Electrofishing Efficiency and Sampling Design 6

Electrofishing Efficiency and

Sampling Design6

Session PurposeThe primary purpose of this module is to help biologists increase the accuracy and precision of sampling by

1. improving efficiency of capture, and2. using a well-planned sampling design.

• Sampling design includes standardization (controlling important efficiency factors), optimizing sample unit number and location, and refining monitoring variables (should you use an index as CPUE or a population estimate?).

• We also will use some simple models to investigate the implications of capture probability and sampling variance, number and placement of sampling units, and fish population characteristics on our decision to monitor abundance by CPUE alone or by using population estimates instead.

First, A Couple Concepts

• Definition of capture efficiency

• The Catch Equation

Definition of Capture Efficiency

??

Definition of Capture Efficiency

• Proportion of population captured by a particular sampling gear and 1 unit of effort

• Example: N = 1000 fish*Captured = 150 fishEfficiency = 15%

*Population size usually estimated by capture-recapture, depletion sampling, or total recovery (rotenone, draining)

Capture Efficiency (“q”) and Capture Probability (“p”)

C=N*q*EC=N*pp=q*E

where,N = true number of fish in a

sample areaC = number of individuals captured in a

sample areaq = capture efficiencyE= effort (e.g., proportion of sample area

fished)p= capture probability

Area (A) containing the population total population (N)Area Sampled (a)

Catch =N*q*E=N*q*(a/A)

N = Catch/(q*E)N = Catch/(q*(a/A))

Area (A) containing the population total population (N)Area Sampled (a)

Catch =N*q*E=N*q*(2*a/A)

N = Catch/(q*E)N = Catch/(q*(2*a/A))

Area (A) containing the population total population (N)Area Sampled (A)

Catch =N*q*E=N*q*(A/A) =N*q

or N = Catch/q

Effort =1 sop=q If you sample all of A,

then capture probability (p) equals efficiency (q)

Improving Efficiency of Capture

• A strong approach is outlined in Framework for Increasing Sampling Efficiency and Precision of a Species

– This outline incorporates much of what has been learned to this point in the course.

– This protocol combines field and lab work to identify most effective electrical waveforms and electrode designs. Derived power standardization tables are coupled with equipment power analysis.

Sampling Design

• Sampling design is about reducing bias and increasing accuracy and precision– i.e., results in estimation of a population

parameter (as abundance) that is likely representative and has narrow confidence intervals

• This function is accomplish by– where and how much to sample– standardization– refining the variables monitored

Sampling Design

• Where and how much to sample

– Three common frameworks: simple random sampling (SRS), stratified random sampling (STRS), and systematic sampling (SYS).

– (There are others as adaptive cluster sampling, etc.)

Sampling Design

• NOTE: calculation of parameter estimates as total abundance, means, and confidence intervals depends upon the sampling design.

• For a tool to learn about these 3 common designs and to use for analysis of your data, see

– Sampling Design Analysis

Sampling Design

• Another tool to learn about how sample size, capture probability, and target fish population size influences point estimates and confidence intervals of catch-per-unit-effort , see

– CPUE Analysis Planner

Sampling Design

• For a slightly different purpose of estimating the ability to detect rare species, again with considerations of sample size, capture probability, and target fish population size, see

– Detection Probability

Sampling Design

• Standardization

– Standardization is a set of approaches to control the influence of important efficiency factors.

Sampling Design• We have theoretical and empirical basis to

approach controlling the effects of water conductivity (“power standardization”)

• The influence of other efficiency factors identified as important for certain water bodies can be addressed by• Sampling only above, below, or within a range of

values (as only sample when water temperature is above a particular reading)

• Incorporate into efficiency equations for adjusting catch to a population estimate (this is not a way of standardization within a sampling design and will be discussed later in this module).

Sampling Design

• The following section is a in-depth view of potential efficiency factors and how the sampling variation introduced by these factors can be minimized by standardization.

Three Categories of Efficiency FactorsBiological

Environmental

Technical

Biological Factors

• Fish factors– Size– Habitat preference– Behavior – Population size or density– Effective conductivity

Biological FactorsMore efficient with larger-sized fish

Several studies have shown greater proportion of larger-sizedindividuals in a population are captured (higher q) by EF. Capture efficiency models often have fish length as a factor.

Study on Size-based Efficiency

Efficiency of electrofishing as a function of total length forlargemouth bass in 27 ponds. Primary sampling by boat electrofishing one lap of shoreline. Secondary sampling by rotenone. Reynolds & Simpson (1978) AFS Spec Pub #5.

Biological Factors

Less power needed per fish volume withlarger-sized fish

Also, the main factor in susceptibility toelectroshock was size (volume or length)

Dolan & Miranda 2003

PDC 15-6

0

500

1000

1500

2000

2500

3000

3500

0 5 10 15 20 25 30 35

Fish Length (cm)

Pt .9

5 (µ

W/c

m3 )

0

10,000

20,000

30,000

40,000

50,000

60,000

est. Total Power

Dolan and Miranda (2003)

Power vs. Fish Length

Pt.95 = 95% probability power threshold for immobilization

PDC 110-1

0

100

200

300

400

500

600

0 5 10 15 20 25 30 35

Fish Length (cm)

Pt .9

5 (µ

W/c

m3 )

01,000

2,0003,0004,000

5,0006,0007,000

8,0009,000

est. Total Power

Dolan and Miranda (2003)

Power vs Fish Length

Pt.95 = 95% probability power threshold for immobilization

PDC 15-6

0.0

1.0

2.0

3.0

4.0

5.0

6.0

0 5 10 15 20 25 30 35

Fish Length (cm)

est.

Volt

Gra

d (V

/cm

)

0

5

10

15

20

25

30

35

Head-Tail Voltage

Dolan and Miranda (2003)

Voltage Gradient vs Fish Length

Est. Volt Grad = voltage gradient requiredfor immobilization

PDC 110-1

0.0

0.5

1.0

1.5

2.0

2.5

0 5 10 15 20 25 30 35

Fish Length (cm)

est.

Volt

Gra

d (V

/cm

)

0

2

4

6

8

10

12

14

16

Head-Tail Voltage

Dolan and Miranda (2003)

Voltage Gradient vs Fish Length

Est. Volt Grad = voltage gradient requiredfor immobilization

Biological Factors

Note: in many cases, lowest efficiencies have been observed for extreme sizes (very small and large individuals); although less power may be required for larger fish and they are easier to see, larger fish may have a greater ability to escape the electric field (“fright bias”)

Biological Factors

Habitat Use (behavior, anatomy)

Sculpins are benthic with absent swim bladders and often reside under rocks in shallow water

More power required to immobilize and need careful technique to recover specimens

Biological Factors

Habitat Use

Lower efficiencies possible with deep water benthic fishes, especially those species with reduced or absent swim bladders

Lower efficiencies possible for wide-ranging pelagic species (gizzard shad, striped bass)

Biological FactorsHabitat Use

Species # of PondsDetection

Success %Bluegill sunfish 27 100LM bass 24 96Crappie 8 25Green sunfish 9 67Redear sunfish 4 100Channel catfish 5 20Bullhead 5 20Golden shiner 5 20

Biological FactorsBehavior

Spawning aggregations: often high catchability because fish individauls are larger, maybe more territorial, and often in shallower water

Beware of the possibility of injury and reproductive impairment issues

Biological FactorsPopulation size or density

Gear saturation: catchability declines at high fish abundances or density

Hansen et al. (2004) found that catchability of Age-0 walleyes (Sander vitreum) to electrofishing declined with increasing population density.

Steeves et al. (2003) observed that capture efficiency declined with higher densities of sea lamprey (Petromyzon marinus) larvae

Biological Factors

Kolz suggested an effective fish conductivity of 150 µS/cm

Miranda & Dolan (2004) suggested an effective fish conductivity of 115 µS/cm (eight species)

Fish evaluated were primary freshwater fish. Species with other life histories, as secondary freshwater fish, may havedifferent effective body conductivities.

Effective Fish Conductivity

Channel Catfish - 60 Hz PDC(Miranda and Dolan 2003)

Quadratic MethodPf = 14.6, Cf = 138, r² = 0.627

y = 0.5673x2 - 2.4264x + 3.7602R2 = 0.627

0.8

1.0

1.2

1.4

1.6

1.8

1.0 1.5 2.0 2.5 3.0 3.5

log Water Conductivity (µS cm-1)

log

App

lied

Pow

er D

ensi

ty (µ

W c

m-3

)

Effective Fish Conductivity for an Ictalurid* Catfish

*considered an “outlier” family as regards EF in the U.S.

Immobilization thresholdsLimited size range

Channel Catfish - 60 Hz PDC(Miranda and Dolan 2003)

Miranda & Dolan (2003) MethodPf = 15.2, Cf = 162, r² = 0.967

0.0

0.4

0.8

1.2

1.6

2.0

0 200 400 600 800 1000 1200

Water Conductivity (µS cm-1)

Volta

ge G

radi

ent (

V0.

95 c

m-1

)

Required Voltage Gradients across Water Conductivities for Channel catfish

Immobilization thresholdsLimited size range

Biological FactorsPDC (50%) - 300V

0.0

0.5

1.0

1.5

2.0

0 200 400 600

Conductivity (µS/cm)

ε (V

/cm

)

100

120

140

160

180D (µW

/cm3)

ε D

Electrofishing power requirements (Kolz & Reynolds 1989)

Environmental Factors• Water quality

– Ionic concentration– Temperature– Conductivity (ionic concentration & temperature)– Turbidity– Depth– Lotic flow rate (discharge)

Environmental FactorsAmbient water conductivity results from ionic

concentration and temperature

VIPE: very important piece of equipment

PDC (50%) - 300V

0

3

6

9

12

0 200 400 600

Conductivity (µS/cm)

Cur

ent (

A)

0

1000

2000

3000

4000

Power (W

)

Amps Watts

PDC (50%) - 300V

0.0

0.5

1.0

1.5

2.0

0 200 400 600

Conductivity (µS/cm)

ε (V

/cm

)

100

120

140

160

180

D (µW/cm

3)

ε D

Effects of Conductivity Change

Typical Patterns

Electrical output characteristics Electrofishing requirements

Appalachian Aquatics AA-24Backpack Electrofisher

0

100

200

300

400

500

600

700

0 50 100 150 200 250 300 350

Ambient Water Conductivity (µS/cm)

Volta

ge S

ettin

g G

oal

Voltage Goal vs. Conductivity

Environmental Factors

Water temperature:

• water temperature as a standardization variable, when used, is often a value selected "not too exceed", due to concerns about higher temperature induced stress/mortality, particularly of salmonids.

• less commonly, temperature standardization is a value selected "not to go below", due to low temperature mediated slow-down of fish floatation rates. Largemouth bass catchability has been observed to decline when water temperatures fall below 6 degrees C. Electric seine hauls in Illinois are conducted more slowly in “winter temperatures”.

• Burkholder and Parsons (2001) recommended that yoy walleye are sampled within a restricted temperature range (10 – 20°C) in Fall due to the curvilinear relationship between CPUE and water temperature.

Environmental FactorsTurbidity or clarity

Turbidity to an intermediate range can increase catchability

Environmental FactorsQuiz question: high or low water conductivity?

Environmental Factors

Depth, how low can you go?

• Physical Habitat

Environmental FactorsHigh discharge: fishes spread out and can be more difficult to sample

Environmental Factors

• Substrate– Size distribution and conductivity

• Lentic water body shape– Round vs. many coves

• Coarse woody debris (e.g., downed logs)

Environmental FactorsSubstrate size and conductivity

Conductive substates can help by lowering cathode resistance (e.g., trailing cathode)Non-conductive substrates do not shield buried eggs from electric fields

Environmental FactorsWater body shape

Catchability of some species higher in more elongated lakes with coves

Environmental FactorsCoarse Woody Debris

Catchability of some species higher in structure

Environmental FactorsCoarse Woody Debris

Electrofishing catchability higher in ponds with structure; all ponds stocked with same number of fish.

Chick et al. (1999) found that catch rates were higher in areas with greater emergent-stem density within the Florida Everglades.

CWD Effects on Catch in Ponds

Cedar

Hardwood

No structu

re

Structure Type

1

2

3

4

5

6

7

8

Aver

age

fish

capt

u red

Technical Factors

• Equipment (power capacity, electrode design, waveform, type)

• Crew experience• Time of day• Sampling design

Technical Factors

Power source: may reach limitation in conductivity extremes

Electrodes: can be modified for the conductivity regime; lower cond. = largerhigher cond. = smaller(moderate cond.: try to make as large as system will drive and logistics allow while keeping a favorable power allocation).

Technical Factors

Waveform: AC can be more effective in high and low conductivities. DC (maybe PDC) can give good taxis. Fish usually more susceptible to higher frequencies. Waveform shape can have relatively small effect (little unstudied, however).

Voltage gradient thresholdsfor common carp attractionor immobilization acrossthree PDC waveform shapes.

D.J. Bird & I.G. Cowx. 1993.Fisheries Research 18:363-376

50 Hz PDC; Vertical line = 1 standard error

Pulsed Direct CurrentWaveform Shape

Technical Factors

PDC 15-6

0

500

1000

1500

2000

2500

3000

3500

0 5 10 15 20 25 30 35

Fish Length (cm)

Pt .9

5 (µ

W/c

m3 )

0

10,000

20,000

30,000

40,000

50,000

60,000

est. Total Power

Fish usually more susceptible to higher frequencies.

FrequencyPDC 110-1

0

100

200

300

400

500

600

0 5 10 15 20 25 30 35

Fish Length (cm)

Pt .9

5 (µ

W/c

m3 )

01,000

2,0003,0004,000

5,0006,0007,000

8,0009,000

est. Total Power

The power threshold for immobilization of a 5 cm fish by 15 pps PDC is six times that needed using 110 pps PDC

Technical Factors

Frequency

1 10 100 10000

0.5

1

1.5

2

2.5

Frequency vs. Threshold Voltage Gradientfor Immobilization of Salmo irideus

Frequency (pulses per second)

Volta

ge G

radi

ent (

Volts

/inch

)

Burnet, A.M.R. 1959. Electric Fishing with Pulsatory Direct Current. New Zealand Journal of Science 2:46-56.

Effects of Duty Cycle • Miranda & Dolan (2004) found that

electrofishing effectiveness was strongly associated with duty cycle.

• Duty cycles of 15 – 50% required the least peak power to immobilize fish.

Dolan and Miranda (2003)Power Density vs Duty Cycle - 100 cm3 fish

Dm = 70.5 µW/cm3 fish at 30.9% D.C.

y = 0.9898x2 - 2.9493x + 3.0454R2 = 0.9471

0.5

1.0

1.5

2.0

2.5

3.0

0.00 0.50 1.00 1.50 2.00 2.50

log Duty Cycle

log

Pow

er D

ensi

ty to

Fis

h

Minimum Power at 31% duty cycle

What is a frequency and pulse width setting that would give a ~31% duty?

Dolan and Miranda (2003)Power Density vs Duty Cycle - 300 cm3 fish

Dm = 37.0 µW/cm3 fish at 30.1% D.C.

y = 0.922x2 - 2.7269x + 3.0615R2 = 0.973

0.5

1.0

1.5

2.0

2.5

3.0

0.00 0.50 1.00 1.50 2.00 2.50

log Duty Cycle

log

Pow

er D

ensi

ty to

Fis

h

Minimum Power at 30% duty cycle

Standardization by Waveform• May be a factor of 4 characteristics:

– Waveform type (AC, DC, PDC)

– PDC waveform shape (exponential, ¼ sine, rectangular, etc.)

– Water & Fish conductivity mismatch (standardizing by power)

– Duty cycle for PDC, which includes frequency and pulse width (e.g., 30%)

Technical Factors• To minimize sampling bias, should collect fishes with the most efficient

method or combination of methods (for which bias is known – best) - many gear comparison studies, see references for examples

• May need specialized equipment (electric seine vs. backpack, pre-positioned area shockers vs. boat, deepwater cathode deployment);

• May need to use multiple gear types (electrofishing and non-electrofishing gears; e.g., see Ruetz et al. 2007)– Small-bodied species selected by fyke nets– Large-bodied species selected by boat electrofishing– In the Colorado River (large river), electrofishing was more effective for

collecting juveniles and trammel nets more appropriate for adults (Paukert 2004 J. Fish Biology 123:1643-1652)

• May need to change sampling design (e.g., point-transect sampling with a boat or backpack)

Technical Factors

Inexperienced crews, or crews inexperienced with a particular water body, have been shown to have lower catch rates than experienced crews.

Technical Factors

Night fishing often more effective in reservoirs and lakes (see McInerny & Cross (2000) for LMB). Predators move into shallows. Fish assemblage in area can change composition from day to night.

Technical Factors (sampling design)

Point-sampling (15 min. with chase boats) for large Ictalurids

*Also, boats with long dropper cathodes drift down-stream followed by a chaseboat for large Ictalurid catfish

Refining Monitoring Variables

• This is the final aspect of sampling design covered in this course.

• The main questions are

– Should you use an index as catch-per-unit-effort (CPUE) or a population estimate to assess your fish populations?

– How do you use CPUE to assess population status?

– How do you adjust catch (CPUE) to calculate a population estimate?

Indices in Fisheries• Catch-effort measures (e.g., relative

abundance and catch per unit effort [CPUE]) are indices– an index is a count of fish number or species

richness without an estimate of the ability to make the counts

– desirable attributes• usually require less effort to collect the data• can be more precise than population estimators

(as depletion estimation)– often used to assess fish population size or

assemblage structure for determining biotic integrity

Relating Indices to Fish Abundance

• Valid use of indices for assessment of fish populations or assemblages requires that the relationship between an index (CPUE) and abundance is relatively constant– across the observable range of index values;– through time when evaluating trends at a single

location; and– across space when making comparisons among

locations

Relating Indices to Fish Abundance

• What if efficiency (q) is NOT relatively constant and we assume it is?

Why Worry? Thought Experiment 1Suppose we want to estimate the rate of change in

population size of LMB in Lake Conway:

This is a question if we are monitoring population trend.

What should we use for the N’s?

2009

2010

NN

Why Worry? Thought Experiment 1Suppose we use Index data to estimate rate of

change:

2009

2009

2010

2010

ˆ

EC

EC

20092009

20102010

**ˆqNqN

2009

2010*ˆqq

Remember,

C = N*q*Eso,C/E = N*q

Why Worry? Thought Experiment

How likely is this to be true?What could cause it to be false?

What if this relationship is false?

12009

2010 qq

Why Worry? Thought Experiment 2Suppose we want to know whether the density in

Lake Conway and Lake Maumelle are the same:

?MaumelleConway DensityDensity

MaumelleConway CPUECPUE

MaumelleMaumelleConwayConway qDenqDen

Conway

MaumelleMaumelleConway q

qDenDen

Why Worry? Thought Experiment

How likely is this to be true?What could cause it to be false?

What if it is not true?

1Conway

Maumelle

qq

Capture Efficiency (“q”)Fish Density vs. Electrofishing CPUE

0 100 200 300CPUE (#/hr)

0

50

100

150

200

Fish

Den

sity

(#/h

a)

Hall (1986)

Higher q

Lower qSlope = 1 ∕ q

Fish Density vs. Electrofishing CPUE

0 100 200 300CPUE (#/hr)

0

50

100

150

200

Fish

Den

sity

(#/h

a)

Capture Efficiency (“q”)

• Example: two sites are sampled; equal q is assumed but in reality Site A has a higher q than Site B. Although CPUE is lower in site B and the conclusion from the index is lower abundance, the true abundance is the same for both sites.

Higher q

Lower q

Site ASite B

N = CPUE ÷ q

Slope = 1 ∕ q

Relating Indices to Fish Abundance

CPUE

Abundance

Hyper-stable

Hyper-depletion

Proportional

Relating Indices to Fish Abundance

• Problem: one or more of these constraints unlikely to be met due to changing conditions

– the relationship between CPUE and abundance at a sample site may vary across time

– the relationship between CPUE and abundance among sample sites may be different

– the relationship between CPUE and abundance may not be constant across range of abundances• hyper-stability or hyper-depletion

Relating CPUE to Fish Density(Single Site Across Time)

• Some studies have demonstrated a strong relationship between CPUE and fish density.– Largemouth bass density across size classes in a 0.66

ha pond over 5 yrs. (#/ha vs. CPUEhr; r = 0.97) (Gabelhouse 1987)

Relating CPUE to Fish Density(Across Sample Sites)

• Reynolds & Simpson (1978) found that boat electrofishing catch per 30.5 m of shoreline in ponds (0.5 ha average) was correlated with abundance per 30.5 m shoreline for

≥ 8” bass (r = 0.6 to 0.7); and ≥ 3” bluegill (r = 0.40 to 0.48)

• Abundance was estimated by rotenone census

Relating CPUE to Fish Density(Across Sample Sites)

• Hall (1986) derived linear regression prediction model for LMB (> 199 mm) in Ohio impoundments

log10 (LMB # per hectare) = -0.55 + 1.23(log10(CPUEhr))

R2 = 0.83Water temperature, conductivity, and turbidity were

measured but not included directly in the statistical analysis

Relating CPUE to Fish Density(Across Sample Sites)

– Largemouth bass densities (#/ha) in 2 large reservoirs (4,900 ha & 13,160 ha) were related to CPUE (#/100 m), R2 = 0.96; stratified random sampling used (McInerny & Degan 1993)

– Largemouth bass densities (#/ha) in 12 impoundments (0.4 to 8.3 ha) were modeled by CPUEhr and by water conductivity (a covariate).

log10(density) = -2.144+1.082(log10(CPUE)+0.847(log10(Conductivity)R2 = 0.88

- Specific water conductivity varied from 410 to 1,700 μS/cm- Water temperature was not a significant variable in the

stepwise multiple linear regression analysis(Hill and Willis 1994)

Relating CPUE to Fish Density(Across Sample Sites)

• Chick et al. (1999) investigated whether airboat electrofishing CPUE in shallow, vegetated habitats is a useful index of overall fish (SL > 8 cm) abundance.– 17 species– Log10(CPUE5min) alone vs. fish/0.1 ha R2 = 0.84– Log10(CPUE5min) & emergent stem density vs. fish

density R2 = 0.96– no relationship with floating-mat volume, water

depth, or water conductivity– standardized by power and removed water

conductivity as a factor.

Relating CPUE to Fish Density (Across Sample Sites)

– However, length-frequency and species relative abundance (assemblage structure) data differed between electrofishing and block net rotenone samples

• yellow bullheads, Seminole killifish, sunfishes, and small size-classes of all species lower for electrofishing

• largemouth bass, Florida gars, and large size-classes of all species had greater relative abundance in the electrofishing samples

Relating CPUE to Fish Density• But…

– McInerny & Cross (2000) in Minnesota found that • “q” decreased with increasing LMB density• Turbidity, water conductivity, temperature, and percent littoral

area affected CPUE depending upon season and time of day• Recommended that

– CPUE obtained in Spring provides best index of density– Effects of density on q must be determined and environmental

conditions must be similar before CPUE can be a useful index of LMB density

– Minnesota DNR developing standardized protocols for largemouth bass sampling using factors found to affect CPUE

– Hansen et al. (2004): “q” decreased with increasing walleye age-0 density; variability in q related to water conductivity but not to shoreline complexity, littoral area, pH, or alkalinity; recommended that CPUE only be used as a crude index of age-0 walleye population density

To Use or Not to Use CPUE ?• If q has low variance, then CPUE may serve as a reliable

population density index or predictor variable of population size; reason for “standardization”.– For a tool to compare the efficacy of CPUE vs. population

estimation, see Trend Analysis

• Your choices (most to least rigorous):– Estimate q for every survey.

• Appropriate for high resolution management

– Model q as a function of important covariates.• Considered essential by some for monitoring.

– Stabilize q (especially for indices)• Main purpose of standardization protocols.• Also makes modeling q more reliable.

– Pray• Use CPUE as an index without knowing q and hope for the

best.

Estimate q for Every Survey

• Capture efficiency or capture probability can by estimated by depletion sampling, mark-recapture, or census techniques as rotenone.

• For investigating data and estimating capture efficiency, see– Depletion sampling 4 Pass– Depletion sampling 3 Pass– Mark-Recapture Analysis

Estimate q for Every Survey

• Example:

– Colorado River rainbow trout

• AVG p^=.17• CV(p^)=0.46

Rainbow trout abundance over time

p^ = estimatedcapture probability

CV = coefficientof variation

Catch per uniteffort adjusted toabundance bycapture probability

• AVG p^=.07• CV(p^)=0.34

Rainbow trout abundance over time

Note that catch per unit effort can increase because of increasing abundance or increasing capture probability.

Model q as a Function of Important Covariates

Modeling Capture EfficienciesEstimate capture efficiencies (e.g., via mark-recapture or

depletion) and measure important efficiency factors (as meandepth) at a number of sample sites

Derive a model relating capture efficiency to the measured variables using logistic regression

Use the model to adjust catch to abundance at future sample sites by measuring efficiency factors

Modeling Capture Efficiencies

• For an example of three efficiency equations and how they are used to adjust catch to a population estimate, see

– Capture Efficiency Models

Next Step

“Electrofishing Equipment” (Module 7)