Wild Turkey Harvest

Management: Biology,

Strategies, and Techniques

Biological Technical Publication

BTP-R5001-1999

U.S. Fish & Wildlife Service
R. Wayne Bailey, pioneering wild turkey researcher, checking successful fall turkey hunters, Pocahontas

County, West Virginia, 1969. U.S. Forest Service, John D. Gill
Wild Turkey Harvest

Management: Biology,

Strategies, and Techniques

Biological Technical Publication

BTP-R5001-1999

William M. Healy

U.S. Forest Service, Holdsworth Natural Resources Center, University of Massachusetts,

Amherst MA 01003 telephone 413/545 1765

Shawn M. Powell

Department of Forestry and Wildlife Management, University of Massachusetts, Amherst,

MA 01003. Present address: 660 Mountain Road, West Hartford, CT 06117 

telephone 860/233 8132 

U.S. Fish & Wildlife Service
ii Wild Turkey Harvest Management: Biology, Strategies, and Techniques

The Authors

William M. Healy is a wildlife biologist with the U.S.

Forest Service, Northeastern Research Station,

located in Amherst, Mass. Bill has a Bachelor’s degree

in forestry and a Master’s degree in wildlife

management from Penn State University. He began

his research career with the U.S. Forest Service in

Morgantown, W.Va. where he studied turkey brood

and winter habitat use. He earned a Ph.D. from West

Virginia University studying the relationships among

poult feeding activity, insect abundance, and

vegetation structure. Since moving to Massachusetts

in 1982 Bill has continued studies of forest wildlife

habitat relationships including the effects of whitetailed deer on forest vegetation, and the relationship

between acorn crops and small mammal abundance.

Shawn M. Powell earned a Bachelor’s degree in

ecology and evolutionary biology at the University of

Rochester, New York. He completed a Master’s

degree in biology at the University of California, Los

Angeles, studying the feeding ecology of mink and

river otters inhabiting coastal Vancouver Island,

British Columbia. He worked for the Environmental

Resources Section at the New England Division of the

U.S. Army Corps of Engineers, and later as a biologist

with the U.S. Fish and Wildlife Service in Carlsbad,

Calif. With the Fish and Wildlife Service he studied

California gnatcatcher ecology, and worked in several

other projects involving threatened or endangered

songbirds, reptiles, and mammals. He returned to

New England to work as a research associate for the

University of Massachusetts and the Massachusetts

Cooperative Fish and Wildlife Research Unit, where

he studied various aspects of the ecology of fishers

and black bears inhabiting western Massachusetts and

southern Vermont.

Acknowledgments

This report would not have been possible without the

help of all the members of the Northeast Wild Turkey

Technical Committee. Under the direction of Bob

Sanford, committee members had been compiling

background information for two years before we

started. Lively discussions about the goals and content

of the project at two annual meetings provided

direction to the project. Committee members

generously supplied data on population status, hunting

regulations, and data collection; and reviewed our

efforts along the way. Special thanks go to committee

members Jim Cardoza, Mike Gregonis, Gary Norman,

Jim Pack, Bob Sanford, and Dave Steffen for their

frequent and thoughtful discussions about various

aspects of turkey biology and state regulatory

procedures. John Organ, Wildlife Program Chief,

Division of Federal Aid, U.S. Fish and Wildlife

Service, provided the funding that made this project

possible, and encouragement when we needed it most.

In addition to committee members, helpful reviews

were provided by Russell Alpizar, Liz Brooks, Dick

Kimmel, and Steve Roberts. Special thanks go to

Georgette Healy for copy editing and proofreading.

Northeast Wild Turkey Technical Committee

Members:

Karen Bellamy, Ministry of Natural Resources,

Ontario

Steven L. Bittner, Wildlife and Heritage Division,

Maryland

Douglas Blodgett, Fish and Wildlife Department,

Vermont

Philip Bozenhard, Department of Inland Fisheries and

Wildlife, Maine

James E. Cardoza, Division of Fisheries and Wildlife,

Massachusetts

William E. Drake, Pennsylvania Game Commission,

Pennsylvania

Robert E. Eriksen, Division of Fish, Game and

Wildlife, New Jersey

Michael Gregonis, Department of Environmental

Protection, Connecticut

Howard Kilpatrick, Department of Environmental

Protection, Connecticut

Gary W. Norman, Department of Game and Inland

Fisheries, Virginia

James C. Pack, Division of Natural Resources, West

Virginia

Kenneth M. Reynolds, Division of Fish and Wildlife,

Delaware

Robert M. Sanford, Department of Environmental

Conservation, New York

David E. Steffen, Department of Game and Inland

Fisheries, Virginia

Brian C. Tefft, Division of Fish and Wildlife, Rhode

Island

Theodore Walski, Fish and Game Department, New

Hampshire.

This publication was funded by purchases of hunting

equipment through the Federal Aid in Wildlife

Restoration Program of the U.S. Fish and Wildlife

Service.
Section I. Introduction........................................................................................................................................................................1

Chapter 1. Background and Goals ....................................................................................................................................2

Section II. Biology ..............................................................................................................................................................................4

Chapter 2. Life History ........................................................................................................................................................5

Chapter 3. Population Dynamics and Models ................................................................................................................7

Turkey Population Models ......................................................................................................................................................7

Parameters Used in Population Models ................................................................................................................................8

Conclusions: Evaluation of Parameters..............................................................................................................................11

Section III. Harvesting Strategies ................................................................................................................................................15

Chapter 4. Spring Gobblers-Only Harvest ....................................................................................................................16

Hunting Regulations ..............................................................................................................................................................17

Decision Variables and Regulatory Procedures ................................................................................................................20

Additional Comments About Decision Variables ..............................................................................................................22

Chapter 5. Spring Gobbler Harvest With Limited Either-Sex Fall Harvest........................................................24

Fall Hunting Regulations ......................................................................................................................................................25

Decision Variables and Regulatory Procedures ................................................................................................................28

Chapter 6. Maximize Combined Spring and Fall Harvests ......................................................................................31

Section IV. Techniques ....................................................................................................................................................................33

Chapter 7. Introduction to Population Estimation ....................................................................................................34

Why Measure Abundance? ....................................................................................................................................................34

Census, Estimate, and Index ................................................................................................................................................34

Scales of Abundance................................................................................................................................................................35

Parameters, Techniques, and Tools ......................................................................................................................................36

Population Index, Density Index, and Production Index ................................................................................................36

Assumptions ............................................................................................................................................................................37

Bias, Accuracy, Precision, and Sample Size ........................................................................................................................37

Observability and Sampling ..................................................................................................................................................38

Selection of an Abundance Estimation Method ................................................................................................................38

Chapter 8. Population Census: Direct Winter Counts ..............................................................................................41

Background and Prior Use of the Technique ....................................................................................................................41

Assumptions ............................................................................................................................................................................41

Advantages................................................................................................................................................................................41

Disadvantages ..........................................................................................................................................................................42

Design, Standardization, and Quality Control....................................................................................................................43

Cost and Manpower Requirements......................................................................................................................................44

Conclusion and Recommendations ......................................................................................................................................44

Chapter 9. Population Index: Harvest Data ................................................................................................................45

Background and Prior Use of the Technique ....................................................................................................................45

Classes of Removal Methods ................................................................................................................................................45

Assumptions ............................................................................................................................................................................46

Advantages................................................................................................................................................................................47

Disadvantages ..........................................................................................................................................................................47

Design, Standardization, and Quality Control....................................................................................................................48

Cost and Manpower Requirements......................................................................................................................................49

Conclusion and Recommendations ......................................................................................................................................49

Chapter 10. Population Index: Brood Surveys ............................................................................................................51

Assumptions ............................................................................................................................................................................51

Contents
iv Wild Turkey Harvest Management: Biology, Strategies, and Techniques

Sources of Variation ................................................................................................................................................................51

Other Approaches....................................................................................................................................................................52

Design and Standardization ..................................................................................................................................................52

Chapter 11. Population Index: Reports From Hunters ............................................................................................54

Background and Prior Use of the Technique ....................................................................................................................54

Assumptions ............................................................................................................................................................................54

Advantages................................................................................................................................................................................54

Disadvantages ..........................................................................................................................................................................55

Design, Standardization, and Quality Control....................................................................................................................55

Cost and Manpower Requirements......................................................................................................................................55

Conclusion and Recommendations ......................................................................................................................................56

Chapter 12. Population Index: Gobbling Counts........................................................................................................57

Assumptions ............................................................................................................................................................................57

Advantages................................................................................................................................................................................57

Disadvantages ..........................................................................................................................................................................57

Design, Standardization, and Quality Control....................................................................................................................58

Cost and Manpower Requirements......................................................................................................................................59

Chapter 13. Population Estimate: Mark-Recapture..................................................................................................60

Prior Use of the Technique....................................................................................................................................................60

Assumptions ............................................................................................................................................................................61

Advantages................................................................................................................................................................................61

Disadvantages ..........................................................................................................................................................................61

Design, Standardization, and Quality Control....................................................................................................................62

Cost and Manpower Requirements......................................................................................................................................63

Conclusion and Recommendations ......................................................................................................................................63

Literature Cited ..................................................................................................................................................................................64

Appendix A. ........................................................................................................................................................................................72

Appendix B. ........................................................................................................................................................................................75

Appendix C...........................................................................................................................................................................................76
1

Section I. Introduction
2 Wild Turkey Harvest Management: Biology, Strategies, and Techniques

Chapter 1. Background and Goals

Wild turkey (Meleagris gallopavo) populations

probably reached their low point in the United States

in the 1930s. The invention of the cannon net in the

early 1950s marked the beginning of serious

restoration efforts, and by 1975 restoration efforts

were in full swing in the Northeast. Since then

turkeys have been restored to their ancestral range in

the Northeast and beyond. Wild turkey hunting

seasons are now held in all 48 continental states and

the Province of Ontario, Canada. Habitat conditions

improved steadily in the Northeast during the period

of restoration as forests that regenerated in the early

part of the century matured and farmland continued

to revert to forest.

During the past 3 decades managers have emphasized

restoration, and the job is now essentially complete.

Research focused on learning more about the turkey’s

population dynamics and habitat requirements. The

progress of research and restoration can be traced in

the proceedings of 7 National Wild Turkey Symposia,

held first in 1959 and then at 5-year intervals since

1970. Major population dynamics studies, involving

hundreds of radio-tagged birds, have been completed

in Iowa, Mississippi, Missouri, New York, Oregon,

Virginia, West Virginia, and Wisconsin. Advances in

our understanding of the species were summarized in

1992 in The Wild Turkey, Biology and Management,

edited by James G. Dickson.

This project began in 1993 with a request from the

Northeast Wildlife Administrators Association to the

Northeast Wild Turkey Technical Committee “to

identify the minimum set of information needed to

properly manage wild turkey populations and

facilitate state standardization of methods and

protocols for data collection within the region.” The

Technical Committee is composed of the wild turkey

project leaders from the northeastern United States

and the Province of Ontario. The Wildlife

Administrators represent state and provincial

agencies with authority for managing wildlife. 

At the time, 3 basic harvest strategies were in use

among the 13 northeastern states (Maine to Virginia)

and the Province of Ontario. Those strategies were:

spring gobbler harvest only, spring gobbler harvest

with limited either-sex fall harvest, and spring and fall

hunting designed to maximize total harvest. There

were no explicit demographic models for the various

harvesting programs and there was no consensus

among resource professionals about the essential data

needed to evaluate the effect of harvest on wild turkey

populations (Vangilder and Kurzejeski 1995:40). The

criteria used to evaluate population status and the

techniques used to collect data varied among the

states. Consequently, few biologists were completely

comfortable with the theoretical basis for harvest

management or the methods used for assessing

population status. 

Despite these misgivings, all state and provincial

management programs were viewed as successful by

biologists, administrators, and the general public. Wild

turkeys had been restored across their historic range

in eastern North America, hunting opportunities were

increasing, populations continued to grow and expand

their range while experiencing either a spring harvest

or a combination of spring and fall harvest. Harvest

management was based on conservative regulations

and a thorough understanding of the species’ life

history. 

Our objective is to synthesize what is known about the

effects of hunting on wild turkey populations, and

provide models for regulating harvest that provide

ample hunting opportunity with minimal risk of

overharvest. In Section II we review the biology of the

species because we believe harvest management must

be based on a thorough understanding of population

dynamics. The chapters on life history and population

dynamics describe the critical relationships among life

history, population characteristics, and harvest. These

chapters provide the ecological basis for harvest

management. 

In Section III we review the 3 basic strategies for

harvesting wild turkeys. We review the assumptions

on which each strategy is based and recommend

procedures for regulating harvest under each

strategy. The procedures we propose are

straightforward and based on existing methods used

in various states. The procedures are based on biology,

use explicit decision variables, require collection of

data at regular intervals, and specify periodic review

of progress.

In Section IV we describe the techniques that are

most useful for obtaining data used to regulate

harvest. An enormous number of techniques has been

used to study wild turkeys, and the list of techniques

we reviewed is contained in Appendix C. Most of those

techniques are useful research tools, but we limited

this review to those that are most useful for collecting

data used for harvest management. 

Our basic premise is that harvest strategies must be

based on population dynamics, but regulation of
Section I. Introduction 3

harvest does not require detailed measurement of

demographic parameters. We believe that sound

management decisions can be made from harvest data

and indices of relative abundance, provided harvest

goals and remedial actions to be taken when goals are

not met are specified in advance, and data are

reviewed periodically. The discussions of the 3 basic

harvest strategies and the assumptions underpinning

them will be useful to all states, because every state

permitting turkey hunting uses one of these

strategies. The material presented provides the

ecological basis for harvesting programs, and can be

used to explain the goals and reasoning behind turkey

hunting regulations to the public. 

In contrast, the procedures that we recommend for

implementing harvest strategies should be viewed as

models to be used as the need arises. The regulatory

procedures provide varying degrees of protection for

the turkey population. The choice of harvest strategy

and regulatory procedure depends on program goals,

the demand for hunting opportunities, the perceived

risk of overharvesting the population, and hunter

safety and satisfaction. The adage “don’t fix it if it ain’t

broke” applies to regulating turkey harvest. 

In general, risk of overharvest can be reduced by

increasing control over harvest rates, and the greatest

control over harvest rate is achieved by regulating

hunter numbers. Strong control over harvest is not

always necessary or desirable. For example, hunters

in Connecticut enjoy liberal fall seasons: a 2-week

firearms season and about 11 weeks of bow hunting.

Hunter numbers are not regulated in either fall

season. In 1997 the total fall firearms harvest was

about 140 turkeys—insignificant from a population

standpoint. Because the demand for the resource is

low, further regulation is unnecessary. 

A review of hunting programs and harvest strategies

is timely. Substantial progress has been made in

understanding wild turkey ecology and population

dynamics. Turkey hunting continues to grow in

popularity despite a decline in the rate of hunting

participation among the general population. Recent

trends in expanding turkey populations and improving

habitat quality cannot go on indefinitely. Habitat

conditions are likely to stabilize and decline as mature

timber is harvested, development continues, and

northeastern dairy farms and pastures disappear. As

turkey populations respond to these landscape

changes, turkey hunters and others will likely

question harvesting programs with increasing

frequency. Wildlife managers need to be able to clearly

explain the biological basis for hunting, the

mechanisms for regulating harvest, and the effect of

harvest on populations. We view this report as a first

step in providing managers with the information

needed to regulate harvest. We hope this report will

encourage the development of explicit harvest goals,

models, and decision rules. We also hope to stimulate

the development of more effective and efficient

methods for monitoring harvest and estimating

population abundance.
4 Wild Turkey Harvest Management: Biology, Strategies, and Techniques

Section II. Biology
Section II. Biology 5

Chapter 2. Life History

This brief review provides the natural history

background needed to follow the chapters on

Population Dynamics and Harvest Strategies. We

emphasize life history attributes that provide the basis

for harvest programs. Comprehensive reviews of the

species’ natural history can be found in the works by

Schorger (1966), Hewitt (1967a), and Dickson (1992). 

The wild turkey is a large-bodied bird with striking

plumage and spectacular visual and auditory displays.

Average weights for adult females range from 8 to11

pounds, and for adult males from 17 to 21 pounds, but

individuals can be substantially heavier (Pelham and

Dickson 1992). Audubon (1967:42) noted “the great

size and beauty of the wild turkey,” and “its value 

as a delicate and highly prized article of food.”

Archaeological evidence indicates that Native

Americans used turkeys extensively for food, so 

some form of harvesting has occurred for several

thousand years (Aldrich 1967:6). 

Wild turkeys exhibit a moderate potential for

population growth. Reproduction is seasonal and the

population is composed of distinct age classes. Turkeys

may survive for as long as 15 years in the wild

(Cardoza 1995), so theoretically there could be 

as many as 15 age classes. The age structure of the

population serves as the basis for most population

analysis and modeling efforts. Mathematical models

usually simplify the age structure into 2 to 4 age

classes, such as poults (0-28 days), juveniles (29 days9 months), yearlings (10-21 months), and adults (>21

months) (Vangilder and Kurzejeski 1995, Roberts and

Porter 1996). 

The mating system is polygamous; males play no role

in rearing young. A variable percentage of 1-year-old

males is reproductively active; all males ≥ 2 years old

can breed. Females are capable of reproducing at 1

year of age, but adult hens are usually more successful

at hatching nests than are yearlings (Vangilder

1992:146). The polygamous mating system and age

structure of the population provide the basis for a

spring harvest strategy that removes primarily adult

males after most hens have been mated. 

Mortality rates are greatest and most variable during

the early stages of life. Both eggs and poults suffer

substantial losses to predators. The proportion of

poults dying during the first 4 weeks after hatching

generally ranges from 53% to 76%, but may be

greater in some years (Vangilder 1992:151). In

comparison, adult mortality rates are moderate. Mean

annual survival rates of hens and gobblers, based on

radio telemetry studies, have ranged from 54% to

62%. In harvested populations, hunting can account

for a significant part of the annual mortality.

Wild turkey populations are characterized by annual

fluctuations that may approach ±50% of the long-term

mean (Mosby 1967:115-117). In northern populations,

where snow cover influences food availability, winter

mortality may cause short-term fluctuations by

reducing the breeding population (Wunz and Hayden

1975, Porter et al. 1983). Annual fluctuations, however,

are most strongly related to variation in hen nesting

success and poult survival, which determine the

number of young joining the population each autumn

(Roberts and Porter 1996). 

Nesting success and poult survival are influenced

primarily by predation and weather conditions. The

relationships among these variables are complex,

incompletely understood, and involve direct and

indirect effects of weather and interactions between

weather and predation (Roberts and Porter 1998a). 

In south Texas, the annual productivity of Rio Grande

turkeys (M.g. intermedia) is strongly influenced by

rainfall. The timing and abundance of vegetative

growth in spring depends on the amount of rain

received the previous autumn, because fall rains

recharge soil moisture and most spring rainfall is lost

to evaporation. In dry years, vegetative growth is

minimal; there is little cover for nests or food for hens

and poults. Under these conditions, predation on nests

and poults is increased and hen nesting effort is

reduced (Beasom 1970, 1973). There may also be a

relationship between weather and predation in humid

eastern forests where nesting cover is generally

abundant. An analysis of nest survival during

incubation in southcentral New York found that nest

survival was greatest during cool, dry periods and

poorest during warm, wet periods. Warm, moist

conditions may enhance the ability of predators to use

olfactory cues to locate nests (Roberts and Porter

1998a).

Most poult mortality occurs within the first 2 weeks

after hatching (Hubbard et al. 1999), and predators

are responsible for most losses (Paisley et al. 1998).

Weather conditions also affect poult survival, and cold,

wet weather is generally associated with lower poult

survival (Rolley et al. 1998). Predicting the effect of

weather on reproduction is difficult because the effects

of temperature and precipitation vary with poult age

(Healy and Nenno 1985, Roberts and Porter 1998b).

Direct loss of poults to exposure seems to be

associated with extreme conditions, such as flooding or
6 Wild Turkey Harvest Management: Biology, Strategies, and Techniques

prolonged cold rain (Healy and Nenno 1985). Cold,

wet weather also affects poults indirectly by reducing

the availability of invertebrate foods upon which

poults depend during the first weeks of life. In

southcentral New York, poult survival to 2 weeks of

age was negatively associated with cold temperatures

during the first week and precipitation during the

second week (Roberts and Porter 1998b). In general,

the more temperature and precipitation deviated from

the long-term average, either above or below, the

greater the reduction in productivity (Bailey and

Rinell 1968:32). Years of better-than-average

reproduction are characterized by higher rates of nest

success, poult survival, and renesting by hens. Years of

poor reproduction are characterized by low rates of

nest success and renesting. These annual population

fluctuations need to be considered when regulating fall

harvest.

In populations of most species, birth rates decline as

population size increases, so maximum population

growth rates occur at low population density. This

phenomenon, called density-dependent population

growth, is important for harvest management because

classic population theory and most game and fish

harvesting models are based on density-dependent

growth (McCullough 1979, Getz and Haight 1989).

Density-dependent population growth has not been

convincingly demonstrated in wild turkeys. Behaviors

or feedback mechanisms that might limit the rate of

increase as turkey populations grow from low to high

numbers have yet to be identified, although

interference between nesting hens is a potential

limiting mechanism (W.M.H. personal observation,

Weinstein et al. 1996). Recent radio-telemetry studies

have shown no change in recruitment rate as

population density changed (Vangilder and Kurzejeski

1995). In contrast, analysis of harvest data from New

York suggested that maximum population growth

occurs at low population densities (Porter et al. 1990a).

Numerous observations of rapid growth in newly

introduced populations also suggest that turkey

populations may exhibit density-dependent growth.

Because the evidence for density-dependent growth is

ambiguous, most turkey population models assume a

constant growth rate regardless of population size.

The absence of a clear density-dependent population

response in turkeys also suggests that turkeys may be

more vulnerable to overharvest than species that

exhibit density-dependent growth because increased

growth rates will not compensate for increased

harvest rates. 

Mean population density varies among landscape

types, such as farmland with scattered woodlots,

extensive forest, or farmland-forest mosaic. We do not

know the ecological carrying capacity of most

landscapes for wild turkeys because both the habitat

and turkey populations have been changing in recent

decades as eastern forests matured and turkeys

expanded into new ranges. In predominantly forested

landscapes, the resources available to turkeys, and

hence carrying capacity, vary annually because of the

turkey’s dependence on acorns and other tree seed

crops. In forested landscapes, mast crops influence

turkey movements during fall and winter, and some

evidence suggests that fall harvest increases in years

of poor mast production because flocks are more

vulnerable to hunting when concentrated around

alternate food sources (Menzel 1975, Wunz 1986, Pack

1994). Birds in agricultural areas are less affected by

changes in natural foods because of available waste

grains. In some mixed forest-agricultural areas, mast

crops can still have a significant effect on movements

and habitat use, especially during winter (Kurzejeski

and Lewis 1990). Annual fluctuations in population

size and an absence of knowledge about population

response to habitat conditions require a conservative

approach to fall harvest management.
7

Chapter 3. Population Dynamics and Models

Section II. Biology 

“Understanding the dynamics of wild turkey

populations and the role of harvest are critical factors

in the development of a harvest management

program” (Vangilder and Kurzejeski 1995:40). This

statement sums up the importance of population

dynamics studies, both through field studies to

estimate population parameters, and through

modeling studies aimed at simulating population

changes based on estimated or actual population

parameters. Population dynamics has been defined as

changes in the size of a group of animals inhabiting a

specific area (Vangilder 1992). In wild turkeys and

other animals, population dynamics is determined by 

3 broad processes: birth (reproduction), death

(mortality, or conversely, survival), and movement

(immigration and emigration) (Vangilder 1992). These

3 broad processes are affected by a number of more

specific factors. Some factors are easily measurable

and can be used to construct turkey population

models. Other factors are difficult to quantify and can

be thought of as background factors that contribute to

the apparently random element of population

dynamics (Fig. 3.1). Of the 3 broad processes,

movement has been largely ignored in studies of

turkey population dynamics, because of the difficulty

in defining and monitoring a closed population of study

animals. Turkey population models therefore usually

assume a closed population and focus on quantifying

reproductive and mortality parameters.

The following review of factors influencing the

population dynamics of the wild turkey is based on

studies conducted on Meleagris gallopavo subspecies

throughout the United States, although most

information exists for the eastern subspecies (M. g.

silvestris). We attempt to summarize data for the

species as a whole, and provide specific results for the

eastern subspecies. 

Turkey Population Models

Vangilder (1992:159) offers the following description of

a population model: 

A population model is a way of mathematically

combining survival- and reproductive-rate

estimates to produce projections of population

size and age structure through time. For a

specific combination of survival and

reproductive rates, a model may tell you

whether a wild turkey population will grow,

decline, or remain stable. A model may be

useful in determining the effects of varying

death rates (e.g., harvest mortality) on

population size and age structure. A model

may also be useful in pointing out deficiencies

in data on certain parameters or in our

understanding of how population parameters

are interrelated.

Given the initial population size and the sex and age

structure (top of Fig. 3.1), and accurate values for

several reproductive and mortality parameters (terms

shown in rectangles in Fig. 3.1), it is possible to predict

or project the dynamics of a given turkey population

over time (Vangilder 1992). Accurate values are often

difficult to obtain, so models usually project a range of

possible outcomes given the best available population

data. 

Types of Population Models

The various types of models used to project wild

turkey population dynamics, and the different levels of

classifying such models, were thoroughly discussed by

Porter et al. (1990b). The first distinction they made

was between detailed mechanistic models that

encompass many specific life history attributes, and

general models that use only a few parameters.

Detailed models can be useful in identifying specific

attributes of a species’ natural history that contribute

to its population dynamics, but they require

comprehensive data that may be difficult, timeconsuming, and expensive to collect. Another

disadvantage of detailed mechanistic models is that the

combined statistical variance of many variables, when

assembled into a large model, may become

unacceptably large. 

Porter et al. (1990b) argue that general models are

often more useful than mechanistic models,

particularly when the goal is projecting population

growth. It is difficult, however, to determine what

simple estimates of survival and reproductive rates

(which may be used in more general models) mean in

terms of population dynamics, because survival and

reproductive rates are usually both age-specific and

time-specific (Vangilder 1992:158). Suchy et al. (1990)

found that models of intermediate complexity provided

the best fit to actual population data.

Models may also be classified as deterministic or

stochastic. A deterministic model produces one unique

and repeatable prediction for a given set of

parameters. In contrast, stochastic models may more

realistically incorporate the natural random variation

inherent in these turkey population variables.

Stochastic models may be preferred for management

purposes when “uncertainty in prediction is a primary

concern” (Porter et al. 1990b).
8 Wild Turkey Harvest Management: Biology, Strategies, and Techniques

Studies Utilizing Turkey Population

Models

There have been few efforts aimed at modeling wild

turkey populations (Suchy et al. 1990) due in part to a

lack of data, particularly for mechanistic models

(Vangilder and Kurzejeski 1995). Opinions differ

among resource professionals about the essential data

needed to either manage or model wild turkey

populations, and about the necessary level of model

complexity (Porter et al. 1990b). The following is a

synopsis of the main turkey modeling studies

conducted to date. The population parameters used or

discussed in each of these studies are indicated in Fig.

3.1, and are discussed later in further detail.

The hypotheses of additive and compensatory

mortality need to be considered before discussing

specific models because the choice of hypothesis has a

profound effect on model results (Suchy et al. 1983,

Nichols et al. 1984, Vangilder 1992). Under the

compensatory mortality hypothesis, mortality from

hunting simply replaces natural mortality, and is

compensated for by a decrease in natural mortality

after the hunting season. Under the opposing

hypothesis, hunting mortality is additive to natural

mortality. There is also an intermediate hypothesis,

that hunting mortality is compensatory until a

threshold is reached, after which it becomes additive

to natural mortality. It is important to investigate

which of the 3 hypotheses appears to fit the observed

cause-specific mortality data most closely. Spring

hunting mortality of gobblers has usually been

assumed to be additive to non-hunting mortality

because natural mortality rates of gobblers are low

and allow little room for compensatory mortality

processes (Vangilder 1992). Fall hunting mortality

appeared to be additive to natural mortality in

populations in Iowa (Little et al.1990) and Wisconsin

(Rolley et al. 1998). Vangilder’s (1992) review of the

literature found little evidence that hunting mortality

was compensatory for natural mortality in turkey

populations.

Lobdell et al. (1972) developed a stochastic model “to

analyze the long-term effect of the addition of a spring

gobbler hunt on the dynamics of a hypothetical

population of eastern wild turkeys.” This relatively

simple model made use of, among other parameters,

the immature:adult female ratio, which they assumed

to be the best indicator of annual production. They

used this ratio to simplify the model by taking the

place of several more difficult-to-measure parameters

that were used in more mechanistic turkey population

models. They also assumed, and presented some

evidence to support, that hunting mortality was

compensatory for a simulated fall-only hunting.

However, they assumed that spring hunting of

gobblers was additive to the total annual mortality

rate. 

Suchy et al. (1983) used a deterministic model

(TURKEY) to simulate autumn harvest in Iowa. They

simulated both the additive and compensatory

hypotheses, and found that the allowable fall hunting

mortality rates were higher under the threshold

hypothesis than under the additive hypothesis (9.5%

vs. 4.7% for females, and 28.4% vs. 16.7% for males).

They concluded that a conservative management

approach, assuming that wild turkey mortality due to

hunting is additive to natural mortality, is preferable.

Later, Suchy et al. (1990) used a modified version of

the TURKEY model (TURK4) to evaluate its ability

“to project wild turkey numbers by determining if

projections were correlated with winter counts of

turkeys” in Iowa. Another model was used to simulate

the dynamics of a Missouri wild turkey population 

(Vangilder and Kulowiec 1988, Vangilder 1992,

Vangilder and Kurzejeski 1995). This model was both

deterministic and stochastic, and assumed that

hunting mortality is additive.

Finally, Roberts et al. (1995) used a deterministic

model to conduct sensitivity analyses to “determine

the relative importance of demographic parameters to

annual population change.” They also discussed the

distinction between realized and potential rates

(Krebs 1985:167) for nesting rates, renesting rates,

and hen success (Fig. 3.1). The difference is that

realized rates included mortalities, whereas potential

rates excluded mortalities (Roberts et al. 1995). This

dichotomy raises the point that parameters used in

population models have been defined in different

ways, so the results from different studies are not

always directly comparable unless they are

recalculated using a common methodology.

Parameters Used in Population Models

Each of the 14 parameters discussed below was used

in at least one of the population models we reviewed.

The relationships among parameters and population

processes are diagrammed in Fig. 3.1, where

parameters are shown in rectangles, and numbers

within rectangles index the literature citation for the

appropriate model. Turkey populations were typically

partitioned into 3 age classes for modeling purposes:

poults (hatch to 0.4 year old), juveniles (0.4 to 1.4

years old), and adults (≥ 1.4 years old) (Rolley et al.

1998). The terms “subadult” and “juvenile” are used

interchangeably in the literature. We used these

conventions for age class in the discussion of

population parameters. We exclude data from studies

for which age classes were combined when reviewing

values that require age-specific data, unless otherwise

noted.

One of the most important aspects of research on

population dynamics is defining the range of

parameter values (Vangilder and Kurzejeski 1995).

Parameter values presented below are taken from

studies summarized in Vangilder (1992), with
Section II. Biology 9

additional data from Vangilder and Kurzejeski (1995),

Roberts et al. (1995), Miller et al. (1998a,b), Paisley et

al. (1998), Hubbard et al. (1999), and Keegan and

Crawford (1999). Several problems arise in comparing

reported parameter values. First, field methods,

parameter definitions, and analyses differ among

studies. Second, for many parameters, year-to-year

variation is large, and generally, the longer the study

the larger the range in parameter values reported.

Third, there is marked regional variation in some

parameter values. Regional variation is particularly

evident in juvenile nesting rates and juvenile nesting

success rates. Fourth, the precision of parameter

estimates is influenced by sample size. Extreme

values, such as 0% or 100% nest success, are often

associated with small sample sizes. Finally, parameter

values reflect the status of the population, with

extreme values being reported for either declining or

growing populations. Because of the difficulties of

measuring population attributes and interpreting

reported values, the initial values inserted into models

are usually a combination of hard data, educated

guesses, and estimates from the literature (Porter et

al. 1990b).

To indicate the range in variation for individual

parameters that characterize turkey populations, we

report mean values and the annual extremes from

each study. We give ranges of reported values for all

subspecies combined, and corresponding values based

only on the eastern subspecies of wild turkey.

1. Nesting rate (age-specific).

Nesting rate has been defined as the proportion of

hens that attempt to nest (Vangilder 1992:145).

Although the nesting rate of adult hens is generally

high, that of juveniles varies among populations within

a subspecies (Vangilder 1992:145-146, Miller et al.

1998b, Keegan and Crawford 1999). Considering the

mean values for all years of each study, for adults this

variable ranged from 72.3% (Miss.) to 100.0% (Mo.,

Mass., Ore.) for 13 studies on all subspecies, and from

72.3% (Miss.) to 100.0% (Mo., Mass., Ore.) for 9

studies on the eastern subspecies.

For juveniles, the mean values ranged from 7.7%

(N.M.) to 100.0% (Mo., N.Y., Wis.) for 13 studies on all

subspecies, and from 42.0% (Iowa) to 100.0% (Mo.,

N.Y.) for 9 studies on the eastern subspecies.

The extreme values (i.e., the mean of all observations

for any given year) for adults ranged from 30.0%

(Miss.) (based on 4 studies) to 100.0% (Iowa, Mo.,

Mass., Ore., N.Y., Miss., Wis.) (based on 7 studies) for

all subspecies, and the same values resulted for

studies on the eastern subspecies alone.

For juveniles, the extremes ranged from 0.0% (Iowa)

(based on 2 studies) to 100.0% (Mo., Miss., N.Y., Wis.)

(based on 5 studies). These 5 studies were all done on

the eastern subspecies, and no data were presented on

extremes from other subspecies.

2. Renesting rate (age-specific).

Renesting rate is defined as the proportion of hens

not successful on their first nesting attempt that

attempt to renest (Vangilder 1992:145, Vangilder and

Kurzejeski 1995). In most studies, renesting rate was

lower for juvenile hens than for adult hens (Vangilder

1992:145).

The means for adults ranged from 0.0% (Ore.) to

69.4%1 (N.Y.) for 10 studies on all subspecies, and from

32.0% (Iowa) to 69.4% (N.Y.) for 7 studies on the

eastern subspecies. 

For juveniles, the mean values ranged from 0.0%

(N.M., Ore.) to 70.0% (Minn.) for 10 studies on all

subspecies, and from 12.0% (Iowa) to 70.0% (Minn.)

for 7 studies on the eastern subspecies (EasternMerriam’s subspecies for Minn.).

The extreme values for adults ranged from 0.0% (Ore.,

Miss.) (based on 7 studies) to 100.0% (Miss.) (based on

6 studies) for all subspecies, and from 4.0% (Ore.)

(based on 5 studies) to 100.0% (Miss.) (based on 6

studies) for the eastern subspecies.

For juveniles, extremes ranged from 0.0% (Iowa, N.Y.,

N.M., Ore., Mo., Wis.) (based on 7 studies) to 75.0%

(Mo.) (based on 5 studies) for all subspecies, and from

0.0% (Iowa, N.Y., Mo.) (based on 5 studies) to 75.0%

(Mo.) (based on 5 studies) for the eastern subspecies.

3. First-nest success rate (age-specific).

First-nest success rate is the proportion of first nests

from which at least 1 poult hatches (Vangilder

1992:145, Roberts et al. 1995). Considering the means

for all age classes (many studies did not differentiate

between juveniles and adults), this variable ranged

from 7.7% (Wis., juveniles) to 62.0% (Minn., combined

ages) for 8 studies on the eastern subspecies (EasternMerriam’s subspecies for Minn.).

The extremes for all age classes ranged from 0.0%

(Wis., Mo., juveniles; Miss., adults) (based on 7

studies) to 100.0% (Mo., juveniles) (based on 7 studies)

for the eastern subspecies (no data presented for

other subspecies).

4. Renest success rate (age-specific).

Renest success rate is the proportion of renests from

which at least 1 poult hatches (Vangilder 1992:145,

Roberts et al. 1995). Considering the means for all age

classes (many studies did not differentiate between

juveniles and adults), the mean for this variable

ranged from 0.0% (Iowa, juveniles) to 72.7% (Minn.,

combined ages) for 8 studies on the eastern subspecies

(Eastern-Merriam’s subspecies for Minn.).

The extremes for all age classes ranged from 0.0%

(Iowa, N.Y., Miss., Mo., Tex., Wis.) (based on 7 studies)

to 100.0% (Miss., adults) (based on 6 studies) for the

eastern subspecies (no data presented for other

subspecies).

1 Potential rate, as defined by Roberts et al. (1995).
10 Wild Turkey Harvest Management: Biology, Strategies, and Techniques

5. Hen success rate (age-specific).

Hen success rate is the proportion of hens that are

successful in hatching 1 or more live poults in the first

or a subsequent nesting attempt. This rate

incorporates first-nest success, renesting rate, and

renest success. Hens that do not attempt to nest are

not included (Vangilder 1992). For eastern wild

turkeys, adult hen success rate tends to be higher

than juvenile hen success rate.

The means2 for adults ranged from 24.6% (Miss.) to

82.8% (Pa.), based on 25 studies on all subspecies. The

same values result from the 16 studies on the eastern

subspecies alone. 

For juveniles, the means range from 0.0% (Tex.) to

100.0% (Pa.), based on 15 studies on all subspecies. For

the eastern subspecies alone, the means range from

15.4% (Ala.) to 100.0% (Pa.), based on 10 studies.

The extreme values for adults ranged from 0.0% (Tex.)

to 90.0% (Ga.), based on 16 studies on all subspecies.

For the eastern subspecies alone, the means range

from 5.0% (Miss.) to 90.0% (Ga.) based on 11 studies.

For juveniles, the extremes range from 0.0% (Mo.,

Iowa, Ala., Tex., Wis.), to 100.0% (Mo., Pa.) based on 9

studies on all subspecies. The same extremes result

from examining 8 studies of the eastern subspecies

alone.

6. Clutch size (age-specific, nesting-attempt-specific).

Clutch size is simply the number of eggs laid by a hen,

whether fertile or not, for a given nesting attempt.

Vangilder and Kurzejeski (1995) observed no

difference in clutch size between juveniles and adults.

However, no distinction was made between clutch size

in juveniles versus adults in many studies, so only 1

value is reported here.

For first nests, the mean for this variable ranged from

8.5 (N.M.) to 12.7 (Mich.) for 11 studies on all

subspecies, and from approximately 9 (Iowa)3 to 12.7

(Mich.) for 8 studies on the eastern subspecies.

In most studies, clutch sizes were larger in first nests

than in renests. For renests, the mean ranged from 7.0

(Iowa, juveniles)4 to 11.9 (N.Y.) for 11 studies on all

subspecies, and from 7.0 (Iowa, juveniles) to 11.9

(N.Y.) for 8 studies on the eastern subspecies.

7. Hatchability: hatching success, fecundity; agespecific, nesting-attempt-specific.

Hatchability is generally defined as the percentage of

all eggs that hatch for successful nests (Vangilder

1992:149-150, Roberts et al. 1995, Vangilder and

Kurzejeski 1995). Thus, as indicated in Fig. 3.1, egg

fertility, embryo viability, and partial predation are all

encompassed by hatching success (Vangilder

1992:149). Some seasonal differences are apparent, but

hen age does not seem to have much effect on

hatching success (Vangilder 1992:149). For all nesting

attempts combined, the mean values for this variable

range from 80.0% (Minn., Pa.) to 93.0% (N.Y.) for 15

studies on all subspecies. The same values result from

the 10 studies on the eastern subspecies.

8. Poult mortality rate.

There is some evidence that poult mortality is higher

for broods from juvenile hens than from adult hens

(Glidden and Austin 1975). Poult mortality is greatest

during the first 2 weeks post-hatching (Vangilder

1992:150), after which time poult mortality declines

sharply (Vangilder and Kurzejeski 1995). It is difficult

to monitor poult mortality after 4 weeks of age

because hens tend to form multiple brood flocks

(Suchy et al. 1990, Roberts et al. 1995, Vangilder and

Kurzejeski 1995). Rolley et al. (1998) assumed that

poult survival between 28 days post-hatching and 1

October equaled that of adult females. They point out,

however, that not much is known about poult mortality

during this phase. Furthermore, it is difficult to

compare estimates of poult survival among studies

because of differences in the methods used to assess

poult survival (Roberts et al. 1995). To give a rough

idea of the range of variation, considering the means,

at 2 weeks of age mortality rate ranged from

approximately 46-47% (Mo., Wis.) to 73% (Ala.) for 11

studies on all subspecies, and the same values resulted

from 10 studies on the eastern subspecies only.

At 4 weeks of age, the means ranged from 40.4%

(Iowa) to 76% (N.Y.) for 9 studies on all subspecies,

and the same values resulted from 8 studies on the

eastern subspecies only. 

Considering the extreme values, at 2 weeks of age the

range was from 21.1% (Iowa) to 88% (Tex.) for 4

studies on the eastern subspecies (no data presented

for other subspecies). At 4 weeks of age the extremes

ranged from 35% (Tex.) to 88% (Tex.) for 3 studies on

the eastern subspecies (no data presented for other

subspecies).

9. Juvenile and adult survival rate.

Population models require separate survival rates for

each season, and for each age and sex class.

Mortalities are attributed to specific causes. The

largest source of mortality in most studies is predation

(Vangilder 1992:155, Roberts et al. 1995, Vangilder and

Kurzejeski 1995). Categorized mortality data are not

generally comparable among studies because of

differences in calculating mortality rates, classifying

mortality factors, and defining time intervals for

calculating mortality (Vangilder and Kurzejeski 1995).

Examining overall annual survival rates provides a

rough comparison of the variation in survival rates

among different regions (Vangilder 1992:154). The

following data are for various combinations of sex and

age classes.

2 Excluding a study in Texas (Beasom and Pattee 1980) in which the age of the hens was not specified and where it was unknown whether

a hen attempted to nest unless then hen reached incubation. The mean was in this study was 17.0%

3 Ranged from 8.8 for juveniles to 9.4 for adults.

4 From 8.0 (Ala.), if only the 9 studies where age classes were combined are considered.
Section II. Biology 11

Considering means, survival rate ranges from 15%

(Va., juvenile and adult gobblers, hens) to 75% (Mass.,

adult hens) for 15 studies on all subspecies. The same

data result from 13 studies on the eastern subspecies. 

Considering the extremes, this variable ranges from

7.8% (Iowa, juvenile gobblers) to 100.0% (Iowa,

juvenile hens) for 4 studies on the eastern subspecies

(no data for other subspecies).

10. Harvest mortality rate.

Harvest mortality rates differ between spring gobbler

season and fall either-sex seasons. Adult gobblers are

more vulnerable than juvenile males to spring harvest

(Vangilder 1992:156-157). Fall harvest mortality rates

also differ by age class and sex, with juvenile males

being the most vulnerable and hens being least

vulnerable to fall harvest (Little et al. 1990). Harvest

rates are often calculated for combinations of age or

sex classes to increase sample size. 

It is usually desirable to enter a range of harvest

mortality rates into a model to examine how the

turkey population would respond to different

management strategies. After performing such

manipulations, both the Missouri model (Vangilder

and Kulowiec 1988) and the Suchy et al. (1983) model

predict that sustained fall harvests of <10% (for both

males and females) permit continued population

growth, but sustained fall harvests greater than this

level predict rapid population declines (Little et al.

1990, Vangilder 1992:163). Furthermore, based on data

input into these models, spring gobbler harvests of

<30% will also allow continued population growth

(Vangilder 1992:163).

For gobblers, during a spring hunting season, previous

management regimes have reported harvest of up to

51% of the estimated population (Ala., Gardner et al.

1973). For hens, during a fall hunting season up to 89%

of a radio-tagged population has been reported

harvested (Fla., Williams et al. 1980).

11. Young per adult female. 

This parameter has been used in simple mechanistic

models to represent the number of young entering the

population at some specific time. Values have been

expressed as number of young per adult female

(Lobdell et al. 1972) and female young recruited per

female (Porter et al. 1990b). This parameter has been

used when other demographic data, such as hen

success rate, hatchability rate, and poult survival,

were unavailable. More detailed models predict

recruitment from estimates of fecundity and survival

rates, and validate predictions by comparing them

with observations of poults/hen or juveniles/adult in

the harvest (Rolley et al. 1998). 

Lobdell et al. (1972) calculated that a mean of 3.025

young per adult female (range 2.275-3.775) was

needed to maintain a population of approximately

1,000 turkeys for a 100-year period. No directly

comparable data were presented in other population

model papers reviewed. 

12. Sex ratio of poults at hatching.

There are no comparative data presented in any of the

population model papers reviewed. Therefore, no

among-studies range of variation is reported here.

Vangilder and Kurzejeski (1995) and Suchy et al.

(1983) assumed a 50:50 sex ratio for their models.

13. Sex ratio of adults.

This parameter is highly variable among studies

depending in part on the time of year and the intensity

of the spring gobbler hunt. For Suchy et al.’s (1983)

study in Iowa, this percentage ranged from 43.1% to

64.1% female for juveniles, and from 61.6% to 74.0%

female for adults. Rolley et al. (1998) initialized their

model with 50% adult females and 50% adult males

and generated a mean stable sex and age distribution

of 33% adult females, 18% juvenile females, 30% adult

males, and 18% juvenile males. 

14. Starting population size: age-specific and sexspecific.

This parameter varies among studies depending on

the current status of the population, and on how the

population is defined. It is difficult to estimate turkey

population sizes with current census techniques

(Vangilder 1992:161), but it is necessary to estimate a

value for this parameter to use many population

models. Initial population size has been estimated in

many ways, and initial values are often ad hoc

estimates derived from several sources of information.

Conclusions: Evaluation of Parameters

Sensitivity analysis is an important step in the

modeling process. This analysis involves

systematically varying the values for each variable to

determine which variable has the greatest influence on

population change, and the values of each variable that

produce significant population change (Porter et al.

1990b).

Sensitivity analysis by Suchy et al. (1983) suggested

that variation in female survival was more important

than variation in reproduction in influencing

population trends. The simulations of Vangilder and

Kurzejeski (1995) show that increasing nest success or

decreasing poult mortality could have a large positive

effect on turkey population growth. Similarly, after

performing a sensitivity analysis on the variables

entered into their model, Roberts et al. (1995) found

that nest success was the most important and most

variable parameter influencing population dynamics.

Nest success was more important than poult survival

or annual adult survival, but all 3 variables had a

significant effect on population change. Other

parameters they examined were unimportant

contributors to population change. Roberts and Porter

(1996) later found that variation in nest success and

survival of juveniles, yearlings, and adults had similar

effects on annual changes in abundance. Rolley et al.

(1998) similarly placed greater importance on the

influence of poult survival, and found that both

reproduction and survival are important in controlling

population growth. Nest success and poult survival are
12 Wild Turkey Harvest Management: Biology, Strategies, and Techniques

difficult to control through management practices

(Vangilder and Kurzejeski 1995). Harvest, particularly

fall either-sex harvest, is easier to control, and

perhaps more important for population regulation

(Vangilder and Kurzejeski 1995, Rolley et al. 1998). 

Regardless of the parameter values used to model

population dynamics, all parameters should be

carefully defined, preferably using definitions common

to other recent modeling literature. Long-term studies

are necessary for defining the yearly range of

variation of a given parameter for a given geographic

area. In the absence of sufficient data on yearly

variability for a population of interest, extreme or

mean values from other areas can be used as

boundaries within which a stochastic population model

can be allowed to vary to test the effects of various

permutations of these variables. If a sensitivity

analysis can be used to identify parameters that have

a large influence on population change, the model can

be simplified to reduce the problems of excessive

variances associated with complex models. In this

manner the cost and effort of collecting data needed to

predict changes in turkey populations can be

minimized. 
Figure 3.1. Some measurable parameters and factors known to or suspected of affecting

wild turkey population dynamics.

Birth

(reproduction)

B

ch

Clutch size

(renests) 

(age-specific)

5 6 7

Hatching success

(hatchability rate/fecundity)

(age specific & nest-attempt-specific)

1 2 3 5 6 7 11 12

Clutch size

(first nests) 

(age-specific)

5 6 7 8 11 12

Nesting rate

(potential vs. realized*)

(age-specific)

1 6 8 11 12

Initiation date of

incubation

Predators

Climate

(temperature,

precipitation)

Hen condition

Annual fluctuations

of nest predator

populations

Abandonment Embryo

viability

Egg 

fertility

Partial

predation

First nest success rate

(age-specific)

5 6 8 10 11 12

Renesting rate

(potential vs. realized*)

(age-specific)

5 6 8 10 11 12

Renest success rate

(age-specific)

5 6 8 10 11 12

Sex ratio of poults 

at hatching 3 6

Young per adult female

(an indicator of annual production) 1 4 7 

Climate

(temperature,

precipitation)

Predators

Hen success rate

(potential vs. realized*)

(age-specific)

5 6 7 8 11 12

?

hen mortalit

1st nests re-nests

*Potential vs. realized are discussed in Roberts et al.

(1995) and Krebs (1985:167)

Variables entered into turkey population 

models, as discussed in the following:

1. Porter et al. (1990)

2. Suchy et al. (1990)

3. Suchy et al. (1983)

4. Lobell et al. (1972)

5. Roberts et al. (1995)

6. Vangilder and Kulowiec (1988), Vangilder (1992), 

Vangilder and Kurzejeski (1995)

7. Rolley et al. (1998)

8. Miller et al. (1998)

9. Roberts and Porter (1998a)

10. Roberts and Porter (1998b)

11. Paisley et al. (1998)

12. Keegan and Crawford (1999)

Other factors influencing turkey population dynamics

Section II. Biology 13
Death

(mortality) Movement

Snow depth

Disease

Parasites

Predation

Exposure

Flooding

Birth defects

Habitat 

characteristics

weather

Juvenile and adult

mortality

(survival rate)

(age & sex specific rates

for each season)

1 2 3 4 5 6 7 12

Poult

mortality

(survival rate)

1 2 5 6 7 8 9 11

Food supply Environment

Incubation

status

Variation in

gobbling

activity

Hunter

density

Gobbler

density

POPULATION DYNAMICS

s

Natural

mortality

3

Poaching

(illegal harvest)

Spring 

gobbler 

hunting

Fall

hunting

(both

sexes)

Crippling 

losses

Legal harvest

(age and sex specific) 3 4 6 7

Starting population size

(age and sex specific) 1 2 3 6 7

Sex ratio

(sex specific) 3

additive

vs. completely

compensatory

vs.

compensatory

beyond a

threshold

tality

Immigration Emigration

14 Wild Turkey Harvest Management: Biology, Strategies, and Techniques
15

Section III. Harvesting Strategies

Three basic strategies are used to harvest wild

turkeys: (1) harvest only gobblers in the spring; 

(2) harvest gobblers in the spring and allow limited

harvest of birds of either sex in the fall; and 

(3) attempt to maximize the combined spring gobbler

and fall either-sex harvests. It might seem that many

harvest strategies could be developed, but given the

species’ life history and North American hunting

traditions we believe these 3 strategies are the only

practical options for turkey harvest management.

Spring gobbler hunting is biologically the most

conservative approach. This strategy yields stable

harvests and is unlikely to result in overharvest. In

contrast, experience has shown that fall harvest can

depress population growth. Population studies and

models have shown that turkey populations are

particularly sensitive to adult hen mortality. Removals

of more than 10% of the population during fall season

are likely to lead to population decline (Suchy et al.

1983, Vangilder and Kurzejeski 1995). Vulnerability of

adult hens to fall harvest is influenced by poult

recruitment because juveniles are more susceptible to

harvest than adults. When reproduction is poor there

are few juveniles in the population to buffer the effect

of fall hunting on adults. Mast crops may also affect

fall harvest. In Pennsylvania and West Virginia some

of the largest fall harvests occurred during the years

of poor mast production. Strategies that include fall

harvest are inherently more complex than the

strategy of harvesting gobblers in the spring because

all age and sex classes are harvested, managers must

deal with annual variation in population size, 

and under some conditions food resources affect

vulnerability to hunting. 

The strategy of maximizing the combined spring and

fall harvest is analogous to the maximum sustained

yield strategies used for white-tailed deer and fish

populations. Turkey harvest strategy, however, differs

in important ways. First, turkey harvest occurs twice

during the year and each harvest removes different

segments of the population. Second, maximum

sustained yield strategies for other species assume a

stable carrying capacity and density-dependent

population responses that cause the population to

oscillate around carrying capacity. In contrast, we

assume that wild turkey populations are density

independent and subject to short-term fluctuations

that are related to annual variation in recruitment.

Another complication arises in some northeastern

habitats where vulnerability to fall harvest may

increase when mast crops fail. Both turkey

recruitment and mast crops are correlated with

weather variables, but, as yet, neither can be

predicted from weather data. Furthermore, neither

recruitment nor mast production have regular cycles

that can be used to predict future conditions.

Therefore, fall turkey populations are moving targets.

Optimizing fall harvest requires a measure of turkey

abundance prior to hunting and the ability to regulate

hunting effort. In this section we will start with spring

gobbler harvest and end with optimizing the combined

spring and fall harvest.
16 Wild Turkey Harvest Management: Biology, Strategies, and Techniques

Chapter 4. Spring Gobblers-Only Harvest

Spring harvest strategies are designed to remove

primarily adult males after most breeding has taken

place. In theory, this type of harvest has minimal

effect on population growth rates because mating is

polygamous and males play no role in rearing young.

Extensive management experience has shown the

strategy of post-breeding male harvest to be

ecologically sound and sustainable. Although the

strategy is clear, actually regulating the harvest is

complex.

The following assumptions form the core of spring

harvest management in the Northeast. The

assumptions are not independent. Here we review

each assumption, show how it may be violated, and

identify hunting regulations designed to assure the

assumptions are met. 

Assumption 1: Spring harvest is limited to males.

Violations of this assumption are insignificant for legal

harvests. Regulations in 10 northeastern states and

Ontario allow taking bearded birds. In theory, this

regulation puts up to 10% of the adult hen population

at risk (Schorger 1957), but in practice the legal

harvest of bearded hens is rare (Vangilder 1996). In

Massachusetts, over 19 years, there was an average of

1 bearded hen taken per 122 males (J.E. Cardoza,

unpubl. data).

Illegal taking of hens is a potentially serious violation

of this assumption. In Missouri, over 7 spring seasons,

5.2% of the hens were shot illegally (Vangilder and

Kurzejeski 1995). Illegal taking of hens was also

significant in Virginia and West Virginia (J.C. Pack,

pers. commun.). In Missouri, Virginia, and West

Virginia, the loss of hens to poaching can exceed the

legal harvest of hens during fall either-sex seasons.

Losses of this magnitude can have a negative effect on

population growth, especially in populations also

subjected to fall either-sex harvest (Suchy et al. 1983,

Vangilder and Kurzejeski 1995). Population modeling

suggests that removal of more than 10% of the adult

hen population will reduce rates of population growth. 

Illegal taking of hens is difficult to detect and manage.

Regulations that establish the dates of hunting,

restrict shooting hours, and prescribe hunting

methods (e.g., calls must be used) are designed to

minimize the loss of hens. 

Assumption 2: Hunting mortality is additive to

natural mortality.

Most game harvest models assume that hunting

mortality partially or wholly replaces natural

mortality (Caughley 1985). In the simplest model,

complete compensation, harvesting does not affect the

population size unless its rate exceeds some threshold

value. As long as harvest remains below the threshold,

a corresponding decrease in other forms of mortality

will compensate for increased hunting mortality. More

complex models of partial compensation assume a

density-dependent population response in which rate

of population growth increases as population size

decreases. 

For wild turkeys, a growing body of evidence indicates

that hunting mortality is additive to natural mortality

(J.C. Pack, unpubl. data). Wild turkey populations

have generally shown a linear increase in overall

mortality with increasing hunting mortality (Little et

al. 1990, Vangilder and Kurzejeski 1995). Adult

gobblers have high survival rates outside of hunting

season, so there is little opportunity for compensating

increases in survival to offset hunting mortality

(Suchy et al. 1983, Little et al. 1990). As a result,

turkey population models are generally based on the

assumption that hunting mortality is additive to

natural mortality, and we recommend this approach to

harvest management. 

Assumption 3: Spring harvest does not affect longterm population levels.

This assumption is widely held, and experience with

the management of harvested populations suggests

the assumption of no long-term effect has generally

been true. Turkey populations have expanded and

thrived throughout North America while being

subjected to conservative spring harvests. Detailed

population studies show that the rate of spring

harvest can affect population dynamics and breeding

behavior. This assumption would be violated if most

adult males were harvested each year, or if breeding

behavior were disrupted by either an inappropriately

timed season or excessive harvest. 

Population modeling suggests that a spring harvest of

30% of the male population would allow for continued

population growth and provide quality hunting

(Vangilder 1992). Precise estimates of gobbler harvest

rates, either as a percentage of the gobbler population

or the total population, are unavailable for spring

hunting in the Northeast. Harvest rates from other

eastern states, determined primarily from radiotracking and band recovery studies, have been

variable, ranging from about 15% to 51% of the

gobbler population (Vangilder 1992:156-157). In

southwestern Wisconsin, spring harvest removed an

average of 32% of the gobbler population (Paisley et
al. 1996). Long-term population studies and simulation

models from Missouri illustrate the relationships

between harvest rate and population characteristics

(Vangilder 1992:162, Vangilder and Kurzejeski

1995:29). As harvest rates increase, the proportion 

of adult males in the population declines and the

proportion of juveniles in the harvest increases.

Harvesting more than 25% of the adult gobblers each

year would shift the age structure in favor of juvenile

males. A total harvest rate of 60% would remove all

adult males in some years and, on average, produce a

spring population consisting of only 10% adult males.

Such an intensive harvest would affect both population

dynamics and the quality of hunting. 

Although the harvest of gobblers in the spring has

been described as “practically foolproof” (Bailey and

Rinell 1968:53), it is clear that this assumption

depends on maintaining a reasonable rate of harvest.

None of the northeastern states or provinces has

expressed spring harvest goals in terms of desired

harvest rate, but all agencies have established

regulations to ensure a conservative harvest rate. 

The regulations that directly affect gobbler harvest

rate include season timing, season length, bag limit,

and limits on numbers of hunters. The best available

evidence suggests that gobbler harvest <30% are

sustainable, and that current regulations are

maintaining harvest rates at sustainable levels. 

Assumption 4: Spring gobbler hunting does not

disrupt breeding behavior.

This assumption is an integral part of the preceding

one that spring hunting does not affect population

levels. Discussion of Assumption 3 emphasizes the

effects of harvest rate on population demography.

Assumption 4 also considers harvest rate, but focuses

on the timing of the hunt relative to the chronology of

reproduction and the direct and indirect effects of

hunting on nesting success. 

The goal of spring hunting has been to maximize

hunter opportunity for taking a gobbler and minimize

risk to nesting hens. One strategy for accomplishing

this goal has been to start the spring hunt near the

median date for the onset of incubation. Thus, hunting

starts well after the onset of seasonal gobbling activity

and after most breeding has taken place. In theory,

incubating hens are protected by their secretive

nature and the advanced stage of vegetative growth.

Hunter opportunities are good because “gobbling

reaches a second peak when the bulk of the hens are

incubating and no longer meet with the males” (Bailey

and Rinell 1967:73). Conventional wisdom held that

gobblers were more vulnerable to hunters’ calls early

in the reproductive season, and that early seasons

might lead to excessive harvest rates and a shortage

of breeding males (Kurzejeski and Vangilder

1992:177). This effect was reported from southern

Alabama, where intensive hunting apparently caused a

shortage of breeding males resulting in a significant

proportion of infertile clutches and lowered overall

productivity (Exum et al. 1987:43-44). 

The effect of the timing of spring season on the rate of

illegal killing of hens has not been measured. There is

some evidence that incubating hens are less

vulnerable to poaching than non-reproductive hens

(Miller 1997). In Missouri, more hens were lost to

poaching in years when spring gobbler season began

before peak incubation (Vangilder 1992:155). In

Virginia and West Virginia, illegal kill was also greater

when gobbler season began before the peak of

incubation, and survival rates of nesting hens were

always greater than those of non-nesting hens (J.C.

Pack, unpubl. data). Reported rates of poaching vary

regionally, and the rates are not necessarily greater

where spring seasons start earlier and last longer

(e.g., Miller 1997). Because poaching is believed to be

a potential problem, most eastern states cite

protection of hens as another reason for holding

spring seasons while the bulk of the hens are

incubating. In contrast to poaching, legal spring

hunting clearly has minimal effect on hen nesting

success. 

Hunting Regulations

Regulations affecting spring gobbler season in the

Northeast are summarized in Table 4.1. In this

section, we describe the relationship of each regulation

to the assumptions underpinning spring harvest

strategies, and we examine the effect of each

regulation on harvest rate. 

Section III. Harvesting Strategies 17
18 Wild Turkey Harvest Management: Biology, Strategies, and Techniques

Season Dates and Season Length

Proper timing of the spring gobbler season is

considered critical for protecting the population. 

The dates and length of the spring hunting season

affect Assumptions 1, 3, and 4 about spring harvest.

The biological gobbling season spans the entire

reproductive cycle. Gobbling activity begins in late

winter before breeding takes place and gradually ends

about 3 months later when nests begin to hatch. Thus,

the later a hunting season starts in the reproductive

cycle the closer it is to the end of gobbling activity and

the shorter it must be. 

The northeastern states and provinces generally have

seasons that coincide with the incubation period rather

than the full gobbling season. Thus, in the Northeast

most seasons are 3 or 4 weeks long, starting in late

April or early May and running through May. Starting

the hunting season near the onset of incubation serves

2 goals: providing hunters ample opportunity to hear

gobblers and protecting hens from inadvertent kill

(Miller et al. 1997). Recent studies have reported a

single peak in gobbling that did not coincide with the

start of incubation (Kienzler et al. 1996, Miller et al.

1997). In West Virginia, the number of gobblers heard

per hour declines steadily over the season, which

starts on the fourth Monday of April and lasts 4 weeks

(Igo et al. 1997). That pattern of gobbling activity

seems typical for populations in the Northeast. 

Table 4.1. Summary of spring gobbler season regulations in use in the Northeast in 1996.

State/Province

Regulation CT DE ME MD MA NH NJ NY ONT PA RI VT VA WV

Season length, weeks

3 xx x

4+ x x x x x x x x x x x

Bag limit

1 xxx x x x x

2 x xx x x x x xx

Bearded birds x x x x x x x x x x x

Males x x x

Regulate hunter numbers

None x x x x x x x x

Number x x x x x x

Area

Time x

Time and area x x

Method of taking

Calling only x x

Unspecified x x x x x x x x x x x x

Hunting implement

Shotgun & shot size x x x x x x x x x x x x

Archery gear x x x x x x x x x

Muzzle-loading

shotgun x x x x x

Shotgun, rifle, bow x x

Harvest reporting

Check station x x x x x x x x x x x x

Report card x x x x x
Incubation behavior does appear to provide hens some

protection from poaching. West Virginia gobbler

hunters reported seeing and calling-in most hens

during the first week of the season, and the numbers

declined steadily as the season progressed (Igo et al.

1997: Table 10, p. 15). Human attitudes, however, may

affect poaching rates more than the dates of the

hunting season. Illegal taking of hens during spring

gobbler season is significant in Virginia and West

Virginia (G. Norman, J.C. Pack, pers. commun.), and

in some years in Missouri (Vangilder and Kurzejeski

1995), but not in Mississippi (Miller 1997). Strong

regional variation obscures the relationships among

hen mortality and the dates and length of spring

hunting season. 

The following generalizations can be made about the

affects of hunting season timing and length on harvest.

■ Starting seasons early in the reproduction cycle will

maximize hunter opportunity to hear and harvest

gobblers (Miller 1997). Early, intensive harvest may

affect population dynamics (Assumption 3) and

breeding behavior (Assumption 4) (Exum et al.

1987). Risk of overharvest can be minimized in early

seasons by using a permit system to regulate

hunter density. 

■ Shortening the spring season will reduce rates of

gobbler harvest. The reduction in harvest will be

greatest when a shortened season occurs late in the

reproductive cycle (Kurzejeski and Vangilder

1992:177). 

■ At present, we cannot predict the size of the

reduction in harvest that will be accomplished by

incrementally shortening or delaying spring hunting

season. Managers should recognize that season

length and timing provide only weak control over

harvest rate. Strong control over harvest rate

requires regulating hunter density. 

■ Strong regional variation obscures the relationship

between season timing and illegal taking of hens. 

It is clear, however, that the timing of spring season

has the potential to reduce illegal killing of hens,

and experience in several eastern states

recommends the conservative approach of having

spring seasons coincide with peak incubation.

Regional variation in poaching activity also suggests

that hunter training and law enforcement may be

appropriate tools for reducing hen mortality during

spring season. 

Bag Limits

Bag limits are pertinent to all 4 assumptions about

spring gobblers harvest. Bag limits are either 1 or 2

birds in the Northeast. Ontario and 5 states have a 1-

bird limit, 7 other states allow taking 2 birds, and

Connecticut has a 1-bird limit on public land and a 2-

bird limit on private land. Bag limits restrict the

activities of the most efficient hunters, distribute the

harvest among hunters, and set an upper limit on the

harvest. Increasing bag limits can increase hunting

opportunity. In practice, raising the limit from 1 to 2

birds has had little effect on total harvest. In West

Virginia, a total of 22,741 gobblers were reported

harvested during the 1997 and 1998 spring seasons,

and 7.5% of the hunters reported killing 2 birds (J.C.

Pack, unpub. data). Bag limits provide only weak

control over harvest rate, because only states with

multiple bird limits have the option to reduce harvest

by restricting bag limit. When conditions permit,

increasing bag limits can increase hunting opportunity

with little risk to the population. 

Only New Hampshire, New Jersey, and Rhode Island

specify that spring hunting is limited to males; 10

other states and Ontario allow taking bearded birds.

Legal harvest of bearded hens is insignificant. 

Control of Hunter Numbers

Hunter numbers and hunter effort affect harvest rate

(Assumption 3) and breeding behavior (Assumption 4).

Ontario and 7 northeastern states do not regulate

hunter numbers. In some of these states hunters are

required to have a special permit or license, but all

applicants receive a permit. Connecticut, Delaware,

New Jersey, Rhode Island, and Maine, use a lottery or

random drawing system to restrict hunter numbers.

In Connecticut and New Jersey applicants must

choose a season specific to an area and time period. 

Regulation of hunter numbers by zone or turkey

management unit provides strong control over

harvest. Permit allocations can be adjusted annually in

response to changes in the turkey population or other

factors within the management zone. The permit

system allows managers to identify the hunting

population and obtain additional information as

needed. 

Methods of Taking

New Jersey and Connecticut specify the method of

taking as “calling” and prohibit “stalking.” This

restriction is intended to protect hens from

inadvertent kill (Assumption 1) and enhance safety.

None of the other states explicitly state a method of

taking, but many states prohibit the use of bait,

electronic calls, and dogs. The effectiveness of these

regulations is unknown. 

Hunting Hours

Hunting hours are from 30 minutes before sunrise

until noon or 1:00 pm. These hours are intended to

protect nesting hens (Assumption 1), because

incubating hens are believed to leave the nest to feed

in the afternoon. The effect of this regulation on hen

mortality is unknown. 

Hunting Implement

Restrictions on hunting implements are intended to

promote hunting safety. Virginia and West Virginia

have no limits on hunting implement, but Ontario and

other northeastern states restrict hunters to the use of

shotguns and specific shot sizes. Delaware,

Massachusetts, New York, and Ontario allow muzzleloading shotguns in addition to modern shotguns. Nine

states also allow hunters to use bows. 

Section III. Harvesting Strategies 19
20 Wild Turkey Harvest Management: Biology, Strategies, and Techniques

Harvest Reporting

An accurate measure of harvest is considered critical

for harvest management programs. In the Northeast,

reporting of kill is universal and generally mandatory.

The majority of states and Ontario require successful

hunters to bring birds to check stations. Maine and

Rhode Island also require hunters to return a kill

report form by mail. Connecticut requires fall

firearms hunters to present birds at a check station;

spring gobbler and fall archery hunters use kill report

forms. Pennsylvania and New York do not operate

check stations and require hunters to return a kill

report form, although in New York reporting is not

legally mandatory. Compliance with reporting

requirements varies among states, and over time

within states. All states consider non-reporting a

serious source of error in harvest estimates and

formally evaluate reporting rates with post-season

mail or phone surveys. 

Decision Variables and Regulatory

Procedures

Agencies in the Northeast generally express turkey

management goals in qualitative rather than

quantitative terms. The most common goals include

re-establishing viable populations in all suitable

habitat and allowing sustained harvest without

adverse impact on the resource. Other commonly

expressed goals include maintaining populations in

balance with available habitat and other resources,

and sustaining populations for esthetic, scientific, and

educational values. Agencies seldom express harvest

goals in terms of harvest density (kill/unit area),

harvest rate (percent of population), or population

density. The West Virginia Division of Natural

Resources has a harvest goal of a minimum of 1

gobbler/mile2 of range. Minnesota has a harvest goal

of 30% of the gobbler population (Appendix A).

Regulations used to achieve these goals fall into 2

categories: those designed to protect the resource and

those addressing hunting quality and safety. Some

agencies regulate hunter numbers and others do not.

Controls on hunter numbers are used primarily to

protect the population, but limiting hunter

participation directly affects hunting quality by

establishing a maximum hunter density. Hunter

numbers generally are controlled through a system of

random drawings for a limited number of permits

(Connecticut, Delaware, Maine, New Jersey, Rhode

Island). Hunters must choose a hunting unit, time

period, or a combination of the two. Limited permit

systems provide a mechanism for distributing the

harvest among hunters and management units. States

that do not limit hunter numbers may require a

turkey hunting permit, but permits are available to all

applicants, and generally hunters may hunt when and

where they want within the region open to hunting.

Both management approaches–limited and

unrestricted hunter numbers–use harvest trend data

to monitor population response and are based on the

same assumptions. The strategy of unrestricted

hunter numbers allows only coarse control over

harvest rate. Permit systems that limit hunter

numbers allow adjusting harvest annually in response

to harvest and population trends and provide much

finer control over harvest rate. 

Spring Gobbler Harvest, No Control on Hunter

Numbers 

This approach represents the simplest harvest

management model in terms of regulatory structure

and information needs. The turkey population is

protected by the regulation of season timing, season

length, and bag limit. Hunt quality and safety are

addressed by regulations that specify hunting

methods (calls must be used; no stalking), hunting

implement (shotgun, shot size restrictions), hunting

hours, and blaze orange. In addition, hunt quality,

hunter ethics, and safety are maintained through

hunter education and a variety of continuing

education and out-reach activities. These efforts also

serve to protect the population by encouraging

behaviors that limit the harvest to males (Assumption

1). 

Harvest trends are used to monitor population status.

Declining harvests may trigger more restrictive

regulations (e.g., later, shorter seasons) and

increasing harvest may lead to more liberal harvest

(increase bag limit). Accurate measure of harvest

represents the basic information need, and harvest is

assumed to provide a reliable index to population size

(Lint et al.1995). 

This harvest strategy is simple and has been

successful. Many eastern states began their modern

turkey hunting seasons with restrictions on hunter

numbers and then relaxed controls when trends in

hunter numbers and harvest suggested controls were

unnecessary. The beauty of this system is in its

regulatory simplicity. Usually, there is 1 statewide

season and license or permit holders can hunt when

and where they want. Harvest is measured with

mandatory check stations, mail-back report cards, or

a combination of the two. The system regulates statewide harvest. This strategy cannot address local

problems of under- or over-harvest and it cannot

distribute hunters to reduce interference among

hunters and improve hunting quality. Safety can be a

concern when accident rates increase (e.g., Pa. and

Mo.). 

Permit systems facilitate identifying the turkey

hunting segment of the general hunting population for

delivering safety messages or obtaining additional

information. Permit systems allow random sampling

of turkey hunters to estimate harvest, effort,

reporting compliance rates, and other attributes. 

We recommend the following procedures for

regulating spring gobbler harvest where hunter

numbers are uncontrolled. 

1. Stratify the overall area into Turkey Management

Units (TMUs).

Options:

a. ecologically based units, e.g., physiographic regions.
b. political units, e.g., counties, townships.

c. combination of ecological and political units, e.g.,

aggregate counties to approximate physiographic

units.

2. Establish a long-term minimum harvest goal for

each TMU, and specify remedial actions to be taken if

harvest falls below the goal.

For example, if harvest falls below goal for 2

consecutive years, season opening will be delayed by 1

week and the season will be shortened by 1 week.

Note that when hunter numbers are unregulated, the

controls over harvest rate are limited to season

timing, season length, and bag limit. An alternative

response to declining harvests is to institute a permit

system that limits hunter numbers. An existing TMU

system provides the basis for a permit system and a

minimum harvest goal provides a decision rule for

protecting the population before it experiences a

serious decline. Therefore, we recommend that TMUs

and minimum harvest goals be established as

safeguards. 

3. Measure harvest annually by TMU.

Options include one or more of the following:

a. mandatory check stations.

b. mandatory kill report card. 

c. post-season surveys of hunters.

Harvest data are a critical information need. Postseason surveys are often used to check on reporting

rates obtained with check stations or report cards.

4. Monitor hunter effort as expressed by numbers of

hunters and trips or hours per hunter.

Options include:

a. post-season surveys.

b. mail-back questionnaires with permit.

Hunter effort data provide a check on the validity of

harvest as an index to population size. Effort data

needs to include unsuccessful hunters so a permit

system facilitates gathering these data.

5. Obtain an annual index to population size that is

independent of harvest.

Options include: 

a. random surveys of hunters pursuing other species. 

For example, Minnesota conducts a random survey of

antlerless deer permit holders to derive indices of

turkey abundance and data on the distribution of

turkeys (Welsh and Kimmel 1990, Kimmel et al. 1996).

b. volunteer-cooperator reports. 

For example, West Virginia uses a volunteer deer bow

hunter survey to obtain data on the abundance and

distribution of many species (Glassock et al. 1997). 

c. staff/volunteer sighting records.

For example, summer brood sighting records

maintained by Pennsylvania Game Commission field

officers (Wunz and Shope 1980, Wunz and Ross 1990).

An independent index to population is not critical to

the decision process with this strategy. However, we

recommend obtaining a second index to abundance as

a check on harvest trend data. Should the

management agency decide to restrict harvest by

limiting hunter numbers an independent index to

abundance is particularly useful for establishing

permit quotas.

Spring Gobbler Harvest, Hunter Numbers

Regulated

Regulating hunter numbers through a permit system

allows much finer control over harvest than the

preceding approach. Permit systems generally have

been used to protect the population, but they have also

been used to satisfy hunter demand for a “quality” low

hunter-density experience. Hunter numbers can be

controlled by TMU and hunting season segment. The

number of permits can be adjusted annually in

response to harvest trends and other available

information on population trends and hunter

satisfaction. Issues of hunt quality that relate to

crowding and interference among hunters can be

addressed directly. 

A good example of the degree of control that can be

achieved by regulating hunter numbers is provided by

the model developed by R. O. Kimmel (Appendix A)

and used in Minnesota to allocate spring permits.

Permits are allocated annually by TMU and season

segment based on previous harvest, population trends,

harvest goals, habitat quality, and hunter interference

rates. The Minnesota system requires data on harvest,

an independent index to population size, information

on hunter effort and interference, and a subjective

estimate of habitat quality. 

The following procedures for regulating spring

harvest where hunter numbers are controlled do not

incorporate measures of hunter effort and habitat

quality, but we indicate where those measures could be

incorporated in the decision process. 

1. Stratify the overall area into Turkey Management

Units (TMUs).

Options include:

a. ecologically based units, e.g., physiographic regions.

b. political units, e.g., counties, townships.

c. combination of ecological and political units, e.g.,

aggregate counties to approximate physiographic

units. 

2. Establish hunting season segments.

Options include:

a. one season.

b. divide season into 2 or more segments, e.g., New

Jersey has 5 sequential segments, each of which

includes at least 1 Saturday. 

3. Establish an annual harvest goal by TMU.

Options include:

a. minimal harvest density. This is the same approach

taken where hunter numbers are unregulated. If

harvest falls below the goal, permit numbers are

reduced the following year in a predetermined way. 

b. proportion of population. For example, Minnesota

has established maximum harvest rate of 30% of the

gobbler population. Population estimates are based on

the previous spring kill. Permit quotas are established

Section III. Harvesting Strategies 21
22 Wild Turkey Harvest Management: Biology, Strategies, and Techniques

for each TMU by considering trends in population,

hunter success rates, habitat quality, and hunter

interference rates (Appendix A). Note that this

system also establishes a hunter density goal for each

TMU. 

4. Establish hunter density goals.

Options include:

a. no restrictions; all hunters are assigned the TMU

they request.

b. maximum densities are established based on quality

of hunt and safety considerations, e.g., 2 hunters/mile2

of habitat; the number of permits for the TMU is

equal to the area of habitat divided by the desired

maximum number of hunters per mile2. 

c. hunter density based on harvest goals, see 3.b.

above.

5. Measure harvest annually by TMU.

Options include one or more of the following:

a. mandatory check stations.

b. mandatory kill report card.

c. post-season surveys of hunters.

6. Monitor hunt quality as expressed by proportion of

hunters experiencing interference by other hunters or

numbers of hunters seen.

Options include:

a. post-season surveys.

b. mail back questionnaires with permit.

In the Minnesota system, hunter interference rates

are used to create a weighting factor that can be used

to increase or decrease the number of permits issued

by TMU. These same surveys or questionnaires can

be used to check kill reporting rates, estimates of

hunter success derived from check stations or kill

report cards, and hunter access problems on private

land. 

7. Obtain an annual index to population size that is

independent of harvest.

Options include:

a. random surveys of hunters pursuing other species.

b. volunteer-cooperator reports.

c. staff/volunteer sighting records.

The Minnesota system (Appendix A) uses a population

trend index based on the proportion of deer hunters

observing wild turkeys in each of the last 3 years

(Kimmel et al. 1996). Increasing trends yield a value

>1 and decreasing trends yield a value <1. The trend

index is used to adjust the harvest based population

estimate for calculating permit allocations. 

Additional Comments About Decision

Variables

Harvest Rate 

The model that we propose for regulating spring

harvest assumes that harvest can be measured

accurately, and that gobbler harvest is a valid index to

population size. For the latter assumption to be true,

hunters must remove a constant proportion of the

population each spring (Steffen and Norman 1996).

We recognize that variation in weather and other

factors can cause variation in gobbler harvest rates

that are independent from population size. Changes in

hunter numbers or hunter effort are another

important source of variation in harvest rate. A review

of studies of spring gobbler harvest mortality

(Vangilder 1992:156-157) confirms annual variation in

gobbler harvest rate within sites and regional

variation among sites. Testing the assumption that

harvest rate is constant requires good estimates of

population size. Recent, long-term studies conducted

in Mississippi confirm that both gobbler harvest and

harvest/unit of hunter effort provide reliable indices of

population size and population trends (Lint et al.

1995). Gobbler harvest was the least expensive index

to obtain. 

To guard against uncontrolled sources of variation in

gobbler harvest, we recommend collecting data on

hunter effort and, if resources permit, obtaining an

independent index of turkey abundance. Data on

hunter numbers and effort can be used by

management agencies for general planning purposes

in addition to turkey harvest management. An

independent index of population size adds validity to

the management program regardless of the harvest

strategy being used. Where hunter numbers are

regulated, the population index can be used directly in

the permit allocation process. 

Juvenile: Adult Gobbler Age Ratios 

We do not recommend using the ratio of juvenile to

adult gobblers in the spring harvest as a basis for

regulating harvest because much of the variation in

this ratio seems unrelated to harvest rate. We do

recommend, however, that agencies that collect age

and weight data while recording harvest continue to

do so. Hunters are especially interested in this type 

of descriptive data, and details about the harvest are

useful when agencies communicate with hunters about

turkey hunting and management programs. 

Population modeling suggests that the percentage of

adult males in the population will decrease as the

overall harvest rate increases (Vangilder 1992:162).

Thus, the ratio of adults to juveniles in the harvest

should contain information about harvest rate. Some

evidence suggests that when the proportion of adults

in the spring harvest drops below 70%, the rate of

harvest may be excessive (Paisley et al. 1996:42).

Reported age ratios in the spring harvest, however,

vary substantially among states (Kurzejeski and

Vangilder 1992:188, Table 2), and show no clear

relationship with season length or hunter effort. 

Reported age ratios are affected by errors in data

collection, annual population fluctuations, and hunter

preferences for adult gobblers. Although juvenile and

adult gobblers are readily distinguished, there seems

to be considerable misclassification in data collected at

cooperator run check stations or reported by hunters.

Annual population fluctuations also influence spring

harvest age ratios because the proportions of juveniles
and adults in the population depend on the previous

summer’s reproduction. There is a higher proportion

of juveniles in the spring population following years of

good reproduction than following years of poor

reproduction. Hunting traditions also seem to play a

role because hunters from states such as Mississippi

with long, liberal spring seasons harvest few juvenile

gobblers (Miller 1997). In summary, age ratios of

harvested birds are useful for describing the harvest,

but much less useful than total harvest or

harvest/hunter effort for regulating harvest. 

Section III. Harvesting Strategies 23
24 Wild Turkey Harvest Management: Biology, Strategies, and Techniques

Chapter 5. Spring Gobbler Harvest With Limited

Either-Sex Fall Harvest

The general goals of this harvest strategy are to allow

continued population growth, to protect and enhance

spring gobbler hunting opportunities, and to provide

additional recreation by allowing limited fall hunting.

The emphasis is on spring gobbler hunting. Fall

hunting is of secondary importance and fall hunting

opportunities may be curtailed if the spring gobbler

harvest declines. This limited fall harvest strategy is

the model used by most northeastern states that have

both spring and fall hunting seasons. The interest in

and demand for fall hunting varies across the

Northeast. Fall hunting traditions are well developed

in New York, Pennsylvania, Virginia, and West

Virginia; fall hunting pressure remains light in

Connecticut and Massachusetts. 

The conservative limited fall harvest strategy was

designed in response to population sensitivity to

either-sex harvest, annual fluctuations in fall

population size that can exceed 50% of the long-term

mean, and the increased risk of overharvest associated

with poor mast crops and low poult production.

Management experience and extensive research have

demonstrated the sensitivity of turkey populations to

fall either-sex harvest. Simulation modeling shows

that fall harvests that remove less than 10% of the

total population generally allow long-term population

growth. Removals of more than 10% of the fall

population invariably led to decreases in population

size. Populations were sensitive to changes in adult

hen mortality rates. When illegal loss of hens during

the spring season was incorporated into these

simulations, the “safe” fall harvest was between 5%

and 10% of the total population. These simulations

were based on extensive data from turkey populations

in northern Missouri where over 7 spring seasons at

least 5.2% of the radio-marked hens were shot illegally

(Vangilder and Kurzejesky 1995). Rates of illegal hen

loss were similar in Virginia and West Virginia (G.

Norman and J. C. Pack, pers. commun.). In New York,

the crude annual mortality rate attributed to human

activity averaged 11.7% and ranged from 5.0% to 7.1%

for radio-marked hens over 4 years (Roberts et al.

1995). These estimates include legal harvest and

wounding, but poaching was the primary source of

human induced mortality. Data are lacking for other

northeastern states. 

The limited fall-harvest strategy minimizes the risk of

overharvest to an annually fluctuating population. The

goal is to sustain a relatively constant fall harvest that

is small enough to protect the population under worstcase conditions characterized by low reproduction and

poor mast crops. No effort is made to exploit the 

larger fall populations that occur in years of betterthan-average reproduction by liberalizing fall seasons.

The limited fall harvest strategy has been successful

in the Northeast. 

Basic Assumptions

In contrast with spring gobbler hunting, biologists

recognize that excessive either-sex harvests in the fall

can slow population growth and cause populations to

decline (Bailey and Rinell 1968). The decline of turkey

populations during the eighteenth and nineteenth

centuries has been attributed to a combination of

habitat destruction and excessive harvest. The limited

fall harvest strategy incorporates several new

assumptions associated with 2 discrete harvest

seasons and the removal of all age and sex classes.

Here we review the new assumptions associated with

limited fall harvest and discuss their relationship to

the 4 assumptions underpinning spring gobbler

harvest. 

Assumption 1: Fall either-sex harvest affects

population growth.

Simulation modeling suggests that harvesting 5-10%

of the fall population will allow for continued

population growth, but harvests exceeding 10% will

usually lead to population decline. Spring and fall

harvests are believed to be additive; illegal taking of

hens during the spring season will reduce the

allowable fall take (Vangilder and Kurzejeski 1995).

The demographic factors that have the greatest effect

on long-term population growth are nest success, hen

survival, and poult survival (Vangilder and Kurzejeski

1995, Roberts and Porter 1996). Fall harvest strategies

have a direct effect on hen survival; nest success and

poult survival are not under the direct control of

managers. 

Regulations that control harvest rates and affect hen

survival include limiting hunter numbers, season

length, season timing in relation to seasons for other

game species, and restrictions on hunting implement.

Regulating hunter numbers with a permit system

provides the greatest control over harvest. Where

hunter numbers are not limited, shortening fall

seasons has effectively reduced harvest rates.

Experience with 11- to 12-week fall seasons in West

Virginia has shown that harvest rates are greatest

during the first week, decline steeply during the

second week, and thereafter decline slowly. Thus,

extended seasons add substantially to the total

harvest. Shortening the West Virginia fall season to 
5-6 weeks resulted in a rapid increase in turkey

populations and fall harvest (Pack 1986). 

Scheduling the opening of fall turkey season so it does

not coincide with the opening date of other game

seasons has also been effective at reducing harvest

rates (Pack 1986). During concurrent seasons, and

especially on opening days, much of the turkey

harvest was associated with opportunistic taking by

hunters pursuing other species. New Hampshire

restricts fall turkey hunters to the use of archery

equipment to minimize harvest while providing

substantial recreational opportunity. 

Assumption 2: Hunting mortality is additive to

natural mortality.

This assumption was discussed under spring gobbler

hunting (Chapter 4, Assumption 2). Here, we consider

the additive interaction between fall and spring

harvest. Gobblers removed during the fall season are

unavailable for harvest the next spring. The direct

effect of removing gobblers is less important than 

the effect that removing adult hens has on longterm population growth. In Virginia, there was no

relationship between the annual fall harvest totals 

and the subsequent spring gobbler harvest over 10

consecutive years. There was a significant inverse

relationship between the number of birds

harvested/km2 of forest in the fall and the growth 

rate of spring gobbler harvest over the same period.

Spring gobbler harvest increased most rapidly (10.4%

annually) where fall harvest was <0.1 bird/km2 of

forested habitat. At higher levels of fall harvest the

annual growth rate in spring gobbler harvest was

about 6%. An identical relationship was observed in an

analysis of fall and spring harvest data from West

Virginia (Steffen and Norman 1996). These results are

consistent with population modeling predictions and

confirm the need for conservative fall harvests where

the program emphasis is on spring gobbler hunting. 

Assumption 3: Turkey populations fluctuate

annually.

The magnitude of annual fluctuations among

northeastern turkey populations is illustrated in

Figure 5.1 by a 10-year series of summer turkey

sightings collected by conservation officers in

Pennsylvania (W.E. Drake, unpubl. data). The total

number of hens and poults observed in these statewide surveys is significantly correlated with the

subsequent fall harvest, and the counts provide a

reliable index to fall population size (Wunz and Shope

1980, Wunz and Ross 1990). In this example, 4 years

were “average” or within 5% of the 10-year mean. Two

years (1984, 1989) were >25% below the 10-year

mean, and 2 years (1987, 1990) were >25% above the

10-year mean. The largest count (1987) exceeded the

10-year mean by 40%, and the smallest count (1984)

was 33% below the mean. 

This strategy is designed to minimize the risk of

overharvest in years when populations are well below

average. In general, juvenile turkeys are more

vulnerable to harvest than are adults. When

reproductive success is high juveniles form the bulk 

of the fall harvest (Steffen and Norman 1996).

Conversely, when reproduction is poor, relatively

fewer juveniles are harvested, thus the harvest must

come largely from the adult component. The risk of

overharvesting the adult hen population is usually

greatest when fall populations are lowest. 

Assumption 4: Vulnerability to harvest increases in

years of poor mast production.

Although this assumption is commonly held by

biologists, the evidence supporting it is mostly

observational and correlational. The evidence that fall

turkey harvests tend to increase in years of poor mast

production and decrease when mast is abundant

comes primarily from Pennsylvania and West Virginia

where forests are extensive and fall hunting is a

strong tradition (Wunz 1986, Pack 1994). For example,

mast conditions were poor statewide in West Virginia

in 1982 and the fall kill was among the highest on

record. Gobbler harvests declined by 19% the

following spring in counties open to fall hunting, but

gobbler harvest increased by 52% in counties closed to

fall hunting (Pack 1994).

The true relationship between harvest rate and mast

abundance is unknown because turkey populations

and mast crops are difficult to measure. In the

Northeast, only Virginia, West Virginia, and Maryland

conduct statewide mast surveys. The assumption that

mast affects harvest is incorporated in the West

Virginia fall harvest management model (Pack et al.

1995, Appendix B). At present, mast crops cannot be

predicted until August or September and our basic

understanding of the patterns of mast production

among species and over time remains limited (Koenig

et al. 1994, Healy 1997, Healy et al. 1999).

Relationships among mast production and turkey

populations merit further study because food

resources affect seasonal movements and habitat use

(Kurzejeski and Lewis 1990) and under some

conditions, fall harvest (Menzel 1975, Wunz 1986, Pack

1994).

Fall Hunting Regulations

Season Dates and Season Length

In contrast to the biological basis for spring hunting

seasons, fall seasons are determined largely by

tradition. “Harvest of turkeys usually begins in

October or November in the northern parts of the

range, later in the southern portions. November was

seemingly designed especially for turkey hunting”

(Bailey and Rinell 1967:90). The regulations affecting

fall turkey seasons in the Northeast are summarized

in Table 5.1.

Season length has a significant effect on harvest. Most

of the turkeys killed are taken on opening day, and

daily kill diminishes rapidly through the second and

third weeks. The daily kill then remains relatively

constant for as long as the season continues (Bailey

and Rinell 1968:35). Shortening fall hunting season

has been effective at reducing harvest and permitting

continued population growth (Pack 1986). Because

most of the kill occurs early in the season a substantial

Section III. Harvesting Strategies 25
26 Wild Turkey Harvest Management: Biology, Strategies, and Techniques

Figure 5.1. Variations in total number of wild turkey hens and poults observed by conservation officers in

Pennsylvania during June, July, and August over a 10-year period, 1981-1990. 

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

-3,000 -2,000 -1,000 0 1,000 2,000 3,000

Deviation from mean (5,940)

-1,990

-1,376

-54

-8

-1,625

251

678

25

2,418

1,651
reduction in season length may be required to achieve

a significant reduction in harvest. For example, in

West Virginia, about half of the harvest occurred in

the first week of an 11-week fall season. Reducing this

season by 5 weeks would have reduced the kill by 20%,

and shortening it 10 weeks would have reduced kill by

50% (Bailey and Rinell 1968:53). Fall firearms seasons

for wild turkeys are either 1 or 2 weeks long in 8

northeastern states (Table 5.1). Thus ample

opportunity exists to increase fall hunting opportunity,

but the potential to reduce harvest by shortening the

season is limited.

Timing of the season also affects harvest. When

hunting seasons for turkey and other game species

start at the same time, harvest is substantially

increased because of increased hunter effort and

opportunistic harvest of turkeys. Separating the start

of turkey season from other hunting seasons has been

effective at reducing harvest (Pack 1986). Snow fall

during the late November seasons usually leads to

increased harvest by enhancing the ability of hunters

to locate flocks.

In the Northeast, fall seasons have generally been

scheduled as late as possible to take advantage of the

rapid growth of juvenile turkeys during autumn. A

juvenile turkey harvested in mid-November will weigh

about a pound more than one taken in mid-October.

Season dates and length are pertinent to all 4

assumptions about fall harvest. Although adjusting

season dates and length has been effective at

providing a sustained fall harvest, these measures

provide only weak control over harvest.

Bag Limits

Bag limits are pertinent to all 4 assumptions about fall

either-sex harvest. In the Northeast, bag limits are

generally 1 bird, but fall hunters may take 2 birds in

parts of New York and Virginia. States with spring

and fall hunts generally have an annual limit of 2

birds, only one of which can be taken in the fall. This

regulation is believed to encourage spring hunting. In

most states, little room exists to control fall harvest by

manipulating bag limit.

Table 5.1 Regulations used for fall either-sex turkey seasons in the Northeast. Delaware, Maine, Ontario, and

Rhode Island do not have fall seasons.

Regulation CT MD MA NH NJ NY PA VT VA WV

Season length, weeks

Firearm or 

combination 2 1 1 <1a 2-7 1-2 2 2-8 2-4

Archery only 10 14 3 7

Bag limit

Firearms or 

combination 1 1b 1b 1 1-2 1 1c 2d 1b

Archery only 1 1 1c 2d

Regulate hunter numbers

None xe x xe xe xe x xe

Number x xf

Area xg x xg

Time

Time and area

Hunting implement

Shotgun and

shot size x x x x x

Archery gear x xxxx x

Muzzle-loading

shotgun x x

Rifle, shotgun, bow x x x x

Harvest reporting

Check station xh xxxx xxx

Report card xh x x

a Three-day fall turkey season in 1999. b Bag limit: 2 bearded birds in spring season with no fall bird, or 1 bearded bird in spring and 1 bird of either sex in fall season. c Combined fall bag limit of 1 for both the firearms and archery seasons. d Bag limit: 1 per day, 3 per license year, no more than 2 of which may be taken in the fall or spring. e Permit or license required, but numbers not restricted. f

Hunter numbers are regulated by permit in selected counties.

g Some management units are closed to turkey hunting. h Successful fall firearms hunters must bring the bird to a check station; successful archery hunters must mail a report card within 24 

hours of the kill. 

Section III. Harvesting Strategies 27
28 Wild Turkey Harvest Management: Biology, Strategies, and Techniques

Control of Hunter Numbers

Only 2 northeastern states, New Jersey and West

Virginia, regulate hunter numbers with a permit

system. West Virginia regulates fall turkey hunter

numbers in management units that have recently

been opened to hunting. Hunters in Connecticut are

required to obtain written permission to hunt on

private land, a practice that probably limits hunter

numbers. Regulation of hunter numbers is pertinent

to all assumptions about fall harvest.

Hunting Implement

Of the northeastern states, only New Hampshire uses

restrictions on hunting gear as the method for limiting

fall harvest. New Hampshire has a 14-week fall

archery season and no fall firearms season; during

1994 bow hunters took 43 turkeys. Restrictions on

hunting implement in other states are intended to

promote safety. Five states (Conn., Mass., N.J., N.Y.,

Vt.) restrict hunters to shotguns and specific shot

sizes. Connecticut and Vermont have separate fall

firearms and archery seasons; Massachusetts, New

Jersey, and New York permit shotguns and archery

gear in fall seasons. Four states (Md., Pa., Va., W.Va.)

allow the use of rifles in addition to shotguns and

archery equipment.

Harvest Reporting

Biologists consider an accurate measure of harvest

critical for management programs. Of the 10 states

with fall seasons, 8 require successful hunters to bring

birds to check stations. New York and Pennsylvania

provide license holders with report forms to be

returned by successful hunters. Reporting is

mandatory in Pennsylvania and voluntary in New

York. Compliance with reporting requirements varies

geographically and temporarily. All states consider

non-reporting a serious source of error in harvest

estimates and use either post-season mail or phone

surveys to improve harvest estimates.

Decision Variables and Regulatory

Procedures

Limited Fall Harvest, Hunter Numbers Regulated

This strategy requires regulating fall harvest in

conjunction with spring harvest. Procedures for

regulating spring gobbler harvest have been

discussed, therefore we examine options for

regulating fall, either-sex harvests in this section.

We recommend the strategy developed by the West

Virginia Division of Natural Resources, Wildlife

Resources Section to implement fall hunting in

counties that had traditionally been hunted only in

spring (Pack et al. 1995, Appendix B). This regulatory

approach explicitly links fall harvest with spring

harvest, controls harvest at the TMU level, and

provides strong protection against overharvest.

The decision and regulatory process for a limited fall

harvest is similar to that recommended for spring

gobbler harvest where hunter numbers are controlled

(see Chapter 4). Spring gobbler harvest goals drive

the decision process. Counties must meet a preestablished spring gobbler harvest-goal for 2

consecutive years to be opened to fall hunting, and

counties are closed to fall hunting if the spring

harvest goal is not met. The actual spring gobbler

harvest is used to estimate population size. Hunter

density is controlled with a permit system. Initial

permit allocations are based on the population

estimate, but permits are not issued until 1 October.

Consequently, the final allocation can be adjusted in

response to data from brood and mast surveys, which

become available in September. The process depends

on accurate measurement of the spring and fall

harvest by TMU. Spring harvest must be tabulated

promptly because it is used to estimate population size

and the number of fall harvest permits to be issued.

The process used to regulate fall harvest is outlined

below. We have generalized the process developed for

West Virginia (Pack et al. 1995, Appendix B). Steps 1

and 2 are completed when the season is established,

although TMU boundaries and harvest goals may be

revised as the need arises. Steps 3 through 5 form an

annual cycle of decision making and permit allocation.

Recommended Procedures

1. Stratify the overall area into Turkey Management

Units (TMUs).

Options include:

a. ecologically based units, e.g., physiographic regions.

b. political units, e.g., counties, townships.

c. combination of ecological and political units, e.g.,

aggregate to approximate physiographic units.

The West Virginia model we are following uses

counties as TMUs.

2. Establish spring harvest goals.

The spring harvest goal is the critical decision

variable in this strategy and the factor that links fall

with spring harvest. Minimum desirable spring

harvest goals are established by TMU. Goals are

based on long-term spring harvest records and goals

are usually expressed as gobbler kill per unit area of

wild turkey range. Fall hunts are permitted as long as

the spring harvest equals or exceeds the established

goal; fall hunting is curtailed if the spring harvest falls

below the goal.

In West Virginia, the minimum spring gobbler harvest

goal is 1 bird/mile2 (0.4/km2

) of wild turkey range.

Counties can be opened to fall either-sex hunting only

when the spring gobbler harvest goal has been

exceeded for 2 consecutive years. Counties are closed

to fall hunting if the spring harvest goal is not met. 

3. Establish fall harvest goals and permit allocations

for each TMU.

Population size is estimated by assuming spring

gobbler harvest is equal to 10% of the total

population. Multiplying the population estimate by a

desired harvest rate, usually 5-10%, produces the
target harvest. The number of permits required is

estimated by dividing the target harvest by the

estimated hunter success rate. 

West Virginia uses a conservative harvest rate of 5%

because illegal taking of hens is known to be a

problem. Hunter success rates are estimated from

harvest report data, and West Virginia currently uses

a fall hunter success rate of 0.27. 

The timing of the permit allocation process is

important. In West Virginia, spring gobbler harvest

data are compiled by June and counties that will be

open to fall hunting are identified. Harvest goals and

permit allocations are then established and fall permit

applications are available by 1 July. Fall harvest

permits are mailed no later than 1 October for a late

October hunting season. Permit allocations seldom

change after 1 July, but the late mailing date allows

biologists the option of changing allocations after

reviewing summer brood reports and mast condition

information that becomes available in September. 

4. Measure harvest annually by TMU.

Options include one or more of the following:

a. mandatory check stations. This is the option used in

West Virginia.

b. mandatory kill report card.

c. post-season surveys of hunters.

5. Obtain an annual index to population size that is

independent of harvest.

Options include:

a. random surveys of hunters pursuing other species.

b. volunteer-cooperator reports.

c. staff-volunteer sightings records.

There are 2 ways that independent indices to

population size can be used to strengthen the

manager’s hand in regulating limited fall harvests.

The first is to provide a check on spring harvest data,

which are used as the decision variable on whether to

hunt in the fall and also serve as the basis for the

population estimate. This is the approach we

recommended under “Spring gobbler harvest, hunter

numbers regulated,” where we described the process

used in Minnesota (Appendix A). In the Minnesota

model, spring gobbler harvest is assumed to equal

10% of the total population. The population estimate is

then multiplied by a trend index derived from a

random sample of antlerless deer permit holders. The

trend is defined by changes in the proportion of deer

permit holders observing turkeys over the past 3

years.

The second way an independent index can be used is

to adjust the fall permit quota. In this case, data from

summer brood counts are used to index fall population

size. This approach has been selected in West Virginia

where managers have the option of changing fall

permit quotas in response to summer brood sighting

data. The West Virginia system is new and formal

decision rules have not been developed. With

experience, formal rules can be developed. For

example, permit quotas could be increased when

brood counts were >25% above the long-term mean.

Alternatively, permit quotas could be decreased in

proportion to the decline in brood counts when brood

counts were >25% below the long-term mean.

Limited Fall Harvest, No Control on Hunter

Numbers

The strategy of limited fall harvest has also been

successfully implemented without any control over

hunter numbers. Where hunter numbers are

unregulated, fall harvests have been limited to a level

that allows continued population growth by regulating

season length, season timing, and bag limit. Of these 3

regulations, season length has the greatest effect on

total harvest. In general, states that traditionally had

extended fall seasons were able to increase rates of

population growth, as indexed by spring gobbler kill,

by shortening fall seasons (Pack 1986). The length of

fall firearms seasons in states without control over

hunter numbers currently ranges from 1 to 9 weeks.

Shorter seasons are the rule where hunting pressure

is greatest.

The scheduling of fall turkey season relative to

seasons for other game species also has a significant

effect on harvest rate. In West Virginia, band recovery

rates for wild turkeys in the fall season ranged from

15% to 23% during a 10-year period when turkey and

squirrel seasons opened concurrently. During the

decade after fall turkey season started 2 weeks later

than squirrel season, the band recovery rates of

marked birds varied from 1.6% to 6.2% (Pack 1994).

Bag limits for fall seasons are usually 1 bird/hunter.

The general practice is to have an annual limit of 2

turkeys, only one of which can be taken in the fall. The

option of allowing hunters to take both birds in the

spring is intended to encourage spring harvest.

Another effective approach to providing fall hunting

recreation while limiting harvest has been to restrict

hunters to the use of archery gear.

Procedures for Regulating Limited Fall Harvest

Without Controlling Hunter Numbers

Except for establishing fall harvest and permit goals,

we recommend the same procedures used when

hunter numbers are regulated. Again, spring harvest

goals and an accurate measurement of spring and fall

harvests are critical features of the system. Specifying

remedial actions to be taken when spring harvest

goals are not met is more difficult when hunter

numbers are unregulated. The procedure is outlined

below, but we will elaborate only where the process

differs from the previous one.

1. Establish Turkey Management Units (TMUs).

2. Establish long-term spring harvest goals for each

TMU, and specify remedial actions to be taken if

harvest falls below the goal. 

We recommend expressing spring harvest goals as

kill/unit of habitat, and basing them on program goals

and past harvest records. Where hunter numbers are

uncontrolled, the options for restricting fall harvest

Section III. Harvesting Strategies 29
30 Wild Turkey Harvest Management: Biology, Strategies, and Techniques

include adjusting season length, season timing, bag

limits, and hunting implement. Season length and

season timing have proven effective on the long-term

maintenance of fall harvest objectives. Adjusting

season length may be the only practical option for a 

short-term response to declining spring harvest. For

example, fall seasons would remain unchanged as long

as spring harvest goals were met. If the spring

harvest goal was not met, the following fall season

would be shortened, or stopped altogether, until the

spring harvest goal was again achieved. Clearly, the

option of regulating fall hunter density should be

considered as a response to declining spring harvests.

3. Measure harvest annually by TMU.

4. Obtain an annual index to population size that is

independent of harvest.

Combining Regulatory Procedures for the SpringPlus-Limited-Fall-Harvest Strategy

The regulatory procedures outlined so far can be

combined in 4 ways to implement the spring-pluslimited-fall-harvest strategy. Each combination of

procedures provides a different degree of control over

harvest. The greatest control over harvest and

protection against excessive harvest is obtained by

regulating hunter numbers in both spring and fall

seasons. The least control over harvest occurs when

hunter numbers are unregulated in both seasons. The

general relationships between regulation of hunter

numbers and control over harvest are shown in Table

5.2.

The choice of regulatory procedures depends on the

perceived risk of overharvest and the degree of

emphasis placed on spring versus fall hunting by the

management agency. Different combinations of

regulatory procedures can be used within a state. 

For example, West Virginia uses 2 distinct regulatory

approaches to implement the same harvest strategy.

In the region of West Virginia where turkeys were

never extirpated and fall hunting has a long tradition,

biologists chose not to restrict hunter numbers in

either spring or fall seasons. Where turkey

populations have recently been restored, biologists

have chosen a more conservative approach and

regulate hunter numbers in the fall seasons but not in

the spring.

In addition to allowing greater control over harvest,

procedures that regulate hunter numbers also allow

prompt management response to changes in harvest

and population status. This is an important distinction

between regulatory procedures that affect hunter

numbers and those that do not. Regulating hunter

numbers allows managers to make immediate,

incremental changes in response to harvest and

population data. Where hunter numbers are

uncontrolled, managers are limited to making

structural changes in seasons, such as shortening

seasons or changing opening dates and bag limits.

Populations respond slowly to these kinds of

regulatory changes and at least 5 years is required to

assess their effect on populations (Weaver and Mosby

1979). Making structural changes in hunting seasons is

often difficult because hunters tend to resist changes

(Wunz 1986). Once a permitting system has been

established, adjusting permit quotas becomes a

routine administrative procedure. Regulating hunter

numbers allows a constant season structure while

controlling hunter effort.

Table 5.2. General relationships among procedures that regulate hunter numbers, degree of control over 

harvest, and risk of overharvest.

Control of Risk of Hunter numbers regulated

harvest overharvest Spring hunt Fall hunt Example

Greatest Least Yes Yes New Jersey

No Yes W.Va., newly occupied range

Yes No

Least Greatest No No Va. and W.Va., historic range
Chapter 6. Maximize Combined Spring and Fall

Harvests

This strategy represents an effort to derive the

maximum harvest from a fluctuating population

without diminishing population growth rate. This

strategy is based on the same assumptions and uses

the same hunting regulations as the previously

described spring and fall harvest strategies. Material

on assumptions and hunting regulations will not be

repeated.

Maximizing the total spring and fall turkey harvest is

similar to maximum sustained-yield approaches used

for other species, but the turkey model differs in

important ways. Most maximum sustained-yield

models assume a constant carrying capacity and

density-dependent population responses that cause

the population to grow toward carrying capacity

whenever it has been displaced. The models envision a

relatively stable system, with self-regulating

mechanisms that cause the population to oscillate

around a mean value. In contrast, we assume that

turkey populations are volatile, routinely fluctuating

by 50% or more around long-term mean levels.

Density-dependent population responses have not

been identified for turkeys, and the available

population models do not assume density-dependent

population growth (Chapter 3). No explicit

assumptions have been made about carrying capacity,

but we recognize that variations in weather and mast

production affect turkey populations in ways that are

independent of population density.

For wild turkeys, a fixed harvest rate will result in

annually fluctuating harvests. A fixed harvest quota

based on mean fall population size would result in

under- or overharvest about as often as it would

produce a desired harvest (Fig. 5.1). Optimizing total

harvest requires an estimate or index of population

size just prior to harvest and a means for regulating

hunter numbers after the population estimate is

available. Obtaining reliable indices of population size

and regulating hunter numbers just prior to hunting is

often administratively impractical.

Two potential models for optimizing fall harvest are

available from the Northeast. In Pennsylvania,

biologists capitalized on years of good reproduction by

extending fall seasons when summer brood counts

were >25% above the 10-year mean for the

management unit. Seasons were initially set at 1 or 2

weeks with the option of extending them depending on

the results of summer brood counts. Season

extensions were announced in September. This system

was used because hunter numbers were uncontrolled.

It did provide additional recreation and harvest in

years when turkeys were abundant.

The current system used in West Virginia to limit fall

harvest could also be used to optimize fall harvest.

Hunter numbers are regulated. Populations are

estimated from spring gobbler harvest; permit

allocations are based on a 5% maximum harvest and

27% success rate. Biologists can review brood and

mast survey data before making the final allocation 

of permits on 1 October. This system contains all the

elements needed to obtain a fixed fall harvest rate.

Maximizing the combined annual spring and fall

harvest would involve combining the regulatory

procedures we have outlined for spring and fall

harvests where hunter numbers are regulated.

Hunter numbers would be regulated in both spring

and fall and permit allocations would be based on

population estimates. The permit allocation process

would follow the guidelines developed in Minnesota

(Appendix A) and West Virginia (Appendix B).

Those procedures have already been described in

detail, so we will only outline the annual decision

process.

1. Establish spring harvest goals.

Population estimate is based on previous spring

harvest adjusted by an independent population trend

index. 

2. Establish hunter density goals.

This step is used to provide as much hunting

opportunity as possible when the demand for permits

exceeds the supply. The season is divided into

segments and a maximum hunter density goal is used

to partition the permit quota among hunting season

segments.

3. Measure spring harvest.

Includes estimating hunter interference rates.

4. Establish fall harvest goals and permit allocations. 

Based on spring harvest, desired harvest rate, and

estimated hunter success rate.

5. Obtain index to late summer population size.

Based on summer brood counts.

6. Adjust fall permit allocation in response to brood

surveys.

Section III. Harvesting Strategies 31
32 Wild Turkey Harvest Management: Biology, Strategies, and Techniques

7. Measure fall harvest.

8. Obtain index of fall population size. 

Random surveys of deer hunters or

volunteer/cooperator surveys such as the West

Virginia Bow Hunter Survey. This index is used to

adjust the spring population estimate. 

Maximizing the total harvest has some stringent

requirements for data and regulations. There must be

an estimate of population size in late summer and a

means of regulating season structure or hunter

numbers in September before the hunt. The West

Virginia model shows how those tasks can be

accomplished. We also believe that maximizing the

total harvest would require regulating hunter

numbers in spring and obtaining a second index to

population size in late fall. It is not clear to us that

there is enough demand to implement the maximum

harvest strategy even in the states of Virginia, West

Virginia, and New York where fall hunting is popular.
33

Section IV. Techniques
34 Wild Turkey Harvest Management: Biology, Strategies, and Techniques

Chapter 7. Introduction to Population Estimation

Why Measure Abundance?

Three types of information are needed to manage

wildlife populations: (a) total numbers, (b) population

dynamics, and (c) distributions and movements

(Norton-Griffiths 1978). In this chapter we focus on

estimating population size. “The future ability of

researchers and managers to provide for more

detailed evaluations of the impacts of both habitat

management practices and harvest regulations on

turkey populations requires the means to detect

changes in population size” (Kurzejeski and Vangilder

1992). Understanding population dynamics requires

knowledge of the population size (Verner 1985), thus

population dynamics and abundance estimation are

linked. Population models that project changes in

numbers over time require an initial estimate of

population size (see Fig. 3.1). Many management

programs for wild turkeys include provisions for

removing a fixed percentage of the population each

hunting season (Mosby 1967, Hayden and Wunz 1975),

thus requiring estimation of the pre-hunting

population size. However, estimating animal

abundance presents many problems, especially for

species like wild turkeys that are inconspicuous,

mobile, and wide ranging (Bull 1981). It is difficult to

know what proportion of the population is detectable

and be sure that the same proportion of the population

is detected each time the technique is used. Thus,

counts need to be standardized to ensure comparable

results (Davis and Winstead 1980:224).

Census, Estimate, and Index 

The term census is often used to refer to any

abundance estimation technique (e.g., Caughley and

Sinclair 1994). It is strictly defined, however, as a

complete count of all individuals in a given population,

such as all the wild turkeys inhabiting a state, or all

wild turkeys at a particular winter roosting site. In

contrast, a survey samples some proportion of the

individuals in a population. An estimate is derived

from a sample of the overall population, which can be

extrapolated to the overall population size or density

by using various statistical or mathematical

manipulations. An estimate is an approximation of the

true population size and may be subject to varying

degrees of bias. An index is any measurable factor

presumed to be related to population size in some way.

The index changes in a predictable manner with

changes in population size, but does not estimate it

directly. An index is used because this factor is easier

to count than the animal itself. It may be possible to

convert or “calibrate” the index to the true population

size, if a suitable proportionality constant or correction

factor can be determined (McClure 1939; Overton and

Davis 1969:415; Eberhardt 1978a:213; Davis and

Winstead 1980:231; Bull 1981; Seber 1982:54-55,451).

The correction factor, however, is rarely known, or the

proportionality may be variable. An index is a

measure of relative abundance that is useful for

comparing the relative size of a population among

different areas, years, or seasons. A census or

estimate, in contrast, is a measure of absolute

abundance. 

An index may be direct or indirect (Bull 1981). A

direct index is obtained by using a sampling scheme 

to count the animals. Although animals are counted

directly, a direct population estimate does not result.

An example would be the numbers of turkeys seen per

kilometer of road driven, or reports of numbers of

turkeys seen by deer hunters. An indirect index

involves factors associated with the presence of the

animals, such as tracks or dens.

The main advantage of a census is that if the

procedure is done accurately, it produces the actual

population size, not an estimate subject to error. A

census may be easier to explain and defend in court

than a method that extrapolates abundance from a

sample, or an index of relative abundance (Weinrich et

al. 1985). A census makes the assumptions that (1) the

entire area is searched, (2) all groups of animals are

located, and (3) all groups are counted accurately

(Norton-Griffiths 1978:5). These assumptions are

difficult to meet under field conditions. All animals in

the population are not equally visible (Eberhardt et al.

1979), and counting errors can be a significant source

of bias. Animal behavior and movements may

compound counting errors, and complete coverage is

difficult for large areas (Caughley 1977a). As a result,

most censuses are subject to some unknown degree of

error. Furthermore, censuses can be prohibitively

expensive, or technically impossible for large areas.

Because wild turkeys are elusive and distributed

among diverse cover types censusing the birds is

generally impractical (Donohoe et al. 1983). Still, a

census may be feasible for a small area, in a habitat

where turkeys are easily seen. 

The main advantage of estimates is that every

individual need not be counted to obtain an estimate of

abundance. Sampling procedures can be used to

greatly reduce the effort, time, and cost of the project.

Problems caused by duplicate counts or missed

individuals may be greatly reduced compared with a
census, and an estimate need not be completed in a

short time (Caughley 1977a:25). Many sources of error

in estimates must be accounted for or controlled,

however, and obtaining reliable samples may be

difficult. Because it is often difficult to reveal the

magnitude or sources of these errors, the resulting

population estimate may be inaccurate. Nevertheless,

if a population size need only be known within a

certain confidence level (e.g., ±10%), then a sample

will usually suffice. The cost and manpower required

for a complete census make estimates the only feasible

alternative for determining the population size of most

wildlife species.

Often, knowing the actual population size is

unnecessary. In many management programs it is

more important to know about trends among seasons

or years, or whether one area has more or fewer

individuals than another. In these situations, an index

is appropriate. Caughley (1977a:14) advised that, “a

problem defined in terms of absolute density can

usually be redefined in such a way that estimates by

relative density will provide a solution.” Relative

methods are adequate for many problems, and they

should be used when adequate because they are much

easier and less costly than absolute methods (Krebs

1989). Indices are frequently used to monitor state

and regional population trends (Kurzejeski and

Vangilder 1992:171), and they are generally adequate

to manage wildlife populations (Caughley 1977a:14).

An index may be considerably less expensive and

labor-intensive than either an estimate or a census

because indices can be obtained from observations by

volunteers or cooperators (Welsh and Kimmel 1990,

Applegate 1997). Furthermore, an index may have a

smaller variance than the corresponding population

estimate (Eberhardt and Simmons 1987). Indices have

the additional advantage that they cause minimal

disturbance to the animals. An index may be the only

practical method in situations where obtaining

accurate counts of animals is difficult. Indices may be

sufficient, or even necessary, if the only goal is to

determine the distribution of a species in an area or

across a state (Hoffman 1962).

Indices, however, have several disadvantages. Indirect

indices are not time-specific because animal sign may

have been left over a long period. Because counts of

sign index abundance over time rather that at a

specific time, indirect indices may be less reliable than

direct counts (Caughley 1977a). Indices cannot be

used for management decisions that require absolute

numbers. A serious problem can arise if the

relationship between index and population size varies

over time or with the size of the population

(Eberhardt and Simmons 1987). Differences among

observers can affect data and make it necessary to

evaluate variation among participants in the survey

(Davis and Winstead 1980:231). Typically, a sampling

procedure needs to be developed to collect data

because large areas cannot be searched entirely. It is

just as important to consider sampling design for

indices as for estimates, and replicate samples should

be obtained to calculate sampling estimates of

variances (Seber 1982:451, 564).

White et al. (1982:32) took an extreme view of indices,

saying that “the use of indices in science is to be

discouraged because indices lack the basic factors

required for making inferences about parameters

based on data. Indices are useful only when they have

been calibrated with the parameter of interest by

using, for example, the theory of double sampling....”

Seber (1982:54) considered indices generally less

reliable than direct methods of abundance estimation.

Indices are based on the difficult-to-prove assumption

that the ratio of the index to the true population size is

the same among the different localities, years, or

populations being compared (Davis and Winstead

1980). An index is useful only if it is relatively stable

under varying environmental conditions, and should

therefore be carefully studied to examine this stability

(Seber 1986). According to Lancia et al. (1994), “use 

of indices is often restricted to comparisons between

populations on the same area over time or between

different areas at the same time, because the exact

relationship between the index and the true

population frequently is not known.”

Table 7.1 lists the population abundance techniques

that we evaluated and indicates which techniques are

treated as censuses, estimates, or indices. The

assignment of a technique into one of these 3

categories is not always clear-cut. A given technique

may be used to census one subspecies or habitat type

and to estimate or index abundance in other

situations. For example, counting wild turkeys at

roosts may be an accurate census for the Rio Grande

subspecies in Texas, where the habitat is open and

turkeys congregate in specific areas for roosting. In

other regions, it may be possible to census accurately

a given flock (a partial census) in winter, although

there may be uncounted individuals or flocks in the

area. Such counts could provide an index if the size of

a flock or the combined count of all known flocks is

related to the size of the overall population (Caughley

and Sinclair 1994:191).

Aerial counts may also be classified in different ways.

For some species, such as large mammals in an open

habitat, a thorough search can locate all individuals

present. In most cases, however, the cost of

completely surveying a large area can be prohibitive.

Furthermore, in some areas or for some species, an

aerial count may fail to account for all animals present,

due to hidden animals or observer errors. It is usually

necessary to sample a portion of the available habitat

and correct for the unobservable fraction of the

population, in which case the aerial count is at best an

estimate, or at worst an index. This point is illustrated

by Krebs (1989:103), who states that “in some cases

the biases may remain of unknown magnitude, and

aerial counts should then not be used as absolute

population estimates.”

Scales of Abundance

Abundance can be measured at several scales (Verner

1985:248). Proper study design depends on selecting

Section IV. Techniques 35
36 Wild Turkey Harvest Management: Biology, Strategies, and Techniques

an appropriate scale. A nominal scale records

presence or absence. This scale might be useful for

distribution studies, or for frequency indices. An

ordinal scale ranks populations in the correct order of

abundance, and might be used for an index that ranks

study areas, or years for the same study area, from

most to least abundant. A ratio scale assumes that all

groups are sampled equally (i.e., the same percentage

of the animals present are detected), and therefore

allows relative percentages to be calculated. For

example, management area A has 25% more

turkeys/km2 than management area B has, or the

turkey population is 25% higher than last year’s.

Because the ratio scale does not require actual

population numbers, it is a measure of relative

abundance, or an index. It is an appropriate scale for

studying annual or seasonal population trends. Finally,

an absolute scale assumes a total count, or at least

accurate correction coefficients for adjusting a biased

count to the actual numbers in the population. A

census produces an absolute count directly, whereas

an estimate approximates the absolute count by

extrapolation of a sampled area to the overall

population. At least a ratio scale of abundance is

needed to address most study objectives (Verner

1985:251). An absolute scale is indicated if the

objective is measuring population fluctuations, or for

determining the actual size of the population of a given

area.

Parameters, Techniques, and Tools

The terms parameter and technique have not been

used consistently when applied to methods for wild

turkey management. A parameter is defined as a fixed

or true quantity (a constant) characterizing a given

population, expressed as a number (White et al.

1982:7, Lancia et al. 1994:216), which may be entered

into a population model. Examples of parameters

include true population size, population density, agespecific survival rate, average clutch size, fecundity,

sex ratio, or capture probability. Because the values of

parameters are seldom known, we generally use an

estimator, “a mathematical expression that indicates

how to calculate an estimate of a parameter from the

sample data” (White et al. 1982:16). A technique is a

field or statistical method for measuring this

parameter. We will also refer to a tool as some

facilitator of the main techniques that does not

estimate or index population abundance in itself, but

assists with the implementation of the technique. A

tool can either be a physical piece of equipment, such

as a camera, a tape recorder, or a postcard

questionnaire sent to hunters, or some specific

modification of a technique, such as the use of mail

carriers to facilitate roadside counts, which are

normally done by agency personnel.

Population Index, Density Index, and

Production Index

In publications discussing animal abundance, the

terms “abundance index/estimate” and “density

index/estimate” are often used as if they were

equivalent concepts. This usage is understandable,

because density is simply population size divided by

area occupied. It would seem that as long as the area

being sampled was known, then both abundance and

density would be known. However, calculating density

becomes complicated when the area sampled is not the

same as the area from which the animals are drawn

(White et al. 1982:120). For example, in the case of

animals with well-defined home ranges, a line of traps

may barely intersect an animal’s home range that lies

mostly outside the study plot. The area from which the

animals were drawn therefore includes additional area

outside the study plot encompassing every home

range intersecting the study plot boundary.

Furthermore, traps on the edge of the study plot tend

to catch many immigrants. This “edge effect” may

result in a severe overestimate of the actual population

density (Dice 1938, Seber 1982:446). Thus, the

calculated area of the study plot may need to be

adjusted by adding a “boundary strip” to the

perimeter (White et al. 1982:120). Furthermore, the

area of the sample plot may include habitat unsuitable

for the species being surveyed. For example, a study

plot for a terrestrial species that includes a lake

covering 30% of the plot would be only 70% occupiable

habitat. This distinction is the difference between

absolute density, or population size divided by the

absolute size of the area sampled, and ecological

density, or population size divided by the total area of

suitable habitat within the area sampled (Brower

1990).

Density may be the most appropriate measure for

many comparisons (Caughley 1977a:12, Eberhardt

1978a), but it is more difficult to estimate than

population size (White et al. 1982:3). Population

estimates from different-sized areas cannot be

compared directly. Density estimates are more useful

for making ecological comparisons between

populations occupying different size ranges. If the

objective is to compare a population in a given area

among years or seasons, or to track population trends

over time, a measure of abundance may work as well

as, or better than a measure of density. Density is

sampled “by dividing the area under survey into

sampling units and counting animals on a pre-selected

proportion of these. The mean density per unit

sampled is taken as an estimate of the mean density

on sampled and unsampled units combined” (Caughley

1977a:25). When boundary effects or ecological density

are not at issue, average density, as estimated from

the sample areas, can be multiplied by the total area

occupied by the population to produce an estimate of

the total population size (Seber 1982:20).

Sometimes the objective is to index population

parameters such as reproduction, rather than

population abundance or density. Reproduction (Fig.

3.1) is one of the main parameters related to

population size, and studying reproduction may

provide insight into population trends. A production

index is used to index reproductive parameters.

Production indices “are valuable in assessing
population status and evaluating harvest strategies”

(Kurzejeski and Vangilder 1992:173). Because some

techniques used to index abundance also serve to

index production, separating these 2 types of

techniques into distinct groups is difficult. Some

techniques simultaneously produce information on

abundance and production. Roadside counts, for

example, may record the total number of turkeys seen

or heard, and numbers of poults, hens, and broods

observed. The total number of turkeys or broods seen

may provide an index of abundance, whereas average

brood size, poult:hen ratios, or percent of hens without

poults may be used for production indices (Mosby

1967:126, Beasom 1970). The total number of broods

seen may produce both an index of abundance and a

separate index of productivity.

Assumptions

Every abundance estimation method rests on a

number of assumptions. Where mathematical or

statistical models are employed, these assumption are

explicitly stated. In other methods, these assumptions

may not be clearly defined, but they are nonetheless

important. For example, a winter flock count, as a

census technique, does not require statistical models

or mathematical calculations other than addition. It is

assumed, however, that no animals are missed or

counted twice. Assumptions in more rigorous models

are important because they take the place of

additional parameters that would otherwise need to be

measured to make the technique valid. For example,

the line transect technique incorporates the

assumption that all animals present directly on the

survey line are counted with probability 1.0. As long

as there is no reason to doubt that this assumption is

valid, there is no need to measure this probability

before the technique can be used. The more

assumptions a model makes, the fewer parameters

need to be measured in the field.

Two basic assumptions from Davis and Winstead

(1980:222) must be considered for any population

estimation method. First, “mortality and recruitment

during the period when data are collected are

negligible, or if not, are corrected in the estimates.”

Second, “all members of the population have an equal,

or known, probability of being counted.” Because

many population estimating techniques violate one or

both of these assumptions to varying degrees, the

choice of method should be carefully considered

before collecting data to minimize potential violations

of the assumptions. According to Seber (1982:561), “if

there is likely to be any question about the validity of

the underlying assumptions, the sample data should

be collected in such a way that the empirical variance

estimates are available for replicated samples. A

comparison of the sampling variance with the

estimated theoretical variance predicted by the model

will often throw some light on the validity of the

model.”

As the number of assumptions increases, the more

likely it is that at least one of them will be violated.

Some assumptions are more critical than others, and

most estimation techniques are sensitive to the

violation of certain assumptions (White et al. 1982). If

a non-critical assumption is seriously violated, or if a

critical assumption is violated in a minor way, the

model may become invalid and produce unreliable

abundance estimates, or alternatively, it may produce

results similar to those that would have been produced

if the assumptions were strictly followed. If a model

can tolerate some deviations from the assumptions

and produce reliable results, the model is considered

robust. Robust models are preferable to non-robust

models, but there may be little choice if the robust

methods are prohibitively expensive or are

inappropriate for a given species or habitat.

Bias, Accuracy, Precision, and 

Sample Size

Population abundance techniques vary in their

accuracy and consistency. Accuracy refers to how

close an estimate is to the true population size or

density. Inaccurate estimates are biased, where bias is

the difference between the true size of the population

and the estimate when repeated many times. Bias is

an error in a constant direction. It is one of two

general types of error, the other being normal

sampling variation (Norton-Griffiths 1978:37). 

Precision refers to how repeatable an estimate is if it

is measured several times independently. Precision

can be optimized by careful experimental design

(Seber 1982:454), but accuracy may be difficult to

control if the magnitude and direction of bias is

unknown. A population estimate can be accurate (and

therefore unbiased) but imprecise if several estimates

of the same population vary widely, even though the

average of the estimates is close to the true population

size. Conversely, a population estimate can be precise

but inaccurate (therefore biased) if several estimates

of the same population are close to each other, but on

average are far from the true population size. 

Another important consideration is the sample size

needed to produce a reliable abundance estimate or

detect changes of a certain magnitude—generally, the

larger the sample size, the better the accuracy and

precision of the estimate. When animals occur in a

clumped distribution, more plots must usually be

sampled to produce an accurate estimate of density

(Caughley 1977a:26-27). If there is bias in the data

that cannot be corrected, increasing sample size will

have little effect on accuracy. For example, if males are

consistently more difficult to observe than other

subgroups, every estimate, no matter how many

repetitions are done, will be an underestimate.

Estimating sample size is complicated because the

magnitude or direction of existing bias is rarely known

(Bibby et al. 1992:34). Excessive sampling wastes

resources, and a balance must be sought between

quality of results and available resources (Cochran

1977:72). Robson and Regier (1964) suggested 3

standards for accuracy in determining population size,

depending on management objectives: (1) for

Section IV. Techniques 37
38 Wild Turkey Harvest Management: Biology, Strategies, and Techniques

preliminary surveys, where only a rough idea of

population size is needed, accuracy of ±50% is

sufficient; (2) for management work, where a

moderate level of accuracy is desired, ±25% is

recommended; (3) for research where accurate data

are needed, ±10% is recommended.

Most estimation techniques have such low precision

that only large changes in the population can be

detected (Davis and Winstead 1980). According to

Caughley (1977b) “appropriate sampling techniques

coupled with standardized methods of survey ensure

that even if our estimates are inaccurate at least they

are repeatable. At worst they will be usable as indices

of density, if not measures of absolute density.” Seber

(1982:458) also recommended handling the inaccuracy

of estimation methods by recognizing that the

estimates are biased and treating them as relative

rather than absolute measures of abundance. Bias can

be held constant by rigorously standardizing the

methods, and the indices obtained can be used for

monitoring changes in the population size and

distribution, and determining preferences for different

habitats. Similarly, Krebs (1989:58-59) points out that

“biased estimates may be better than no estimates,

but you should be careful to use these estimates only

as indices of population size. If the bias is such as to

be consistent over time, your biased estimates may be

reliable indicators of changes in a population.” 

Estimation methods with both high accuracy and high

precision can be very expensive (Davis and Winstead

1980:222). It may be preferable to sacrifice either

accuracy or precision to attain a study design that will

produce acceptable results for the purpose in mind.

Accuracy is more important for obtaining an

abundance estimate if a population is to be reduced by

culling, or if biomass estimates are of interest. In

contrast, precision is more important when repeated

abundance estimates will be used to monitor trends

over time, although bias must also remain constant

among censuses (Norton-Griffiths 1978:41-42,

Rabinowitz 1993:120). However, both accuracy and

precision should be considered even if one is more

important than the other for a given management

objective, or if it is only possible to maximize one of

the two. 

Sources of bias include (1) skill of observer, (2)

conspicuousness of animal, (3) weather conditions, (4)

species activity related to time of day or season, (5)

duplicate counts of individuals driven ahead by

flushing, (6) variation of the screening effect of

habitat, and (7) distance from the observed animal

(Brower 1990:120). Verner (1985:254-259) discusses

these factors in detail, and adds various effects of

study design as an additional source of bias.

Observability and Sampling

Observability and sampling must be considered in

any abundance estimation project (Lancia et al.

1994:218). Most abundance estimating methods do not

account for all animals present. It is necessary to

estimate the proportion of the animals observed to

develop a proportionality constant for transforming

the count into an estimate of abundance. Limitations

of time and money usually prohibit a survey from

being conducted over the entire study area (Lancia et

al. 1994:218). Several decisions must be made to deal

with the problems of observability and sampling. The

first decision is whether the study area is large

enough to require sampling instead of a complete

census. Decisions must then be made about the size

and number of plots, and a sampling scheme, such as

stratified random sampling or simple random

sampling, must be selected (Lancia et al. 1994:247).

Selection of an Abundance 

Estimation Method

A wide selection of census, estimate, and index

techniques is available. Choosing the “best” method

depends largely on the management or study

objectives (Järvinen 1976, Eberhardt 1978a, Verner

1985:249), which should be clearly defined beforehand.

Absolute estimates may be necessary if a population is

managed for maximum sustained-yield harvesting or

to limit abundance to a specific level (Bull 1981, Seber

1982:458, Eberhardt and Simmons 1987). Measures of

relative density may be sufficient for studies of habitat

use, rate of increase, dispersal, and responses of the

population to manipulation (Bull 1981). Absolute

estimates are also necessary for studies where

population density is to be related to behavior,

reproductive rate, survival, emigration, or

immigration (Caughley 1977a). Complete counts will

usually be unattainable, thus an estimate will have to

be used. Choice of estimating technique will depend on

available manpower and funding. Estimating

techniques that provide the most accurate and

consistent results may be prohibitively expensive, and

the most expensive and labor-intensive techniques do

not necessarily always produce the most reliable

results. Each technique should be reviewed to

determine (1) how critical the assumptions are and the

consequences of a departure from these assumptions,

and (2) whether the assumptions are valid for the

subspecies and habitat type under consideration. 

After selection of a potential technique, it may be

desirable to conduct a pilot study to test for violations

of the assumptions, and determine if the variance of

the estimates is within acceptable limits for the

expected sample size. The accuracy or precision

needed will depend on management objectives. If

trends, rather than actual numbers, are all that is

desired, then an index may suffice. As with estimates,

some indices are more reliable than others given the

species or habitat under consideration, and some will

not be feasible given the cost or manpower required.

The selection of a method will also depend on habitat

and landscape characteristics (Stauffer 1993), the size

of the area, and how the species is distributed

throughout the habitat (Seber 1982:560). Finally, the

selection of a method should depend on the

techniques’ advantages and disadvantages, and how

they relate to wild turkey ecology and behavior. Krebs
(1989:12) presents a flowchart for selecting an

appropriate method based on the factors discussed

above. Caughley and Sinclair (1994:190) and Bibby et

al. (1992:22-23) also provide lists of questions to be

considered before selecting a suitable method. After

considering all these factors, one might construct a list

of several potentially suitable methods. It would be

preferable, resources permitting, to use more than one

estimating method simultaneously and then compare

the results (Rusch and Keith 1971).

There is no general agreement as to which abundance

estimation technique is best for wild turkeys (Welsh

and Kimmel 1990). There is probably no completely

reliable technique for estimating turkey abundance

over extensive areas of eastern forest habitat (Mosby

1967, Wise 1973). Existing methods for inventorying

wild turkey populations need further evaluation and

development (Wunz and Ross 1990). In attempting to

determine which of the many available techniques is

“best,” we suggest following Pollock and Kendall’s

(1987:509) thinking: “the question here should not be

which of the techniques . . . is the best, but which one

is the best to be used, given the population, the

species, the habitat, the budget, and the importance of

the estimate.”

We evaluated each population abundance technique

with respect to the following considerations: (1) prior

and current use of the technique; (2) assumptions

(including a list of assumptions for the technique,

which of these assumptions can be relaxed and which

are critical, and how these assumptions relate to wild

turkeys); (3) advantages; (4) disadvantages; (5) design,

sample size requirements, and quality control; and (6)

cost and manpower requirements.

A long list of disadvantages relative to advantages

(Appendix C) need not make a method unacceptable

for wild turkeys. Disadvantages and biases can be

corrected for in many instances (Overton and Davis

1969:424), and assumptions can sometimes be relaxed.

A few strong advantages may outweigh many minor

disadvantages, but the reverse is also possible.

Some techniques listed in Table 7.1 and Appendix C

have severe limitations, which may explain why they

are not used for wild turkeys in the Northeast. Aerial

counts have rarely been used for turkeys in the

Northeast because of the difficulties of observing

turkeys in forested habitats. Line transects and strip

transects have seldom been used in this region

because of the difficulty of obtaining an adequate

sample size with low density populations in forested

habitats. Adequate samples would require

prohibitively long or numerous transects, and it is

difficult to meet the assumption that the animals do

not move before they are sighted. Drive counts are of

limited utility because of the elusive nature and

scattered distribution of wild turkeys. The personal

interview-map plot technique has not been used

recently to estimate turkey abundance, probably

because of changes in land use and the distributions of

suitable participants. The personal interview may be

useful for determining distribution, but probably not

for estimating abundance. The use of feeding sites,

dusting sites, or dropping counts has been suggested

as indices to abundance for wild turkeys, but the

ability to locate such signs, and the environmental

factors confounding these measures, limit their use for

abundance estimation. Tape-recorded poult distress

calls are useful for estimating brood size and survival

of broods of radio-tagged hens. Tape-recorded calls

may also be useful to determine distribution, but

probably not for abundance estimation. The remaining

techniques and tools listed in Appendix C have some

potential for abundance estimation, and the following

discussions will focus on these methods. Some

techniques will be appropriate for a specific research

question, but not for management programs, whereas

others will be broadly applicable.

Section IV. Techniques 39
40 Wild Turkey Harvest Management: Biology, Strategies, and Techniques

Table 7.1. Techniques evaluated for wild turkey abundance estimation

Census techniques

1. Direct winter counts (winter flock/roost counts), including interviewing cooperators

May be a true census only for the Rio Grande subspecies in Texas; otherwise, an index.

Estimates

2. Aerial counts (may be considered an index, or a subheading of plot/quadrate sampling or transect sampling)

3. Line transects 

4. Strip transects

5. Plot sampling (including quadrates)

6. Drive counts (also an index. See Lancia et al. 1994)

7. Mark-recapture

a. Banding studies

b. Radiotelemetry

8. Removal methods

a. Change-in-ratio

b. Catch-effort

c. Index manipulation method

9. Map plotting

a. Personal interview-map plot technique

b. Map plotting from field observations

10. Double sampling 

For example, combined aerial/ground counts

Indices

11. Brood surveys (may be considered a subheading of roadside counts)

a. Cooperator brood surveys

b. Data from field officers/ancillary sightings by department personnel (during routine field duties)

12. Reports from hunters

a. Deer hunters

b. Turkey hunters

13. Roadside counts/survey routes

Includes use of tape-recorded poult distress calls

14. Gobbling counts

15. Track counts (snow track counts may be a subset of, or tool for, direct winter counts)

16. Nuisance/damage complaints

17. Harvest data (related to #8.)

18. Feeding sites

19. Dusting sites

20. Dropping/fecal/pellet counts

21. Frequency indices (may be useful for distribution)

22. Poult survival studies

Tools for the above techniques

23. Radiotelemetry

24. Summer baiting (may be a tool for brood counts, or for capturing birds for mark-recapture)

25. Winter baiting (may be a tool for capturing birds for mark-recapture)

26. Mail-carrier surveys (a tool for roadside counts/brood surveys)

27. Tape-recorded calls (poult calls, male vocalizations)

28. Camera stations (a tool for brood surveys; may be used in conjunction with summer or winter baiting)

29. Adaptive sampling

30. Other sampling schemes

a. Stratified random

b. Simple random

c. Systematic

31. Infrared sensing imagery

32. Hunter check stations

33. Hunter report cards

34. Harvest surveys

a. Mail

b. Telephone

35. Field bag checks

36. Agency license/permit sales records

37. Multiple observers

38. Personal interviews/mail surveys of residents or hunters
Chapter 8. Population Census: Direct Winter

Counts

Background and Prior Use of the

Technique

Stokes and Balph (1965) noted that “accurate

inventories of wildlife species clearly depend on a

knowledge of behavior. . . . Season of the year and

even time of day influence behavior and thus dictate

appropriate census methods.” Direct winter counts

take advantage of the tendency of wild turkeys to

form stable winter flocks (Porter 1978), use the same

roosting sites repeatedly (Cook 1973), and restrict

their movements during periods of heavy snow cover

(Hayden 1980). Roost counts have been used

primarily with the Rio Grande subspecies, where

roosting sites are limited and flocks use traditional

roosting areas. Flock counts and various snow

tracking techniques have been used across the

northern range of the eastern subspecies.

Theoretically, once flocks or roost sites are located it is

possible to count all turkeys present. If all flocks or

roost sites can be located, it may be possible to count

the entire population in a given area.

DeArment (1975) used roost counts to estimate the

numbers of Rio Grande turkeys in a 2-county area in

Texas and calculate the percentage of the population

and the percentage of each sex that was harvested.

The precision and accuracy of the counts were not

evaluated. Roost counts were also examined in Texas

by Smith (1975), who attempted to “describe how

habitat factors combined with a declining population

affected the roosting behavior of Rio Grande turkeys

and thus affected roost counts.” Cook (1973) examined

whether populations of Rio Grande turkeys in Texas

could be reliably estimated by interviewing

landowners where winter turkey flocks were located.

Landowners were asked for roost locations and

numbers of turkeys present. Project personnel,

assisted by local game-management officers, were also

used to locate roosts and to make their own counts for

comparison. It should be noted that the Rio Grande

subspecies forms socially stable winter flocks and

generally uses permanent winter roost sites (Watts

and Stokes 1971). Still, many of the concepts can be

applied to winter flock counts in other regions where

turkeys form flocks and have restricted home ranges,

but generally roost at a different site each night. 

Little and Varland (1981) used observations of turkey

flocks reported by landowners in Iowa to determine

minimum population growth and dispersal distance of

newly reintroduced populations. Porter (1978) and

Porter and Ludwig (1980) used direct counts of all

known winter flocks, in conjunction with gobbling

counts, to estimate spring population densities in

Minnesota. Menzel (1975) obtained information on

winter concentrations of turkeys in Nebraska from a

mail survey of landowners and observations made by

field personnel. Tefft (1996a,b) used flock counts in the

fall, making use of cooperator sighting reports in

conjunction with several other methods, to develop a

population estimate in Rhode Island. Weinrich et al.

(1985) used cooperators (landowners and other

selected individuals such as postal carriers and

wildlife agency personnel) in Michigan to obtain

counts of turkeys in winter flocks to estimate turkey

populations for the purpose of distributing spring

hunters by area. Hoffman (1962) used winter flock

counts to determine wintering populations in

Colorado. Weaver and Bellamy (1989) conducted

winter flock counts in Ontario to document turkey

population growth from a release 5 years earlier, and

have since then been conducting the surveys annually

during a 1-week period in March. Lewis and Kelly

(1973) performed winter flock counts on a 12-mile2

area in Missouri to estimate the population directly

between 1964 and 1969.

Assumptions

In using direct winter counts as a census technique, it

is assumed that all turkeys aggregate into observable

flocks, that flock ranges and flock compositions are

stable, and that all flocks can be located. These

assumptions may be invalid if some turkeys remain

solitary or flock composition and ranges shift during

the counting period. In the northern part of the wild

turkey’s range, “flocks” of as few as 1-3 individuals

may be common. Variation in flock size is not a

problem if the flock’s range is stable. When snow

cover is deep, flock ranges will be small regardless of

the total number of birds present, so an investigator

may be as likely to detect a flock of 1 individual as a

flock of 20.

If snow tracks are used to locate flocks, and if

abundance data are inferred from the tracks

themselves, it must be assumed that the number of

tracks correlates with the numbers of birds (Bull

1981). This assumption may not always be valid

because accurate track counts are impossible under

some snow conditions.

Advantages

In some areas, wild turkeys are concentrated by

supplemental feeding or by feeding frequently in

harvested fields in winter. As a result, turkeys may be

Section IV: Techniques 41
42 Wild Turkey Harvest Management: Biology, Strategies, and Techniques

more visible in winter than at other times of the year,

making direct counts of individuals easier. Visibility of

turkeys is also enhanced because of the lack of

vegetative cover in winter (Hurt 1968).

One important advantage of the direct winter count

technique, provided the assumptions are met, is that it

results in a total count of the population rather than

an estimate or index. The method involves censusing

an entire area instead of sampling and using statistical

manipulations to estimate the population of the entire

area (Weinrich et al. 1985). A total count is often easier

to explain to the public than estimates based on

sampling or indices of relative abundance (Weinrich et

al. 1985).

Another advantage of the technique is that it is useful

in areas with severe winters because heavy snow

cover will restrict the movements of wild turkeys and

make them easier to observe (Weinrich et al. 1985). In

Indiana, for example, a large percentage of turkeys

confined their movements to small areas during

periods of heavy snow cover (Wise 1973). The

northeastern United States and adjacent Canadian

provinces typically experience severe winter weather

making this technique potentially useful.

Unfortunately, those snowfall conditions that restrict

movements of wild turkeys may also interrupt data

collection (Hayden 1980). Predictability of snow cover

can also be a problem when counts require the

participation of many observers.

The cooperation of landowners can facilitate the

application of winter counts to areas as large as an

entire state. No special sampling skills are required

for landowners to count turkeys on their own

property, and using landowners minimizes problems

associated with agency access to private property

(Weinrich et al. 1985). Turkey flocks are rather

conspicuous, and they attract attention and interest

among residents, which facilitates alerting cooperating

landowners to their presence (Hoffman 1962). Cook

(1973) compared counts made by biologists with

estimates made by landowners of turkeys at winter

roosts in Texas and found that landowner estimates

“could be used to determine levels and trends of

populations where there is little nightly movement

from one roost site to another.”

Direct winter counts are potentially useful for

establishing spring harvest goals. Population

estimates based on direct winter counts were

correlated with the subsequent spring’s harvest in

Michigan (Weinrich et al. 1985). In Minnesota, density

estimates based on winter flock counts were highly

correlated (r = 0.99, P < 0.05) with spring density

estimates based on gobbling counts and observations

of the average number of males per group (Porter and

Ludwig 1980).

Being able to count the number of animals in a group

has the advantage of allowing accurate and

repeatable calculations of mean group size (Caughley

1977a:25). Because “the mean size of social groups of

gregarious animals tends to increase as density

increases . . . group size usually provides a workable

index of density” Caughley (1977a:24). Leopold (1944)

found this relationship to be true for turkeys,

observing winter flock size to increase with density.

This measure may also be useful for comparing mean

group size among areas or among years, perhaps as

an index to food abundance.

Another advantage of direct winter counts is that the

method is not labor intensive. This benefit is evident

when cooperators are enlisted to collect data. The

labor is restricted to the amount of time required to

collect the data from the cooperators and process it.

In regions with persistent winter snow cover, flocks

can be located by following tracks in the snow. The use

of tracks to locate wild turkey flocks or to index

populations has been employed by Glover (1948) and

Bailey (1973) in West Virginia, and by Eaton et al.

(1970) in New York. Plots or transects may be used to

find tracks. Following tracks in the snow can provide

information on the number and sex of turkeys, their

behavior, and habitat use (Healy 1977, Hayden 1980,

Burke 1982). Winter sex ratios can provide an index of

the potential breeding population and also may serve

to give a post-season estimate of the numbers of

gobblers harvested relative to hens (Ontario Ministry

of Natural Resources 1985). Wunz and Hayden (1975)

used snow tracks directly to estimate turkey

populations, and found the method reliable in

Pennsylvania provided at least 2 days had elapsed

since the last snowfall and that snow depth had been

>25 cm for at least 2 weeks. Jahn (1973) also noted

the advantage of obtaining winter flock counts when

deep snow concentrates turkeys.

Disadvantages

Movement of flocks from one location to another can

result in inaccurate counts. Midwinter movements

were a problem in Minnesota, but flocks did not move

extensively after 1 January and few individuals moved

between flocks during winter (Porter 1978). Movement

of flocks between winter roosts was also a problem in

Texas (Cook 1973), and can be a potential problem in

any region. Texas landowners often made inaccurate

counts of turkeys because of the instability of roost

sites and the infrequency of observation (Cook 1973).

In many situations observers tend to underestimate

the size of large groups (Eberhardt et al. 1979), but

Cook found the reverse with turkeys. When

landowner estimates were compared with roost counts

done by biologists, the landowner estimates were 

7-203% greater (Cook 1973). This difference was

apparently caused by movement of flocks to different

roosts, with subsequent double counting by the

landowners. Cook (1973) concluded that roost counts

were not a reliable indicator of populations in areas

with unstable roost sites.

Smith (1975) also reported that roost counts were not

reliable estimators of the winter populations of Rio

Grande turkeys in Texas. Roosting patterns were

variable and the size of a roost site dictated the size of

a flock that could use it. The factors that led to
unstable roosting patterns in Texas where turkey

densities are low included human activity and land use

patterns, relative availability of roost sites, and

heightened sensitivity of small flocks to disturbance

(Smith 1975). The roosting habits of the Rio Grande

subspecies of wild turkey in Texas differ from those of

subspecies in other regions. Eastern wild turkeys are

usually not limited by the availability of trees for

roosting, and they can easily shift to a new roost if

disturbed. The problem of flock movement can be

minimized by having a short census period (Weinrich

et al. 1985), because flocks are less likely to move

large distances in a short time. A short census period

can also reduce labor costs, especially where biologists

are used to conduct the surveys.

In Nebraska, winter counts were not correlated with

other survey methods (Menzel 1975). Although

Weinrich et al. (1985) found a correlation between

winter flock counts and the subsequent spring’s

harvest, the relationship was affected by sex and age

composition, dispersal patterns, land ownership and

use, and weather. These factors are potential sources

of variation in the use of direct winter counts to

estimate wild turkey populations.

The use of landowner cooperators may result in

uneven coverage across a study area because the

distribution of private land or willing cooperators may

be patchy. Weinrich et al. (1985) noted a non-random

distribution of wintering turkey flocks in their

Michigan study area, associated with private land and

the abundance of farming-related waste, storage

grains, and supplemental feeding stations found there.

However, this concentration of turkeys on private land

also made them more visible to the public than if they

were on less accessible state or federal land. It may be

difficult to find landowners who are willing to

participate in the surveys, and cooperator interest in

conducting the counts may decline over time (Welsh

and Kimmel 1990). Declines in cooperator response

rates can lead to underestimates of the turkey

population. These problems can be alleviated by using

wildlife agency personnel to conduct the surveys, but

the cost of such efforts may be prohibitive for large

areas.

At present, mean winter flock size cannot be used to

estimate population size or index relative abundance.

The difficulty with using group size to estimate

density in animal populations is that “the regression of

density on group size is seldom linear and it usually

cuts the vertical axis below the origin; hence the index

ranks densities but does not reveal the proportional

difference between them” (Caughley 1977a:25).

Winter flock size varies within regions (Ontario

Ministry of Natural Resources 1985), and apparently

responds to many factors in addition to population

density (Smith 1975). 

The biggest problem with using snow tracks to locate

flocks is obtaining suitable tracking conditions.

Consistent and persistent snow cover is required, and

this condition cannot be guaranteed or predicted in

many parts of the wild turkey’s range. Bailey (1973)

also found that tracks often could not be located, even

when turkeys were known to be present in the general

area.

Counts of tracks in the snow are difficult to convert to

abundance estimates. An animal can cross a transect

or plot many times, obscuring the relationship

between numbers of tracks and numbers of birds (Bull

1981). Brower (1990) considered track counts an index

of activity rather than a measure of abundance. To use

tracks as a density estimate, track counts must be

calibrated for animal activity and spatial distribution

(Brower 1990:122). Alternatively, tracks can be

counted during a brief period following snowfall to

eliminate duplicate counting (Wunz and Hayden 1975).

Design, Standardization, and 

Quality Control

There is no standard method for conducting winter

flock counts on wild turkeys, and little methodological

discussion of the technique can be found in the

literature. The approach used in Ontario by Weaver

and Bellamy (1989) might be adapted to other areas of

the Northeast:

The area to be censused was broken up into

blocks approximately 4 square miles in area.

Landowners and other interested individuals

were recruited in each block to observe and

report the number of different turkeys seen

during a one week period in March. At least two

cooperators were used for each block to allow for

verification and non-reporting. Whenever

possible, the same observers were used for the

same block each year. The cooperators were

asked to plot all observations on topographic

maps to allow for elimination of duplicate

observations. Additional blocks and cooperators

were added as the turkey range expanded.

Weinrich et al. (1985) used the following methods to

count winter flocks in Michigan:

Counts of winter turkey flocks in northern Lower

Michigan were made each January by 6 to 8

people working full time for a period of about 2

weeks. The short census period minimized errors

caused by movement of birds between flocks. The

census was based on personal contact with

observers in turkey range. Census personnel did

not actually attempt to count all the turkeys in

their area, but utilized networks of contacts

which were screened for reliable reports of flock

sightings. Initially, the networks were developed

from various sources, including newspaper and

radio publicity, turkey observation report cards,

and observations by postal route carriers, school

bus drivers, utility workers, United Parcel

Service drivers, and from door-to-door contact

with the local residents.

Annual winter flock counts were conducted by the

same people whenever possible. Most of these

Section IV: Techniques 43
44 Wild Turkey Harvest Management: Biology, Strategies, and Techniques

personnel were local technicians, laborers, or parttime employees working under the direction of the

district wildlife biologists.

A network of transects that followed ridge lines and

drainages were used to estimate winter populations

on areas ranging from 81 km2 to 263 km2 in northcentral Pennsylvania (Hayden and Wunz 1975, Wunz

and Hayden 1975). Data collection began after snow

depth had exceeded 25 cm for at least 2 weeks, and

transects were searched starting 2 days after the last

snowfall. Counts were made on foot and with

snowmobiles. Tracks were examined to determine

number and sex of birds. Data were summarized to

determine distribution and minimum number of birds

present. The minimum number of birds present was

considered a reliable index. Counts could not be

conducted in 2 of 12 years because of unsuitable snow

conditions (Wunz and Hayden 1975).

Other investigators have made brief comments on

their winter count procedures. Smith (1975) made

roost counts in Texas at unequal intervals from

December through February, in early morning or late

evening. DeArment (1975) attempted to calculate an

average adjusted population estimate by adding the

fall harvest to the late winter population, as

determined from winter flock counts. Weaver and

Bellamy (1989) conducted counts annually during a 

1-week period in March.

Cost and Manpower Requirements

The 1994 cost for the Ontario survey (Weaver and

Bellamy 1989) was approximately $1,500 (Canadian),

mainly for maps and postage for about 60 cooperators.

For the direct winter count done by Weinrich et al.

(1985) in Michigan, 6-8 agency personnel were needed

full time for a 2-week period. Because cooperators

were able to collect all of the field data, the only

agency effort required would have been to collect and

analyze the data from the cooperators.

Conclusion and Recommendations

Except for specialized circumstances, which include

persistent snow cover and restricted flock movements,

direct winter counts have limited utility as a census

technique (Kurzejeski and Vangilder 1992:176). After

considering several techniques for estimating

abundance in northern Michigan, Weinrich et al.

(1985) concluded that winter counts were more

applicable than roost counts (Smith 1975), brood

counts (Schultz and McDowell 1957), winter track

counts (Bailey 1973), or gobbling counts (Porter and

Ludwig 1980) for their area. The Michigan area was

characterized by persistent snow cover and

agricultural practices and supplemental feeding that

caused turkeys to congregate in specific locations.

In regions of the Northeast with persistent snow

cover, systematic snow tracking of areas on the order

of 5,000-20,000 acres has the potential for providing

accurate counts of wild turkeys (Wunz and Hayden

1975). The cost of having biologists conduct the census

over larger areas may be prohibitive. The

supplemental use of cooperators may result in a

reliable index of turkey abundance, and provide

accurate information on distribution (Weaver and

Bellamy 1989). The main problem with the technique

in most of the region is that it is impossible to

guarantee a continuous series of counts from year to

year because weather conditions are not suitable for

making counts in some winters. If the goal is to obtain

yearly estimates of population size, a method that is

less dependent on weather would be preferable. If

absolute estimates of population size are not needed

for a management program, one of the index methods

discussed in the other chapters may be more

appropriate for evaluating population trends.
Chapter 9. Population Index: Harvest Data

Background and Prior Use of the

Technique

Wildlife agencies commonly use harvest data collected

by mandatory checking or mail surveys to obtain

turkey population estimates (Kennamer et al. 1992).

Table 9.1 shows the types and methods of harvest data

collection used in the Northeast. These data can be

used to index population trends and densities; index

fall hunting mortality rates (DeGraff and Austin 1975,

Lewis 1980); or obtain information on hunter

satisfaction, attitudes, and field observations (Norman

and Steffen 1996). The joint evaluation of fall and

spring harvest data can provide information on

population trends and the effects of hunting on the

population (Steffen and Norman 1996). The most

common method of gathering harvest data is mailing

surveys to a random sample of permit buyers

(Kurzejeski and Vangilder 1992:176). In the Northeast,

mandatory check stations are used more often than

hunter surveys to monitor turkey harvest. 

Several investigators have examined hunting data as a

means of estimating or indexing wild turkey

populations, or for comparing hunting data with the

results of other indices. Palmer et al. (1990) examined

hunter success rates relative to population size and

found them to be only weakly correlated over a 6-year

period in Mississippi. Steffen and Norman (1996) used

harvest data from check stations in Virginia to assess

the effect of fall hunting on population trends. These

investigators, as well as Pack (1993) in West Virginia,

found a nonlinear relationship between fall harvest

levels and the growth rates of spring gobbler

harvests, although fall harvest totals were not related

to the size of the subsequent gobbler harvest. In

Pennsylvania, the length of the fall either-sex hunting

season was inversely related to brood counts in the

next year (Wunz and Ross 1990). For Merriam’s

turkeys in northwestern Nebraska spring hunting

success was unrelated to the preceding fall’s hunting

success or total harvests (Menzel 1975). Porter et al.

(1990a) developed an effort-based index to abundance,

using the waiting time (number of hunter-days) before

the first wild turkey kill of the season in a given town.

The reciprocal of this waiting time produces an index

that is positively related to turkey density. DeGraff

and Austin (1975) used band recoveries from birds

harvested in the fall hunting season to calculate the

pre-season population size. They acknowledged that

banding and band recovery strongly favored hens with

broods because adult hens without broods were

captured and harvested at lower rates than successful

hens were. Shaw and Smith (1977) used harvest data

from both check stations and post-season mail

questionnaires in Arizona, and concluded that “the

number of turkeys taken by hunters was simply a

function of population size.” They also found that the

percentage of hunters who were successful was

directly correlated with the number of turkeys

counted during fall roadside surveys. They concluded

that the most important factor influencing both

success rate and total harvest is the number of

turkeys available.

Harvest data can be used to estimate populations with

the class of abundance estimating techniques termed

removal methods. When animals can be readily seen,

methods based on observation are generally

preferable. For difficult-to-observe species, however,

mark-recapture or removal methods are often better

choices (Lancia et al. 1994:249). Removal methods are

conceptually similar to mark-recapture methods, but

“capturing” is done by hunting. Removal has the

advantage that it avoids any behavioral response

caused by trapping and subsequent release (White et

al. 1982), and it avoids some difficult-to-meet

assumptions of mark-recapture surveys. Removal

violates the assumption of closure, however, and it

disrupts the local population, making the method

unsuitable for studies to be repeated in the near

future (White et al. 1982). This difficulty can be

overcome in non-hunting removal studies by marking

and releasing captured animals , but not counting

these marked animals in subsequent captures (Brower

1990:115-118). The statistics and equations used in

some specific removal methods are presented in

Brower (1990:115-118).

Classes of Removal Methods

Removal methods include 2 general classes of

estimators: change-in-ratio estimators and catch-perunit-effort estimators. Change-in-ratio estimators

“can be used if the proportions of ‘types’ (e.g., sexes,

age classes) of animals in the removals are

substantially different from the proportions of the

same types in the pre-removal population” (Lancia et

al. 1994). This method should be appropriate for

spring gobbler seasons, or even for fall either-sex

seasons if there is a tendency to selectively harvest

certain age and sex classes of birds. The method

involves calculating the relative abundance of the 2

types of animals in a population, then removing a

known number of 1 type, and then re-estimating the

relative abundance of the 2 types, using the resulting

change in ratio to calculate the total number of

Section IV: Techniques 45
46 Wild Turkey Harvest Management: Biology, Strategies, and Techniques

animals in the population (Norton-Griffiths 1978:107,

Caughley and Sinclair 1994:208). The method requires

surveys, both before and after the removal (Caughley

1977a:38-44, Lancia et al. 1994:250). The change-inratio method has been used to estimate the size of

pheasant and deer populations, as well as other

terrestrial species (see Seber 1982:353), and is

frequently used in fisheries studies (Scattergood

1954:280). In one change-in-ratio method, called

Kelker’s method (see Krebs 1989:155), if a population

is classifiable into 2 or more classes, such as age or

sex, the population size can be estimated after

removing a known number of animals from 1 class.

Kelker’s method is often used to estimate the size of a

population prior to hunting that removes mostly males

(Caughley 1977a:38-44). This and other methods are

described more fully in Caughley (1977a:38-48). Krebs

(1989:158) recommends that researchers use the

approach outlined by Paulik and Robson (1969) when

planning the procedure. Large samples are usually

required for change-in-ratio estimators (Krebs

1989:166). Sample size calculations are discussed in

Krebs (1989:190-194).

Catch-per-unit-effort removal models may be used if

the removals are not selective, and they have the

advantage that “population estimates can be derived

from removals that are part of a routine management

activity such as hunter harvests” (Lancia et al.

1994:231). The method is only recommended for

exploited populations, because it would be too effortintensive and disruptive otherwise, and is generally

less accurate than other methods (Caughley 1977a:17-

19). Harvest of wild turkeys is related to the numbers

of turkeys present and to the intensity of hunter

effort. The catch-per-unit-effort method will

presumably eliminate or control the effects on harvest

data of changing hunter effort. Like the change-inratio methods, the catch-per-unit-effort methods are

widely used in commercial fisheries (Seber 1982:569),

and have also been used extensively for small

mammals. Both methods have been used to develop

models for estimating animal abundance. Dupont

(1983) developed models for estimating fish

populations. Novak et al. (1991) and Laake (1992)

modified Dupont’s models and applied them to whitetailed deer and elk, respectively. These models have

stringent requirements, such as great variation in

effort, high harvest rates, and precise measures of

harvest (Laake 1992), which may make the models

difficult to use in management programs. Williams

and Austin (1988) commented that an index

incorporating hunting effort was needed to compare

gobbler harvest on different areas, and Lint et al.

(1995) used harvest per unit effort and numbers of

harvested gobblers to derive an estimate that was

correlated with Buckland mark-recapture estimates.

Glidden (1980) recommended an index based on the

number of days of effort per turkey harvested in New

York. Gefell (1991) and Porter et al. (1990a), also

working in New York, recommended the use of

abundance indices based on take and effort on the

opening day of the wild turkey season. In all these

studies involving wild turkeys, catch-per-unit-effort

indices appeared to be the best of the indices

examined to track population trends, but the actual

population size was not known.

The catch-per-unit-effort method estimates population

sizes based on the decline in catch-per-unit-effort over

time. This method requires data on the number of

animals removed and statistics indicating effort, such

as the number of hunters per unit time or the number

of birds seen per unit time. Obtaining data on effort

usually requires use of a hunter survey. This method is

only appropriate if the removal does not greatly affect

the size of the population (Caughley 1977a:17-19). In

areas where a substantial proportion of the turkey

population is removed, this method may be

inappropriate. In these situations, “absolute catch per

unit time is a better index” (Caughley 1977a:17-19).

Krebs (1989:162), in a seemingly contradictory

statement, says that “this method is highly restricted

in its use because it will work only if a large enough

fraction of the population is removed so that there is a

decline in catch per unit effort . . .it will not work if the

population is large relative to the removals.” Brower

(1990:115-118) also indicated that population estimates

would be most precise when large proportions of the

population have been removed.

Eberhardt (1982), elaborating on a method proposed

by Davis (1963), provides another way to use removal

data to estimate population size, called the indexremoval method or index-manipulation method.

According to Krebs (1989:162), “if an index of

population size (like roadside counts) can be made

before and after the removal of a known number of

individuals, it is possible to use the indices to estimate

absolute density . . . Eberhardt (1982) gives the

necessary estimation formulas and discusses how this

removal method compares with methods based on

mark-recapture.” Because this method requires 2

surveys in addition to harvest data, it may be too

expensive for some management programs. However,

harvest data may already be collected as part of the

normal monitoring program, and the completion of 2

relatively inexpensive surveys before and after the

hunting season may be worth the expense for the

quality of data they provide.

Assumptions

A critical assumption for removal methods is that the

population is closed (Caughley and Sinclair 1994:208).

In many studies, this assumption is violated because

“after a few days, individuals originally too far from

the grid to have been caught will start moving into the

area vacated by the removals (White et al. 1982).

Certain catch-per-unit-effort models, however, can be

applied to open populations (see Seber 1982:296-352,

541-545 for a full discussion of techniques for these as

well as closed-population models). The problem of

animal ingress is greater for small grids than for large

study areas, because the ratio of perimeter to area

decreases as area increases. In the case of wild turkey

removal by hunting across a state or large

management zone, movements of animals into the

study area should be insignificant. Another
assumption with removal methods is that aside from

the effects of the removal, the population is neither

increasing nor decreasing.

The catch-per-unit-effort method also is based on the

critical assumption, which should be examined before

attempting to use this method, that the probability of

capture remains constant. This assumption may be

violated because of changes related to weather, food

supply, population level, or other factors (Davis and

Winstead 1980:233). It is also assumed that catch is

proportional to the population and the probability of

hunter success depends primarily on population

density. This assumption may be violated if factors

such as weather or changes in food supply alter

hunter success or the population’s vulnerability to

hunting. It remains to be determined for wild turkeys

if there is a threshold above which hunter competence

is limiting, which would limit the ability of the

technique to track population growth above that

threshold, but should not invalidate the index as a

warning tool against populations falling below this

threshold.

Other assumptions of the catch-per-unit-effort method

are that conditions of catching, catching efficiency, and

gear are standardized; the removal of one animal does

not interfere with the removal of another; and the

animals do not learn to avoid capture or become more

prone to capture (Caughley 1977a:17-19). It is also

assumed that hunter effort and harvest can be

measured accurately. This assumption requires that all

hunters, including those who were unsuccessful, be

included in the sample to obtain an accurate

estimation of effort. Seber (1982:296) summarizes the

underlying assumption of catch-per-unit-effort

methods by emphasizing that the size of a sample

removed from the population should be proportional

to the effort put into removing the sample. More

specifically, “one unit of sampling effort is assumed to

catch a fixed proportion of the population, so that if

samples are permanently removed, the decline in

population size will produce a decline in catch per unit

effort.”

With change-in-ratio methods, there are 2 critical

assumptions (Krebs 1989:155). First, it is assumed

that the population is composed of 2 types of

organisms, such as males and females, or adults and

young. Second, there must be a differential change in

the numbers of these 2 types during the observation

period (hunting season). It is also assumed that the 2

types of animals have the same probability of removal.

Because of sex- and age-related differences in the

behavior of wild turkeys, this assumption probably will

be violated. Pollock et al. (1985) proposed a method for

estimating population size when there is unequal

probability of removal. Pollock’s method probably

would not work for wild turkey harvests because it

requires 2 periods of removal, with one class removed

in the first period and a second class removed in the

next period.

Advantages

The main advantage of using harvest data as a

population index is that the data are relatively simple

and inexpensive to collect (Stauffer 1993, Lint et al.

1995). Biologists do not need to spend time and

manpower conducting censuses, but instead have

hunters report the data to them. In some states the

necessary information can be collected at hunter

check stations. Check stations can provide an accurate

count of legally harvested birds at the county or

regional level. They also allow the collection of

biological data such as age, sex, and weight

(Kurzejeski and Vangilder 1992:176). Postal surveys of

licensed hunters are also commonly used to collect

harvest data.

Lint et al. (1995) showed that harvest per unit effort

and number of harvested gobblers were related to

mark-recapture population estimates for gobblers in

Mississippi. They recommended that these 2 indices

be used when examining gobbler population trends,

and believed that the number of harvested gobblers

“properly indexed gobbler population size.” In many

states, the data for the harvested-gobblers index are

already collected. It may be an easy matter to use

existing data on hunter effort “to examine possible

trends and relations between harvested gobblers and

hunter effort” (Lint et al. 1995). Glidden (1980) found

that fall harvest in south-central New York was

correlated with the pre-season wild turkey population

and with hunting pressure.

If the removal is performed by hunters, removal

methods may yield more information than markrecapture (Hanson 1967), but the reverse may be true

if hunters are not used to perform the removal

(Chapman 1955). An additional advantage is that when

removal methods are used in conjunction with an

index method, such as roadside surveys, they can be

used to estimate absolute density (Krebs 1989:162).

Disadvantages

The greatest difficulty in using harvest data as a

population index is meeting the assumption that the

proportion of the total population harvested remains

constant for the areas or time periods being compared

(Lancia et al. 1994:223). Most investigations of harvest

rate find evidence of variation over both time and

space (Anderson 1975, Clark 1987).

Harvest data from a fall, either-sex season may be

difficult to interpret because uncontrollable factors,

such as food availability or unseasonable weather, can

affect harvest. Years of low natural food availability

may force turkeys to forage in open areas such as

harvested cornfields, where they are more likely to be

seen and shot by hunters, thus increasing harvest

rates or harvest per unit effort. Alternatively,

abundant natural food supplies may disperse

populations, resulting in fewer encounters with

hunters and lower harvest rates. Concurrent seasons

for other species can also affect turkey harvest. For

Section IV: Techniques 47
48 Wild Turkey Harvest Management: Biology, Strategies, and Techniques

example, turkey harvest may remain relatively

constant over the years even as the population

declines, if turkeys are taken incidentally by hunters

pursuing other species. Changes in hunter density

may also influence hunter success rates (Porter et al.

1990a).

Harvest rate can be standardized by calculating the

harvest per unit hunting effort. This measure may not

be useful, however, for indexing population abundance

when the population size is heavily influenced by

harvest. This situation may arise if the population is

small, or if hunting pressure is especially intense (Lint

et al. 1995), because of liberal hunting seasons or the

abundance of hunters. In Mississippi, the relationship

between hunter effort and harvest varied between

years, perhaps because of changes in gobbler

population condition and age structure (Palmer et al.

1990). In New York, traditional harvest-based

techniques such as total kill per unit area, change-inratio, or catch-per-unit-effort did not produce

estimates that met basic tests for accuracy or

conformed to normal statistical distributions (Porter

et al. 1990a, Gefell 1990). These investigators found no

clear relationship between hunter effort and

abundance in the following year, and hypothesized that

reproduction following the hunting season can offset

losses attributable to hunting in the previous year.

They therefore used the reciprocal of the waiting time

to the first kill of the season as an abundance index,

which overcame the problems mentioned for the

traditional techniques and provided high precision in

their analyses. The relationship among hunter effort,

hunter success, and harvest have varied among

regions and type of hunting season. Positive

correlation between hunter effort and harvest have

been reported for New York (DeGraff and Austin

1975) and Florida (Williams and Austin 1988). There

was no relationship between total hunter effort and

hunter success rate in Florida (Williams and Austin

1988) and Missouri (Lewis 1975). Palmer et al. (1990)

observed an inverse correlation between gobbler

hunter effort and hunter success rates in a Mississippi

population that was declining while hunter effort was

increasing.

Another problem with the use of harvest data as an

index to population abundance is that hunters may

selectively harvest certain age and sex classes

(Hanson 1967, Hayden and Wunz 1975, Wunz and

Shope 1980). The use of bag checks and wing and leg

collections was discontinued in Pennsylvania because

the harvest sample was not considered representative

of the population (Hayden 1985). Turkeys of certain

age classes may be particularly vulnerable to hunting,

and differences in behavior between males and

females may also lead to a disproportionate harvest of

one sex.

The usual goal of obtaining a population estimate or

index is to use this information to regulate future

hunting seasons. Winter flock counts may provide

information necessary to set the following spring

season, and summer brood counts may be useful for

setting the subsequent fall season. Harvest data are

not always helpful for planning future hunting seasons

(Wunz and Shope 1980). Data from a fall either-sex

hunt, for example, may have little relationship to the

subsequent spring’s gobblers-only hunt, and little

bearing on the population 1 year later before the next

fall hunt. For this reason, harvest data are useful for

charting trends, but not as valuable as indices or

estimates of the preseason population for regulating

harvests.

The time and expense required to operate hunter

check stations may limit their utility when large

numbers of hunters participate and seasons are long.

The use of hunter report cards to collect harvest data

may greatly decrease agency manpower and costs, but

the accuracy of the data may be compromised (Myers

1973). Hunters tend “to inflate their reported hunting

success through pride, prestige, or memory loss . . .

[causing] large positive biases . . . . Those hunters who

bagged nothing are the worst offenders” (Seber

1982:489). Other problems are (1) response rates may

differ for successful hunters and unsuccessful hunters,

and (2) surveys do not allow collection of some

biological data such as bird weights. Furthermore,

sample size may be inadequate to provide regional or

county information (Kurzejeski and Vangilder

1992:176), making it difficult to calculate variance

estimates or otherwise measure data quality. When

uncertainty is unknown it is impossible to statistically

test data, and the results may only be useful for

providing general statements about population trends

(Stauffer 1993).

Mail surveys are widely used to eliminate the labor

associated with check stations. Mail surveys also have

the advantage of providing data from unsuccessful

hunters. However, a major problem with the use of

mail surveys is that it may take months to obtain

names and create a mailing list of licensees. Mail

survey data are not available in some states for 12 to

18 months after the close of the season (Kurzejeski

and Vangilder 1992:176).This difficulty can be reduced

where turkey hunters are required to apply for

licenses, because names and addresses of hunters are

available immediately. A long delay in getting survey

materials to hunters may cause them to forget

pertinent details of their hunts (Kurzejeski and

Vangilder 1992:176). Connecticut has addressed these

problems by including a hunter survey card with

every turkey permit and requiring all hunters to

complete and return the survey form within 10 days 

of the close of the season.

Design, Standardization, and Quality

Control

Choosing a method of data collection (check stations,

report cards, mail surveys) depends largely on the

financial and manpower resources available, but is also

influenced by tradition and politics. The particular

type of data collected is dependent on what type of

information the state needs. Hence, it is difficult to

recommend standards for harvest data collection.
Table 9.1 lists the methods used and types of data

collected in the Northeast.

The basic data required to use removal methods to

estimate population size include numbers of birds

harvested, hunter effort, and sex and age of harvested

birds. Harvest and biological data can be obtained

most accurately from mandatory check stations,

although report cards and surveys can provide a

lesser-quality source of information. Even when

hunters are legally required to present harvested

birds at check stations, harvest data must be

corrected for non-response. Because reporting rates

vary among years, periodic corrections for nonresponse are necessary.

Catch-per-unit-effort methods require the use of

surveys to determine hunter effort, with total hours or

number of days spent hunting being the usual

measures (Table 9.1). Porter et al.’s (1990a) finding

that time to first kill of the season can be used to index

abundance suggests that date and time of kill should

be recorded. In addition to harvest data, change-inratio methods require independent estimates of the

abundance of turkeys in the harvested age or sex class

before and after the hunting season. Check stations

and surveys can also be used to collect data on hunter

attitudes and experiences, although check stations

generally reach only successful hunters.

Cost and Manpower Requirements

The Missouri Department of Conservation compared

costs of post-season mail surveys with check stations

(Kurzejeski and Vangilder 1992). For mail surveys, the

calculation of harvest estimates with 95% confidence

limits of ±5% for each county in Missouri would

necessitate surveying 99.2% of spring turkey permit

buyers. The cost of the survey at 1985 postage rates

was approximately $70,450. If the resolution of the

survey had been reduced to estimating harvest in the

8 zoogeographic regions of Missouri, the cost would

still have exceeded $60,000. To obtain the same

reliability level for the fall harvest, an additional

$35,000 would be required. These costs do not include

data entry and analysis time, so the actual expenses

could be greater. The costs of running mandatory

spring and fall check stations in Missouri average

$40,000 a year. Although the actual costs will vary

from state to state, the information from Missouri

gives some idea of the relative differences in costs.

Lint et al. (1995) compared the number of harvested

gobblers, harvest per unit effort, number of gobblers

heard per day, and mark-recapture as indices or

estimates of abundance. They found that the number

of harvested gobblers was the least expensive,

followed by harvest/effort, gobblers heard/day, and

mark-recapture.

Conclusion and Recommendations

Harvest data are the easiest and least expensive data

to obtain for monitoring program success and

population trends. Harvested gobblers and gobbler

harvest per unit hunter effort are reliable indices of

population trends (Lint et al. 1995). Porter et al.’s

(1990a) index to time of first turkey kill in the fall

season also provides a useful index to abundance that

can be used to monitor trends over time. Because

harvest is never perfectly correlated with population

size, an independent index of abundance will be

required in some management situations.

Harvest data usually need to be corrected for nonreporting bias to obtain an accurate estimate of total

harvest. Reporting rates can be estimated with

random surveys of permit holders after the season, or

by comparing the results from field bag checks with

check station and report card returns. Reporting rates

need to be estimated periodically because they vary

over time. Hunter check stations elicit higher

reporting rates than do report cards that successful

hunters return by mail. In Missouri, mandatory check

stations were less costly than random mail surveys for

estimating harvest. Check stations are the preferred

method for collecting harvest data in the Northeast.

When the proportion of the population harvested is

known from band-return or radio-telemetry data,

harvest can be used as an ad hoc estimator of

abundance. Harvest data are used to estimate

population size in the harvest management models

developed in Minnesota (Appendix A) and West

Virginia (Appendix B). Mathematically modeled catchper-unit effort and change-in-ratio estimates of

abundance have strict assumptions and data

requirements, but they may be useful for research.

We recommend that harvest data collection include

date and time of kill, location to the turkey

management zone, age, and sex. These data allow

calculation of the most useful indices of abundance and

provide a complete description of the harvest. 

Section IV: Techniques 49
50 Wild Turkey Harvest Management: Biology, Strategies, and Techniques

Table 9.1. Types and methods of turkey harvest data collection by eastern states and provinces.a

Method CT DE ME MD MA NH NJ NY ONT PA RI VT VA WV

Check station xb xx xxx x x x x xx

Report card, survey x xc x xxxxx

Data collected

Sex and age RCb MC MC MC MC MC MC RC MC MC MC MC MC

Weight RCb MC MC MC MC MC MC VQ

Spur length RCb MC MC MC MC MC MC RC MC MC MC MS VQ

Beard length RCb MC MC MC MC MC RC MC MC MC VQ

Location of kill RCb MC MC MC MC MC MC MS MC RC MC MC MC MC

MS RC VQ

Date of kill RCb MC MC MC MC MC MC MS MC RC MC MC MC MC

MS RC VQ

Hunter characteristics MS MS MS MS MS RC MS VQ

Participation or permit use MS MS MS MS MS RC MS MS

Hunting area(s) MS MS MS MS MS RC VQ

Hunting dates MS MS MS MS

VQ

N days spent hunting MS MS MS MS MS RC VQ

Total hours spent hunting MS MS RC VQ

N turkeys harvested MS MS MS MS RC RC VQ

MS MS MS

N turkeys seen MS MS MS RC VQ

N gobblers heard MS MS RC VQ

N hens flushed from nests MS VQ

Type of hunting implement MS MS MC MS RC MC MC VQ

RC MS MS

Distance and result of 

shots fired MS RC MS MS

N hunters seen MS MS MS RC MC

MS

N other hunters interfering MS VQ

N hunts disrupted MS RC MS

Calls, decoys, or guides used MS MS RC MS VQ

Illegal activity observed MS RC MS VQ

Hunter experience level MS I MS RC VQ

Time spent preseason scouting RC MS VQ

Amount of money spent MS MS MS RC VQ

Hunter attitudes, opinions MS MS RC MS VQ

State/federal/private land MS RC MC VQ

MS

a Abbreviations:

MC - Mandatory Check

MS - Mail Survey

RC - Report Cards

VQ - Voluntary Questionnaire

x - State uses

I - Incidental/Informal studies.

b Connecticut uses mandatory check stations during the fall firearms season. Spring gobbler and fall archery harvests

are monitored with kill report cards, and permits for all seasons include a survey form, which must be returned by all

hunters within 10 days of the season’s closing date. Harvest and biological data are collected with 2 or more methods.

c Mandatory survey included with all permits; to be completed by all hunters and returned within 10 days of the season’s

closing date.
Chapter 10. Population Index: Brood Surveys

Brood surveys have been widely used to obtain indices

of abundance or measures of productivity. Field

methods varied among studies and the statistics

reported include total counts of hens and poults, total

number of broods observed, poult-per-hen ratios,

mean brood size, and proportion of hens with poults

(Kennamer et al. 1975, Menzel 1975, Wunz and Ross

1990). The results of many brood surveys are difficult

to interpret because of differences in methods,

statistics reported, and the frequent misinterpretation

of age ratios. Poult-per-hen ratios alone contain little

information about population dynamics (Caughley

1974a). Poult-per-hen ratios do not indicate either the

direction or rate of change in a population. Large

poult-per-hen ratios can occur with decreasing,

stationary, or increasing populations. Age ratios are

not adequate substitutes for accurate estimates of

relative or absolute density, which are the measures

needed for harvest management (Caughley 1974a).

This discussion will be limited to using summer brood

surveys to obtain an index to population size prior to

fall hunting season. The variable of interest is the total

number of hens and poults observed. Other statistics

obtained from brood surveys, such as poult-per-hen

ratios and mean brood size, are of interest to the

observers but are not useful for management

decisions. We do not recommend using data from

brood surveys to measure productivity.

Assumptions

The basic assumption is that brood counts are related

to population size in a constant way. Hens and broods

are of most interest during summer because nest

success, hen survival, and poult survival are the

demographic factors having the greatest effect on

population growth (Vangilder and Kurzejeski 1995,

Roberts and Porter 1996). Hens with broods are also

the most visible segment of the population during

summer (Glidden 1980, Wunz and Shope 1980). Brood

counts, if conducted in a standardized way, should

reflect population size. 

We found no formal expression of the assumptions for

brood counts and roadside counts in the literature.

The relationship between brood counts and population

size has not been tested because of the practical

impossibility of conducting counts in populations of

known size (Weinstein et al. 1995). Population

dynamics studies (Vangilder and Kurzejeski 1995) and

correlational studies (Wunz and Shope 1980, Wunz and

Ross 1990) do support the basic assumption that

brood counts reflect population size. In Pennsylvania,

total counts of hens and poults are correlated with fall

harvest estimated from mail surveys (r = 0.78) and

hunter report cards (r = 0.88), but not with

subsequent spring harvest (Wunz and Ross 1990).

Similar correlations between brood counts and fall

harvest have been found in West Virginia (J.C. Pack,

pers. commun.).

It is important to recognize that brood counts have

been assessed by comparing them with fall harvest,

another index of population size. Fall harvest is

influenced by hunter effort, food availability, and

weather, so population size is not the only factor

influencing the relationship between brood counts and

harvest. Despite these extrinsic effects on harvest,

brood counts are good predictors of subsequent fall

harvest.

Sources of Variation

The most common methods for conducting brood

counts are observations by staff and cooperators made

during the course of routine field duties, and

systematic roadside surveys. Both methods are

subject to a number of sources of variation.

Lack of Standardization

Lack of standardization has been a major problem

within studies and in comparisons among studies. The

problem is greatest when observations are recorded

during routine field work, where effort is seldom

standardized among years or management units. The

general approach has been to include large numbers

of observers and assume that variation in effort is

randomized across years and management units.

Efforts have been made to standardize observer days

or miles driven in these surveys with some success.

Keeping observers motivated to follow protocol in

recording observations is difficult, and changes in staff

or field duties can affect the counts. Finally, it is

difficult to standardize effort among management

units because duties among conservation officers or

district biologists vary.

In contrast, formal roadside counts are designed to

control effort in terms of numbers of observers, hours

of effort, miles driven, time of day, and weather

conditions. These surveys are also designed to obtain

adequate spatial distribution and comparable samples

among management units.

Adequate Sample Size

Obtaining adequate sample sizes has been a problem

with both methods. Adequate statewide samples are

Section IV: Techniques 51
52 Wild Turkey Harvest Management: Biology, Strategies, and Techniques

difficult to obtain with systematic roadside surveys

because sampling costs represent a direct expense to

agencies. Systematic roadside surveys have generally

been applied to specific study areas (Bartush et al.

1985). To obtain adequate samples from observations

made during routine field work it has been necessary

to extend the observation period through June, July,

and August.

Roadside Bias

Roads do not represent a random sampling of the

available habitat, and the data from roadside counts

cannot be treated like a random sample from a set of

transects (Norton-Griffiths 1978, Caughley and

Sinclair 1994). These biases have been ignored for

statewide surveys where we assume road density and

distribution is adequate to give a representative

sample of management units.

Observability

Counts of broods are always biased toward

underestimation. The observability of poults varies

with poult age and habitat type. In good habitat young

poults can seldom be seen. Observability increases as

poults age, and brood counts tend to increase from

June through August (Wunz and Shope 1980). When

using total numbers of birds observed as an index to

population size, we assume differences in observability

due to habitat and age are randomly distributed

among years and management units.

Multiple Broods

Multiple broods are often mentioned as a confounding

factor in studies attempting to calculate poult-per-hen

ratios or mean brood size. Multiple broods do not pose

a problem for using total counts of hens and poults to

index population size.

Repeat Counts

The issue of repeat observations has not been formally

addressed for counts obtained during routine field

work. Broods usually cannot be identified individually

because birds are not marked, brood size can change,

broods move, and counts are often incomplete.

Duplicate counting cannot be eliminated because the

counts extend over 3 months. Counts can be inflated

when an observer reports the same brood from the

same place over a period of many days. Such obvious

duplicate counts are often deleted, but we know of no

formal rules for screening these counts. Repeat

counting is assumed to be relatively constant among

years and areas for observations made during routine

field work.

Repeat counting can be minimized in formal roadside

counts by using unique survey routes, or by

scheduling runs of the same route over time and

controlling for time of day.

Other Approaches

Tape-recorded calls, cameras and bait stations, and

mark-resight methods have been used to enhance the

efficiency of brood counts or improve the estimates of

population size.

Tape-Recorded Poult Distress Calls

Tape-recorded calls have been used to detect many

bird species (Stirling and Bendell 1966, Braun et al.

1973, Johnson et al. 1981, Marion et al. 1981). Kimmel

and Tzilkowski (1986) evaluated the use of taperecorded poult calls for locating turkey broods. Only

hens with broods responded to the calls, but not all

hens with broods came to the calls. Overall, 67% of the

radio-tagged hens responded to tape-recorded poult

calls, and the response rate of brood hens declined as

poults grew older. Tape-recorded calls were effective

at luring radio-tagged hens and their broods into

areas where poults could be counted accurately. Taperecorded calls have been useful for getting accurate

counts of young broods of radio-tagged hens to

estimate poult survival in studies of population

dynamics (Vander-Haegen 1987). Tape-recorded calls

were not effective in increasing sample size in

roadside surveys in Rhode Island (Tefft 1996b). Taperecorded poult distress calls are a useful research tool,

but we do not recommend them for state-wide brood

surveys. 

Bait Stations and Automated Camera Systems

Bait stations have been used with great success to

attract turkeys for capture, and in a variety of ways to

detect the presence of turkeys and enhance brood

surveys (Speak et al. 1975, Hayden 1985). Monitoring

bait stations with automated camera systems holds

promise for estimating turkey abundance (Cobb et al.

1996). The efficiency and effectiveness of monitoring

bait sites with cameras was compared to standard bait

station transect surveys conducted from a vehicle in

Florida (Cobb et al. 1996). The camera system

produced a much larger sample than the transect

surveys (154 vs. 7 observations in 14 days). The

camera system revealed that all age and sex classes

visited bait sites, and because of the larger number of

observations, camera surveys could be completed in 7

days as opposed to 14 days for roadside transect

surveys.

The initial purchase of the infrared-activated camera

systems was expensive, but these costs can be

amortized over several years. Because surveys could

be conducted in 7 days (following 7 days prebaiting)

camera systems can be moved to sample many areas

in one season. The system was clearly effective at

indexing abundance on areas of about 3,000 ha; more

research is needed to develop the technique for

broader application (Cobb et al. 1996).

Design and Standardization

We believe that brood surveys offer the greatest

potential for obtaining an estimate of relative density

that can be used to regulate fall harvest. Brood

surveys have been successfully used for this purpose

in Pennsylvania and West Virginia. Based on the

available literature we cannot make specific
recommendations for conducting these surveys, but

we can offer general recommendations for their

design.

Observations Made During Routine Field Work

At present, this is probably the most cost-effective

approach for state-wide surveys, but the existing

models contain a high level of uncontrolled variation.

We recommend that these surveys use standardized

field procedures and meet specific goals, such as

detecting a 10% change at the 80% level of probability.

The design should be done in consultation with a

biometrician, and should include careful consideration

of the geographic sampling units. Once established,

sampling design should not be changed unless there is

compelling reason to do so. Changing procedures may

invalidate comparisons among years. The goal is to

produce estimates of relative abundance that are

comparable from year to year.

Automate the Tabulation and Analysis Process

This step is important because data come from many

observers and results must be available quickly to be

applied to fall harvest. Kimmel et al. (1996) described

a computerized system for handling turkey

observations that included menu-driven database

routines and a geographic information system that

allowed displaying data as maps or tables. Most states

have systems that can accommodate brood survey

data.

Consider Enlisting Enthusiastic Amateurs as

Volunteers

Volunteers have contributed to the development of

large-scale databases such as breeding bird surveys,

Christmas bird counts, and numerous state wildlife

atlases. Surveys of bowhunters and spring-gobbler

hunters can produce detailed and reliable information

about many species (Glassock et al. 1997, Igo et al.

1997). Many hunters are skilled observers who spend

many hours in the field. National Wild Turkey

Federation members are obvious candidates for

turkey surveys. Rural mail carriers have been

especially helpful (Stauffer 1993), and several states

have used mail carriers to obtain abundance estimates

of their principal game species (Greeley et al. 1962).

Roadside Surveys

Roadside surveys have generally been carefully

designed and have used trained personnel to collect

data. One of the most difficult problems is

determining an adequate sample size. Several

preliminary samples are usually needed to estimate

sample size. The model for roadside surveys

developed for Florida provides a good example of

design considerations (Bartush et al. 1985, Cobb et al.

1996). The Florida system includes bait stations along

a 32-km survey route. Criteria are provided for

selecting roads, establishing bait stations, and

recording data. Surveys are conducted daily over a 

2-week period and controlled for time of day, direction

of travel, rate of travel, and weather conditions.

Details of survey protocol are given in Cobb et al.

(1996:217-218).

Section IV: Techniques 53
54 Wild Turkey Harvest Management: Biology, Strategies, and Techniques

Chapter 11. Population Index: 

Reports From Hunters

Wild turkey sightings by hunters have been used to

provide an index to population trends, determine

distribution, and estimate relative abundance (Welsh

and Kimmel 1990, Kimmel et al. 1996). Data on

sightings by hunters can be collected by random

survey, cards attached to permits, volunteer records,

or by interviewing hunters at check stations. Hunter

report data for wild turkeys have typically been

collected from deer hunters, but can be collected from

hunters of any other game, including wild turkeys

(Kennamer 1986). Deer hunter surveys have been

used most often because they can usually provide

large, well-distributed samples. The same data

collection procedures will apply to any game survey,

although the timing of various game seasons may

make certain hunts more useful for predicting

subsequent wild turkey harvest.

Background and Prior Use of the

Technique

In Minnesota, a random survey of antlerless-deer

hunters is used to index yearly trends in wild turkey

abundance (Kimmel and Welsh 1987, Welsh and

Kimmel 1990, Kimmel et al. 1996). Biologists

examined differences in the mean number of turkeys

seen per day to detect population changes between

geographical areas within a year, and between years

within an area. Wild turkey population estimates

obtained by wildlife personnel were correlated with

number of turkeys seen per hunter per day (TPD),

and the percentage of hunters seeing turkeys

(HOWT). The HOWT index predicted changes in

abundance better than TPD did (Kimmel et al. 1996).

The HOWT index had more desirable statistical

properties than the TPD index and it was less affected

by hunters reporting observing a very large number

of turkeys (Kimmel et al. 1996). Hunters may also

more reliably remember observing turkeys than

recalling the number of turkeys seen. Furthermore,

the HOWT index was better than mean TPD scores at

reflecting known geographic and annual differences in

abundance (Kimmel at al. 1996).

Rolley and Kubisiak (1994) mailed a postage-paid

survey form to deer hunter licensees, and evaluated

several indices derived from the survey as predictors

of turkey population density. They found that the

percentage of deer hunters reporting turkey sightings

(HOWT) was strongly correlated with the subsequent

spring turkey harvest density. They also reported a

significant positive correlation between the number of

turkeys seen per hunter per day (TPD) and harvest

density. They used a helicopter survey to check the

hunter report data, and found that the helicopter

survey population density estimates were significantly

correlated with the HOWT but not with the TPD

index. Deer hunter surveys were used to gather data

on turkey distribution in Missouri (Lewis 1980). Both

the number of turkeys seen and the percentage of

hunters seeing turkeys were correlated with the

subsequent spring harvest. The HOWT index had the

strongest relationship with spring harvest. 

Interviews of successful gun hunters conducted at

check stations were used to assess wild turkey

populations in Illinois (Garver 1986). The percentage

of hunters seeing wild turkeys was more sensitive to

changes in turkey numbers than was the number of

turkeys seen. The reported locations of turkey

sightings were used to determine range expansion.

Similarly, Bailey (1973) used interviews with hunters

as the most effective means of determining the

success of transplanted wild turkeys in West Virginia.

Donohoe (1985) used interviews of deer hunters,

turkey hunters, and landowners to monitor the

distribution and population status of wild turkeys in

the Midwest.

Turkey sightings by West Virginia bowhunters are

being used to develop an index of abundance that can

be used to predict spring harvest (Glasscock et al.

1997). Cooperators, mostly members of the West

Virginia Bowhunters Association, maintain detailed

records of their hunting activity and wildlife

observations during the 6-week fall archery deer

season. Examination of the first 2 years of data

suggests that turkeys seen per 100 hours may be a

better predictor of spring population trends than

brood surveys or fall harvest.

Assumptions

The method assumes that the observers are uniformly

distributed across the area of interest. Formal models

have not been developed for this method.

Advantages

Using hunter reports as an index to wild turkey

abundance is cost- and labor-effective, particularly 

in comparison with route-based surveys (Welsh and

Kimmel 1990). Because hunters are already present in

the field and require no deployment or compensation

by the wildlife management agency, it is a relatively

simple matter to collect incidental observation data
from them. Other survey methods require

expenditures of agency manpower to conduct field

surveys, and such surveys can be limited by lack of

sufficient numbers of biologists. Because hunters are

distributed across an entire state or province, data can

be collected over a large area with minimal effort.

Hunters are usually the easiest to identify and most

widely distributed group of cooperators an agency can

enlist for surveys. It is often possible to sample

hunters from specific turkey management units, and

data can be collected over a relatively short time

period (Welsh and Kimmel 1990). The response rate of

hunters remains relatively constant in contrast with

landowner cooperator participation, which can decline

over time because of lack of interest or other factors

(Welsh and Kimmel 1990). The number of hunters is

typically consistent from year to year, and hunters

may be more inclined to respond to surveys than

landowners would be if they perceive that the data will

aid in successful management. Alternatively, the

submission of sighting reports could be mandatory. 

Wild turkey sightings by hunters have proven to be an

effective index to wild turkey trends (Welsh and

Kimmel 1990, Kimmel et al. 1996).

In addition, hunter reports may be one of the most

useful tools for determining wild turkey distribution

and relative abundance (Kimmel et al. 1996). Because

hunters may thoroughly cover a jurisdiction, sighting

reports may be sufficient to produce a map of wild

turkey distribution and relative density across a state

or province without requiring the use of agency

personnel.

Disadvantages

Obtaining an adequate sample size has been the main

disadvantage of random surveys of hunters. In

Minnesota, antlerless-deer hunters are sampled

because permits are issued for specific management

units. Too few permits are issued for some units to

obtain an adequate sample. A larger problem was that

the sample size needed to detect the desired level of

annual change (≥15%) for individual TMUs was

prohibitively expensive. The problem was overcome by

aggregating the 76 permit areas into 15 larger TMUs

(Kimmel et al. 1996).

Volunteer cooperator surveys, such as the West

Virginia bowhunter and gobbler hunter surveys,

provide detailed biological data on many species

(Glasscock et al. 1997, Taylor et al. 1998). Inadequate

sample size and poor spatial distribution of

cooperators are the main disadvantage to these

surveys. Indices of abundance can generally be

calculated at a regional or statewide level, and little

information may be available for some TMUs.

Design, Standardization, and 

Quality Control

Design and analysis procedures are well developed for

random surveys of hunters (Welsh and Kimmel 1990,

Kimmel et al. 1996). Methods used in Minnesota to 

sample antlerless-deer hunters can be applied directly

in states where antlerless-deer permits are issued by

management unit. Where permits are not issued for

specific management units, the sample can be drawn

by location of the hunter’s residence. Several indices

derived from hunter sightings have been evaluated to

determine their statistical properties and relationship

to population size. Percentage of hunters seeing

turkeys (HOWT) has generally been the most efficient

index for monitoring population trends (Lewis 1980,

Welsh and Kimmel 1990, Rolley and Kubisiak 1994).

Sample size requirements to meet specific levels of

precision are discussed by Kimmel et al. (1996). The

Minnesota methods have the distinct advantages of

automated data processing and mapping capabilities.

We recommend using the Minnesota procedures as a

model for sampling hunters to obtain sighting data,

whether the data are collected from antlerless-deer

hunters or another class of hunters.

The other indices and methods for obtaining hunter

sighting data have not been as carefully evaluated or

standardized. Two other potentially useful methods for

collecting hunter observations are used in the

Northeast. In Connecticut, postage-paid hunter

survey forms are included with all turkey permits:

spring gobbler, fall archery, and fall firearms. These

surveys have been used to obtain data on hunter

attitudes toward regulations, the status of the

population, and expenditures for turkey hunting. The

surveys always obtain data on dates, locations, and

hours per trip; gobblers heard; gobblers seen; hens

seen; and birds harvested. Data are summarized

annually to produce a variety of statistics including

hours hunted per town and hours per kill per town.

The Connecticut survey has the great advantages of

including the entire turkey hunting population and

being completed shortly after season’s end. The data

can be used to produce a variety of indices of

abundance, and the detailed hunter effort could be

used for catch-per-unit-effort estimators of population

size.

West Virginia enlists volunteer bowhunters and spring

gobbler hunters to obtain wildlife sighting data. These

surveys produce detailed information about hunter

activity, game activity, and observations of many

species. The data can be used to produce several

indices of abundance. Both methods, mandatory

hunter surveys and volunteer sightings, deserve

further study and evaluation of the abundance indices.

Section IV: Techniques 55
56 Wild Turkey Harvest Management: Biology, Strategies, and Techniques

Cost and Manpower Requirements

In 1995, the Minnesota Department of Natural

Resources budgeted about $10,000 for postage and

materials for the survey of antlerless-deer hunters

used to index turkey abundance (Kimmel et al. 1996).

Labor costs are not included, but the survey is

conducted from the office by one biologist and no field

work is required. The survey cost was estimated to be

63% of the cost of gobbling counts conducted over the

same area (Welsh and Kimmel 1990).

Conclusion and Recommendations

Turkey sightings by hunters provide statistically

reliable indices of turkey abundance (Kimmel et al.

1996). Sightings obtained by deer hunters after the

fall either-sex turkey season are probably the most

practical method available for indexing spring

population size. Indices based on observations made

by fall or spring turkey hunters can be used to

validate harvest reports or calculate harvest-per-uniteffort indices of abundance. Surveys used to collect

hunter sighting data require minimal labor and

expense in comparison with field techniques used to

estimate abundance. At present, mail surveys of

random samples of deer hunters provide the best

method for allocating sampling effort among

management units. 
Chapter 12. Population Index: Gobbling Counts

Gobbling counts involve recording the number of

gobbles and number of individual birds heard from

listening stations spaced regularly along transects.

Similar call count methods have been used for many

upland game bird species. Gobbling counts have been

used extensively with wild turkeys for various

purposes (Leopold 1944, Dalke et al. 1946, Donohoe

and Martinson 1963, Scott and Boeker 1972, Wise

1973, Bevill 1975, Porter 1978, Porter and Ludwig

1980, Hoffman 1990, Palmer et al. 1990, Pack 1993,

Lint et al. 1995, Tefft 1996a,b). In the Northeast,

gobbling counts have been used in Virginia, West

Virginia, Rhode Island, Maine, Massachusetts, and

Ontario. Gobbling counts provide 3 types of

information: phenology of gobbling, population

distribution, and population abundance. Phenology

studies have been used to establish the timing of

spring season. The success of reintroductions and

range expansion of newly introduced populations have

frequently been monitored with gobbling counts, and

gobbling counts have been used to monitor population

trends in many studies.

Assumptions

Gobbling count methods have not received rigorous

mathematical treatment and the underlying

assumptions have not been explicitly defined. Several

assumptions must be made when the gobbling counts

are used to index abundance, estimate population size,

or determine the phenology of gobbling. First, it is

assumed the survey does not influence gobbling

activity. If turkey calls or other auditory stimuli are

used to elicit gobbling, the data can be used only to

determine distribution. Second, it is assumed all

observers are equally efficient. This assumption can

be tested, or the observers can be screened to meet

proficiency standards. Third, gobbling activity is

influenced by time of year, time of day, and weather.

Gobbler surveys must be standardized to control these

sources of variation (Tefft 1996a,b). Fourth, the

relationship between gobbling activity and population

size is constant. This assumption is common to all

index methods, and it is probably only approximated

even for specific populations. There is substantial

variation among individuals within populations

(Hoffman 1990), and gobbling seems to be influenced

by the age structure and physical condition of the

population (Lint et al. 1995).

Additional assumptions have been made to convert

gobbling counts to population estimates. For example,

Donohoe and Martinson (1963) estimated the

population for their Ohio study area by assuming only

adult males gobbled, and adult males made up 25% of

the population. In contrast, Bevill (1973) found that

92% of adults and 69% of juveniles gobbled. Porter

(1978) derived a density estimate from gobbling count

data by assuming juveniles did not gobble, the sex

ratio was 50:50, and the age ratio of yearling to adult

males was similar to that of females. Because of the

uncontrollable variation associated with phenology,

weather, and population status, abundance estimates

derived from gobbling counts are usually treated as

indices.

Advantages

Gobbling is a distinctive vocalization that can be heard

for up to a mile under favorable conditions. Gobbling

males are easier to detect than hens and juveniles

during the breeding season. Gobbling counts do not

disturb the population (Bull 1981) and surveys can be

conducted in all habitat types (Amman and Ryel 1963).

The general location of the gobbler can be determined

and fairly accurate locations can be obtained if 2

observers triangulate the direction of the call (Davis

and Winstead 1980:224). Gobbler counts are

particularly useful for detecting the presence of lowdensity populations and determining the distribution

of flocks prior to the spring hunt (Tefft 1996a,b).

Porter and Ludwig (1980) reported that a combination

of extensive and intensive gobbling survey data

provided good estimates of relative abundance

between years and areas of similar size in Minnesota.

The maximum number of groups of gobblers heard in

spring, adjusted by the average number of individuals

per male group, was significantly correlated with

winter flock counts. Gobbling counts were correlated

with spring hunter success rates in Minnesota (Porter

and Ludwig 1980) and Mississippi (Palmer et al. 1990).

Gobbling counts were also significantly correlated

with total gobbler harvest in Mississippi (Palmer et al.

1990).

Disadvantages

Gobbling counts are subject to large day-to-day

variation (Scott and Boeker 1972, Hoffman 1990),

which generally results in weak correlations between

gobbling counts and other estimates of abundance

(Palmer et al. 1990, Lint et al. 1995). Daily gobbling

activity is affected by chronology of breeding activity,

gobbler condition, population age and sex ratios,

Section IV: Techniques 57
58 Wild Turkey Harvest Management: Biology, Strategies, and Techniques

individual variation, and weather; all factors that are

difficult or impossible to control with sampling design.

Weather affects both gobbling activity and the ability

of observers to detect gobbling. Cloud cover, dew, wind

velocity, and rain influence gobbling activity (Davis

1971, Bevill 1973), but the relationship among weather

variables and gobbling is complex and correction

factors have not been developed for these variables.

To account for daily variation, gobbling counts are

typically conducted on several days each season that

meet specific weather criteria. As a result, gobbling

counts require a large number of worker-days and a

large number of personnel (Wise 1973, Welsh and

Kimmel 1990:132). Difference in hearing ability among

observers is an important concern (Ammann and Ryel

1963), and conducting counts over large areas creates

many logistical problems. Finally, gobbling counts

require an adequate road system, and listening points

must be free from outside background noises.

Design, Standardization, and 

Quality Control

Many limitations of the gobbling count technique can

be overcome or minimized by standardizing the

methodology and carefully designing the survey.

Differences in habitat or available resources among

agencies may lead to differences in detail, but the

general procedure is well established. The technique

was summarized in the 1995 report of the Northeast

Wild Turkey Technical Committee as follows:

The methods employed in conducting gobbling

counts are similar wherever they are used. In

gobbling counts, a series of road routes are

sampled annually over a period of weeks during

the peak of gobbling. This is usually during April

and May, but the exact timing varies by year and

state. The technique as described by Porter and

Ludwig (1980), which is typical, involves survey

routes consisting of listening stations at

approximately 1 mile intervals along a route 9 to

12 miles long [according to Wise (1973), “gobbler

counts were satisfactory when conducted over

limited areas so that listeners were no more than

0.5 to 1 mile apart”]. At each stop the observer

records for a 4 minute listening period the

number of individual birds heard gobbling, the

total number of gobbles heard and any noise

interference encountered. Routes are run twice

weekly from opposite directions starting at 40

minutes before sunrise on days of calm or low

winds with no rain or snow. Consistent listening

stations along the route are used throughout the

period of the survey and from year to year...Scott

and Boeker (1972) state that the survey period

must include the peak of gobbling activity. They

also indicate that due to extreme day to day

variation in gobbling activity each route should

be run from [at least] three to six times.

Indices produced by gobbling count surveys include

(a) the percentage of stops that recorded any gobbling

activity for a given year, (b) an activity index equal to

the total number of gobblers counted divided by the

total number of stops in a given year, and (c) a 

gobbling intensity equal to the total number of calls

counted divided by the number of active gobblers

counted (Tefft 1996a,b).

This methodology controls many of the sources of

variation in gobbling counts. Details of sampling

design are given by Porter and Ludwig (1980) and

Tefft (1996a,b). Key design features include standard

route length and distance between stations, with

permanent reference points for stations. The same

routes and stations are used each year. Starting and

stopping times, number of times to run route, and

direction of travel are specified in advance. Data forms

and listening times are standardized, and suitable

weather conditions are clearly defined. Artificial calls

should not be used to stimulate gobbling, and the

observer should not make any unnecessary loud

noises.

Ideally, survey dates should correspond with the peak

gobbling activity, which may vary from year to year

and from region to region. In practice, gobbling

surveys are planned to encompass the range of peak

gobbling dates for the region, and the maximum

counts are used in the analysis. If the peak of gobbling

is unknown, a pilot study may be necessary, or

information from adjoining jurisdictions may used to

estimate peak gobbling dates. Because peak gobbling

dates may vary across a state or province, gobbling

count survey dates should be specific to a given

management unit or physiographic region.

Variability among observers is important and should

be controlled. All observers must be thoroughly

trained in survey protocol. Standardization of the

method with respect to elimination of interfering

noises is essential; but, equally trained observers may

still differ in their ability to hear calls. This source of

error can be minimized by using the same observers

every year. Alternatively, the relative hearing abilities

of observers can be evaluated by having 2 observers

simultaneously run a route and comparing the

numbers of gobblers heard by each observer, or by

administering a hearing test to each observer before

running the survey. It may then be possible to develop

a correction factor to standardize the data. The

investigator should decide on an acceptable level of

precision before conducting the survey, and then

replicate the survey, spatially or temporally, to attain

this level of precision and to accumulate an acceptable

sample size. A pilot study may be desirable to

estimate the expected levels of variation and to predict

the sample size resulting from a given level of effort.

If the above considerations for quality control or

standardization are followed, gobbling counts are

potentially useful as an abundance index for wild

turkeys. If such standardization is not feasible, or the

sources of variability in gobbling activity cannot be

adequately controlled, the technique may still be

useful for studying the phenology of gobbling and for
monitoring distribution. The utility of the technique as

an abundance estimate is limited by the difficulty in

determining the relationship between population size 

and gobbling activity.

Cost and Manpower Requirements

Welsh and Kimmel (1990) projected that it would

require 70 worker-days and a large staff to conduct

gobbling counts over a 4,410-km2 area in Minnesota.

The cost of the gobbling survey led to the

development of alternative hunter survey methods to

monitor population trends (Welsh and Kimmel 1990).

Gobbling counts were conducted on a 14,140-ha area in

Mississippi by 2 people working 24 mornings each

spring (Lint et al. 1995). In Indiana, gobbling count

surveys required a minimum of 6-7 person-hours per

bird heard (Backs et al. 1985). Using a combination of

intensive and extensive gobbling count methods,

Porter and Ludwig (1980) were able to survey a 2,000-

km2 area in Minnesota for <100 man-days per year.

The 1995 report of the Northeast Wild Turkey

Technical Committee described the costs associated

with gobbling counts as follows: 

Gobbling counts, while not requiring large

amounts of overhead, can be labor intensive.

Assuming three to four hours for running each

route, including travel time, and each route being

run four times, the time commitment is about two

staff days per route. The number of routes is

then determined by variability in counts between

routes and desired accuracy of the mean count.

The cost could be reduced by using volunteers,

but one of the problems identified with the use of

gobbling counts is inter-observer variability so

great care must be used.

Section IV: Techniques 59
60 Wild Turkey Harvest Management: Biology, Strategies, and Techniques

Chapter 13. Population Estimate: 

Mark-Recapture

The mark-recapture technique involves capturing,

marking, and releasing animals, and subsequently recapturing the animals on one or more occasions to

determine the proportions of marked animals in the

subsequent captures (Bibby et al. 1992:106-107).

Captures and recaptures need not be done physically

by trapping if the animals can be recognized by some

other means, such as wing streamers or radio tagging. 

One of the common mark-recapture estimators is the

Lincoln-Petersen estimator or the Lincoln “index.”

The term “index” is misleading because this method

produces a population estimate, not an index. This

method is the simplest of the mark-recapture methods

because it uses a single marking period, followed by a

single occasion of recapturing the animals. This

estimator is biased upward by birth and immigration,

so is best used for populations sampled between birth

periods (Caughley 1977a:145). 

Because animals are marked only at the initial

capture, it may not be possible to mark or sample

enough animals to attain reasonably reliable estimates

with the Lincoln-Petersen estimator (Eberhardt et al.

1979:9). Methods that employ more than one marking

occasion may be preferable, but these methods may

increase the time and effort needed to conduct the

experiment. The Schnabel method and Schumacher’s

modifications of this method (Caughley 1977a:145)

employ multiple marking occasions, and are somewhat

more efficient than the Lincoln-Petersen estimator

(Seber 1982:567). Schumacher’s method allows a

check on the assumption of equal catchability, but the

method has more constraints than the LincolnPetersen estimator and requires that the population

maintain a constant size during the experiment and

that no animal dies or leaves the area (Caughley

1977a:145). Several other methods have commonly

been used, including the Jolly-Seber method (Jolly

1965) and Bailey’s triple catch, which is useful when

immigration or birth is occurring, and which gives

estimates of birth and death rate as well as population

size.

Prior Use of the Technique

The mark-recapture technique has been used with

varying degrees of success with wild turkeys and

other birds. Everett et al. (1980) used the Schnabel

estimator to obtain wild turkey population estimates

that were found to agree closely with flock counts

conducted during routine field activities. DeYoung and

Priebe (1987) evaluated the use of the mark-recapture

technique for wild turkeys in Texas, using patagial

tags as the markers. They concluded that the

technique may be useful for obtaining population

estimates in research studies, but believed the cost

and effort required were impractical for management

applications. Gribben (1986) evaluated the markrecapture technique for wild turkeys in Mississippi

and concluded that it was too costly and timeconsuming to obtain sufficient sample sizes, but

suggested that the method may be useful in setting

management objectives. 

Lint (1990) compared several mark-recapture models

for a Mississippi wild turkey population, and found

that the Buckland model (Buckland 1980), a

modification of the Jolly-Seber model that allows the

use off known deaths in the analyses, provided the

best estimates of population size. Lint et al. (1995)

used the Buckland model to estimate gobbler

population sizes for an open population. They

compared the Buckland estimate with several harvestbased indices of turkey abundance, and found capturerecapture techniques to be the most expensive.

Weinstein et al. (1995) compared 2 mark-recapture

models (Jolly-Seber and Buckland) and 2 markresight models (Minta and Mangel 1989 and Arneson

et al. 1991) with counts made at summer bait sites in

Mississippi. They experienced poor performance with

both mark-recapture models, probably due to small

sample sizes. The mark-resight models performed

better, but still produced biased estimates due to

violations of assumptions and small sample sizes. They

noted that even after large expenditures of money and

labor their mark-recapture methods provided

inadequate sample sizes. 

Bailey and Rinell (1968) used the Lincoln-Petersen

estimator, employing band returns, to estimate fall

wild turkey populations in West Virginia. Similarly,

DeGraff and Austin (1975) used voluntary band

recoveries to estimate the proportion of the wild

turkey population that was legally harvested each fall

in New York. They calculated the pre-season

population size by dividing the reported harvest by

the direct band recovery rate. An adaptation of this

method has been used to estimate spring harvest

rates for wild turkeys in Mississippi, where Palmer et

al. (1990) calculated harvest rates as the percentage of

gobblers tagged January-March that were recovered

in the subsequent spring hunting season. They used

Buckland’s modified Jolly-Seber method to estimate

gobbler population size. In Missouri, wild turkeys

were captured and marked with patagial wing tags;

and data on age and sex ratios, mortality rates, and

longevity were obtained from recovery of tags (Lewis
1980). In spite of their sound theoretical basis, markrecapture techniques have been used infrequently for

wild turkeys and other bird species because the

techniques are extraordinarily time consuming

(Verner 1985:288).

Assumptions

There are 3 general assumptions for all markrecapture studies (Otis et al. 1978, White et al. 1982) : 

1. The population is closed.

Closure refers to the size of the population remaining

constant during the sampling period. More generally,

closure means that there are no unknown changes to

the initial population (Otis et al. 1978, White et al.

1982). In actuality, the population may not need to be

completely closed, as long as death and emigration do

not occur at different rates between marked and

unmarked animals (Eberhardt et al. 1979:7-9).

Geographic closure due to physical boundaries is often

distinguished from demographic closure that is

influenced by birth, death, and emigration (White et

al. 1982). Tests for closure are difficult. The

assumption is never completely true in natural

biological populations and any violation of this

assumption biases the various population estimators

that have been developed for this technique (White et

al. 1982). Models have been developed for open

populations, but these methods require more data and

more assumptions than closed models require. Openpopulation models may be useful for conducting longterm population monitoring and for obtaining

information on survival and recruitment rates (Otis et

al. 1978). The Jolly-Seber model is the most commonly

used open population estimator (White et al. 1982:180-

187). It allows estimating population size at each of 3

or more sampling occasions and provides estimates of

the probabilities of survival and recruitment between

sampling occasions. Pollock et al. (1990) believed that

Jolly-Seber and related models are preferable to

other mark-recapture models, because these

estimates are derived from statistical theory. The

Jolly-Seber model assumes that all animals in the

population have the same probabilities of survival and

capture for any one sampling period (Pollock et al.

1990). Pollock (1981) developed a more generalized

model that allows different capture and survival

probabilities for different age classes. 

2. Animals do not lose their marks over the course

of the experiment, or the effect of tag loss is

corrected in the analysis. 

For example, wild turkeys marked with leg bands or

wing tags are assumed to retain these tags until their

final capture. 

3. All marks are correctly noted and recorded at

each sampling. 

Thus, if turkeys are “recaptured” by remote

observation, it is assumed that tagged birds will be

correctly identified. 

Most mark-recapture models also assume that equal

effort is expended on each trapping occasion, and that

capturing and marking an animal does not affect its

subsequent catchability (Otis et al. 1978:7-18, White et

al. 1982). Methods for testing for equal catchability

are discussed in Krebs (1989:43-44). Several ways

exist that may relax this last assumption (Otis et al.

1978:7-18, Pollock et al. 1990), but the first 3

assumptions are considered essential.

Methods based on analyses of hunting returns have

also been developed (Caughley 1977a:157-167, Seber

1982:511-530, Seber 1985). In addition to Assumption

2, these methods also assume that (a) hunting

pressure does not steadily increase or decrease over

time, although year-to-year fluctuations are allowable;

(b) only marks recovered by hunting are included in

the analysis; and (c) all ages are equally susceptible to

hunting. These assumptions and a set of 7 models

used to analyze these types of data are discussed in

Caughley (1977a:158-167) and Seber (1982:239-255).

Advantages

The main advantage of the mark-recapture technique

is reliability and the sound theoretical basis compared

to other abundance estimators or indices. In addition

to its utility as a technique for estimating abundance,

the method can also provide useful information about

other demographic variables (Krebs 1989:15, Pollock

et al. 1990, Bibby et al. 1992:105, Caughley and

Sinclair 1994:208). The following properties may be

examined by mark-recapture experiments: (1)

movement, (2) growth rate, (3) age-specific fecundity

rates, (4) age-specific mortality rates, (5) size of the

population, (6) rate of birth and immigration

combined, (7) rate of death and emigration combined,

(8) rate of harvesting, and (9) rate of increase

(Caughley 1977a:133). Additionally, the method can

also be used “to investigate habitat selection and other

distributions. . . and to measure reproductive success

of individual birds” (Bibby et al. 1992:105). Gribben

(1986) also noted the utility of the technique for

setting management objectives.

Disadvantages

One of the main problems of the mark-recapture

technique is that it is costly, effort-intensive, and time

consuming (Caughley 1977a:134, Verner 1985, Gribben

1986, DeYoung and Priebe 1987, Krebs 1989,

Weinstein et al. 1995). Another consideration is that

“mark-recapture techniques are difficult to use in the

real world, and you should be certain they are needed

before starting to use these techniques” (Krebs 1989).

Davis and Weinstead (1980) provide a list of

references demonstrating that mark-recapture

methods may underestimate actual population levels

considerably.

Mark-recapture methods incorporate several

restrictive assumptions that are difficult to meet

(DeYoung and Priebe 1987, Krebs 1989:16, Kurzejeski

and Vangilder 1992). Indeed, Caughley (1977a:134)

concludes that “results are often inaccurate because

mark-recapture models are seldom more than a vague

Section IV: Techniques 61
62 Wild Turkey Harvest Management: Biology, Strategies, and Techniques

approximation to reality,” and that the mark-recapture

models are not very robust, and “even small deviations

from their implicit assumptions can produce large

errors in the results.” Departures from the

assumption of equal probability of capture, in

particular, are difficult to detect and may produce

biased estimates (Seber 1982:565). Caughley and

Sinclair (1994:209-210) believed that mark-recapture

methods were of limited utility for wildlife

management because the assumption of equal

catchability is violated by almost all wildlife

populations. In contrast, these methods may be of

considerable use in wildlife research studies.

The problem of differences in catchability is common

among animal studies because age, sex, type and

location of traps, type of bait, and environmental

conditions can affect catchability. The act of capturing

and tagging can greatly alter the probability of

recapture of any given animal (Seber 1982:487). For

wild turkeys in Texas, DeYoung and Priebe (1987)

believed that the assumption of equal probability of

capture was not met, because the road network used

for the recaptures (resightings in this case) did not

uniformly sample the study area. Caughley

(1977a:134-139) discusses sources of error due to

unequal probability of capture, and presents tests for

equal catchability. The problem of unequal catchability

potentially can be overcome by using a combined

approach employing both closed and open models, as

described by Pollock et al. (1990). 

Where harvest is used to recover tagged animals,

unequal vulnerability to harvest is a problem. Markrecapture models assume that the probability of

shooting an animal of a given age is proportional to

the frequency of that age class in the population. This

assumption usually is not valid in game bird mortality

studies (Caughley 1977a:158). Differences in the

behavior of adult versus juvenile wild turkeys lead to

disproportionate harvest among the age classes. In

this situation, estimates usually need to be made for

each age or sex group, leading to greatly increased

sample size requirements. If separate data are

collected for age or sex groups, however, the method is

not appropriate for estimating population structure

(Gribben 1986). Non-reporting or mis-reporting of

tags from harvested birds may lead to an

overestimation of the population size (Seber 1982:489).

Because tag returns by hunters are frequently on the

order of 30-50%, the marks recovered by hunters can

rarely be used to estimate population size (Caughley

1977a:157). Generally, only mortality rates can be

calculated from mark-recapture experiments using

hunting returns.

A related problem is that different types of tags have

different recovery or retention rates on wild turkeys

(Myers 1973). Thus, fewer birds may be counted as

recaptures than would be the case if all tags were

retained, leading to biased or non-comparable data.

Caughley (1977a:139) points out that few systems for

marking animals are permanent (particularly bird

bands), and he also discusses testing for loss of marks.

Obtaining a sufficient sample size may restrict the use

of the mark-recapture technique because a large

proportion of the population needs to be marked for

adequate accuracy. Several investigators have

suggested marking at least 50% of the population for

Lincoln-Petersen estimates (Caughley 1977a, Seber

1982, Krebs 1989). For wild turkey populations

occupying areas of more than a few thousand acres,

such a sample size may be unattainable. Capture of

turkeys is often too difficult to provide adequate

sample sizes for population studies (Gribben 1986). 

Skalski and Robson (1992) noted that mark-recapture

methods typically serve to estimate abundance at a

single plot, which means that statistical inference is

limited to abundance of the animals in that study area,

rather than for the overall contiguous population or

the mean abundance of the overall area. Caughley

(1977a:140) similarly noted that in attempting to

interpret population size by mark-recapture, the

population often has no distinct boundary, and

therefore limits the estimation to a study area of

arbitrary size. Lint et al. (1992) discussed the

problems of using arbitrary study area boundaries to

derive population and density estimates for wild

turkeys and described a method for estimating

effective study area size from tag-returns of marked

and harvested wild turkeys. Thus, if a mark-recapture

study were conducted in a local study area, the

extension of the results to the entire management

area would be unjustified. 

Design, Standardization, and 

Quality Control

The methodology for mark-recapture field techniques

and statistical analyses is well developed. The

selection of a particular mark-recapture estimator

depends on the assumptions that can be made about

the population and the procedure (Eberhardt et al.

1979:7). We cannot recommend a single best protocol

for use with any wild turkey population, because the

assumptions need to be evaluated for each population

before a method can be selected. The mark-recapture

method should always be selected before attempting to

collect field data, and testing of the assumptions

should be incorporated into the sampling program

(Krebs 1989:59-60). The investigator who wants to

employ mark-recapture methods should first consult

the pertinent literature. 

Krebs (1989:59-60) and Lancia et al. (1994 239-247)

summarize the various methods used to analyze markrecapture data. A comprehensive synthesis of the

wildlife and statistical literature on mark-recapture

studies can be found in Cormack (1968) and Otis et al.

(1978). Seber (1973) is an earlier but useful review

summarizing the existing literature on this method to

that date. Seber (1982) incorporates more recent

developments, and gives an excellent discussion of the

Jolly-Seber and other mark-recapture models. He also

discusses in detail departures from the various

assumptions, and statistical tests to determine

whether these departures are significant. White et al.
(1982) provides a follow-up to Otis et al. (1978),

providing a comprehensive primer on closedpopulation mark-recapture methods including

references on field procedures. White et al. (1978)

provide a computer program (CAPTURE) to carry

out the calculations, along with a commentary on the

use of the program. Verner (1985:288) commented that

this program “is an essential tool for anyone using

capture-recapture methods that assume a closed

population.” CAPTURE, however, “does not perform

well when capture probabilities are small and

population sizes are small” (Pollock et al. 1990).

Pollock et al. (1990) present methodology to design,

analyze, and conduct long-term mark-recapture

experiments, and present new material that was not

previously discussed by Seber (1982). They discuss

several variations of the Jolly-Seber model for open

populations, and present 2 computer programs for the

analysis of mark-recapture data (JOLLY and

JOLLYAGE). A recent program, NOREMARK,

which produces 4 estimators of population abundance,

was presented by White (1996) for situations such as

radiotelemetry studies where animals are initially

marked but are only observed, rather than

recaptured, in subsequent samples.

The critical assumption of closure can be met or

approximated by timing the sampling periods to avoid

times of migration or recruitment, and by running the

experiment over as short a period of time as possible

(White et al. 1982:162). If there is no way to ensure

that the population size is not undergoing change

during the period of the investigation, an openpopulation model should be used. The problem with

open models, however, is that these methods require

more data than closed models do because assumptions

are more rigorous and more parameters are involved

(Otis et al. 1978:7-18).

Skalski and Robson (1992:58) recommend that unless

prior estimates of population parameters are available

and reliable, preliminary surveys should be considered

as the first step in most field investigations that will

use capture data to make inference to population

effect. They also provide recommendations for the

design and analysis of these preliminary surveys.

Cost and Manpower Requirements

Buckland et al. (1993) lists several references

indicating that mark-recapture techniques are

relatively expensive compared to line transects. One

such comparison was performed for bobwhite by

Shupe et al. (1987), who used a helicopter to count

birds along transects. They found that mark-recapture

estimates were more expensive than the aerial

transects, which in turn were more expensive than

drive counts. They also noted that mark-recapture

methods were 3 times more expensive than groundbased (walking) line transects in rangeland studies in

Texas. Thus, mark-recapture was the most expensive

of the 4 methods examined. DeYoung and Priebe

(1987) evaluated mark-recapture techniques for wild

turkeys in Texas and concluded that the required cost

and effort made the technique impractical for largearea management uses. Similarly, Gribben (1986), who

evaluated the mark-recapture technique for wild

turkeys in Mississippi, concluded that the technique is

costly and time consuming, but may be of some use in

setting management objectives. Lint et al. (1995)

compared various methods of indexing or estimating

wild turkey gobbler abundance and ranked 4 methods

in order of decreasing cost as (a) mark-recapture, (b)

number of gobblers heard per day, (c) harvest per unit

effort, and (d) number of harvested gobblers. In

contrast, Guthery (1988) examined bobwhite density

on rangeland and found that labor required for the

Lincoln-Petersen mark-recapture method was

comparable to that for the line transect method.

Compared with change-in-ratio removal methods, the

Lincoln-Petersen method has been claimed to be

“better value for the money” (Seber 1982). Skalski and

Robson (1992) discuss the calculation of costs

associated with trapping studies. 

Conclusion and Recommendations

The mark-recapture technique will be most useful as a

research tool for studying population dynamics, or for

calibrating and validating population indices obtained

with other techniques. The technique will be useful

mainly in intensive studies, as opposed to statewide or

provincial management applications. In research

situations where the technique is feasible, it can

provide reliable estimates of absolute abundance and

other demographic variables. In Seber’s (1982:564)

comprehensive book discussing the main methods for

estimating or indexing animal abundance, he

concludes that “of all the methods considered in this

book, the [Lincoln-Petersen] method appears to be the

most useful, provided that the assumptions underlying

the method are satisfied and there are sufficient

recaptures in the second sample.” Compared with

removal methods, populations can be sampled more

reliably and efficiently using mark-recapture

techniques (Pollock and Kendall 1987). Otis et al.

(1978:67) and White et al. (1982:162) recommend the

use of live trapping (mark-recapture) studies over

removal methods because of the “wider array of

options available for the data analysis.” In addition,

removal methods disrupt the population, which may

lead to immigration when large numbers of animals

are removed, thus violating the assumption of closure. 

The mark-recapture technique is not useful for routine

long-term monitoring applications because of the costs

associated with capturing adequate numbers of birds

and the difficulty of observing marked birds

(Kurzejeski and Vangilder 1992). Estimates of

absolute abundance are seldom necessary for harvest

management, and population trends can generally be

monitored with indices of abundance. Intensive

population studies involving large numbers of marked

birds offer opportunities for employing markrecapture methods to validate other, less costly indices

of abundance (Lint et al. 1995).

Section IV: Techniques 63
64 Wild Turkey Harvest Management: Biology, Strategies, and Techniques

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Appendix A. 

Minnesota Department of Natural Resources

regulates spring wild turkey hunting by area and time

period. For the spring 1999 turkey season, hunters

applied to hunt during 1 of 8, 5-day time periods

(between April 14 and May 23) and in 1 of 36 Permit

Areas. More than 35,000 applications were received

for 18,360 available permits.

When turkey hunting was initiated in Minnesota in

1978, the number of available hunting permits was

held at low levels to maintain a minimal harvest

because of a developing wild turkey population. As

Minnesota’s turkey population and hunter interest

increased, the number of available permits was

increased requiring a systematic approach to setting

permit levels. The mathematical model I describe

incorporates available information to estimate the

turkey population for the previous spring and for the

upcoming spring hunting seasons, and to calculate the

optimal number of hunting permits to issue to meet

management objectives.

Program Goal — to issue the maximum number of

turkey hunting permits without harming the resource

or reducing the quality of hunting.

Factors considered in model:

1) Turkey Population Factors

■ previous harvest — to estimate the previous spring 

population

■ population indices — to estimate the change in

population over time; the population trend

2) Quality Factors

■ habitat distribution

■ hunter interference rates

Other factors (such as size of permit areas, number of

hunters and harvest success rates from past hunts,

and problems of hunter access to private land) are also

considered before making final determinations.

Turkey Population Factors

1. Estimate the previous spring turkey population for

each permit area (PSP = previous spring population).

PSP = Registered Harvest / fraction of population

harvested

We assume that the registered harvest represents

15% of the spring population. Past models used 10%,

as indicated from past wild turkey research (e.g.,

Lewis and Kelly 1973). However, more recent

information for Wisconsin, with a season framework

similar to Minnesota’s spring season, indicates 16% of

the population was harvested (Paisley et al. 1996). We

plan to refine this value for Minnesota. 

2. Determine population trend

We use percent of “hunters observing wild turkeys”

(HOWT), from a survey of turkey observations by

deer hunters, as a population index (Welsh and

Kimmel 1990, Kimmel et al. 1996b). Population indices

from the 3 most recent population surveys are used to

determine the population trend or HOWT change.

3. Estimate the turkey population for each permit

area for the upcoming spring turkey hunting season

(FSP = future spring population).

FSP = PSP x HOWT change

4. Estimate the gobbler population using future spring

population and % males in the population. We assume

% males is 46% from Minnesota pheasant modeling

(another males-only hunted species) (A. Berner, pers.

commun.).

gobblers = FSP x 0.46

5. Set goal — Minnesota’s current harvest goal

(acceptable harvest) is 30% of males for all permit

areas.

6. Calculate the number of permits to issue for each

permit area to achieve the harvest goal using the

estimated gobbler population, previous year’s hunter

success and the assumed % of permit recipients (95%)

that actually hunt as determined by turkey hunter

surveys (Kimmel et al. 1996a). This projects

recommended number of permits to issue based on

the estimated turkey population. (Quality factors are

taken into consideration in steps 7-9.)

Minnesota spring wild turkey population and hunting permit allocation model.

R. O. Kimmel, 1998. Minnesota Department of Natural Resources, Rt. 1, Box 181, Madelia, MN 56062, 

phone 507-642-8478, e-mail richard.kimmel@dnr.state.mn.us
Appendix A. 73

Quality Factor

7. Habitat Distribution (HD) factors are developed

from Geographic Information System (GIS) maps of

forest cover. Amount and spatial distribution of forest

cover are subjectively evaluated for each permit area.

This factor becomes a multiplier; i.e. factors <1

reduce the permit numbers. 

Habitat Distribution Factor

Good (Permit Area 349) 1.0

Moderate-plus 0.8

Moderate (Permit Area 225) 0.7

Moderate-minus 0.6

Limited (Permit Area 450) 0.5 

8. Interference Rate (IR) factors are determined

from turkey hunter survey data (Kimmel et al. 1996a).

Factors are based on interference rates and used as

multipliers as with Habitat Distribution.

Interference Rate Factor

>25% 0.7

>20% - 25% 0.8

>15% - 20% 1.0

</ = 15% 1.2

9. The number of hunting permits recommended for

each permit area is determined using permit numbers

based on population estimate, Habitat Distribution,

and Interference Rates.

Recommended Permits = Permits (step 6) x HD x IR

The recommended number of permits for each area is

compared to the number of permits offered in past

years. The wildlife manager, however, makes the final

decision for permit numbers to be offered for permit

areas within his or her work area.

Spreadsheet Columns

Column A — Permit Area Number

Column B — Registered Harvest for Spring 1998

Turkey Season

Column C — Previous Spring Population (PSP)

(Column B/0.15)

Column D — Population index (HOWT) for 3rd most

recent survey

Column E — Population index for 2nd most recent

survey

Column F — Population index for most recent survey

Column G — Annual population trend (HOWT

change) calculated from D, E, F; formula takes into 

consideration the number of years between each pair

of surveys

Column H — Estimate of future spring population

(FSP) (C x G)

Column I — Forest abundance - mi2 of forest cover in

the permit area (% forest cover (GIS data) x total mi2

in permit area)

Column J — Gobblers per mi2 in the permit area ((H x

0.46) / I)

Column K — Harvest goal expressed as % of males

Column L — Number of permits issued during

previous hunt 

Column M — Hunter success during previous hunt

(B/L)

Column N — Number hunters per mi2 of forest to

achieve harvest goal ((K x J) / M)

Column O — Number of permits to issue for each

Permit Area to achieve goal ((N x I) / 0.95) - 

assumes 95% of permit recipients actually hunt

Note — Square miles of habitat is actually entered

into the model for comparison between permit areas.

This value enters the model in column J and is then

removed from the model in Column O.

Column P — Hunters per season (for 8 season

framework) to achieve population goal (Column O / 8)

Column Q — Permits issued per season for previous

year’s turkey hunting season. This is listed for

comparison to columns P and R.

Note — Column R is model value for number of

permits to issue

Column R — Number of turkey hunting permits to

issue per season which includes both population and

quality factors (P x S x T)

Column S — Habitat Distribution factor (HD)

Column T— Interference Rate factor (IR)

Column U — Number of permits actually issued

following input from local wildlife managers and the

public. This would ideally fall between the model value

(column R) and the number of permits issued for the

previous year’s hunting season (column Q).

Column V — Hunter density - hunters per square

mile of forest placed in each permit area (U / I).
74 Wild Turkey Harvest Management: Biology, Strategies, and Techniques

Literature Cited

Kimmel, R.O., G.C. Nelson, and S.L. McDonald. 1996a.

Spring 1996 wild turkey hunter survey. Pages

54-58 in B. Joselyn, ed. Summaries of wildlife

research findings 1996. Minnesota

Department of Natural Resources, Section of

Wildlife. St. Paul.

Kimmel, R.O., J.H. Poate, and M.R. Riggs. 1996b.

Spatial handling of wild turkey survey data

using Geographic Information System

mapping procedures. Proc. National Wild

Turkey Symposium 7:219-223.

Lewis, J.B., and G. Kelly. 1973. Mortality associated

with the spring hunting of gobblers. Pages

295-299 in G.C. Sanderson and H.C. Schultz,

eds., Wild turkey Management: current

problems and programs. University of

Missouri Press, Columbia. 

Paisley, R.N., R.G. Wright, and J.F. Kubisiak. 1996.

Survival of wild turkey gobblers in

southwestern Wisconsin. Proc. National Wild

Turkey Symposium 7:39-44.

Welsh, R.J., and R.O. Kimmel. 1990. Turkey sightings 

by hunters of antlerless deer as an index to 

wild turkey abundance in Minnesota. Proc. 

National wild Turkey Symposium 6:126-133.
Appendix B.

Wild Turkey Regulation Strategy

All counties currently closed to fall turkey hunting will

remain closed until the county has a spring harvest of

1 bird per square mile of turkey range for a

minimum of 2 years in succession. The county must

also be adjacent to a county which now has fall turkey

hunting or it must be adjacent to 2 other counties

which have a spring harvest of at least 1 bird per

square mile for 2 years in succession.

Any county qualifying for fall hunting will initially be

opened to 6 days of either-sex hunting with a limited

number of permits. The season will open the same

date as the traditional fall hunted counties. Any

county opened must maintain a spring gobbler kill of

at least 1 bird per square mile of range to remain

open.

Only resident hunters will be permitted to participate

in hunting in counties open to permit hunting. All

resident hunters except landowners will be required to

apply for permits.

Other calculations used in issuing permits are as

follows: 

1. Permit numbers will be based on maintaining a

maximum fall harvest of no more than 5 percent of the

turkey population. 

2. Population levels are based on spring gobbler

harvests which are equal to 10 percent of the total

population. 

3. Hunting success is 27 percent. 

4. Permit allocations and counties to be open for a

current year will be adjusted by brood reports and

mast data available by September prior to opening of

a fall season. 

5. Permit numbers will be adjusted based on the

anticipated number of landowners.

Proposed Time Schedule

Operation of Turkey Permit System

1. Proposed counties to have permit system to be

presented at Sportsmen Meetings in March.

2. Proposed counties to be presented to Natural

Resources Commissioners in April for approval with

the understanding that modifications may be

necessary based on May Gobbler Harvest.

3. In June, Spring Gobbler Harvest Data are used to

correct the county list. Counties are added or

subtracted depending on whether or not they meet the

harvest goal of 1 gobbler per square mile for 2

successive years. To be added to the list a county must

also be adjacent to a fall hunt county or 2 adjacent

counties that also qualify for the first time.

4. Permit availability to be announced by July 1 with

information on applications, deadline for receiving

applications, planned number of permits available, etc.

5. Commissioners will be informed of progress of

system and specifics on numbers available at July

meeting.

6. The number of permits to be issued will seldom

change after July, but the system must include the

option to revise permit allocations based on brood

reports and mast condition information available in

September. This part of the system is vital to open

additional counties for fall hunting.

7. Permits will be mailed no later than October 1.

Appendix B. 75

Strategy for implementing fall hunting of wild turkeys into additional counties. 

Adapted from J.C. Pack, L. Berry, J. Evans, J.R. Hill, R. Knotts, and C. Taylor. 1995. West Virginia Division of

Natural Resources, Wildlife Resources Section.
76 Wild Turkey Harvest Management: Biology, Strategies, and Techniques

Appendix C. 

Direct winter

counts (e.g.

interviewing

cooperators)

(includes roost

counts)

This method

may be better

classified as an

index (see

Caughley

1977a:24-25),

except in certain

populations

where all

turkeys

associate into

flocks. 

• Estimates of population size

were correlated with the following

spring’s harvest in MI (Weinrich et

al. 1985).

• Supplemental foods in winter

attract birds, thus resulting in

greater visibility.

• Provides a total count rather

than an index, which makes it

easier to explain to the public,

media, and legislature (Weinrich

et al. 1985).

• Involves an entire area rather

than a sampling for statistical

expansion to area estimates

(Weinrich et al. 1985).

• Not very labor intensive

• Use of cooperating landowners

limits access problems that might

arise from other censusing

methods (Weinrich et al. 1985).

• Where roosting patterns are

stable, may be a reliable inventory

method (Cook 1973).

• “Turkey flocks, being relatively

conspicuous, attract attention and

create interest among local

residents so that observations of

flocks made by reliable persons

can be valuable...” (Hoffman 1962).

• An advantage of counting the

number of animals per group is

that mean group size can be

calculated very accurately and with

high repeatability (Caughley

1977a:25).

• Use of cooperators can provide

data from a relatively large area

(e.g., an entire state).

• Works well in areas with severe

winters where weather conditions

restrict movement of the birds

(Weinrich et al. 1985, as cited in

Ontario 1985).

• Short census period limits

movements between flocks and is

not labor intensive (Ontario 1985).

• In TX, flocks moved to different

winter roosts, resulting in double

sampling by landowners (Cook

1973); this can be minimized by a

short census period (Weinrich et al.

1985).

• Except under rare circumstances

such as winter flocking in northern

MI and traditional roost sites in

TX, direct counts have limited

feasibility (Kurzejeski and

Vangilder1992).

• Landowner interest may decline

over the years, with individuals

responding less frequently (Welsh

and Kimmel 1990).

• The correlation between winter

census and spring kill is affected

by sex and age composition,

dispersal patterns, land ownership

and use, and weather (Weinrich et

al. 1985).

• In TX, instability of roost sites

and infrequent observations caused

many landowners to make

inaccurate estimates of turkey

numbers (Cook 1973).

• In TX, roosting patterns were so

variable and unstable that roost

counts were not reliable estimators

of the winter population (Smith

1975).

• Midwinter movements were

problematic in MN (Porter 1978)

• Unstable roosting patterns can

be produced by (1) human activity

and land use practices, (2) relative

availability of roost sites, (3)

heightened sensitivity of small

flocks.

• Winter concentration counts

were not correlated with other

survey methods in NE (Menzel

1975).

• The regression of density on

group size is seldom linear and it

usually cuts the vertical axis below

the origin; hence the index ranks

densities but does not reveal the

proportional difference between

them (Caughley 1977a:25) . 

Advantages, disadvantages, cost and manpower notes on wild turkey population censusing, estimating, and

indexing methods. Notes particularly applicable or worth special consideration for wild turkeys in the Northeast

are in italics.

I. Census Techniques

Method Advantages Disadvantages Cost Manpower

• 6-8 people

full time for 

a 2-week

period to

collect data

from

cooperators,

in MI

(Weinrich et

al. 1985). 
Appendix C. 77

Aerial counts

(Beasom 1970,

in TX)

NOTE: Some

investigators

treat aerial

surveys as

relative (i.e.,

indices) rather

than absolute

measures of

abundance

(Caughley and

Goddard 1972 as

cited in Ontario

1985, Eberhardt

1978b).

May be

considered a

subset of

plot/quadrat

sampling or

transect

sampling (Krebs

1989). 

II. Estimates

Method Advantages Disadvantages Cost Manpower

• Useful in open habitats.

• Permits direct counts of

individuals.

• Causes less disturbance than

ground surveys (Caughley

1977a:38).

• A practical method over large

areas (Ontario 1985). “The only

practicable means of estimating

the number of large animals

inhabiting an extensive area on

land or in the sea” (Seber 1982:454,

Caughley 1977b).

• Useful (considering cost and

logistics) for extensive inaccessible

areas and on a broad scale

(Ridpath et al. 1983).

• Requires little manpower and

time (Caughley 1974b as cited in

Ontario 1985).

• Could be conducted in winter to

determine the distribution of a

new population (Ontario 1985).

• May be used in conjunction with

ground surveys to improve the

accuracy of population estimates

(Caughley 1974b as cited in

Ontario 1985) [see Double

sampling].

• Ground counts are only

practicable when there is good

access and visibility and when the

animals are reasonably tame to

vehicles (Norton-Griffiths 1978:94)

[aerial surveys largely circumvent

these problems].

• “Although the estimate is usually

inaccurate and often imprecise it

answers a broad range of

ecological and management

questions to an acceptable level of

approximation” (Caughley 1977b). 

• Limited utility in

heavily vegetated areas

• Ability to see the animals

may vary among individual

observers (Scattergood

1954).

• Animals may stay tend to

stay under cover with

certain weather conditions

or at certain times of the

day).

• Speed of airplane, height

above ground, transect

width, and skill of different

observers have significant

effects on population

estimates (Seber 1982,

Caughley et al. 1976,

Caughley 1977b, NortonGriffiths 1978, Eberhardt

et al. 1979, Ridpath et al.

1983—speed only);

observed densities may not

be directly comparable

between different surveys

(estimates) (Caughley et al.

1976).

• Variation of pilots

(Hoskinson 1976), snow

conditions, terrain, and

time of day appear to bias

results (LeResche and

Rausch 1974, Davis and

Winstead 1980).

• Amount of cloud cover or

size of aircraft affected

results for coastal

waterfowl (Stott and Olson

1972).

• Extremely variable

results in comparison with

surface counts, for coastal

waterfowl (Stott and Olson

1972).

• Underestimates seem to

be inherent in the method

(Stott and Olson 1972,

LeResche and Rausch

1974) (cited in Davis and

Winstead 1980)

(Scattergood 1954)

(Caughley 1974b, with

references). This can be a

problem when accurate

population estimates are

needed to establish harvest

rates and quotas

(LaResche and Rausch

1974 as cited in Ontario

1985). “Even inexperienced

observers can overlook as

many as 20% or more of

the animals 

• $25 per km2

for aerial

transects of

deer in South

Dakota

(Naugle et al.

1996).

• Shupe et al.

(1987)

counted

bobwhite

from a

helicopter

along

transects.

The cost of

aerial

transects was

less than for

markrecapture

estimates,

but above the

cost of

conducting

drive counts

(cited in

Stauffer

1993).

• Helicopter

> fixed-wing

> ground

(Ridpath et

al. 1983, for

large tropical

mammals in

Australia). 

• Helicopter

> ground >

fixed-wing...

When

counting in

wooded

habitats only,

the effort in

counting

from the

ground or in

a fixed-wing

aircraft is

much the

same

(Ridpath et

al. 1983, for

large tropical

mammals in

Australia). 
II. Estimates (continued)

Method Advantages Disadvantages Cost Manpower

Aerial counts

(continued)

[see references in Seber 1982:456],

so that all estimates, whether

based on quadrats or transects are

underestimates” (Seber 1982:456).

• “Animals missed in heavy cover,

observer fatigue from boredom

from flying over low density areas,

variable snow cover and terrain,

different observers in subsequent

surveys, the surveyors’

competency or level of experience,

and pilot interest, attitude, and

skill all result in low population

estimates” (Ontario 1985).

• Provide only a rough estimate of

population size (Ontario 1985).

• Aerial summer census with

helicopters was not recommended

by Myers (1973) in CO.

• Fixed-wing plane census in

winter at feed stations was

unsuccessful in CO (Myers 1973)

• Usually cannot be used to

estimate densities of birds, because

birds are easily missed and

because one normally cannot relate

flocks sampled to total area

sampled (Verner 1985).

• The resulting estimate “is

usually inaccurate, biased, and

often imprecise (i.e. has large

variance)” [but] can be used to

answer a broad range of ecological

and management questions to an

acceptable level of approximation

(Seber 1982).

• Ground counts are more useful

for obtaining data on the seasonal

patterns of distribution within

different vegetation types, and also

provides information on the

behavior and condition of the

animals that cannot be obtained

from aircraft (Norton-Griffiths

1978).

• “Counting from the ground is

the only method suitable to obtain

detailed demographic and

environmental data of the

population. It may be a cheap

alternative to the use of aircraft

where the area is small enough (as

Norton-Griffiths 1978) and access

is feasible (Ridpath et al. 1983).

• Ground vehicles travel more

slowly and counting rate is higher

(Norton-Griffiths 1978:94).

• Ground vehicles can stop as

necessary to make highly accurate

counts, and can make incidental

observations on behavior, age and

sex structure, condition, state of

the vegetation, etc. (NortonGriffiths 1978:94). 

78 Wild Turkey Harvest Management: Biology, Strategies, and Techniques
II. Estimates

Method Advantages Disadvantages Cost Manpower

Line transects • Compensates for differences in

observability along survey routes,

compared with strip transects

(DeYoung and Priebe 1987).

• All animals observed in a survey

are used in forming an estimate of

abundance, as contrasted with strip

transects (Eberhardt et al. 1979,

Burnham et al. 1985, Caughley and

Sinclair 1994). This tends to

increase the precision of the

estimate (Caughley and Sinclair

1994).

• “In habitats where it is difficult to

spot every animal you are looking

for or for you to know that you are

not scaring animals away, line

transects are more appropriate

than fixed-width [strip] transects”

(Rabinowitz 1993)

• It is not necessary to detect all

animals present in the study plot.

Only a small percentage of the

animals actually present might be

detected (Buckland et al. 1993).

• Allows a relaxation of the strong

assumptions required for strip (i.e.,

plot or quadrat) sampling

(Burnham and Anderson 1984, as

cited in Buckland et al. 1993).

• Less expensive than markrecapture (Buckland et al. 1993

with references).

• Possible advantage for wild

turkeys: this method “is perhaps

more appropriate [than strip

transects] when the counts are

likely to be low” (Seber 1982:461).

• A particularly useful method

when the animals are too mobile to

be sampled using sample plots, or

when the animals are difficult to

locate and must be flushed into the

open (Seber 1982:562, Brower

1990:119).

• “The line transect method will

give more value for the money than

quadrat sampling, provided the

underlying assumptions are

satisfied (Seber 1982:563).

• Best suited to large areas that are

relatively uniform within sections of

hundreds of meters or

more...Particularly suitable in

extensive, open, uniform, or

species-poor habitats” (Bibby et al.

1992:66,84).

• Probably more accurate than

point counts (Bibby et al. 1992:67).

• “The major advantage of the line

transect sampling scheme is the

relative ease of its implementation

in the field once a proper design

has been chosen” Anderson et al.

1979).

• “[The inclusion of objects

outside a narrow strip, where

• Larger standard errors

than strip transects

(DeYoung and Priebe

1987).

• Line transects “are

known for their ease of

performance in some

situations, not for their

accuracy and precision.

Estimates of variance and

computation of confidence

intervals are difficult, as

specific theoretical models

or mathematical

distributions typically

must be assumed; but

confidence intervals as an

expression of precision of

the population estimate

may be expressed readily

if replicate transects are

sampled” (Brower

1990:120).

• Difficult to observe

sufficient numbers of

turkeys to derive accurate

estimates.

• All animals located

directly on the line must

be detected (Lancia et al.

1994:231, Pollock et al.

1990:66). This may be

especially difficult in aerial

surveys (Pollock et al.

1990:66). 

• Animals cannot move

before they are sighted,

and no animals can be

counted twice (Lancia et

al. 1994:231).

• Distances (e.g.

perpendicular distance of

the animal from the line)

must be measured exactly

(Lancia et al. 1994:231).

This may be impractical,

especially in aerial surveys

(Pollock et al. 1990:66).

“Studies based on sighting

distances and angles are

generally subject to more

severe biases” (McDonald

1993). 

• Sightings must be

independent events (e.g.,

the flushing of one animal

does not cause another to

flush) (Lancia et al.

1994:231).

• Animals must be

detected or missed in

random fashion (Quang

and Becker 1996).

• Probability of detection

must depend only on

distance (Quang and

Becker 1996).

• Shupe et al.

(1987) counted

bobwhite from

a helicopter

along transects.

The cost of

aerial transects

was less than

for markrecapture

estimates, but

above the cost

of conducting

drive counts

(cited in

Stauffer 1993). 

• Mark

recapture

exceeded costs

for walking line

transects by a

factor of three

in rangeland

studies in TX

(Shupe et al.

1987, as cited in

Buckland et al.

1993).

• Labor is

comparable to

estimates

based on the

LincolnPetersen index

(Guthery 1988).

• For a study

on northern

bobwhites in

Texas, cost was

about $47/km

to establish

transects, plus

$3.30 to sample

them (Guthery

1988). 

• For a

study on

northern

bobwhites

in Texas,

labor

involved

about 4.7

hours per

km to

establish

transects

on

moderately

brushy

rangeland

(using a

crew of 3

using a

chainsaw,

bow saw,

and limb

cutters),

plus 17-23

minutes

per km for

sampling

(Guthery

1988). 

Appendix C. 79
II. Estimates (continued)

Method Advantages Disadvantages Cost Manpower

Line transects

(continued)

detection is less than perfect] and

the ability to collect and analyze

grouped data (rather than

individual distance) represent

major advantages of line transect

over strip transect sampling”

(Burnham and Anderson 1984).

“Line transect width may be large

(effectively unbounded) and the

number counted (sample size, n)

typically much greater than on a

narrow strip transect” (Burnham

et al. 1985).

• Advantage over strip transects:

“Strip data often result in biased

estimates because objects in the

strip are missed” (Burnham and

Anderson 1984).

• Advantage over strip transects:

“A properly conducted line

transect survey will provide valid

estimates of density even if a

substantial fraction of objects go

undetected” (Burnham and

Anderson 1984).

• Advantage over strip transects:

“The results [of this paper’s

analysis] indicate a preference for

the line transect survey method

over strip transects on the basis of

bias and efficiency...The results

show that line transect sampling

generally has a smaller MSE than

strip transect sampling...Bias does

not increase as transect width

increases” (Burnham et al. 1985).

• Line transect “indices are

efficient in that they collect a large

amount of data quickly and with

relatively small effort” (Brower

1990). 

• Detection of animals may depend

on distance, group size,

environmental conditions, time of

day, and animal behavior (Buckland

et al. 1993, Quang and Becker

1996).

• Likely to be more appropriate in

relatively heterogeneous habitats

such as rangelands, as opposed to

patchy habitats such as croplands

(Stauffer 1993, in a paper on quail).

• Requires a greater investment of

time and effort than methods to

derive indices (Stauffer 1993, in a

paper about quail).

• Transects are not practical for

species that occur at such low

densities that you are not able to

obtain a reasonable sample size

(Rabinowitz 1993); sample size of

at least 25-30, and preferably 40-80,

is recommended (Burnham et al.

1980).

• It is believed that the line

transect sampling method can be

applied only if the geometric center

of the cluster can be determined

adequately. Animals in loose

groups cannot be analyzed

appropriately as a clustered

population because they usually do

not flush simultaneously and the

geometric center is nearly

impossible to establish accurately

(Burnham et al. 1980, Anderson et

al. 1979).

• For clustered populations, it may

be difficult to meet the

requirement that cluster size be

determined accurately (Buckland

et al. 1993).

• Not a very good approach in

small areas or for detecting the

effects of fine-grained habitat

variation (Bibby et al. 1992:66).

• “Transect methods do not always

work because dense shrub and

rough terrain make it difficult for

the observer to walk quietly and

simultaneously look for birds”

(Seber 1986).

• Large sampling effort (total

distance walked) may be required

for acceptable levels of precision

with species such as northern

bobwhites (Guthery 1988). 

• “Application of transects is

difficult on large areas because of

time requirements” (Guthery

1988).

• Accuracy “depends heavily on

which model is chosen for the

analysis” (Caughley and Sinclair

1994). 

80 Wild Turkey Harvest Management: Biology, Strategies, and Techniques
II. Estimates (continued)

Method Advantages Disadvantages Cost Manpower

Strip transects • Simpler to use than variabledistance line transects, because

one records only the birds detected

within the strip...The observer

must be trained to estimate only

one distance accurately—that to

the limit of the strip (Verner 1985).

• Strip transects “have the

immense advantages of simplicity

and realism. If the transect width

is appropriately chosen, what the

observer sees is what the observer

gets. The mathematics of such

sampling are simple, elegant, and

absolutely solid” (Caughley and

Sinclair 1994).

• Provides a density estimate

(Verner 1985).

• Better than line transect if

animals are quite numerous

(Eberhardt et al. 1979).

• Statistically more accurate

(unbiased) than line transects

(because you are counting total

numbers instead of estimating)

(Rabinowitz 1993, Burnham et al.

1985).

• Generally easier to locate and

sample than quadrats (Seber

1986). 

• Too variable over time, compared

with line transects (DeYoung and

Priebe 1987).

• Does not meet the assumption that

all individuals in the fixed-width strip

were sighted. It is generally recognized

that belt (strip) surveys often fail to

detect 100% of the animals present in

the strip when used for estimation of

density (McDonald 1993).

• Difficult to observe sufficient

numbers of turkeys to derive accurate

estimates.

• Sample size could be small, leading to

an estimator with a large variance

(Burnham et al. 1985).

• Animals observed outside the

transect must be ignored; this could be

a problem when densities are very low

(Eberhardt et al. 1979)

• Not appropriate for most forest

situations (Rabinowitz 1993)

• Transects are not practical for species

that occur at such low densities that

you are not able to obtain a reasonable

sample size (Rabinowitz 1993)

• If the objects counted are either rare

or not readily seen (or both),

restricting coverage to a strip of

definite width may require an

unreasonably large number of

transects to provide suitably narrow

confidence-limits on an estimate

(Eberhardt 1978b).

• All animals within the strip must be

detected, in contrast to line transects

(Buckland et al. 1993).

• Possible disadvantage for wild

turkeys: this method “is appropriate

when the population is fairly numerous

and readily visible” (Seber 1982:460).

• Are more affected by boundary

effects (e.g. movement of animals on

the boundary) than quadrats (Seber

1986).

• “Strip transects are generally

inappropriate for birds because of lack

of visibility” (Burnham et al. 1980, as

cited in Seber 1986).

• “Transect methods do not always

work because dense shrub and rough

terrain make it difficult for the

observer to walk quietly and

simultaneously look for birds” (Seber

1986).

• “[The inclusion of objects outside a

narrow strip, where detection is less

than perfect] and the ability to collect

and analyze grouped data (rather than

individual distance) represent major

advantages of line transect over strip

transect sampling” (Burnham and

Anderson 1984).

• “Strip data often result in biased

estimates because objects in the strip

are missed” (Burnham and Anderson

1984). Appendix C. 81
82 Wild Turkey Harvest Management: Biology, Strategies, and Techniques

II. Estimates (continued)

Method Advantages Disadvantages Cost Manpower

Drive counts • Can be useful to completely

count all birds on relatively small

(i.e., <500 ha) areas (Stauffer 1993,

in a paper on quail).

• Dogs can be used to assist. 

• Should not be used with

animals that remain quiet or

hide, or escape into trees

(Rabinowitz 1993:110).

• Large number of people

required (Rabinowitz

1993:110). 

• Shupe et al.

(1987) counted

bobwhite from a

helicopter along

transects. The

cost of aerial

transects was

less than for

mark-recapture

estimates, but

above the cost

of conducting

drive counts

(cited in

Stauffer 1993). 
II. Estimates (continued)

Method Advantages Disadvantages Cost Manpower

Markrecapture

• Useful for estimating population

size in detailed research studies

(Kurzejeski and Vangilder 1992)

or for evaluating indices.

• May be of some use in setting

management objectives (Gribben

1986).

• “Of all the [abundance] methods

considered in this book, the

Petersen method appears to be

most useful, provided that the

assumptions underlying the

method are satisfied and there are

sufficient recaptures in the second

sample” (Seber 1982:564).

• Can be used to provide

information on birth, death, and

movement rates in addition to

information on absolute abundance

(Krebs 1989:15, Caughley and

Sinclair 1994:208). In addition to

estimating population size, can be

used “to investigate habitat

selection and other distributions, to

calculate survival rates, measure

dispersal and other movements,

and to measure reproductive

success of individual birds” (Bibby

et al. 1992:105).

• In general, populations that can

be sampled by either markrecapture or removal methods are

sampled more reliably and

efficiently using mark-recapture

procedures (Brower 1990). 

• Costly, effort-intensive, and

time-consuming (Gribben 1986,

Caughley 1977a:134).

• Several assumption need to be

met (e.g., the critical assumption

of equal probability of recapture

for marked and unmarked

turkeys) (DeYoung and Priebe

1987, Kurzejeski and Vangilder

1992, Krebs 1989).

• It is not always easy to detect

departures from the assumption

that marked and unmarked

animals have the same

probability of being caught in the

second sample, which may lead

to biased estimates (Seber

1982:565).

• Capture of turkeys is too

difficult to result in the necessary

sample sizes needed for detailed

population studies (Gribben

1986).

• Not an appropriate method for

estimating population structure

(Gribben 1986).

• Different types of tags may

have different recovery/retention

rates (Myers 1973) [so data may

be biased or non-comparable]

• Very time consuming compared

with other methods (Verner

1985). “Require considerable time

and effort to get the required

data” (Krebs 1989:16).

• “Results are often inaccurate

because mark-recapture models

are seldom more than a vague

approximation to reality”

(Caughley 1977a:134).

• Not very robust, and “even

small deviations from their

implicit assumptions can produce

large errors in the results”

(Caughley 1977a:134). “To be

accurate, they require a set of

very restrictive assumptions

about the properties of the

population being studied” (Krebs

1989:16).

• “When animals are readily

observable, observation-based

methods for estimating

population size often will be

preferable to capture-recapture

methods.”

• Relatively expensive, compared

to line transects (Buckland et al.

1993 with references).

• Variable catchability is a

common problem. Catchability

can vary with age and sex, size,

type of trap and its location, type

of bait, and environmental

conditions. The process of

• Shupe et al. (1987)

counted bobwhite

from a helicopter

along transects. The

cost of aerial transects

was less than for

mark-recapture

estimates, but above

the cost of conducting

drive counts (cited in

Stauffer 1993). 

• Mark recapture

exceeded costs for

walking line transects

by a factor of 3 in

rangeland studies in

TX (Shupe et al. 1987,

as cited in Buckland et

al. 1993).

• Labor for LincolnPetersen index is

comparable to

estimates based on the

line transect method

(Guthery 1988). 

Appendix C. 83
84 Wild Turkey Harvest Management: Biology, Strategies, and Techniques

II. Estimates (continued)

Method Advantages Disadvantages Cost Manpower

Markrecapture

(continued)

catching and tagging, and even the

tag itself, can seriously affect the

subsequent catching of an animal

(Seber 1982:487 with references).

Unequal vulnerability of age and

sex groups to harvest is a problem;

estimates usually need to be made

for each sex-age group—so sample

sizes increase.

• Dependence on hunters for tag

return may lead to non-reporting

or mis-reporting of tags, leading to

an overestimate of population size

(Seber 1982:489).

• A considerable proportion of the

population must be marked for a

reasonable accuracy... a number of

authors have commented on the

need to mark at least 50% of the

population (Seber 1982:565 with

references).

• “Mark-recapture techniques are

difficult to use in the real world,

and you should be certain they are

needed before starting to use these

techniques” (Krebs 1989:59).

• Since mark-recapture methods

typically only serve to estimate

abundance at a single plot, the

statistical inference is limited to

abundance of the animals in that

study area, rather than for the

overall contiguous population or

the mean abundance of the overall

area (Skalski and Robson 1992). 
II. Estimates (continued)

Method Advantages Disadvantages Cost Manpower

Removal

methods

(Includes next 2

techniques:

change-in-ratio

and catch-effort)

Change-inratio 

Catch-effort 

Personal

interview-map

plot technique

(Mosby and

Handley 1943,

Weaver and

Mosby 1979)

Double

sampling 

• May yield more information for

the same amount of effort than

mark-recapture when hunters

carry out the removal (Hanson

1967) [but the reverse may be true

if hunters are not used (Chapman

1955)].

• When used with an index method

(such as roadside surveys), the

index can be used to estimate

absolute density (Eberhardt 1982

as cited in Krebs 1989:162). 

• Survey-removal methods (one of

several types of removal methods)

cannot be used when no obvious

sex or age or other distinction

between the classes can be readily

recognized in the field (Hanson

1967).

• The principal difficulty with the

survey-removal method is that sex

or age or other class of the

population often exhibit different

behavior, leading to

unrepresentative samples from the

field (Hanson 1967).

• In general, populations that can

be sampled by either markrecapture or removal methods are

sampled more reliably and

efficiently using mark-recapture

procedures (Brower 1990). 

• If the 2 types (sexes, ages, etc.) of

animals do not have the same

probability of capture (probable for

turkeys), one of the critical

assumptions is violated (Krebs

1989:161).

• Designed for situations where

one group (sex, age, etc.) is

removed more than the other (e.g.,

spring gobbler hunting) (Krebs

1989). 

• “This method is highly restricted

in its use because it will work only

if a large fraction of the population

is removed so that there is a

decline in the catch per unit effort.

It will not work if the population is

large relative to the removals”

(Krebs 1989:166).

• Large samples are usually

required (Krebs 1989:166). 

• Doesn’t census the relatively

solitary adult gobblers, which are

added to the population estimate

by using an approximation formula

(Weaver and Mosby 1979). 

• If it is assumed that accurate

ground counts can be made, then it

may be more efficient to take more

ground counts and not do any

aerial surveying (Eberhardt et al.

1979) (see Cochran 1977:342 for a

chart to help make this

determination). 

• One person

can cover an

entire county in

approximately 

1 week (Zirkle

1982, probably

paraphrasing

Weaver and

Mosby 1979 or

Mosby and

Handley 1943).

• Permits the interviewer the

opportunity to assess sportsman

attitudes regarding the status and

problems of the species in the

inventory area (Zirkle 1982,

probably paraphrasing Weaver

and Mosby 1979 or Mosby and

Handley 1943). 

Appendix C. 85
86 Wild Turkey Harvest Management: Biology, Strategies, and Techniques

III. Indices

Method Advantages Disadvantages Cost Manpower

Brood surveys

(e.g. total N

hens and poults

seen during

routine duties

by field officers,

or seen by

cooperators)

(Schultz and

McDowell 1957,

Wunz and Shope

1980, Wunz and

Ross 1990,

Lewis 1978,

Craig and

Suetsugu 1973)

May be

considered a

subheading of

roadside counts

(Ammann and

Ryel 1963). 

• Correlated with fall harvest

(Wunz and Shope 1980 in PA,

Vangilder unpubl. data, Wunz

and Ross 1990).

• Correlated with the

proportion of juvenile gobblers

in the subsequent spring’s

harvest in MO (Kurzejeski and

Vangilder 1992).

• Correlated with annual

harvest as estimated from a

postal survey in PA (Wunz and

Ross 1990).

• Correlated with hunter report

card data in PA (Wunz and Ross

1990).

• Can be used to derive poultto-hen ratios, which are

correlated with the proportion

of juveniles in the fall harvest

and the proportion of juvenile

males in the following spring

harvest.

• Provide reliable indices to

annual reproduction for all

subspecies (Kurzejeski and

Vangilder 1992)

• Cooperator surveys are

relatively inexpensive

(compared with gobbling

counts) (Backs et al. 1985)

• Poult counts provide

immediate knowledge of

reproduction during the

previous year (Smith 1975).

• Number of young per hen

with brood was shown to

provide a reasonable estimate of

relative production in Nebraska

(Menzel 1975).

• Use of cooperators can

provide data from a relatively

large area (e.g., an entire state).

• Are useful to determine the

population status prior to the

fall, and can be used to develop

recommendations for a fall

hunting season (Ontario 1985).

• Summer brood surveys are

more accurate than gobbling

counts in spring for determining

the fall pre-season population

status because of the effects of

inclement weather on gobbling

(Ontario 1985). 

• An index, so lacks the

precision of quantitative

estimates of density.

• No relationship found

between poult:hen ratios and

subsequent fall harvest in

Nebraska (Menzel 1975) (a

disadvantage to using brood

surveys as a production index,

not as an abundance index).

• Not correlated with spring

harvest data in PA (Wunz and

Ross 1990)

• Low sample size due to

infrequent sightings (Wunz and

Shope 1980 as cited in Ontario

1985, Bartush et al. 1985)

• Inexperienced surveyors may

fail to record sightings (Bartush

et al. 1985).

• Lack of standardization (i.e.,

with respect to same degree of

effort each year) may be a

problem (Bartush et al. 1985).

• Many variations in the

method exist (Ontario 1985).

• Broods are often encountered

where it is difficult to see the

poults (Kimmel and Tzilkowski

1986) (a disadvantage to using

brood surveys as a production

index, not as an abundance

index).

• Broods may form multiplehen broods (Schultz and

McDowell 1957) as the summer

progresses (Leopold 1944:166)

(a disadvantage to using brood

surveys as a production index,

not as an abundance index).

• Biased toward observation

associated with public roads

and open agricultural areas

(Backs et al. 1985).

• Brood counts over designated

routes were not recommended

by Myers (1973) in CO because

they are “time consuming and

often fruitless.”

• Broods may be difficult to

count because of dense cover,

high vegetation, or wariness of

the birds (Hoffman 1962) (a

disadvantage to using brood

surveys as a production index,

not as an abundance index).

• Underestimates of the

broodless hen component of the

population are a concern (Lewis

and Kurzejeski 1984 as cited in

Ontario 1985) (a disadvantage

to using brood surveys as a

production index, not as an

abundance index). 

• $450-

600/route/

year

(including

expenses

and

salaries)

for 16

annual

surveys 

in FL

(Bartush

et al.

1985). 

• One manhour per

turkey

observation

(averaging 

3 birds/

observation)

(compared 

with at least 

6-7 man-hours

per bird heard

for gobbling

count surveys),

in a study done

in Indiana

(Backs et al.

1985).

• At least 39

samples were

needed to

reflect yearly

changes in

brood averages

of 10% in CO

(Hoffman

1962). 
II. Indices (continued)

Method Advantages Disadvantages Cost Manpower

Reports from

deer hunters

(Welsh and

Kimmel 1990, in

MN; Lewis

1980, in MO;

Garver 1986, in

IL; Kimmel et.

al. 1996, in MN)

and turkey

hunters

(Kennamer

1986). 

• Cost- and labor-effective,

compared to route-oriented

surveys (Welsh and Kimmel

1990).

• An advantage over landownercooperator surveys is that a

number of potential survey

participants can be easily defined,

and data can be collected over a

shorter period of time (length of

the hunting season)

• Distribution of hunters may be

relatively even over permit areas,

thus making the technique an

effective indicator of wild turkey

abundance and distribution

(Welsh and Kimmel 1990).

• Relatively inexpensive.

• Requires a minimal amount of

labor.

• Response rate remains

relatively constant (compare with

interviewing cooperators, above)

(Welsh and Kimmel 1990).

• Can detect 10-15% changes in

turkeys seen per hunter per day

(Welsh and Kimmel 1990).

• If hunters are asked for

locations of turkey sightings, can

also provide useful information on

distribution (Kimmel et al. 1996).

• Percent of hunters observing

wild turkeys (HOWT) is more

robust to outliers, and better

reflects known geographical and

annual differences in abundance,

than number of turkeys

observed per days hunting

(TPD); hunters may better

recollect general sightings than

exact numbers seen (Kimmel et

al. 1996, with references).

• Strongly correlated with

subsequent spring harvest

(Rolley and Kubisiak 1994, in WI).

• Can provide data from a

relatively large area (e.g. an

entire state). 

• An index, so lacks the precision

of quantitative estimates of

density.

• Not possible in areas

without hunting seasons (e.g.

where turkey populations are

not yet well established)

• Correlated with the

following spring harvest

(Welsh and Kimmel 1990, in

MN).

• Cannot predict actual

turkey densities (Welsh and

Kimmel 1990)

• Percent of hunters

observing wild turkeys

(HOWT) is more robust to

outliers, and better reflects

known geographical and

annual differences in

abundance, than number of

turkeys observed per days

hunting (TPD); hunters may

better recollect general

sightings than exact numbers

seen (Kimmel et al. 1996, with

references). 

• An antlerless deer survey

conducted 

in MN cost

$3,500 for 

4 mailings

(Welsh and

Kimmel

1990); this

was 63% of

the cost of

gobble count

routes

conducted

over the

same area. 

• Can be

done by 1

individual 

in an office . 

• Can

provide data

on

Appendix C. 87
88 Wild Turkey Harvest Management: Biology, Strategies, and Techniques

II. Indices (continued)

Method Advantages Disadvantages Cost Manpower

Roadside

counts/

survey routes

(Speake 1980,

Beasom 1970,

Pattee and

Beasom 1979,

Shaw 1973,

Smith 1962)

May be

considered to be

a tool for

gobbling counts.

production, age, and sex, including

poult:hen ratios, brood size, and

percentage of hens with poults.

• Can traverse large areas quickly

and easily by using only 2 persons

and a vehicle (Brower 1990:120).

• “Population sampling is quick,

easy, and inexpensive” (Hewitt

1967b, discussing red-winged

blackbirds in New York State).

• “Counting from a car is

particularly good for large and

conspicuous birds which occur at

low densities, such as raptors”

(Bibby et al. 1992:77).

• Useful for large regions such as

a state.

• Useful when access off the

existing road system is difficult

(Norton-Griffiths 1978:4). 

• An index, so lacks the precision

of quantitative estimates of

density.

• More useful in the more open

habitats occupied by the

Merriam’s and Rio Grande

subspecies.

• Requires an adequate road

system.

• Turkeys encountered on roads

are not visible long enough to

make an accurate count (Kimmel

and Tzilkowski 1986)

• No relation found between

average N turkeys seen on

established survey routes and

hunter harvest in Nebraska

(Menzel 1975).

• Condition of the roadside cover

affects visibility (Davis and

Winstead 1980).

• Numbers of animals seen are

determined by activity of the

animals as affected by hour of

day, food supply, and weather

(Davis and Winstead 1980,

Hewitt 1967b) . . . and

potentially height and density of

vegetation and cloud cover

(Sauder et al. 1971).

• Activity may vary

quantitatively temporally,

seasonally, and selectively (Davis

and Winstead 1980).

• “Although this method

provides a reliable index of

population changes from year to

year or between geographical

areas with similar habitats, it is

limited for seasonal comparisons

because of serious sampling

biases caused by changes in food

supply and cover” (Brower

1990:121). “The counts obtained

from roadside censuses are. . .

not comparable. . . from one

season to another” (Howell

1951).

• Edges of roads tend to be

“habitat” for some species (and

are avoided by others-SMP), and

this leads to a consistent

overestimate (underestimate) of

numbers or density (NortonGriffiths 1978:4, Hewitt 1967b).

• Roads are rarely distributed

randomly across an area...they

tend to pass along contours

rather than across them (NortonGriffiths 1978:4). . . road counts

are open to considerable bias

because the road system is

unlikely to be representative of

an area. . .more important, the

bias is likely to change in the

course of the year as the

• A single

observer could

cover 275

acres per hour,

surveying a

strip 75 yards

on each side of

the road

(Hewitt 1967b,

discussing redwinged

blackbirds in

New York

State). 
II. Indices (continued)

Method Advantages Disadvantages Cost Manpower

Roadside

counts/

survey routes

(continued)

Gobbling

counts (Scott

and Boeker

1972, Porter and

Ludwig 1980,

Bevill 1975, Lint

et al. 1995)

success rates (Porter and Ludwig

1980, in MN; Palmer et al. 1990, in

MS).

• Can increase the total number of

birds seen or heard compared to a

conventional census (see

references in Kimmel and

Tzilkowski 1986).

• Gobbling activity shown to be

linked to the hen:gobbler ratio (sex

ratio) and “might be useful as

population indexes where outside

disturbances do not interfere.”

• Can be used to monitor range

expansion, trends in population

growth and the magnitude of

differences in population

abundance (Porter and Ludwig

1980, in MN).

• May be useful to derive indexes

to population levels over time or to

compare relative densities between

different areas (but not to predict

fall harvest) (Stauffer 1993, in a

paper about quail).

• Also defines the phenology of

gobbling [to determine peak

gobbling activity], and that

information can then be used to

time spring hunting seasons with

peak gobbling activity (Porter and

Ludwig 1980 as cited in Ontario

1985).

• May be conducted to determine

the effect of a severe winter on the

breeding population (Ontario

1985).

• Doesn’t disturb birds as much as

more direct estimating methods

(Bull 1981).

• “Relatively few observers can

cover a large area and obtain a

large number of observations

rather inexpensively even when

the density of a species is low”

(Bull 1981).

• Can sample all types of habitat

(Ammann and Ryel 1963).

• Quality of data is questionable

distribution of the animals changes

(Norton-Griffiths 1978:100).

• “There is no straightforward

method of calculating the sample

error from a road count, for a road

system cannot be regarded as a

sample of units in the same way as

a set of transects” (NortonGriffiths 1978:102). (But perhaps

this is only a problem if roadside

counts are to be used as an

estimate, rather than an index?) 

• Good correlation with hunter

(Welsh and

Kimmel

1990); this

was 160% 

of the cost

of an

antlerlessdeer hunter

survey

conducted

over the

same area. 

• >70

worker-

(Welsh and Kimmel 1990) and

has been claimed to be

unreliable as a population

indicator (Scott and Boeker

1972).

• Population estimates only

weakly correlated with call

counts (Palmer et al. 1990)

• May be related to gobbler

condition, such that indices

based on call counts may falsely

indicate population decline

after winters with food

shortages (Palmer et al. 1990).

• Gobbling activity appeared to

be unrelated to poult production

in AZ (Scott and Boeker 1972)

• Gobbling varies greatly among

individuals and with the

chronology of breeding activity,

and thus is a limited technique

as a population measure

(Hoffman 1990, in CO).

• Daily variability in gobbling

intensity (Wise 1973, Porter

1978).

• Gobbling activity may be

affected by weather conditions

such as cloud cover (Bevill 1973,

Davis 1971, as cited in Bevill

1975), dew factor, wind velocity,

and rain (Bevill 1973).

• Vocalization rates and

detectability can be influenced

by such factors as wind, rain,

time of day, temperature,

seasonality, species response

traits, lunar cycles, and

disturbance by humans or other

predators, effects of terrain and

vegetation on sound,

territoriality, breeding condition

(Johnson et al. 1981, Bull 1981)

• Gobbling activity was reduced

after winters that had food

shortages (Palmer et al. 1990, in

MS).

• WMH: George Hurst’s group

in Mississippi has found that

gobbling is influenced by the

days by 

a large

number of

personnel,

in a MN

study (see

Welsh and

Kimmel

1990:132).

• Two

people for

24 mornings

each spring

harvest

season (Lint

et al. 1995).

• At least 

6-7 manhours per

bird heard

(compared

with 1 manhour per

turkey

observation,

averaging 3

birds per

observation,

for a

cooperator

survey, in a

study done

in Indiana

(Backs et al.

1985).

• Not

necessary

Appendix C. 89
90 Wild Turkey Harvest Management: Biology, Strategies, and Techniques

II. Indices (continued)

Method Advantages Disadvantages Cost Manpower

Gobbling

counts

(continued)

condition of the gobblers—so

there is some variation that is

not associated with population

size.

• Surveys may be hampered by

inclement weather (Backs et al.

1985, Donohoe and Martinson

1963).

• More expensive and labor

intensive compared with

techniques such as reports from

deer hunters (Welsh and Kimmel

1990), and harvest data (Lint et

al. 1995).

• Large number of personnel

required (Wise 1973)

• Requires an adequate road

system.

• Outside noises may interfere

with ability to hear gobbling

(Porter and Ludwig 1980 as cited

in Ontario 1985).

• Difficult to estimate precision

(Porter and Ludwig 1980).

• Only actively gobbling males

are detected (Backs et al. 1985)

• Estimates of population size

may be dependent on manpower

used to survey an area (Donohoe

and Martinson 1963).

• “In most cases it probably is

risky to use call counts to make

predictions concerning potential

fall harvests, unless such data

are supplemented by information

on nesting success and survival”

(Stauffer 1993, in a paper about

quail).

• Small sample sizes may be a

problem with wild turkeys

(Porter and Ludwig 1980 as cited

in Ontario 1985).

• “Gobbling counts in spring are

not as accurate as summer

brood surveys for determining

the fall pre-season population

status because of the effects of

inclement weather on gobbling”

(Ontario 1985).

• Shifting personnel may differ

in hearing ability and so produce

records that may not be

comparable (Ammann and Ryel

1963).

• Methods used to monitor and

quantify gobbling have differed

among studies, making

comparisons difficult (Hoffman

1990). 

• $5,600 for a study in MN
II. Indices (continued)

Method Advantages Disadvantages Cost Manpower

Snow-track

counts (track

counts)

May be

considered a

subset of direct

winter counts.

Nuisance/

damage

complaints

Harvest data

(may be

standardized

using hunter

effort), e.g.,

postal surveys,

bag checks. 

to observe birds directly

• Can provide sex information

(Burke 1982 as cited in Ontario

1985). Winter sex ratio functions as

an index of the potential breeding

population, and may also provide a

post-season estimate of the number

of gobblers versus hens harvested

(Wooley et al. 1978 as cited in

Ontario 1985).

• The resulting winter sex ratios

provide a post-season estimate of the

number of gobblers vs. hens

harvested (Wooley et al. 1978 as

cited in Ontario 1985). The counts

then also function as an index of the

potential breeding population

(Ontario 1985).

• Tracks are easily found

• Birds are easy to observe because

of lack of cover in the winter (Hurt

1968 as cited in Ontario 1985).

• Easily and inexpensively

conducted (Wooley et al. 1978 as

cited in Ontario 1985).

• Can be used to find and map flock

locations during periods of good

snow cover.

• In some areas (e.g. Indiana, Wise

1973), a large percentage of

turkeys do not appear to move

appreciably during periods of

temporary snow cover.

• Data collection may be

interrupted by inclement

weather (Hayden and Wunz

1975 as cited in Ontario 1985).

WMH: The biggest problem is

getting suitable tracking

conditions; depends on

consistent and persistent snow

cover.

• Toms and hens behave

differently in the winter where

hens are often found in large

aggregates (Ontario 1985)

• Small sample size, especially in

populations not yet established

(Wooley et al. 1978 as cited in

Ontario 1985).

• Tracks are remote from the

animal in time; and it is difficult

to determine how many birds

made the tracks (Bull 1981).

• “Caution must be exercised in

using these data, as one animal

may cross a transect or plot

several times. Therefore, these

counts should be considered an

index of activity rather than a

measure of abundance” (Brower

1990:122). 

• Harvest/unit effort and

survey at 1985

postage rates cost

$70,450

(Kurzejeski and

Vangilder 1992),

plus data entry

and analysis costs;

a fall survey cost

an additional

$35,000 plus data

entry and analysis

costs.

• In Missouri, the

costs for spring

and fall

mandatory check

stations average

$40,000 annually

(Kurzejeski and

Vangilder 1992). 

• Can estimate

number of harvested gobblers has

been shown to be related to markrecapture population estimates (Lint

et al. 1995).

• May indicate trends better than

population estimates (Bailey 1980).

• Many wildlife management areas

may already be collecting data

needed for the harvested-gobblers

index; managers can compile

existing hunter effort information to

examine possible trends and

relations between harvested gobblers

and hunter effort for their gobbler

population (Lint et al. 1995)

• Harvest data are relatively easy

and inexpensive to collect (Lint et al.

1995, Stauffer 1993).

• Direct biological data, such as

weight, age, and sex, may be

collected from harvested birds at

check stations.

• Bias in sex and age ratios (Wunz

and Shope 1980).

• After-the-fact information may

have little use in planning future

hunting seasons (Wunz and

Shope 1980).

• Harvest/unit effort not as

useful an index when harvest

greatly influences population size

(Lint et al. 1995).

• Operation of a check station is

time consuming.

• The proportion of the total

population that is harvested

(harvest rate) must be constant

for areas or time periods being

compared (Lancia et al.

1994:223). WMH: Fall, eithersex harvest data are difficult to

interpret because (a) food

availability affects harvest, (b)

concurrent seasons for other

species also affect harvest

(harvest rate may remain

constant as population declines

because turkeys are taken by

hunters pursuing other species).

• Hunter report cards provide

Appendix C. 91
92 Wild Turkey Harvest Management: Biology, Strategies, and Techniques

II. Indices (continued)

Method Advantages Disadvantages Cost Manpower

Harvest data

(continued)

less accurate data than check

stations (Myers 1973).

• Hunter surveys may provide

an insufficient sample size to

provide regional or county

information (Kurzejeski and

Vangilder 1992).

• Results may not be available in

time for the regulation setting of

the next year’s hunting season or

for making available to the public

in a timely fashion (Kurzejeski

and Vangilder 1992).

• Hunters may have forgotten

pertinent information by the time

surveys reach them (Kurzejeski

and Vangilder 1992).

• The data source may be of

variable reliability (hunters) and

there is a lack of control over

data quality (lack of variance

estimates, etc.). The data may

not lend itself to statistical

analyses, and thus it is difficult to

identify real differences between

areas or years. At best, we are

limited to general statements

about population trends from

hunter data (Stauffer 1993).

• Hunters tend to inflate their

reported hunting success

through pride, prestige, or

memory loss, causing large

biases. Those hunters who bag

nothing are the worst offenders

(Seber 1982:489).

• Successful hunters tend to

respond at a higher rate than

unsuccessful hunters (Kurzejeski

and Vangilder 1992).

• Hunter surveys do not allow

the collection of some biological

data that may be collected at

check stations (Kurzejeski and

Vangilder 1992).

• Obtaining mailing lists and

recovering mail surveys takes a

lot of time—may be up to a year

and a half after the end of the

season before the data are

available (Kurzejeski and

Vangilder 1992). 

• In Missouri, a spring mail
II. Indices (continued)

Method Advantages Disadvantages Cost Manpower

Feeding sites

(scratch marks,

pellets, etc.)

Dusting sites 

Dropping/fecal/

pellet counts 

Frequency

indices

Poult survival

studies,

based on

radiotelemetry 

the relative numbers of individuals,

demonstrate population fluctuations,

and also determine preferred

habitat types and seasonal use

patterns (Neff 1968 as cited in

Ontario 1985).

• Dropping configuration is useful in

revealing the sex of the birds (Bailey

1956 as cited in Ontario 1985).

• “Some of the problems associated

with counting actual populations do

not occur when counting droppings”

[the author does not elaborate]

(Ontario 1985). 

• Useful when it is difficult to count

the number of individuals in a unit,

but easy to determine the presence

or absence of individuals.

• SMP: Useful for measuring

distribution. 

• High poult mortality was shown to

be the major factor controlling

population density in AL (Everett et

al. 1980). 

• “Because dusting regulates the

amount of lipid substance on the

feathers,... the amount of dusting

may be in response to

environmental factors (e.g., diet),

so caution should be used in

comparisons between

populations” (Bull 1981). 

• Observer fatigue, boredom,

and lack of experience contribute

to missed groups [and therefore

cause bias] (Ontario 1985).

• The possibility of counting the

same droppings from a previous

year is a source of error (Ontario

1985).

• Durability of scat and

resistance to weather, diet,

behavior, and time must be

considered (Bull 1981).

• Loss of dropping groups by

erosion and insect attack

increase variations between

years as do defecation rates

where an increase in dropping

density may be due to an ample

food supply in a particular year,

not an increase in population

(Ontario 1985). 

• May not be an accurate

indicator of overall productive

success when only a small

percentage (<40%) of the hens

are radio-tagged (Everett et al.

1980). 

Appendix C. 93
94 Wild Turkey Harvest Management: Biology, Strategies, and Techniques

IV. Tools for the above techniques

Method Advantages Disadvantages Cost Manpower

Radiotelemetry

Summer

baiting

(2 indices

described by

Hayden 1985)

May be used as

a tool for brood

surveys, or for

capturing birds

for use in markrecapture

estimates. 

Winter baiting

May be used as

a tool for

capturing birds

for use in markrecapture

estimates.

Mail-carrier

surveys

(a tool for

roadside

counts/brood

surveys)

• Advantage over mark-recapture as

a separate technique: often provides

insight into sources of mortality (and

sometimes even estimates of sourcespecific mortality risks) and

information about emigration

(Pollock et al. 1990:67).

• When used for mark-recapture

studies, resightings are generally

much cheaper to acquire than

physically capturing and handling

the animals (White 1996).

• Allows determination of the

location and status of individuals

without having to flush or disturb

the birds (Stauffer 1993).

• Could increase the effectiveness of

using tape-recorded calls to lure

hens for brood counts (Tefft

1996a,b). 

Relatively expensive, in both

equipment costs and the

substantial field effort needed to

obtain frequent locations on a

large number of animals (Pollock

et al. 1990:67).

• There may sometimes be a lack

of independence between losses

of contact and animal status, and

such a dependence could cause

problems in estimation (Pollock

et al. 1990:67).

• When used for mark-recapture,

unmarked animals are not

marked on subsequent occasions

(White 1996). 

• Resulting fall population

estimates need to be adjusted

depending on mast production

(Hayden 1985).

• Turkeys may not find the bait

attractive if a natural food supply

is plentiful (Ontario 1985). 

The majority (25 of 31) feed

stations were not found by

turkeys in CO (Myers 1973) . 

• Makes visible turkeys that might

otherwise never be seen (Hayden

1985); quickly attracts birds to roads

where they can be counted (Ontario

1985).

• Bait station routes can be easily

established.

• Personnel can be trained quickly to

bait and examine stations .

• Counts appeared to be reliable in

CO (Myers 1973).

• Recommended technique to

concentrate birds for census in CO

(Myers 1973).

• Provide “as accurate an indicator of

population trends as is possible to

obtain” (Myers 1973).

• Turkey counts can be tallied by

counting tracks in fresh snow (need

not see birds themselves) (Myers

1973). 

• The daily routines of the

participants are consistent (Ammann

and Ryel 1963).

• The counts can be taken whenever

the information is needed the most,

or when the habits of the birds are

most likely to yield consistent results

(Ammann and Ryel 1963).

• Helps acquaint the carriers (many

of whom are hunters) with the

management program (Ammann and

Ryel 1963). 
IV. Tools for the above techniques (continued)

Method Advantages Disadvantages Cost Manpower

Camera

stations

May be used as

a tool for brood

surveys.

Adaptive

sampling 

Stratification

A statistical tool

for various

sampling

techniques.

Infrared

sensing

imagery 

Tape-recorded

calls 

(poult distress

calls, male

vocalizations)

(Kimmel and

Tzilkowski 1986)

• Sampling intensity used in a given

area depends on the density found in

the previous area(s), with sampling

then concentrated in the higherdensity areas; useful if the

population is very patchy and the

population area is very large (Seber

1986). 

• “As populations tend to be very

patchy, stratified sampling seems to

be the most appropriate method”

(Seber 1986).

• WMH: Many states are already

stratified along management zones. 

• Can greatly increase detection

rates over standard aerial survey

techniques (Naugle et al. 1996).

• High cost may be justifiable in

areas where current methods of

estimating density are questionable

(Naugle et al. 1996).

• Untrained interpreters may

lack the ability to distinguish

false targets (see Naugle et al.

1996 with references) and usually

count more individuals than

skilled interpreters (see Naugle

et al. 1996 with references).

• Overlapping transects may

cause biases (Naugle et al. 1996

discuss ways to correct for

these).

• Higher cost than visual aerial

transect sampling (Naugle et al.

1996).

• May not be cost-effective in

open habitats where less

expensive techniques (e.g., visual

aerial counts) reliably estimate

density (Naugle et al. 1996). 

$99 per km2

scanned

(including cost

of flying to

study area, per

diem for 2

people, and

analysis of

infrared tapes),

for a deer

survey in South

Dakota in 1994

(Naugle et al.

(1996). 

• Cost and time/labor effective

(Johnson et al. 1981).

• Useful for luring broods into

open areas where visibility of

poults would be optimum (Kimmel

and Tzilkowski 1986).

• Can be used “to elicit a vocal or

visual response from an otherwise

silent or invisible animal”

(Norton-Griffiths 1978:107).

• Can increase sample size by

eliciting responses from birds that

are elusive and not easy to detect

visually (Marion et al. 1981,

Johnson et al. 1981).

Appendix C. 95

• An index, so lacks the precision

of quantitative estimates of

density.

• Males do not respond to taped

call.

• An important source of bias is

the failure of a certain proportion

of the population to respond to

auditory signals (Marion et al.

1981, Johnson et al. 1981).

• For some bird species,

“indiscriminate use of playback

recordings on repeated visits

during the breeding season can

bias the results as birds may alter

their habits or their terrestrial 
96 Wild Turkey Harvest Management: Biology, Strategies, and Techniques

IV. Tools for the above techniques (continued)

Method Advantages Disadvantages Cost Manpower

Tape-recorded

calls 

(poult distress

calls, male

vocalizations)

(continued)

• A playback recording census can

increase the total number of birds

seen or heard for a given species in

comparison to a conventional census,

especially for species with low song

activity (Johnson et al. 1981).

• Some species of birds may

respond to tape recordings at times

when they would otherwise remain

silent (Johnson et al. 1981, with

references).

• In a study of blue grouse, hooting

males were counted faster and a

total census was obtained sooner by

eliciting the response of males to the

recorded calls of a hen (Stirling and

Bendell 1966). 

boundaries if they believe a

competing member of the same

species is holding territory

nearby” (Johnson et al. 1981).

• “If censused too often some

individuals and/or species may

become less responsive”

(Johnson et al. 1981).

• Response rates and

detectability can be influenced by

such factors as wind, rain, time of

day, temperature, seasonality,

species response traits, lunar

cycles, and disturbance by

humans or other predators,

effects of terrain and vegetation

on sound, territoriality, breeding

condition (Johnson et al. 1981,

Bull 1981, Stirling and Bendell

1966-possible effects of wind and

other weather on blue grouse).

• Proved ineffective at locating

turkey broods in Rhode Island

(Tefft 1996a,b), but could be

improved with the aid a radio

transmitters. 
The end of a successful spring gobbler hunt, E. S. “Sam” Nenno, Monongalia County, West Virginia, 1982.

U.S. Forest Service, E.S. Nenno.
U.S. Department of the Interior

U.S. Fish & Wildlife Service

Route 1, Box 166

Sheperdstown, WV 25443

http://www.fws.gov/

April 2000