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All an ecologist wants to know, but never can find

All an ecologist wants to know, but never can find. Peter M.J. Herman Netherlands Institute of Ecology Yerseke p.herman@nioo.knaw.nl. Total N vs. Total P. Anorganic N vs. Anorganic P. What makes us jealous ?. Large datasets Reliably measured data Covering most of the ocean

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All an ecologist wants to know, but never can find

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  1. All an ecologist wants to know, but never can find Peter M.J. Herman Netherlands Institute of Ecology Yerseke p.herman@nioo.knaw.nl

  2. 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

  3. Cross-system comparisons of benthic biomass and primary production in estuaries Herman et al. 1999 Adv.Ecol.Res 70 System-averaged benthic biomass relates to system-averaged primary production Possible implications for effects eutrophication Possible norm for biomass But: system coverage poor! 60 YT GR 50 OS 40 System-averaged macrofauna biomass g AFDW m-2 B2 30 VM EW 20 SFB CB B1 LIS BF LY B=-1.5 + 0.105 P 10 WS 2 r =0.77 ED COL 0 0 100 200 300 400 500 600 700 System primary production (gC.m-2.y-1)

  4. Benthic data from shelf break Omex project: benthic fauna and sediment biogeochemisty Heip et al. 2001 DSR II SCOC Macro Meio 14 ) -1 12 .y -2 10 8 6 Respiration (gC.m 4 2 0 0 2000 4000 Depth (m)

  5. Shelf break data compared with shallow systems 100 Shallow systems Estimated as 1/3 PP ) -2 10 Consistent pattern over orders of magnitude of organic loading Biomass macrofauna (gAFDW.m 1 0.1 1 10 100 1000 -1 -2 .y ) (Estimated) SCOC (gC.m

  6. 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

  7. Sediment community oxygen consumption Henrik Andersson et al. submitted

  8. Refining with PP-depth gradients

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

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

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

  12. Macrobenthos Westerschelde: depth & salinity Tom Ysebaert Peter Herman

  13. 140 WS OS GR VM 120 100 intertidal biomass (g AFDW.m-2) 80 shallow subtidal 60 deep subtidal 40 channel 20 0 Comparison other regional systems Distribution ~ * macro- vs. micro- vs. non-tidal * wave vs. current * transparancy * oxygen conditions Grevelingen Oosterschelde Veerse Meer Westerschelde Tom Ysebaert Peter Herman

  14. Functional guilds and depth distribution : Oosterschelde Biomass (g AFDW.m-2) Deposit feeders Biomass (g AFDW.m-2) Suspension feeders 0 1 2 3 4 0 20 40 60 -1 - 2 m -1 - 2 m 2 - 5 m 2 - 5 m 5 - 8 m 5 - 8 m > 8 m > 8 m

  15. æ ö ¶ ¶ ¶ ¶ C C ( ) ç ÷ = - w + - K C P C ç ÷ ¶ ¶ ¶ ¶ t z z z è ø Model for suspension feeder occurrence Phytoplankton growth at depth z: production - P consumption sinking mixing P -> food depletion suspension feeders depends on production, mixing, pelagic losses -> suspension feeders deeper as water gets more transparant P

  16. 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

  17. 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

  18. 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

  19. 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

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