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

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Presentation on theme: "Status on jellyplankton modeling Stemmann, L Laboratory of Oceanography of Villefranche sur Mer."— Presentation transcript:

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

2 Content 1.Introduction 2.Population based modes 3.Individual based models 4.Functionnal type model 5.Bayesien approach 6.ECOSIM/ECOPATH models 7.Back to data

3 Introduction

4 1967-1993 PointB Zooplankton time series study on target species Introduction 3 Jellyfish sps. (Liriope tetraphylla, Solmundella bitentaculata, and Rhopalonema velatum.) 2 Siphonophore sps. (Abylopsis tetragona and Chelophyes appendiculata) 1 Ctenophore sp. (Pleurobrachia rhodopis) 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 Shift and reorganization of zooplankton community at the end of the ‘80s (1987 shift year) toward an oligotrophic system. « dry years »

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

6 Introduction

7 Population matrix model

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

13 Individual based model

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19 NPZD models but also the talk of Arthur yesterday

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

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

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

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

27 Zooplankton Datasets Results  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 Chaetognatha (log10(ind.m -3 +1)) Medusae + Siphonophores (log10(ind.m -3 +1)) Copepods (log10(ind.m - 3 +1))

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

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 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). Conclusion

31 Conclusions Conclusion  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)http://www.zooscan.com

33 Zooplankton Datasets Results Chaetognatha Siphonophora Medusae Decapoda

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

35 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. Material and Method Point B ASLO

36 500 SAMPLES Time Series TRAINING SET TEST SET Defining groups 1 2 Prediction by the model (Random Forest) Accuracy (confusion matrix) cross-validation Material and Methods Automatic recognition of copepods (ZOOSCAN) COPEPODS 96% recognition 18% contamination 3 (confusion matrix) PREDICTION >10 6 copepods were extracted and sized

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

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

41 Introduction

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