Status on jellyplankton modeling

1. Status on jellyplankton modeling Stemmann, L Laboratory of Oceanography of Villefranche sur Mer

2. Content • Introduction • Population based modes • Individual based models • Functionnal type model • Bayesien approach • ECOSIM/ECOPATH models • Back to data

3. Introduction

4. Introduction « dry years » Shift and reorganization of zooplankton community at the end of the ‘80s (1987 shift year) toward an oligotrophic system. 3 Jellyfish sps. (Liriope tetraphylla, Solmundella bitentaculata, and Rhopalonema velatum.) 2 Siphonophore sps. (Abylopsis tetragona and Chelophyes appendiculata) 1 Ctenophore sp. (Pleurobrachia rhodopis) 1967-1993 PointB Zooplankton time series study on target species 5 copepod sps (C. typicus, T. stylifera, Acartia clausi, Oithona spp., and Oncaea spp.) 3 chaetognath sps (S. enflata, S. setosa and S. minima) Molinero et al., 2005 and 2008

5. Introduction 1967-2003 PointB Zooplankton time series study What about the whole jellyfish, chaetognath and copepod populations? Are the authors’ predictions confirmed after 10 more years of observations? 2003 2003 2003 Molinero et al., 2008

6. Introduction

7. Population matrix model

8. Population matrix model

9. Population matrix model

10. Population matrix model

11. Population matrix model

12. Population matrix model And previous presentation by Tamara Shiganova and Paul Nival

13. Individual based model

14. Individual based model

15. Individual based model

16. Individual based model

17. Individual based model

18. Individual based model

19. NPZD models but also the talk of Arthur yesterday

20. NPZD models Population dynamics model (number and weight) NPZD model

21. Probabilistic approach Bayesien : See J. Ruiz yesterday (temp and food) Predicting the distribution of the scyphomedusa Chrysaora quinquecirrha in Chesapeake Bay M. B. Decker1,*, C. W. Brown2, R. R. Hood3, J. E. Purcell4, T. F. Gross5, J. C. Matanoski3,6, R. O. Bannon7, E. M. Setzler-Hamilton8,† MEPS 329:99-113(2007)  -  doi: 10.3354/meps329099 (temp and salinity)

22. Ecopath with Ecosim

23. Ecopath with Ecosim

24. Ecopath with Ecosim

25. The talk will not focus on large ‘dangerous’ Scyphozoa but rather on smaller Hydrozoa and their relation with their changing environnement. BACK to data Pelagia noctiluca

26. Results Zooplankton Datasets Chaetognatha(log10(ind.m-3+1)) Medusae + Siphonophores(log10(ind. m-3+1)) Copepods(log10(ind. m- 3+1)) • The different groups do not share the same seasonal cycle. Chaetognaths and jellyfish may not be in competition for the same ressource.

27. Results Zooplankton Datasets Chaetognatha(log10(ind.m-3+1)) Medusae + Siphonophores(log10(ind. m-3+1)) Copepods(log10(ind. m- 3+1)) • All groups were abundant during the « dry years » • All groups decreased in concentrations. There is a change in the phenology mainly of copepods (lack of second peak). • Since chaetognaths are more abundant in summer/autumn and specific predators of copepods, their decrease may have been greater than jellyfish

28. Results ~synchronised long-term changes in environment and zooplankton 1st PC Zooplankton datasets (40%): S.cops., L.cops., Chaeto., Jelly., Dol.,Dec. 1974-1980: 1980-1990: 1990-1998 1998-2003 copepods and Jellies All groups (phyto. 2 peaks) All groups All groups (phyto. 2 peaks) Relation with hydro-climate Dry years Dry years 1st PC Environmental datasets (33%): 20,50m T. and S., at.P, Ek.D., Rain, Sun 1974-1980: 1980-1990: 1990-1998 1998-2003 Salinity and Temp. Salinity and Mean Temp .autumn Correlation between the winter NAO and 1st PC of environnemental conditions (r=0.44 / p=0.02) Salinity and Temp Salinity

29. Conclusion What is missing ? Ecophysiology of several species and mainly adult phase that cannot be cultivated in the lab. - what is the impact on mesozooplankton ? - what is the impact on recycling elements ? - are temp. and food the main forcings ? - what are their main predators ? Spatial distribution in the open sea What could help ? IBM coupled to Lagrangian models

30. Conclusion Conclusions • Previous observations on target species (Molinero et al., 2008) are confirmed at the level of some groups’ populations (copepods and chaetognaths) but not for others (jellyfish). Trends in target jellyfish species may not be representing the whole community trend. • Using the additional data, we observe quasi-decadal oscillations (8-9 years) in the plankton community rather than a regime shift in 1987 as previously proposed (Molinero et al., 2008).

31. Conclusion Conclusions • Zooplankton changes are closely linked to hydroclimate, and a bottom-up control is suggested (Garcia-Comas, session 64). The winter convection (low T and high S) may be the principal factor to initiate the bloom and zooplankton development in spring. Autumn bloom may be function of salinity stratification. • Imaging techniques (here the ZooScan) can be used to monitor changes in the plankton ecosystem. Automatic recognition is reliable only for a few taxonomic groups but deeper taxonomic details can be easily obtained by semi-automatic analysis. The produced datasets are reliable and homogenous (required for long term study).

32. Thanks to : SESAME project, EC Contract No GOCE-036949, funded by the European Commission's Sixth Framework Programme under the priority 'Sustainable Development, Global Change and Ecosystems'. CTD, plankton and coulter data are part of the RADE-HYDRO and SOMLIT programs carried out by the Observatory of Oceanography of Villefranche/mer and the Laboratory of Villefranche sur Mer. Isabelle Palazzoli for providing the meteo data and to Corinne Desnos for the help with the scanning on the ZOOSCAN (http://www.zooscan.com)

33. Results Zooplankton Datasets Chaetognatha Medusae Siphonophora Decapoda

34. Results Environmental Datasets Water Temp.(20 and 50 m.) Salinity (20 and 50 m.) Ekman depth Rainfall

35. Material and Method Sampling Site Point B (Villefranche Bay, Ligurian Sea) from 1966 to 2003 Daily sampling with the JB net (330 μm mesh) by Vertical hauls (70-0m). Samples are pooled in weekly jars. ASLO Point B

36. Material and Methods 1 2 3 Automatic recognition of copepods (ZOOSCAN) Defining groups TRAINING SET cross-validation (confusion matrix) Prediction by the model (Random Forest) Accuracy (confusion matrix) 500 SAMPLES Time Series COPEPODS 96% recognition 18% contamination TEST SET >106 copepods were extracted and sized PREDICTION

37. Material and Methods Semi-Automatic recognition of other groups (ZOOSCAN) Medusae Chaetognatha Automatic recognised Objects (8000) Siphonophora (only 1st bell and no colonies ind.) Doliolida Decapoda Franck Prejger: Gelatinous zooplankton final sorting

38. Material and Methods Dataset Validation • The automatic counting is similar to the visual counting for copepods.

39. Material and Methods Dataset Validation • The long term evolution of the three groups are similar as counted independently on the ZOOSCAN or by a taxonomist many years before (S. Dallot, Oliver Beck).

40. Results Point B time series (Medusae + Siphonophores) • Chaetognaths, medusae and copepods have quite similar long term changes. • Total jellyfish abundance deacreases after 1990 and increases slightly after 1994.

41. Introduction

42. Introduction