Pelagic Predators Food Habits Project
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1 Pelagic Predators Food Habits Project Tim Essington and Mary Hunsicker University of Washington Robert Olsen and Mark Maunder IATTC Enric Cortes SWFSC
2 Using a Food Habits Database Our hypothesis: Reduced abundance of large pelagic predators may gave enhanced the productivity of economically important tunas If true then: Tunas should comprise a significant component of pelagic predator diets, throughout the range of their populations No single sampling program can possibly address this question
3 Project Objectives Develop database of apex predator food habits How does predation vary with species, body size, season, location of capture? Also provide database product for wide use Evaluate the scope for predation impacts on tuna recruitment Simulation and mass-balance modeling
4 Towards a Food Habits Database We presently have a database containing summarized data from 37 peerreviewed publications Predators (# studies): Tunas: Yellowfin (10); Skipjack (6); Bigeye (4); Albacore (5); Bluefin (1) Billfish: Striped marlin (4); Blue marlin (3), Black marlin (2), Swordfish (2) Sharks: Galapagos shark (1), Tiger shark (1); Hammerhead sharks (3); Shortfin mako (1); Silky shark (1); Bigeye thresher (1) Others: Mahi mahi (3), Indo-Pacific sailfish (1); Eastern spinner dolphin (1); Coastal spotted dolphin (1); Sailfish (1);
5 Tunas Yellowfin Bigeye Skipjack Yellowfin 7.8% Skipjack Skipjack 7.8% V, 3.9%FO Yellowfin 2.2%V, <1% FO Bigeye,Yellowfin Skipjack Yellowfin Skipjack Yellowfin Data post 1980s Data prior to1980s Study locations: Tunas
6 Marlins Blue Marlin Black Marlin Blue Marlin Striped Marlin Striped Marlin Blue Marlin Data post 1980s Data prior to1980s Study Locations: Billfishes
7 Summarized Data Reveal Substantial Predation on Skipjack Tuna % occurrence % volume % number % Skipjack (mean, SD) Skipjack Yellowfin Other tunas Billfish Predator
8 Summarized Data Show Variation by Ocean Region % occurrence % volume % number % mass % Thunnus & Katsuwonus spp. (mean, SD) Central Pacific, Hawaii Eastern Pacific Western Pacific New Zealand/Samoa Region
9 Summary Statistics from Enric Cortes Shark database % K. pelamis in the diet of sharks 25.00% 20.00% 15.00% 10.00% 5.00% 0.00% Katsuwonus pelamis SE Pacific SW Pacific C Pacific Region % T. obesus in the diet of sharks 25.00% 20.00% 15.00% 10.00% 5.00% 0.00% %N %W %O %IRI N = 42 % T. albacares in the diet of sharks 40.00% 35.00% 30.00% N = % 20.00% 15.00% 10.00% 5.00% 0.00% Thunnus obseus SE Pacific Thunnus albacares SE Pacific N = 45 %N %W %O %IRI Region C Pacific %N %W %O %IRI Region
10 PS set locations for two ETP diet studies
11 Coryphaena hippurus Elagatis bipinnulata Katsuwonus pelamis Lobotes pacificus Makaira indica Makaira nigricans Sphyrnidae Tetrapturus angustirostris Thunnus albacares Thunnus obesus Carcharhinus longimanus % ETP % Yellowfin tuna 10% Predators Unid. Thunnus spp Thunnus obesus Thunnus albacares Thunnus alalunga Unid.Scombridae Sarda orientalis Katsuwonus pelamis Elagatis bipinnulata Coryphaena hippurus Auxis spp Prey Acanthocybium solandri Acanthocybium solandri Carcharhinus falciformis Diet Proportion
12 Elagatis bipinnulata Katsuwonus pelamis Lobotes pacificus Makaira indica Makaira nigricans Sphyrnidae Tetrapturus angustirostris Thunnus albacares Thunnus obesus Carcharhinus longimanus Coryphaena hippurus Acanthocybium solandri Carcharhinus falciformis 0.0 Bigeye tuna 0.02% ETP Predators Prey Unid. Thunnus spp Thunnus obesus Thunnus albacares Thunnus alalunga Unid.Scombridae Sarda orientalis Katsuwonus pelamis Elagatis bipinnulata Coryphaena hippurus Auxis spp Acanthocybium solandri Diet Proportion
13 Elagatis bipinnulata Katsuwonus pelamis Lobotes pacificus Makaira indica Makaira nigricans Sphyrnidae Tetrapturus angustirostris Thunnus albacares Thunnus obesus Coryphaena hippurus Carcharhinus longimanus ETP Albacore tuna 6% Predators Unid. Thunnus spp Thunnus obesus Thunnus albacares Thunnus alalunga Unid.Scombridae Sarda orientalis Katsuwonus pelamis Elagatis bipinnulata Coryphaena hippurus Auxis spp Prey Acanthocybium solandri Acanthocybium solandri Carcharhinus falciformis Diet Proportion
14 Thunnus obesus Elagatis bipinnulata Katsuwonus pelamis Lobotes pacificus Makaira indica Makaira nigricans Sphyrnidae Tetrapturus angustirostris Thunnus albacares Coryphaena hippurus Carcharhinus longimanus % 22% 40% ETP Skipjack 11% 33% Predators Unid. Thunnus spp Thunnus obesus Thunnus albacares Thunnus alalunga Unid.Scombridae Sarda orientalis Katsuwonus pelamis Elagatis bipinnulata Coryphaena hippurus Auxis spp Prey Acanthocybium solandri Acanthocybium solandri Carcharhinus falciformis Diet Proportion
15 Why do we need primary data? Summarized data don t allow us to address: Size-structure of feeding relations Variation in feeding due to: Location Method of Capture Season Other confounding variables
16 Primary Data Sources: Acquired Olson et al (ongoing) Alverson 1950 s data (ongoing, 75% complete) POFI data 1950 s (ongoing, just obtained) Olson et al s (not yet started) Wish List: ECOTAP data, plus ongoing SPC data collection efforts Anything else!
17 Why the details matter: Size Selectivity and Cannibalism Juvenile Skipjack account for % of adult skipjack diets What does this mean for juvenile skipjack? How many survive?
18 Model Schematic Fishing Mortality Fishing Mortality Fishing Mortality Fishing Mortality Recruit Stock Natural Mortality Natural Mortality Natural Mortality Natural Mortality Inputs: Model Calculations Diet contribution (% mass) Mean Size Consumed (variance) Allometry of consumption rate Size-at-age Post recruit age-structure (M, F) Pre- recruit other mortality Onset of cannibalism Size-based vulnerability function Recruitment rate (age mo) Age-specific predation mortality rate Proportion of cohort surviving cannibalism
19 Size structure of feeding is critical 25.0% 21% Proportion of Cohort Surviving 20.0% 15.0% 10.0% 5.0% 2% 4% 0.0% Mean Size of Consumed Skipjack (cm) Results based on age-structured modeling analysis
20 Next Steps: Modeling Sensitivity analysis and write up skipjack model for publication Apply to skipjack, yellowfin and bigeye Realistic expectations!
21 A new culture of data sharing We can all benefit from a centralized database Without archival efforts, hard earned data have limited life span Some questions can only be addressed via synthesis of many studies Archival and analysis promotes serendipitous discoveries
22 But I m not done with my data yet This new culture of data sharing also requires an ethic of data use No data will be used without the permission of the provider for a specific requested use We are developing guidelines to ensure fair data use and exchange
23 Want to learn more? Funding is provided by Pelagic Fisheries Research Program
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