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Monitoring and modeling of estuarine benthic macrofauna and
their relevance to resource management problems
Tom Ysebaert, Peter Herman, Herman Hummel, Bart Schaub, Wil
Sistermans & Carlo HeipNetherlands Institute of Ecology
(NIOO)
[email protected] Colour of Ocean Data - The Palais des
Congrs, Brussels, Belgium, 25-27 November 2002
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OUTLINEIntroduction: estuarine management and the problem of
scale
Benthic monitoring programmesPredictive modelingSpatio-temporal
dynamicsTrend calculations
General conclusions
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FIELD STUDIES - EXPERIMENTSENVIRONMENTAL PROBLEMSSCALETIDAL
FLAT+ multidisciplinary research + detailed process studies+ food
web and stable isotope studies + sediment processes
LINKS monitoring integrative studies time-series data
modelingSmallLargeSCHELDE ESTUARY- large-scale dredging operations-
habitat loss- water quality- fisheries
INTRODUCTION
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Benthic monitoring programmesBenthic organisms: suitable
indicators for changes in environmental qualityDutch Delta area (SW
Netherlands): long tradition in monitoring of estuarine benthic
macrofaunadesigned to detect long-term trends in large parts of
different systems (e.g. Grevelingen) Explore relationships between
biota and environmental variables to improve prediction and trend
calculations
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SCHELDE ESTUARYLarge data set available (>5000
samples)Different sampling designs (stratified random, fixed
stations)Environmental variables (model derived)
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Predictive modelingLogistic regression: model probability of
occurrence of species as a function of environmental variables
Ysebaert et al. 2002, MEPS
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Macoma balthica: comparison pred./obs.Observed
presencesPredicted presencesYsebaert et al. 2002, MEPS
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Predictive modeling: conclusionsfor 20 macrobenthic species
response surfaces were modeled (Ysebaert et al., MEPS 2002)
the overall prediction performed very well (>75%).
% predicted observed vs actually observed: 25%-85%.
Within-estuary validation: successful
where patterns of distribution are strongly and directly coupled
to physico-chemical processes, our modeling approach is capable of
predicting macrobenthic species distributions with a relatively
high degree of success
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Time-averaged approach - no temporal dynamicsExtrapolation to
other systems limited - needs incorporation of system-wide
characteristics (e.g. SPM content, productivity, wave vs. tide
dominance)No prediction of abundance or biomass
Limitations of the approachAnalysis of spatio-temporal
variability of abundance and biomassAnalysis of dependence on
environmental factors
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11 transects in 3 salinity zones, 2-4 stations per transect15
replicates per stationsampled twice yearly 1994-2000 height, mud
content, chl a monitoredFit hierarchical Anova model to
observations (variance components)Regression on environmental
variables
Spatio-temporal dynamics
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R0.41Mud0.37 ***MedianChl aHeight0.53 ***SlopeSalinityFlood0.33
***Ebb-0.16 MudChl a0.15 Height-0.16 Salinity0.21 *Macoma balthica:
spatial and temporal variabilityYsebaert et al., in press, MEPS
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In general fair proportion of variance explained by
station-averaged environmental variablesTemporal variation in
environmental variables poor explanatorsTemporal variation
synchronized over estuary or region for bivalves (recruitment) but
seldom for other speciesLargest proportion of variance usually in
unsyn-chronized, station-dependent, temporal variation points to
important patchiness and independent development at a scale >
replicate scale (1m2), but < transect scale -> biological
interactions?Spatio-temporal dynamics: conclusions
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Application to trend calculationsUse information on the
environment in trend calculationsBIOMON Westerschelde: stratified
random design
Approach : define relationships between environment and biota
(presence-absence, abundance, biomass)Compare regression models
where year is considered the only independent variable with
regression models with year and environmental variables as
independent variables
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Trends 1992-2001
Chart4
7.06603023857.053215574
6.58449705525.0113479639
6.1357792156.1797382047
5.71764043025.8061064273
5.32799681084.2661010592
4.96490647883.701248232
4.62655989093.9991305371
4.31127081954.371222843
4.01746794984.479861118
3.7436870484.4082393282
trend [year]
trend [year+env]
count
Heteromastus filiformis
Sheet1
hetefilimacobaltbathpilopygoeleghydrulvaaphemarinephcirrneredivearenmaricorovoluceraedul
trend [year]trend
[year+env]predpred-envpredpred-envpredpred-envpredpred-envpredpred-envpredpred-envpredpred-envpredpred-envpredpred-envpredpred-env
19927.06603023857.0532155743.36009784423.49818265864.05222232523.50499502237.11507843714.90925506725.1524047445.35665438682.99337833462.96815356190.29957435250.28578022071.34972937081.00154005980.13456869070.13791135595.1049584946.25827152552.45207373072.6084427824
19936.58449705525.01134796393.12671397292.75195667764.10331069934.38630690357.43843838625.39009690144.92445280934.14307009293.13052797962.91696997840.25518267390.26129082641.26307402640.87886691550.13156781450.1252280285.12766489353.16839358142.29315349341.7485409957
19946.1357792156.17973820473.06706420022.9849478484.21244589954.60407090087.6446190876.7456576955.00137096145.43296221843.42288314192.41083600590.21655846570.21230954441.23550855691.17882376260.12819498450.13877132425.23056224377.5306240922.22622306531.83619622
19955.71764043025.80610642732.99794022622.49728739464.32061423824.91966068217.86510037047.0713715355.05873954194.67487835353.73146304312.79564844440.18362483930.16975331521.20498334511.07016138070.12549841850.12346786696.98451277748.6608646011.90741590482.3061898959
19965.32799681084.26610105922.90466506143.34134284194.42141452974.01677424538.11508232357.49482567545.06507440985.83434572284.03773498334.82468247780.15438985270.16789576691.16654611560.96110239510.12507732280.11975071778.17305779987.24854710211.38584950341.12775744
19974.96490647883.7012482322.8631546863.24857254134.54505305323.77987008428.32567539996.98345284665.17282157016.38663055554.43317380966.06013110990.13081646940.15179612931.14581358170.84546735130.12210072080.11472333589.11914544845.42074794061.2901525661.2585268231
19984.62655989093.99913053712.74011362713.07395353514.6360502794.07088624118.62533335817.29254612155.10647030865.32370548264.74734169145.4403556930.11100845410.12111793711.09782029460.94328223320.11943838940.10779418337.93875051985.54833945021.12646685221.0615432582
19994.31127081954.3712228432.67714286072.86957128564.75462593695.11189763178.87502277279.39040437235.16225435985.25109416245.17341054985.14526396010.09405604450.0889249511.07030301960.93399346370.11715518480.11724203218.31344598797.71313108671.06086551781.1874273017
20004.01746794984.4798611182.46393258182.82265701874.80141260625.44958081619.308928601410.90459177744.8711369965.11936563375.36091856115.37404340790.07995022960.07477021870.99246070881.1348461060.11501913990.128981203510.697991957814.27362896840.74087043530.7820145884
20013.7436870484.40823932822.66779720411.97695888395.05967833875.13106270769.255807421110.22065920175.54129008224.06629035216.37272583034.54479528940.06778437020.05506056191.05508529860.94369737660.11106622120.118195627113.084355909410.16073130360.79275997960.9817382348
Sheet1
trend [year]
trend [year+env]
count
Heteromastus filiformis
Sheet2
trend [year]
trend [year+env]
count
Macoma balthica
Sheet3
00
00
00
00
00
00
00
00
00
00
trend
trend env
count
Bathyporeia pilosa
00
00
00
00
00
00
00
00
00
00
trend
trend env
count
Pygospio elegans
00
00
00
00
00
00
00
00
00
00
trend
trend env
count
Hydrobia ulvae
trend [year]
trend [year+env]
count
Aphelochaeta marioni
trend [year]
trend [year+env]
count
Nephtys cirrosa
00
00
00
00
00
00
00
00
00
00
trend
trend env
count
Nereis diversicolor
00
00
00
00
00
00
00
00
00
00
trend
trend env
count
Arenicola marina
00
00
00
00
00
00
00
00
00
00
trend
trend env
count
Corophium volutator
trend
trend env
count
Cerastoderma edule
7.06603023857.053215574
6.58449705525.0113479639
6.1357792156.1797382047
5.71764043025.8061064273
5.32799681084.2661010592
4.96490647883.701248232
4.62655989093.9991305371
4.31127081954.371222843
4.01746794984.479861118
3.7436870484.4082393282
trend [year]
trend [year+env]
count
Heteromastus filiformis
-
Trends 1992-2001
Chart6
3.36009784423.4981826586
3.12671397292.7519566776
3.06706420022.984947848
2.99794022622.4972873946
2.90466506143.3413428419
2.8631546863.2485725413
2.74011362713.0739535351
2.67714286072.8695712856
2.46393258182.8226570187
2.66779720411.9769588839
trend [year]
trend [year+env]
count
Macoma balthica
Sheet1
hetefilimacobaltbathpilopygoeleghydrulvaaphemarinephcirrneredivearenmaricorovoluceraedul
trend [year]trend
[year+env]predpred-envpredpred-envpredpred-envpredpred-envpredpred-envpredpred-envpredpred-envpredpred-envpredpred-envpredpred-env
19927.06603023857.0532155743.36009784423.49818265864.05222232523.50499502237.11507843714.90925506725.1524047445.35665438682.99337833462.96815356190.29957435250.28578022071.34972937081.00154005980.13456869070.13791135595.1049584946.25827152552.45207373072.6084427824
19936.58449705525.01134796393.12671397292.75195667764.10331069934.38630690357.43843838625.39009690144.92445280934.14307009293.13052797962.91696997840.25518267390.26129082641.26307402640.87886691550.13156781450.1252280285.12766489353.16839358142.29315349341.7485409957
19946.1357792156.17973820473.06706420022.9849478484.21244589954.60407090087.6446190876.7456576955.00137096145.43296221843.42288314192.41083600590.21655846570.21230954441.23550855691.17882376260.12819498450.13877132425.23056224377.5306240922.22622306531.83619622
19955.71764043025.80610642732.99794022622.49728739464.32061423824.91966068217.86510037047.0713715355.05873954194.67487835353.73146304312.79564844440.18362483930.16975331521.20498334511.07016138070.12549841850.12346786696.98451277748.6608646011.90741590482.3061898959
19965.32799681084.26610105922.90466506143.34134284194.42141452974.01677424538.11508232357.49482567545.06507440985.83434572284.03773498334.82468247780.15438985270.16789576691.16654611560.96110239510.12507732280.11975071778.17305779987.24854710211.38584950341.12775744
19974.96490647883.7012482322.8631546863.24857254134.54505305323.77987008428.32567539996.98345284665.17282157016.38663055554.43317380966.06013110990.13081646940.15179612931.14581358170.84546735130.12210072080.11472333589.11914544845.42074794061.2901525661.2585268231
19984.62655989093.99913053712.74011362713.07395353514.6360502794.07088624118.62533335817.29254612155.10647030865.32370548264.74734169145.4403556930.11100845410.12111793711.09782029460.94328223320.11943838940.10779418337.93875051985.54833945021.12646685221.0615432582
19994.31127081954.3712228432.67714286072.86957128564.75462593695.11189763178.87502277279.39040437235.16225435985.25109416245.17341054985.14526396010.09405604450.0889249511.07030301960.93399346370.11715518480.11724203218.31344598797.71313108671.06086551781.1874273017
20004.01746794984.4798611182.46393258182.82265701874.80141260625.44958081619.308928601410.90459177744.8711369965.11936563375.36091856115.37404340790.07995022960.07477021870.99246070881.1348461060.11501913990.128981203510.697991957814.27362896840.74087043530.7820145884
20013.7436870484.40823932822.66779720411.97695888395.05967833875.13106270769.255807421110.22065920175.54129008224.06629035216.37272583034.54479528940.06778437020.05506056191.05508529860.94369737660.11106622120.118195627113.084355909410.16073130360.79275997960.9817382348
Sheet1
trend [year]
trend [year+env]
count
Heteromastus filiformis
Sheet2
trend [year]
trend [year+env]
count
Macoma balthica
Sheet3
trend [year]
trend [year+env]
count
Bathyporeia pilosa
trend [year]
trend [year+env]
count
Pygospio elegans
00
00
00
00
00
00
00
00
00
00
trend
trend env
count
Hydrobia ulvae
trend [year]
trend [year+env]
count
Aphelochaeta marioni
trend [year]
trend [year+env]
count
Nephtys cirrosa
trend [year]
trend [year+env]
count
Nereis diversicolor
trend [year]
trend [year+env]
count
Arenicola marina
00
00
00
00
00
00
00
00
00
00
trend
trend env
count
Corophium volutator
trend
trend env
count
Cerastoderma edule
7.06603023857.053215574
6.58449705525.0113479639
6.1357792156.1797382047
5.71764043025.8061064273
5.32799681084.2661010592
4.96490647883.701248232
4.62655989093.9991305371
4.31127081954.371222843
4.01746794984.479861118
3.7436870484.4082393282
trend [year]
trend [year+env]
count
Heteromastus filiformis
3.36009784423.4981826586
3.12671397292.7519566776
3.06706420022.984947848
2.99794022622.4972873946
2.90466506143.3413428419
2.8631546863.2485725413
2.74011362713.0739535351
2.67714286072.8695712856
2.46393258182.8226570187
2.66779720411.9769588839
trend [year]
trend [year+env]
count
Macoma balthica
-
Trends 1992-2001
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Trend calculations: conclusionsFor some species, regression
models with the factor year as independent variable or regression
models with the factor year and environmental variables as
independent variables showed similar results, but for several
species the significant trend disappeared when environmental
variables were included
environmental variables, incorporated into regression models,
might improve long-term trend calculations, as they allow to
compensate for differences in local environmental variability.
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GENERAL CONCLUSIONSThe results demonstrate the important role
environmental variables play in explaining variability of
soft-sediment benthic macrofauna at scales from 100m to complete
estuarine systems.
Predictions of presence-absence data of macrobenthic species
successful within the Schelde estuary
environmental variables, incorporated into regression models,
might improve long-term trend calculations, as they allow to
compensate for differences in local environmental variability.
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GENERAL CONCLUSIONSA large proportion of variance is in 10m -
100 m unpredictable patchiness and (biologically induced?)
year-to-year variation
Emphasis of monitoring of impacts should be on long-term (>
3yr) average populations, and should be related to long-term
changes in environment
There is a gap in the monitoring scheme at scales between 1m and
~200 m, which could be important to cover
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Thank youData obtained in co-operation with RIKZ,the National
Institute for Coastal and Marine Management (The Netherlands)
