Tuna and tuna forage: reconciling modeling and observation in a spatial mixed-resolution ecosystem model

Transcription

1 Tuna and tuna forage: reconciling modeling and observation in a spatial mixed-resolution ecosystem model Patrick Lehodey Oceanic Fisheries Programme Secretariat of the Pacific Community Noumea, New Caledonia

2 Conclusion Existing data on tuna and the pelagic ecosystem are numerous but at multiple temporal and spatial scales Using them to parameterize and evaluate an ecosytem model requires to develop a model that produces predictions at these same multiple temporal and spatial scales

3 Top-to-bottom integrated food web in marine ecosystem Level of information (observation and knowledge) available for the pelagic ecosystem Top Pred. Interm. TL. Zoopk. Prod I Phys. marine ecosystem information Catch and effort, length freq., large scale tagging exp, individual trackings, biology, physiology, stat. pop. Dyn. Mod. Catch cruises biology cruises, OPC, mesocosm physiology, dynamics Satellites, cruises, moorings photosynthesis, physiology, dynamics Obs. Satellites, scientific cruises and ship of opportunity network, arrays of moorings physical laws (Navier-Stoke, ) Evaluation same level of details Pred. marine ecosystem model(s) Basin scale spatial pop. dyn. mod. Functional group (s) NPZD 3D NPZD 3D GCM

4 Pelagic ecosystem N2 Phytopk Gel. org. mesozpk DOM microbial Bact. loop microzpk seabirds EpiPel. DAY Swordfish Adult yft tuna mar. mam. skj, young tuna &scombrids div. pisc. Adult bet tuna NIGHT Billfish Sharks 200m Pico & Nanopk MesoPel. Detritus 500m macro and micro nutrients BathyPel. Figure 1. A top to bottom schematic view of the pelagic food web. Most of the organisms in the meso- and deep- pelagic layers have nycthemeral migration patterns leading to higher concentration in the upper layer at night and in the deeper layer during the day.

5 Vertical structure of the pelagic food web Figure 2. The different daily vertical distribution patterns of the micronekton in the tropical pelagic ecosystem. 1, epipelagic; 2, mesopelagic migrant; 3, mesopelagic nonmigrant; 4, bathy-pelagic migrant; 5, bathypelagic non-migrant. Group 4 can be detailed into bathypelagic migrant into the intermediate layer (4a) or the surface layer (4b) night sunrise day a 4b surface layer day deep sunset, sunrise layer night sunset intermediate layer surface 200m 500m Figure 3. Five typical vertical movement behaviours simulated using a 3-layer and 2-type of prey pelagic system (adapted from Dagorn et al. 2000): 1- epipelagic predators (e.g., skipjack, marlins and sailfish); 2- predators moving between the surface and intermediate layers during the day (e.g., yellowfin tuna); 3- predators mainly in the intermediate layer during the day (e.g., albacore tuna); 4- predators moving between deep and intermediate layer during the day (e.g., blue shark); 5- predators mainly in the deep layer during the day (e.g., bigeye tuna and swordfish).

6 Modelling forage components: Total primary production E 3-layer 6-forage functional groups day sunset, sunrise night surface T o C & currents in the 0-200m layer 200m 500m T o C & currents in the m layer T o C & currents in the m layer Day Length (DL) as a function of latitude and date

7 Forage functional groups: modelling forage as multispecies populations New Primary Production Ecological transfer E S S S F 1 e t Age (day) /lambda obs (age at maturity) Tr Tr+1/lambda obs (min age of prey) ambient Temperature (oc) S. e t t 0 1% Tr mean age F 1 lifespan t 1 t Ln Tr Lehodey P. et al., Fisheries Oceanography 7(3/4): Lehodey P Progress in Oceanography 49: Lehodey P., Chai F., Hampton J Fisheries Oceanography 12(4):

8 2-layer, 3-component forage Epipelagic forage Top : La Niña Bottom: El Niño Migrant mesopelagic forage Deep mesopelagic forage

9 Spatial forage biomass Day: 0-200m Night: 0-200m Day concentrations (ml / 1000 m3) of skipjack forage measured during the EASTROPAC cruise (left) and biomass of epi-pelagic forage predicted by the model for the same period (February-March 1967). (Blackburn and Laurs, 1972)

10 Acoustic data Spatial forage biomass (A. Bertrand, E. Josse)

11 Biomass epi-pelagic epi eq2 migrant eq2 deep eq Biomass migrant- adn deep-pelagic Biomass time series Predicted biomass of forage components in eastern and western equatorial regions Jan-48 Jan-51 Jan-54 Jan-57 Jan-60 Jan-63 Jan-66 Jan-69 Jan-72 Jan-75 Biomass epi-pelagic Jan-78 Jan-81 Jan-84 Jan-87 Jan-90 Jan-93 Jan-96 Jan-99 Jan Jan-48 Jan-51 60N 50N 40N 30N 20N 10N 0 10S 20S 30S 40S Jan-54 50S epi eq3 migrant eq3 deep eq3 Kurex Eq4 Eq3 Eq2 Tas Eq1 Jan-57 Jan-60 Jan-63 Jan-66 Jan-69 Jan-72 Jan-75 Jan-78 Jan-81 Jan-84 Jan-87 Jan-90 Jan-93 Jan-96 90E 110E 130E 150E 170E 170W 150W 130W 110W 90W 70W Chili Jan-99 Jan Biomass migrant- and deep-pelagic Jan-48 Jan-51 Jan-54 Jan-57 Jan-60 Jan-63 Jan-66 Jan-69 Jan-72 Jan-75 Jan-78 Jan-81 Jan-84 Jan-87 Jan-90 Jan-93 Jan-96 Jan-99 Jan-02 Biomass epi-pelagic epi eq4 migrant eq4 deep eq Biomass migrant- and deep-pelagic

12 Structure of the tuna population Time / age structure Size Transport / movement (advectiondiffusion) Habitat factors Spawning t0 2 mm T o, Food (P), Larvae 1 st month 2 mm -5 cm Predators (F) Juvenile 2 nd and 3 rd month 5-15 cm Currents in upper layer T o, Food (Zpk), Predators (all largest tuna) Young 2 nd quarter to age of 1 st maturity Adult 1 st maturity to last quarter 15 - > 40 cm > 40 cm 1- Habitat based movement (following increasing gradient) 2- Proportional to fish size 3- Decreasing with increasing habitat 4- impact of currents? T o, oxygen, Food (F), Predators (all adult tuna) T o, oxygen, Food (F), spawning seasonality Natural mortality Growth Independent estimates + habitat-related variability Independent estimates (+ habitat-related variability)

13 Spawning Habitat : (Temperature, Food, Predators, currents) distribution of skipjack larvae (Nishikawa et al.) Jun 1971

14 Fisheries: Pred. vs Obs. cpue obs skj_pswfad pred skj_pswfad CPUE J-72 J-74 J-76 J-78 J-80 J-82 J-84 J-86 J-88 J-90 J-92 J-94 J-96 J-98 J-00 J-02 Spatially-disaggregated monthly catch 100 sum_pswfad_skj sum_pswuna_skj E+04 3.E+04 2.E+04 1.E+04 0.E+00 1.E+05 1.E+05 8.E+04 6.E+04 4.E+04 2.E+04 0.E+00 Length-frequency distribution ( by fishery, time and space)

15 Accessibility to forage components bathypelagic migrant 1 0% bathypelagic non-migrant 0% bathypelagic migrant 2 2% bathypelagic migrant 1 3% bathypelagic migrant 2 8% bathypelagic non-migrant 1% epipelagic 36% mesopelagic migrant 46% epipelagic 48% mesopelagic migrant 39% mesopelagic non-migrant 4% Skipjack (age = 8 quarter) mesopelagic non-migrant 13% Yellowfin (age = 16 quarter) By species, age, space and time Build up a database to classify all tuna prey species according to these 6 groups

16 Adult Habitat: (temperature, food, spawning seasonality) Bigeye tuna (FL = 80 cm) habitat Jun 1999

17 Observed individual movements and predicted habitat Nov 1999 Predicted movement (arrows) of bigeye tuna (FL ~ 80 cm) based on the gradient of habitat (background colour) combining forage, temperature and oxygen, and estimated track of a bigeye released wit h an archival tag during the same period.

18 Mixed resolution (PFRP project)

19 Mixed resolution (PFRP project) Physical-biogeochemical outputs: ESSIC (R. Murtugudde, Univ. Maryland) 1950-present ½ deg spatial resolution 10 day time resolution Forage and tuna: SEAPODYM - 6 forage components (production and biomass) - Tuna species (Larvae, juveniles, young, adults, total biomass) - Tuna fisheries (many)

20 Visualization software : SeapodymView: Replay the simulations Extract/aggregate data Can be distributed with files of predicted variables to colleagues interested to compare their results/observations to these predictions