ARROW: system for the evaluation of the status of waters in the Czech Republic

Jiří Jarkovský

1) Institute of Biostatistics and Analyses, Masaryk University, Czech Republic2) Research Center for the Environmental Chemistry and Ecotoxicology, Masaryk University, Czech Republic

The ARROW project

Project aims

• Good status of surface waters is demanded by WFD EU

• Identification of good status

• We need system for the evaluation of state of surface waters

• Utilization in water management and decision support

+

DATA

Components of state evaluation

Ecological stateEcological state

MacrophytesMacrophytes

FytobenthosFytobenthos

Benthic macroinvertebratesBenthic macroinvertebrates

FishesFishes

Supporting parametersSupporting parameters

HydromorphologyHydromorphology

Chemical and physics parameters

Chemical and physics parameters

One out One out all outall out

Mul

timet

ric e

valu

atio

n

Norms

Overall evaluation

of state

Complex statistical

methodology

5 levels5 levels

2 levels2 levels

Specific pollutantsSpecific pollutants

Selected chemical

parameters

Selected chemical

parameters

What data we need?

• Structure and composition of biological communities for all biological compounds– Properties of taxa – species traits

• Influential abiotic factors– Natural parameters (altitude etc.)– Stressors

• Chemical contamination

• Data for reference and contaminated sites

General methodology of state evaluation

Comparison with reference datasetComparison with reference dataset

Definition of reference conditionsDefinition of reference conditions

Statistical analysisStatistical analysis

Expert evaluationExpert evaluation

Levels of stateLevels of state

Reference stateReference state

Reference conditions define very good environmental state.Complex multivariate data.

Possible range of statePossible range of state

Evaluated localityEvaluated locality

Distance from reference condition

Distance from reference condition

Biomonitoring network

Reference network (green) and its extension in 2006 (red)

Sampling network and WFD typology I

• B system of WFD EU: 35 river types• Evaluation of reference conditions within river types

Sampling network and WFD typology II

• Aggregation of river types for some types of analyses• Parameters no influenced by region, stability of statistical estimates

DATA ANALYSIS

Data analysis in implementation of WFD

• The objective evaluation of ecological state is impossible without correct data analysis

• Data analysis is included in several steps of the project:– Searching for sufficient level of taxonomic determination that can be

accepted in biomonitoring networks – Preliminary analysis of relationship of biological communities and their

environment – definition of model for evaluation– Robust methodology of evaluation of ecological state for routine

biomonitoring– Reporting of results of evaluation of ecological state

Statistical analysis in evaluation of ecological state

Biological community

Biotic indices• Simple computation, taxa individuality is lost• Comparison with reference conditions • Reference conditions from data analysis or expert judgment

Biotic indices• Simple computation, taxa individuality is lost• Comparison with reference conditions • Reference conditions from data analysis or expert judgment

= X

Multivariate modeling of reference biological community• Comparison of expected reference community and measured community • Complex analysis, nevertheless data and methodology demanding

Multivariate modeling of reference biological community• Comparison of expected reference community and measured community • Complex analysis, nevertheless data and methodology demanding

Comparison with community defined by experts•Comparison of expected reference community and measured community

Comparison with community defined by experts•Comparison of expected reference community and measured community

Supporting parameters

Multivariate similarity with reference conditionsMultivariate similarity with reference conditions

Ref.

Predictive modeling of macrozoobenthos communities

Standard data of monitoring Standard data of monitoring (unknown sites)(unknown sites)

Reference data

Classification into reference groups Classification into reference groups according to natural heterogeneityaccording to natural heterogeneity

Definition of reference categories according to biological communities

Difference between observed and Difference between observed and expected ecological stateexpected ecological state

Reference model: step IReference model: step I

Environmental Environmental parametersparameters

Community Community compositioncomposition

Description of reference categories = reference model

Environmental parameters

CommunitiesReference model: step IIReference model: step II

I.

II.III.

IV.

V.VI.

Partial Partial evaluation Aevaluation A

Partial Partial evaluation Bevaluation B

Biological community

Final Final evaluation of evaluation of

statestate

VII.

Final result is a single metric with straightforward interpretation

based on different communities,

possibly also on environmental

evaluation

Natural heterogeneity

Pollution

I. II. III. IV. V. VI. VII.

Independent problems to solve

Definition of reference categories• defining homogenous groups within the reference database according to

composition of biological communities • statistical method: Hierarchical agglomerative clustering on distance matrix

(Gower distance metric) of biological communities followed by algorithm for definition of optimal number of clusters and expert opinion will be used for definition of reference groups.

Reference dataReference model consists of several homogeneous

categories according to their community composition

Statistical analysis

Results of the analysis have to be• confirmed by expert knowledge (valuable reference groups)• at least 10 – 15 sites in each reference group

Searching for optimal number of clusters

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Expert separation into 21 clusters

0 0.05 0.1 0.15 0.2 0.25

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10111213141516171819202122232425262728293031323334353637383940

„Silhouette“ of given number of clusters

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Small number of clusters easy to separate

No significant differences

No discrepancy in expert and statistical

solution

Reference model• Reference categories are described by environmental parameters which are

minimally influenced by human activity = natural heterogeneity • These parameters are used for classification of unknown sites (standard

monitoring) into reference groups according to theory that sites with similar environmental conditions should have similar biological communities

Unknown siteUnknown site– Classified to reference

category according to natural heterogeneity

Reference categories– Defined according to communities’

composition– Description of their natural heterogeneity

(forming communities)

??

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– Natural conditions should be defined for biological communities, environmental metrics and chemical pollutants

Description of final classification

0

20

40

60

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Distance from

the source

100

200

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400

500

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800

Altitude

• Example of abiotic data description

Multimetric evaluation of ecological state

100%

100%

100%

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Me

tric

2

Met

ric 3

Different metrics - > indication of stressors

Ref.

Position of evaluated locality towards reference

conditions?

Position of evaluated locality towards reference

conditions?

Stressor identification Aggregation of metrics into final multimetrics

Reporting

Levels of state

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0,010,10,10,950,5

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Makrozoo-bentos

Celkové

hodnocení

Biologická složka

Fyzikálně-chemické

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DatumNázev

Popisnécharakte-

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HodnotaHodnotaHodnotaHodnotaHodnotaHodnota/xx. xx. xxxxNázevPovodí

0,010,10,10,950,5

MakrofytaFyto-

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Makrozoo-bentos

Celkové

hodnocení

Biologická složka

Fyzikálně-chemické

odběry

DatumNázev

Popisnécharakte-

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profilů

Multimetric evaluation of ecological state combine several views on biological community and influencing stressors.

Selection of indices for final multimetric

• Identification of suitable indices– Metrics which distinguish between

reference and standard localities– Relationship to abiotic stresors

• Aggregation of indices– Similar metrics aggregated into modules

with relationship to specific stresor

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• Selection of candidate indices– Indices decribing different aspects (species composition, diversity, saprobity)– Indices which are not correlated– Indices without outliers, etc.

• Altogether 19 indices selected for final multimetric

– Indices with significant difference between reference and non-reference state

– or important indices from expert point of view (number of taxa, etc.)

Final multimetric

• Number of individuals• Q statistic stochastic• EPT – number of taxa of

Ephemeroptera, Plecoptera, Trichoptera

• P – number of taxa of Plecoptera

• Number of individuals• Q statistic stochastic• EPT – number of taxa of

Ephemeroptera, Plecoptera, Trichoptera

• P – number of taxa of Plecoptera

MakrozoobentosFinal multimetric

MakrozoobentosFinal multimetric

A. Saprobity and trophismA. Saprobity and trophism

• Saprobic index• RETI

• Saprobic index• RETI

B. DiversityB. Diversity

C. Habitat degradationC. Habitat degradation

D. Predictive model of communityD. Predictive model of community

• Expected community predicted - similarity index with expected community

• Expected community predicted - similarity index with expected community

Zonation • zonation hypocrenal (WSES)• zonation epirhithral (WSES)• zonation epipotamal (WSES)• zonation metapotamal (WAES)• zonation hypopotamal (WAES)• zonation litoral (WAES)• zonation profundal (WAES)Microhabitat preference• microhab. psammal (WSES)• microhab. pelal (WAES)• microhab. lithal (WAES) Feeding preference• feeding type: grazer and scrapers (WAES)• feeding type: active filter (WAES)

Ecological state evaluation and its components

Ecological state

MacrophytesMacrophytes

FytobenthosFytobenthos

MacrozoobentosMacrozoobentos

FishesFishes

Supporting characteristicsSupporting characteristics

HydromorphologyHydromorphology

Chemical and physicsChemical and physics

DiversityDiversitySaprobity and trophism

Saprobity and trophism

Habitat degradation

Habitat degradation

Predictive model of community

Predictive model of community

Partial indices

Migration MigrationReproductionReproduction Tolerance Tolerance

Oxygen consumption

Oxygen consumption

10 hydromorphology parameters

10 hydromorphology parameters

Biotic indexBiotic index Expert communityExpert community

Biotic index (saprobity)Biotic index (saprobity) Expert communityExpert communityBiotic index (mineralisation)

Biotic index (mineralisation)

N, PN, P ConductivityConductivity pHpH

Specific pollutantsSpecific pollutants

Comparison with normComparison with norm

Reference conditions• Reference conditions of indices and abiotic parameters is based on percentile

method

Ecological state

MacrophytesMacrophytes

FytobenthosFytobenthos

MacrozoobenthosMacrozoobenthos

FishesFishes

Supporting parameters

Supporting parameters

IndicesIndices

Reference conditions

Community composition

Community composition

IndicesIndices

IndicesIndices Community composition

Community composition

IndicesIndices Community composition

Community composition

Expert opinion + data analysisExpert opinion + data analysis

Data analysisData analysis

Macrozoobenthos: data analysis and expert opinion

Expert opinion on reference conditions

Data analysis of reference conditions

Fishes: data analysis and expert opinion

Expert opinion on reference conditions

Data analysis of reference conditions

Overall evaluation of state

Chemical stateChemical state

2 levels2 levels

Weighted computation of ecological state from its components

Weighted computation of ecological state from its components

3. Macrophytes3. Macrophytes

4. Fytobenthos4. Fytobenthos

1. Macrozoobenthos1. Macrozoobenthos

2. Fishes2. Fishes

Overall score:Overall score:

5. Supporting parameters

5. Supporting parameters

max ( 1. 2. 3. 4. 5. )

Levels of state

Levels of state

One out all outOne out all out

Overall evaluation of state

Norms

Implementation

Central ARROW Database

ARROW Client

Monitoring

ScientificARROW

Routine analysis

Scientific analysis, preparation of

templates for routine work

ARROW Client

ARROW Client

ARROW Client

ARROW Client

Monitoring Monitoring

PHPJavaScript

Oracle

PHPJavaScript

Java

Web based system

• The Arrow system have web-based interface for both data input and results reporting

CONCLUSIONS

Conclusion The presented methodology is universal for any type of date

(i.e. any biological communities) and respects the problems of data distribution and variability as well as WFD EU demands.

• Multimetric approach which combine both site and type specific approach is used for the final evaluation of ecological state

The software implementation use standardized tools and have central management of rights, data and reporting

Complex system for evaluation of ecological state of surface waters with standardized procedure of data processing.