1. Peter Ward RAM Myers Dalhousie University The effects of soak time and depth on longline catch rates EB WP-3 EB WP-12

2. 200 m 400 m 500 m 1950s 1990s 1950s 1990s Depth25–175 m 25–500 m Dawn35%30% Dusk 0%70% Soak time 5 hr 9 hr

3. Observer data from six fisheries 0 20S 40S 20N 40N 140E180E140W Western Pacific Bigeye (1,915 sets) Central Pacific Bigeye (3,243 sets) Central Pacific Bigeye (3,243 sets) Western Pacific Distant (234 sets) North Pacific Swordfish (1,539 sets) South Pacific Yellowfin (1,419 sets) South Pacific SBT (666 sets) >500,000 fish >6,000 daily sets >500,000 fish >6,000 daily sets

4. Swordfish 2.5 2.0 1.5 1.0 0.5 0 Catch rate (no./1000 hooks) Soak time (hr) 05101520 Data + Estimate of deployment time from start and end of time of set Observer record of time when each hook was retrieved

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

6. Soak time effect -0.20.0+0.2 bigeye skipjack swordfish Billfishes Tunas blue shark Sharks and rays albatross Other fishes Soak time effect varies among species Seabirds

7. Soak time effect correlated with survival 020406080100 Alive (%) Soak time effect 0.0 -0.1 +0.1 r = 0.54 blue shark skipjack tuna

8. -0.50.00.51.0 -0.5 0.0 0.5 1.0 Dusk effect Dawn effect oilfish Dusk has a positive effect for many species blackmarlin Ray’s bream

9. Effects make a substantial difference $1,500 vs $5,000 Swordfish 5 hr 20 hr no dawn or dusk 14 dawn and dusk 310

10. 012 Bigeye tuna Day 500 400 300 200 100 Depth (m) Relative catchability 0 Night Striped marlin Blue shark Opah Distribution of catches of most species varies with depth... and with time of day

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

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

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

15. Catch is the product of capture and loss rates 20151050 Soak time (hr) Probability of being on a hook No captures after deployment e.g. seabirds β < 0 Captures exceed losses e.g. blue shark β > 0 β = 0 β < 0 Losses eventually exceed captures e.g. skipjack Captures balance losses e.g. yellowfin

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

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