TECHNOLOGIES CURRENTLY DIRECTLY OR INDIRECTLY USED IN

TECHNOLOGIES CURRENTLY USED DIRECTLY OR INDIRECTLY IN

FISHERIES MANAGEMENT
Ian Hampton

Cape Town

South Africa

Surveys of stock size (absolute or relative)

Acoustic

Moving platforms
Stationary platforms

Trawl (swept-area)
Ichthyoplankton
Aerial
Sightings from surface

Stock definition

Distribution from surveys
Migration

Surveys
Mark/recapture
Spatial/temporal trends in size
Hydrodynamic models

Stock characteristics

Genetic characteristics (Electrophoresis, mitochondrial DNA)
Morphometric characteristics
Natural tracers (external markings, parasites)

Analysis methods

Survey data ® Stock Estimate (mean and variance)

Design-based methods
Model-based methods

Stock Estimate ® Stock Assessment (population modelling)

Survey estimates
Commercial catch data (VPA etc.)
Biological parameters

age & growth (optical image analysis, SEM etc.)
mortality (cohort analysis, predation estimates)
reproduction (histology)
recruitment strength (young fish surveys, cohort analysis)

Environmental inputs

temperature (remote sensing, CTD etc.)
production/ocean colour (fluorometric methods, SeaWIFS)
currents (ADCP, moorings)
models of fish/environment interactions

Stock Assessment ® Management Procedure

Simulation modeling of harvesting strategies
Socio-economic input
Interaction with interest groups

NEW TECHNOLOGIES/METHODS

ACOUSTIC SURVEYS

Problem	Abs/Rel	New or improved technologies/methods
Target Strength	A (+R?)	Extend density range of in situ method Improved single target recognition (multi-freq. split-beam, tracking) Shorten range to target (towed transducers, AUV) Integration + high-resolution counter Theoretical studies-improved models Improved ex situ experiments?
Calibration	A	Improve beam mapping - beam factor
Effective sampling volume (echo-counting)	A (+R?)	Better-defined beams - beam- forming
Inadequate resolution (echo-counting)	A (+R?)	Better-defined beams - beam- forming
Target identification	A + R	Multiple net samplers DFA Neural network analysis Frequency-dependence of back-scatter
Near-surface targets (avoidance and surface blind zone)	A + R	Multi-beam sonar (vertical +horizontal?) + distance-sampling analysis AUV
Near-bottom targets (dead-zone)	A + R	Towed transducers AUV Theoretical corrections
Sampling error	A + R	Improve survey designs effort allocation according to expected spatial structure adaptive sampling
Trawl selectivity (target identification)	A + R	Target identification by acoustics Observation of fish reaction to trawl (trawl sonars, video cameras etc.)
Aeration	A + R	Keel-mountings for transducers Towed transducers, AUV
Vessel motion	A + R	Towed transducers, AUV Transducer stabilisation
Noise limitations	A + R	Reduce vessel noise (e.g. diesel/electric propulsion) Towed transducers, AUV
Data analysis	A + R	Improve estimators of mean and sampling variance based on spatial modelling
Estimation of overall error	A + R	Error modelling ( e.g. Monte Carlo simulations), based on best estimate of nature and magnitude of individual sources of error

TRAWL SURVEYS

Problem	Abs/Rel	New or improved technologies/methods
Catchability coefficient (avoidance, herding, escapement)	A (+R?)	Observation of fish reaction to trawl (trawl sonars, video cameras etc.) Gear comparison experiments
Fish above net	A (+R?)	Assess acoustically (trawl-mounted or hull-mounted transducers)
Trawl performance	A (+R?)	Better use of net-monitoring equipment
Untrawlable grounds	A (+R?)	Use heavier gear More precise positioning of trawls in relation to bottom characteristics and features (make better use of sea-bed characterisation and trawl positioning systems) Re-survey acoustically when/if fish off bottom Sample by other fishing methods ( e.g. long-line)
Sampling error	A + R	Improve survey designs Trawl allocation according to expected spatial structure Adaptive sampling
Data analysis	A + R	Improve estimators of mean and sampling variance based on spatial modeling
Estimation of overall error	A + R	Error modeling (e.g. through Monte Carlo simulations), based on best estimate of nature and magnitude of individual sources of error

ICHTHYOPLANKTON SURVEYS

Problem	Abs/Rel.	New technologies/methods
Sampling variance	A +R	CUFES - use adaptively
Spawning fraction (egg production methods)	A + R	New histological methods ?
Egg counting	A + R	Optical plankton counters
Egg mortality	A + R	Increase sample size - CUFES
Net selectivity (larval surveys)	A (+R?)	Multi -samplers Compare with size distribution from multi-frequency acoustic methods
Survey design, data analysis and error quantification	A + R	As for acoustic and trawl surveys

AERIAL SURVEYS

Problem	Abs/Rel	New or improved technologies/methods
Low detectability	A + R	LIDAR Image-intensification, image enhancement and object- recognition systems
Identification	A + R	Combine with acoustic ground-truthing (cf. acoustic target identification methods) Automated object-recognition systems
Target quantification	A + R	Automated optical image-recognition and processing systems
Safety		Unmanned platforms?
Survey design, data analysis and error quantification	A + R	As for acoustic and trawl surveys

SHIPBOARD SIGHTINGS SURVEYS

Problem	Abs/Rel	New or improved technologies/methods
Detectability	A + R	Distance-sampling analytical methods Double-platform/observer methods (e.g. sightings + multi-beam sonar)
Biomass of schools	A (+R?)	Assess acoustically ( sonar + echo-sounder) - research and commercial vessels equipped with multi-beam sonar
Survey design, data analysis and error quantification	A + R	As for acoustic and trawl surveys

This page is maintained and was last updated 15-Nov-00 by Pip Sumsion (sumsionp@dfo-mpo.gc.ca).