPENDIX 2 Example metric uncertainty standard deviations cceeeeseeeteeneeeeeeeeeeeeneee 47 Abbreviations WB Water Body the entity of water management according to the Water Framework Directive WFD BQE Biological Quality Element organism groups demanded for assessment and monitoring of water ecological status fish benthic macroinvertebrates aquatic macrophytes angiosperms marine macroalgae benthic freshwater algae phytoplankton EQR Ecological Quality Ratio ratio of observed assessment index value to the expected value under reference conditions EQRs represent normalised index values on a numerical scale between 0 and 1 Page 4 47 WISER d fd i FEATS Deliverable D6 1 3 WISERBUGS WISER Bioassessment Uncertainty Guidance Software tool for assessing confidence of WFD ecological status class 1 Overview The aim of the software program WISERBUGS is to assist in quantifying uncertainty in the assessment of the ecological status of lakes rivers transitional estuarine and coastal waters WISERBUGS WISER Bioassessment Uncertainty Guidance Software is a product of the WISER Water bodies in Europe Integrative Systems to asse
30. erbody bioassessments As an example above the Number of Families metric for observed samples is assumed to have an average bias of 1 5 families with a additional SD component of 1 22 V1 5 The effect of sample processing errors on other metrics can be more complex is thus harder to synthesise in a simple form In the STAR project sampling programme all of the samples taken from sites within one stream type including those used to set trial estimates of Reference Condition were taken and processed to the same standard usually by the same people In such cases there is no bias for Page 23 47 WISER 14141 AS Deliverable D6 1 3 WISERBUGS WISER Bioassessment Uncertainty Guidance Software tool for assessing confidence of WFD ecological status class the Observed sample metric values relative to the RC values and these components can be ignored or set to zero Column M Uncertainty in Reference Condition E value The Reference Condition value 7 of a metric for a site waterbody is usually determined by the observed value of the metric for a set of high quality sites considered to be in ecological Reference Condition which are of the same or similar physical environmental type The RC value could be either site specific and even site and season specific as in the case of RIVPACS type predictive models or stream type specific as implied by using the WFD System type A typology for grouping sites within an Ecoregion
31. es Program WISERBUGS links the metric specification details in the Metric Specification file with the observed and other metric values in the other input files FOBS FEZERO FEONE and FCORR entirely by matching their metric names Therefore THE NAME OF ANY PARTICULAR METRIC MUST BE EXACTLY THE SAME IN ALL OF THE INPUT FILES 4 1 2 Metrics used Observed values or EQR Columns B D Cells left blank are treated as zero 0 values Column B Use in error assessment To involve a metric in the bioassessment enter a 1 in the spreadsheet Column B labelled Use in error assessment This highlights the cell in pink Where multi metric indices MMI are involved enter the name for the derived MMI in a new row of column A and the enter a 6 in Column B of the same row This highlights the cell in light blue the colour used to denote multi metric information To remove a metric from the bioassessment either delete its value in Column B or enter 0 c D Base assessment on Indexis integer S 1 Observed values 1 Yes 2 EQR values 0 or blank No Metric name 22 Abundance ind m 24 Number of Taxa 26 Saprobic Index Zelinka amp Marvan 28 Average score per Taxon 30 DSFI 33 Number of Families 35 Index A 36 Index B 37 Index C 38 Multi metric Index based on Indices A B and C
32. es all of those specified for use in the site waterbody bioassessment by the Metric Specification file see Section 4 1 but can include other currently unused metrics The name for a particular metric must be EXACTLY THE SAME in both files If the name of any of the metrics specified for use is not found in the FEZERO or FEONE file then program WISERBUGS issues an error message in the program log file WISERBUGS LOG and warns the User that the program failed 4 3 2 Methods for setting the Reference Condition E1 value of a metric Various methods of setting the Reference Condition 7 value of a particular metric for a particular site waterbody or environmental similar group of sites waterbodies can be used depending the data available Obviously the reference condition or high quality sites used to determine the E values should be sampled in the same way as the samples for the sites being assessed The following are several possible options in roughly decreasing order of preference i If a suitable RIVPACS type predictive model involving an adequate number of environmental similar reference condition sites is available then the E values are best based on RIVPACS type site and season specific predictions of the expected fauna and metric values ii In the absence of a RIVPACS model if a suitable number of reference condition sites of an environmentally similar type are available the E values can be based on t
33. etric is given equal weight in determining the group class based on either the average of the individual metric classes or the MMI based on the average of the individual metric EQR values However the User can specify a non uniform set of relative weights for the metrics in a group The weights are given in column O for a metric or even MMI in top level group or in column Q for a metric in a bottom level group Lies oa ne px A N ie B Q Required Hierarchy of metric groups Bottom level group Top level group 19 Metric Weight if not equal Metric YYeight if not equal Metric name Metric Group Metric Group 24 Saprobic Index Zelinka amp Marvan 2 26 Average score per Taxon 2 28 DSFI 2 30 Diversity Shannon Wiener Index 1 Lis his 32 Diversity Margalef Index EE EA e aa 34 Acid Index Hendrikson amp Medin 3 36 Number of Families 1 37 r a 38 Index A 4 40 Index C 4 4 41 Multi metric Index based on Indices A B and C 4 42 Ss Saas an In the first example above there are only Top level groups the metrics Diversity Shannon Wiener Index and Number of Families are in top group 1 Saprobic Index Average Score per Taxon and DFSP are in group 2 and the metric Acid Index Hendrikson amp Medin is group 3 on its own The final top level metric group 4 comprises the three metri
34. gh 1 x good 3 x poor 1 1 3 1x 5 1x 4 3x 2 5 15 5 3 0 moderate In the second illustrative example below all the macroinvertebrate metrics of the previous example keep their groups but these are now treated as bottom level groups and the whole set of macroinvertebrate metrics become one top level group of metrics In addition in determining the overall average class for the top level group comprising macroinvertebrates the bottom level groups 1 2 3 and 4 are given relative weights 1 3 1 and 2 as specified in Column O were oa gx A N ie P Q Required Hierarchy of metric groups Top level group Bottom level group 19 Metric name Metric Group Misit ttle Metric Group ENJE elfen 20 if not equal if not equal 23 24 Saprobic Index Zelinka amp Marvan 1 3 2 25 26 Average score per Taxon 1 2 27 28 DSFI 1 2 29 30 Diversity Shannon VVviener Index 1 1 A 32 Diversity Margalef Index 34 Acid Index Hendrikson amp Medin 1 i 3 36 Number of Families 1 38 Index A 1 4 1 39 Index B 1 4 2 40 Index C 1 4 1 41 Multi metric Index based on Indices A B and C 1 2 4 42 43 EFI European Fish Index 2 44 45 MTR Mean Tropic rank of Macrophytes K3 46 47 HMI Habitat Modification Index 4 os Page 30 47 WISER didi Se em Deliverable D6 1 3 W
35. h assumed and generated independent sampling errors would under estimate the true variability in a MMI average and underestimate the uncertainty in status class estimation In practice such sampling correlations are only important if they are at least 0 5 or so Program WISERBUGS has the facility to incorporate sampling correlations between metrics The User can supply estimates of the replicated sampling correlations between either all metrics or those pairs of metrics which are thought to be highly correlated Program WISERBUGS simulates sampling variation for the metrics some perhaps on a transformed scale assuming a multivariate normal distribution for the joint sampling variation based on the User supplied estimates of the sampling SD and the sampling correlations If a particular metric is specified as having constant sampling variance on a square root or double square root scale then the correlations involving this metric are also assumed to be for when this variable is on its transformed scale Page 36 47 WISER didi Se em Deliverable D6 1 3 WISERBUGS WISER Bioassessment Uncertainty Guidance Software tool for assessing confidence of WFD ecological status class Layout of the FCORR metric correlation file The first row is a header line Each subsequent row specifies one practically significant correlation between two metrics The metric names are given in Columns A and B and the correlation is given in Column C Corre
36. he mean or median metric value for these sites iii If a suitable number of an environmentally similar type of high quality sites of uncertain reference condition are available the E values can be based on the mean median or perhaps an upper percentile 75 or 90 value of the metric for these sites iv If only a very small number of an environmentally similar type of high quality sites of uncertain reference condition are available then the E values cannot be reliable estimated and might be based using the maximum of the few values available However the maximum value is not a stable measure and increases with the number of sites on which it is based v If no reference condition or high status sites are available then some form of hind casting or extrapolation to reference conditions will be necessary to provide appropriate values of E7 Page 35 47 WISER d fd i EELT Deliverable D6 1 3 WISERBUGS WISER Bioassessment Uncertainty Guidance Software tool for assessing confidence of WFD ecological status class 4 4 Correlations between metrics in sampling variation File code name FCORR The values of two or more metrics especially if they are intended to measure the same type of stress are often correlated across a range of sites When one such metric gives values indicating relative high quality the other is also likely to give a similar message Highly correlated metrics would hopefully give
37. ile good mod mod 4 3 3 3 10 3 3 33 which is rounded to 3 mod The difference between Rules 2 and 3 is when the average is mid point between two classes Thus high good 5 4 2 9 2 4 5 for which Rule 2 rounds up better to 5 high while Rule 3 rounds down worse to 4 good Rules 4 and 5 are both based on the median class of the group of metrics median class is always one of the observed classes Case a Odd number of metrics in group same for Rules 4 and 5 median is middle ranking status class e g median of high good poor good median of high high high moderate poor high Case b Even number of metrics in group Rule 4 Rule 5 round better round worse e g median of good bad good bad median of high high moderate poor high moderate Page 26 47 WISER d d i EE SD Deliverable D6 1 3 WISERBUGS WISER Bioassessment Uncertainty Guidance Software tool for assessing confidence of WFD ecological status class Rule 6 makes a Multi metric index MMI equal to the average value of the Ecological Quality Ratios EQRs all of the metrics in the group and then classifies the MMI into status classes All metrics involved in the MMI must have been converted into an EQR by specifying option 2 Base assessment on EQR value
38. imulate a large number of other possible observed metric or EQR values which could have been obtained for the site waterbody If the sampling SD is considered to be constant on a specified transformed scale then in each simulation the observed value x is first transformed then a random sampling error z added and the result back transformed to obtain a simulated observed value e g for the square root transformation simulated observed value Vx z Each simulation leads to a status class based on each individual metric and then groups of metrics in the specified multi metric rules for site waterbody bioassessment From the statistical distribution of simulated values and classes estimates of the probability of belonging to each status class are obtained The content and format of the EXCEL output file give the ecological status assessment and the associated uncertainty for each site waterbody is explained in Section 5 WISERBUGS and its precursor STARBUGS was written and produced by Ralph Clarke of Bournemouth University BourneU in the UK Page 8 47 WISER d fd i FEATS Deliverable D6 1 3 WISERBUGS WISER Bioassessment Uncertainty Guidance Software tool for assessing confidence of WFD ecological status class Disclaimer Uncertainties in estimates of the ecological quality and status class of a site or water body are potentially due to many factors ranging from the field sampling and sample processing methodology to the
39. ioassessment Uncertainty Guidance Software tool for assessing confidence of WFD ecological status class The program then reads all of the input files and carries out the simulations to assess the uncertainty in your bioassessment of each of the sites waterbodies in the Observed metric values file Whilst the files are being read and the analysis is being carried out for each site waterbody in turn the program Processing Please wait displays the message Progress on reading the input files and working through the analysis is written out to a standard file called WISERBUGS LOG in the WISERBUGS start up directory Any detected errors are also written to the same file which should always be checked on program completion On completion of the analysis or error detection a P i this button is displayed Press the button to continue EE ce If the program detected an error in any of the input files or did not complete the analysis for all or any of the Analysis not completed Press to exit Program and view Progress and sites waterbodies then the following message errors in file STARBUGS LOG is displayed On pressing this button the Log file WISERBUGS LOG is displayed using Microsoft Windows NotePad Scroll down this file to see how far the reading of the input files and the analysis progressed The last few lines should usually indicate where the program or analysis failed and what input data a
40. k STAR project replicates samples were taken at a wide range of stream types throughout Europe and this information has been used to provide the example estimates of the sampling standard deviation SD of a range of metrics see Appendix 2 Sampling variance here is the variance between samples from a water body over the assessment Page 22 47 WISER d fd i EAT Deliverable D6 1 3 WISERBUGS WISER Bioassessment Uncertainty Guidance Software tool for assessing confidence of WFD ecological status class period Sampling SD is simply the square root of the sampling variance but is quoted in preference because it is in exactly the same units as the metric values and thus more easily interpreted Columns I and J Sampling variation SD The variability between replicate sample values of a metric for a site may tend to increase or vary systematically with their average value for the site In such cases statistical analysis may show that the sampling variance is less variable when the metric values are transformed using the square root Vx double square root Vx arsine Vx or arsine V x 100 transformation As an example Clarke et al 2002 found that sampling SD of the square root of the number of macroinvertebrate families present in RIVPACS samples from UK rivers showed no systematic pattern and was on average estimated as 0 228 To allow for this in program WISERBUGS the User should enter a 1 in Column J and 0 228 in Column J of the
41. lations can be specified in any order The name for a particular metric must be EXACTLY THE SAME as in the Metric Specification file see Section 4 1 If a metric name in the FCORR file does not match up exactly with the name of a metric specified for use in the Metric Specification file then the correlation will be ignored Any sampling correlations not specified are treated as zero A simple example of an FCORR file is given below B E 1 Metric i Metric j Sampling correlation ij 2 Saprobic Index Zelinka amp Marvan Average score per Taxon 0 6 3 Average score per Taxon Biological Monitoring VYorking Party 0 8 4 Average score per Taxon Number of Families 0 5 5 4 5 Direct entry of multi metric index MMI values and their uncertainty If you have previously decided on the metrics and combination rules to be used for your multi metric index MMI then you may have already calculated your MMI values for each sample from a prior sampling variability study such as those carried out in the STAR or WISER projects In such cases it may be possible to calculate an appropriate estimate of the sampling variability and uncertainty in the MMI values directly The estimates of the observed values of the MMI and estimates of its sampling SD can be supplied directly to the WISERBUGS input files This avoids the need to derive and supply the sampling correlations betweens the individual metrics involved in the MMI a
42. ll over estimate the precision and under estimate the true uncertainty in the assessment of status classes Any User needs to be made aware of these obvious limitations especially from the point of view of taking catchment management decisions However this software system approach provides a good framework for uncertainty assessment and is a major step forward Page 9 47 WISER Wahid ve Deliverable D6 1 3 WISERBUGS WISER Bioassessment Uncertainty Guidance Software tool for assessing confidence of WFD ecological status class Page 10 47 WISER didi Se em Deliverable D6 1 3 WISERBUGS WISER Bioassessment Uncertainty Guidance Software tool for assessing confidence of WFD ecological status class Installation e Using Windows Explorer copy the supplied zip file WISERBUGS_1 1 zip to the directory from which you wish to install the WISERBUGS program e Within the selected directory click on the zip file and extract all of the files to the same directory e To begin the installation click on the file SETUP EXE in this installation directory and follow the instructions e By default the WISERBUGS software package including the supplied example test data files will be installed to the directory C Program Files WISERBUGS Page 11 47 WISER Wahid ve Deliverable D6 1 3 WISERBUGS WISER Bioassessment Uncertainty Guidance Software tool for assessing confidence of WFD ecological status class Page 12 47 WISER j
43. ly click on the Edit button on the right hand side of the box and select the required directory which can be on any directory and drive This will change the directory of ALL input and output files to this new directory but will keep the last used filenames e Irrespective of whether or not you have just changed the default working directory you can now click on the Edit button for any type of input file to select the filename and if required independent alternative directory for your own required previously created input files of data or parameters When you change default working directory it is useful to do this anyway to check that the input files all exist in this new working directory e You can click on the Edit button for the Results Output file or for the Log output file to select the filename and if required independent alternative directory for these output files If the output file already exists you will asked if you wish to overwrite it If the output file does not exist or you type the name of a new file in the Windows File Save dialog box it will be created 3 3 Stages of a program run Press to Quit program e Ifyou want to quit the program press the button e To begin the analysis using the selected files Prose ta Ganyout he uncenainy press this button analysis and store results in the specified Output file Page 14 47 WISER d d i rl SS Deliverable D6 1 3 WISERBUGS WISER B
44. macrophytes diatoms or fish For each set of sites waterbodies to be assessed the program reads the observed values of each metric to be used from a User specified Observed metric values EXCEL file The observed values of the metrics must have been calculated previously outside of program WISERBUGS The layout of this input file was designed to provide immediate compatibility with the metric values EXCEL files derived and output from the freshwater macroinvertebrate sample software known as AQEMrap or ASTERICS obtainable from the EU Fifth Framework Programme river classification project STAR Web site at www eu star at The AQEMrap software could be used to calculate and export observed metric values for freshwater macroinvertebrate samples for input to the WISERBUGS program The program also needs three other User specified input files EONE specifying the Reference Condition E1 values of each metric for which EQR 1 EZERO specifying the values Eo of each metric for which EQR 0 ECORR optional specifying the correlations between metrics due to sampling variation The content and format of the User specified EXCEL input files are explained in detail in Section 4 Page 7 47 WISER didi DS em Deliverable D6 1 3 WISERBUGS WISER Bioassessment Uncertainty Guidance Software tool for assessing confidence of WFD ecological status class WISERBUGS uses the uncertainty estimates for each metric to s
45. n Section 4 4 Page 17 47 WISER didi Se em Deliverable D6 1 3 WISERBUGS WISER Bioassessment Uncertainty Guidance Software tool for assessing confidence of WFD ecological status class Blank input files for modification by the User File Code name Example file FMETSPEC WISERBUGS BlankMetricsSpec11 xls FCORR WISERBUGS BlankCorr11 xls Page 18 47 WISER Waa Se Deliverable D6 1 3 WISERBUGS WISER Bioassessment Uncertainty Guidance Software tool for assessing confidence of WFD ecological status class 4 1 Metric specification file File code name FMETSPEC This EXCEL worksheet file specifies most of the parameters of the analysis namely the metrics to be used their uncertainty component estimates and the sets of multi metric rules to be applied It is divided into several main sections as indicated below The layout but not the User defined parameters of the spreadsheet and in particular the rectangular block of cells Al Q20 is assumed by the program to be fixed and should not be altered or the program will probably crash The program is supplied with a working example of this type of file To make their own version of a Metric Specification file Users should begin by making a copy of the supplied example file which they then edit as required Coie tor each tps BE Multi metric rule to be used for
46. name Saprobic Index Zelinka amp Marvan Average score per Taxon Index A Index B Index C Multi metric Index based on Indices A B and C If the observed values of the metric are to be classified then the limits should be in terms of the observed values e g as for the Saprobic Index in the example below If higher values of a metric or its EQR indicate poorer status then the limits entered should be the upper inclusive value for each class e g in the Saprobic Index example below Page 21 47 WISER didi EA Sa en Deliverable D6 1 3 WISERBUGS WISER Bioassessment Uncertainty Guidance Software tool for assessing confidence of WFD ecological status class sites waterbodies with observed values greater than 1 7 but less than or equal to 2 0 are classed as good all values gt 2 6 are classed as bad If lower values of a metric or its EQR indicate poorer status then the limits entered should be the lower inclusive value for each class e g in the Average score per Taxon example sites waterbodies with EQR values lt 0 89 but greater than or equal to 0 78 are classed as good all EQR values lt 0 56 are classed as bad Metrics which are part of a multi metric index MMI but are not individually assigned to status classes as part of the bioassessment do not need any class limits specified However the derived MMI usually scaled to lie in the range 0 1 whose value
47. nd files need to be corrected i i Analysis completed ok Press to exit If the uncertainty analysis was completed ok for all Proqer and view results in EXCEL sites waterbodies then the following message is displayed On pressing this button the results of the uncertainty analysis and multi metric rules for the assessment of all sites waterbodies are displayed in the User specified EXCEL Output file Details and explanation of the output are given in Section 5 to which you are referred You could optionally save the WISERBUGS Results Output EXCEL file to another name You must close the Results output file before re running the software and outputting to the same file or the program will stop with an appropriate warning On either closing EXCEL or otherwise switching back to the still open WISERBUGS program you can re run the program with the same or different input and output files or press the Press to quit program button to close the program Page 15 47 WISER Wahid ve Deliverable D6 1 3 WISERBUGS WISER Bioassessment Uncertainty Guidance Software tool for assessing confidence of WFD ecological status class Page 16 47 WISER d fd i FELTS Deliverable D6 1 3 WISERBUGS WISER Bioassessment Uncertainty Guidance Software tool for assessing confidence of WFD ecological status class 4 Input files details All of the input files are EXCEL XLS files Five input files are required I
48. nput EXCEL files File Code name Content FMETSPEC Metric specification file uncertainty estimates multi metric rules FOBS Observed values of each metric for each site waterbody FEONE Reference Condition E values of each metric for which EQR 1 FEZERO Values Eo of each metric for which EQR 0 FCORR optional Correlations between metrics in their sampling variation The contents layout and format of each type of input files are explained in Sections 4 1 4 4 Cells left blank are treated as missing values zero values or ignored as appropriate Working examples of each type of input file are listed below and in Appendix 1 Example Test files File Code name Example file FMETSPEC WISERBUGS _TestMetricsSpec11 xls FOBS WISERBUGS TestObs11 xls FEONE WISERBUGS _TestEZero11 xls FEZERO WISERBUGS TestEOne 1 xls FCORR WISERBUGS TestCorr1 1 xls with some sampling correlations WISERBUGS TestCorrNulll1 xls with zero sampling correlations Two blank input files listed below are supplied with the software package The blank FMETSPEC file should be used as the starting point to build up your own Metric Specification File detailing the metrics you wish to involve and their class limits and other details as described in Section 4 1 The contents layout and purpose of the FCORR file are described i
49. nt The first example below shows the uncertainty assessment for the metric Saprobic index Zelinka amp Marvan Because the status classes for this metric were based on its observed Page 41 47 WISER d d DS em Deliverable D6 1 3 WISERBUGS WISER Bioassessment Uncertainty Guidance Software tool for assessing confidence of WFD ecological status class values rather than normalised EQR values no metric conversion parameters Eg and E were used and these values were set to missing values in the output The EQR values were set to the observed Saprobic index values and the SD and confidence limits represent uncertainty in the observed metric values The status class is based on User supplied class limits for the observed metric values The probabilities of belonging to each class are derived from simulations of observed metric values In this example the first site waterbody U2301173 Clun at Marlow has an observed sample Saprobic Index value of 2 008 but based on its supplied sampling SD the 95 confidence limits for possible values are estimated as 1 771 2 248 The sample s status class is moderate based on this metric but there is a 47 probability the site waterbody could have been classed as good and a very small probability it could have been either high or poor status A AK AL AM AN AO AP AQ AR AS AT AU Aav AW 9 SiteAWaterbody Identifier 10 Saprobic Index
50. nty in the estimate of the usually mean value of a metric for a water body depends on the level of sampling replication on which it was based in terms of replicate sampling spatial and temporal sampling coverage over the area of the water body to be assessed and the period of time for which the water body assessment is to apply The estimates of uncertainty in individual metric values can include the sampling standard deviation SD due to sampling sub sampling variation and optionally the SD and bias due to sample sorting and identification In practice the uncertainty SD estimates for each metric for each water body or site to be assessed within WISERBUGS must be based on best available information from replicated sampling studies on this or environmentally similar water bodies The ecological status class assessment for individual metrics can be based on just the observed O values of metrics or on normalised Ecological Quality Ratios EQRs involving the ratio of the observed metric values to the Reference Condition values E1 of the metric More generally EQRs are determined by O E EQR 9 E Eg equation 1 where O observed value E Reference Condition value value of metric for which EQR 1 and Eo value of metric for which EOR 0 Any EQR values calculated from equation 1 which are negative are always reset to zero Page 6 47 WISER FLAAT Deliverable D6 1 3 WISERBUGS WISER Bioassessment Uncert
51. ny queries or comments about the software please contact Ralph Clarke at Bournemouth University rclarke bournemouth ac uk This Release of the software and User manual is the first version of the software for trial general use It would be helpful if you could report any bugs errors found with the software or User Manual to Ralph Clarke at Bournemouth University rclarke bournemouth ac uk REFERENCES Clarke R T Furse M T Wright J F amp Moss D 1996 Derivation of a biological quality index for river sites comparison of the observed with the expected fauna Journal of Applied Statistics 23 311 332 Clarke R T 2000 Uncertainty in estimates of river quality based on RIVPACS In Assessing the biological quality of freshwaters RIVPACS and similar techniques Wright J F D W Sutcliffe and Furse M T eds pp 39 54 Freshwater Biological Association Ambleside Clarke R T Furse M T Gunn R J M Winder J M amp Wright J F 2002 Sampling variation in macroinvertebrate data and implications for river quality indices Freshwater Biology 47 1735 1751 Clarke R T amp Hering D 2006 Errors and uncertainty in bioassessment methods major results and conclusions from the STAR project and their application using STARBUGS Hydrobiologia 566 433 440 Staniszewski R Szoszkiewicz K Zbierska J Lesny J Jusik S amp Clarke R T 2006 Assessment of sources of uncertainty in macrophyte sur
52. or each site waterbody are all stored in a single row of the Output file with the site waterbody name in Column A The results for each site waterbody are given in the following order from left to right i Overall observed status class and probabilities of belonging to each of the five possible classes ii For each of the top level metric groups in turn Observed status class for the group and probabilities of belonging to each of the five possible classes iii For each used metric in turn Observed metric value Eo E7 EQR standard deviation SD of the EQR lower and upper 95 non parametric confidence limits of the EQR Observed status class for the metric and probabilities of belonging to each of the five possible classes iv For each derived multi metric index MMI in turn Observed MMI EQR value standard deviation SD of the EQR lower and upper 95 non parametric confidence limits of the EQR Observed status class for the MMI and probabilities of belonging to each of the five possible classes If the status classes for a metric were based on its Observed values indicated by a 1 in Column C of the FMETSPEC file described in Section 4 1 2 rather than normalised EQR values then the columns headed E0 and E1 in the output for that metric are set to values of 9 i e missing values the EQR is set equal to the Observed metric values and all information on confidence limits in EQR
53. particular site or waterbody Empty or blank cells are assumed to be missing values and set to 9 Be careful and check the output The AQEMrap program outputs the phrase Not Calculated as the metric value for a site waterbody sample where it cannot be calculated usually for reasons related to lack of taxonomic resolution Program WISERBUGS automatically converts such cells into missing values 9 Program WISERBUGS can cope with some metrics within a multi metric group having missing values in that it makes the hierarchal bioassessment using the multi metric rules on the remaining metrics and indicates where one or more metrics values were missing at each stage of the overall assessment see Section 5 for further details 4 3 EQR metric parameters E and E File code names FEZERO amp FEONE An observed O metric value is normalised into any EQR using the formula in equation 1 repeated here O E EQR 2 E E equation 1 where O observed value Ei ll Reference condition value value of metric for which EQR 1 and Eg value of metric for which EQR 0 The Eo values to be used for each site waterbody for each used metric must be read from a User specified EXCEL file of any chosen name but referred to here as the FEZERO file The E values to be used for each site waterbody for each used metric must be read from a User specified EXCEL file of any chosen name but referred to here
54. relates to uncertainty in Observed metric values Page 40 47 WISER didi Se em Deliverable D6 1 3 WISERBUGS WISER Bioassessment Uncertainty Guidance Software tool for assessing confidence of WFD ecological status class Overall status class and probabilities of class membership The example below shows the results on Overall status class for the six example sites The first site waterbody U2301173 Clun at Marlow was assigned an overall status class of good However the uncertainty analysis using the sampling and other variability estimates suggests that although there is a 57 1 probability the site waterbody would be classified to good there is a substantial 42 9 probability that the site waterbody would be classified as moderate status class F3 Microsoft Excel WISERBUGS_TestOut11 xIs File Edit View Insert Format Tools Data Window Help Adobe PDF DOSER SRY BB Slo m z 4 21 io Bara 710 B z U e s S th Rl te te A20 SA f Metric Specification file C WISERBUGS WISERBUGS_TestMetricsSpec11 xls Observed Metric values file C WISERBUGS WISERBUGS_TestObs11 xls EZERO E0 values file C AWISERBUGS WISERBUGS_TestEZero11 xls EONE E1 values file C WISERBUGS WISERBUGS_TestEOne11 xIs Metric sampling Correlations file C AWISERBUGS WISERBUGS_TestCorr11 xls Results Output file C WWISERBUGS WISERBUGS_TestOut11 xls Log Output file C WISERBUGS WISERBUGS LOG Site
55. row for Number of Families metric as illustrated above Raw abundance metrics are more likely to have constant sampling SD on the double square root scale which is similar to a logarithmic scale but avoids the need to add one to zero abundances Metrics which are proportions or percentages e g EPT taxa as a percentage of all individuals tend to have highest sampling variability at intermediate values and their sampling SD are less variability on a arsine Vx or arsine V x 100 transformed scale respectively For many metrics the sampling SD can at least initially be assumed to be constant Columns K and L Sorting identification bias and SD This is only applicable where the Reference Condition RC metric values E for a site waterbody were derived from field samples sorted and identified to a higher standard than used for the observed sample metric values In this case the Observed sample may tend to have more taxa missed or mis identified than for the RC sites This means that certain taxonomic richness type metrics will tend to be under estimated bias in the observed samples and under estimate the true site waterbody quality For example in the UK CEH used an quality audit re analysing a proportion of the UK government agencies RIVPACS samples to provide annual estimates of the average under estimation in the number of taxa present this bias and the SD in the number of taxa missed can be incorporated into the site wat
56. s in Column C of the Metric Specification file This converts the observed O value into any EQR based on equation 1 repeated here 2 O E EQR s Ler equation 1 where O observed value E Reference condition value value of metric for which EQR 1 and Eo value of metric for which EOR 0 Notes When EQRs are used in a MMI their values are always forced to lie within the range 0 1 Thus any EQR values gt 1 are reset to 1 and any EQR values lt 0 are reset to 0 Eo and E are specified in separate input files described in Section 4 3 The name for the derived MMI and its status class limits are specified by adding a new row to the Metric Specification file spreadsheet giving a name for the MMI in Column A entering a 6 in Column B to indicate it is a derived MMI rather than an ordinary metric entering the lower limits of each status class of the MMI in Columns E H Finally these details are linked to the individual metrics upon which the MMI is based by assigning the MMI to the same top level metric group column N and optionally bottom level group Column P as was used for the individual metrics Different weights can be attached to each metric involved in determining the average EQR value used as an MMI as described below under the heading Weights in Column O and Q Page 27 47 WISER didi EA Sa em Deliverable D6 1 3 WISERBUGS WISER Bioassessment Uncertainty Guidance
57. s are to be classified into status classes does need its class limits defined See the example above where metrics Index A Index B and Index C are to be individually normalised to EQRs in the range 0 1 and then their EQR values averaged to form a new MMI whose name and class limits are specified in the worksheet 4 1 4 Estimates of uncertainty components Columns M For each metric to be used in the bioassessment best available estimates are required of the various potential sources of uncertainty in the observed metric and EQR values ote X pe A J K E M 17 Uncertainty in observed O value Uncertainty in ues reference Sampling SD constant when a conation E wan Observed values Sorting identification variation value 19 transformed to 0 untransformed 1 square root Average Metric name SD 2 fourth root ba bias SD SD Vv 3 arcsine for p 20 4 arcsine for p 25 26 Saprobic Index Zelinka amp Maran 0 12 0 0 0 0 27 28 Average score per Taxon 0 249 0 0 0 0 081 29 30 Number of Families 0 228 1 eS M22 0 53 31 32 Index A 0 08 33 Index B 0 12 34 Index C 0 14 35 MMI based on Indices A B and C The overall joint effect of field sampling variation and subsequent sub sampling procedures on observed metric values can be estimated by statistical analysis of variance techniques Within the European 5 framewor
58. s the prior estimate of the sampling SD of the MMI will automated take account of the natural sampling correlations of the component metrics Page 37 47 WISER Wahid ve Deliverable D6 1 3 WISERBUGS WISER Bioassessment Uncertainty Guidance Software tool for assessing confidence of WFD ecological status class Page 38 47 WISER didi DS em Deliverable D6 1 3 WISERBUGS WISER Bioassessment Uncertainty Guidance Software tool for assessing confidence of WFD ecological status class 5 Output file details All of the output is written to a single EXCEL file An example output file WISERBUGS _TestOutl11 XLS is provided with the software package as described in Appendix 1 The contents layout and format of the output files are explained below 5 1 Log file detailing analysis progress and any problems file WISERBUGS LOG Text file WISERBUGS LOG in the same directory as the software details progress on reading in information from the User input files and carrying out the uncertainty bioassessment on each sample in turn This provides very useful information on whether the analysis has proceeded as intended and should be checked If program WISERBUGS fails to complete its uncertainty simulation analysis for all sites waterbodies in the User specified Observed metrics values file then the program informs the User who is ask to check through the program log text file WISERBUGS LOG to hopefully find information or clues
59. ss Ecological status and Recovery research project Grant 226273 WISER supported by the European Commission under the Seventh Framework Programme http www wiser eu The WISER project aim was to assist in developing methods for calibrating different biological survey results for lakes transitional and coastal waters against ecological quality classifications to be developed for the Water Framework Directive WFD http europa eu int comm environment water water framework index_en html The WFD requires Member States to assess monitor and where inadequate improve the ecological status of water bodies rivers lakes transitional and coastal waters All such water bodies are to be classified to one of five ecological status classes high good moderate poor and bad with the aim of eventually achieving or maintaining good or better status for all water bodies The ecological status i e condition of a water body is often measured using one or more metrics derived from the taxonomic composition and or abundance obtained from field samples surveys and or habitat surveys The term metric here usually refers to any biological index or other single valued measure which is designed to measure some aspect of the biological community and its taxonomic composition at a site or water body The Articles of the Water Framework Directive Annex V section 1 3 require that Estimates of the level of confidence and precision of the results provide
60. ual metrics and Ecological Quality Ratios EQRs into an overall bioassessment for a site Indices derived from two or more other metrics or indices are often referred to as multi metric indices denoted here by MMI EQRs are as defined in Equation 1 in Section 1 Two intermediate hierarchal levels of grouping the metrics are possible these are referred to as Top level groups and Bottom level groups the latter occurring within a subset of a Top level group of metrics Up to 8 groups of metrics are permitted at each level Sequence of optional steps in the overall assessment based on all selected metrics i convert individual metrics observed values into EQRs ii classify individual metric observed or EQR values into status classes iii combine individual metric EQRs into a Bottom level group MMI and class iv combine individual metric classes into a Bottom level group class v combine individual metric EQRs into a Top level group MMI and class vi combine individual metric classes and or Bottom level group classes into a Top level group class vii combine individual metric EQRs into an Overall MMI and class for the site viii combine Top level group classes into a Overall status class for the site Section 4 1 5 describes the rule types available within program WISERBUGS for combining metric EQRS or their status classes Section 4 1 6 describes how the rule type is specified for combining metrics in each bottom
61. ulations to assess uncertainty in estimates of ecological status class for water bodies based on either single metrics or a combination of metrics multi metric indices MMIs and multi metric rules The User provides prior estimates of the relevant sampling uncertainty for each metric and metric value to be involved in the water body assessments together with metric status class limits and the rules for combining metrics into an overall water body assessment WISERBUGS can also be used just to test the effect of new status class limits and multi metric rules on site waterbody status assessments without any uncertainty assessment by setting all uncertainty components to zero Although initially designed for use with river macroinvertebrate data and metrics program WISERBUGS is designed to be as generic as possible so that it can be used with a wide range of metrics derived from field site sampling and survey data for any single or combination of biological quality elements BQEs namely phytoplankton aquatic flora macroinvertebrates and or fish and any type of water body rivers lakes transitional or coastal waters The program requires the User to provide a Metric Specification File in EXCEL format in which they specify which metrics are to be used to determine the site or waterbody bioassessments the individual metric uncertainty estimates and the multi metric rules for combining information from individual metrics The uncertai
62. variation Weenie value O untransformed Metrio name praua Average Ibias sD so Metric Group TA an Metric Group ee om a 4 arosine for p z Abundance indim z Saprobic Index Zelinka amp Marvan 1 012 o 0 o o 2 27 28 Average score per Taxon 0 249 o 0 0 0 081 2 29 30 OSFI 1 1 0 56 o 0 0 o 2 Kil 32 IBE Agem 33 34 Diversi sity Shannon Wiener Index Bf 0 23 o o o o il 35 38 Diversity Margalef Indes z Asid Index Hendrikson amp Medin 1 1 12 a a a a 3 i 2 Number of Families 2 0 228 15 122 O53 1 B Inder A 1 0 08 4 45 Index B 1 O12 4 EI 46 Index C t cx 4 1 47 MMI based on Indices A B and C 6 4 48 I 1 3 7 Metric Ecological status multi metric grouping Names 2 class limits 4 and weights for metrics i metric used Estimates of uncertainty Observed or EQR components sampling SD etc Page 19 47 WISER didi DS em Deliverable D6 1 3 WISERBUGS WISER Bioassessment Uncertainty Guidance Software tool for assessing confidence of WFD ecological status class 4 1 1 Metric names Column A Individual metrics can be given any name up to 100 characters providing they are valid as strings within EXCEL cells In the examples provided the names used are often those output from the AQEMrap metric calculation software However the program is designed to also be able to use metric values calculated by other software or methods and if required for taxonomic groups other than macro invertebrat
63. veys and the consequences for river classification Hydrobiologia 566 235 246 Sundermann A Pauls S U Clarke R T amp Haase P 2008 Within stream variability of benthic invertebrate samples and EU Water Framework Directive assessment results Fundamental and Applied Limnology Archiv f r Hydrobiologie 173 21 34 Wright J F Sutcliffe D W amp Furse M T eds 2000 Assessing the biological quality of fresh waters RIVPACS and other techniques Freshwater Biological Association Ambleside Page 44 47 WISER PsA r AA ee Deliverable D6 1 3 WISERBUGS WISER Bioassessment Uncertainty Guidance Software tool for assessing confidence of WFD ecological status class APPENDIX 1 WISERBUGS Example input and output files supplied with program Input EXCEL file types File Code name Content FMETSPEC Metric specification file uncertainty estimates multi metric rules FOBS Observed values of each metric for each site waterbody FEONE Reference Condition E values of each metric for which EQR 1 FEZERO Values Eo of each metric for which EQR 0 FCORR Correlations between metrics in their sampling variation Output EXCEL file File Code name Content FOUT Results of uncertainty amp status analysis for each site waterbody in turn Example Test files File Code name Example file

PDF (WISERBUGS Software User manual)

Contents

Download Pdf Manuals

Related Search

Related Contents