Diagnostic Tool Development
This project (WFD60) forms part of the UK Strategy for the
implementation of the EC Water Framework Directive (WFD: European
Union, 2000). Within its broad remit the WFD requires the development
of ecological classification tools for the purpose of determining
ecological status, with reference to specific environmental pressures.
The WFD requires that these tools should assign lakes to one of five
categories, (High, Good, Moderate, Poor, Bad) to indicate conditions
relative to what is considered to be “good status”.
This report focuses on the development of a tool with which to
determine the extent of the pressure of acidification on lake
Objectives of research
The primary objective is the development of a method and tool with
which to assess the pressure of acidification (a major threat to the
ecology of acid-sensitive fresh waters, particularly in the UK uplands)
on the benthic macroinvertebrate assemblage of lakes.
Key findings and
Tool development under WFD60 was severely delayed due to problems
obtaining sufficient high quality biological and chemical data. The
dataset used to support this phase is still less than satisfactory,
comprising data for only 105 sites and representing a subset only of
the chemical variables that would have been useful for explanatory data
analysis. Due to the paucity of acid anion data from one source and
dual endpoint (or Gran) alkalinity from another, the final
physico-chemical dataset was built using one of two commonly used
expressions of acid neutralising capacity (ANC) and a few associated
Our assessment of the literature regarding
macroinvertebrate-acidification inference techniques concluded that
none were appropriate for this assignment. In most cases
macroinvertebrate communities have been used to infer pH, but pH per se
carries little information on acid sensitivity or the likelihood that a
site has acidified.
We show, through an investigation of the output of the Steady State
Water Chemistry (SSWC) Model and palaeoecological diatom-pH
reconstructions, how ANC can be used as an indicator of damage, in
terms of modelled ANC change, diatom-inferred pH change and the
mobilisation of labile inorganic aluminium (Allab)
Furthermore, we show that prediction of the likelihood and level of
acidification can be refined by using ANC in conjunction with calcium
Assessment of chemical data from the UK Acid Waters Monitoring Network
demonstrates that Allab concentration, possibly the most important
agent of damage associated with acidification, will rarely if ever
reach biologically toxic concentrations in sites with an ANC above 40
Conversely, sites which currently have a negative ANC are highly likely
to exhibit biologically toxic Allab
We show that ANC and Allab explain as much
variance in a small high quality macroinvertebrate dataset as pH and
propose that macroinvertebrate community structure may carry sufficient
information for the level of physico-chemical damage to be inferred
through its relationship with ANC and calcium concentration.
In the expanded dataset, representing 105 lakes, we again show that ANC
is strongly related to the principal axis of macroinvertebrate species
variation between sites.
We show that certain attributes of macroinvertebrate community
structure pertinent to normative definitions also vary along an ANC
gradient. In particular, a crude measure of macroinvertebrate species
richness, as inferred by the total number of species identifiable to
species level, is tightly related to ANC. This is consistent
with observations in the literature that macroinvertebrate diversity
may be reduced by anthropogenic acidification but not by natural
acidity (i.e. at sites where pH is depressed by organic acids only).
Several individual species show sharply truncated distributions on Allab
gradients and species often ceased to be present in waters with mean
annual Allab concentrations over 10 µg
We created a “damage matrix” to provide an a priori
physico-chemical classification of all sites in the WFD60 database by
ANC and calcium concentration into WFD compliant classes, i.e. HIGH,
GOOD, MODERATE, POOR, BAD. Owing to the sparsity of the data
we then condensed these classes into three representing HIGH-GOOD,
MODERATE and POOR-BAD.
We used a classification tree approach to predict the a priori defined
class of each site using its macroinvertebrate assemblage.
Classification trees are a powerful yet simple way of predicting
classes from a set of predictor variables (in this case,
macroinvertebrate species and broader macroinvertebrate groups).
After using a large range of biological input variables, including data
at species level (i.e. the proportions of individual taxa) we found
that summary data only, in the form of minimum species richness (MSR)
of the full assemblage, the minimum number of species in certain
biological groups, and the proportion of individuals represented by
certain groups, was necessary to maximise the successful classification
rate. The final tree classification used these variables only.
We found that a simple rule, i.e. MSR >or<12.5, provided
the most powerful criterion for distinguishing between damage classes
at the primary level. Further splits were based on the number of
non-leptophebid (i.e. mostly acid-sensitive) Mayfly taxa, the
presence/absence of bivalves, the proportion of Ephemeropteran,
Plecopteran and Trichopteran individuals in the entire assemblage, and
the minimum number of stonefly taxa. The apparent misclassification
rate of this tree was 18.3%. We determined that the tree should be able
to correctly assign class status to random independent samples between
77 – 78% of the time.
This simple approach was able to distinguish between acidified and
naturally acid (i.e. high DOC, low sulphur deposition) lakes that tend
to support relatively large numbers of taxa. Apparently more complex,
species-based, models such as the Acid Water Indicator Community model
(AWIC) are perhaps better tuned to predict pH but have limited value in
While the divisions on this tree form our current
“best” model, we have major reservations with
respect to the total number of sites in the dataset and the
distribution of sites at the acidified end of the gradient. The model
as it stands is clearly not fit for purpose but would benefit greatly
from the addition of 30-40 more sites in an acidified condition.
While this is a categoric approach to classification, class predictions
could be converted to EQR-compatible site scores to meet WFD
requirements. There are a number of methods to achieve this, but the
most robust would use a method known as “bagging”
to determine the probability of membership of each site in the most
likely class and neighbouring classes, to provide a sliding score. The
proposed increased number of sites would be essential for this
technique to be used effectively.
We tested the tool qualitatively on 51 sites for which chemical data
were not adequate to be included in the original training set.
Generally the classification of sites was highly consistent with
geographical location although a few sites were clearly misclassified.
Current model weaknesses are likely to be principally due to the
paucity of sites for which data are available at the acid and acidified
end of the physico-chemical gradient. The imbalance of sites in the
training set also prevents us from deriving predictions of the
probability of correct classification using a “tree
We recommend that biological and physico-chemical data are gathered for
a further 30-40 acidified sites before any attempt is made to refine
the existing model.
Before implementation, we recommend the tool is tested on 1) time
series data, to allow an assessment of temporal variability of output,
and 2) sites for which detailed multi-proxy biological records are
available, so that the macro-invertebrate inferred damage class can be
related to wider-ecosystem indications of damage by acidification.
Water Framework Directive, Lakes, Acidification, Littoral
Macroinvertebrates, Classification, Classification Trees, Acid
Neutralising Capacity, Aluminium, pH.
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£25.00, less 20% to FWR members.
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