Productivity Report

Southeast Regional Wild Turkey Reproductive Decline Study
A Retrospective Regional Analysis of Wild Turkey Productivity in the Southeast
Investigators: Michael E. Byrne and Michael J. Chamberlain
Warnell School of Forestry and Natural Resources
University of Georgia
180 E Green St
Athens, GA 30602
Bret A. Collier
School of Renewable Natural Resources
Louisiana State University
227 Highland Rd
Baton Rouge, LA 70803
History and background
This study was initiated as a response to persistent declines in annual reproductive
indices of eastern wild turkeys (Meleagris gallopavo silvestris) based on surveys conducted by
member states of the Southeast Wild Turkey Working Group (SEWTWG). There was concern
among state biologists that the observed reproductive declines were an indicator of large-scale
regional declines in wild turkey populations that was presently occurring, or was likely to occur
in the near future. At the 2010 SEWTWG meeting held in North Carolina, focused discussion on
reproductive declines led to the identification of research priorities relating to the decline,
including population dynamics, habitat, and survey techniques. At the 2011 SEWTWG meeting
in Florida, research priorities were formalized and member states decided that prior to initiating
new field studies, it would be informative and cost effective to study demographic trends
retrospectively using existing datasets maintained by member states. The analysis of historic
trends in wild turkey demographic parameters would provide a long-term, large-scale perspective
on wild turkey populations in the region. In doing so, survey techniques could be examined
critically, and hypotheses could be developed from the existing data to further refine research
priorities.
At the 2011 SEWTWG meeting, it was agreed that the cost of the project would only be that
necessary to cover 2 years’ salary for a post-doctoral researcher stationed at the University of
Georgia (UGA). The idea was to hire a post-doctoral researcher and have the cooperating states
each contribute equally to the effort, thereby minimizing costs to all cooperators. Each state was
given flexibility to decide how funds were contributed; some state agencies provided funds
whereas other states provided funds through their respective state chapters of the National Wild
Turkey Federation (NWTF). All monies were directed to NWTF headquarters in Edgefield, SC,
who ultimately routed the funds to UGA through a contract with the Warnell School of Forestry
and Natural Resources. As per NWTF policy, there were no indirect costs associated with these
funds.
A full report consisting of a critical examination of survey techniques used by states to
measure productivity of wild turkeys and potential improvements to such techniques was
delivered to the SEWTWG in January 2014. The present report deals specifically with the
analysis of historic demographic trends. Our specific goals were two-fold:
1) Summarize and analyze trends in measurable demographic parameters temporally and
spatially.
2) Generate hypotheses to account for observed declines in productivity and identify
avenues of future research.
As we received and summarized the available data, our approach naturally had to evolve
to suit what data were available. Ultimately, we decided to take a relatively broad-scale
approach and examine trends at the state level in the style of a meta-analysis. This was primarily
a consequence of data availability and quality, which as we detail below, was highly disparate
among states and years. After examining trends for individual states, we were able to identify
regional commonalities and draw biological inferences. We ultimately decided on this approach
because we did not want to sacrifice biological interpretability for the statistical complexity that
would have been required to standardize and combine data at smaller spatial scales and across
state boundaries.
The following report does not proceed in the format of a standard scientific paper, with
distinct sections devoted to methods, results, and discussion. Given the complexity of the study
and the many aspects incorporated in making inferences from the data and developing
hypotheses, we instead decided to present our results in the form of a case-study. As such, we
detail the process step-by-step, from data collection, through various analyses, and finally
through our development of a theory to explain the observed reproductive declines. Our goal
with this presentation was to allow the reader to follow our thought process, thereby providing
insight into why we chose the various approaches outlined below, and how we ultimately
reached conclusions and developed hypotheses.
Data collection and availability
Data acquisition began in April 2012, coinciding with the initiation of the contract that
allowed funding to be routed from NWTF to UGA. At that time, we contacted the turkey
coordinators of cooperating states and asked them to provide all available data and historical
records regarding productivity indices, harvest records, and turkey restoration and restocking
information. We requested coordinators provide as much of the associated metadata and
background information as was available for each dataset.
Thirteen states were able to provide records of productivity indices (Table 1). Data from
Oklahoma includes only that from the southeastern portion of the state occupied by the eastern
subspecies. Alabama initiated a state-wide 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 the poult per hen ratio (PPH). In a
general sense, the PPH ratio is defined as the ratio of the total number of poults to the total
number of females observed during the summer brood-rearing period. The methods of data
collection and calculation of PPH ratios varied somewhat among states. In most states, sightings
of turkeys are recorded opportunistically during the summer months by agency personnel, or a
combination of agency personnel and citizen volunteers, as they go about their daily activities.
Generally observers are asked to record observations of females without broods, as well as
females with broods. West Virginia is different in this regard, as observations of females
without young were often not recorded. Thus, the reader should bear in mind that the reported
PPH ratios for West Virginia have a slightly different biological meaning than other states.
For most states, the observation period included the months of June – August; Tennessee
reports PPH ratios based only on observations recorded in August, the sample period for
Kentucky and North Carolina is July – August, and West Virginia uses observations of broods
made from May - September. All states ask observers to record individual sightings as separate
events. When calculating PPH ratios however, the total numbers of poults and females from
individual observations are combined to provide a single estimate. Guidelines used for filtering
spurious and unlikely observations prior to calculating PPH ratios, if they exist at all, are not
standardized across states and in many cases are not standardized across time within a state (for
instance, a current turkey biologist may filter observations whereas his predecessors may not
have done so).
Of the states that monitored productivity, Virginia was the only state in which the PPH
ratios we used were not calculated from summer observations. Rather, PPH ratios were derived
from the ratio of yearling to adult females in the fall harvest based on reports at mandatory
hunter check stations. While Virginia does record summer brood observations, inferences
regarding productivity were traditionally based on fall harvest data as the spatial distribution was
more consistent, and sample sizes larger, than summer observations. Virginia discontinued using
fall harvest as a means of indexing productivity in 2010, but since this represents a continuous
data set of 26 years and was traditionally used by the state as the primary measure of
productivity, we considered it the most appropriate for our purposes.
All states were able to provide harvest records. However, there was significant
inconsistency regarding what data were available, how data were obtained, and length of
historical records. The one metric common to all states was an annual estimate of total spring
harvest; however, the analytical methods used to develop estimates differed among states and
often changed within states through time. Estimates of total 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. Harvest estimates are often used as proxies of population density, although few
studies have tested the veracity of this relationship (Lint et al. 1995). A more robust index would
theoretically be gained by accounting for hunter effort. Unfortunately, the data necessary to
estimate hunter effort was only available in 9 states and normally only for a limited number of
years. Nine states allow, or have allowed, a fall turkey harvest in the past, but we concentrated
our analyses on spring harvest because 1) all cooperating states have a spring harvest season and
2) spring seasons see much greater hunter interest and participation than fall seasons, especially
in recent decades. Alabama, for example, experienced an 89% reduction in fall hunter numbers
between the years 1977-2011, and these trends are not restricted to the southeast (Tapley et al.
2010). Presently fall harvest is greatly surpassed by spring harvest in terms of birds killed and
numbers of participating hunters in all states in the region.
Detailed historical restoration information was available from 6 states that also provided
productivity data (GA, LA, MO, MS, NC, and TN). This information primarily consisted of the
number of turkeys released into a specific area. In some cases, releases were detailed to the
specific release site, and in other cases releases were summarized by county. Given the largescale nature of this study, we tabulated the total number of birds released state-wide in a given
year.
State agencies work independently in collecting and summarizing turkey population
metrics and the resulting inconsistencies makes comparisons among states problematic. As such,
we used data from the USGS Breeding Bird Survey (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 once annually, and is often run by the same observer consecutively for a series of
years. Each route contains 50 evenly spaced survey sample points. At each sample point, the
observer conducts a 3 minute point-count, recording all bird species seen and heard. BBS data
are summarized for individual states as well as larger physiogeographic regions of the continent.
For any geographic region, a Bayesian-based hierarchical model which accounts for varying
regional survey quality and observer effects (Link and Sauer 2002) is used to estimate an annual
population index (measured as observations/route). For our purposes, the standard protocol over
space and time is an obvious advantage to the incorporation of BBS indices. Additionally, the
BBS has been ongoing since 1966, providing a long term data set that includes the major
restoration efforts of the wild turkey in the southeast through the present day. The BBS index
incorporates observations of all turkeys observed regardless of age or sex, and as such provides
an index of total turkey populations as opposed to most state-derived estimates which are based
on data pertaining to specific sexes (i.e. spring harvest data). We queried annual abundance
indices and associated 95% credibility intervals for each individual state for the years 1966 –
2011 through the interactive online BBS results and analysis portal (http://www.mbrpwrc.usgs.gov/bbs/bbs.html).
Analysis
Because considerable differences in data availability and quality among states precluded
us from being able to pool data across states, we decided to adopt a meta-analysis approach. We
accomplished this by first analyzing available data from each state independently, then making
biological inferences based on the broad-scale similarities in trends observed across states. All
productivity and harvest datasets are statistically imprecise, primarily driven by inherent biases
in data collection methodologies and haphazard survey protocols. Additionally, stochastic
annual variation in conditions that may influence nesting success can lead to considerable annual
variation from long term productivity averages (Vangilder et al. 1987, Healy 1992). Similarly,
factors such as weather conditions and the timing of harvest seasons relative to the timing of a
given year’s nesting cycle may introduce noise into estimates of spring harvest. The variation
Table 1. The historical availability of statewide productivity data (PPH ratios) from 13
states in the southeastern United States; including the total number of years (n), and the
availability of raw data.
State
Years
n
Raw data Notes
AL
2010-2011
2
NA
AR
1982-2012
31
NA
GA
1978 -2012
35
1978-2011
KY
1984-2011
28
NA
LA
1994 -2010
17
1994-2010
MS
1995 -2012
18
1995-2012
MO
1959 -2012
54
1990-2011 PPH ratio only calculated for brood groups with ≤
2 hens
NC
1988 -2012
25
2001-2011
OK
1985-2012
28
2001-2012 Data only includes SE portion of the state where
the eastern sub-species occurs
SC
1982 -2012
31
NA
TN
1983 -2012
30
2003-2012 PPH ratios calculated only from observations
during month of August
VA
1979-2010
32
NA
Productivity calculated from ratio of adults/young
in fall harvest
WV
1967-2012
46
1967-2012 Based only on observations of females with broods
Table 2. Historical availability of state-wide spring harvest from 15 states in the southeast
United States; total harvest = estimated number of total males harvested, hunter numbers
= estimates of total hunters or eligible hunters based on permit sales.
State
Total harvest
Hunter numbers
AL
1972 – 2012
1972 - 2012
AR
1961 - 2012
NA
FL
1989 – 2012
1989 - 2010
GA
2005 - 2013
2005 - 2013
KY
1978 – 2012
1997 - 2012
LA
1980 – 2012
1980 – 2010
MS
1981 - 2012
1981 - 2009
MO
1960 - 2012
1960 - 2011
NC
1977 – 2011
1976, and every 7th year since 1983
OK
1990 – 2012
NA
SC
1976 - 2011
1977 - 2010
TN
1990 – 2010
NA
TX
1995 – 2012
1983 - 2011
VA
2004 – 2013
NA
WV
1996 – 2012
NA
caused by such stochastic factors may obfuscate the underlying historical trends representing the
larger population-level processes in which we are interested. To account for the expected
nonlinearity in our data due to these stochastic effects, we used generalized additive models
(GAM, Wood 2006) to fit smoothed spline regression functions to time series data sets of
productivity and harvest to elucidate the underlying historical trends. We adjusted the number of
knots (similar to piecewise regression) used in producing the spline curve to improve the fit of
each model and evaluated goodness of fit for each model visually based on diagnostic residual
plots. We report the model predicted estimate and 95% confidence interval for each year.
GAM’s were fit in the statistical program R (R Core Team 2013) using the mgcv package (Wood
2013).
Establishment of long term demographic trends
Our first priority was to identify long-term trends in demographic data regarding
productivity and estimated population size. Productivity, as measured by PPH indices, has
generally declined across all states where data were available (Figure 1). The nature and severity
of declines was somewhat variable however; for example Tennessee has experienced a
particularly steep historical decline whereas productivity in Mississippi has remained relatively
stable over the record-keeping period (Figure 1). We offer that direct comparisons among states
regarding the 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 sample protocols. For
example, West Virginia has consistently greater estimates than other states, but West Virginia’s
sampling protocol also varies considerably from other states since females without poults are not
recorded. The important observation is the consistency in the generally declining trend regionwide, and not the absolute PPH estimates. This applies to total harvest data, and to an extent, the
BBS data as well.
Trends in spring harvest numbers were highly variable across states, and it is difficult to
generalize an overall region-wide trend (Figure 2). A number of states, including Kentucky,
North Carolina, and Tennessee exhibited 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 1990’s or early 2000’s. The most
precipitous and persistent decline was observed in Mississippi (Figure 2).
Inferring a direct relationship between harvest and population is tenuous as harvest is a
function of availability and rate, and there is obvious age and location specific selection in
harvests of wild turkeys. 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 more so than turkey populations. We performed simple linear regression
on harvest data from 8 states (AL, FL, GA, KY, LA, MO, MS, and SC) that provided some
estimate of hunter numbers (derived variously through license sales or hunter surveys) to test for
independence between hunter numbers and spring harvest. We did not include Texas in this
analysis because hunter numbers and total harvest are calculated for a number of species based
on hunter survey data, including eastern wild turkeys in the portion of the state in which they are
extant. However, harvest data of eastern wild turkeys in Texas are also collected each year based
on the mandatory check-in of harvested birds. Estimates of eastern wild turkeys harvested each
spring in Texas derived from the hunter survey differed considerably from the corresponding
number of turkeys reported from mandatory check-ins (which we assume to be more accurate).
This, combined with the fact that estimates of hunter numbers calculated from survey data
exhibited very large confidence intervals, leads us to believe that these estimates are inaccurate.
We suspect the lack of accuracy stems from a low sample size of hunter response in the
relatively small area of the state in which eastern wild turkeys are hunted.
We found that correlations between hunter numbers and spring harvest were high in all
states, and that hunter numbers were a useful predictor of total harvest (Table 3). The
implication is that caution should be used in extrapolating information regarding population
trends from estimates of total harvest alone, as these estimates may be more representative of
hunter participation than wild turkey population density.
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 to an extent that hunting quality is affected, then there should be
corresponding changes in harvest success. To test for this, we modeled trends in spring hunter
success (defined as the % of hunters to harvest ≥ 1 turkey), or harvest proportion (defined as total
turkeys harvested ÷ turkey hunters) for 7 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 harvest success rather than harvest rates because this metric was based
directly on the responses to hunter surveys, and thus did not introduce bias associated with an
Table 3. Results of linear regression models (F statistics, degrees of freedom (df), P-values,
and adjusted R2 values) of the influence of spring hunters on total spring harvest estimates
for 8 states in the southeast. Sample size (n) is the number of years in which both estimates
of harvest and hunter numbers were available.
State
n
F
df
P- value
adjusted R2
AL
41
120.0
39
<0.001
0.75
FL
21
112.4
19
<0.001
0.85
GA
9
11.4
7
0.012
0.57
KY
16
32.7
14
<0.001
0.68
LA
27
33.4
25
<0.001
0.55
MS
27
136.9
25
<0.001
0.84
MO
53
936.7
51
<0.001
0.95
SC
35
361.4
33
<0.001
0.91
estimation of hunter numbers. We modeled hunter success for Mississippi as well, as these
values were reported annually for the years 1980 – 2009 in records maintained by Mississippi
State University (Hunt 2010). We found that harvest trends that accounted for hunter effort were
generally more stable than raw harvest trends (Figure 3). For instance, hunter success in
Mississippi has consistently hovered around 50%, despite persistent declines in the numbers of
total birds harvested. The regional implication of these trends is that broadly speaking, hunters
participating in spring hunts have not experienced any significant historical declines in success
as we measured it.
Trends in data from the BBS were relatively consistent region-wide and suggested
relative population increases to some degree over time in all states (Figure 4). The magnitude of
increase varied considerably across states; Tennessee for instance exhibited a particularly sharp
population increase beginning in 2000. These trends were least pronounced in Louisiana and
Mississippi. BBS estimates for any given geographic region are partially contingent on route
coverage and birder interest as surveys are run by volunteers (Link and Sauer 2002). This likely
explains to some degree the variation we observed among states in terms of the absolute values
of annual indices. A lack of volunteer observers to sample routes in rural areas of Louisiana and
Mississippi might partially account for the low trend estimates we observed in these states. We
did not show BBS data for Oklahoma and Texas because the eastern subspecies only occurs in a
small portion of each state. The BBS analysis is performed at the state level, thus it is impossible
to separate the index of eastern wild turkeys from Rio Grande wild turkeys present in large
portions of these states.
Our findings demonstrate that there is evidence of long term declines in productivity (to
varying degrees) region-wide; however, there is little evidence to support a congruent large scale
decrease in overall population densities. Unfortunately, no robust way to directly measure wild
turkey population densities on large scales presently exists. However, we can infer from the data
available that at a regional scale, populations are presently in a state of relative increase or
stabilization. We draw this inference from examination of harvest rates and hunter success, and
trends in BBS indices. Annual BBS indices for any species are modeled as expected
observations per route in a given state. Considering BBS routes are surveyed only once annually
and that not all routes in a given state necessarily traverse ideal turkey habitat, the fact that
turkeys are encountered with sufficient frequency so that relative abundance estimates have
consistently increased range-wide strongly suggests turkey population increases over time.
Possible exceptions may be southeastern Oklahoma and eastern Texas which represent the
western margin of the eastern wild turkey’s range (Stangel et al. 1992). Populations existing at
the distributional margin of a species range are often highly dynamic in nature (Williams et al.
2003, Guo 2005), so this is not unexpected. Eastern Texas in particular has had a history of
difficulty in introducing and maintaining stable populations.
Demographic trends and landscape change
We originally postulated that large-scale landscape changes may have acted as potential
drivers of the observed demographic changes through time. However, we decided to reduce
effort along this line of inquiry after our initial investigations indicated that this was not a fruitful
path to follow. For the time periods in which large-scale land cover data were available, we
found only slight variations in relevant habitat types, and the observed demographic trends 1)
appeared to operate independent of landscape and 2) suggested that some metrics were
statistically different that were biologically implausible. Furthermore, given the general
contrasting trends in the various demographic parameters within states, attempting to draw
inferences on the relationship between land cover and demography would be problematic
because inferences could contradict depending on what dependent variable was considered.
We illustrate this for MO and TN. The most suitable data for quantifying long term
trends in land cover change was the USGS National Land Cover Database (NLCD, Homer et al.
2012). The NLCD characterizes land cover at 30m resolution across the conterminous United
States based primarily on Landsat Enhanced Thematic Mapper satellite imagery, and is available
for the years 1992, 2001, and 2006. For each year, we calculated the percentage forested and
urbanized land respectively for each state based on the percentage of cells classified as each land
cover type. To ensure consistent classification methodology between years, we used the
1992/2001 Retrofit Land Cover Change Product, which updated the classification methodology
of the 1992 data for accurate comparison with the later datasets. All NLCD data was accessed
from the Multi-resolution Land Characteristics Consortium (MRLC) website
(http://www.mrlc.gov/). We plotted changes in the percentage of forest cover and urbanization
for each state from 1992 – 2006, along with respective trends in PPH ratio, spring harvest, and
BBS index.
We found that from 1992 – 2006, total forest cover and urbanization changed by < 1%
within each state (Figure 5). During the same period, PPH ratios, spring harvest numbers, and
BBS indices exhibited considerable changes as detailed herein. Thus, we can infer that at the
state-level, land cover as quantified by the NLCD was not responsible for the observed changes
in demographic parameters. This is not to say that habitat has no bearing on turkey populations,
but these effects are likely localized, and as such are not detectable or appropriate for causative
statistical analysis at the large spatial scale in which we are concerned.
Characterization of productivity declines
Before we could develop hypotheses to explain productivity declines, it was necessary to
characterize the nature of these declines, which could be the result of 2 things: 1) an increasing
proportion of females being observed without broods, or 2) a decline in the average size of
observed broods. This is important because, while not necessarily mutually exclusive, each
scenario suggests a different set of possible mechanisms underlying the observed trends. A
proportional increase in the number of females observed without broods may suggest that nesting
success, or the proportion of females attempting to nest, has been declining. If however the
proportion of females observed with broods has remained relatively stable while brood sizes
have declined, this may be indicative of declining poult survival or decreased fecundity.
Eight states reported the annual percentage of females observed without broods, or
provided raw data that allowed us to calculate this value. We observed that, especially in the last
15 years, the percent of females observed without young has generally increased through time in
all states (Figure 6). The strength of this trend varied to some extent among states, but in 5 states
the ranges in model predicted estimates approached or exceeded 20% (MS = 18.7%, LA = 19%,
MO = 25.8%, OK = 26.8%, TN = 28.8%). Since 1995, the only state to show evidence of a
declining trend was Missouri, and only since 2010. The implication is that observed declining
trends in PPH ratios have been at least in part due to an increasing percentage of females
observed without broods, suggesting a general historic decline in per capita nesting success is
likely partially responsible for observed declining trends in productivity.
Brood sizes were harder to estimate based on the nature of the available data. Simply
calculating the annual ratio of total poults to total females based on observations of females with
young was superficially appropriate. However, there were several confounding factors with this
method that have potential to introduce considerable bias into the results. Among them is the
fact that females without broods are known to travel with females tending broods, and the fact
that this method assumed that observed young were distributed evenly among all females.
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 the 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 records reported in the literature, clutch sizes > 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. On the surface, there
appeared to be a general negative trend, especially for states with long term data availability
(Figure 7). However, the scale of variation was rather small, with the difference between the
largest and smallest mean brood sizes < 2 poults for all states except Oklahoma and West
Virginia. Likewise, there was considerable overlap in confidence intervals among years. The
most persistent downward trend appeared in West Virginia, although sample sizes early in the
historical record were generally low, which is reflected by the wide confidence intervals. Thus,
the apparent decline in mean brood size from very high values in the early part of the West
Virginia record was likely influenced to some degree by sampling effort. There appears to be
little evidence that changes in brood sizes have occurred through time in the southeast,
suggesting that wild turkey populations are not experiencing any significant large-scale
reductions in fecundity or poult survival.
Productivity and restoration efforts
The fact that turkey populations exist in many parts of the southeast is largely the result
of intensive large scale restoration efforts. Restoration was largely accomplished through the
live release of turkeys wild captured from extant populations into areas in which turkeys were
absent (or existed at very low densities) that contained suitable habitat (Kennamer et al. 1992).
Given the large scale introductions and dispersals of birds into new areas, we wanted to
investigate whether releases conferred a noticeable signal in productivity trends. If a consistent
signal was found, this would provide reason for us to examine the relationship between
restoration efforts and productivity in more detail. We tallied the total number of birds released
each year for each of the 6 states that were able to provide detailed restocking information. We
determined when restoration was 50%, 75%, and 95% complete in each state based on total
cumulative releases. For example, restoration was considered 50% complete when 50% of the
total amount of birds released during all years of restoration was reached. When comparing
restoration efforts to productivity trends, we noted that declines in productivity began prior to
restoration efforts reaching 50% completion in states that have historical productivity data
extending back beyond that point (Figure 8). 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
1990’s. However, in all of these states the major downward trend in productivity began prior to
these intensive restoration efforts and continued unaltered for the duration of the restocking
years. Restocking efforts in Louisiana and Mississippi did not exhibit the 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 suggest a link between the
intensity of restoration efforts, or a consistent change following the completion of restoration, on
productivity trends.
Relationship between productivity and population size
It appears that large-scale declines in productivity have occurred concomitant with
overall increasing and/or stabilizing population trends. To illustrate this, we plotted GAM
predicted productivity (PPH) values as a function of relative population size (BBS indices). To
account for differing scales among states, we first scaled PPH ratios and BBS indices from 0 – 1
for each state, with 0 being the lowest value of each metric, and 1 being the largest value of each
metric for each respective state. We observed a clear negative relationship between population
size and productivity in all states (Figure 9). There are 2 potential conditions which may allow
turkey populations to remain stable or increase in the face of low levels of reproduction.
 High survival rates of adults, especially females. Long-lived females theoretically have
more opportunities to reproduce during their lifetime. To maintain a population each
female would have to successfully reproduce only once in her lifetime.
 High recruitment rates of young into the adult population. To maintain a stable
population, the number of young recruited in the breeding population need only equal the
number of adult deaths. Recruitment above this threshold would act as a buffer, and
potentially lead to population increases.
If high female survival is playing a part in maintaining populations, we would expect to see
some evidence of a historical trend towards increasing female survival to counter-act declining
productivity. To explore this potential, we reviewed the literature of studies that reported female
survival. We limited our review to studies which used robust statistical methods such as KaplanMeier (Pollock et al. 1989), Heisey-Fuller (Heisey and Fuller 1985), or known-fate survival
models in program MARK (White and Burnham 1999) to derive annual survival estimates from
radio-telemetry data (Table 4). Additionally, we queried researchers currently engaged in studies
of female survival to provide us preliminary results (Table 4). 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 the 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 the years 1984 – 1989,
and for the years 1990 – 1994. If a study spanned 2 time periods but did not report individual
survival rates for each year, we counted the study as belonging to 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 that spanned 1998 – 2000. Because most of the study occurred prior to
2000, we grouped this study into the 1990’s. 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
date reported in Wilson et al. (2005), and as such only the results from Byrne (2011) were used.
We found evidence that 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 1980’s to 0.68 (range
0.58 - 0.78) for studies in the 2000’s (Figure 10). While the notion of low productivity and
simultaneous stability in wild turkey populations may initially seem counter-intuitive, this
relationship has been discussed in the turkey literature before. Most notably, Vangilder et al.
(1987) used modeling approaches to conclude that even when female success rates (defined as
the portion of females alive each spring to successful hatch a brood) were as low as 30-40%,
high population densities could be maintained as long as annual female survival rates averaged
~ 0.44.
Table 4. Studies reporting annual survival estimates of female eastern wild turkeys used in
tracking survival trends over time. All studies derived survival rates based on radio
telemetry data.
Study years Study location
Landscape
Reference
1981 – 1989
MO
Mixed forest/ag
Vangilder and Kurzejeski 1995
1984 – 1985
MO
Mixed forest/ag
Kurzejeski et al. 1987
1984 – 1994
MS
Mixed hardwood/pine
Miller et al. 1998a
1987 – 1990
MS
Pine plantation
Palmer et al. 1993
1988 – 1994
WI
Mixed forest/ag
Wright et al. 1996
1990 – 1993
NY
Mixed forest/ag
Roberts et al. 1995
1990 – 1993
MO
Mountain hardwood
Vangilder 1995
1990 – 1994
VA/WV
Mountain hardwood
Pack et al. 1999
1993 – 1996
IA
Mixed forest/ag
Hubbard et al. 1999a
1998 – 2000
SC
Coastal pine
Moore et al. 2010
2002 – 2006
OH
Hardwood
Reynolds and Swanson 2010
2002 – 2010
LA
Bottomland hardwood
Byrne 2011
2003 – 2005
IN
Agricultural
Humberg et al. 2009
2010 – 2012
DE
Pine plantation
J. Bowman, pers com
2012
AR
Mixed hardwood/pine
T. Pittman, pers com
Previous studies have detailed poult survival rates through the summer brood-rearing
period (Vangilder et al. 1987, Miller et al. 1998b, Godfrey and Norman 1999, Hubbard et
al.1999b, Thogmartin and Johnson 1999, Norman et al. 2001). While studies such as these offer
possibilities to draw some indirect inferences regarding recruitment, there are no studies that we
are aware of that directly quantified eventual recruitment into the breeding population. The
dearth of information in the literature on this subject prevents us from being able to comment on
historical and current trends in realized recruitment, except to postulate that it has likely declined
in conjunction with productivity declines. Clearly, this is a fertile and important research area in
which to focus future efforts.
Potential mechanism driving productivity trends
The negative correlation between population and productivity indices leads us to
hypothesize that the large-scale historical declines in productivity observed in the southeast are
evidence that reproduction is mediated in a density-dependent manner. The idea of population
self-regulation (i.e. density-dependence) is one of the oldest and most well established aspects of
population ecology (Turchin 2001), and the phenomena is ubiquitous across a wide range of
organisms (Brook and Bradshaw 2006). It is physically impossible for any population to
continue to grow indefinitely, thus increasing population densities are expected to trigger
negative feedback loops that limit population growth. As such, it is expected that a negative
relationship should exist between population size and the rate of population increase. A review
by Guthery and Shaw (2013) points out that evidence of density dependence in upland game
birds has existed in the literature since the 1940’s. More recently, Porter et al. (1990) and
McGhee and Berkson (2007) both found evidence of density-dependent population growth in
wild 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 (Larson 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 1940’s; specifically Errington (1945) found such a
relationship in both northern bobwhite (Colinus virginianus) and ring-necked pheasant
(Phasianus colchicus) populations. The existence of a density-dependent effect on wild turkey
reproduction has been hypothesized by several authors who have noted lower 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). In the present study, we
observed this phenomenon across an extensive spatial and temporal scale.
The influence of stochastic environmental variables such as rainfall and temperature on
short term annual variations in reproduction is well documented in the literature on eastern wild
turkeys (Warnke and Rolley 2005). However, the functional relationship between population
density and reproduction, which is more likely to drive long term trends, has not been explored.
Despite the observational evidence, there is no experimental evidence of a density-dependent
relationship between population density and reproduction for wild turkeys, and as a result
traditional models of wild 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 wild turkey populations that ties together reproduction, survival, and potential
limiting aspects of landscapes. This theory should generate a set of testable hypotheses and
highlight particular aspects of wild turkey ecology in which our knowledge is lacking. We hope
our thoughts ultimately will help drive future wild turkey research.
To understand how density-dependence potentially works in wild turkey populations, it is
important to consider life history traits of turkeys. Wild turkeys exhibit life-history traits
commonly associated with r-selected species, such as early age of maturity, short life spans, high
reproductive potential, and an association with dynamic and/or early successional environments
(Stearns 1977). Species with these life-history traits are hypothesized to exhibit their greatest
population growth rates at low population densities relative to environmental carrying capacity
(K, Fowler 1981). This hypothesis is supported for wild turkeys by the findings of Porter et al.
(1990) and McGhee and Berkson (2007), who both found population growth to be greatest at low
densities. A correlate of this is that the highest levels of recruitment would thus be expected to
be associated with these periods of high growth occurring at low densities, and the historical
trends in the southeast largely conform to this pattern.
Female mortality rates are often observed to exhibit seasonal variation. In most studies
that have detected such seasonal variation, the lowest survival rates are often associated with
reproductive seasons (Palmer et al. 1993, Vander Haegen et al. 1988, 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 high (i.e. Pack et al. 1999). This makes intuitive sense, as
incubation and brood-rearing activities are behaviors that ostensibly leave females especially
vulnerable to predation. Beyond this, it is reasonable to assume that the physiological stress
associated with reproduction may extend the increased mortality risk of reproductively
successful females beyond the reproductive seasons.
Two general mechanisms to explain how density-dependent reproduction arises in
populations have been hypothesized. The first, which may be termed the interference
hypothesis, considers that in territorial species as population density increases the frequency of
agonistic encounters between individuals competing for space results in a hostile social
environment. As a result of the increased energy expended towards territorial behavior at the
expense of reproduction, reproductive output is uniformly decreased across the landscape with
little variation across individuals (Dhondt and Schillemans 1983, Sillett et al. 2004). The second
hypothesis is the concept of site-dependent population regulation (also termed the habitat
heterogeneity hypothesis) which suggests that as population density increases a progressively
larger proportion of the population is forced into using low-quality habitat, resulting in declines
in per capita reproductive success (Rodenhouse et al. 1997, McPeek et al. 2001). Under this
paradigm, while per capita reproductive output declines, variation among individuals is high as
individuals that are able to access high-quality habitat reproduce more successfully than
individuals in marginal habitat. Wild turkey males play no part in nesting or brood rearing, and
no territorial behavior between nesting females has been documented. Additionally, the
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 believe that density-dependent
reproduction in wild turkeys is most likely to be a form of site-dependent population regulation.
To understand how site-dependent population regulation may influence the reproductive
output of wild turkeys, consider that wild turkeys inhabit heterogeneous landscapes. A natural
consequence of this is that the quality of nesting habitat in any given system is heterogeneous as
well, ranging from high quality to unsuitable. Thus, only a certain portion of any landscape will
be high-quality nesting habitat. When we refer to high-quality nesting habitat we are specifically
referring to habitats that are conducive to successful nesting attempts. Given that predation is
often identified as the 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 may be
reasonable in this sense to assume that high-quality areas are those associated with low nest
predation risk. At low population densities, a large proportion of the breeding population will be
able to access what high quality nesting habitat is available. As a result, per capita recruitment
would be expected to be high as per capita nesting success is also expected to be high. As the
population continues to grow the proportion of females forced to nest in sub-optimal habitat
grows as well. At high population densities, a small proportion of the total female population
will be able to nest in these high-quality areas, with the remainder forced to attempt to nest in
lower quality areas. As a result per capita recruitment is reduced, as nest-loss is significant for
the proportion of the population nesting in sub-optimal habitat. 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.), the
absolute numbers of females successfully producing poults may be relatively consistent across
years. However, despite increasing population density the absolute number of successful
females remains relatively static, and the proportion of the population that experienced failed
nesting attempts rises. The expected result is what we see in the historical trends; reduced PPH
ratios concomitant with an increasing percentage of the female population observed without
broods.
The decline in per capita nesting success as a result of increased population density
should precipitate a rise in female annual survival rates. This is because females that experience
early nest loss, or alternatively do not attempt to nest at all, are spared the increased predation
risk associated with reproductive activities. Collier et al. (2009) documented the negative
influence of reproductive effort on survival for Rio Grande wild turkeys (M. gallopavo
intermedia). As the proportion of these reproductively unsuccessful females increases, the
overall population-level survival rate increases. In populations subject to density-dependent
regulation, this inverse relationship between reproduction and survival is a mathematical
necessity to maintain stable populations (Guthery and Shaw 2013). In this regard, a stable
population is one that has occupied the available habitat and shows no trend towards an
increasing or decreasing population size, although year to year variability may be substantial.
Essentially, this refers to a population that has reached a saturation point, and in which annual
demographic variation is ruled primarily by stochastic density-independent factors (i.e. weather).
To maintain such populations, survival and reproduction cannot increase in tandem, as doing so
would lead to biologically implausible geometric population increases over time. Conversely,
concomitant persistent declines in reproduction and survival would result in population crashes.
Thus, to maintain stable populations, survival and reproduction rates must be negatively
correlated (Guthery and Shaw 2013).
We have formulated this theory as a framework to explain patterns in the observed
historical demographic trends in the southeast based on our knowledge of wild turkey biology
and our personal research experience, as well as general concepts of population ecology. From
this we are able to develop a number of specific hypotheses based on assumptions we used that
could be explicitly tested via properly designed studies. The results of such studies would
provide evidence to support or challenge our hypotheses, and ultimately serve to better elucidate
the mechanisms linking density and reproduction. The key hypotheses are:
1) For any given landscape, population-level reproductive performance is negatively
correlated with population density.
2) For a given landscape, the mean annual survival rate of females is positively correlated
with population density.
3) For a given landscape, females that do not attempt to reproduce or experience early nest
failure have greater survival rates than females that are reproductively active.
4) If quality nesting areas are settled first as site-dependent 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 high. This is
to say that early nesting females will first settle and occupy limited available quality
habitat, and as a result late nesting females will be forced into marginal habitats. The
strength of this relationship should be weak when population densities are low.
Well-designed studies of an experimental nature are required to specifically test the
veracity of these hypotheses; however, there are still inferences we can draw from published
observational studies. The results of research conducted on Sherburne WMA (hereafter
Sherburne), located in the Atchafalaya Basin in southern LA offers an interesting case-study.
Intensive radio-telemetry studies of wild turkeys on this area occurred from 2002 – 2010
(Grisham et al. 2008, Byrne 2011, Byrne and Chamberlain 2013). Studies specifically concerned
with nesting ecology and female survival occurred during the years 2002-2005, and 2007-2010
(Byrne 2011, Byrne and Chamberlain 2013). At the time of these studies, before the catastrophic
flooding that occurred in May 2011, turkey densities on Sherburne were considered among the
highest in the state (J. Stafford, pers. comm.). Female success rates (percentage of females alive
each spring to successfully hatch ≥ 1 egg) were among the lowest reported in the literature at
24%, as were nesting rates (percentage of females alive each spring to successfully reach
incubation) at 60% (Byrne and Chamberlain 2013). However, nest success rates (percentage of
known nests in which ≥ 1 egg was successfully hatched) at 39% were relatively consistent with
the range-wide average (Byrne and Chamberlain 2013). These results broadly conform to
several predictions of site-dependent population regulation. For example, based on female
success rates, we see that per capita reproductive success is low, as hypothesized for a high
density population (hypothesis 1). Quality nesting habitat appeared to be of limited availability
in this bottomland hardwood system, where dense canopy coverage and persistent seasonal
floods resulted in a relatively sparse understory with little available ground-level cover. In a
high density population scenario, we expect only a small proportion of the females to be able to
access what high quality nesting cover was available, and this appears to be reflected in the low
nesting rates. The implication is that a large number of females were forced to attempt nesting in
sub-optimal habitat which resulted in a high percentage of nest loss prior to incubation and
subsequent observer detection. However, nest success rates suggest that the females that were
able to access suitable nesting habitat were as successful in hatching a brood as females in other
landscapes.
As expected by hypothesis 2, with low per capita reproductive success and a high
population density mean annual female survival rates were relatively high at 0.58±0.06 (Byrne
2011). Additionally, Byrne (2011) found that annual survival rates of reproductively active
females (defined as those that reached nest incubation) were lower than non-reproductive
females as expected by hypothesis 3. Most known female mortalities (67.7%) occurred during
the nesting or brood-rearing seasons. Similarly, Grisham et al. (2008) found male annual
survival rates on Sherburne to be high as well (0.64±0.06), although this is likely at least partly
due to conservative spring harvest regulations. These studies provide some support for the idea
of a negative correlation between reproduction and survival, and suggest that despite low
reproductive rates, high female survival rates allowed the population to maintain itself at
relatively high densities. As such we believe that what we observed at Sherburne can be
interpreted as a microcosm of the historical trends observed on larger scales in the present study
study.
The few studies that have examined the relationship between incubation start date and
nesting success have found that earlier nesting attempts are generally more successful than later
attempts (Thogmartin and Johnson 1999, Norman et al. 2001). Recent work in Georgia has
shown Julian date of nest initiation to be the strongest determinant of nest survival (Little et al. in
review). This suggests support for hypothesis 4, which predicts that earlier settling females are
able to access the highest quality habitat, which translates into higher rates of nest success for
those individuals. While there are numerous examples in the literature that seemingly provide
some level of support for our hypotheses, and our proposed mechanism of site-dependent
population regulation in general, we stress the need for future research aimed at specifically
testing these hypotheses. Ideally these studies would allow for some level of experimental
manipulation of wild turkey population densities, while controlling for extraneous variables as
much as possible.
Management Implications
Understanding the mechanisms by which density-dependence operates in wild turkeys
would provide tangible benefits to both habitat and harvest management applications. Perhaps
the most consequential implication of density-dependence is that in populations that have
reached the point of stability and self-regulation some management actions may be in some
respects self-defeating. This is due to the fact that actions aimed at increasing reproduction will
necessarily entail a decrease in survival, and vice versa; as discussed above this is a necessary
relationship in maintaining stable populations. In the case of habitat management for instance,
schemes aimed at increasing nesting and brood-rearing habitat under the assumption that
increasing productivity will necessarily translate to increasing population sizes may be naïve. In
populations that have already occupied the available habitat and stabilized, such a strategy may
increase reproduction but we would expect those gains to be counter-acted by associated
decreases in survival. The end result is that even with increased productivity the population
remains stable (in terms of numbers at least, age ratios may very well change). Population
growth could potentially be stimulated by providing new habitat for turkey populations to
expand into. Essentially, creating new habitat (as opposed to managing or improving habitat)
equates to lowering population densities in relation to habitat availability, and we would expect
populations to increase as new habitat is colonized. However, population growth will only
increase until the new stabilization point is reached, at which point self-regulatory processes
again take control.
The realization of the importance of accounting for density-dependence in regards to
harvest management strategies has been increasingly recognized in recent years (Warnke and
Rolley 2005, McGhee et al. 2008). By accounting for density-dependence in population models,
managers could theoretically use a yield curve paradigm to estimate expected yield for a given
population given its size relative to K. Managers could use this information to determine the
relative population density that maximizes yield, and could enact harvest management practices
to maintain populations at said densities (McGhee et al. 2008).
Population density relative to K should factor prominently into management goals as
practices that are effective at low population densities may not provide similar benefits at high
densities, and vice versa. While it may not be feasible to measure population density directly, an
understanding of the mechanistic workings of density-dependence would allow sufficient
inferences to be drawn on the status of a given population by tracking the relationships between
demographic parameters such as reproduction, survival, and harvest rates as a function of hunter
effort. Furthermore, an understanding of the 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 ostensibly allow for the generation of more precise predictive
models, and promote more informed decisions regarding harvest management.
Additional research questions and needs
In the course of analyzing and interpreting data for this study, we were able to identify
several additional areas in which information is limited and where we believe future work would
be beneficial.
1: Measures of population density
The lack of a universally accepted and accurate tool to measure wild turkey population size is a
problem that was first identified several decades ago (Mosby 1967). As an absolute count of
turkey numbers is not practical, a variety of methods have been used to attempt to derive an
index of population density. These include the total numbers of females and poults in summer
brood counts (Wunz and Shope 1980), gobbling counts (Porter and Ludwig 1980), observations
of summer bait-site use (Hayden 1985, Weinstein et al. 1995), winter flock surveys (i.e.
Weinrich et al. 1985), capture-mark-recapture modeling based on band returns (Lint et al. 1995)
and camera trap surveys (Olsen et al. 2010). While some of these indices have shown some
degree of correlation with harvest, the veracity of these methods has not been tested in respect to
actual population size. Given the importance that population density may have on determining
demographic changes through time, research aimed at assessing the accuracy of common indices
to measure density, or developing new methods to better estimate population size are paramount.
2: Immigration, emigration, and metapopulation dynamics
Within-patch demographic parameters regarding reproduction and survival are commonly
studied aspects of wild turkey populations. However, population dynamics are also influenced
by between-patch processes of immigration and emigration, and to this end little work has been
done focused on wild turkeys. The vast majority of turkey demographic studies ignore the
potential roles of dispersal and connectivity between populations, although these parameters may
have important influences on population stability and fluctuations. This likely due in part to the
fact that a majority of studies are focused on single, relatively small, study areas where these
processes are unlikely to be detectable. Understanding connectivity across large ecological
scales would aid in identifying source and sink populations, as well as important corridors that
permit movements between populations. Likewise, researchers could then begin to quantify the
permeability of landscapes to large scale movements, and to quantify how isolated specific
populations are. This last point may be particularly relevant for populations in east Texas and
southeast Oklahoma. Populations on the margins of a species distribution are generally more
dynamic than populations towards the center. There is evidence to suggest that densitydependent population regulation affects marginal and interior populations similarly, but marginal
populations are more susceptible to density-independent population fluctuations (Williams et al.
2003). This places an isolated, marginal population at greater risk of extinction, as a number of
consecutive years of poor reproduction or survival may drive the population below the point
from which it can recover. Theoretically, this risk could be mediated to some degree if
connectivity existed to allow immigration from the larger population to “rescue” these marginal
populations. It is a plausible hypothesis that the lack of connectivity to the larger population,
rather than local habitat quality, may be the major factor underlying the historical difficulties in
establishing populations in eastern Texas
Figure 1: Historic trends in productivity, as measured by poult per hen ratios, of 12 states in the
southeastern United States. Lines represent generalized additive model estimates (solid line) and
95% confidence intervals (dashed lines).
Figure 2: Historic trends in spring harvest of male wild turkeys in the southeastern United States.
Lines represent generalized additive model estimates (solid line) and 95% confidence intervals
(dashed lines). No estimate provided for Georgia because data was not sufficient to fit a model.
Figure 2 continued: Historic trends in spring harvest of male wild turkeys in the southeastern
United States. Lines represent generalized additive model estimates (solid line) and 95%
confidence intervals (dashed lines).
Figure 3: Historic trends in hunter success or harvest rates of male wild turkeys during spring
hunting seasons in the southeastern United States. Lines represent generalized additive model
estimates (solid line) and 95% confidence intervals (dashed lines).
Figure 4. Historic state-specific trends in USGS breeding bird survey indices for eastern wild
turkeys in 13 states in the southeastern United States. Dashed lines represent 95% credible
intervals.
Figure 4 continued: Historic state-specific trends in USGS breeding bird survey indices for
eastern wild turkeys in 13 states in the southeastern United States. Dashed lines represent 95%
credible intervals.
Figure 5. Percentage of urban and forest land cover for Missouri and Tennessee during the years
1992, 2001, and 2006 based on National Land Cover Data database, and concomitant trends in
demographic variables; PPH ratio, spring harvest, and BBS index (red lines).
Figure 6. Historic trends in proportion of females observed without broods during summer brood
counts in 8 states in the southeastern United States. Lines represent generalized additive model
estimates (solid line) and 95% confidence intervals (dashed lines).
Figure 7. Mean brood size (± 95% C.I.) based on observations of single females with ≤ 16 poults
for 7 states in the southeastern United States.
Figure 8: Productivity trends as determined through poult per hen ratios (solid lines) and annual
restoration efforts as measured through numbers of wild turkeys released within a given state
(grey bars) for 6 states in the southeastern United States. Vertical dashed lines represent 50%,
75%, and 95% cumulative releases respectively.
Figure 9. The 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. Values for both indices are scaled from 0 (lowest values)
to 1 (highest value) for each state.
Figure 10: Mean (± SE) survival estimates of female wild turkeys from studies conducted on
radio-marked individuals during the 1980s, 1990s, and after 2000. References to specific studies
can be found in Table 4
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