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THE USE OF ECO-EXERGY IN OCEANOLOGY: APPLICATION TO POSIDONIA OCEANICA MEADOWS Dorothée Pête, Branko Velimirov & Sylvie Gobert PhD student, University.

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Presentation on theme: "THE USE OF ECO-EXERGY IN OCEANOLOGY: APPLICATION TO POSIDONIA OCEANICA MEADOWS Dorothée Pête, Branko Velimirov & Sylvie Gobert PhD student, University."— Presentation transcript:

1 THE USE OF ECO-EXERGY IN OCEANOLOGY: APPLICATION TO POSIDONIA OCEANICA MEADOWS Dorothée Pête, Branko Velimirov & Sylvie Gobert PhD student, University of Liège

2 Introduction  Exergy = « Useful work a system can perform when brought into equilibrium with its environment » (Szargut et al., 1988 ) = distance from thermodynamic equlibrium  Applying this theory to understand ecosystems and to detect environmental perturbations What a mystification! It’s metaphysics! Are you crazy?

3 Thermodynamic theory for Ecosystems (S. E. Jørgensen) Thermodynamic equilibrium = Inorganic soup

4 Take energy in: Matter Storage in biochemical constituents Thermodynamic theory for Ecosystems (S. E. Jørgensen)  Trends to keep away from thermodynamic equilibrium when becoming more complex (Prigogine, 1980) Loose energy: Matter Maintenance Trophic webs

5 Exergy index or eco-exergy: a practical way to apply the exergy theory to ecosystems Ex (kJ/volume or surface) = distance between the system and the thermodynamic equilibrium   when the ecosystem is moving away from thermodynamic equilibrium    when the ecosystem is getting closer from its climax, its ecological optimum = « work capacity possessed by organisms and ecological networks of organisms due to biomass and information embodied in their genome and the amino acid sequence of proteins » (Jørgensen et al., 2010)

6 β i : -β-factor of the ith organism -defined on a genetic basis: enzymes and proteins, defined by DNA, are driving life processes (Jørgensen et al., 2005) - kind of approximation of organisms complexity - higher for « specialised » organisms - expressed relatively to detritus (no genetic information, only free energy of the organic matter, ≅ 18,7 kJ.g -1 ) - ex: β = 1 for detritus, 8,5 for bacteria and 133 for nematods Exergy index or eco-exergy Formula: C i : Biomass of the ith organism

7 Specific exergy (Structural exergy, Silow, 1998) Ex sp = expresses the presence of more specialised organisms in the ecosystem  Ex = informations on the capacity of the ecosystem to develop and get more complex  Ex sp = information on the « quality » of the biomass

8 Use of Ex and Ex sp in Oceanology 2 main uses: - Modeling of ecosystems development (plankton dynamics) - Indicators of environmental quality

9 Use of Ex and Ex sp in Oceanology 2 main uses: - Modeling of ecosystems development (e.g. plankton dynamics) - Indicators of environmental quality Interest as indicators: - Complete part of an ecosystem  Global  More sensitive ? - Reflect ecosystem development and complexity.

10 Can we use them to detect a perturbation in a marine ecosystem early? Exportation of vegetal biomass Production of vegetal biomass Production of animal biomass Biodiversity hot spot Basis for food webs Spawning and breeding ground Hydrodynamic protection Stabilization of the bottom Trapping of suspended particules Focus ecosystem = Posidonia oceanica meadow - What? Posidonia oceanica = endemic seagrass of the Mediterranean Sea

11 Focus ecosystem = Posidonia oceanica meadow - Why? P. oceanica = descriptor of the quality of the Mediterranean coastal zone University of Liege: - Tradition of marine research (Biology, chemistry, physics, modeling) - Research station in Calvi Bay, Corsica: STARESO (STAtion de REcherche Sous-marine et Océanographique) - Years of experience in the Mediterranean Sea with a special focus on the Posidonia oceanica ecosystem Can we use them to detect a perturbation in a marine ecosystem early? In Calvi Bay, pristine and perturbated meadows are well known.  Good zone to test the use of Ex and Ex sp

12 Can we use them to detect a perturbation in an ecosystem early?  Posidonia oceanica meadow has a low turnover.  Sediment = final container of pollutants (sedimentation)  Microbenthic loop: organic matter (OM), microphytobenthos (microscopic algae), meiofauna (microscopic animals), bacteria  Important sub-system in P. oceanica meadows  High turnover

13 Goals  Clarification and validation of the use of Ex and Ex sp as descriptors of anthropogenic perturbations in P. oceanica beds  Effects of nutrients and organic matter inputs which are the main perturbations in the Mediterranean coastal zone  New method to measure and detect perturbations affecting P. oceanica meadows  Precise, early and global method

14 Sampling What? - sediment cores (vertical profile) - Biomass determination for every component of the microbenthic loop. - sediment and environment parameters

15 How to validate an index and a method? Spatial heterogeneity at small scale Comparison between a pristine and a perturbated site In situ experiments

16 Sampling sites 10 m, 22 m Small scales Alteration Shading = Reference site Fish farm 22 m  Seasonal variations Perturbated site Adapted from Vermeulen et al., 2011 From STARESO SA

17 Spatial heterogeneity STARESO 125 cm 25 cm  3 grids  March, June, November 08, March 09  12 nodes/grid (uniform random)  3 cores/node

18 Results : DIVA analysis Biomass of bacteria 0-1 cm1-2 cm 5-10 cm cm Heterogeneity and « hot spots » of biomass

19 Spatial heterogeneity : Estimation of Ex & Ex sp Median ± range For 10 cm For 1 cm Important heterogeneity especially for the 1 st cm of the sediment  Most dynamic slice, exchanges with the water column  BUT probably the most affected by environmental perturbations The less heterogenous slice is the 5-10 cm  Less dynamic slice and no exchanges with the water column  Anoxic conditions for most samples  BUT « old » sediment  Choose the 5-10 cm to prevent heterogeneity effects

20 Spatial heterogeneity : Ex & Ex sp 5-10 cm Important heterogeneity in spite of the choice No real seasonal variability  Seems stable along the year Median ± range

21 STARESO vs. Fish farm: 5-10 cm Awaited results for November 2008 only  Not an estimation… Median ± range No difference in Ex sp  No difference in biomass « quality » between sites This estimation is not able to catch the difference in EX between sites. In November 2008, Ex STARESO>Ex Fish farm  STARESO is closer from the ecosystem climax than the fish farm. No difference in Ex sp.  No difference in the « complexity » of organisms living in the ecosystem.  The ecosystem is able to adapt itself to this perturbation (Silow, 1998).

22 In situ experiments: Sediment alteration  Site: STARESO, 10 m depth.  Duration: 3 months (from end of May to end of August 2009).  Alteration (mimic pollution by fish farms or dredging): ml of sediment were added once a week on 21 marked points in a 3x3 m frame.

23 In situ experiments: Shading  Shading (  in turbidity because of  in nutrients concentration, fish farms, sewages, land farms): - 3 nets (3x1 m, mesh size: 0,5 mm 2 ) about 50 cm from the canopy. - Light extinction: 52 ± 1,6 % - Cleaning once a week to avoid fouling

24 In situ experiment : Ex 5-10 cm No difference between periods  Estimation?  Too short experiment? Median ± range

25 In situ experiment : Ex sp 5-10 cm Median ± range No real difference between periods  Estimation?  Too short experiment?

26 Conclusions  Spatial variability Important heterogeneity BUT less important in the cm sediment depth zone  Choice of the 5-10 cm sediment horizon to compare samples even if it is maybe less precise  Fish farm vs. STARESO Ex STARESO>Ex Fish farm in November 2008 for the cm horizon  Ex seems able to dicriminate both sites  In situ experiment No difference along the experiment.  Too short experiment to see an impact…

27 Use of Ex and Ex sp as a tool to detect perturbations in the Mediterranean coastal zone is not easy to validate in this part of P. oceanica ecosystem. Important to link the results with environmental parameters to understant why it works or not. Work in progress… Conclusions

28 Thank you! Tanks to Loïc Michel, Renzo Biondo, Gilles Lepoint, Sylvie Gobert, Branko Velimirov, people of the STARESO, students, cleaning team, spreading team, repairing team,…


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