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Peter Ward RAM Myers Dalhousie University

The effects of soak time and depth on longline catch rates. Peter Ward RAM Myers Dalhousie University. EB WP-3 EB WP-12. 400 m. 1950s 1990s. Depth 25–175 m 25–500 m . 200 m. 500 m. Dawn 35% 30% Dusk 0% 70% . Soak time 5 hr 9 hr . Observer data from six fisheries.

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Peter Ward RAM Myers Dalhousie University

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  1. The effects of soak time anddepth on longlinecatch rates Peter WardRAM Myers Dalhousie University EB WP-3 EB WP-12

  2. 400 m 1950s 1990s Depth 25–175 m 25–500 m 200 m 500 m Dawn 35% 30% Dusk 0% 70% Soak time 5 hr 9 hr

  3. Observer data from six fisheries North Pacific Swordfish (1,539 sets) 40N Western Pacific Bigeye (1,915 sets) 20N >500,000 fish>6,000 daily sets Central PacificBigeye (3,243 sets) 0 20S Western Pacific Distant (234 sets) South PacificYellowfin (1,419 sets) 40S South Pacific SBT (666 sets) 140E 180E 140W

  4. Data Observer record of time when each hook was retrieved 2.5 Swordfish 2.0 + Estimate of deployment time from start and end of time of set 1.5 Catch rate (no./1000 hooks) 1.0 0.5 0 0 5 10 15 20 Soak time (hr)

  5. Generalized linear mixed model (1) Random effects (O) (2) Fixed effects • soak time (T) • season (S) • year (Y) • dawn (A) • dusk (P)

  6. Seabirds Billfishes Other fishes Sharks and rays albatross Soak time effect varies among species swordfish blue shark skipjack Tunas bigeye -0.2 0.0 +0.2 Soak time effect

  7. Soak time effect correlated with survival +0.1 blue shark Soak time effect 0.0 skipjack tuna r = 0.54 -0.1 0 20 40 60 80 100 Alive (%)

  8. 1.0 Ray’s bream 0.5 oilfish Dusk effect 0.0 Dusk has a positive effect for many species -0.5 black marlin -1.0 -1.0 -0.5 0.0 0.5 1.0 Dawn effect

  9. Swordfish 5 hr 20 hr no dawn or dusk 1 4 dawn and dusk 3 10 Effects make a substantial difference $1,500 vs $5,000

  10. Bigeye tuna Day Blue shark Striped marlin Opah Night 0 Distribution of catches of most species varies with depth 100 200 . . . and with time of day Depth (m) 300 400 500 0 1 2 Relative catchability

  11. Conclusions (1) Abundance indices need to be adjusted for: • soak time • dawn and dusk • depth range (2) Mortality of several species may be greater than indicated by catch records

  12. πis the probability of catching a fish: h e p = ( ) h + 1 e p æ ö = h ç ÷ log - p 1 è ø Generalized linear mixed model Logistic regression catch y has a binomial distribution: y~b(n,π) η is the ‘soak time effect’:

  13. Operations drawn from a larger population of operations Random effects in catch rate – soak time relationship for each operationare independent and normally distributed: Random effects

  14. Catch is the product of capture and loss rates No captures after deployment e.g. seabirds β < 0 Captures exceed losses e.g. blue shark β> 0 Probability of being on a hook Losses eventually exceed capturese.g. skipjack β< 0 Captures balance losses e.g. yellowfin β= 0 0 5 10 15 20 Soak time (hr)

  15. Soak time effect generally consistent among areas swordfish escolar oilfish blue shark porbeagle

  16. Epipelagic community 0m striped marlin 100m yellowfin tuna 200m wahoo 300m 400m bigeye tuna swordfish opah 500m Mesopelagic community

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