Northern Adriatic sea ecosystem model: trophic network analysis, time simulation and spatial dynamics

Transcription

1 Northern Adriatic sea ecosystem model: trophic network analysis, time simulation and spatial dynamics Barausse, A., Artioli, Y., Palmeri, L., Duci, A., Mazzoldi, C. Environmental Systems Analysis Lab and Department of Biology - Italy

2 Goals of this presentation Illustrate the main results of a static, time simulation and spatial simulation model of Northern Adriatic sea trophic network, constructed using Ecopath with Ecosim software For time reasons, major focus on static and time simulation model

3 Modelled area and period Reference averaging period for the static model: (1990 s) Northern Adriatic Sea: km2, average depth 29 m, mean temperature 14,5 C Po river: enormous freshwater and nutrient input Eutrophicated; among the most productive areas of the Mediterranean Sea Most fished Italian sea

4 Static trophic network Modelling software: Ecopath Steady state: mass balance and energy balance written for each group: Production = sum of the different sources of mortality Assimilated consumption = respiration + production A linear system, whose solution gives the final network. Constraint: mass and energy must be balanced.

5 Balanced model Group name Seabirds Sharks Rays European hake Zoobenth. fish - hard bottom Zoobenth. fish - soft bottom Mackerel Horse mackerel Other small pelagics Anchovies Sardines Nectobenthic zooplankt. fish Omnivorous fish Benthic piscivorous fish Flatfishes Squids Benthic cephalopods Crustacea 1 Crustacea 2 Mantis shrimp Non commercial bivalves Commercial bivalves Gastropods Filter feeding invertebrates Echinoderms Polychaetes Jellyfish Zooplankton Pelagic bacteria Macroalgae and phaner. Phytoplankton Discard Detritus B (t/km²) 0,0106 0,04 0,012 0,1 0,52 0,73 1,33 1,28 0,439 9,49 6,28 0,61 0,16 0,18 0,135 0,12 0,17 5,5 1,2 0,085 24,496 0,981 7, ,847 26,989 1,02 3,5 4 39,917 13,1 0, ,93 P/B (y-1) 4,61 0,53 0, ,1 1,4 0,62 0,57 1,891 0,95 0,87 1,162 1,624 1,15 1,5 3,75 3,3 3,3 8,7 1,5 1,415 1,415 1,699 0,85 0,803 1,644 8, , Q/B (y-1) 69,34 4,4 4,137 4,12 6,408 6,306 6,506 5,941 8,296 11,8 8,709 6,442 15,04 3,9 6,975 25,74 6,6 17,785 51,181 4,56 6,35 6,35 9,51 3,804 2,514 14,27 25, EE 0 0,796 0,731 0,989 0,997 0,989 0,332 0,585 0,989 0,951 0,994 0,848 0,902 0,97 0,966 0,986 0,93 0,983 0,906 0,966 0,496 0,9 0,95 0,971 0,522 0,575 0,15 0,947 0,401 0,216 0,383 0,976 0,993 Main parameters: B biomass P/B production Q/B consumption EE percentage of production used in the system DC diet C catches

6 Trophic Level 33 groups: 2 detritus, 4 plankton, 1 macroalgae, 11 invertebrates, 14 fish, 1 birds 6 fleets: 5 italian gears (midwater, beam and botton trawling, hydraulic dredge, artisanal and other fisheries) and a croatian-slovenian fleet

7 Ecopath model - input data Currency: wet weight (t/km2) Many different data sources: Literature Field measures (e.g. biomasses from trawl and acoustic surveys) Laboratory measures (e.g. consumption rates) Other models (e.g. total mortality from VPA s) Empirical equations (e.g. for natural mortality for fishes) Regional and local database: landings Qualitative considerations (e.g. diets for some fishes) personal and expert knowledge (e.g. benthos diet) direct measurements (discard)

8 Results: flow and network analysis Total PP / Total Biomass (excl. detritus) = 14,1 y-1 Strongly productive ecosystem, production mainly in the pelagic compartment (94%) Total PP / Total Respiration = 4,424 (neglecting bacteria) Coll et al. (2007) found 2,73 for Central and Northern Adriatic and it seems to be lower even if accounting for different group and parameter choices Finn s Cycling Index (24,23 %) >> Predatory Cycling Index (1,04 %) Total ascendency = 25,2 % of development capacity Not developed network

9 Flow and network analysis Source \ TL Producer Detritus All flows II 19,8 8,8 11,2 III 13,5 21,6 18,4 IV 11 12,1 11,8 V 13,3 13,5 13,5 VI 15,5 16,1 16 VII 16,3 16,8 16,7 VIII 16,5 16,8 16,7 Transfer efficiencies (calc. as geometric mean for TL II-IV) From primary producers: 14,3% From detritus: 13,2% Total: 13,4% Aggregation in discrete trophic levels sensu Lindemann shows anomalies: transfer efficiencies of energy not decreasing from lower to higher trophic levels. Fishing impact has been suggested to be the cause (Coll et al., 2007)

10 Trophic structure: mixed trophic impact Sum of direct/indirect impacts of one group on another. Rectangle up = positive impact, rect. down = negative fish groups are not impacting the network, not even other fishes (exception: pelagics). fish groups are not impacted by fisheries FISH FISH { depleted upper trophic levels, fishing state (depressed upper food web, not reactive) FISHERIES {

11 Time simulation: Ecosim software Parameters: Vulnerabilities (one for each predator-prey couple and determining if predation is bottom-up or top-down controlled), changed during calibration Other parameters (max relative change in feeding time, feeding time adjustment rate, etc.) all kept to default recommended values, after a sensitivity analysis on the goodness of fit, before and after calibration (exception: mantis shrimp)

12 Ecosim data Simulation from 1996 to Forcing factors are: Fishing mortalities (from VPA, or combining landing and biomass timeseries) Effort timeseries if no F s available (combination of number of boats, GT, fishing days several sources) Chl-a and VGPM primary production timeseries, starting from 1998 and based on Seawifs data ( Behrenfeld and Falkowski, 1997) Calibration on 17 relative biomass timeseries from: Medits trawl surveys Acoustic surveys (pelagic groups) CPUE (mainly used to smooth trawl surveys data which appeared artificially rough)

13 Calibration SS (= goodness of fit measure) Sum of squared deviations of log biomasses from log predicted biomasses, scaled y the maximum likelihood estimate of the relative abundance scaling factor q in the equation y=qb (y=relative abundance, B=absolute abundance) All parameters default, effort forcing SS = 42,09 Adding F s SS = 34,4 (-18%) F s + all v s=1,3 SS = 27,1 (-35%) Adding PP forcing and automatic calibration on v s SS = 19,7 (-53%) Reducing all v s > 10 to v = 10 (final fit) SS = 20,1 (-52%)

14 Calibration Some good fits. Final fit is not sensitive to changes in v s untouched during calibration. Mantis shrimp biomass Flatfish biomass

15 Calibration Some bad fits (especially with groups with strong oscillations and whose reproduction is connected with environmental variability not accounted by the model, like SST and Po river) Sardine biomass Squid biomass

16 Simulated scenarios Model run for a further 15 years ( ). Two scenarios: keeping effort and F s from 2006 unchanged fishing reduced from 5 to 4 days per week (management approach considered feasible by local fishermen). This means reducing effort and F s by 20% Looking at the results from 2021: Biomass of fish and commercial invertebrates, with respect to 2006, are Constant in the unchanged scenario (increase is 2% on average) Always increasing more and by 10% on average in the 4/5 scenario Landings increase on average with respect to 2006 in both scenarios Slightly higher increase in the 4/5 scenario (3% vs. 2%) In the 4/5 scenario only croatian-slovenian fleet catches less than in 2006, but better (8% difference) than the other scenario Work less to fish more?

17 Role of primary production Environmental variability (PP, which seems to be decreasing in Northern Adriatic sea, but not only) can be important. E.g. If PP is not kept constant but lowered of 5% during the 15 years in the 4/5 scenario, landings for 4 fleets out of 6 will decrease with respect to PP sustains the fisheries.

18 Dealing with uncertainty Some, but not all, groups (flatfishes, rays, omnivorous fish, small pelagics) are sensitive in the long term to parameters that do not change the fit in the short term. Flatfishes Time adjustment factor = 0 Time adjustment factor = 0,2 Calibration Simulated scenario Is calibration period too short to make predictions? Currently testing if those groups can bias results.

19 Spatial simulation: Ecospace software 27x29 spatial grid. The model is replicated over each cell and then simulated. Movement rates of groups can be modified by habitat (6 here) preference and by the presence of food and predators. Movement rates are the main calibration parameters. PP map Habitat map N Primary production maps and circulation field can be imported. Fleets fish where it is more convenient (low sailing costs, more fish): gravity model. Area closed to fishing can be represented (MPA s, territorial waters).

20 Ecospace model The fits on Medits data still to be improved (a lot of free parameters ) but results make sense Sardines Benthic cephalophods Po river inflow On smaller scales results can be dependent on the choice of parameters not influencing the overall fit. E.g. testing the influence of Tegnue MPA (1 cell) on surrounding waters. Doubling base movement rate for Zoobenthivorous fish hard bottom: Btegnue = - 55 % Bsurrounding cells = - 75 % Overall SS = - 0,7 %

21 Conclusions Nothern Adriatic Sea ecosystem is strongly productive and not developed (sensu Odum), but its health is difficult to quantify: stress is due to BOTH natural (shallowness, river inputs) and anthropogenic causes (fisheries and eutrophication) Upper trophic network appears to be depressed Exploitation seems to be higher than the optimal one, both for fish and fishermen Environmental variability and primary production play a key role in Nothern Adriatic dynamics. Eutrophication can have also a positive role because it sustains the operating fisheries In both time and space simulations, parameters not influencing the fit can become important in the long term and on small scales. Every prediction should be made taking into account this issue

22 This work was developed under the EU-sponsored INCOFISH project (contract INCO ). Thank you for your attention!

23 Results flow and network analysis Source \ TL Producer Detritus All flows II 19,8 8,8 11,2 III 13,5 21,6 18,4 IV 11 12,1 11,8 V 13,3 13,5 13,5 VI 15,5 16,1 16 VII 16,3 16,8 16,7 VIII 16,5 16,8 16,7 Transfer efficiencies (calc. as geometric mean for TL II-IV) From primary producers: 14,3% From detritus: 13,2% Total: 13,4% Importance of microbial loop is comparable to grazing (66% of fluxes originates from detritus): maturity? No, probably shallowness and Po river input Phytoplankton chain is also more efficient: limiting, optimized factor. Contradiction with high productivity can be explained: phytoplankton blooms are so intense that population crashes before it is grazed (low predation mortality is a confirmation)