1. INTO THE BIG GREENY-BROWN YONDER

2. INTO THE BIG, GREENY-BROWN YONDER Challenges Modelling individual to population parameterisation of the mesoscale Observations biological synoptic Analysis how should we describe patchiness?

3. PARAMETERISING INDIVIDUAL BEHAVIOUR Prey tracking - cross diffusion? Swarming - use ideas from statistical physics? Vertical migration - imposed population advection?

4. SWARMING At the level of individual, what causes it? social forces proximity arrayal forces matching speed and direction of neighbours environmental effects chemical gradients hydrography currents

6. Flierl et al. (1999), J.Theor.Biol.

7. INTO THE BIG, GREENY-BROWN YONDER Challenges Modelling individual to population parameterisation of the mesoscale Observations biological synoptic Analysis how should we describe patchiness?

9. “The sea surface variability of all properties shows a marked increase when the internal Rossby radius of deformation is resolved. However, there are indications that tracers and processes…vary on yet smaller scales…” “New production…increases with model resolution.”

10. INTO THE BIG, GREENY-BROWN YONDER Challenges Modelling individual to population parameterisation of the mesoscale Observations biological synoptic Analysis how should we describe patchiness?

11. LIMITATIONS ON BIOLOGICAL DATA Very little spatial data, especially for… zooplankton exacerbated by behaviour rates (e.g. growth rate, grazing etc) vital for deriving functional forms governing modelled processes community structure has the potential to significantly alter the dynamics of a non-linear system We have just enough data to show that all can display considerable spatial structure.

12. Holligan, 1978

13. EMERGING SAMPLING TECHNIQUES HPLC  FRRF  Hologrammetry   Video sampling  Multifrequency acoustics  Zoo. Rates C.C. Also in situ nutrient sampling being developed. All must still be used in conjunction with traditional techniques.

14. Prieur et al., 1993

15. HOLOCAM Up to 100litres Rate 0.1Hz Depth 100m Target speed<1m/s P.Hobson (Brunel) R.Lampitt (SOC)

16. INTO THE BIG, GREENY-BROWN YONDER Challenges Modelling individual to population parameterisation of the mesoscale Observations biological synoptic Analysis how should we describe patchiness?

20. ACCOUNTING FOR LACK OF SYNOPTICITY OMEGA Project J.T.Allen(SOC), M.Rixen (Liège) 3 techniques: Geostrophic relocation Use estimate of velocity field to relocate observations to common time. Iterative procedure. Interpolation Interpolate between two or more spatial surveys to create spatial coverage at a given time. Spatio-temporal correlation method As the 2 nd technique but weighted according time between observation and required common time.

21. Harvard Ocean Prediction System Srokosz et al., 1997; McGillicuddy et al., 1999; Popova, 2001 Includes: - open ocean regional model - 6-cpt ecosystem model - nitrate, phytoplankton, 2 zooplankton, detritus, ammonium - data assimilation - temperature, salinity, nitrate, chlorophyll, 2 zooplankton, physical forcing Resolution: - typically 2km for 200kmx200km domain Can be used… - …to model data post-cruise - …to predict field evolution on cruise -near real-time

22. Comparison with data Chl (mgC/m^3) Phytoplankton*1.6 (mmolN/m^3) Day 9 Day 15 Day 21 Day 24

23. AUTOSUB

24. INTO THE BIG, GREENY-BROWN YONDER Challenges Modelling individual to population parameterisation of the mesoscale Observations biological synoptic Analysis how should we describe patchiness?

25. Describing patchiness The manner in which we describe a phenomenon affects both our understanding of it and the way in which we can interrogate it. Stating the obvious: The manner of description must suit the question that is to be asked. Time to let spectra go. But what alternatives are there… wavelets, 3 point correlation functions, fractals…

26. Old theories of turbulence - big eddies begat little eddies - they do so the same everywhere But observation contradicts this. Reality is intermittent. True at all scales. Patchiness theories need to be revised in light of intermittency. Intermittent forcing? Seuront et al. (1999)

27. A QUICK GUIDE TO FRACTAL BEHAVIOUR Structure function: = Scaling: = (  /T)  (q)  (q) is the scaling exponent Monofractal:  (q) is linear Multifractal:  (q) is non-linear Universal multifractal:  (q)=qH-[C 1 /(  -1)](q  -q)  =1+  (2)

28. Pascual et al., 1995

29. Seuront et al., 1999

30. Colour sensor on aircraft June 2001 700km transect 5m resolution 5 decades of data

31. “We expect that the connection between pattern and process for multifractal variability in the plankton will develop along a similar path to spectral analysis. the initial uses of spectral analysis were purely descriptive. That use was followed by a connection of spectral analysis to phenomenological models and only later by a connection to mechanistic models.” Pascual et al., 1995

32. Toroczkai et al., 1998 dS B /dt = -fS B + cvS B 

33. Are distributions of biomass and production in the ocean controlled by the geometrical structure of the flow? How can the underlying geometrical structure be found? - “full” velocity field required - at what temporal and spatial scales? - how can it be found?