Coupling OSMOSE and ROMS NPZD models: towards end to end modelling of the Benguela upwelling ecosystem

Transcription

1 Coupling OSMOSE and ROMS NPZD models: towards end to end modelling of the Benguela upwelling ecosystem Yunne SHIN Morgane TRAVERS PICES XVI th annual meeting, October 26 - November 5, 2007, Victoria BC, Canada

2 End to end modelling () DEFINITION (Travers, Shin, Jennings, Cury Progress in Oceanography) aims to represent the entire food web and the associated abiotic environment, requires the integration of physical and biological processes at different scales, implements two-way interaction between ecosystem components accounts for the dynamic forcing effect of climate and human impacts at multiple trophic levels.

3 models for EBFM WHAT FOR? - How fishing and climate (F & C) effects propagate down and up marine foodwebs? - What are the combined effects of F & C on target and non-target species? Physiological rates Spatial distribution Match-mismatch Recruitment success Food web for the Benguela ecosystem (Field et al. 1991) Primary production

4 Structure of models (Shin et al. MS) HOW? - Vertical integration Coupling of LTL and HTL models - needs integration of multi-disciplinary knowledge - relies on pre-existing models - ensures that processes addressed at appropriate scales at each TL - Horizontal integration Biodiversity integration / simplification How can we select the key components to be explicitly modelled?

5 Alternative foodchains Parsons and Lalli 2002 The Effect of Fishing Down the Food Chains of the Sea Fishing Van der Lingen et al Physical and Chemical Forcing Diatoms Macrozooplankton Fish Flagellates Mesozooplankton Jellyfish Smaller Fish Stibor et al. 2004

6 Pathways oriented models VERTICAL / TROPHIC integration Rhomboïd approach (De Young et al. 2004) Fish-centered models Fish Fish Fish Fish Fish Fish Fish Fish Zooplankton Phytoplankton HTL model LTL model HORIZONTAL / BIODIVERSITY integration

7 Pathways oriented models VERTICAL / TROPHIC integration Rhomboïd approach (De Young et al. 2004) Plankton-centered models Fish Zooplankton Zooplankton Zooplankton Phytoplankton Phytoplankton Detritus HTL model LTL model HORIZONTAL / BIODIVERSITY integration

8 Pathways oriented models Pathways-oriented approach (Shin et al. MS) VERTICAL / TROPHIC integration Fish Fast TOR FC Slow TOR FC Fish Zooplankton Phytoplankton Fish Low energy FC Fish Fish Fish Fish Fish Zooplankton Phytoplankton High energy FC Zooplankton Detritus HTL model LTL model HORIZONTAL / BIODIVERSITY integration

9 models: review Functional groups Spatial model Adapted from Travers, Shin et al COMPONENTS Multi-species 1 or 2 species Non spatial model 2-ways trophic coupling Top predators Forage species Zooplankton Phytoplankton Detritus Nutrients Environment Larval IBM Sourisseau NEMURO.Fish SEAPODYM APECOSM EwE ERSEM IGBEM / BM2 Size spectra «NPZD» OSMOSE MODELS

10 Osmose Npzd Roms Top predators Forage species Zooplankton Phytoplankton Detritus Nutrients Environment OSMOSE ROMS- NPZD Representation of fishing (Osmose) and climate (Roms-NPZD) drivers Vertical integration Predation as the coupling process: propagation of C & F effects via trophic interactions Horizontal integration Multi-species and multi-compartment coupled model: possible alternative foodchains

11 OSMOSE Object oriented Simulator of Marine ecosystems Exploitation Shin & Cury 2001, 2004 Patterns in fish diets Variability in time and space of fish diets Cannibalism Omnivory

12 Size and marine foodwebs Species Size PREDATION MORTALITY

13 Size based predation 1- Thresholds for predator/prey size ratio 2- Spatio-temporal co-occurrence 200 m -32 Lamberts Bay m St Helena Bay Saldanha Bay Port Elizabeth -34 Hout Bay -34 Gansbay Prey size Ratio max Ratio min pred size 200 m -32 Lamberts Bay m St Helena Bay Saldanha Bay Km Port Elizabeth -34 Hout Bay -34 Gansbay Km log abd Modelled food webs are variable in structure Opportunistic predation: dampening role on the foodweb 1 µm 1 mm 1 m log Size

14 OSMOSE structure - Size-based predation Size-structured and spatial model - Fisheries management and conservation issues Species-based model Model dimensions: Abundance and biomass by species, age, size, space and time

15 OSMOSE structure Class SYSTEM - abundance, biomass - species richness S - carrying capacity - size spectrum - S species Class SPECIES - abundance, biomass - growth parameters - reproduction parameters - distribution area/age - fishing mortality/age - (longevity+1) cohorts Class COHORT - species - abund., biomass - n schools Class SCHOOL - species, age - abundance, biomass - spatial coordinates - length, weight - predation efficiency

16 Fish life cycle anchovy Spatial distribution Natural mortality By age/size class Implicit migration patterns Eggs and larvae surplus mortality (export, sinking, non-fecundation, starvation, critical stage..) Estimation by calibration (genetic algorithm, Versmisse et al. MS) Mortality due to other predators (marine mammals, birds etc)

17 Fish life cycle Spatial distribution Natural mortality Piscivores Forage Predation 3 constraints: predator/prey size ratio spatio-temporal co-occurrence maximum ingestion rate Predation efficiency ξ

18 Fish life cycle Spatial distribution ξ crit = ration of maintenance maximal ration Natural mortality Piscivores Forage Predation Μ ξ M ξ max M ξ Mξmax = ξ+ ξ crit M ξ max ξ Starvation 0 ξ crit 1 ξ ξ N = N ( 1 e ) M ξ M

19 Fish life cycle Spatial distribution Natural mortality Piscivores Forage Predation ξ Starvation ξ Growth Von Bertalanffy model: Δ L Δ L sai,, sai,, ΔL = L ( e ) e sa, s K K ( a t ) s s 0 1 s = 0 if ξ i si < ξ crit < ξ 2 Δ L sa, = ( ξ i ξ crit ) if ξ si ξ 1 ξ i ξ crit crit

20 Fish life cycle Spatial distribution Natural mortality Piscivores Forage ξ Predation ξ Starvation Growth Fishing spatial distribution - MPAs (Yemane, Shin, Field MS) Fishing mortality Fishing periods

21 Fish life cycle Spatial distribution Natural mortality Piscivores Forage ξ Predation ξ Starvation Growth N 0 = φ. SSB. SR Fishing mortality Reproduction

22 Fish life cycle Spatial distribution Natural mortality Piscivores Non piscivores Forage Predation Carrying capacity maximal biomass of non-piscivorous fish which implicitly feed on plankton ξ ξ Starvation Growth Fishing mortality Reproduction

23 Forcing/coupling: approach Spatial distribution Natural mortality Food availability ROMS-N 2 P 2 Z 2 D 2 (Penven, Machu, Koné) (Travers and Shin, MS) Forage B t,x,y,i Ciliates 20 µm 200 µm Copepods 200 µm 2 mm Predation Predation mortality Flagellates 2 µm 20 µm Diatoms 20 µm 200 µm Starvation M t,x,y,i Nitrates Ammonium Growth Small detritus Large detritus Fishing mortality Reproduction PROCESSES: Grazing, growth, excretion, egestion, mortality, sinking, photosynthesis, respiration, nitrification, remineralization

24 Application southern Benguela 12 fish species modelled: 76% total fish biomass, 94% total catch Silver kob Kingklip Snoek Deep water hake Shallow water hake Horse mackerel Chub mackerel Redeye Anchovy Lanternfish Lightfish Sardine

25 Biological parameters Ex: Southern Benguela (Shin, Shannon, Cury 2004; Travers et al. 2006) GROWTH REPRODUCTION SURVIVAL L inf K t 0 c φ a mat M F a rec ANCHOVY CHUB MACKEREL HAKE shallow water HAKE deep water HORSE MACKEREL KINGKLIP LANTERNFISH LIGHTFISH REDEYE SARDINE SILVER KOB SNOEK

26 Predation constraints No a priori functional response and pre-determined diets, but predation constraints: 1- Maximum ingestion rate 2- Predator/prey size ratios Prey size Ratio max Ratio min pred size

27 Spatial distributions Age Shallow water hake Southern Benguela Namibia -28 S -28 Orange river -30 South Africa Lesotho m -32 Lamberts Bay m St Helena Bay Saldanha Bay N W E Port Elizabeth -34 Hout Bay -34 Gansbay REFERENCES Maps - Badenhorst and Smale Payne, Punt Punt et al Km Ages 1-2 Ages Ages N N N W E Namibia -28 S -28 W E Namibia -28 S -28 W E Namibia -28 S -28 Orange river -30 South Africa Lesotho -30 Orange river -30 South Africa Lesotho -30 Orange river -30 South Africa Lesotho m -32 Lamberts Bay m St Helena Bay Saldanha Bay Port Elizabeth -34 Hout Bay -34 Gansbay 200 m -32 Lamberts Bay m St Helena Bay Saldanha Bay Port Elizabeth -34 Hout Bay -34 Gansbay 200 m -32 Lamberts Bay m St Helena Bay Saldanha Bay Port Elizabeth -34 Hout Bay -34 Gansbay Km Km Km

28 OSMOSE Outputs Size-based indicators Lmean, L95%, L at age Size spectrum Species-based indicators Shannon index W-statistic Size Age 100% 80% 60% 40% Abundance W Biomass Ln (abd) 20% 0% Size

29 OSMOSE Outputs Trophodynamic indicators Spatial indicators Diet matrix/species/size Mean TL, TL-at-age TL distribution TL Shallow water hake Community mean length age

30 Preliminary results Trophic structure of the Southern Benguela using Osmose-LTL Zoo-plankton spatio-temporal mortality Fish trophic levels and life-history

31 Copepods mortality spatial patterns FEEDBACK from OSMOSE to NPZD model Applying fish-induced mortality rates on plankton (Travers and Shin, MS) For copepods: fish-induced mortality: day -1 in average m=0.05 day -1 in the biogeochemical model ~ ¼ of the mortality due to modelled fish Fish-induced mortality on copepods (month -1 ) S Upwelling St Helena Bay Agulhas Bank E

32 Copepods/Fish seasonal patterns (Travers and Shin, MS) Fish-induced mortality on copepods (month -1 ) 2 Mortality rate (month -1 ) J F M A M J J A S O N D 0-2 Biomass Upwelling

33 Trophic structure mean TL Comparison OSMOSE-NPZD-ROMS with ECOPATH snoek silver kob (Travers, Shin, Jennings) sardine redeye lightfish lanternfish kingklip Ecopath ECOPATH Model OSMOSE horse mack. 2+ horse mack. deepw. Hake 2+ deepw. hake shall. w. hake 2+ shall. w. hake euphausiids anchovy Mean TL per species

34 Species TL distributions TL OSMOSE TL ECOPATH anchovy lantern fish euphausiids Density TL horse mackerel round herring sardine Small forage species not always specialists Sardine more generalists than anchovy Van der Lingen et al. 2006

35 Species TL distributions silver kob kingklip Density TL shallow-water hake deep-water hake snoek Large fish species are life-history omnivores (TL species may vary F, csq on trophic spectrum) In the 1990s, low predation of eggs and larvae of demersal fish by small pelagics (cultivation hypothesis)

36 Conclusion What s next: Defining and simulating «What if scenarios» for quantifying propagation of climate and fishing changes Strategy 1: appropriate vertical and horizontal integration of models for being able to anticipate ecological «surprises» Strategy 2: Comparative approach across models for strengthening simulation results Strategy 3: Validation using Pattern-Oriented Modelling approach (Grimm & Railsback 2005) Multiple patterns validation at different hierarchical levels (Cury, Shin, Travers et al. 2007) Diets Species composition Trophic level TL hake = 4.2 TL redeye = 3.6 Spatial patterns Chla distribution Community indicators Size spectrum Predator prey size ratio Population indicators Mean size Prey size Predator size Evolution of size frequency Pelagic Demersal ABC curves INDIVIDUAL PATTERNS POPULATION PATTERNS COMMUNITY PATTERNS

37 MANY THANKS FOR YOUR ATTENTION!!!