Reza Hakimimofrad- fish populations Abundance Estimates

7/31/2019 Reza Hakimimofrad- fish populations Abundance Estimates

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Estimating Abundance

Reading: Chapter 10

Survey design Visual censuses

Acoustic methods

Trawl surveys

Depletion estimates

Mark-recapture estimates

Egg Production Methods

Fishery-dependent CPUE


Why do we need to estimate abundance?

To estimate:

1. Stock size

2. Recruitment

3. Mortality

4. Spatial distribution


Survey design

A central problem is obtaining an abundanceindex that is proportional to stock size

Well-designed survey should provide estimates of: average fish abundance or density and

Spatial distribution (survey boundaries?)

Accuracy vs. Precision


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Accuracy Precision


Survey design

A central problem is obtaining an abundanceindex that is proportional to stock size

Well-designed survey should provide estimates of:

average fish abundance or density and

Spatial distribution (survey boundaries?)

Accuracy vs. Precision

Bias vs. Variance

precision ( error) = $

Sample size (n)

5 10 15 20 25 30 35

SampleE

rror(%)

40

50

60

70

80

90

100

110

Sample error vs. sample size


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Survey design

Stratification by habitat type or depth Combine abundance estimates across strata

Increases precision

Systematic vs. Random sampling

Systematic can be more precise and generallyreduces costs


Visual censuses

Require clear, shallow waters

Best with non-cryptic fish that dont avoid divers

Can see fish and habitat Transects most common

Point counts (timed or instantaneous)

Behavior


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Acoustics

Use of sound waves to detect fish (swim bladder)

Best for pelagic fishes

Target strength is species-specific and must bedetermined experimentally

Simultaneous trawling to ground-truth catch

Problems with acoustic shadows and avoidance

Very promising for well understood pelagic stocks


Depletion (or Removal) estimates

Relation between abundance and catch rate

Requires:

Closed population

Short fishing period (no recruitment) Catchability proportional to abundance

CPUE (C/f) = qNtNt = N0 KtCPUE (C/f) = qN0 qKt

Plot CPUE vs. cumulative catch (K) (known as Leslie method)


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Kt

CPUE

Leslie Method

Estimate of N0Slope = -q

Consecutive sweeps with 100ft. seine (Fall 2003)

1st haul 2nd haul 3rd haul

Numbercaptured

0

5

10

15

20

25

30

35

40

45

Pinfish

Cumulative Catch

40 50 60 70 80 90 100

CPUE

0

10

20

30

40

50

Mullet

Cumulative Catch

20 25 30 35 40

CPUE

0

5

10

15

20

25

Spot

Cumulative Catch

5 10 15 20 25

CPUE

0

2

4

6

8

10

12

14

Shrimp

Cumulative Catch

5 10 15 20 25

CPUE

0

2

4

6

8

10

Depletion estimates of abundance (Fall 2003)


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60ft seine pulled inside 100ft seine (Fall 2003)

1st haul 2nd haul 3rd haul 4th haul

Numbercapture

d

0

5

10

15

20

25

Mullet

Spot

Pinfish

Blue crab

Fort Fisher Field trip 2004Depletion estimation using 100ft. seine

Seine haul

1st haul 2nd haul 3rd haul

Numberscaptured

0

20

40

60

80

100

120

140

Pinfish

Mojarra

Atl silverside

Ladyfish

Total fish

Pinfish

K70 80 90 100 110 120 130

CPUE

0

20

40

60

80

100

All species

K75 100 125 150 175 200 225 250 275 300

CPUE

0

20

40

60

80

100

120

140

160

180

200

Mojarra

K0 50 100 150 200

CPUE

0

10

20

30

40

Atl. silverside

K0 10 20 30 40 50

CPUE

0

5

10

15

20



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Pinfish

Cumulative catch (K)

500 520 540 560 580

CPUE(#perhaul)

-100

0

100

200

300

400

500

600

Atl. silverside


70 80 90 100

CPUE(#perhaul)

0

10

20

30

40

50

60

70

80

Atl. croaker


7.8 8.0 8.2 8.4 8.6 8.8 9.0 9.2 9.4

CPUE(#perhaul)

0

2

4

6

8

10

All species


850 900 950 1000 1050

CPUE(#perhaul)

-200

0

200

400

600

800

1000


Total Length (mm)

0 20 40 60 80 100 120 140

Relativefrequency(%)

0

10

20

30

40

50

60

20 ft seine

Total Length (mm)0 20 40 60 80 100 120 140

Relativefrequency(%)

0

2

4

6

8

10

12

14

16

60 ft seine

n = 71

n = 210

Size-selectivity of beach seines (Fall 2005)


Depletion (or Removal) estimates DeLury Method

CPUE (C/f) = qNtCPUE (C/f) = qN0(Nt/N0)

ln CPUE = ln qN0 + ln (Nt/N0)

Substitute Nt/N0 = e-qE

ln CPUE = ln qN0qE

Plot ln CPUE vs. cumulative effort (E)


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Cumulative effort (E)

lnCPUE

DeLury Method

slope = -q

y-int. = ln qN0


Trawl surveys

Very widely used, most common

Mesh size regulates fish size

Constant catchability (q) essential; lack ofstandardization is major problem

Consistent gear design, tow speed, duration helpto maintain q

C = qfN

CPUE = qD

Stock biomass = D x area


Trawl surveys

Many factors affect catchability (q)

Tow speed

Depth

Time of day Vessel noise

Mostly, q is unknown, but..

If q is constant, then estimated stock biomass willbe proportional to actual stock size


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Surveys

Annual method

Daily method


Whats wrong with using CPUE from fishery?

It provides catch and effort data from large areasover long time scales, so why not use it?

Often times it is used, only data available

Landings data omits discards (bycatch, undersize)

Catch/effort data hard to get for every boat

CPUE (LPUE) rarely proportional to abundance

No gear standardization

Capture efficiency increases with time

Fishers dont fish randomly


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Fig. 10.15. Spatialdistribution

of commercialtrawling effort(hours per year)in the North Sea

Fig. 4.16. Distributionof Atlantic cod in theGulf of St. Lawrence,showing range expansionand contraction overtwenty years

Fig. 4.17. Occurrence oflow, medium, and highcatches of Atlantic cod

in research vesselsurveys as thefishery collapsed


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Fig. 4.18. How the

calculation of meancatch rate can affectthe interpretation offishery trends, examplefrom northern cod

0

1

2

3

4

5

6

7

8

9

0 1000 2000 3000 4000

Biomass (Tons)

CatchabilityCoefficien

Abundance


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Hypoaggregation

Hyperaggregation

Abundance

Density

Abundance

CatchperEffort(CPUE)

CPUE remains high due to aggregation of fish

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Reza Hakimimofrad- fish populations Abundance Estimates