Monitoring and modeling of Monitoring and modeling of estuarine benthic macrofauna and estuarine benthic macrofauna and

their relevance to resource their relevance to resource management problemsmanagement problems

 Tom Ysebaert, Peter Herman, Herman Hummel, Bart Schaub, Wil Sistermans & Carlo Heip

Netherlands Institute of Ecology (NIOO)

t.ysebaert@nioo.knaw.nlThe Colour of Ocean Data - The Palais des Congrès, Brussels, Belgium, 25-27 November 2002

OUTLINEOUTLINE

• Introduction: estuarine management and Introduction: estuarine management and the problem of scalethe problem of scale

• Benthic monitoring programmesBenthic monitoring programmes– Predictive modelingPredictive modeling– Spatio-temporal dynamicsSpatio-temporal dynamics– Trend calculationsTrend calculations

• General conclusionsGeneral conclusions

FIELD STUDIES - EXPERIMENTS

ENVIRONMENTAL PROBLEMS

SCALE

TIDAL FLAT

+ multidisciplinary research

+ detailed process studies

+ food web and stable isotope studies

+ sediment processes

LINKS• monitoring• integrative studies• time-series data• modeling

Small Large

SCHELDE ESTUARY

- large-scale dredging operations

- habitat loss

- water quality

- fisheries

INTRODUCTIONINTRODUCTION

Benthic monitoring programmesBenthic monitoring programmes

• Benthic organisms: suitable indicators for Benthic organisms: suitable indicators for changes in environmental qualitychanges in environmental quality

• Dutch Delta area (SW Netherlands): long Dutch Delta area (SW Netherlands): long tradition in monitoring of estuarine benthic tradition in monitoring of estuarine benthic macrofaunamacrofauna

• designed to detect long-term trends in large designed to detect long-term trends in large parts of different systems (e.g. Grevelingen)parts of different systems (e.g. Grevelingen)

• Explore relationships between biota and Explore relationships between biota and environmental variables to improve environmental variables to improve prediction and trend calculationsprediction and trend calculations

0 10 km

SCHELDE ESTUARYSCHELDE ESTUARY

• Large data set available (>5000 samples)• Different sampling designs (stratified

random, fixed stations)• Environmental variables (model derived)

Predictive modelingPredictive modeling

Logistic regression: mLogistic regression: model probability of occurrence odel probability of occurrence of species as a function of environmental variablesof species as a function of environmental variables

Ysebaert et al. 2002, MEPS

Macoma balthicaMacoma balthica: comparison pred./obs.: comparison pred./obs.

Observed presences

Predicted presences

Ysebaert et al. 2002, MEPS

• for 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

Predictive modeling: conclusionsPredictive modeling: conclusions

• Time-averaged approach - no temporal dynamics• Extrapolation 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 approachLimitations of the approach

Analysis of spatio-temporal variability of abundance and biomass

Analysis of dependence on environmental factors

• 11 transects in 3 salinity zones, 2-4 stations per transect

• 15 replicates per station

• sampled twice yearly 1994-2000

• height, mud content, chl a monitored

• Fit hierarchical Anova model to observations (variance components)

• Regression on environmental variables

Spatio-temporal dynamicsSpatio-temporal dynamics

R² 0.41

Mud 0.37 ***MedianChl aHeight 0.53 ***SlopeSalinityFlood 0.33 ***Ebb -0.16 °

MudChl a 0.15 °Height -0.16 °Salinity 0.21 *

Variation between strong and weak recruitment yearslarge unsynchronized variation at small (station) scale

Spatial variation at station (100 m) scale, depending on height, current, mud content

YY*R

Y*T(R)Y*S(T R)

RT(R)

S(T R)Res

0.00

0.02

0.04

0.06

0.08

0.10

0.12 ***

***

***

Macoma balthica

Macoma balthicaMacoma balthica: : spatial and spatial and temporal variabilitytemporal variability

Ysebaert et al., in press, MEPS

• In general fair proportion of variance explained by station-averaged environmental variables

• Temporal variation in environmental variables poor explanators

• Temporal variation synchronized over estuary or region for bivalves (recruitment) but seldom for other species

• Largest 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: conclusionsSpatio-temporal dynamics: conclusions

Application to trend calculationsApplication to trend calculations

• Use information on the environment in trend calculations

• BIOMON 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

Heteromastus filiformis

0

1

2

3

4

5

6

7

8

1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001

coun

t

trend [year]

trend [year+env]

Trends 1992-2001Trends 1992-2001

Trends 1992-2001Trends 1992-2001

Macoma balthica

0

0.51

1.52

2.5

33.5

4

1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001

coun

t

trend [year]

trend [year+env]

Species Regression [year]

Regresssion[year + env]

Heteromastus filiformis - -

Macoma balthica - =

Bathyporeia pilosa + =

Pygospio elegans + +

Hydrobia ulvae = -

Aphelochaeta marioni + +

Nephtys cirrosa - -

Nereis diversicolor - =

Arenicola marina = =

Corophium volutator + +

Cerastoderma edule - -

Trends 1992-2001Trends 1992-2001

Trend calculations: conclusionsTrend calculations: conclusions

• For 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.

GENERAL CONCLUSIONSGENERAL CONCLUSIONS

• The 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.

• A 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

GENERAL CONCLUSIONSGENERAL CONCLUSIONS

Data obtained in co-operation with RIKZ,the National Institute for Coastal and

Marine Management (The Netherlands)

Thank youThank you