Assessing the ecosystem impacts of fishing in the South Catalan Sea by developing dynamic simulations on fishing effort and target species Marta Coll, Isabel Palomera, Sergi Tudela and Francesc Sardà Institut de Ciències del Mar (ICM-CSIC) Barcelona, Spain

1. The South Catalan Sea ecosystem model (SCMEE 2004) 2. The SCS model calibrated with time series of data 3. Temporal dynamic simulations of 5 fishing scenarios

1. The South Catalan Sea ecosystem model Mass balance model of trophic interactions Ecopath with Ecosim software version functional groups from primary producers to main top predators Includes trawling, purse seining, long lining and troll bait fisheries 6 fishing harbours: Tarragona to Les Cases d’Alcanar Area modelled of 4300 km m depth: continental shelf and upper slope Represents the ecosystem in 1994 Coll et al., accepted to Journal of Marine Systems

Ecopath mass balance modelling Basic parameters required per compartment (i): B: Biomass P/B: Production per unit of biomass Q/B: Consumption per unit of biomass EE: Ecotrophic efficiency (production used within the ecosystem) 1-EE: Other mortality DCij: Fraction of (i) in the diet of (j) Y: Catches; E: Net migration; BA: Biomass accumulation Expressed on an annual basis per unit surface area and WW (t·km -2 ·yr -1 ) Pauly et al., ICES J. Mar. Sci., 57: ; Christensen and Walters Ecol. Model., 172(2-4): The South Catalan Sea model Production = Predation + Yield + Net Migration + Biomass accumulation + Other mortality

Overview of trophic flows and ecosystem structure 1. The South Catalan Sea model Pelagic Demersal Benthic Large pelagics Bonito Zooplankton Phytoplankton Discards By catch Detritus Suprabenthos Macro zooplankton Various small pelagics Squids Fin whale Dolphins Anchovy Jellyfish Sardine Marine turtles Seabirds Adult hake Benthopelagic fishes Juvenile hake Audouin gull Shrimps Blue whiting Demersal fishes(2) Poor cod Demersal fishes(3) Demersal sharks Anglerfish Conger eel Demersal fishes(1) Flatfishes Mullets Octopuses Crabs Norway lobster Polychaetes Benthic invertebrates TL V IV III II I Trawl Purse seine Troll bait Long line Horse mackerel Mackerel

Wide and intense fishing impact Target species, predators and by-catch Target species, preys and by- catch Low TLc Low OI High PPR 1. The South Catalan Sea model Impact of fishing activities

1. The South Catalan Sea model The mass balance modelling is a good tool to summarize and integrate the available information in a coherent way, identifying critical gaps and describing the ecosystem structure and functioning: * Quantification of trophic flows, globally or by components * Estimation of TLs, OI, Mortalities: M2, M0, F * Indices related with network and information analysis * Quantification of fishing impact through the MTI, PPR, TLc, GE… The starting point from where to develop dynamic simulations with the temporal dynamic module Ecosim: * Assessing the impact of fishing trough time by changing fishing mortalities or fishing effort by gear (from an initial value of Ecopath) * Fitting the model to available data, searching for trophic interactions parameters and environmental anomaly * Applying optimization routines to include economic and social data Walters et al Rev. Fish Biol. and Fish., 7: , Christensen and Walters Ecol. Model., 172(2-4): In summary…

2. Temporal dynamic modelling and calibration process Ecosim takes the Ecopath master equation and sets up a series of differential equations of biomass dynamics to calculate changes of each group over time: dBi/dt: growth rate during time dt of group (i) in terms of its biomass P/Q: net gross efficiency MOi: other non-predation natural mortality Fi: fishing mortality Ii: immigration rate; ei: emigration rate; Ii-ei·Bi: net migration rate Total consumption by group i Total consumption on group i by all predators j Walters et al Rev. Fish Biol. and Fish., 7: , Christensen and Walters Ecol. Model., 172(2-4):

Qs are calculated based on the Foraging arena where Bi is divided into vulnerable and non-vulnerable components and the transfer rate vij determines the flow control: top-down, bottom-up or intermediate Walters et al Rev. Fish Biol. and Fish., 7: , Christensen and Walters Ecol. Model., 172(2-4): * vij is expressing the rate with which B move between being vulnerable and not vulnerable * Bi is prey biomass; Bj is predator biomass * aij is the effective search rate for i by j * Ti and Tj is relative feeding time for prey and predator * Dj represents effects of handling time as a limit to consumption rate * Sij are seasonal or long term forcing effects * Mij are mediation forcing effects 2. Dynamic modeling and calibration process

Fitting the model to data … EwE now includes an iterative process to fit the model and calibrate it with empirical data To explore how changes in functional groups can be attributed * to internal ecosystem factors: feeding interactions and population factors * to external ecosystem factors: fishing activity and environmental forcing - From an ecosystem model of a past situation - Using to force the model changes in: * fishing effort, fishing mortality, total mortality - Using available information on biomasses and catches to modify model variables (mainly vulnerability factor vij) based on the reduction of the goodness of fit measure that it is the summed-squared residuals (SS) of a predicted from an observed value 2. Dynamic modeling and calibration process Walters et al Rev. Fish Biol. and Fish., 7: , Christensen and Walters Ecol. Model., 172(2-4):

South Catalan Sea calibrated ecosystem model: from We developed an ecosystem model representing We used changes in fishing mortality and nominal fishing effort of trawling, purse seining and longline fishery ► best fit: Cv > TRB > nº boats > days fishing Absolute and relative biomasses to fit the model Corrected catches from to compare results Change vulnerabilities of most sensible interactions (vij) and prediction of an environmental anomaly 2. Dynamic modeling and calibration process

* Trophic flow control for the most sensible prey-predator interactions: e.g. sardine * Anomaly function linked with primary production correlated with NAO indexes (annual and winter values) and time series of temperature * Identification of compensation in recruitment of hake when adult stock is low (commonly defined in many stocks, Myers and Cadigan, 1993) Predicted and empirical biomasses and catches 1 1. Relative and absolute values (y) over time (x); Myers and Cadigan, Can. J. Fish. Aquat. Sci., 50:

2. Dynamic modeling and calibration process 1. Trophic interactions play a key role in explaining the variability 2. Fishing dynamics: adult hake, sardine, anchovy and demersal sharks 3. Environmental forcing paying its role in the pelagic compartment The model predicts important changes in the ecosystem structure and functioning, highly exploited from 1978 and overexploited in 2003 * Biomass decrease of top predators like adult hake and demersal sharks * Biomass decrease of target low TL organism: like small pelagics and juv. hake * Increase of benthopelagic fishes, jellyfish, conger eel and small demersal fishes: preys and competitors * Lower biomass of sardine than anchovy in 2003 * Lowest levels of anchovy in the late 1990s and showing modest recovering… Summary: what seems to have happened in these 26 years?

3. Development of dynamic simulations of fishing options * Changing the fishing mortality (F) by group or fishing effort by fleet * Assuming constant the predicted environmental anomaly * Making simulations of 20 years from 2003 * Assessing the impact of changing fishing activity * Comparing predicted values of Bf/Bi and Cf/Ci ( ) 5 Simulations - If nothing changes… - If global fishing effort decreases 20% (≈ one fishing day) - If demersal fishery or purse seine fishing effort decreases 20% - How to recover high levels of hake, anchovy and sardine 3. Dynamic simulations

Simulation 1: If nothing changes…. 3. Dynamic simulations * Low biomasses of adult hake, sardine * Decreasing biomass of juv. hake and several demersal species * High biomasses of benthopelagic fishes, conger eel, other small pelagics (mainly round sardinella), jellyfish, shrimps and horse mackerel * Anchovy shows a recovery trend * Biomasses are maintained and catches don’t increase

Simulation 2: If fishing effort is globally reduced by 20% 3. Dynamic simulations * Some partial recovery on biomass of demersal and pelagic depleted species * Still high biomasses of benthopelagic fishes, conger eel, other small pelagics, jellyfish * Increasing catches of anglerfish, conger eel, demersal fishes, sardine, horse mackerel * Global biomass maintained, non clear recovery of global catches 2003

Simulation 3: If fishing effort is reduced by 20% for purse seine 3. Dynamic simulations Simulation 4: If fishing effort is reduced by 20% for the demersal fishery * Some recovery on biomass of pelagic depleted species * Still high biomasses of benthopelagic fishes, conger eel, other small pelagics, jellyfish * Increasing catches of sardine and some demersal fishes * General recovery on biomass of demersal depleted species * Still high biomasses of benthopelagic fishes, conger eel, other small pelagics, jellyfish * Increasing catches of some demersal fishes

Simulation 5: how to recover high levels of hake, anchovy and sardine 3. Dynamic simulations If we reduce the fishing rate of adult hake to F/Z <0.8 1 ; eliminating fishing on juv. hake < 25cm (immature ones) to 80%, reducing F/Z for sardine <0.5 2 and maintaining F/Z for anchovy < * Recovery of biomasses of adult and juv. hake, sardine and other benthic and pelagic species * Lower levels for anchovy comparing 1978 but higher ones respect 2003 (25%) * Lower biomasses of benthopelagic fishes, jellyfish, conger and other pelagic fishes * Higher levels of caches for target demersal and pelagic species * Global increase of biomasses and catches 1. As recommended for ground fished stocks: Mertz and Myersl, Can. J. Fish. Aquat. Sci., 55: ; 2. As recommended for small pelagic fishes: Patterson Rev. Fish Biol. Fish, 2:

Conclusions Simulation examples are showing interesting results: * Target species are driven by fishing activity and we need to lower fishing impact to recover them, probably preventing as well the proliferation of other species (jellyfishes and benthopelagic fishes: trophic cascades) * A reduction of 20% of effort would imply some improvement of the ecosystem respect the actual state * To recover the system we need an intervention in both pelagic and demersal fisheries to increase top predator biomasses and relax the impact on target small pelagic fishes, while increasing the predation on preys of top predators and competitors of low TL target species Ecological modeling in the Mediterranean context is shown as an appropriated tool to investigate fishing management options To answer important ecological questions, to pose new ones and to assess the ecosystem effects of fishing This is especially relevant in the Mediterranean because it take into account the multispecific nature of ecosystem and fisheries: essential under the EAF

Conclusions The ecosystem modeling approach is a tool to improve the strategic nature of the management: where we are, where we are going? Complementing the tactical management from stock assessment and evaluation tools This can contribute to evolve the reactive management of fishing resources into a more adaptive and strategic one, in line with recommendations of GFCM Ecological modeling is nourished by conventional assessment methods, information that we already have and we organize into an ecosystem context We need: to continue collecting this essential information to increase it: some critical gaps (diet of key specie, ontogeny) to collect new data to monitor model predictions (validate or refuse) Are benthopelagic fishes increasing in Mediterranean exploited ecosystems? Models are always under construction in the sense that when new data or new ideas are available, they can be improved

Conclusions 1. Libralato et al., Submitted to Journal of Applied Ecology; 2. Granzotto et al., Chemistry and Ecology, 20(1): ; 3. Walters et al., Ecosystems, 2: EwE Ecological modeling shows an essential improvement with the ability to fit models to data: * From calibrated models we can derive ecosystem indicators like L index (presented by S. Libralato and collaborators 1 ) * They can be used to derive classical indicators as fishing mortalities (F), predator mortalities (M2), maximum sustainable catches (MSY) from an ecosystem context * They can also include socioeconomic data to assess the optimum equilibrium of different fishing options taking into account social, economic and ecological criteria (example in Venice lagoon 2 ) * They are the baseline from where to develop spatial simulations 3 We suggest to the Sub-Committee of Stock Assessment: To foment the ecosystem modeling application in the Mediterranean by implementing EwE and other tools by implementing them to different scales We are also working in the Adriatic Sea (1970s to 2000s): This will enable us to have another example to compare observed patterns and different model scales

THANKS!