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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