All an ecologist wants to know, but never can find Peter M.J. Herman Netherlands Institute of Ecology Yerseke

Total N vs. Total P Anorganic N vs. Anorganic P What makes us jealous ? Large datasets Reliably measured data Covering most of the ocean Far-reaching interpretations

System primary production (gC.m -2.y -1 ) System-averaged macrofauna biomass g AFDW m B= P r 2 =0.77 GR OS VM WS B1 B2 ED EW CB SFB LIS LY COL YT BF Cross-system comparisons of benthic biomass and primary production in estuaries System-averaged benthic biomass relates to system- averaged primary production Possible implications for effects eutrophication Possible norm for biomass But: system coverage poor! Herman et al Adv.Ecol.Res

Depth (m) Respiration (gC.m -2.y ) SCOCMacroMeio Benthic data from shelf break Heip et al DSR II Omex project: benthic fauna and sediment biogeochemisty

(Estimated) SCOC (gC.m -2.y ) Biomass macrofauna (gAFDW.m -2 ) Shelf break data compared with shallow systems Shallow systems Estimated as 1/3 PP Consistent pattern over orders of magnitude of organic loading

What could be mined further ? §More data sets on benthic biomass, PP and sediment oxygen consumption §Breakdown of datasets: regionally, with water depth, with physical conditions, with nature of primary production etc.. §Breakdown of benthic biomass into different functional groups, even species. §Better resolution of variability behind the averages – what are determining factors for these

Sediment community oxygen consumption Henrik Andersson et al. submitted

Refining with PP-depth gradients

Derived: rates of pelagic oxygen consumption with depth + relative role of water column / sediment in mineralisation + estimate of benthic denitrification Corrected for lateral production gradient Uniformly productive ocean

What could be mined further? §Relation with macro/meiobenthic biomass, species composition and diversity Depth (m)Latitude Oxygen (ml/l) % Org. Carbon E.g. Levin & Gage (1998) Macrobenthic diversity as a function of depth, oxygen, latitude, carbon content of sediment

Danish monitoring: relation mussels – chl a Kaas et al. (1996) Bloom Decay Koseff et al., 1993 ? -> mixing rates?

Macrobenthos Westerschelde: depth & salinity Tom Ysebaert Peter Herman

Comparison other regional systems biomass (g AFDW.m -2 ) intertidal shallow subtidal deep subtidal channel WSOSGRVM Tom Ysebaert Peter Herman Grevelingen Oosterschelde Veerse Meer Westerschelde Distribution ~ *macro- vs. micro- vs. non-tidal *wave vs. current *transparancy *oxygen conditions

Functional guilds and depth distribution : Oosterschelde m m m > 8 m m m m > 8 m Biomass (g AFDW.m -2 ) Deposit feeders Biomass (g AFDW.m -2 ) Suspension feeders

Model for suspension feeder occurrence  CPC zz C K zt C                     mixing sinking production - consumption P P P Phytoplankton growth at depth z: -> food depletion suspension feeders depends on production, mixing, pelagic losses -> suspension feeders deeper as water gets more transparant

Some common denominators §Data sets must come from both similar and dissimilar systems §Comparability of methods is prerequisite §Not valuable without physical and/or chemical metadata §Taxonomy problems when analysed at species level ; autecology often lacking when analysed at functional group level §Models needed to make data meaningful

What would we want? §Easily accessible, highly resolved ecological data §Georeferenced §Consistent taxonomy §Auto-ecological information §Well-documented methods §Physical and chemical data (depth, light, chlorophyll, nutrients, sediment composition, physical stress,…) linked §Spatiotemporal variation represented

What could we do with it? §Inter-system comparison of limiting factors on species / functional guilds / trophic groups §Deriving norms and indicators adapted to local circumstances §Detecting general temporal trends ~ global change §Better exploitation of remotely sensed variables §Testing ecological hypotheses §Detecting patterns that suggest experimental approach or detailed research

What would we need for it ? §Linking of existing databases from national / regional monitoring programmes §Quality control on data sets §Exchange formats §Resolution of the taxonomic mess §Better linking between ecological, physical and biogeochemical datasets