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

Slides:



Advertisements
Similar presentations
Population dynamics Zoo 511 Ecology of Fishes.
Advertisements

A spatial integrated population model applied to black-footed albatross Simon Hoyle Mark Maunder.
Are the apparent rapid declines in top pelagic predators real? Mark Maunder, Shelton Harley, Mike Hinton, and others IATTC.
Dealing with interactions between area and year Mark Maunder IATTC.
Modeling silky shark bycatch
The current status of fisheries stock assessment Mark Maunder Inter-American Tropical Tuna Commission (IATTC) Center for the Advancement of Population.
University of Hawaii OCN 331 Living Marine Resources October 4, 2007 Honolulu, Hawaii Brooks Takenaka United Fishing Agency Hawaii Seafood Project 2 (NOAA)
Confidence intervals. Population mean Assumption: sample from normal distribution.
BHS Methods in Behavioral Sciences I
Are pelagic fisheries managed well? A stock assessment scientists perspective Mark Maunder and Shelton Harley Inter-American Tropical Tuna Commission
Mark N. Maunder, John R. Sibert, Alain Fonteneau, John Hampton, Pierre Kleiber, and Shelton J. Harley Problems with interpreting catch-per-unit-of-effort.
Important events in the history of Hawaiian commercial fisheries 1976 – Congress passes the Fisheries Management and Conservation Act 1984 – closure of.
The Global Environment Facility 4 th Biennial International Waters Conference July 31 – August , Cape Town, South Africa Managing and Conserving.
Effect of Circle Hooks and Bait on Target and Bycatch Species in Pelagic Longline Fisheries 2007 Inter-Sessional Meeting of the Subcommittee on Ecosystems.
August 17, 2015 ICCAT 2009 & 2010 Review and Preview.
The length structure of bigeye tuna and yellowfin tuna catch at different depth layers and temperature ranges: an application to the longline fisheries.
Definition of Bycatch FIGURE 16 Definition of Bycatch CAPTURE = CATCH + BYCATCH + RELEASE.
Population Dynamics Mortality, Growth, and More. Fish Growth Growth of fish is indeterminate Affected by: –Food abundance –Weather –Competition –Other.
ASSESSMENT OF BIGEYE TUNA (THUNNUS OBESUS) IN THE EASTERN PACIFIC OCEAN January 1975 – December 2006.
Humans and the Sea -- Fisheries, management, and sampling Millions of people depend on fisheries… in what ways? –Food 86 million tons/year –Jobs –Products.
Seabird and Shark Bycatches in the Pacific Ocean from Taiwanese Observer Data of ******** Shui-Kai Chang, Ju-Ping Tai & Chih-Hao Shiao.
REPORT OF THE 2007 MEETING OF THE SUB- COMMITTEE ON ECOSYSTEMS (Madrid, Spain - February 19 to 23, 2007)
Pelagic Longline Fishing Procedures & Gear
Spatial issues in WCPO stock assessments (bigeye and yellowfin tuna) Simon Hoyle SPC.
Humans and the Sea -- Fisheries, management, and sampling
Report of Chinese Observer Program in the Tropical Eastern Pacific Ocean in 2006 Xiao-jie Dai, Liu-xiong Xu and Li-ming Song Shanghai Fisheries University,
Fisheries in the Seas Fish life cycles: Egg/sperm pelagic larvaejuvenile (first non-feeding – critical period – then feeding) (first non-feeding – critical.
Historical and recent estimates of the body-size and abundance of pelagic species taken by longline Peter Ward RAM Myers Dalhousie University EB WP-7.
1 II-Main scientific and management results expected from the tagging programme 1) Stock structure and migrations 2) Tuna growth 3) Natural mortality as.
Summary of Atlantic Swordfish Species Working Group Discussion (see also SCI -021)
2007 ICCAT SCRS Executive Summay for Atlantic Bigeye Tuna 2007 ICCAT SCRS Executive Summay for Atlantic Bigeye Tuna.
CATCH LIMITS FOR INDIVIDUAL PURSE-SEINE VESSELS TO REDUCE FISHING MORTALITY ON BIGEYE TUNA IN THE EASTERN PACIFIC OCEAN.
Incorporating spatial autocorrelation into the general linear model with an application to the yellowfin tuna (Thunnus albacares) longline CPUE data 將空間自我相關與泛線性模式結合,並應.
POPULATION DYNAMICS Zoo 511 Ecology of Fishes 2009.
Recreational Fishing Organizations: The Missing Link in Cooperative Fisheries Research and Management Jason Schratwieser Conservation Director.
Management of the brown crab (Cancer pagurus) fishery in Ireland Oliver Tully Irish sea Fisheries Board (BIM)
Introduction to Multilevel Analysis Presented by Vijay Pillai.
CAN DIAGNOSTIC TESTS HELP IDENTIFY WHAT MODEL STRUCTURE IS MISSPECIFIED? Felipe Carvalho 1, Mark N. Maunder 2,3, Yi-Jay Chang 1, Kevin R. Piner 4, Andre.
A general covariate based approach for modeling the population dynamics of protected species: application to black footed albatross (Phoebastria nigripes)
O. Maury, MACROES meeting Brest 4-5 May 2010 MACROES WP 2 : Methodology in Ecosystems TASK 2.2: Improve the description of functional biodiversity within.
Yellowfin Tuna Major Changes Catch, effort, and length-frequency data for the surface fisheries have been updated to include new data for 2005.
Modelling population dynamics given age-based and seasonal movement in south Pacific albacore Simon Hoyle Secretariat of the Pacific Community.
1 PIRO’s Pelagic Ecosystem Management Needs PIFSC External Science Review April 5, 2016.
Day 4, Session 1 Abundance indices, CPUE, and CPUE standardisation
CPUE analysis methods, progress and plans for 2011 Simon Hoyle.
Seamounts hotspots of pelagic biodiversity in the open ocean Telmo Morato, S. Hoyle, V. Allain, S. Nicol I.S.R Portugal, IMAR-Açores Portugal, Secretariat.
Pacific-Wide Assessment of Bigeye Tuna
Estimation of Catches of Non-Target Species Using Observer Data
8.6 BLUE MARLIN AND WHITE MARLIN
ASAP Review and Discussion
Day 1 Session 1 Overview of tuna fisheries and stock assessment in the WCPO
TDW10: April 2016, Noumea, New Caledonia
Day 3 Session 3 Parameter estimation – Catchability and Selectivity
11/19/2018 Day 3 Session 3 Special Session – Uncertainty, the stock recruitment relationship and “steepness”
SESSION 5.4 Consequences for scientific data collection/management as a result of recent WCPFC decisions Sixth Tuna Data Workshop (TDW-6) April 2012.
WCPFC Ecosystem & by-catch Conservation and Management Measures
ANALYSIS OF SKIPJACK CATCH PER UNIT OF EFFORT (CPUE) Mark N
Longline CPUE standardization: IATTC 2006
SESSION 4 Annual Catch Estimates Introduction/Objectives – WCPFC Obligations Seventh Tuna Data Workshop (TDW-7) April 2013 SPC, Noumea, New Caledonia.
Observer data analyses: bycatch composition
Evaluation of the 2004 Resolution on the Conservation of Tuna in the eastern Pacific Ocean (Resolution C-04-09)
SESSION 4 Annual Catch Estimates Introduction/Objectives – WCPFC Obligations Sixth Tuna Data Workshop (TDW-6) April 2012 SPC, Noumea, New Caledonia.
NMFS Report SWFSC Activities Highly Migratory Species
Western and Central Pacific Tuna Fishery: Status and Challenges
Fifth Tuna Data Workshop (TDW-5)
Evaluation of the 2004 Resolution on the Conservation of Tuna in the eastern Pacific Ocean (Resolution C-04-09)
Ocean temperatures are projected to rise by 1. 4°C by 2050 and 2
Projected changes to ocean food webs and oceanic fisheries
Projected changes to ocean food webs and oceanic fisheries
Data from Barkley et al. Data from Barkley et al. (1978); Brill (1994); Dagorn et al. (2000); Schaefer and Fuller (2002) rendered into a three-dimensional.
Presentation transcript:

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

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

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

Swordfish Catch rate (no./1000 hooks) Soak time (hr) Data + Estimate of deployment time from start and end of time of set Observer record of time when each hook was retrieved

(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

Soak time effect bigeye skipjack swordfish Billfishes Tunas blue shark Sharks and rays albatross Other fishes Soak time effect varies among species Seabirds

Soak time effect correlated with survival Alive (%) Soak time effect r = 0.54 blue shark skipjack tuna

Dusk effect Dawn effect oilfish Dusk has a positive effect for many species blackmarlin Ray’s bream

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

012 Bigeye tuna Day 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

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

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’:

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:

Catch is the product of capture and loss rates 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

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

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