POTENTIAL DENSITY
DEPENDENCE IN WILD
TURKEY
PRODUCTIVITY IN THE
SOUTHEASTERN
UNITED STATES
Michael E. Byrne1,2
Michael J. Chamberlain
Warnell School of Forestry and Natural Resources,
University of Georgia,
180 E Green St,
Athens, GA 30602, USA
Warnell School of Forestry and Natural Resources,
University of Georgia,
180 E Green St,
Athens, GA 30602, USA
Bret A. Collier
School of Renewable Natural Resources,
Louisiana State University Agricultural Center,
227 Highland Rd,
Baton Rouge, LA 70803, USA
Abstract: Observations in recent years by state agency biologists in the southeastern United States that are members of the
Southeast Wild Turkey Working Group (SEWTWG) have indicated region-wide declines in productivity indices of wild
turkeys (Meleagris gallopavo; hereafter, turkey). Concerned that productivity declines were indicative of general large scale
population declines, we initiated a study to assess productivity and population trends in the southeastern United States based
on historical data collected by SEWTWG member states. Our goals were to summarize and analyze trends in demographic
parameters temporally and spatially and generate hypotheses to account for observed declines in productivity. Thirteen states
provided historical records (range: 17–54 years) of annual productivity, which we used to evaluate reproductive trends. We
used a combination of spring harvest data (range: 8–52 years) and data from the U.S. Geological Survey’s Breeding Bird
Survey (BBS; 1966–2011) to quantify trends in turkey population sizes. Because of wide discrepancies in data collection
methodology and availability across states, we characterized productivity and population trends for individual states and
made biological inferences based on similarities observed across the region. At the state level, our results suggested that
productivity has declined concomitant with increasing or stabilizing population sizes. Declines in productivity indices
appeared to be best characterized by historical increases in number of females observed without broods. However, brood size
appeared to have remained relatively stable. A subsequent literature review suggested a historical trend of increasing annual
female survival rates. This led us to hypothesize that productivity may be limited in a density dependent manner. Specifically,
we posit that productivity is mediated through site dependent regulation: as population density increases, availability of good
quality nesting habitat becomes a limiting factor and a greater percentage of the hen population is forced to attempt to nest in
poor quality nesting habitat, thus reducing per capita reproductive success.
Proceedings of the National Wild Turkey Symposium 11:329–351
Key words: Breeding Bird Survey, brood counts, density dependence, harvest, Meleagris gallopavo, population ecology,
productivity, reproduction, southeastern United States, survival, wild turkey.
Associate Editor: Porter
1
Present address: Halmos College of Natural Resources and Oceanography, Nova Southeastern University, 8000 North Ocean
Drive, Dania Beach, FL 33004, USA.
2
E-mail: [email protected]
329
330
Productivity and Survival
Restoration of wild turkeys (Meleagris gallopavo;
hereafter, turkey) is one of the greatest success stories in
wildlife management and populations in many regions
increased rapidly in the last decades of the 20th century
(Kennamer et al. 1992, Eriksen et al. 2015). However,
recent perceived declines in turkey abundance and
reproductive output have caused concern (Eriksen et al.
2015). We initiated our study in response to these
perceived, persistent declines in annual productivity indices
of turkeys based on surveys conducted by biologists of state
agencies that are members of the Southeast Wild Turkey
Working Group (SEWTWG). There was concern among
these state biologists that observed declines were an
indicator that large scale, regional declines in turkey
populations were presently occurring, or were likely to
occur in the near future.
Influence of stochastic environmental variables, such
as rainfall and temperature, on short term annual variations
in reproduction of turkeys has been well documented in the
literature (reviewed in Warnke and Rolley 2005). However,
large scale, population level drivers of long-term reproductive trends remain largely unexplored. Density dependent mechanisms are potential drivers in long-term
productivity trends, and need to be considered given rapid
expansion of turkey populations following restoration
efforts (Kennamer et al. 1992). A basic tenant of population
biology is that if increasing populations reach sufficient
densities, they will trigger negative feedback loops that
limit population growth. As such, it is expected that a
negative relationship should exist between population size
and rate of population increase. Guthery and Shaw (2013)
observed that evidence of density dependence in upland
game birds has existed in the literature since the 1940s.
Porter et al. (1990) and McGhee and Berkson (2007) both
suggested density dependent population growth in turkey
populations based on analyses of various harvest indices.
One way in which density dependence may manifest
itself is through decreased recruitment. Density dependent
effects on reproduction have been documented across a
variety of avian taxa through both experimental (Dhondt et
al. 1992, Both 1998, Pöysä and Pöysä 2002, Sillett et al.
2004, Brouwer et al. 2009) and observational studies
(Larsson and Forslund 1994, Ferrer and Donazar 1996,
Bennetts et al. 2000, Armstrong et al. 2005, Carrete et al.
2006). Negative relationships between population density
and reproduction in gallinaceous birds were documented in
the literature as early as the 1940s (Guthery and Shaw
2013). Specifically, Errington (1945) found such a
relationship in both northern bobwhite (Colinus virginianus) and ring-necked pheasant (Phasianus colchicus)
populations. Existence of a density dependent effect on
turkey reproduction has been hypothesized by several
authors, who have noted reduced productivity in populations considered stable, compared to recently introduced
and expanding populations (Vangilder et al. 1987, Vander
Haegan et al. 1988, Miller et al. 1998b, Bond et al. 2012).
At the 2011 meeting, SEWTWG formalized research
priorities and decided that, before initiating regional field
studies, it would be informative and cost effective to
examine demographic trends retrospectively, using existing
datasets maintained by member states. Historical trend
analyses would provide a long-term, large scale perspective
on turkey population ecology in the region. In doing so,
survey techniques could be examined critically and
hypotheses could be developed from existing data to
further refine research priorities moving forward. In the
present study, we use long-term trend data for both
productivity and population size collected on a large scale
to assess plausibility of density dependent effects on
reproduction in turkeys. Our specific goals were 2-fold:
(1) summarize and analyze trends in demographic parameters temporally and spatially, and (2) generate hypotheses
to account for observed declines in productivity. It is our
hope that these hypotheses will stimulate discussion of
turkey population dynamics and identify fruitful avenues of
future research.
METHODS
Data Collection and Availability
We began data collection in April 2012 by contacting
turkey coordinators of cooperating states within SEWTWG
and asking them to provide all available data and historical
records regarding productivity indices for turkeys. Inferences regarding productivity need to be made in the context
of population density because, while productivity declines
may result in population declines, under many density
dependent scenarios, productivity declines may in fact be
indicative of increasing population densities. As such, we
also asked for historical data regarding harvest records,
turkey restoration, and restocking information. We requested coordinators to provide as much associated metadata and
background information as was available for each dataset.
Data included 2 subspecies, eastern turkey (M. g. silvestris)
and Florida or Osceola turkey (M. g. osceola).
Productivity
Thirteen states provided productivity index records
(Table 1). Oklahoma data were from the southeastern
portion of that state occupied by the eastern subspecies.
Alabama initiated a statewide productivity monitoring
program in 2010, and we did not use those data for making
inferences on long-term productivity trends (Table 1). The
primary metric used to index reproduction region-wide was
poult per hen (PPH) ratio. This ratio was defined as ratio of
total number of poults to total number of females observed
during the summer brood-rearing period. In most states,
sightings of turkeys were recorded opportunistically during
summer months by agency personnel, or a combination of
agency personnel and citizen volunteers, as they went about
their daily activities (e.g., Butler et al. 2015). Generally,
observers were asked to record observations of females
with and without broods. West Virginia was different in
this regard, as observations of females without young often
were not recorded. Thus, the reader should bear in mind
that reported PPH ratios for West Virginia have a slightly
different biological meaning than other states.
For most states, the observation period included June–
August; Tennessee reported PPH ratios based only on
observations recorded in August, the sample period for
Kentucky and North Carolina was July–August, and West
Turkey Productivity Byrne et al.
331
Table 1. Historic availability of statewide productivity data (poult per hen ratios [PPH]) of eastern turkeys from 13 states in the
southeastern United States, including number of years (n) and availability of raw data. PPH = the ratio of observed poults to adult
females observed during summer brood surveys. Data obtained from respective state agencies.
State
Years
n
Raw dataa
Alabama
Arkansas
Georgia
Kentucky
Louisiana
Mississippi
Missouri
North Carolina
Oklahoma
2010–2011
1982–2012
1978–2012
1984–2011
1994–2010
1995–2012
1959–2012
1988–2012
1985–2012
2
31
35
28
17
18
54
25
28
NA
NA
1978–2011
NA
1994–2010
1995–2012
1990–2011
2001–2011
2001–2012
South Carolina
Tennessee
Virginia
WestVirginia
1982–2012
1983–2012
1979–2010
1967–2012
31
30
32
46
NA
2003–2012
NA
1967–2012
a
Notes
PPH ratio only calculated for brood groups with 2 hens
Data only includes SE portion of the state where the eastern subspecies occurs
PPH ratios calculated only from observations during month of August
Productivity calculated from ratio of adults/young in fall harvest
Based only on observations of females with broods
NA = raw data not available.
Virginia used observations of broods during May–September. All states asked observers to record individual
sightings as separate events. However, when states
calculated PPH ratios, total numbers of poults and females
from individual observations were combined to provide a
single estimate. Guidelines for filtering spurious and
unlikely observations prior to calculating PPH ratios, if
they existed at all, were not standardized across states and
in many cases were not standardized across time within a
state. For example, a current biologist may have filtered
observations considered spurious based on an improbably
large ratio of females to poults, whereas his or her
predecessors did not.
Of states that monitored productivity, Virginia was the
only state in which PPH ratios were not calculated from
summer observations. Rather, PPH ratios were derived
from ratio of juveniles to adult females in fall harvest based
on reports at mandatory hunter check stations. While
Virginia did record summer brood observations, inferences
regarding productivity were traditionally based on fall
harvest data because spatial distribution was more consistent over time and sample sizes were larger than summer
observations. Virginia discontinued using fall harvest to
index productivity in 2010. However, we considered it the
most appropriate for our purposes because it represented
continuous data for 26 years and was traditionally used by
Virginia as the primary measure of productivity. These
methodological disparities hindered our ability to make
direct comparisons among states. Despite this, as long as
methodological biases present within a state were relatively
consistent over time, we offer that any significant, longterm changes in statewide productivity would still be
identifiable in regards to relative historical trends in PPH
ratios.
We attempted to characterize observed trends in PPH
ratios based on the assumption that long-term changes in
PPH ratios could result from 2 potential underlying factors:
(1) changes in number of females observed without broods,
and (2) changes in mean size of observed broods. While not
necessarily mutually exclusive, each scenario suggests a
different set of possible mechanisms underlying observed
trends. For example, a proportional increase in number of
females observed without broods may suggest that nesting
success, or proportion of females attempting to nest, had
declined. However, a relatively stable proportion of
females observed with broods with declining brood sizes
may indicate declining poult survival or decreased
fecundity.
Eight states (Georgia, Louisiana, Mississippi, Missouri, North Carolina, Oklahoma, South Carolina, and
Tennessee) reported annual percentage of females observed
without broods, or provided raw data that allowed us to
calculate PPH. We were unable to calculate brood sizes
from available data. Additionally, simply calculating
annual ratio of total poults to total females based on
observations of females with young was inappropriate
because of the necessary assumption that observed young
were distributed evenly among all females. This has
potential to introduce considerable bias, because females
without broods will travel with females tending broods
(e.g., Byrne et al. 2011). Therefore, we reasoned that the
most accurate way to measure mean brood size was to rely
solely on observations of single females with young. For
states that provided raw data that included records of
individual observations, we estimated mean annual brood
size and corresponding 95% confidence interval based on
observations of single females with 16 poults. We chose
16 poults because, based on our personal field experiences
and mean clutch sizes reported in the literature (Vangilder
1992), clutches .16 are exceptionally rare. Thus, we
assumed that observations of single females with broods of
more than 16 poults likely represented an erroneous or
incomplete observation.
Abundance Indices
All states provided spring harvest records. Nine states
had fall turkey seasons during all or part of our study
period. However, we concentrated our analyses on spring
harvest because (1) all cooperating states had a spring
turkey season and (2) range-wide, spring seasons generally
Productivity and Survival
332
Table 2. Historical availability of statewide spring eastern turkey
harvest data from 15 states in the southeast United States. Total
harvest was estimated number of total males harvested and
hunter numbers were estimates of total hunters or eligible
hunters based on permit sales. Data obtained from respective
state agencies.
State
Total harvest
Hunter numbersa
Alabama
Arkansas
Florida
Georgia
Kentucky
Louisiana
Mississippi
Missouri
North Carolina
1972–2012
1961–2012
1989–2012
2005–2013
1978–2012
1980–2012
1981–2012
1960–2012
1977–2011
Oklahoma
South Carolina
Tennessee
Texas
Virginia
West Virginia
1990–2012
1976–2011
1990–2010
1995–2012
2004–2013
1996–2012
1972–2012
NA
1989–2010
2005–2013
1997–2012
1980–2010
1981–2009
1960–2011
1976, and every third
year since 1983
NA
1977–2010
NA
1983–2011
NA
NA
a
NA = estimates of hunter numbers not available.
see much greater hunter interest and participation than fall
seasons, especially in recent decades (Eriksen et al. 2015).
There was considerable inconsistency regarding data
availability, how data were obtained, and length of
historical records (Table 2). The 1 metric common to all
states was an annual estimate of spring harvest, although
methods of obtaining this estimate differed among states
and methods often changed within states through time.
Estimates of spring harvest were variously derived from
information gathered at check stations, or through hunter
surveys conducted via mail, phone, internet, or various
combinations thereof. Calculations of harvest metrics were
variously conducted in-house by agency personnel, or by
outside entities such as universities or consulting firms.
Data sufficient to provide estimates of hunter effort were
available in 8 states (Alabama, Florida, Georgia, Kentucky,
Louisiana, Mississippi, Missouri, and South Carolina) over
a variable number of years. These data consisted of annual
estimates of spring hunter effort derived from license sales
or hunter surveys.
Harvest estimates are often used as proxies of
population density, although few studies have tested
veracity of this relationship (Lint et al. 1995). Inferring a
direct relationship between harvest and population size is
difficult, as harvest is a function of availability of turkeys to
hunters, and rate at which hunters are able to harvest
turkeys. Likewise, there is obvious age and location
specific selection in turkey harvest events. Harvest may
be a sufficient index if hunter effort remains constant over
time (Lint et al. 1995); otherwise, trends in overall harvest
may be representative of hunter population dynamics and
behavior more so than changes in turkey populations. To
test for a relationship between hunter numbers and spring
harvest, we performed a simple linear regression for the 8
states above that provided estimated hunter numbers. A
great correlation between harvest estimates and hunter
numbers would indicate that raw harvest estimates are
unsuitable as indices of turkey populations. Theoretically,
accounting for hunter effort should provide a more accurate
link between harvest data and turkey population density. If
turkey populations are experiencing noticeable long-term
changes in density, there should be corresponding changes
in hunter success per unit effort. To account for effort, we
examined trends in spring hunter success (defined as
percentage of hunters to harvest 1 turkey), or harvest
proportion (defined as turkeys harvested per number of
turkey hunters) for states that reported such information
directly, or in which we were able to calculate these metrics
ourselves from data provided. In Florida, we chose to
model trends in hunter success rather than harvest
proportion because this metric was based directly on
responses to hunter surveys, and thus did not introduce bias
associated with estimating hunter numbers. We also
modeled hunter success for Mississippi, as these values
were reported annually during 1980–2009 (Hunt 2010). We
note that more information is required to derive a truly
accurate measure of catch per effort. For example, ratio of
harvested turkeys to number of hunters may remain the
same, but days required to harvest a turkey may change.
However, this level of information was not commonly
available.
State agencies work independently to collect and
summarize turkey population metrics and resulting inconsistencies make comparisons among states problematic
(also see Eriksen et al. 2015). This, along with issues
inherent in using harvest data as an index of population,
prompted us to use data from the U.S. Geological Survey’s
Breeding Bird Survey (BBS; https://www.pwrc.usgs.gov/
BBS/) as an independent and methodologically consistent
index to overall population trends for each state. The BBS
is an annual road-based survey designed to track large scale
changes in avian abundance over time. Over 4,000 survey
routes exist in the United States and Canada; each route is
39.43 km in length, is surveyed by a qualified observer
once each year, and is often surveyed by the same observer
consecutively for a series of years. Each route contains 50
evenly spaced sample points from which an observer
conducts a 3-minute point-count, recording all bird species
seen and heard. Annual surveys in the southeastern United
States are normally conducted in early–mid June, with the
exception of Florida, where surveys may occur in May.
BBS data are summarized for individual states and larger
physiogeographic regions of the continent. For any
geographic region, a hierarchical model, which accounts
for varying regional survey quality and observer effects
(Link and Sauer 2002, Sauer and Link 2011), is used to
estimate an annual population index (measured as observations per route).
For our purposes, the standard protocol over space and
time was an obvious advantage of BBS indices. The BBS
has been ongoing since 1966, providing a long-term data
set that encompasses restoration of turkeys in the
southeastern United States to present day. Additionally,
the BBS index incorporates observations of all turkeys
regardless of age or sex and, as such, provides a general
index of total turkey population density as opposed to most
state-derived estimates, which are based on data pertaining
to specific sexes (e.g., spring harvest data). Timing of BBS
Turkey Productivity Byrne et al.
333
Table 3. Studies reporting annual survival estimates of female eastern turkeys used in tracking survival trends over time. All studies
derived survival rates based on radiotelemetry data and known fate models.
Study years
Study location
Landscape
Reference
1981–1989
1984–1985
1984–1994
1987–1990
1988–1994
1990–1993
1990–1993
1990–1994
1993–1996
1998–2000
2002–2006
2002–2010
2003–2005
2010–2012
2012
Missouri
Missouri
Mississippi
Mississippi
Wisconsin
New York
Missouri
Virginia–West Virginia
Iowa
South Carolina
Ohio
Louisiana
Indiana
Delaware
Arkansas
Mixed forest–agriculture
Mixed forest–agriculture
Mixed hardwood–pine (Pinus spp.)
Pine plantation
Mixed forest–agriculture
Mixed forest–agriculture
Mountain hardwood
Mountain hardwood
Mixed forest–agriculture
Coastal pine
Hardwood
Bottomland hardwood
Agricultural
Pine plantation
Mixed hardwood–pine
Vangilder and Kurzejeski 1995
Kurzejeski et al. 1987
Miller et al. 1998a
Palmer et al. 1993
Wright et al. 1996
Roberts et al. 1995
Vangilder 1995
Pack et al. 1999
Hubbard et al. 1999
Moore et al. 2010
Reynolds and Swanson 2010
Byrne 2011
Humberg et al. 2009
J. Bowman, personal communication
T. Pittman, personal communication
surveys coincides with end of nesting season and beginning
of the brood survey period over much of the region.
Theoretically, this should allow all members of the
population, except late nesting females, to be available
for detection in surveys.
We queried annual BBS abundance indices and
associated 95% credibility intervals for each individual
state during 1966–2011 through the interactive online BBS
results and analysis portal (http://www.mbr-pwrc.usgs.gov/
bbs/bbs.html). We did not include BBS data for Oklahoma
and Texas because the eastern subspecies only occurred in
a relatively small portion of each state, containing few BBS
routes. Additionally, BBS analyses are performed at the
state and species level, making it difficult to separate
eastern turkeys from Rio Grande turkeys (M. g. intermedia)
present in large portions of these states.
Restoration Efforts
Turkey populations in many parts of the southeastern
United States are largely the result of intensive, large scale
restoration efforts. Restoration was largely accomplished
through live release of turkeys captured from extant
populations into areas containing suitable habitat in which
turkeys were absent or existed at very small densities
(Kennamer et al. 1992). Given large scale introductions and
dispersals of turkeys into new areas from restoration
efforts, we investigated if release efforts conferred a
noticeable change in productivity trends. Historical restoration information was available from 6 states that also
provided productivity data (Georgia, Louisiana, Mississippi, Missouri, North Carolina, and Tennessee). This
information primarily consisted of counts of turkeys
released, sometimes detailed to specific release sites, and
in other cases summarized by county or parish. Given the
large scale nature of this study, and inconsistencies in
reporting, we tabulated number of turkeys released
statewide in a given year. We determined when restoration
was 50%, 75%, and 95% complete in each state based on
total cumulative releases and qualitatively compared
historical releases to trends in PPH indices.
Female Survival
To provide further context for interpreting observed
productivity trends, we reviewed the literature for female
survival estimates. We limited our review to studies which
used robust statistical methods such as Kaplan–Meier
(Pollock et al. 1989) or Heisey–Fuller (Heisey and Fuller
1985) to derive annual survival estimates from radiotelemetry data. Additionally, we queried researchers currently
engaged in studies of female survival to obtain preliminary
results (Table 3). To bolster sample sizes, we considered
studies that spanned the entire geographic range of the
eastern subspecies. To investigate how survival may have
changed over time, we binned studies into one of 3 time
periods, (1980–1989, 1990–1999, and 2000) and recorded
mean annual survival estimate for each study. For multiyear studies that spanned across time bins and reported
annual survival estimates for individual years, we classified
years into their respective time period and calculated a
mean annual survival rate. For example, Miller et al.
(1998a) reported annual survival rates during 1984–1994.
In this case, we calculated a mean survival rate for 1984–
1989 and 1990–1994, respectively. If a study spanned 2
time periods but did not report individual survival rates for
each year, we placed it in the time period in which most of
the study occurred. For example, Moore et al. (2010)
reported only a single survival rate estimate from a study
during 1998–2000. Because most of the study occurred
prior to 2000, we grouped this study into the 1990s.
Additionally, in cases in which successive studies of
specific study sites built on and incorporated date reported
in earlier studies, we only used survival estimates reported
in the most recent study. For example, Byrne (2011)
incorporated data reported in Wilson et al. (2005) and, as
such, we only used results from Byrne (2011).
Analyses
Because considerable differences in data availability
and quality among states precluded pooling data across
states, we used a comparative analysis approach. We
accomplished this by first analyzing available data from
334
Productivity and Survival
each state independently, then making biological inferences
based on broad scale similarities in trends observed across
states. Statistical imprecision in productivity and harvest
datasets is caused by inherent biases resulting from
unquantified levels of measurement error in data collection
methodologies and inconsistent survey protocols. Additionally, stochastic annual variation in conditions that
influence nesting success can lead to annual variation in
long-term productivity averages (Vangilder et al. 1987,
Healy 1992). Similarly, factors such as weather conditions
and timing of hunting seasons relative to timing of a given
year’s nesting cycle may introduce annual variation in
harvest numbers. These combined factors cause variation
that may obfuscate underlying trends representing larger,
population level processes in which we were interested.
To account for expected nonlinearity in our data due to
stochasticity, we used generalized additive models (GAM;
Wood 2006), which provide a flexible, regression based
method for fitting a curve to noisy, non-linear data. General
additive models are similar in nature to generalized linear
models, but in a GAM, the linear predictor incorporates a
set of smooth functions of any number of predictor
variables (in our case, time). Variables are smoothed via
a spline function, which is essentially a series of multiple
polynomial regressions connected at various points (knots)
to create a continuous smooth line through data. Applying
GAMs allowed us to smooth time series data to elucidate
underlying trends. Specifically, we fit GAMs to time series
data of PPH ratios, proportion of females observed without
broods, spring harvest numbers, and spring harvest
proportions. We adjusted number of knots used to produce
spline curves for each model to improve model fit. We
evaluated goodness-of-fit for each model based on visual
inspection of residual normality via standard diagnostic
residual plots, Q-Q plots, and residual histograms. We
report model predicted estimate and 95% confidence
interval for each year. We fit GAMs in the statistical
program R (R Core Team 2013) using the mgcv package
(Wood 2013).
To illustrate relationships between trends in abundance
and productivity, we plotted GAM predicted productivity
(PPH) values as a function of relative population size (BBS
indices) for each state in which productivity data were
available. To account for differing scales among states, we
first scaled PPH ratios and BBS indices from 0 to 1 for each
respective state, with 0 being least value of each metric and
1 being largest value of each metric.
RESULTS
Productivity
Twelve states provided historical productivity data
ranging from 17 to 54 years (mean = 31 years, Table 1).
Productivity generally declined across all states where data
were available (Fig. 1), but the nature and severity of
declines was variable. For example, Tennessee experienced
a particularly steep decline, whereas productivity in
Mississippi remained relatively stable over the record
keeping period (Fig. 1). Percentage of females observed
without poults generally increased through time in the 8
states from which data were available (Fig. 2). Again,
strength of this trend varied among states but, in 5 states,
ranges in model-predicted estimates approached or exceeded 20% (Mississippi = 18.7%, Louisiana = 19%, Missouri
= 25.8%, Oklahoma = 26.8%, Tennessee = 28.8%).
Difference between largest and smallest annual mean brood
size was ,2 poults for all states except Oklahoma and West
Virginia (Fig. 3). Mean observed brood size (6 95% CI)
ranged 4.5 (6 0.6) to 6.4 (6 0.8) in Louisiana, 5.1 (6 0.2)
to 6.7 (6 0.3) in Missouri, 4.5 (6 0.3) to 6.1 (6 0.4) in
Mississippi, 4.4 (6 0.3) to 5.4 (6 0.4) in North Carolina,
and 5.1 (6 0.4) to 6.2 (6 0.4) in Tennessee. Mean observed
brood size ranged 4.6 (6 0.9) to 7.3 (6 0.7) and 5.2 (6 0.6)
to 9.0 (6 1.2) for Oklahoma and West Virginia,
respectively.
Abundance
All 15 states provided historical data on estimated
spring harvest ranging 8–52 years (mean = 28 years, Table
2). Trends in spring harvest were greatly variable across
states, and it was difficult to generalize a region-wide trend
(Fig. 4). A number of states, including Kentucky, North
Carolina, and Tennessee, exhibited consistently increasing
trends in spring harvest through time, whereas states such
as Arkansas, Missouri, South Carolina, and West Virginia
exhibited a trend of stabilizing or decreasing harvest
following a peak in the late 1990s or early 2000s. The
most precipitous and persistent decline was observed in
Mississippi (Fig. 4).
We found that correlations between hunter numbers
and spring harvest were great in all states, and that hunter
numbers were a significant predictor of total harvest (Table
4; Fig. 5). Thus, harvest estimates were likely strongly
biased by hunter participation. We also noted an additional
potential confounding relationship between harvest and
hunter numbers in that hunter participation may itself be
influenced to some degree by turkey population densities
(i.e., a functional response in which, when turkey
populations are perceived to be great, a greater number of
hunters may participate in spring hunting). When accounting for harvest effort, harvest trends were generally less
variable over time than raw harvest estimates (Fig. 6). For
instance, hunter success in Mississippi has consistently
hovered around 50% despite persistent declines in numbers
of turkeys harvested. In Missouri, an increase in estimated
harvest of 60,650 turkeys between 1960 and 2004
corresponded with an increased harvest proportion of only
0.32 turkeys harvested per hunter.
Trends in data from BBS suggested population
increases over time, but magnitude of increase varied
considerably across states (Fig. 7). Tennessee, for example,
exhibited a particularly sharp population increase beginning in 2000, with estimated observations/route increasing
from 0.4 to 4.9 between years 2000 and 2010. Conversely,
trends were least pronounced in Louisiana and Mississippi,
with total net increases of model estimated observations per
route of 0.21 and 0.36 for each state, respectively. When
plotting annual productivity indices as a function of BBSderived population indices, a clear negative relationship
between population size and productivity was apparent in
all states from which both data sets were available (Fig. 8).
Turkey Productivity Byrne et al.
335
Figure 1. Historical trends in turkey productivity, as measured by poult per hen ratios, of 12 states in the southeastern United States
based on available records during 1960–2012. Lines represent generalized additive model estimates (solid line) and 95% confidence
intervals (dashed lines).
336
Productivity and Survival
Figure 2. Historical trends in proportion of turkey females observed without broods during summer brood counts in 8 states in the
southeastern United States based on available records during 1978–2012. Lines represent generalized additive model estimates (solid
line) and 95% confidence intervals (dashed lines).
Turkey Productivity Byrne et al.
337
Figure 3. Mean turkey brood size (6 95% C.I.) based on summer brood survey observations of single females with 16 poults for 7
states in the southeastern United States with data availability during 1967–2012.
338
Productivity and Survival
Figure 4. Historical trends in spring harvest of male turkeys in the southeastern United States based on available records during 1961–
2012. Lines represent generalized additive model estimates (solid line) and 95% confidence intervals (dashed lines). No estimate
provided for Georgia because data were not sufficient to fit a model.
Turkey Productivity Byrne et al.
339
Figure 4. Continued: Historical trends in spring harvest of male turkeys in the southeastern United States based on available records
during 1961–2012. Lines represent generalized additive model estimates (solid line) and 95% confidence intervals (dashed lines).
Productivity and Survival
340
Table 4. Results of linear regression models (F statistics,
degrees of freedom [df], P-values, and adjusted R2 values) of
the relationship between spring hunters on total spring harvest
estimates of male turkeys for 8 states in the southeastern United
States. Sample size (n) is number of years in which both
estimates of harvest and hunter numbers were available. Data
obtained from respective state agencies.
State
n
F
df
P-value
adjusted R2
AL
FL
GA
KY
LA
MS
MO
SC
41
21
9
16
27
27
53
35
120.0
112.4
11.4
32.7
33.4
136.9
936.7
361.4
39
19
7
14
25
25
51
33
,0.001
,0.001
0.012
,0.001
,0.001
,0.001
,0.001
,0.001
0.75
0.85
0.57
0.68
0.55
0.84
0.95
0.91
Curve shape varied somewhat among states but, in general,
years with greatest PPH ratios corresponded to years when
BBS indices were least.
Productivity and Restoration
When comparing restoration efforts to productivity
trends, we noted that declines in productivity began prior
to restoration efforts reaching 50% completion in states
that had historical productivity data extending back
beyond that point (Fig. 9). Restoration efforts in Georgia,
Missouri, North Carolina, and Tennessee were characterized by time periods in which restocking activity was
especially intense. For instance, in North Carolina, there
was a clear peak in releases in the 1990s. However, in all
of the above states, steep downward trends in productivity began prior to these intensive restoration efforts and
continued for duration of restocking years. Restocking
efforts in Louisiana and Mississippi did not exhibit clear
spikes in activity observed in other states. Louisiana is
the only state in which productivity declines began after
restoration was 95% complete. Overall, no clear pattern
existed to allow us to draw inference regarding a direct
link between intensity of restoration efforts and productivity, as it appeared that prevailing productivity trends
began prior to intense restoration efforts. We add the
caveat that it was difficult to ascertain spatial overlap
between counties in which restoration efforts were
concentrated and those in which brood surveys were
being actively conducted, especially during early restoration periods. Thus, potential exists that some small
scale correlations may have been obfuscated by brood
surveys not simultaneously occurring in areas experiencing intense restoration efforts.
Female Survival
We found that range-wide female survival rates have
generally increased over time, from an average annual
survival rate of 0.51 (range: 0.44–0.61) for studies in the
1980s to 0.68 (range 0.58–0.78) for studies in the 2000s
(Fig. 10).
DISCUSSION
Our findings suggest that there has been a general longterm decline in turkey productivity (to varying degrees)
across the southeastern United States, based on trends in
PPH ratios observed across states. We offer that direct
comparisons among states regarding actual PPH ratios are
tenuous, as there are many confounding, latent variables to
consider. As such, it is difficult to parse out whether
differences in scale represent actual differences in true
productivity among states, or are artifacts of differing data
collection or survey protocols. For example, West Virginia
had consistently larger PPH ratios than other states, but
West Virginia’s sampling protocol also varied considerably
from other states, as females without poults were not
recorded. However, the important observation is a consistent, generally declining trend region-wide, and not
absolute PPH estimates.
Based on data available, it appeared that decreasing
PPH ratios were at least in part due to an increasing
proportion of females observed without broods. Conversely, there was little evidence to suggest meaningful declines
in brood sizes for 7 states in which such data were
available, based on small variation in mean brood sizes (,2
poults) in 5 states, and great degree overlapping confidence
interval among years. The most persistent negative
historical trend appeared in West Virginia, although sample
sizes early in the historical record were generally small,
which was reflected by wide confidence intervals. Thus,
apparent decline in mean brood size from very great values
in the early part of the West Virginia record was likely
influenced to some degree by sampling effort.
Congruent with declining productivity indices, we
observed increasing population trends based on BBS data.
Breeding Bird Survey routes are surveyed once annually,
and not all routes in a given state necessarily traverse ideal
or suitable turkey habitat. Additionally, survey methods are
not specifically designed to detect turkeys. Despite these
shortcomings, BBS was consistent in methodology and
spatial coverage over time, and suggested that turkey
populations increased over time.
Raw harvest data are only informative given assumptions of constant hunter effort and availability of turkeys.
However, hunter numbers change through time, and we
demonstrated that raw harvest numbers were greatly
correlated with hunter numbers. The implication is that
caution should be used in extrapolating information
regarding population trends from harvest data. When
considering indices that accounted for hunter effort, we
found no evidence to suggest population declines in the 8
states in which such data was available. At the time of our
study, populations appeared to be in a state of either
relative stability or slow growth.
At the same time, annual survival rates of female
turkeys reported in the literature have generally increased.
We recognize that this is a relatively crude estimation of
survival trends, and that variation in predator communities
and climatic conditions across the species’ range may have
influenced local survivorship differently. However, we
believe that the general trend towards increased survival
over time and across regions is worth noting, especially as
other demographic trends (i.e., reproductive and harvest
Turkey Productivity Byrne et al.
341
Figure 5. Relationships between annual estimates of spring hunter numbers and total male harvests for 8 states in the southeastern
United States based on available records during 1960–2012. Lines indicate linear regression fits.
342
Productivity and Survival
Figure 6. Historical trends in hunter success or harvest proportion of male turkeys during spring hunting seasons in the southeastern
United States based on available records during 1960–2012. Lines represent generalized additive model estimates (solid line) and 95%
confidence intervals (dashed lines).
Turkey Productivity Byrne et al.
data) in other regions are similar to that observed in the
southeastern United States (Eriksen et al. 2015). Thus, our
results indicate that turkeys in the southeastern United
States have followed a historical pattern of large scale
decreases in per capita productivity, increasing and
stabilizing population abundance, and increases in female
survival rates. Almost universally, years with least
productivity indices were associated with greatest indices
of population abundance (Fig. 8). While the notion of little
productivity and simultaneous stability in turkey populations may initially seem counterintuitive, this relationship
has been discussed in previous literature. Most notably,
Vangilder et al. (1987) used modeling approaches to
conclude that, even when female success rates (defined as
portion of females alive each spring to successfully hatch a
brood) were as small as 30–40%, large population densities
could be maintained if annual female survival rates
averaged approximately 0.44.
Negative correlation between population and productivity indices leads us to hypothesize that large scale,
historic declines in productivity we observed are evidence
that reproduction is mediated in a density dependent
manner. Despite observational evidence, there is no
experimental evidence of a density dependent relationship
between population density and reproduction in turkeys
and, as a result, traditional models of turkey population
dynamics have assumed no such relationships (Roberts and
Porter 1995, Vangilder and Kurzejeski 1995, Rolley et al.
1998, Alpizar-Jara et al. 2001). Here, we present a
theoretical mechanism in which density dependent reproduction may operate on turkey populations. While our
proposed mechanism is in some respects speculative, it is
based on our observed trends in reproduction, population
densities, female survival, and our knowledge of turkey
ecology in general. It is our hope that presenting our
thoughts will stimulate discussion and novel thought on
nature of turkey population dynamics and will help guide
future turkey research.
Turkeys exhibit life history traits commonly associated
with r-selected species, such as early age of maturity, short
life span, great reproductive potential, and an association
with dynamic and early successional environments (Stearns
1977). Species with these life history traits are hypothesized to exhibit their greatest population growth rates at
small population densities relative to environmental
carrying capacity (K; Fowler 1981). This hypothesis is
supported for turkeys by Porter et al. (1990) and McGhee
and Berkson (2007), who both found population growth to
be greatest at small densities. A correlate of this is that
greatest levels of per capita recruitment would be expected
to be associated with these periods of great growth
occurring at small densities, and historical trends in the
southeastern United States largely conform to this pattern.
A number of general mechanisms to explain how
density dependent reproduction arises in populations have
been hypothesized. The concept of site dependent population regulation (also termed the habitat heterogeneity
hypothesis) suggests that as population density increases,
a progressively larger proportion of the population is forced
into using poor quality breeding habitat, resulting in
declines in per capita reproductive success (Rodenhouse
et al. 1997, McPeek et al. 2001). Under this paradigm,
343
while per capita reproductive output declines, variation
among individuals is great as individuals that are able to
access good quality breeding habitat reproduce more
successfully than individuals in marginal habitat conditions. Our observed trends of reduced PPH ratios
concomitant with variable proportions of females observed
without young suggests a per capita decrease in recruitment
along with increasingly variable nesting success among
individuals. As such, we hypothesize that density dependent reproduction in turkeys is most likely to be a form of
site-dependent population regulation.
To understand how site-dependent population regulation may influence reproductive output of turkeys, consider
that turkeys inhabit heterogeneous landscapes. A natural
consequence of this is that quality of nesting habitat in any
given system is heterogeneous as well, ranging from good
quality to unsuitable. Thus, only a certain portion of any
landscape will contain suitable nesting habitat (e.g., habitat
conditions that are conducive to successful nesting
attempts). Given that predation is often identified as primary
cause of nest loss (Vangilder et al. 1987, Vander Haegen et
al. 1988, Miller et al. 1998b, Paisley et al. 1998, Byrne and
Chamberlain 2013), it is reasonable to assume that good
quality areas are often those associated with small nest
predation risk. At small population densities, a large
proportion of a breeding population would be able to access
what good quality nesting habitat is available. As a result,
per capita recruitment would be expected to be great, as per
capita nesting success is also expected to be great. As the
population continues to grow, proportion of females forced
to nest in suboptimal habitat conditions grows as well. At
great population densities, a small proportion of total female
population would be able to nest in these good quality areas,
with the remainder forced to attempt to nest in poorer
quality areas. As a result, per capita recruitment would be
reduced, as nest loss becomes significant for proportion of
the population nesting in suboptimal habitat conditions.
This would be expected to be reflected in annual summer
brood counts. After allowing for some degree of annual
variation resulting from density independent environmental
factors (temperature, precipitation, etc.), absolute numbers
of females successfully producing poults may be relatively
consistent across years. However, despite increasing
population density, absolute number of successful females
remains relatively static, and proportion of the population
that experience failed nesting attempts rises. The expected
result is what we observed in historical trends, reduced PPH
ratios concomitant with an increasing percentage of the
female population observed without broods.
Female survival rates may be linked to decreases in per
capita reproductive success. Female survival rates have
been observed to exhibit seasonal variation, with least
survival rates often associated with reproductive seasons
(Vander Haegen et al. 1988, Palmer et al. 1993, Wright et
al. 1996). Female survival outside of reproductive seasons,
assuming harvest (legal or illegal) is not a major cause of
mortality, can be quite great (e.g., Pack et al. 1999). This
makes intuitive sense, as incubation and brood-rearing
activities leave females especially vulnerable to predation.
Conversely, females that experience early nest loss, or
alternatively do not attempt to nest at all, may be spared the
risk associated with reproductive activities. Collier et al.
344
Productivity and Survival
Figure 7. Historic state-specific trends in U. S. Geological Survey Breeding Bird Survey indices for eastern turkeys in 13 states in the
southeastern United States during 1966–2011. Dashed lines represent 95% credible intervals.
Turkey Productivity Byrne et al.
345
Figure 7. Continued: Historic state-specific trends in U. S. Geological Survey Breeding Bird Survey indices for eastern turkeys in 13
states in the southeastern United States during 1966–2011. Dashed lines represent 95% credible intervals.
(2009) documented negative influence of reproductive
effort on survival for Rio Grande turkeys. Thus, as
proportion of reproductively unsuccessful females increases, overall population level survival rate may increase as
well.
A number of hypotheses based on assumptions we used
could be explicitly tested via properly designed studies.
Results of such studies would serve to better elucidate
mechanisms linking turkey reproduction and density. These
hypotheses include:
(1) For any given population, female reproductive success,
defined as proportion of females alive at beginning of a
reproductive season to successfully hatch young
(Vangilder 1992), is negatively correlated with population density.
(2) For a given landscape, females that do not attempt to
reproduce or those that experience early nest failure
(i.e., do not incubate a nest or tend a brood) have
greater annual survival rates than females that are
reproductively active, after controlling for factors such
as legal and illegal harvest.
(3) Following from hypothesis 2, for a given landscape,
mean annual survival rate of females is positively
correlated with population density.
(4) If quality nesting areas are settled first as sitedependent regulation would suggest, then for a given
landscape, with all else being equal, there should exist
a negative correlation between nest initiation date and
nest success when population densities are great. This
is to say that early nesting females will first settle and
346
Productivity and Survival
Figure 8. Relationship between annual indices of population density (measured through Breeding Bird Survey data) and productivity
(measured through poult per hen ratios) for 11 states in the southeastern United States during 1966–2011. Values for both indices are
scaled from 0 (least values) to 1 (greatest value) for each state.
Turkey Productivity Byrne et al.
347
Figure 9. Productivity trends as determined through poult per hen ratios (solid lines) and annual restoration efforts as measured through
numbers of turkeys released within a given state (grey bars) for 6 states in the southeastern United States during 1950–2012. Vertical
dashed lines represent 50%, 75%, and 95% cumulative releases respectively.
348
Productivity and Survival
Figure 10. Mean (6 SE) survival estimates of female turkeys
from studies conducted on radiotagged individuals during 1980s,
1990s, and after 2000. References to specific studies can be
found in Table 4.
occupy limited available quality habitat and, as a
result, late nesting females will be forced into marginal
habitat conditions. Strength of this relationship should
be weak when population densities are small.
Clearly, our inferences are based on data summarized
over coarse spatial scales. In reality, a state’s turkey
population likely consists of a number of relatively distinct
subpopulations with varying levels of connectivity (Fleming and Porter 2007). By making inferences at the state
level, we are necessarily making inferences based on data
sets that represent aggregations of these various subpopulations. At any given point in time, it is reasonable to
expect that variation exists between these populations, in
that some populations may be growing, whereas others are
stable or decreasing (e.g., Butler et al. 2015). Furthermore,
these populations are likely influenced to different degrees
by localized changes in factors such as habitat conditions
and availability, predator populations, and harvest regimes.
When we aggregated across these populations, we necessarily missed some of this localized variation. For these
reasons, we did not attempt to draw correlative inferences
regarding state level habitat changes and productivity
trends, as attempting to draw inferences between habitat
conditions and demographic trends on such scales would
not have been especially informative. It is our hope to
instigate future research aimed at testing these hypotheses,
further refining these ideas, and exploring alternative
hypotheses as appropriate. However, we stress that such
work should focus on elucidating population processes at
scales relevant to turkey population ecology.
Our work highlights an important issue regarding use
and interpretation of methods used to survey turkey
demographic parameters. The lack of a universally
accepted and accurate tool to measure turkey population
size is a problem that was first identified several decades
ago (Mosby 1967) and is still an issue today. While
commonly used, current harvest metrics are likely poor
indices of population size or density. Hunter numbers
change through time, and the great correlation observed
between hunter numbers and raw harvest estimates
invalidates use of raw harvest as a meaningful index to
population size. Additionally, not all turkeys are available
to hunters, and hunters may exhibit selectivity in turkeys
they harvest (adult males are preferable to juveniles for
instance; Isabelle and Reitz 2015). Accounting for hunter
effort and reporting harvest as a measure of catch per effort
should provide a more accurate index. However, often the
information necessary to accurately quantify effort is
missing. Finally, male-only spring harvest metrics only
serve as a good index of total population assuming there are
no long term changes in sex ratios. Recent advances in
statistical techniques to reconstruct populations from
harvest data are promising (Gast et al. 2013), but these
techniques are still developing and have yet to be adopted
by management agencies. Unfortunately, given lack of
required information and violation of multiple important
assumptions, we feel that attempting to draw inferences
regarding population changes based on currently available
harvest data is tenuous at best.
BBS routes are surveyed once annually, and not all
routes in a given state necessarily traverse ideal or suitable
turkey habitat. Additionally the survey methods are not
specifically designed to detect turkeys. Despite these short
comings, given consistency in methodology and spatial
coverage over time, the fact that turkeys are encountered
with sufficient frequency so that relative abundance
estimates increased strongly suggests actual population
increases over time. Thus, the BBS appears useful for
detecting long-term population changes, but lack of turkeyspecific survey protocols likely limits its effectiveness at
detecting short-term population changes and its usefulness
as a short-term monitoring tool.
Summer brood surveys represented the most common
method of indexing productivity, but methodologies varied
widely, hindering direct comparisons across states and
regions, and little work has been done assessing ability of
such surveys to accurately index productivity. The few
studies that have been conducted in this regard, on Rio
Grande turkeys, indicated poor correlations between survey
results and reproductive parameters of radiotagged populations (Butler et al. 2005) and lack of statistical power
necessary to detect biologically meaningful changes
(Schwertner et al. 2003). Future work should assess ability
of brood surveys to accurately index productivity and
measure detection probabilities in a variety of landscapes.
MANAGEMENT IMPLICATIONS
We offer that the time has come for studies specifically
designed to elucidate mechanistic relationships between
population density and demographic processes in turkeys.
An understanding of processes that regulate turkey
populations, and how these processes may vary with
respect to population density, would allow for further
refinement of population models. This would allow
generating more precise predictive models and promote
more informed decisions regarding harvest management.
Additionally, while it may not be feasible to measure
population density directly at large scales, an understanding
of mechanistic connections between density and demographic parameters would potentially allow sufficient
inferences to be drawn by tracking relationships among
reproduction, survival, and harvest. Accounting for density
dependence is an important aspect of creating sound policy
Turkey Productivity Byrne et al.
to meet specific management goals (Stevens et al. 2015).
However, given how little is presently known about
processes linking turkey population density and demographic parameters, we hesitate to provide specific
management recommendation until further research is
conducted.
ACKNOWLEDGMENTS
We thank the cooperating states of the SEWTWG
(Alabama, Arkansas, Florida, Georgia, Kentucky, Louisiana, Mississippi, Missouri, North Carolina, Oklahoma,
South Carolina, Tennessee, Texas, Virginia, and West
Virginia) that provided access to their respective historical
data, and agency personnel who provided information
regarding data collection and helpful thoughts and
comments that improved this manuscript: S. Barnett, B.
Bond, A. Butler, S. Dobey, D. Godwin, J. Hardin, J. Honey,
C. Hunter, J. Isabelle, K. Krantz, K. Lowery, G. Norman,
C. Ruth, D. Sawyer, R. Shields, J. Stafford, E. Stanford, D.
Steffen, C. Taylor, and J. Waymire. We thank our
cooperating state agencies and state chapters of the
National Wild Turkey Federation (NWTF) that provided
funding for this project, and we thank the NWTF for
logistical support in handling distribution of funding.
Additional support was provided by the University of
Georgia Warnell School of Forestry and Natural Resources.
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Turkey Productivity Byrne et al.
Michael E. Byrne is currently a postdoc at the Nova Southeastern
Oceanographic Institute. He received his Ph.D. from Louisiana State
University and his M.S from the University of Rhode Island. Mike
is a vertebrate ecologist with research interests in behavioral and
population ecology, with a particular focus on animal movement
ecology, habitat use, and elucidating the links between individual
behaviors and population level processes. He conducts research on a
wide variety of species in both marine and terrestrial ecosystems,
and has been involved in wild turkey research since 2007.
Michael J. Chamberlain is a Professor of Wildlife at the Warnell
School of Forestry and Natural Resources at the University of
Georgia. Mike received his B.S. degree from Virginia Tech, and his
M.S. and Ph.D. degrees from Mississippi State University. Mike’s
research interests are broad, but he focuses much effort into
evaluating relationships between wildlife and their habitats. He has
conducted research on wild turkeys for the past 20 years. Mike is a
dedicated hunter and dad, and enjoys spending time outdoors
regardless of the pursuit.
351
Bret A. Collier is an Assistant Professor in the School of Renewable
Natural Resources at Louisiana State University. Bret’s research
focus is wildlife population dynamics and development of statistical
methods for wildlife biologists, although he has been known to
delve into a variety of wildlife-related topics. He has been actively
conducting research on wild turkey demography and spatial ecology
for the past 12 years. Bret and his wife, Reagan, have a daughter,
Kennedy, and he is both a hunter and landowner.