Lecture 2 review Maximizing long term harvest can generally be achieved by following a “fixed...
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Transcript of Lecture 2 review Maximizing long term harvest can generally be achieved by following a “fixed...
![Page 1: Lecture 2 review Maximizing long term harvest can generally be achieved by following a “fixed escapement” harvest rule WHICH BRINGS STOCK TO ITS MOST PRODUCTIVE.](https://reader036.fdocuments.net/reader036/viewer/2022062409/56649ca65503460f94967f9b/html5/thumbnails/1.jpg)
Lecture 2 review
• Maximizing long term harvest can generally be achieved by following a “fixed escapement” harvest rule WHICH BRINGS STOCK TO ITS MOST PRODUCTIVE SIZE AS QUICKLY AS POSSIBLE THEN HOLDS STOCK AT THAT SIZE
• Nearly the same long term harvest can be achieved by following a “fixed exploitation rate” rule, much less damaging to fishers
• Tactics for regulating harvest rates involve either input (effort) or output (catch) controls
• Output controls are dangerous and require accurate assessments of stock size
• Complex management objectives and performance measures are an invitation to gridlock in decision making
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Limits to compensatory responses• Most populations exhibit high juvenile
survival at very low densities
• But occasionally (5-10%?) compensation fails at low densities, leading to low equilibrium or extinction
N
SJ
SJ
N
-Allee effect (eggs don’t get fertilized, eg scallops); rare
-Cultivation/depensation (competitors/predators of juveniles increase when N is low, eg bass-bluegill)
-Trophic cascades (green water/clear water states)
-Botsford’s effect (size dependent cannibalism)
(Invasive species have to exhibit this ability)
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Life history trajectories
• Whenever you handle a fish, ALWAYS ask yourself these questions:– How old is it?– Where was it spawned?– Where will it spawn?
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Life history stanzas (partitions of the life history trajectory)
The eggie
Larval drift, density-independent mortality
Juvenile migrationFirst juvenile nursery area: small, strong density-dependence in mortality
Spread into larger juvenile nursery area(s), mortality much lower
Adult foraging areas, most often with complex seasonal migration patterns
Spawning migration
Fractal, complex diurnal movement
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Characteristics of LHT
• There is typically very strong selection for behaviors that take fish back to spawn in the places where they were successfully produced (this is not just a salmon thing)
• Seasonal migrations become more pronounced as fish grow
Time
Random model
Distance from tagging site
Migration model
Distance from tagging site
Time
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Characteristics of LHT
• Natural mortality rates vary as M=k/(body length), starting at a few percent per day and often falling to a few percent per year
• Body growth typically follows a vonBertalanffy length curve of the form
length=L[1-e-K(a-ao)]• Sometimes there is a “kink” in the growth curve,
with small juveniles either showing extra fast growth (if they seek warm microhabitats) or extra slow growth (if they face very high predation risk).
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Is the Beverton-Holt invariant M/K=1.6 a valid generalization for stock
assessment?
y = 2.1161x
R2 = 0.5872
y = 1.6372x
R2 = 0.3182
y = 2.0041x
R2 = 0.8567
y = 1.1363x
R2 = 0.2195
0.01
0.1
1
10
100
0.01 0.1 1 10
vonBertalanffy K
Na
tura
l mo
rta
lity
ra
te M Age structure data
Length converted catchcurve
Tagging
Z vs E plot
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Characteristics of LHT• Maturation typically occurs at 50%-70% of maximum
body length, with fecundity then being proportional to body weight
0
10
20
30
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60
70
0 2 4 6 8 10 12
Age (years)
Len
gth
(cm
)
0
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1000
1500
2000
2500
3000
3500
4000
Wei
gh
t (g
)
Length (cm)
Hayes model Length
wt (kg)
Hayes Model weight
But some fish like these New Zealand brown trout practically stop growing at maturity, and make massive (45%) investments in eggs (Hayes et al TAFS 2000)
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Representing LHT in models
• Age structure accounting (block trajectory by even age intervals)
• Stanza structure accounting (Ecosim)
• Individual-based models (track movement)
[N1 N2 N3 …]t [N1 N2 N3…]t+1 (easy in spreadsheets)
Log Numbers at age
Age (months)
Weight at age
Log Numbers at age
Age (months)
Weight at age
X,Y positions and fates of large sample of individuals
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Ways to represent space in models
• Total areas by habitat class, without regard to spatial arrangement (A1,A2,…)
• Irregular spatial areas (“polygons”)
• Regular spatial cells (“rasters”)
A1 A2
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Spatial Management
Dealing with complex dynamics
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Spatial management is not just about MPAs
• Dynamic organization of shrimp fisheries: lessons for assessment, cooperative management
• Fishing for information: using logbook data to understand spatial stock structure and opportunities for more selective fishing practices
• Methods for modeling spatial stock dynamics
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A tale of two gulfs
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Spatial stock structure (Western king prawn)
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Spatial life history
Nursery
Juveniles
Fishery(Adults)
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St Vincent Gulf fishery
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Spencer Gulf fishery
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Contrasting management regimes
• St. Vincent Gulf (collapsed)– Ethnic fishery– Combative participants, severe misreporting– Assessments based on simple catch-effort
relationships• Spencer Gulf (sustained)
– Cohesive fishing communities– Neil Carrick: dogged persistence, many bar fights to
develop cooperative approach– Regulatory structure based on adaptive fishing policy
(time-area closures) based on repeated surveys and openings each year
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Cooperative spatial surveys in Spencer Gulf
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Assessment modeling options
• Empirical approach: fishing for information to map stock distribution and abundance several times during each season, cpue-based rule for ending annual fishery before depletion of spawning stock
• “Mechanistic” approach: develop detailed spatial model of physical drivers (currents, temperature, salinity), predict prawn recruitment, survival, movement
• (The mechanistic approach led to very costly research and modeling, never worked)
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Multispecies fisheries: tradeoffs caused by technical interactions so
some stocks overfished at MSY
0
100000
200000
300000
400000
500000
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700000
0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 0.55 0.6
Overall exploitation rate
Ave
rge
Yie
ld
SALIX CREEK
MOTASE LAKE
AZUKLOTZ CREEK
PINKUT BELOW WEIR
PINKUT ABOVE WEIR
PINKUT CHANNEL #1
FULTON BELOW WEIR
FULTON ABOVE WEIR
FULTON CHANNEL #2
FULTON CHANNEL #1
TWAIN CREEK
TSEZAKAWA CREEK
TAHLO CREEK
TACHEK CREEK
SOCKEYE CREEK
SIX-MILE CREEK
SHASS CREEK
PIERRE CREEK
PENDELTON CREEK
NINE-MILE CREEK
MORRISON CREEK
FOUR-MILE CREEK
BABINE - UNACCOUNTED
BABINE RIVER (SECTION4)BABINE RIVER(SECTIONS 1 - 3)ZYMOETZ RIVER - UPPER
NANIKA RIVER
CLUB CREEK - UPPER
CLUB CREEK - LOWER
SOUTHEND CREEK
ALASTAIR LAKE
KITSUMKALUM LAKE
CEDAR RIVER*
WILLIAMS CREEK
SOCKEYE CREEK
SCHULBUCKHANDCREEKBLACKWATER CREEK
SHAWATLAN CREEK
PRUDHOMME CREEK
J OHNSTON LAKE
DIANA CREEK
Lake
Skeena River sockeye salmon example: many stocks overfished at Fmsy
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Selective fishing practices to achieve variable F targets over species
• Most common: forced discarding of sensitive species (e.g. escape ramps for dolphins)
• Modification of gear deployment (e.g. bait types, set depths, mesh sizes, escape gaps and grids)
• Selective space-time openings (e.g. salmon)
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Temporal selectivity: Skeena River gillnet fishery example
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Spatial selectivity
• Use detailed logbook and survey data to map species distributions, identify areas of high overlap and/or density of sensitive species (e.g. Fishmap)
• Adaptive spatial closures based on the mapping (e.g. Carrick’s shrimp fishery)
• Also use the mapping to develop “folly-fantasy” spatial cpue indices for long-term stock assessment
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Partial separations in spatial distributions, BC trawl fishery
Sensitive species (longspine rockfish, Fmsy=0.05)
Productive species (English sole, Fmsy=0.2)