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Reducing uncertainty in assessment with the Benthic Quality Index
Examples from the Swedish west coast
Mats Blomqvist, Hafok Kjell Leonardsson, SLU Rutger Rosenberg, GU, Marine Monitoring Marina Magnusson, Marine Monitoring
Photo: Hans C Nilsson
BQI history • Introduced 2004 (Rosenberg et al 2004) • Legally binding WFD method 2008 (NFS 2008:1, HVMFS 2013:19)
• Further developed 2009 (Leonardsson et al 2009) – discuss uncertainty
• Legally binding MSFD method 2012 (HVMFS 2012:18)
• Species sensiGvity values improved 2015 (Leonardsson et al 2015) – SGll lowest diversity a species is found (5th percenGle)
• Richness instead of Hurlbert ES50 – Based on
• Tolerance or sensiGvity to disturbance • Capability to coexist with other species
– Uncertainty evaluated
Mats Blomqvist, Malmö 6th May 2015 Slide 2
Polyphysia crassa
SensiGvity value
(S0.05)
ProporGon of samples from disturbed areas
Species sensiGvity value
Mats Blomqvist, Malmö 6th May 2015 Slide 3
1411 staGons
Amphiura chiajei
SensiGvity value
(S0.05)
StraGfied: at least 20 disturbed and 20 undisturbed samples Not straGfied: at least 20 samples
Species sensiGvity value • SensiGvity factor =
abundance weighted mean of sensiGvity values
• Randomly select a sensiGvity value within the 95% conf int for each species in a sample
• Calculate factor • Repeat 1000 Gmes for
each sample • Each dot is one sample
calculated one Gme
Mats Blomqvist, Malmö 6th May 2015 Slide 4
SensiGvity factor without uncertainty in S0.05
SensiGvity factor with
uncertainty in S
0.05
Absolute average error is 0.26 ± 0.22 or a CV of 1.8 ± 2.9 %
i.e. uncertainty in sensiGvity values has a very small influence on BQI!
Species sensiGvity value
Mats Blomqvist, Malmö 6th May 2015 Slide 5
Polyphysia crassa
SensiGvity value
(S0.05)
ProporGon of samples from disturbed areas
Upper error bar
Lower error bar
Species sensiGvity values -‐ conclusions
• Uncertainty depends on – proporGon of samples from disturbed environments – number of samples
• Low precision can reduce accuracy – exclude species from sensiGvity list if • low value and high upper range error • large lower error range
Mats Blomqvist, Malmö 6th May 2015 Slide 6
Read more Leonardsson et al: Marine Pollu3on Bulle3n 93 (2015) 94–102
Reducing spaGal variaGon
Mats Blomqvist, Malmö 6th May 2015 Slide 7
VerGcal subtypes: ≤ 20 m BalGc influence > 20 m AtlanGc water
From SGU 2010
Account for environmental variaGon
• Subtypes: e.g. BQI today • Include environmental variables in the index by sefng
reference values for index metric based on environmental variable, e.g. – Danish DKIv2 (reference values for AMBI and H’ as funcGon of salinity) (DKIv2 = ((1-‐ ((AMBI-‐AMBImin)/7))+ (H/Hmax))/2 * (1-‐(1/N)))
– BriGsh IQIvIV (reference values for AMBI, Lambda’ and S as funcGon of salinity and sediment)
• Regression model – include relevant environmental factors salinity, sediment, depth – assessment based on residuals
Mats Blomqvist, Malmö 6th May 2015 Slide 8
Model building – index components
Mats Blomqvist, Malmö 6th May 2015 Slide 9
855 staGons
StaGon medians
Model building
Mats Blomqvist, Malmö 6th May 2015 Slide 10
Model AIC ΔΑΙC
Y=BQI2015(depth) 3241.2 0
Y=BQI2009(depth) 3365.4 124.2
Y=BQI2015 (salinity) 3377.6 136.4
Y=BQI2009 (salinity) 3478.7 237.5
Y=BQI2015 (sediment) 4003.8 762.6
Y=BQI2009 (sediment) 4017.9 776.7
Assessment is based on the residuals from the regression model instead of the index value itself.
Model building
Mats Blomqvist, Malmö 6th May 2015 Slide 11
TransformaGon of residuals
Mats Blomqvist, Malmö 6th May 2015 Slide 12
Residuals per type
Mats Blomqvist, Malmö 6th May 2015 Slide 13
Assessment – GM • SE approach 5 staGons
for spaGal representaGveness
• Boundary GM 5% limit of baseline data – Select assessment unit/year (AY)
– Select 5 staGons in AY – Select 1 sample from each staGon
– Calculate mean
Mats Blomqvist, Malmö 6th May 2015 Slide 14
If mean residual is less than -‐2.52 then status is less than good
EQR = (mean residual + 8.53) / 17.06
Bootstrapped mean of 5 transformed residuals
Conclusions • SpaGal variaGon in BQI is reduced by a regression model based on depth
• Assessment is based on the residuals from the regression model instead of the index value itself
• Bootstrapping is used to find GM boundary (5 samples within assessment unit/year)
• Final step in assessment: compare mean residuals against status class boundaries
Mats Blomqvist, Malmö 6th May 2015 Slide 15