PERPEST version 1.0, manual and technical description
Contents
1. 2 1 The PERPEST database case base The database called the case base consist of two different data sets one containing the updated results of the review on effects of pesticides observed in semi field experiments Brock et al 2000a Brock et al 2000b and one on fate and effect characteristics of insecticides and herbicides Table 2 1 The first data set comprises case studies in which the effect of a certain concentration of a pesticide is evaluated in a microcosm or mesocosm Experiments were selected for evaluation when the model ecosystem simulated a realistic freshwater community the experimental design was appropriate ANOVA or regression design and when the exposure concentrations were clearly described We made a distinction between systems to which a single single or pulse and to which a repeated multiple or chronic dose was applied and between lentic stagnant or recirculating and lotic flow through systems Evaluated experiments normally comprised of several cases i e each evaluated concentration in an experiment is a separate case in the case base The endpoints evaluated were classified in 8 different ecological endpoint groups which were different for insecticides and herbicides see Box 1 The responses Alterra rapport 787 15 observed for various ecological endpoint groups were assigned to 0 not evaluated ot the five effect scores ranging from no to clear long term effects Each record in the case base is2. The pesticide manual Twelfth edition British Crop Protection Council Farnham UK Traas T P J H Janse T Aldenberg and T C M Brock 1998 A food web model for fate direct and indirect effects of Dursban 4E a i chlorpyrifos in freshwater microcosms Aquatic Ecology 32 179 190 Van den Brink P J J Roelsma E H Van Nes M Scheffer and T C M Brock 2002 PERPEST model a case based reasoning approach to Predict the Ecological Risks of PESTicides Environmental Toxicology and Chemistry 21 2500 2506 Van Nes E H and M Scheffer 1993 Best Analogous Situations Information System User s guide for BASIS version 1 0 Final report 93 044 Institute for Inland Water Management and Waste Water Treatment RIZA Lelystad The Netherlands Watson I and F Marir 1994 Case based reasoning a review The Knowledge Engineering Review 9 355 381 38 Alterra rapport 787 Appendix I Supported transformations Before standardization a non linear transformation can be used to shrink certain parts of the scale and to stretch other parts The following transformations are available 1 Logarithmic transformation Commonly used transformation to change a log normally distributed variable into a normal distribution or to give less weight to large quantities a hy 2 Log x 1 transformation Like logarithmic transformation but scaled for variables that can be zero Y ny tI 3 Log x 100 x transformation Spec
3. do not use y Load Previous ak Cancel Help Fig 3 3 The Weight Select using tab sheet In this tabsheet Fig 3 3 you can select or deselect conditional variables or selection variables In the lower part of the dialog screen you can select the hydrology and exposure of the experiment to be predicted Left panel Here you select the conditional variables used for weighing the similarity of cases Toxic unit cases evaluating a concentration with a similar TU have a higher weight Mode of action cases evaluating a a compound with a similar mode of action have a higher weight Molecule group cases evaluating a a compound within the same molecule group have a higher weight Substance cases evaluating a a compound within the same substance have a higher weight Hydrology cases with a similar hydrology have a higher weight Exposure cases with a similar exposure have a higher weight DT50 cases evaluating a substance with a similar DT50 have a higher weight Henry cases evaluating a substance with a similar Henry coefficient have a higher weight K cases evaluating a substance with a similar K have a higher weight Right panel Here you select a part of the case base based on the conditional variables Nearby Toxic unit select cases within a certain range of toxic unit factor see options 26 Alterra rapport 787 Mode of action select cases evalu
4. 4 1 Concentration gradient dialog In this figure the effects of different concentrations on all endpoints are plotted The effect classes are stacked and shown as coloured areas At default the gradient is logarithmic from 0 0125 to 6 4 toxic units Right clicking on the figure brings up a popup menu with the following items Copy to clipboard Copies the figure to the clipboard as metafile You can paste the figure in Word or another program example Fig 3 9 Toxic units Toggles between concentrations ug and toxic units Change Concentration Range Opens the concentration range dialog to change the concentration range or to add observed effects in new studies in the figure Tables Shows the data as tables that can be copied to other applications e g Excel Properties Change the colours axes and titles of the plot Effect of 2 4 D on Community metabolism Hl no effect t A slight effect Hl clear effect not enough data 0 8 06 a oO Q L 04 0 2 50 10015900 Concentration mg l Fig 3 9 Example of a concentration gradient 34 Alterra rapport 787 3 4 2 Concentration range dialog Concentration range xi Concentrations Observed effects Number of concentrations Update logarithmically Cancel Help Fig 3 10 The concentration range dialogue Use this dialog box to change the concentration range or to add observed effects i
5. a nominal parameter and therefore the CRS optimization method may fail 2 Optimize method There are two ways of optimization e Enter all Just optimize the model with all conditional variables e Stepwise Enter the best conditional variable in a similar way as described with stepwise prediction see 3 2 3 but each step is optimized first While this is the best optimization method it may be very time consuming Save optimized model to Type here the file name to save the optimized model 4 Options button If you press this button the options dialog box is displayed with the optimize tab You can change the optimization ranges and other parameters here see 3 2 3 After the OK button is pressed optimization starts The progress of the optimization process is showed in a window Fig 3 8 In this screen the following information is showed Goodness of fit This is the goodness of fit of the last set of parameters e Best goodness of fit This is the best goodness of fit so far Convergence This value is empty while the vase is being filled see Controlled Random Search Thereafter it shows the relative difference between the best and the worst parameter set in the vase gt e Table with parameters In this table all parameters that are being optimized are showed The Minimum Maximum and the Best value in the vase is displayed The last column Current shows the last value of this parame
6. and font here 36 Alterra rapport 787 References Aamodt A and E Plaza 1994 Case Based Reasoning foundational issues methodological variations and system approaches AI Communications 7 39 59 Anderson J R 1983 The architecture of cognition Cambridge Mass Harvard University Press UK Branting L K J D Hastings and J A Lockwood 1997 Integrating cases and models for prediction in biological systems AI Applications 11 29 48 Brock T C M J Lahr and P J Van den Brink 2000a Ecological risks of pesticides in freshwater ecosystems Part 1 Herbicides Report 088 Alterra Green World Research Wageningen The Netherlands Brock T C M R P A Van Wijngaarden and G J Van Geest 2000b Ecological risks of pesticides in freshwater ecosystems Part 2 Insecticides Report 089 Alterra Green World Research Wageningen The Netherlands Campbell P J D J S Arnold T C M Brock N J Grandy W Heger F Heimbach S J Maund et al 1999 Guidance document on Higher tier Aquatic Risk Assessment for Pesticides HARAP SETAC Europe Brussels Belgium Prize M and R Walker 2000 Clinical decision support systems for intensive cate units using case based reasoning Medical Engineering And Physics 22 671 677 Koelmans A A A Van der Heijde L M Knijff and R H Aalderink 2001 Integrated modelling of eutrophication and organic contaminant fate amp effects in aquatic ecosystems A rev
7. be optimized by the computer sce 2 5 2 2 5 Calculate dissimilarities The distance between the question case and all other cases is calculated by a dissimilarity index Appendix HI gives a list of the supported indices At default the Euclidian distance index is used Optionally the dissimilarity coefficients can be scaled between the minimum and maximum dissimilarity which is especially useful with noisy data D MIN D MaxDist MinDist MAX D MIN D D MinDist 2 3 How to predict a response variable After calculation of the dissimilarities of all cases with the question case the cases in the database are ranked according to the obtained values The N nearest cases are used to make a prediction of the selected response variables we call N the number of nearest points The default number for N is 25 With the response variables of these points the prediction is made The following methods are implemented 1 Inverse distance The response variables of these cases are weighted so that the influence of the cases declines with the dissimilarity from the case being estimated N y ly ki DP et N y DP i in which P Prediction of the transformed response variable needs to be transformed back by using the inverse of the transformation N Number of nearest points Yu 7 Transformed not standardized response variable amp of case 7 D Dissimilarity of case with the question case P Distanc
8. button the Brows cases analogous with screen appears Fig 3 6 30 Alterra rapport 787 Browse cases analogous with 2 4 D 123121 ug l O x Order of this record 6 xl B gt Ei Dissimilarity 6 343 Code of substance Community metabolism 34123 59 6 Name of the substance Isoproturon Fish and Tadpoles Type of substance Herbicide Macrocrustaceans amp insects Mode of action Photosynthesis inhibitor Macrophytes Molecule group Urea Molluscs Concentration of substance ug l 90 Periphyton F Concentration as toxical unit 4 2857 Phytoplankton 1 Exposure single pulse Zooplankton 1 Hydrology during experiment stagnant recirculating ip wi Reference Traunspurger et al 1996 Previous i Fig 3 6 The Brows cases analogous with question case screen This dialog box makes it possible to view the details of the 10 most similar experiments The cases are sorted in order of similarity The first case order number 1 is the most similar case The left panel shows the conditional variables of that experiment and the right panel the values of the response variables Press the gt button to view the next case and the lt button to move backwards Pressing the gt button moves to the last most dissimilar case The lt button restores the first case Press Finish to close the dialog box Before you return to the first screen you get the opportunity to save your session all settings are saved
9. composed of the name of the chemical the concentration evaluated the reference to the open literature type of exposure and model ecosystem and the effect scores for the eight ecological endpoint groups The second data set consists of fate characteristics of the different pesticides and their toxicity for standard test species In order to make comparisons possible between studies performed with different herbicides or insecticides we expressed the exposure concentrations as Toxic Units TU For this we divided the studied exposure concentration usually the nominal peak concentration of the pesticide in the water column by the corresponding geometric mean acute EC50 value of the most sensitive standard test species according to OECD guidelines In case of insecticides the most sensitive standard test species usually was Daphnia magna For herbicides the most sensitive standard test alga according to OECD guidelines usually were Scenedesmus subspicatus ot Selenastrum capricornutum Values were taken from Brock et al 2000a 2000b To be able to find analogies related to fate characteristics of pesticides also the field dissipation is taken into account This field dissipation is represented by the DT50 of the water compartment determined in a water sediment study the Henry coefficient partitioning coefficient air water and the K partitioning coefficient water organic matter These variables were when available added to the database fo
10. pesticide Right of this list the following buttons ate displayed Add New Add a new substance to the database see below Delete delete the current substance This is only possible if there are no records with experiments of this substance in the database the button is not grayed then Edit edit the properties of the substance Features the features of the selected substance are listed in this table Below the table the Number of cases with data about this substance is displayed 24 Alterra rapport 787 Concentration ug l type here the concentration of the substance As you type the toxic unit of the currently selected substance is displayed Number of effect classes choose either 3 or 5 effect classes In case of three effect classes the original classes 3 4 and 5 are fused to one clear effects class Reset button resets the selections 3 2 1 1 Add new substance This form appears if you press the New button in the Experiment features tab of the substance data form see above Fill this form to enter a new substance or for aaa the features of an existing substance The following fields should be filled CAS registry number required Fill here the international CAS registry number of the substance Search on Internet button Use this button to find information about pesticides on a number of selected websites Chemical name required The name of the substance Type of substance
11. ranges defined the relative range can be optimized If there is no relative range this option is useless and disabled e Number of nearest points Check this option to optimize the number of nearest points e Distance power When using the inverse distance prediction method the distance power is an important parameter which determines how the influence of the cases declines with the dissimilarity This parameter can be optimized When using other prediction methods this option is grayed e Min and max distance When using the inverse distance prediction method the minimum and maximum distance are important parameters that may prevent that nearby cases are weighted too strongly Both parameter are optimized if this option is checked When using other prediction methods this option is grayed e Prediction method Use this option to find the optimal prediction method Note this is a nominal parameter and therefore the CRS optimization method may fail it is probably better to optimize the different methods separately e Distance measure Use this option to optimize the distance measure Note this is a nominal parameter and therefore the CRS optimization method may fail it is probably better to optimize models with different measures separately e Standardization method Note this is a nominal parameter and therefore the CRS optimization method may fail 32 Alterra rapport 787 e Transformations of conditional variables Note this is
12. 87 45
13. ALTERRA WAGENINGEN UNIVERSITY in WAGENINGEN ui WAGENINGEN TEN PERPEST version 1 0 manual and technical description a model that Predicts the Ecological Risks of PESTicides in freshwater ecosystems Egbert H van Nes and Paul J van den Brink perpest Alterra rapport 787 ISSN 1566 7197 WAGENINGE NIE PERPEST version 1 0 manual and technical description The research reported in this report was financed by the Dutch Ministry of Agriculture Nature Management and Fisheries within the framework of programme 359 and 416 PERPEST version 1 0 manual and technical description A model that Predicts the Ecological Risks of PESTicides in freshwater ecosystems E H van Nes P J van den Brink 1 Wageningen University Department of Aquatic Ecology and Water Quality Management Wageningen University and Research centre P O Box 8080 6700 DD Wageningen The Netherlands 2 Alterra Green World Research Wageningen University and Research centre P O Box 47 6700 AA Wageningen The Netherlands Alterra rapport 787 Alterra Green World Research Wageningen 2003 ABSTRACT Nes E H van amp P J van den Brink 2003 PERPEST version1 0 manual and technical description a model that Predicts the Ecological Risks of PESTicides in freshwatrer ecosystems Wageningen Alterra Green World Research Alterra rapport 787 46 pp 11 figs 1 tables 22 refs This report is a technical description and a user manu
14. al of the PERPEST model able to Predicts the Ecological Risks of PESTicides in freshwater ecosystems This system predicts the effects of a particular concentration of a pesticide on various community endpoints based on empirical data extracted from the literature The method that it uses solves new problems e g what is the effect of pesticide A by using past experience e g published microcosm experiments The database containing the past experience has been constructed by performing a review of freshwater model ecosystem studies evaluating the effects of pesticides The PERPEST model searches for situations in the database which resemble the question case based on relevant toxicity characteristics of the compound The model is described in the scientific paper written by Van den Brink et al 2002 and available via the enclosed CD ROM and the website www perpest alterra nl Keywords Effect model Aquatic community Ecological risk assessment Pesticides Case Based Reasoning ISSN 1566 7197 This report can be ordered by paying 18 into bank account number 36 70 54 612 in the name of Alterra Wageningen the Netherlands with reference to rapport 787 This amount is inclusive of VAT and postage 2003 Alterra Green World Research P O Box 47 NL 6700 AA Wageningen The Netherlands Phone 31 317 474700 fax 31 317 419000 e mail info alterra nl No part of this publication may be reproduced or published in a
15. ating substances with the same mode of action only Molecule group select cases evaluating substances with the same active molecule group only Substance select cases evaluating the same substance only Hydrology select cases having the same hydrology only Exposure select cases evaluating the same exposure only Selected as default 3 2 3 Options tab PERPEST substance data BE oj x Experiment features Weight Select using Options Prediction Bootstrap Optimize Perpest Standardization method Nomaize Dissimilarity measure Euclidean Distance y Prediction method Inverse distance Number of nearest points or Distance power el Min distance or NAN E Max distance or NAN fi 0 100 gt Critical dissimilarity Defaults Load Previous Fig 3 4 The Options tab sheet The option parameters of PERPEST are grouped in various tabs Changes in the parameters are saved till the next session Fig 3 4 Press the Defaults button to reset the default values of the parameters The parameters in each tab are explained below First tab sheet Prediction Second tab sheet Bootstrap Third tab sheet Optimize Fourth tab sheet PERPEST Alterra rapport 787 27 The first tab sheet Prediction parameter description Standardization method This parameter defines the standardization method used to standardize the c
16. e weighting power is always negative The more negative the distance weighting power the faster the decline in influence and the less the effect of points further out will have on the interpolation 2 Moving averages The response variables of the N nearest cases are averaged without weighing 20 Alterra rapport 787 3 Local multiple regression The response variables of the N nearest cases are estimated by multiple regression of the nearest points with the conditional variables This method is suitable for noisy and irregular spaced data 4 Global multiple regression The response variables are estimated by multiple regression with the conditional variables A bootstrapping procedure calculates the confidence intervals for the different effect classes and endpoints Manly 1997 In this resampling technique many default 500 random data sets are generated by selecting cases at random with replacement To be conservative we selected a smaller amount of cases than in the original database default 75 With each data set a prediction is made The generated distribution of predictions serves as an estimate of the uncertainty The 2 5 and 97 5 percentiles from this bootstrap distribution serve as the 95 confidence interval 2 4 How accurate is the prediction The performance of the prediction method is evaluated using leave one out cross validation Stone 1974 With this technique one case is removed from the database Subsequent
17. elected as response variables Nominal variables are used as conditional variables all cases that equal the question case get a value 1 and all other cases get value 0 assigned When nominal variables are used as response variables the variable is translated into binary dummy variables Each class is one variable that can take two values true 1 or false 0 For ordinary variables these restrictions are not made 2 2 4 Transformation standardization and weighing of variables Before standardization a non linear transformation can be used to shrink certain parts and to stretch other parts of the scale of the variable The most commonly used transformation is the logarithmic transformation The available transformations are listed in Appendix I The different variables need to be standardized to give equal weight to different scaled variables There are three options implemented see details in Appendix II 1 Normalization The variable is scaled to be a normal distribution with a mean of 1 and a standard deviation of 1 2 MinMax standardization By this method the variables are scaled between one and zero 3 No transformation Use unstandardized data Use this option only if the data are already standardized Alterra rapport 787 19 The variables are weighted by multiplying their values with weights that are entered by the user The purpose of this weighting is to give important variables more weight The weights also can
18. ertain percentage of the optimal similarity The second tab sheet Bootstrap parameter description N Bootstrap N Bootstrap defines the number of random data sets that are generated for the Bootstrap technique The larger this number the more accurate the bootstrapped confidence limits are Bootstrapped fraction To be conservative we selected a smaller amount of cases than in the original database Bootstrap fraction default 75 for each bootstrapped prediction Confidence limits p This parameter sets the probability of the confidence limits of the bootstrapped predictions The third tab sheet Optimize parameter description Convergence criterion The convergence criterion is a stop criterion for the optimization The lower this parameter the better the optimization but the longer the optimization takes The convergence is defined as the relative difference in goodness of fit between the best and worst parameter set in the vase during controlled random search Vase size This parameter sets the size of the vase in controlled random search optimization A higher vase size is needed if the optimization fails to find the global optimum but stays in a local optimum Goodness of fit This parameter sets the used goodness of fit criterion for cross validation and optimization See 2 4 and Appendix IV Stop criterion stepwise In a step forward analysis
19. ext sensitive Help system The program can be removed from the computer in the following way 1 Open the configuration screen by selecting Configuration from the Start menu 2 Select Add or remove programs 3 Select PERPEST from the list of installed software and click the Remove button Alterra rapport 787 23 3 2 Predicting an effect After pressing the Prediction button in the start screen the following dialog box appears Fig 3 2 PERPEST substance data 101 x Experiment features Weight Select using Options Substance 2 4 D CAS 94 75 7 Code of substance 94 75 7 24D Features Name of the substance Type of substance Herbicide Mode of action Auxine simulator Molecule group Aryloxyalkanoic acid J X Number of cases 11 Concentration pg 0 D toxic units Number of effect classes 3 y Reset Fig 3 2 The substance data dialog box Enter in this screen the features of the experiment to be predicted The dialog box has the following tab sheets Experiment features tab Weigh Select using tab Options tab If you press the Next button you go to the next tab sheet alternatively you can select tab sheets by clicking the tabs You can also load a previous session by clicking the Load button lab file 3 2 1 Experiment features tab In this window you enter the features of the experiment that should be predicted Substance Select here the chemical name CAS number of the
20. h equation are TU lt 3 or Exposute multiple constant These conditions can be combined in complex equations including several functions see calculated variables Note that the result of the equation should be a logical value True or False the result of comparison and logical functions like gt lt and or 18 Alterra rapport 787 There is one special kind of selection that is the relative selection In this case the selected cases may differ from the problem case only with a certain relative value For instance TU lt M 2 and TU gt M 2 in which M is the toxic unit of the problem case The advantage of this relative selection is that this condition can be updated in the cross validation and therefore the relative factor 2 in the example can be optimized see 2 4 and 2 5 2 2 3 Select conditional and response variables The next step in CBR is to select variables that are to be used in the analysis There are two kinds of variables 1 Conditional variables Conditional variables are called independent variables in regression analysis They explain the effect of a substance Examples of these variables in PERPEST are the Mode of action and the concentration of a substance 2 Response variables Response variables are variables that express the effects of a substance In regression these variables are called dependent variables In PERPEST all effect classes of herbicides or insecticides are automatically s
21. he Manhattan Distance The City Block Distance CB is the sum of the differences between all variables It weights the variables that are far out stronger than the Euclidean Distance does n CB y Ya Yay k 1 Cord Distance The Cord Distance CD is geometrically represented by the distance between points where the sample vectors intersect a unit sphere see Jongman et al 1995 It gives more weight to qualitative aspects than the other indices of the program n Vig CD Yki a kj Ge gt kI 2 an Yri Y Ik k 1 k 1 Chebychev Distance The Chebychev Distance ChD is the maximum difference between variables It weights one variable that is far out even stronger than the City Block Distance ChD MAX Yu Yy Symbols jg transformed and standardized value of the variable k in case i multiplied with the weight of the variable default weights are 1 n number of variables The properties of the Euclidean Distance and the Cord Distance are discussed by Jongman e al 1995 Optionally the dissimilarity coefficients can be scaled between the minimum and maximum dissimilarity which is especially useful with noisy data D MinDist D MIN D MaxDist MinDist MAX D MIN D Alterra rapport 787 43 Appendix IV Supported validation measures The following indices are used as goodness of fit measures In case of a model with more than one response variables these mea
22. heo Brock and Gerben van Geest for their help in reviewing the literature on the ecological effects of pesticides in freshwater ecosystems Marten Scheffer Jan Roelsma and Theo Brock for their help developing the model and Mechteld ter Horst for testing the model Alterra rapport 787 7 Summary This report is a technical description and a user manual of the model PERPEST a model that Predicts the Ecological Risks of PESTicides in freshwater ecosystems This system predicts the effects of a particular concentration of a pesticide on various community endpoints based on empirical data extracted from the literature The method that it uses is called Case Based Reasoning CBR a technique that solves new problems e g what is the effect of pesticide A by using past experience e g published microcosm experiments The database containing the past experience has been constructed by performing a review of freshwater model ecosystem studies evaluating the effects of pesticides This review assessed the effects on various endpoints e g community metabolism phytoplankton macro invertebrates and classified them according to their magnitude and duration The PERPEST model searches for situations in the database which resemble the question case based on relevant toxicity characteristics of the compound This allows the model to predict effects of pesticides for which no evaluation on a semi field scale have been published PERPEST resul
23. ial transformation to bring data of percentages as close as possible to a normal distribution er Yki n amp Yu 700 2 4 Square root transformation Square root transformation applied when analyzing Poisson distributed variables Yri T N Yri 5 Inverse transformation Used e g to make the relation between Secchi depth and extinction approximately linear gt 1 Vig Yri 6 Inverse x 1 transformation Like the inverse transformation but scaled for variables that can be zero ei 1 Y ki y 1 7 Angular Percentages transformation Special transformation to bring data of percentages as close as possible to a normal distribution Alternative for Log x 100 x transformation Vig yarcsin JAN 8 Angular transformation Special transformation to bring data of fractions as close as possible to a normal distribution Ye yJaresin y Alterra rapport 787 39 9 No transformation Use unchanged data Symbols Iki value of the variable amp in case 7 cus transformed value of the variable amp in case 7 40 Alterra rapport 787 Appendix II Supported standardization methods The different variables need to be standardized to give equal weight to different scaled variables 4 Normalization By this method the relative position of an observation within a distribution is described The normalized value also called standard or Z score shows how many times the standard deviation an ob
24. iew Water Research 35 3517 3536 Jongman R G H C J F Ter Braak and O F R Van Tongeren Eds 1995 Data Analysis in Community and Landscape Ecology Cambridge University Press Cambridge UK Kolodner J L 1993 Case based reasoning San Mateo USA Morgan Kaufmann Publishers San Mateo CA USA Leake B D 1996 Case based reasoning Experiences Lessons amp Future Directions Menlo Park California USA AAAI Press Menlo Park CA USA Linders J B H J J W Jansma B J W G Mensink and K Otermann 1994 Pesticides Benefaction or Pandora s box A synopsis of the environmental aspects of 243 pesticides Report 679101014 RIVM Bilthoven The Netherlands Alterra rapport 787 37 Manly B F J 1997 Randomization bootstrap and Monte Carlo methods in biology Chapman amp Hall London UR Montani S R Bellazzi L Portinale G d Annunzio S Fiocchi and M Stefanelli 2000 Diabetic patients management exploiting case based reasoning techniques Computer Methods And Programs In Biomedicine 62 205 218 Price WL 1977 A controlled random procedure for global optimisation The computer journal 20 367 370 Scheffer M 1991 On the predictability of aquatic vegetation in shallow lakes Memorie dell Istituto Italiano di Idrobiologia 48 207 217 Stone M 1974 Cross validatory choice and assessment of statistical predictions Journal of the Royal Statistical Society B 36 111 147 Tomlin C D S 2000
25. ing the best set of parameters by trying at random After each iteration the goodness of fit adjusted R is calculated by cross validation see 2 4 The CRS algorithm first selects N sets of model parameters uniformly distributed over prior parameter ranges calculates the goodness of fit for each and puts them in a vase It then selects m 1 points at random from the vase and mirrors the last point over the average centroid of the first m The mirrored point is the new trial point The goodness of fit R is calculated If the R better than the worst set of parameters in the vase the worst element of the vase is replaced by this new guess This process continues until a convergence criterion is reached 22 Alterra rapport 787 3 User manual 3 1 Installation and getting started The program is distributed as a single file Install PERPEST exe To install the program run this file and follow the instructions on the screen The program is installed in the Program Files WUR directory and an icon is added to the Sar Programs menu To start the program click on the PERPEST icon in the Programs menu The start screen will be displayed Fig 3 1 PERPEST x perpest was The PERPEST model a Case Based Reasoning approach to Info predict ecological risks of pesticides Version 1 1 0 1 Fig 3 1 The Start screen Click the Prediction button to make a prediction see 3 2 The He button opens the cont
26. last treatment 5 Clear long term effects lasting gt 8 weeks Convincing reductions in sensitive endpoints and complete recovery of these endpoints later than 8 weeks after the last treatment Negative effects reported over a sequence of sampling dates Alterra rapport 787 17 2 2 How to find similar cases Define question case Filter the data base J Select conditional and response variables Transformation standardization and weighing of variables Calculate dissimilarities Select the N nearest cases Calculate the mean value for the response variables of the N cases Prediction Fig 2 1 The used method of case based reasoning The steps to be taken to find analogies are summarized in Fig 2 1 Each step is explained below 2 2 1 Defining question case The first step in case based reasoning is defining the question case i e which circumstance do you want to predict In PERPEST the minimum information for a question case is the pesticide name and its concentration If the pesticide is not yet available in the fate and effects characteristics database also its CAS number mode of action molecule group type of substance and lowest EC50 for standard test organisms must be entered see also 3 2 2 2 2 Filter the database The first optional step in the finding the similar cases is to select a part of the database on the basis of a logical equation Examples of suc
27. le values or features collected from a patient collected during a consult or visit This case can be compared to earlier collected cases patients incorporated in a case base Montani et al 2000 From this case base the most similar cases can be extracted by applying for instance the nearest neighbor technique From these similar cases some useful statistics like similarity in diagnosis and successful therapy between the cases can be calculated and used for decision making Although CBR is popular in various scientific areas there have been described only very few applications in ecology grasshopper pest control Branting et al 1997 and ecotoxicology ecological risks of pesticides Van den Brink et al 2002 12 Alterra rapport 787 Some of the advantages of the CBR technique are 1 Zo 3 No prior information or assumptions about the nature of relations between variables are needed It is easy to find and browse through all available information of the most analogous cases The system can improve by adding new cases to the case base This learning possibility is an important feature of CBR systems Itis the starting point of the LABDA approach Largely Analogous But Differences Also Scheffer 1991 The LABDA approach is a way of predicting the response of a case It involves two steps A Rough estimations using analogous cases Largely Analogous B Fine tuning of the prediction by quantitative models that predict
28. le to utilize the specific knowledge of previously experienced concrete analogous situations cases for solving new problems CBR is an approach that enables incremental sustained learning since new experience is retained making it immediately available for future problems Aamodt and Plaza 1994 The first system that might be called a case based was the system of Kolodner 1993 a question answering system with knowledge on the various travels and meetings of the former US secretary of State Cyrus Vance Since then the study of CBR is driven by two primary motivations firstly to model human reasoning and learning and secondly to make Artificial Intelligence AI systems more effective Leake 1996 Early applications of CBR are among others in diagnosis setting clinical audiology heart failure building defects aircraft fault diagnosis and repair legal reasoning criminal sentencing patent law injuries to workers building regulations arbitration dispute resolution design landscape mechanical design conceptual design and planning warfare planning manufacturing planning problems Watson and Marir 1994 Examples of interpretive CBR are law application and diagnosis setting A well known application of CBR in medicine is to help medical personnel to assess patient status assist in making a diagnosis and facilitate the selection of a course of therapy Frize and Walker 2000 In this example a case is defined as a set of variab
29. ly the response variables of this case is predicted using the remaining cases The prediction is compared with the removed case This procedure is repeated for all cases and a goodness of fit measure is calculated In case of binary results such as the effect classes in PERPEST only the log likelihood and the percentage correctly predicted are suited Four indices of the fit were implemented 1 The mean adjusted R of the response variables percentage of variance explained by the model 2 The minimal adj R of the response variables 3 The sum of the log likelihood of the response variables This measure is only suited for binary variables Boolean String Calculated string 4 The percentage correctly predicted This simple measure also is only suited for binary variables Details about these options are given in Appendix IV 2 5 Optimization of the prediction The CBR method implies many subjective choices of methods and weights We used the controlled random search procedure Price 1979 to optimize these choices mathematically The following parameters used by the prediction method can be optimized auto matically Weights of conditional variables Distance weighting power Number of nearest points Alterra rapport 787 21 The parameters are optimized iteratively by use of the Controlled Random Search CRS algorithm Price 1979 This algorithm is an improvement of pure random search an algorithm search
30. n the concentration gradient figure Fig 3 10 This screen has two tab sheets Concentrations In this tab sheet you can enter the number of concentrations in the plot default 10 If this number is changed the concentration range is updated logarithmic You can also change the concentrations or toxic units separately If you press the Update Logarithmically button the concentration range is updated logarithmically doubled with the last changed concentration as basis Observed effects In this tab sheet you can enter results from your own experiments to compare the predicted values with 3 4 3 Gradient data dialog The data of the concentration gradient graphs are displayed as tables here There are two buttons to save the data to the clipboard To Clipboard button copy the current table to the Windows clipboard You can paste these data in Excel or Word All to Clipboard button copy all tables to the Windows clipboard This may take some time 3 4 4 Graph properties dialog Use this dialog box to change several features of the concentration gradient figure The screen has the following tab sheets Series Change the colour and legend title of each series Legend Change the font and the position of the legend Alterra rapport 787 35 Axes Select on the left hand side the axis that you want to adjust On the right side you can edit the title font and scaling of each axis Titles Edit the graph title
31. ny form or by any means or stored in a data base or retrieval system without the written permission of Alterra Alterra assumes no liability for any losses resulting from the use of this document Projectnummer 230048 Alterra rapport 787 HM 08 2003 Contents Preface Summary 1 Introduction 1 1 What is PERPEST 1 2 Case based reasoning Methods 2 1 The PERPEST database case base 2 2 How to find similar cases 2 2 1 Defining question case 2 2 2 Filter the database 2 2 3 Select conditional and response variables 2 2 4 Transformation standardization and weighing of variables 2 2 5 Calculate dissimilarities 2 3 How to predict a response variable 2 4 How accurate is the prediction 2 5 Optimization of the prediction User manual 3 1 Installation and getting started 3 2 Predicting an effect 3 2 1 Experiment features tab 3 2 1 1 Add new substance 3 2 1 2 Search Internet 3 2 2 Weigh Select using tab 3 2 3 Options tab 3 2 4 Predicted effects 3 2 5 Browse analogous cases 3 3 Optimizing the prediction 3 4 Creating concentration gradients 3 4 1 Concentration gradient dialog 3 4 2 Concentration range dialog 3 4 3 Gradient data dialog 3 4 4 Graph properties dialog References Appendices I I Supported transformations Supported standardization methods III Supported dissimilarity measures IV Supported validation measures 39 41 43 45 Preface We would like to thank Rene van Wijngaarden Joost Lahr T
32. onditional variables before the dissimilarity with all cases can be calculated See Appendix Il Dissimilarity measure This parameter defines the similarity method used to calculate the dissimilarity between the question case and all cases See Appendix III Number of nearest points For all CRS prediction methods the N most similar cases are used to calculate a prediction The default number of nearest for N is 25 With the response variables of these points the prediction is made using various methods Prediction method Use this parameter to select the method for the prediction See 2 3 Distance power With the inverse distance prediction method the response variables of N most similar cases are weighted so that the influence of the cases declines with the dissimilarity to a power from the case being estimated Min distance or NAN The Min distance defines the minimum of a scaling of the dissimilarity measures which is especially useful with noisy data Assign NAN Not A Number to this parameter if you want to keep the minimum unchanged Max distance or NAN The Max distance defines the maximum of a scaling of the dissimilarity measures which is especially useful with noisy data Assign NAN Not A Number to this parameter if you want to keep the minimum unchanged Critical dissimilarity Optionally the user may limit the cases that are displayed in the Cases Dialog to a c
33. only the differences of the question case from the analogous cases But Differences Also Alterra rapport 787 13 2 Methods The main purpose of the program PERPEST is to find analogous cases based on available information in a data base 2 1 In 2 2 is explained how this is done Based on these analogous cases it is possible to predict the response of the question case In 2 3 the averaging method is explained The next question is how good this prediction is In 2 4 is explained how the goodness of fit is evaluated The methods and parameters used by PERPEST can be optimized automatically 2 5 explains by which method this is done Table 2 1 The most important variables in the PERPEST database Variable Description Type of variable DT50 Field dissipation DT 50 days Float EC50 geometric mean acute EC50 value of the most sensitive Float standard test species according to OECD guidelines ug L FullName Name of the substance Memo Henry Partitioning coefficient air water Pa m3 mol Float Kom Partitioning coefficient water organic matter Kom L kg Float Mode of action Mode of action String Molecule group Molecule group String Type_sub Type of substance String Conc Concentration of substance ug l Float Expos Exposure String Hydrology Hydrology during experiment String Reference Full reference Memo ToxUnit Concentration as toxic unit Calculated Conc EC50
34. onse variable By pressing the right mouse button a menu pops up with the following items Word yields a sharp scalable picture Alterra rapport 787 Change Font change the font of the pie charts Update Figure updates the figure using the latest settings Copy copy the figure as in wmf format to the clipboard Pasting it in Microsoft 29 Predicted effects i ioj xf Substance Chlorpyrifos 2 pg l Effects on Legend E 1 no effect E 2 slight effect Algae and Community Fish n 48 Insects macrophytes metabolism n 52 m 3 clear effect n 52 n 49 Macrocrustacea Microcrustacea Other Rotifers n 45 n 52 macro invertebrates n 52 n 42 LoglLikelihood in cross validation 859 8 Gradient Pe Confi dence lms Previous E Cancel Help Fig 3 5 The predicted effects screen Below the figure the following items are visible Log likelihood loglikelihood is a goodness of fit measure that is determined by cross validation may take some time to appear Gradient button Here you can create a plot of the effects of a concentration gradient Optimize button click this button to optimize the method of prediction See also CRS dialog box Confidence limits button view the prediction dialog box in which the results are presented as table and a bootstrap estimate of the confidence limits is given here 3 2 5 Browse analogous cases After pressing the next
35. optimization or prediction the conditional variable that yields the best fit is added first Subsequently the next parameter is added but only if the goodness of fit increases with a certain factor i e the stop criterion Range for optimizing During optimization several parameters are changed within certain 28 Alterra rapport 787 Weights of vars ranges T variables his parameter defines t he minima and maximal weights of Range for optimizing Ranges of vars During optimization severa ranges This parameter defines t of variables parameters are c he minimal hanged within certain and maxima Range for optimizing Distance power During optimization severa ranges This parameter defines t power parameters are c he minima hin certain distance hanged wit and maxima Range for optimizing Num of nearest points During optimization severa ranges This parameter defines t nearest points parameters are c he minimal hin certain number of hanged wit and maxima The fourth tab sheet Perpest relative ranges parameter description CAS Set the default weight and optionally the transformation used for the CAS substance code ToxUnit Set the default weight and optionally the transformation used for the concentration of the substance expressed as to
36. or have many uncertain parameters so experts may predict effects of toxicants better Anderson 1983 has shown that people use past cases as models when learning to solve new problems Also experts solve problems by analogy 1 e using analogous cases from memory to solve new problems For instance if one asks an expert what the effect of 1 ug L of the insecticide chlorpyrifos will be on the ecology of a freshwater ecosystem he or she will look for analogous cases 1 e experiment he or she has conducted or evaluated in the past Obvious the type of experimental ecosystem test design assessed endpoints etc are different between the experiments so the expert has to make some nuance also In the field of artificial intelligence this process is called Case Based Reasoning CBR Kolodner 1993 Leake 1996 The basics of CBR is that it retrieves similar experience cases about similar situations from the memory a database that is called the case base and reuses this experience in the context of a new situation for a prediction The PERPEST model Van den Brink et al 2002 is based on Case Based Reasoning In this model the prediction of the effects of a certain concentration of a pesticide on a defined aquatic ecosystem is based on published information on effects of pesticides on the structure and function of aquatic ecosystems as observed in semi field experiments This CBR system consists of the database containing this information and a search
37. r each pesticide Values were obtained from Linders et al 1994 and the pesticide manual Tomlin 2000 16 Alterra rapport 787 BOX 1 The grouped endpoints and five effect classes used in PERPEST The grouped endpoints are Herbicides Insecticides Community metabolism Community metabolism Phytoplankton Algae and macrophytes Periphyton Microcrustacea Macrophytes Rotifers Zooplankton Macrocrustacea Macrocrustaceans amp Insects Insects Other macro invertebrates Other macro invertebrates Vertebrates Vertebrates The five effect classes are 0 blank Endpoint not evaluated in the study 1 No effects demonstrated No consistent adverse effects are observed as a result of the treatment Observed differences between treated test systems and controls do not show a clear causality 2 Slight effects Confined responses of sensitive endpoints e g partial reduction in abundance Effects observed on individual sampling dates only and or of a very short duration directly after treatment 3 Clear short term effects lasting lt 8 weeks Convincing reductions in sensitive endpoints Recovery however takes place within eight weeks Effects observed on a sequence of sampling dates 4 Clear effects recovery not studied Clear effects e g severe reductions of sensitive taxa over a sequence of sampling dates are demonstrated but duration of the study is too short to demonstrate complete recovery within eight weeks after the
38. required Select the type of substance herbicide or insecticide here Mode of action The mode of action e g photosynthesis inhibitor is filled here Molecule group The active molecule group e g triazin on e DT50 days The half life of the chemical in water as determined in a water sediment system EC50 ug l required LC50 or EC50 of most susceptible standard test species Henry coefficient Pa m mol The partitioning coefficient air water Ko kg The partitioning coefficient between water and organic matter 3 2 1 2 Search Internet This dialog box may help to find information about substances on internet CAS number LC50 DT50 etc Select a title of an internet site in the upper list The URL and a short description is displayed If you press OK the default internet browser should be started with the URL in the edit box If nothing happens you may not have registered the extension html in the Windows Explorer Alterra rapport 787 25 3 2 2 Weigh Select using tab PERPEST substance data j 5 xj Experiment features Weight Select using Options Weight using Select using v Toxic unit v Nearby toxic unit v Mode of action Mode of action v Molecule group Molecule group v Substance _ Substance Hydrology Hydrology _ Exposure _ Exposure L1 DT50 Henry Kom Exposure do not use hd Hydrology
39. routine named Weighted Analogies Prediction WAP Van Nes and Scheffer 1993 The rationale behind WAP is that based on a few characteristics of the questioned case e g pesticide characteristics exposure concentration type of exposure analogous cases are identified in the database These analogous cases can be weighted and summarised in a prediction This means that although for certain pesticides no microcosm or mesocosm experiment is published one is able to predict its effect on a semi field scale using the results of experiments Alterra rapport 787 11 performed with other pesticides that have a similar toxicological mode of action TMoA and fate characteristics The PERPEST model can be used in the ecological risk assessment when uncertainties are large and data availability is small e g in the case of a new pesticide Using PERPEST an idea can be obtained in which direction uncertainties are likely to be large so in which direction data must be gathered for a refined risk assessment e g endpoints and exposure concentrations of interest Output of PERPEST can also be used to translate spatially and temporal distributed concentration data into effect concentrations i e to use it as a risk indicator In this report the methods incorporated in PERPEST are described and a manual of the graphical user interface GUD of the model is included 1 2 Case based reasoning Case based reasoning CBR is a way of solving problems that is ab
40. servation deviates from the mean of the population The mean of the normalized values is 0 and the standard deviation is 1 It is obtained by subtracting the mean from a value and dividing this difference by the standard deviation Vege ve Yu s d 5 MinMax standardization By this method the variables are scaled between the minimum and maximum values in the database The minimum gets value 0 and the maximum gets value 1 ki Yki MING Yki Yki MAXi Oki MINI Yki 6 No standarization Do not change data Use this option only if the data are already standardized Symbols Yy transformed value of the variable amp in case 7 Hey value of the variable amp in case 7 MIN y minimal value of the variable amp in the database MAX Y maximal value of the variable amp in the database Y mean of variable amp in the database s d standard deviation of variable in the database n number of variables Alterra rapport 787 41 Appendix III Supported dissimilarity measures The distance between the question case and all other cases is calculated by a dissimilarity index The following gives a list of the supported indices 1 Enclidean distance default The Euclidean Distance ED is the most frequently used index It is the distance in the n dimensional space constrained by the conditional variables Each variable is one dimension of the space 2 ED y Yu Vy k 1 City Block distance also called t
41. sures are combined in a single value see 2 4 Adjusted R R sometimes called coefficient of determination is the same statistic that is commonly used in linear regression The sample R usually is an optimistic estimate of how well the model fits the reality The statistic adjusted R attempts to correct R to reflect more closely the goodness of fit of the model in the population Formula 2 residual sum of squares N p 1 adj R 1 total sum of squares N 1 N sample size p number of parameters If the adjusted R equals 1 the model fits perfectly if the adjusted R is negative the mean value of the response variables is a better prediction than the model Log likelihood For binary data the adjusted R is not suitable For these data two other measures are implemented The likelihood is the probability that the observed data occur if the model is correct As this is usually an extremely low probability the logarithm of this value is taken resulting in a negative number In our case the log likelihood L is calculated as follows L Ind 1 r n 2 PR n p ODS Symbols P Prediction gt Sum of the cases where the observed conditional variable equals 0 obs 0 Percentage correctly predicted This simple measure is less accurate as the log likelihood but is also suited for binary variables only It simply gives the percentage of the responses that is predicted correctly Alterra rapport 7
42. ter that has been evaluated e Close button Press this button to stop the optimization and proceed with the currently best parameter set It is not recommended to do this because there is a chance that it might fail e Stop CRS button Press this button to stop the optimization process temporarily Press the button again to proceed LT lala Goodness of fit 691 4 Best goodness of fit 605 09 Convergence 0 058316 an a pe Weight CAS 94 75 7 0 047282 9 966 7 56 Weight Mode of action Auxine simulator 0 035726 9 9854 8 052 3 023 Weight Molecule group Aryloxyalkanoic 0 035281 9 9627 9 956 9 958 Range ToxUnit 8 4069 9 9609 9 956 9 501 NNearestPoints 35 155 118 58 DistPower 4 9879 0 51661 1 891 0 5216 MinDistance 0 046862 9 9774 2 443 0 3025 MaxDistance 0 10245 9 9817 6 654 2 475 Stop CRS Cancel Help Fig 3 8 The progress of the optimizing process Alterra rapport 787 33 If the convergence criterion is reached or if the user has pushed the Close button the CRS results dialog is displayed In this dialog the best parameter set is displayed and you are prompted if you want to use the new parameter set now It is always saved to the lab file that is indicated in the first screen 3 4 Creating concentration gradients In the Predicted effects dialog box see 3 2 4 you can predict concentration gradients Fig 3 9 by pressing the Gradient button The concentration gradient dialog appeats 3
43. to a file that you can load in the Substance data dialog box 3 2 3 3 Optimizing the prediction With this option the weights and some of the parameters can be optimized To start optimization click on the Opzimize button in the Predicted effects dialog box see 3 2 4 The next dialog box appears Fig 3 7 Alterra rapport 787 31 Controlled Random Search xj Optimize Weight ToxUnit Options IV Weight CAS 2921 88 2 Y Weight Mode of action Acetylchlolinesterase inhibitor IV Weight Molecule group Organophosphorous insecticides IV Range ToxUnit IV Number of nearest points IV Distance power IV Min distance IV Max distance 7 Prediction method Distance measure C Standarization method Transformations of conditional variables Optimize method Enter all X Save optimized model to optimized lab Hl Cancel Help Fig 3 7 The controlled Random Search dialog box Use this screen to select the way of optimization and the parameters that must be optimized This dialog box has the following components 1 Optimize Use this box to select which parameters should be optimized The following items can be de selected e Weights of conditional variables This item should be checked to optimize the weights of conditional variables If there is only one conditional variable changing the weight is useless Therefore then this item is disabled e Range of conditions If there is a filter with relative
44. ts in a prediction showing the probability of classes of effects no slight or clear effects plus an optional indication of recovery on the various grouped endpoints The model is described in the scientific paper written by Van den Brink et al 2002 and available via the enclosed CD ROM and the website www perpest alterra nl Alterra rapport 787 9 1 Introduction 1 1 What is PERPEST The tiered ecological risk assessment of pesticides consists of a conservative first tier and more realistic higher tiers These higher tiers can include the use of laboratory tests using more realistic exposure regimes testing of indigenous species the use of a variety of models population food web landscape and conducting experiments in model ecosystems Campbell et al 1999 To this end many experiments performed with microcosms and mesocosms are performed during the last 20 years and published in the open literature Brock et al 2000a 2000b reviewed the open literature for microcosm and mesocosm experiments on the effects of herbicides and insecticides This review was performed to establish ecological threshold values for pesticides in surface waters and to evaluate current standard setting methodologies In order to predict effects of pesticides on aquatic communities and ecosystems large simulation models like for instance food web models can be used Koelmans et al 2001 Traas et al 1998 Ecological models however are either incomplete
45. xic unit Conc EC50 Mode of Action Set the default weight and optionally the transformation used for the mode of action of the pesticide Molecule Group Set the default weight and optionally the transformation used for the active molecule group Hydrology Set the default weight and optionally the transformation used for the hydrology during the experiment Flow through or Stagnant recirculating Expos Set the default weight and optionally the transformation used for the exposute to the substance in the experiment multiple constant or single pulse DT50 Set the default weight and optionally the transformation used for the field dissipation DT50 days Henry Set the default weight and optionally the transformation used for the Partitioning coefficient air water Pa m mol Kom Set the default weight and optionally the transformation used for the Partitioning coefficient water organic matter Kom L kg Max allowable difference toxic unit factor At default the system selects only experiments that differ by a certain factor with the question experiment You can set that factor here 3 2 4 Predicted effects After evaluating all the options and pressing the next button the predicted effects screen appears In this screen a summary of the results is given as pie charts see Fig 3 5 Each pie gives the predicted effect classes for that resp
Download Pdf Manuals
Related Search