1. Monitoring and modeling of estuarine benthic macrofauna and their relevance to resource management problems 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 Heip Netherlands Institute of Ecology (NIOO) t.ysebaert@nioo.knaw.nl The Colour of Ocean Data - The Palais des Congrès, Brussels, Belgium, 25-27 November 2002

2. OUTLINE Introduction: estuarine management and the problem of scaleIntroduction: estuarine management and the problem of scale Benthic monitoring programmesBenthic monitoring programmes –Predictive modeling –Spatio-temporal dynamics –Trend calculations General conclusionsGeneral conclusions

3. 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 SmallLarge SCHELDE ESTUARY - large-scale dredging operations - habitat loss - water quality - fisheries INTRODUCTION

4. Benthic monitoring programmes Benthic organisms: suitable indicators for changes in environmental qualityBenthic organisms: suitable indicators for changes in environmental quality Dutch Delta area (SW Netherlands): long tradition in monitoring of estuarine benthic macrofaunaDutch Delta area (SW Netherlands): long tradition in monitoring of estuarine benthic macrofauna designed to detect long-term trends in large parts of different systems (e.g. Grevelingen)designed 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 calculationsExplore relationships between biota and environmental variables to improve prediction and trend calculations

5. 0 10 km

6. SCHELDE ESTUARY Large data set available (>5000 samples) Different sampling designs (stratified random, fixed stations) Environmental variables (model derived)

7. Predictive modeling Logistic regression: model probability of occurrence of species as a function of environmental variables Ysebaert et al. 2002, MEPS

8. Macoma balthica: comparison pred./obs. Observed presences Predicted presences Ysebaert et al. 2002, MEPS

9. 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: conclusions

10. 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 approach Analysis of spatio-temporal variability of abundance and biomass Analysis of dependence on environmental factors

11. 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 dynamics

12. R²0.41 Mud0.37 *** Median Chl a Height0.53 *** Slope Salinity Flood0.33 *** Ebb-0.16 ° Mud Chl a0.15 ° Height-0.16 ° Salinity0.21 * Variation between strong and weak recruitment years large unsynchronized variation at small (station) scale Spatial variation at station (100 m) scale, depending on height, current, mud content Y Y*R Y*T(R) Y*S(T R) R T(R) S(T R) Res 0.00 0.02 0.04 0.06 0.08 0.10 0.12 *** Macoma balthica Macoma balthica: spatial and temporal variability Ysebaert et al., in press, MEPS

13. 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 (1m 2 ), but biological interactions? Spatio-temporal dynamics: conclusions

14. Application 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

15. Trends 1992-2001

17. SpeciesRegression [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 1992-2001

18. Trend 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.

19. GENERAL 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.

20. 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 CONCLUSIONS

21. Data obtained in co-operation with RIKZ, the National Institute for Coastal and Marine Management (The Netherlands) Thank you