Phytoplankton Classification Tool
(Phase 2)
WFD80
June 2007
EXECUTIVE SUMMARY
Background to research
The Environment Agency and SNIFFER have commissioned Phase 2 of this R
& D project to develop the method to classify the ecological
status of lakes on the basis of phytoplankton. As part of this
assessment, metrics need to be developed for phytoplankton community
composition.
Objectives of research
Specific objectives for the project were to develop a robust
classification, incorporating:
- Prediction of reference scores for UK lakes based on
phytoplankton composition
- Developing criteria for defining the good/moderate boundary
- Classifying the ecological status of a water body in to one
of five status classes (High/Good/Moderate/Poor/Bad), based on
the calculation of an Ecological Quality Ratio (EQR). An EQR
being calculated from the relationship between current observed
and reference phytoplankton community composition for a site
- Determining uncertainty associated with the classification
result, based on statistical confidence or probability of class
Key findings and
recommendations
Following collation of a dataset of matching phytoplankton and
environmental data from 300 lake samples, a multivariate approach to
metric development was adopted. CCA was used to develop a
species-environment model for phytoplankton, with the main typology
variables (alkalinity, altitude, mean depth) included as significant
explanatory variables in the model alongside two variables indicative
of eutrophication pressure (chlorophyll and TP concentrations). The CCA
model indicated strong correlations between the eutrophication pressure
gradients (Chlorophyll and TP) and alkalinity. This highlighted the
potential problem of developing simple univariate optima of
phytoplankton taxa against pressure gradients.
Optima were derived for 112 of the most common phytoplankton taxa
(mixed Genus and species level) along both eutrophication gradients
(chlorophyll and TP) using reciprocal averaging. Although this was
still a univariate approach, the correlative effect with alkalinity is
removed later through the calculation of an EQR by taking account of a
site’s alkalinity in the reference score. A metric, the
Phytoplankton Index of Eutrophication (PIE) was developed which
averages the taxa optima at a site, weighted by their abundance (log10
biovolume), to give an Observed PIE Score. Comparisons of various
weightings of the community data clearly showed that this metric showed
the strongest relationships with both log10TP (r2 = 0.60) and
log10Chlorophyll (r2 = 0.64). The metric showed significant
relationships with the two pressure gradients for all lake alkalinity
types, although was weakest for low alkalinity lakes.
Three approaches were explored for establishing Expected, or Reference,
PIE Scores for a site using data from 50 reference lake samples.
Firstly, stepwise regression was carried out examining which typology
variables explained significant variance in reference site PIE scores.
Alkalinity was the only typology variable selected with a significant
positive relationship between PIE score and alkalinity. Using this
regression model, site-specific reference PIE scores can be predicted
for any UK or Irish lake of known alkalinity. Type- specific reference
PIE scores were also established with the regression model on the basis
of the median alkalinity measures in the UK and Irish lake dataset. It
is recommended that, if possible, modelled site-specific reference
conditions are adopted as they are ecologically more appropriate and
correlate better with observed PIE scores at reference lakes than
type-specific reference scores.
EQRs were calculated from the ratio of Observed to Expected PIE scores,
which were then transformed, to produce an EQR ranging from 0 to 1. The
High/Good (H/G) boundary was determined from the lower 25% of reference
site EQRs. To derive the remaining boundaries, phytoplankton taxa were
classified as “positive” (low eutrophication
pressure) or “negative” (high eutrophication
pressure) indicators of eutrophication pressure, by examining their
optima and tolerances in a constrained CCA model. The % biovolume of
positive and negative taxa was calculated for each sample and
polynomial regression analysis was carried out to examine their
relationships with EQR. The crossover point in these two relationships
was chosen to represent the Good/Moderate (G/M) status class boundary.
The 75% of residuals in the two equations were then used to identify
the Moderate/Poor (M/P) status class boundary and the
remaining Poor/Bad (P/B) boundary was derived from a division
of the remaining EQR scale.
The PIE metric was applied to all UK and Irish lake phytoplankton
samples. Observed and Expected PIE scores and resultant EQRs are given
in Appendix 2. The mean variance in EQR scores between samples from
different months for the same lake was relatively low. The relationship
between observed EQR and variance in EQR was used to estimate the
confidence in classification of results and mis-classification rate for
a given EQR.
A number of sources of uncertainty, or error, in EQRs and associated
constituent measures (observed PIE scores, sample biovolume, number of
taxa) were examined, focusing on sample processing errors due to the
combination of sub-sample and counter analytical error. The resultant
estimates of the average within-site sampling/processing variability
can also be used, in software such as STARBUGS, to derive estimates of
uncertainty in assigning water bodies to a WFD ecological status class.
These results are, however, preliminary, based on limited data and
unstructured sampling. The effects of larger scale within-lake spatial
variability on EQRs also needs to be examined, with estimates in more
lakes over a wide range of ecological qualities using nested replicate
samples and replicate sub-samples before these results can be
considered reliable.
Sources of variation in chlorophyll data due to within-lake spatial
variation and laboratory analytical variability were examined and
compared with previous work on temporal variability. The largest source
of variability in chlorophyll concentrations within a lake is temporal,
but if sampling is carried out monthly then much of the seasonal
variability is eliminated and becomes smaller than the estimated
variance between replicate samples taken on the same day. If sampling
is restricted to the outflow of lakes, the least variable location,
then the estimate of replicate sampling variance becomes less than
laboratory variance.
In terms of minimising uncertainty in both chlorophyll and composition
classifications, it is recommended that sampling is carried out at a
single specific location within a site, where the location is
representative of the lake as a whole (outflow or centre of lake).
Samples should be taken from July to September at a monthly frequency.
Key words: phytoplankton, WFD, classification, lake, ecological status
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N.B.
The report is available for download from the SNIFFER Website