Northern Adriatic sea ecosystem model: trophic network...

University of Padova

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

University of Padova - Italy

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


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



Modelled area and period

Northern Adriatic Sea:

• 32000 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

Reference averaging period for the static model: 1996-1998 (1990’s)



Static trophic network

www.ecopath.org

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.



Balanced model

Main parameters:• B biomass• P/B production• Q/B consumption• EE percentage of

production used in the system

• DC diet• C catches

Group name B (t/km²) P/B (y-1) Q/B (y-1) EESeabirds 0,0106 4,61 69,34 0Sharks 0,04 0,53 4,4 0,796Rays 0,012 0,724 4,137 0,731European hake 0,1 1 4,12 0,989Zoobenth. fish - hard bottom 0,52 1,1 6,408 0,997Zoobenth. fish - soft bottom 0,73 1,4 6,306 0,989Mackerel 1,33 0,62 6,506 0,332Horse mackerel 1,28 0,57 5,941 0,585Other small pelagics 0,439 1,891 8,296 0,989Anchovies 9,49 0,95 11,8 0,951Sardines 6,28 0,87 8,709 0,994Nectobenthic zooplankt. fish 0,61 1,162 6,442 0,848Omnivorous fish 0,16 1,624 15,04 0,902Benthic piscivorous fish 0,18 1,15 3,9 0,97Flatfishes 0,135 1,5 6,975 0,966Squids 0,12 3,75 25,74 0,986Benthic cephalopods 0,17 3,3 6,6 0,93Crustacea 1 5,5 3,3 17,785 0,983Crustacea 2 1,2 8,7 51,181 0,906Mantis shrimp 0,085 1,5 4,56 0,966Non commercial bivalves 24,496 1,415 6,35 0,496Commercial bivalves 0,981 1,415 6,35 0,9Gastropods 7,786 1,699 9,51 0,95Filter feeding invertebrates 8 0,85 3,804 0,971Echinoderms 8,847 0,803 2,514 0,522Polychaetes 26,989 1,644 14,27 0,575Jellyfish 1,02 8,433 25,3 0,15Zooplankton 3,5 66 152 0,947Pelagic bacteria 4 133 750 0,401Macroalgae and phaner. 39,917 1,699 - 0,216Phytoplankton 13,1 175 - 0,383Discard 0,097 - - 0,976Detritus 361,93 - - 0,993



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

Trophic Level



Ecopath model - input dataCurrency: 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)



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



Flow and network analysis

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)

Source \ TL II III IV V VI VII VIIIProducer 19,8 13,5 11 13,3 15,5 16,3 16,5Detritus 8,8 21,6 12,1 13,5 16,1 16,8 16,8All flows 11,2 18,4 11,8 13,5 16 16,7 16,7

Transfer efficiencies (calc. as geometric mean for TL II-IV) From primary producers: 14,3% From detritus: 13,2% Total: 13,4%



Trophic structure: mixed trophic impact

• fish groups are not impacting the network, not even other fishes (exception: pelagics).

{FISH

{FISHERIES

FISH

• fish groups are not impacted by fisheries

depleted upper trophic levels, fishing state (depressed upper food web, not reactive)

Sum of direct/indirect impacts of one group on another. Rectangle up = positive impact, rect. down = negative



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)



Ecosim dataSimulation from 1996 to 2006.

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 (http://web.science.oregonstate.edu/ocean.productivity; 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)



CalibrationSS (= 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%)



CalibrationSome good fits. Final fit is not sensitive to changes in v’s untouched during calibration.

Mantis shrimp biomass Flatfish biomass



CalibrationSome 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



Simulated scenariosModel run for a further 15 years (2007-2021). 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?



Role of primary productionEnvironmental 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 2006. PP sustains the fisheries.



Dealing with uncertainty

Is calibration period too short to make predictions? Currently testing if those groups can bias results.

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.

Time adjustment factor = 0

Calibration Simulated scenario

Flatfishes

Time adjustment factor = 0,2



Spatial simulation: Ecospace software27x29 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.

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).

N

Habitat map

PP map



Ecospace modelThe fits on Medits data still to be improved (a lot of free parameters…) but results

make sense

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 %

SardinesBenthic cephalophods

Po river inflow



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



Thank you for your attention!

This work was developed under the EU-sponsored INCOFISH project (contract INCO-003739).



Results – flow and network analysis

•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)

Source \ TL II III IV V VI VII VIIIProducer 19,8 13,5 11 13,3 15,5 16,3 16,5Detritus 8,8 21,6 12,1 13,5 16,1 16,8 16,8All flows 11,2 18,4 11,8 13,5 16 16,7 16,7

Transfer efficiencies (calc. as geometric mean for TL II-IV) From primary producers: 14,3% From detritus: 13,2% Total: 13,4%

Northern Adriatic sea ecosystem model: trophic network...

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