Workshops on End-to-End models and Mediterranean models

November 15-19, Barcelona

Istituto Nazionale di Oceanografia e di Geofisica Sperimentale – OGS(National Institute of Oceanography and Experimental Geophysics – OGS)Trieste, Italy

Simone Libralato

Marine trophodynamic models:From theoretical developments to tools for

supporting marine policy

2015 Euromarine General Assembly meeting 24-25 February 2015 Stazione Zoologica Anton Dohrn, Naples, Italy

Marine ecosystem:complex, several drivers (goods & services)

Feedbacks between ecosystem components Interactions between stressors

Climatic changes

Fisheries and aquaculture

Pollutants Invasive/exoticspecies

Nutrient inputs

Protected areas

• Understand and predict cumulative impacts of natural and anthropogenic pressures (e.g., climate change, fishing, pollution…) on marine ecosystems and the sustainability of services they provide

• Develop integrated & operational tools for analysing scenarios and suggesting mitigation policies.

Objectives

..focus on the part you are most interested in without forgetting its interactions with the rest of the universe(which can be modelled with less details) …

Representing the universe in a nutshell

..but what is the universe of a fish?

Late 90s – early 2000s change in paradigm

..focus on the part you are most interested in without forgetting its interactions with the rest of the universe(which can be modelled with less details) …

Representing the universe in a nutshell

..but what is the universe of a fish?

Late 90s – early 2000s change in paradigm

Pauly et al., 1998

THE ECOSYSTEM!

What modelling approach?Tr

ophi

c le

vel

Pauly et al., 1998

Number of species

Stock assessment models (VPA, LCA)

Multispecies models (MSVPA)

Individual Based models

Statistical models

(GLM, GAM)Lotka-Volterra adapted multispecies

models

Biogeochemical models (NPZD)

Food web modelling

Size spectra (OSMOSE, APECOSM)

GENERALITY ACCURACY(performan

REALSIM(functionality)

modular approach: using the best available tools already developed and well tested Rose et al. (2010), Mar. Coast. Fish., 2: 115-130.

Increasing relevance

Proportion of published works indexed in the Ngram and Scopus databases containing "food web" or "food webs". The number of works matching the search are divided by the total number of indexed works per year. The search was performed online on August 2014.

(Libralato et al. MEPS, 2014)

What are the key issues?Tr

ophi

c le

vel

Number of species

Bottom-up

Top-down

There is often a focus on ‘vertical dimension’ only,i.e. predation & transfer efficiency

what of the ‘horizontal dimension’ ?

Competition ? Symbiosis ? BIODIVERSITY ?

Difficulties: scale and biological resolution

Spatial scale

Time scale

virus

Top pred

phytozoo

fish

Integrated models of marine

ecosystems

We face the problems of different scales

(time&space) between elements to

be represented

typical scale for hydrodynamic/BGC

processes:Hours & km

typical scale for HTL processes:

months&km

?

What modelling approaches?

FOOD WEB (EFG) and SIZE SPECTRA modelsmight converge to similar complexity

(and borrow solutions/parametrization one from the other)

size

B

We use Ecopath with Ecosim, a food web model because:- Lot of info already gaterhed, parametrized, analysized in this framework for many diff ecosystem-Very good diagnostic and computation of ecosystem index already implemented- allows to include biological resolution aspect (biodiversity) through opportune choice of ecological functional groups

Biological resolution

(1) Working example: targets of fisheries

Fisheries targets are assessed with single species stock assessment tools.Although they might be more accurate in the description of single species population dynamics, other food web effects are negeglected.

Imposing fisheries targetsTyrrheninan Sea

What is the effect of applying Fmsy on the assessed species? Assuming capability to manage selectivity perfectly…..

62 functional groups (3 non living groups; 5 plankton groups; sea birds; turtles; sharks and rays; invertebrates; fishes); 4 fishing fleets (trawl; seine; passive net; longline)

Changes in comm. Catches: -1%

Imposing Fmsy: changes in biomass

Changes in comm. Catches: +0.2% -16.2%

Imposing Fmsy for all assessed species

Changes in comm. Catches: -6.5% +0.8%

Application of assessment Fmsy to group of species allows to benefit of trade-offsemerging from food webs

More realism: optimal effort

Changes in comm. Catches: -21.7%

Bottom trawlers

Purseseines

Passive polivalents Longlines

0.52 0.76 1.20 1.49

FMSY FoptRose shrimp 0.7 0.39Mantis shrimp 0.54 0.75Anchovy 0.43 0.30Sardine 0.32 0.20Hake 0.22 1.75Red shrimp 0.35 0.29Norway lobster 0.21 0.30

(2) Working example: Climate changes & fisheries effects

1) Regional Climate Model (RegCM), one way nested in Global Climate Model HadAM3H (Giorgi et al., 2004)

2) Biogeochemical model of the Northern Adriatic Sea (Cossarini & Solidoro, 2008)

3) Adriatic Sea food web models (Coll et al, 2009)

What are the key issues?

Adriatic Sea

Zoo1

POP DOP

Bact

Zoo2

Phy2

Phy1

PO4

Yearly pool dynamics in P

3 Climate scenarios

27 fishing scenarios1 baseline scenario

16 management scenarios by single commercial

species(Anchovy, Sardine, Hake,

Red Mullet)

10 management scenarios by fleet

(changes in effort for bottom trawl, beam trawl,

purse seine, mid water trawl, Tuna fleets)

81 scenarios

(~ -25% inputs; ~IPCC B2)

(mod. precipitation pattern;~IPCC A2)

RF - ReferenceB2 – Local sustainability

A2 – Market oriented

Outputs for the resulting 81 scenarios

RF + 26 fishing scenarios under RF

B2 + 26 fishing scenarios under B2

A2 + 26 fishing scenarios under A2

Biomass of 46 FG catches of 46 FG Indicators25 Group-based 9 Ecosystem

Color Legend>0.30.15< <0.30.05< <0.15-0.05< <0.05-0.15< <-0.05-0.3< <-0.15<-0.3 % change respect to base scenario (RF with actual fishing: first raw)

>30% increase of P/D ratio (Pelagics over Demersal Fish) in RF scenario with 25% increase of bottom trawl (scenario RF-17)

>30% decline of anchovy biomass B2 scenario with 50% increase of fishing mortality for anchovy (scenario B2-3)

>15% decline of catches of top predators when decreasing tuna fishing effort by 25% in climate scenario A2 (scenario A2-27)

Climate and fisheries effects: synergies and antagonisms

The value of model output obtained in different fishing (ONLY) scenarios Highlighted for all outputs:

Biomass of 46 FG

catches of 46 FGIndicators

25 Group-based 9 Ecosystem

RF baseline

Fishingscenario

Climatescenario

Fisheries &

Climatescenario

RF baseline

Fishingscenarios

Climatescenarios

Fisheries &

Climatescenarios

Mod

el o

utp

uts

Mod

el o

utp

uts

The value of the same model output obtained in different climate (ONLY) scenarios

The value of the same model output obtained in different fishing+climate scenarios

SYNERGISTIC EFFECTclimate + fishing > climate or fishing

ANTAGONISTIC EFFECTclimate + fishing < climate or fishing

What are the key issues?

Libralato et al. (2015b) in prep

Adriatic SeaBiomass Catches Indicators

25 Group-based 9 Ecosystem

RF + 26 fishing scenarios under RF

B2 + 26 fishing scenarios under B2

A2 + 26 fishing scenarios under A2

Reference & anomalies due to fishing

synergies

antagonism

Climate vs Fishing

Scenarios with Increasing fishing mortality or effort

Scenarios with DEcreasing fishing mortality or effort

Scenarios with Increasing fishing mortality or effort

Scenarios with DEcreasing fishing mortality or effort

(3) Moving toward operational

Coupled food web and hydrodynamic/biogeochemicalmodels (data assimilation):Formal skill tests showed large space for improve HTL predictability

Ongoing work for Integrated food web models

Foodweb

Hydro

BGCRCM

• Need for complete feedbacks from food web models to the BGC/hydrodynamic ones: two-ways coupling

Akoglu et al., GMDD, 2015

What next: 0) Consolidating

Coll & Libralato, 2012, Fish & Fish

Reviewed by Coll & Libralato 2012

Moutopoulos et al 2013Univ. Roma + OGSPranovi et al

What next: 0) Consolidating Integrated food web approaches

Akoglu et al., GMDD, 2015

What next: 1) organizing ex isting data + new data

Data requirement is huge for food web modelling: need for continuingand increasing data collection (for increasing accuracy of our models)

- Mesocosm experiments to better define processes

- Need for better define parameters:

- continuing surveys (acoustic, trawl surveys)- better quantification of effort and catches- more information on keyspecies- more information on neglected groups- more information on less studied systems (deep systems, sediment models- more information on diet preferences (isotops, gut content..)

What next: 1) organizing ex isting data + new data

Stomachs Classical morphological identificationof preys

DNA extraction with commercial kit

PCR amplification of target DNA

Illumina MiSeq System

Specific primers with overhang adapters

Indexing

Pooling samples analysed in a single run

Multiple amplifications per subsample tolimit PCR drift problem

diet preference determination with BARCODING STRATEGIES

What next (2) incorporating adaptation, evolution, plasticity

Species can adapt within the time scale of our simulations to new environmental conditions (e.g. new temperature)

(Solidoro et al., 2010)

PlasticitySpecies can be very plastic to changes in environmental conditions

What next (2) incorporating adaptation, evolution, plasticity

Life history traits can evolvequite rapidly on contemporary time scale under the effect of important pressures

Length at maturity vs fishing mortality(Sharpe & Hendry, 2009, Evol Appl)

What next: 3) develop new models of food web

Likely, like in climate change many different models are used (see IPCC), also for marine system predictions (IPBES??) different models would be used to assess possible changes.

Use of different available model structures for making predictions: Atlantis(Fulton et al); Ecopath with Ecosim (Christensen et al); Osmose (Shin et al); MICE Medium Intermediate complexity models (Plagayi et al); Ecotroph(Gascuel et al),….

But also develop fresh new models. New conceptualization of food webs can help better using data, better representing plasticity, but also that might facilitate identification of reference levels (and indicators) ….

- Individual based food web models can be a pathway (VEW, Woods)

- Representations of food webs in terms of trophic levels

- …others….

Thank you!

Filippo Giorgi, ICTP (Trieste, ITALY)

Cosimo Solidoro, Ekin Akoglu, Gianpiero Cossarini, Paolo Lazzari, and the ECHO group, OGS (Trieste, ITALY)

Enrico Arneri, Alberto Santojanni, CNR-ISMAR (Ancona, ITALY)

Marta Coll, Isabel Palomera, Nixon Bahamon, ICM-CSIC (Barcelona, SPAIN)

Baris Salihoglu, Temel Oguz - METU (Mersin, TURKEY)

Acknow ledgments and credits