Water Quality and Ecology

the multi-pressure approachJohn Murray-Bligh

Workshop on Environmental Modelling and Regulation in Catchments

Bristol

28 August 2019

Aim: to describe approaches to multi-

pressure analysis for ecology

Describing ecological quality – biotic indices

WFD status classification and ecological objectives

Modelling biological reference conditions

Artificial intelligence research at Aston and StaffordshireDiagnosing pressures from biological communities

Scenario testing

Other tools

Ecological impacts and the

multi-pressure approach

How would you define environmental

quality?

What are we trying to protect?Uses of the water environment to people

Water resource

• Drinking

• Washing

• Industry

• Power (turbines, cooling)

• Agriculture / Horticulture (irrigation)

• Aquaculture (fish farm, water cress)

Conveyance

• Waste water (gravity)

• Drainage / Flood relief

Waste treatment / purification

Amenity

• Aesthetic

• Cultural

• Biodiversity

• Fishing

• Swimming

• Kayaking

Transport

What aspect of water provides these

uses

Physical resource

Ecosystem services

Chemical quality (toxic chemicals)

Climate

Ecology and modelling

To measure environmental quality (ecological indicators)

To define environmental objectives (the target ecology)

To understand the pressures and processes affecting ecological quality

To identify what measures we should take to protect and improve quality

Biotic indices

Aim

To summarize complex biological information into a simple value that is easy for water managers to understand

Indices of saprobity

Saprobity = organic enrichment / pollution

Until 1980s, sewage effluent the strongest and most widespread pressure, with industrial effluent, metal mining and acidification

Self purification

Waste treatment speeds

some of these natural

processes to convert

suspended organics to

settleable solids

If undamaged, biota in a

river will polish final

effluent and maintain

quality

Hynes, 1960

Impact of sewage effluent

History in UK

Royal Commission 20:30 standard 1898-1915

20 mg/l BOD

30 mg/l SS

Assumes 8x dilution

Saprobic index 1908-09+ extended eusaprobity Saprobic

BOD5 Index

xenosaprobity 0 0

oligosaprobity <1 1.0

β-mesosaprobity <5 2.0

α-mesosaprobity <13 3.0

polysaprobity >20 4.0

isosaprobity ciliates

metasaprobity flagellates

hypersaprobity bacteria

ultrasaprobity abiotic

Biochemical oxygen demand (BOD)

mg/l O2 5 days 20°C

Saprobic index for a site

abundnace totalabundance) x value x (weighting index Saprobic

UK river invertebrate index

WHPT ASPTHigh quality

Good quality

Perlidae 12.7

Heptageniidae 9.7

Caenidae 6.5

Gammaridae 4.4

Gerridae 5.2

Elmidae 6.6 Hydropsychidae 6.6

Simuliidae 5.8

Baetidae 5.5

Chironomidae 1.1

Physiidae 2.4Oligochaeta 2.7Asellidae 2.8

Hydrobiidae 4.2Poor & bad

quality

Saprobic sensitivity

‘Organic’ indices

1. Reduced oxygen

2. Siltation

3. Ammonia toxicity

4. Increased organic matter supports abundance of tolerant taxa

Invertebrate indices used in UKWHPT ASPT saprobity

ARMI index saprobity

WHPT Ntaxa all pressures

TRPI phosphate

E-PSI fine sediment

CoFSI fine sediment

LIFE low flow velocity

DEHLI low flow habitat loss

MIS-index flow intermittence

SAGI saline intrusion

SPEARpesticides toxic organic chemicals

MetTOL metals

AWIC acidification

Co-variancePressures influencing O2

Organic load

Low flow

Physical modification

Canalisation

Siltation

Eutrophication

Oil

Thermal discharges

Phenols/reducing chemicals

Indices sensitive to these pressures will also be sensitive to low oxygen saturation

Northland Regional Council, NZ

Supersaturated

in day

50% saturated

at night

Inextricable link between saprobity

and eutrophyWegl 1983

saprobity BOD5 index NH4 O2 Trophy total P Chl-a summer biomass total N

mg/l value mg/l mg/l mg/m3 mg/m3 Secchi (m) mg/m3 mg/m3

oligosaprobic <1 1 <0.1 >8 oligotrophy <13 <3 >5 <2000 <300

β-mesosaprobic <5 2 <0.5 >6 mesotrohpy <40 <10 5-1 >7000 <400

α-mesosaprobic <13 3 <13 >2 eutrophy <100 <40 1-0.5 <10 000 <1000

polysaprobic >20 4 >20 <1 hypertrophy >100 >40 <0.5 >10 000 >1000

Allosaprobity = saprobity originating outside the river – allocthonous (leaves, soil run-off, waste)

Autosaprobity = saprobity generated by biota in the river (by aquatic plants, algae)

Different aspects of the same thing

Succession within limnosaprobity

and eusaprobityh

m

i

Saprobic degreeh = hypersaprobity

m = metasaprobity

i = isosaprobity

p =polysaprobity

α = α-mesosaprobity

β = β-mesosaprobity

o = oligosaprobity

x = xenosaprobity

α

β

o

x

Microbial taxa

M = mixotrophic phytoflagellates

P = producers

C = ciliates

F = colourless flagellates

Z = zooplankton

B = bacteria

B

B

B

B

B

B

B

B

F

F

F

C

C

Z

Z

Z

Z

P

P

P

P

M

After Sládeček 1973

p

Trophic group

producers

consumers

decomposers

Be careful with biotic indicesA response by a pressure-specific index doesn’t confirm that the impact is caused by that pressure – co-variance

LIFE only responds to one aspect of low flow: flow velocity (and saline intrusion). DEHLI responds to habitat loss, SAGI to saline intrusion, MIS-index to flow intermittence, etc..

Never use indices that you can’t explain:Don’t use scores, use ASPT and Ntaxa

Don’t use diversity indices, use N-taxa (richness) or number of species and

evenness

Indices based on sensitivity values derived from less information/data are less reliable

Taxonomic nomenclature

Status classification and

ecological objectives

http://www.wfduk.org/

High

Good

Moderate

Poor

Bad

Ecological Status

Chemical status

Biological Quality Elements

Specific Pollutants with UK EQS

Hydro-morphological Quality Elements

Priority Substances and Other Pollutants with EU EQS

Surface water

Status

Physico-chemical Elements

H

G

M

H

G

M

P

B

H

G

M

P

B

H

G

M

P

B

H

G

M

P

B

H

G

M

P

B

H

G

M

P

B

H

G

M

H

G

M

H

G

H

G

H

G

H

G

H

G

H

G

H

G

M

P

B

Invasive Species

Worst class

Worst class

Worst class

Worst class

Worst class

GF

GF

GF

GF

GF

GF

GF

GF

GF

GF

GF

GF

GF

H/GM

GoodFailing to achieve goodWorst

class

Worst class

H/GM

H

G

H/GM

H/GM

WHPT ASPT v WHPT N-taxa

High

ASPT

Low

Low High

N-taxa

RIVPACS and ecological assessment

Different natural communities are found

in different types of environments

Values of biotic indices vary between

natural biological communities as well

as environmental damage

WHPT ASPT 4.17

WHPT Ntaxa 41.7

WHPT ASPT 7.14

WHPT Ntaxa 21.4

WFD surface water status

normative definitions

Status

High Near natural, minimally disturbed,

reference

Good Slight deviation from reference (general

objective)

moderate Moderate deviation from reference

Poor Major alteration leading to substantial

deviation from reference

Bad Severe alteration leading to large

portions of community absent

compared to reference

700600500400300200100

200

000

800

600

400

200

0

Group

31-36

37-40

41-43

1-7

8-16

17-26

27-30

River Communities Project 1977-1979The best available unpolluted

rivers were sampled across the

whole of UK, including Northern

Ireland

Sites were chosen from source to

sea to cover all the river zones

The first task was to develop a

standard sampling method (3-

minute kick sample)

Natural river

invertebrate

communities identified

by TWINSPAN

classification

A community has similar species

present in similar abundances

Different invertebrate communities were found throughout the country, wherever the same environmental conditions occur

River Communities Project - result

Abiotic conditions at sites where each community was found were analysed by multiple discriminant analysis to find the smallest group of parameters that differentiated them

River Communities Project - result

If you know what environmental parameters differentiate the communities, you can predict them from those parameters

River Invertebrate Prediction and Classification

Scheme - RIVPACS

Environmental parameters for RIVPACS

prediction

Map dataOS grid reference

→ mean air temperature*

→ air temperature range*

→ latitude*

→ longitude*

Log10 altitudeLog10 distance from sourceLog10 slopeAltitudeDistance from sourceSlopeDischarge or velocity

* Calculated by RICTRICT also calculates log10 values

Sample dataLog10 widthLog10 depthSubstrate % clay/silt

% sand% gravel/pebbles% cobbles/boulders

→ mean particle size*

Geo-chemistryLog10 alkalinitycan be calculated by RIVPACS from:

total hardnesscalciumconductivity

RIVPACS and ecological assessment

We can’t use indices directly to indicate environmental damage in

different streams

The proportional reduction in index value represents a similar level of

damage, whatever the starting value. 10 taxa in the mountain

stream and 20 taxa in the chalk stream both represent about 50%

reduction in taxa

RIVPACS predicts the value an index should have at a site if it is in

un-impacted (near natural) ‘reference’ condition

WHPT ASPT 4.17

WHPT Ntaxa 41.7

WHPT ASPT 7.14

WHPT Ntaxa 21.4

WFD classification

Biological status classification is based on environmental quality ratios (EQRs)

condition referenceat metric of valuesample(s) in observed metric of value EQR

EQR = 0

EQR = 1

RIVPACS is implemented in

RICT software

http://www.sepa.org.uk/science_and_research/what_we

_do/monitoring_and_reporting/ecology/rict.aspx

WFD invertebrate status class

boundaries

The Water Framework Directive (Standards and Classification) Directions (England and Wales) 2015 – official values for biological, physical and chemical standards

http://www.legislation.gov.uk/uksi/2015/1623/resources

Incudes biological and chemical class boundaries

Technical details in UK TAG documents, including methods

www.wfduk.org

RIVPACS IV general model I

Sample data

Width

Depth

Substrate % clay/silt

% sand

% gravel/pebbles

% cobbles / boulders mean particle size*

Geo-chemistry

One of:

alkalinity total hardness

calcium conductivity

Map data

OS grid reference mean air temperature*

air temperature range*

latitude

longitude*

Altitude

Distance from source

Slope

Discharge category or velocity

RIVPACS IV flow and sediment independent Model 44

Sample data

Geo-chemistry

One of:

alkalinity total hardness

calcium conductivity

Map data

OS grid reference mean air temperature*

air temperature range*

latitude *

longitude*

Altitude **

Distance from source **

Slope **

Discharge category **

% drift geology class in upstream catchment

Class 1 – Peat**

Class 3 – clay**

Class 6 – Chalk**

Class 7 – Limestone**

Class 8 – Hard Rocks**

Upstream catchment area**

Mean altitude of upstream catchment**

Flow and sediment independent

RIVPACS Model 44

Key

* Calculated by RICT

** Obtained from database

hosted with RICT using OS

grid reference

Replaced

by

New version of RICT

Drag and drop interface

Corrects minor errors in current RICT classification

Written in R, so more amenable to revision and update

Thousands of sites can be classified in one run, so quicker to use – runs overnight

Will include RIVPACS Model 44

RICT 2

Predicting reference value for

phytobenthos

Biotic index used for WFD status of phytpobenthos (diatoms) = TDI (trophic diatom index)

Prediction is far simpler than for invertebrate indices

eTDI = 9.933 * Exp(Log10(alkalinity) * 0.81)

Prediction is based only on availability of orthophosphate

Artificial intelligence research

at Aston and Staffordshire

Aim

Experienced biologists can not only determine environmental quality but also diagnose the causes of poor quality from an invertebrate sample

Aim of this work was to mimic the thought process used by experienced biologists to interpret invertebrate data

Approach

Use machine learning (artificial intelligence) methods because these are best for analysing very large datasets

Investigated a wide range of AI methods –neural networks, expert systems, etc.

Investigated each in a series of PhDs (School of Engineering at Aston University)

Conceptual basis

Biological community is determined by environmental quality

Expert ecologists use two thought processes to interpret biological survey data:

pattern-recognition -

(unsupervised learning based

on MIR-max) - RPDS

probabilistic reasoning –

(Bayesian expert systems) –

RPBBN

Staffordshire University School of

ComputingFrom Hynes Biology of Polluted Water

River Pressure Diagnostic System

INPUT – biological

invertebrate sample data

RPDS allocates your

biological sample to a cluster

of similar biological samples.

Each cluster is represented by

a circle

OUTPUT - diagnosis

Average concentrations of chemical, flow or

degree of pressure related to samples in a

cluster provides the diagnosis for a new

biological sample allocated to that cluster.

10 and 90-percentile values allow you to

interpret these values as high or low compared

to more common conditions

Aim of RPDS

For Water Framework Directive, we must not only classify ecological quality, but also

identify the causes of poor quality or degradation

so that we can identify an effective programme of measures

RPDS helps us diagnose environmental conditions determining the biological quality

• If we don’t know what may be causing poor quality

• To get a second opinion

• To get subjective evidence to support a diagnosis

Pattern RecognitionRPDS display

Based on the pattern of abundances of

taxa in each sample

Similar invertebrate samples are allocated to each bin (also known as a cluster), based on composition and abundance, and selected RIVPACS environmental variables - this stage is know as classification

Bins with similar biological samples are

closer together

Two separate data sets and displays:

spring

autumn

River Pressure Diagnostic System

Based on the pattern of

abundances of taxa in each sample

Pattern RecognitionRPDS display

MIR-Max

Mutual Information and Regression Maximisation

Information theory-based MIR-max system:

allocates biological data to m bins for best performance, then ...

positions bins so that distances between them relate to similarity of data between bins for better visualisation

Data used in RPDS

RPDS 3.5 RPDS 3.51 RPDS 3.01

Coverage EA & SEPA EA EA & SEPA

Time period 1995-2004 1995-2012 1995-2008

Biological

Sites

Samples

13,562 sp +13,105 au 85,626

8751

86,686

9329

63,565

Environmental variables 13 13 13

Water Quality data RICT predictions Calculated using

RICT methodology

RICT predictions

Matched Chemical and Biological

sites

5666 7720 4894

Chemical

Determinands

Statistics

Samples

42

3-yr %ile

42

3-yr %ile

68,769

42

3-yr %ile

42,676

Perceived stresses (PISCES) 2000

& 2003

Variables

Samples

74

6,579

74

6,579

74

6,579

RPDS 3.01 & 3.51 (continued)

RPDS 3.5 RPDS 3.51 RPDS 3.01

Land cover/use Yes Yes

Flow sites (% impact at Q95 records)

4478 4479 4478

River Habitat Survey

Survey form types

Matched Bio & RHS sites

Data

. 1995, 1996, 1997, 2003

3974

Survey data,

Habitat Quality

Assessment (HQA) values,

Habitat Modification Score

(HMS) values,

6 indices summarising:

substrate, flow, vegetation,

activity, bank-top and bank-

face survey values.

1995, 1996, 1997, 2003

3927

Survey data,

Habitat Quality

Assessment (HQA) values,

Habitat Modification Score

(HMS) values,

6 indices summarising:

substrate, flow, vegetation,

activity, bank-top and bank-

face survey values.

Data limitations

Biology limited to river invertebrates

Pesticide chemical data excluded (pesticides only in PISCES data)

No chemical data to enable Acid Neutralising Capacity to be determined

Setting a new site as the current ‘Input Sample’

New site indicated by green dot

New site allocated to best cluster,

but other good clusters also shown

Values for non-mandatory fields will

only display if you provide data for

them

Manual data entry form

Open Report tab to view of sample or new sample

That you have entered

Value = average value in bin

= diagnosis

Count = number of samples in bin with data

= reliability of diagnosis

Click on cluster to select it

in order to show it in report

Pop’n 10%ile and 90% - Scroll right to see Pop’n 90%ile

These are global statistics based on the mean values in

every bin/cluster – different value for spring and autumn

Compare with average of samples in bin (value = diagnosis)

to see if the value is a particularly high or low

Scrolling down

Multi-site diagnosis

Guidance within RPDS 3.5RPDS (v3.5)

• Indicator tab gives text descriptions for every indicator, its environmental significance and photos of every taxon

• Extensive help files (= user guide) … next slide

Help menu Help opens in a new window so you

can keep it open while using RPDS

RPDS - issues

Output indicates multiple pressures. Managers want to know which pressure to alter

Interactions between pressures are difficult to predict because they

are so complex.

e.g. eutrophication in rivers is controlled by nutrients, flow, depth,

turbidity, turbulence, temperature, contact time [distance from

source, dead zones], shading, chemicals, invertebrate grazing ….

Controlling one pressure improves resilience of ecology to the other

pressures

Experts concentrate on particular pressures – EA

organised around ‘Functions’ (Water Quality, Water

Resources, Chemicals, Fisheries, etc.)

Funds for restoration from particular functional budgets

River Pollution Bayesian Belief Network (RPBBN)

Invertebrate abundance

category

Chemical concentration

category

Green bar = prediction

Red bar = predictor

Click to toggle on/off and to change

predictor concentration/abundance

Predicts river invertebrate abundances

from concentration of major chemicals &

flow and vice versa

Used to test the combinations of

improvement measures that has the

greatest impact on ecological status

Can be used with predictions of flow and

chemistry to predict biology, including

the biotic indices used to determine

WFD ecological status

Derived from matched biology, chemistry

and flow data for 30,000 samples from

3,600 sites

Input: any combination of biological,

physical or chemical data

Output: predictions of all the remaining

variables

RPBBN - River Pressure Bayesian

Belief Network

Normal test statistics tell you

if …. then,,,,,

OK for certain relationships, but ecology is affected by multiple pressures so we need to use uncertain reasoning

Always more than one pressure affects ecology

(if …. then,,,,,) (if …. then,,,,,) (if …. then,,,,,) (if …. then,,,,,)

common statistics can’t cope with all the interactions between these

Bayesian belief network (BBN)

2 parts:

1. causal network that defines cause-effect relationships

2. matrix of conditional probabilities relating

each abundance of each taxon

to

each concentration/ magnitude of each environmental variables

RPBBN causal network

Matrix of probabilitiesEach value of parent is linked by a probability to each value

of daughter parameter

RPBBN causal network

Data

The BBN uses only fully matched datasets

RPBBN v2 based on 16,244 spring and 15,856 autumn samples

Calculates biotic indices on-screen

Two new RPBBN systemsEnvironmental

Variables

Alkalinity

Altitude

Sand-silt

Site type

Season

Flow3

Flow6

Flow12

Flow24

Orthophosphate

Oxygen %

pH

TON

Normal model

5 states

Southern model

+ Ammonium 8 states

BOD5 7 states

Ophos 7 states

Oxygen % 7 states

TON 7 states

RPBBN v2.1

There two forms of the normal (5-state) model:

Single season

Annual

Annual model is based on spring + autumn and enables values of indices to relate to the WFD classification

RPBBN 2 splash screen

Choose model

You can also open the Domain Manager to

change model after opening the system

(File > Domain Manager)

Prior probabilities – normal model

Scroll down to environmental variables

Enter biological data for site (in this case from system database) – predictions of environmental data sharpen

Note RPBBN v2.1 calculates a different N-taxa for each index

Add information about season and site type sharpens

predictions of environmental factors …compare next slide

Add information about season and site type sharpens

predictions of environmental factors …compare next slide

Predict biology from environmental data

Southern model is slow, so you can switch-on Manual Propagation which allows you to

make multiple changes and delay updating rest of the network until you choose to

When you are ready

select Propagate

RPBBN Help

RPBBN - issues

Tried to use RPBBN to predict change in chemical quality to improve biological quality

Enter environmental data for a site. Improve one or two parameters until WHPT ASPT and WHPT Ntaxa improve.

Biotic index values based on averages – central tendency. Cannot predict amount by which a particular environmental parameter has to change to achieve a given magnitude of change of biotic index

Pressures co-vary – so unrealistic to alter just one parameter (e.g. oxygen affected by most of the other parameters)

RPBBN does indicate which environmental parameter has greatest effect

Artificial Intelligence

Beyond classification

RPDS

RPBBN - for scenario testing

MIR-Max classification software for creating systems like RPDS

Current work to make a web version of MIR-Max (& RPDS )

Ecological Statistics ToolKitSingle Site Chemistry Statistics including Cusum, seasonal trend, Kendal,

box plots analysis

Ecological Statistics ToolKit Summary Site Statistics for each determinand

Ecological Statistics ToolKitMulti – Site Statistics including PCA, density plots, time series and box

and whisker plots

Ecological Statistics ToolKitMultivariate Analysis of BIOSYS samples including Cluster and MDS

Sentinel network

Changes in invertebrates 1990-2011

There was a marked increase

in the number of differ taxa

(richness), indicating improved

quality. This was greatest in

more urban areas.

The increases were mainly

taxa that are sensitive to

organic pollution and are

indicators of unpolluted water.

decrease increase

urb

an

incre

ase

rura

l in

cre

ase

Causes of improvement in

invertebrates

Improvement in invertebrate quality matched changes in water

chemistry. They did not appear to be associated with climate change.

Effect of North Atlantic Oscillation

Changes in the sensitivity of taxa fluctuated with the

NAO, which affects weather, but there was still an

underlying improvement

References

Vaughan, I.P. and S.J. Ormerod (2012) Large-scale, long-term trends in British river macroinvertebrates. Global Change Biology 18: 2184–2194

Vaughan, I.P. and S.J. Ormerod (2014) Linking interdecadal changes in British river ecosystems to water quality and climate dynamics. Global Change Biology 20:

Long-term change on

phosphate

Orthophosphate

From harmonized monitoring. 60% drop 1995-2015. Expect 66% by 2020 and c88% by 2027

End