Tuna Stock Assessment Support System Zach Siegrist , D P Harrison, M - PowerPoint PPT Presentation

Tuna Stock Assessment Support System Zach Siegrist , D P Harrison, M G Hinton, F J O’Brien, D A Kiefer, Dimitris Menemenlis, Yibo Jiang, E M Armstrong Program: NASA Data Operations and Assessment

Satellite Imagery Field Surveys Output & Input from ECCO 2 Tuna fishery time series sea surface, temperature, climatology of circulation model for Eastern Tropical height, winds, chlorophyll, the vertical sea surface mixed layer depth, sea Pacific the diffuse attenuation distribution of surface height, & vertical profiles of catch, effort, recruitment coefficient at 490 nm, as oxygen temperature & horizontal and from stock assessment well as primary production. vertical flow rates models Tuna Stock Assessment Support System Algorithm for spatial-temporal matching EOF analysis of fishery data with environmental data routine recruitment Species and fleet habitat characteristics prediction stored as a look-up table or habitat model algorithm Dynamic mapping algorithm TSASS Products • maps of the distribution of fleets and tuna species • predicted recruitment stock assessment system

TSASS screen of habitat interface and selected graphical analyses Selected features of 4-dimensional (latitude, longitude, depth, and time) marine GIS : • automated importing of imagery from satellites, output of circulation models, gridded survey and fishery data, and vector files of coastline, etc. • automated matching in space and time of these diverse data types and built-in statistical analyses including EOF • Play back of time series data and output from models • Dynamic plugin of our EASy marine GIS system which runs dozens of different aquaculture, organism tracking and research-based projects as well as broader fisheries, water quality and climate change analyses

Bigeye Thunnus obesus

Yellowfin Thunnus albacares

Skipjack Katsuwonus pelamis

TSASS can model horizontal and vertical movements and general patterns of tuna based upon cost benefit. Cost of encountering prey and benefit of assimilating prey

CO2 O2 O2 Muscles Gills heat CO2 O2 metabolic CO2 heat fuels search & capture heat Stomach Visual acuity Prey Trophic transfer efficiency Primary Production Conceptual Model of Optimized Energy Budget- assimilation and thermal balance

Longline Fishing Ground Purse Seine & Longline Fishing Grounds Eastern Pacific Tuna Fishery IATTC Catch and Effort at monthly time steps at 1 (purse seine) or 5 (longline) degree resolution starting in 1976.

TSASS screen shots. The purse seine fishing ground (indicated by +) strongly matches surface waters where chlorophyll concentration exceeds 0.1 μ g/L (upper panel) & overlies the hypoxic layer (lower panel). Purse seine recording stations superimposed upon a climatological image of annual average oxygen concentration at 150 m depth. Vertical compression of predators and prey above the hypoxic layer helps explain the purse seine fishery boundaries, also controls chlorophyll concentrations because of more efficient cycling of limiting nutrients.

After automatic import and processing of data by 1.8 PS-tYFT (1994-2012) 0.25 1.6 Triangle Background Frequency the EASy software, a variety of statistical analyses 1.4 Preference Quotient 0.2 1.2 can be run. 1 0.15 0.8 0.1 0.6 0.4 Example of our preference quotient for purse seine 0.05 0.2 0 0 habitat of yellowfin tuna. The orange line in the GHRSST (°C) figure is the preference quotient for sea surface PS-tYFT (1994-2012) temperature, oxygen at 150 depth, and sea surface 2 0.2 Triangle Background Frequency chlorophyll a concentration. The 95% confidence Preference Quotient 1.5 0.15 limits for the value of the preference index is either 1 0.1 above or below that expected for a random 0.5 0.05 distribution of catch, and is depicted by the dashed 0 0 line that is centered on a value of 1. The blue bar NODC-O2 (ml/L) graph is the discrete frequency distribution of the 4 0.5 PS-tYFT (1994-2012) area fished, and the green bar graph is the discrete 0.45 3.5 0.4 Triangle Background Frequency frequency distribution of the total catch weight of 3 0.35 Preference Quotient 2.5 0.3 yellowfin. 2 0.25 0.2 1.5 0.15 1 0.1 0.5 0.05 0 0 Preference and avoidance leads to dynamic 0.05 0.15 0.25 0.35 0.45 0.55 0.65 0.75 0.85 0.95 Chlorophyll (ug/L) mapping as shown in the next slide

March, 1981. Predicted distribution of Yellowfin Tuna overlaid with average purse seine catch in metric tons/month.

We have developed an algorithm to predict the recruitment of yellowfin tuna based upon Empirical Orthogonal Function (EOF) extraction of patterns of variability from time series of satellite imagery. EOF 1 st Seasonal spatial component & temporal expansion coefficient (right hand corner) of sea surface temperature • Seasonal component is the strongest After extracting the seasonal component, we can observe EOF 1 st nonseasonal spatial component & temporal expansion coefficient • Reveals ENSO events • Red/purple areas – the two poles of spatial variability

Yellowfin Tuna Recruits per Spawner Biomass: YFT RecruitsPerSpawnerBiomass: Stock Assessment & Satellite Sea Surface Temperature Predictions Stock Assessment blue , Satellite SST Predictions red 200 El Nino El Nino El Nino 150 100 50 1985 1990 1995 2000 2005 2010 Correlation between EOF temporal expansion coefficients and yellowfin recruitment.

Snapshots of EOF variability in the satellite sea surface temperature as newborn Yellowfin Tuna mature yellowfin strong cohorts are newborn during El Nino strong cohorts are 3 months old strong cohort are 9 months old strong cohorts are 6 months old – transitioning to La Nina

El Nino cohort at birth – same El Nino event, El Nino cohort at 4 months very little chlorophyll, absence of predation El Nino cohort at 7 months El Nino cohort at 9 months El Nino cohort growing up in colder water with higher productivity – as they mature, they can better avoid predation because of rapid growth and possible cohort cannibalism

Findings • We have developed an automated tool to characterize the habitat of the commercial tuna species of the Eastern Pacific Ocean. We find that there are multiple important oceanographic variables including chlorophyll, sea surface temperature, sea surface height, depth of the hypoxic layer, and ocean currents that characterize these habitats. Finally, we have demonstrated that this information can provide reasonably good predictions to dynamically map the distribution of these species. • The distribution of tuna in equatorial waters is driven by the strengths of the equatorial current, the equatorial counter current, and the north equatorial current. It is also driven by the strength of coastal upwelling. This is most clearly illustrated with changes in distribution and catch with ENSO signals. • We propose that the hypoxic layer of the eastern tropical Pacific vertically compresses the planktonic and pelagic ecosystem leading to a more efficient cycling of nitrogen. The depth of the hypoxic layer shapes the vertical and horizontal distribution of the 3 commercial species. • EOF analysis of time series of satellite imagery and the output of NASA’s ECCO 2 global circulation model can be used to monitor the recruitment of yellowfin tuna (we will soon test this with the other 2 species). We have proposed a mechanism for this relationship based on the evolution of temperature and chlorophyll changes during ENSO events.