Potential Density Dependence in Wild Turkey Productivity in the Southeastern United States

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, Warnell School of Forestry and Natural Resources, University of Georgia, University of Georgia, 180 E Green St, 180 E Green St, Athens, GA 30602, USA 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; on turkey population ecology in the region. In doing so, hereafter, turkey) is one of the greatest success stories in survey techniques could be examined critically and wildlife management and populations in many regions hypotheses could be developed from existing data to increased rapidly in the last decades of the 20th century further refine research priorities moving forward. In the (Kennamer et al. 1992, Eriksen et al. 2015). However, present study, we use long-term trend data for both recent perceived declines in turkey abundance and productivity and population size collected on a large scale reproductive output have caused concern (Eriksen et al. to assess plausibility of density dependent effects on 2015). We initiated our study in response to these reproduction in turkeys. Our specific goals were 2-fold: perceived, persistent declines in annual productivity indices (1) summarize and analyze trends in demographic param- of turkeys based on surveys conducted by biologists of state eters temporally and spatially, and (2) generate hypotheses agencies that are members of the Southeast Wild Turkey to account for observed declines in productivity. It is our Working Group (SEWTWG). There was concern among hope that these hypotheses will stimulate discussion of these state biologists that observed declines were an turkey population dynamics and identify fruitful avenues of indicator that large scale, regional declines in turkey future research. populations were presently occurring, or were likely to occur in the near future. Influence of stochastic environmental variables, such METHODS as rainfall and temperature, on short term annual variations in reproduction of turkeys has been well documented in the Data Collection and Availability literature (reviewed in Warnke and Rolley 2005). However, large scale, population level drivers of long-term repro- We began data collection in April 2012 by contacting ductive trends remain largely unexplored. Density depen- turkey coordinators of cooperating states within SEWTWG dent mechanisms are potential drivers in long-term and asking them to provide all available data and historical productivity trends, and need to be considered given rapid records regarding productivity indices for turkeys. Infer- expansion of turkey populations following restoration ences regarding productivity need to be made in the context efforts (Kennamer et al. 1992). A basic tenant of population of population density because, while productivity declines biology is that if increasing populations reach sufficient may result in population declines, under many density densities, they will trigger negative feedback loops that dependent scenarios, productivity declines may in fact be limit population growth. As such, it is expected that a indicative of increasing population densities. As such, we negative relationship should exist between population size also asked for historical data regarding harvest records, and rate of population increase. Guthery and Shaw (2013) turkey restoration, and restocking information. We request- observed that evidence of density dependence in upland ed coordinators to provide as much associated metadata and game birds has existed in the literature since the 1940s. background information as was available for each dataset. Porter et al. (1990) and McGhee and Berkson (2007) both Data included 2 subspecies, eastern turkey (M. g. silvestris) suggested density dependent population growth in turkey and Florida or Osceola turkey (M. g. osceola). populations based on analyses of various harvest indices. One way in which density dependence may manifest Productivity itself is through decreased recruitment. Density dependent effects on reproduction have been documented across a Thirteen states provided productivity index records variety of avian taxa through both experimental (Dhondt et (Table 1). Oklahoma data were from the southeastern al. 1992, Both 1998, Poÿsa¨ and Poÿsa¨ 2002, Sillett et al. portion of that state occupied by the eastern subspecies. 2004, Brouwer et al. 2009) and observational studies Alabama initiated a statewide productivity monitoring (Larsson and Forslund 1994, Ferrer and Donazar 1996, program in 2010, and we did not use those data for making Bennetts et al. 2000, Armstrong et al. 2005, Carrete et al. inferences on long-term productivity trends (Table 1). The 2006). Negative relationships between population density primary metric used to index reproduction region-wide was and reproduction in gallinaceous birds were documented in poult per hen (PPH) ratio. This ratio was defined as ratio of the literature as early as the 1940s (Guthery and Shaw total number of poults to total number of females observed 2013). Specifically, Errington (1945) found such a during the summer brood-rearing period. In most states, relationship in both northern bobwhite (Colinus virgin- sightings of turkeys were recorded opportunistically during ianus) and ring-necked pheasant (Phasianus colchicus) summer months by agency personnel, or a combination of populations. Existence of a density dependent effect on agency personnel and citizen volunteers, as they went about turkey reproduction has been hypothesized by several their daily activities (e.g., Butler et al. 2015). Generally, authors, who have noted reduced productivity in popula- observers were asked to record observations of females tions considered stable, compared to recently introduced with and without broods. West Virginia was different in and expanding populations (Vangilder et al. 1987, Vander this regard, as observations of females without young often Haegan et al. 1988, Miller et al. 1998b, Bond et al. 2012). were not recorded. Thus, the reader should bear in mind At the 2011 meeting, SEWTWG formalized research that reported PPH ratios for West Virginia have a slightly priorities and decided that, before initiating regional field different biological meaning than other states. studies, it would be informative and cost effective to For most states, the observation period included June– examine demographic trends retrospectively, using existing August; Tennessee reported PPH ratios based only on datasets maintained by member states. Historical trend observations recorded in August, the sample period for analyses would provide a long-term, large scale perspective 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 Notes Alabama 2010–2011 2 NA Arkansas 1982–2012 31 NA Georgia 1978–2012 35 1978–2011 Kentucky 1984–2011 28 NA Louisiana 1994–2010 17 1994–2010 Mississippi 1995–2012 18 1995–2012 Missouri 1959–2012 54 1990–2011 PPH ratio only calculated for brood groups with 2 hens North Carolina 1988–2012 25 2001–2011 Oklahoma 1985–2012 28 2001–2012 Data only includes SE portion of the state where the eastern subspecies occurs South Carolina 1982–2012 31 NA Tennessee 1983–2012 30 2003–2012 PPH ratios calculated only from observations during month of August Virginia 1979–2010 32 NA Productivity calculated from ratio of adults/young in fall harvest WestVirginia 1967–2012 46 1967–2012 Based only on observations of females with broods a NA = raw data not available.

Virginia used observations of broods during May–Septem- trends. For example, a proportional increase in number of ber. All states asked observers to record individual females observed without broods may suggest that nesting sightings as separate events. However, when states success, or proportion of females attempting to nest, had calculated PPH ratios, total numbers of poults and females declined. However, a relatively stable proportion of from individual observations were combined to provide a females observed with broods with declining brood sizes single estimate. Guidelines for filtering spurious and may indicate declining poult survival or decreased unlikely observations prior to calculating PPH ratios, if fecundity. they existed at all, were not standardized across states and Eight states (Georgia, Louisiana, Mississippi, Missou- in many cases were not standardized across time within a ri, North Carolina, Oklahoma, South Carolina, and state. For example, a current biologist may have filtered Tennessee) reported annual percentage of females observed observations considered spurious based on an improbably without broods, or provided raw data that allowed us to large ratio of females to poults, whereas his or her calculate PPH. We were unable to calculate brood sizes predecessors did not. from available data. Additionally, simply calculating Of states that monitored productivity, Virginia was the annual ratio of total poults to total females based on only state in which PPH ratios were not calculated from observations of females with young was inappropriate summer observations. Rather, PPH ratios were derived because of the necessary assumption that observed young from ratio of juveniles to adult females in fall harvest based were distributed evenly among all females. This has on reports at mandatory hunter check stations. While potential to introduce considerable bias, because females Virginia did record summer brood observations, inferences without broods will travel with females tending broods regarding productivity were traditionally based on fall (e.g., Byrne et al. 2011). Therefore, we reasoned that the harvest data because spatial distribution was more consis- most accurate way to measure mean brood size was to rely tent over time and sample sizes were larger than summer solely on observations of single females with young. For observations. Virginia discontinued using fall harvest to states that provided raw data that included records of index productivity in 2010. However, we considered it the individual observations, we estimated mean annual brood most appropriate for our purposes because it represented size and corresponding 95% confidence interval based on continuous data for 26 years and was traditionally used by observations of single females with 16 poults. We chose Virginia as the primary measure of productivity. These 16 poults because, based on our personal field experiences methodological disparities hindered our ability to make and mean clutch sizes reported in the literature (Vangilder direct comparisons among states. Despite this, as long as 1992), clutches .16 are exceptionally rare. Thus, we methodological biases present within a state were relatively consistent over time, we offer that any significant, long- assumed that observations of single females with broods of term changes in statewide productivity would still be more than 16 poults likely represented an erroneous or identifiable in regards to relative historical trends in PPH incomplete observation. ratios. We attempted to characterize observed trends in PPH Abundance Indices ratios based on the assumption that long-term changes in PPH ratios could result from 2 potential underlying factors: All states provided spring harvest records. Nine states (1) changes in number of females observed without broods, had fall turkey seasons during all or part of our study and (2) changes in mean size of observed broods. While not period. However, we concentrated our analyses on spring necessarily mutually exclusive, each scenario suggests a harvest because (1) all cooperating states had a spring different set of possible mechanisms underlying observed turkey season and (2) range-wide, spring seasons generally 332 Productivity and Survival

Table 2. Historical availability of statewide spring eastern turkey numbers would indicate that raw harvest estimates are harvest data from 15 states in the southeast United States. Total unsuitable as indices of turkey populations. Theoretically, harvest was estimated number of total males harvested and accounting for hunter effort should provide a more accurate hunter numbers were estimates of total hunters or eligible link between harvest data and turkey population density. If hunters based on permit sales. Data obtained from respective turkey populations are experiencing noticeable long-term state agencies. changes in density, there should be corresponding changes State Total harvest Hunter numbersa in hunter success per unit effort. To account for effort, we examined trends in spring hunter success (defined as Alabama 1972–2012 1972–2012 percentage of hunters to harvest 1 turkey), or harvest Arkansas 1961–2012 NA proportion (defined as turkeys harvested per number of Florida 1989–2012 1989–2010 turkey hunters) for states that reported such information Georgia 2005–2013 2005–2013 directly, or in which we were able to calculate these metrics Kentucky 1978–2012 1997–2012 ourselves from data provided. In Florida, we chose to Louisiana 1980–2012 1980–2010 model trends in hunter success rather than harvest Mississippi 1981–2012 1981–2009 proportion because this metric was based directly on Missouri 1960–2012 1960–2011 responses to hunter surveys, and thus did not introduce bias North Carolina 1977–2011 1976, and every third associated with estimating hunter numbers. We also year since 1983 modeled hunter success for Mississippi, as these values Oklahoma 1990–2012 NA South Carolina 1976–2011 1977–2010 were reported annually during 1980–2009 (Hunt 2010). We Tennessee 1990–2010 NA note that more information is required to derive a truly Texas 1995–2012 1983–2011 accurate measure of catch per effort. For example, ratio of Virginia 2004–2013 NA harvested turkeys to number of hunters may remain the West Virginia 1996–2012 NA same, but days required to harvest a turkey may change. However, this level of information was not commonly a NA = estimates of hunter numbers not available. available. State agencies work independently to collect and see much greater hunter interest and participation than fall summarize turkey population metrics and resulting incon- seasons, especially in recent decades (Eriksen et al. 2015). sistencies make comparisons among states problematic There was considerable inconsistency regarding data (also see Eriksen et al. 2015). This, along with issues availability, how data were obtained, and length of inherent in using harvest data as an index of population, historical records (Table 2). The 1 metric common to all prompted us to use data from the U.S. Geological Survey’s states was an annual estimate of spring harvest, although Breeding Bird Survey (BBS; https://www.pwrc.usgs.gov/ methods of obtaining this estimate differed among states BBS/) as an independent and methodologically consistent and methods often changed within states through time. index to overall population trends for each state. The BBS Estimates of spring harvest were variously derived from is an annual road-based survey designed to track large scale information gathered at check stations, or through hunter changes in avian abundance over time. Over 4,000 survey surveys conducted via mail, phone, internet, or various routes exist in the United States and Canada; each route is combinations thereof. Calculations of harvest metrics were 39.43 km in length, is surveyed by a qualified observer variously conducted in-house by agency personnel, or by once each year, and is often surveyed by the same observer outside entities such as universities or consulting firms. consecutively for a series of years. Each route contains 50 Data sufficient to provide estimates of hunter effort were evenly spaced sample points from which an observer available in 8 states (Alabama, Florida, Georgia, Kentucky, conducts a 3-minute point-count, recording all bird species Louisiana, Mississippi, Missouri, and South Carolina) over seen and heard. Annual surveys in the southeastern United a variable number of years. These data consisted of annual States are normally conducted in early–mid June, with the estimates of spring hunter effort derived from license sales exception of Florida, where surveys may occur in May. or hunter surveys. BBS data are summarized for individual states and larger Harvest estimates are often used as proxies of physiogeographic regions of the continent. For any population density, although few studies have tested geographic region, a hierarchical model, which accounts veracity of this relationship (Lint et al. 1995). Inferring a for varying regional survey quality and observer effects direct relationship between harvest and population size is (Link and Sauer 2002, Sauer and Link 2011), is used to difficult, as harvest is a function of availability of turkeys to estimate an annual population index (measured as obser- hunters, and rate at which hunters are able to harvest vations per route). turkeys. Likewise, there is obvious age and location For our purposes, the standard protocol over space and specific selection in turkey harvest events. Harvest may time was an obvious advantage of BBS indices. The BBS be a sufficient index if hunter effort remains constant over has been ongoing since 1966, providing a long-term data time (Lint et al. 1995); otherwise, trends in overall harvest set that encompasses restoration of turkeys in the may be representative of hunter population dynamics and southeastern United States to present day. Additionally, behavior more so than changes in turkey populations. To the BBS index incorporates observations of all turkeys test for a relationship between hunter numbers and spring regardless of age or sex and, as such, provides a general harvest, we performed a simple linear regression for the 8 index of total turkey population density as opposed to most states above that provided estimated hunter numbers. A state-derived estimates, which are based on data pertaining great correlation between harvest estimates and hunter 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 Missouri Mixed forest–agriculture Vangilder and Kurzejeski 1995 1984–1985 Missouri Mixed forest–agriculture Kurzejeski et al. 1987 1984–1994 Mississippi Mixed hardwood–pine (Pinus spp.) Miller et al. 1998a 1987–1990 Mississippi Pine plantation Palmer et al. 1993 1988–1994 Wisconsin Mixed forest–agriculture Wright et al. 1996 1990–1993 New York Mixed forest–agriculture Roberts et al. 1995 1990–1993 Missouri Mountain hardwood Vangilder 1995 1990–1994 Virginia–West Virginia Mountain hardwood Pack et al. 1999 1993–1996 Iowa Mixed forest–agriculture Hubbard et al. 1999 1998–2000 South Carolina Coastal pine Moore et al. 2010 2002–2006 Ohio Hardwood Reynolds and Swanson 2010 2002–2010 Louisiana Bottomland hardwood Byrne 2011 2003–2005 Indiana Agricultural Humberg et al. 2009 2010–2012 Delaware Pine plantation J. Bowman, personal communication 2012 Arkansas Mixed hardwood–pine T. Pittman, personal communication surveys coincides with end of nesting season and beginning Female Survival of the brood survey period over much of the region. To provide further context for interpreting observed Theoretically, this should allow all members of the productivity trends, we reviewed the literature for female population, except late nesting females, to be available survival estimates. We limited our review to studies which for detection in surveys. used robust statistical methods such as Kaplan–Meier We queried annual BBS abundance indices and (Pollock et al. 1989) or Heisey–Fuller (Heisey and Fuller associated 95% credibility intervals for each individual 1985) to derive annual survival estimates from radiotelem- state during 1966–2011 through the interactive online BBS etry data. Additionally, we queried researchers currently results and analysis portal (http://www.mbr-pwrc.usgs.gov/ engaged in studies of female survival to obtain preliminary bbs/bbs.html). We did not include BBS data for Oklahoma results (Table 3). To bolster sample sizes, we considered and Texas because the eastern subspecies only occurred in studies that spanned the entire geographic range of the a relatively small portion of each state, containing few BBS eastern subspecies. To investigate how survival may have routes. Additionally, BBS analyses are performed at the changed over time, we binned studies into one of 3 time state and species level, making it difficult to separate periods, (1980–1989, 1990–1999, and 2000) and recorded eastern turkeys from Rio Grande turkeys (M. g. intermedia) mean annual survival estimate for each study. For multi- present in large portions of these states. year studies that spanned across time bins and reported annual survival estimates for individual years, we classified Restoration Efforts years into their respective time period and calculated a mean annual survival rate. For example, Miller et al. Turkey populations in many parts of the southeastern (1998a) reported annual survival rates during 1984–1994. United States are largely the result of intensive, large scale In this case, we calculated a mean survival rate for 1984– restoration efforts. Restoration was largely accomplished 1989 and 1990–1994, respectively. If a study spanned 2 through live release of turkeys captured from extant time periods but did not report individual survival rates for populations into areas containing suitable habitat in which each year, we placed it in the time period in which most of turkeys were absent or existed at very small densities the study occurred. For example, Moore et al. (2010) (Kennamer et al. 1992). Given large scale introductions and reported only a single survival rate estimate from a study dispersals of turkeys into new areas from restoration during 1998–2000. Because most of the study occurred efforts, we investigated if release efforts conferred a prior to 2000, we grouped this study into the 1990s. noticeable change in productivity trends. Historical resto- Additionally, in cases in which successive studies of ration information was available from 6 states that also specific study sites built on and incorporated date reported provided productivity data (Georgia, Louisiana, Mississip- in earlier studies, we only used survival estimates reported pi, Missouri, North Carolina, and Tennessee). This in the most recent study. For example, Byrne (2011) information primarily consisted of counts of turkeys incorporated data reported in Wilson et al. (2005) and, as released, sometimes detailed to specific release sites, and such, we only used results from Byrne (2011). in other cases summarized by county or parish. Given the large scale nature of this study, and inconsistencies in Analyses reporting, we tabulated number of turkeys released statewide in a given year. We determined when restoration Because considerable differences in data availability was 50%, 75%, and 95% complete in each state based on and quality among states precluded pooling data across total cumulative releases and qualitatively compared states, we used a comparative analysis approach. We historical releases to trends in PPH indices. accomplished this by first analyzing available data from 334 Productivity and Survival each state independently, then making biological inferences states from which data were available (Fig. 2). Again, based on broad scale similarities in trends observed across strength of this trend varied among states but, in 5 states, states. Statistical imprecision in productivity and harvest ranges in model-predicted estimates approached or exceed- datasets is caused by inherent biases resulting from ed 20% (Mississippi = 18.7%, Louisiana = 19%, Missouri unquantified levels of measurement error in data collection = 25.8%, Oklahoma = 26.8%, Tennessee = 28.8%). methodologies and inconsistent survey protocols. Addi- Difference between largest and smallest annual mean brood tionally, stochastic annual variation in conditions that size was ,2 poults for all states except Oklahoma and West influence nesting success can lead to annual variation in Virginia (Fig. 3). Mean observed brood size (6 95% CI) long-term productivity averages (Vangilder et al. 1987, ranged 4.5 (6 0.6) to 6.4 (6 0.8) in Louisiana, 5.1 (6 0.2) Healy 1992). Similarly, factors such as weather conditions to 6.7 (6 0.3) in Missouri, 4.5 (6 0.3) to 6.1 (6 0.4) in and timing of hunting seasons relative to timing of a given Mississippi, 4.4 (6 0.3) to 5.4 (6 0.4) in North Carolina, year’s nesting cycle may introduce annual variation in and 5.1 (6 0.4) to 6.2 (6 0.4) in Tennessee. Mean observed harvest numbers. These combined factors cause variation brood size ranged 4.6 (6 0.9) to 7.3 (6 0.7) and 5.2 (6 0.6) that may obfuscate underlying trends representing larger, to 9.0 (6 1.2) for Oklahoma and West Virginia, population level processes in which we were interested. respectively. To account for expected nonlinearity in our data due to stochasticity, we used generalized additive models (GAM; Abundance Wood 2006), which provide a flexible, regression based method for fitting a curve to noisy, non-linear data. General All 15 states provided historical data on estimated additive models are similar in nature to generalized linear spring harvest ranging 8–52 years (mean = 28 years, Table models, but in a GAM, the linear predictor incorporates a 2). Trends in spring harvest were greatly variable across set of smooth functions of any number of predictor states, and it was difficult to generalize a region-wide trend variables (in our case, time). Variables are smoothed via (Fig. 4). A number of states, including Kentucky, North a spline function, which is essentially a series of multiple Carolina, and Tennessee, exhibited consistently increasing polynomial regressions connected at various points (knots) trends in spring harvest through time, whereas states such to create a continuous smooth line through data. Applying as Arkansas, Missouri, South Carolina, and West Virginia GAMs allowed us to smooth time series data to elucidate exhibited a trend of stabilizing or decreasing harvest underlying trends. Specifically, we fit GAMs to time series following a peak in the late 1990s or early 2000s. The data of PPH ratios, proportion of females observed without most precipitous and persistent decline was observed in broods, spring harvest numbers, and spring harvest Mississippi (Fig. 4). proportions. We adjusted number of knots used to produce We found that correlations between hunter numbers spline curves for each model to improve model fit. We and spring harvest were great in all states, and that hunter evaluated goodness-of-fit for each model based on visual numbers were a significant predictor of total harvest (Table inspection of residual normality via standard diagnostic 4; Fig. 5). Thus, harvest estimates were likely strongly residual plots, Q-Q plots, and residual histograms. We biased by hunter participation. We also noted an additional report model predicted estimate and 95% confidence potential confounding relationship between harvest and interval for each year. We fit GAMs in the statistical hunter numbers in that hunter participation may itself be program R (R Core Team 2013) using the mgcv package influenced to some degree by turkey population densities (Wood 2013). (i.e., a functional response in which, when turkey To illustrate relationships between trends in abundance populations are perceived to be great, a greater number of and productivity, we plotted GAM predicted productivity hunters may participate in spring hunting). When account- (PPH) values as a function of relative population size (BBS ing for harvest effort, harvest trends were generally less indices) for each state in which productivity data were variable over time than raw harvest estimates (Fig. 6). For available. To account for differing scales among states, we instance, hunter success in Mississippi has consistently first scaled PPH ratios and BBS indices from 0 to 1 for each hovered around 50% despite persistent declines in numbers respective state, with 0 being least value of each metric and of turkeys harvested. In Missouri, an increase in estimated 1 being largest value of each metric. harvest of 60,650 turkeys between 1960 and 2004 corresponded with an increased harvest proportion of only 0.32 turkeys harvested per hunter. RESULTS Trends in data from BBS suggested population increases over time, but magnitude of increase varied Productivity considerably across states (Fig. 7). Tennessee, for example, exhibited a particularly sharp population increase begin- Twelve states provided historical productivity data ning in 2000, with estimated observations/route increasing ranging from 17 to 54 years (mean = 31 years, Table 1). from 0.4 to 4.9 between years 2000 and 2010. Conversely, Productivity generally declined across all states where data trends were least pronounced in Louisiana and Mississippi, were available (Fig. 1), but the nature and severity of with total net increases of model estimated observations per declines was variable. For example, Tennessee experienced route of 0.21 and 0.36 for each state, respectively. When a particularly steep decline, whereas productivity in plotting annual productivity indices as a function of BBS- Mississippi remained relatively stable over the record derived population indices, a clear negative relationship keeping period (Fig. 1). Percentage of females observed between population size and productivity was apparent in without poults generally increased through time in the 8 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). 340 Productivity and Survival

Table 4. Results of linear regression models (F statistics, DISCUSSION degrees of freedom [df], P-values, and adjusted R2 values) of the relationship between spring hunters on total spring harvest Our ﬁndings suggest that there has been a general long- estimates of male turkeys for 8 states in the southeastern United term decline in turkey productivity (to varying degrees) States. Sample size (n) is number of years in which both across the southeastern United States, based on trends in estimates of harvest and hunter numbers were available. Data PPH ratios observed across states. We offer that direct obtained from respective state agencies. comparisons among states regarding actual PPH ratios are

2 tenuous, as there are many confounding, latent variables to State nFdf P-value adjusted R consider. As such, it is difficult to parse out whether AL 41 120.0 39 ,0.001 0.75 differences in scale represent actual differences in true FL 21 112.4 19 ,0.001 0.85 productivity among states, or are artifacts of differing data GA 9 11.4 7 0.012 0.57 collection or survey protocols. For example, West Virginia KY 16 32.7 14 ,0.001 0.68 had consistently larger PPH ratios than other states, but LA 27 33.4 25 ,0.001 0.55 West Virginia’s sampling protocol also varied considerably MS 27 136.9 25 ,0.001 0.84 from other states, as females without poults were not MO 53 936.7 51 ,0.001 0.95 recorded. However, the important observation is a consis- SC 35 361.4 33 ,0.001 0.91 tent, 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 Curve shape varied somewhat among states but, in general, proportion of females observed without broods. Converse- years with greatest PPH ratios corresponded to years when ly, there was little evidence to suggest meaningful declines BBS indices were least. 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 Productivity and Restoration interval among years. The most persistent negative When comparing restoration efforts to productivity historical trend appeared in West Virginia, although sample trends, we noted that declines in productivity began prior sizes early in the historical record were generally small, to restoration efforts reaching 50% completion in states which was reflected by wide confidence intervals. Thus, that had historical productivity data extending back apparent decline in mean brood size from very great values beyond that point (Fig. 9). Restoration efforts in Georgia, in the early part of the West Virginia record was likely Missouri, North Carolina, and Tennessee were charac- influenced to some degree by sampling effort. terized by time periods in which restocking activity was Congruent with declining productivity indices, we especially intense. For instance, in North Carolina, there observed increasing population trends based on BBS data. was a clear peak in releases in the 1990s. However, in all Breeding Bird Survey routes are surveyed once annually, of the above states, steep downward trends in productiv- and not all routes in a given state necessarily traverse ideal ity began prior to these intensive restoration efforts and or suitable turkey habitat. Additionally, survey methods are continued for duration of restocking years. Restocking not specifically designed to detect turkeys. Despite these efforts in Louisiana and Mississippi did not exhibit clear shortcomings, BBS was consistent in methodology and spikes in activity observed in other states. Louisiana is spatial coverage over time, and suggested that turkey the only state in which productivity declines began after populations increased over time. restoration was 95% complete. Overall, no clear pattern Raw harvest data are only informative given assump- existed to allow us to draw inference regarding a direct tions of constant hunter effort and availability of turkeys. link between intensity of restoration efforts and produc- However, hunter numbers change through time, and we tivity, as it appeared that prevailing productivity trends demonstrated that raw harvest numbers were greatly began prior to intense restoration efforts. We add the correlated with hunter numbers. The implication is that caveat that it was difficult to ascertain spatial overlap caution should be used in extrapolating information between counties in which restoration efforts were regarding population trends from harvest data. When concentrated and those in which brood surveys were considering indices that accounted for hunter effort, we being actively conducted, especially during early resto- found no evidence to suggest population declines in the 8 ration periods. Thus, potential exists that some small states in which such data was available. At the time of our scale correlations may have been obfuscated by brood study, populations appeared to be in a state of either surveys not simultaneously occurring in areas experienc- relative stability or slow growth. ing intense restoration efforts. 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 Female Survival survival trends, and that variation in predator communities We found that range-wide female survival rates have and climatic conditions across the species’ range may have generally increased over time, from an average annual influenced local survivorship differently. However, we survival rate of 0.51 (range: 0.44–0.61) for studies in the believe that the general trend towards increased survival 1980s to 0.68 (range 0.58–0.78) for studies in the 2000s over time and across regions is worth noting, especially as (Fig. 10). 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. 343 data) in other regions are similar to that observed in the while per capita reproductive output declines, variation southeastern United States (Eriksen et al. 2015). Thus, our among individuals is great as individuals that are able to results indicate that turkeys in the southeastern United access good quality breeding habitat reproduce more States have followed a historical pattern of large scale successfully than individuals in marginal habitat condi- decreases in per capita productivity, increasing and tions. Our observed trends of reduced PPH ratios stabilizing population abundance, and increases in female concomitant with variable proportions of females observed survival rates. Almost universally, years with least without young suggests a per capita decrease in recruitment productivity indices were associated with greatest indices along with increasingly variable nesting success among of population abundance (Fig. 8). While the notion of little individuals. As such, we hypothesize that density depen- productivity and simultaneous stability in turkey popula- dent reproduction in turkeys is most likely to be a form of tions may initially seem counterintuitive, this relationship site-dependent population regulation. has been discussed in previous literature. Most notably, To understand how site-dependent population regula- Vangilder et al. (1987) used modeling approaches to tion may influence reproductive output of turkeys, consider conclude that, even when female success rates (defined as that turkeys inhabit heterogeneous landscapes. A natural portion of females alive each spring to successfully hatch a consequence of this is that quality of nesting habitat in any brood) were as small as 30–40%, large population densities given system is heterogeneous as well, ranging from good could be maintained if annual female survival rates quality to unsuitable. Thus, only a certain portion of any averaged approximately 0.44. landscape will contain suitable nesting habitat (e.g., habitat Negative correlation between population and produc- conditions that are conducive to successful nesting tivity indices leads us to hypothesize that large scale, attempts). Given that predation is often identified as primary historic declines in productivity we observed are evidence cause of nest loss (Vangilder et al. 1987, Vander Haegen et that reproduction is mediated in a density dependent al. 1988, Miller et al. 1998b, Paisley et al. 1998, Byrne and manner. Despite observational evidence, there is no Chamberlain 2013), it is reasonable to assume that good experimental evidence of a density dependent relationship quality areas are often those associated with small nest between population density and reproduction in turkeys predation risk. At small population densities, a large and, as a result, traditional models of turkey population proportion of a breeding population would be able to access dynamics have assumed no such relationships (Roberts and what good quality nesting habitat is available. As a result, Porter 1995, Vangilder and Kurzejeski 1995, Rolley et al. per capita recruitment would be expected to be great, as per 1998, Alpizar-Jara et al. 2001). Here, we present a capita nesting success is also expected to be great. As the theoretical mechanism in which density dependent repro- population continues to grow, proportion of females forced duction may operate on turkey populations. While our to nest in suboptimal habitat conditions grows as well. At proposed mechanism is in some respects speculative, it is great population densities, a small proportion of total female based on our observed trends in reproduction, population population would be able to nest in these good quality areas, densities, female survival, and our knowledge of turkey with the remainder forced to attempt to nest in poorer ecology in general. It is our hope that presenting our quality areas. As a result, per capita recruitment would be thoughts will stimulate discussion and novel thought on reduced, as nest loss becomes significant for proportion of nature of turkey population dynamics and will help guide the population nesting in suboptimal habitat conditions. future turkey research. This would be expected to be reflected in annual summer Turkeys exhibit life history traits commonly associated brood counts. After allowing for some degree of annual with r-selected species, such as early age of maturity, short variation resulting from density independent environmental life span, great reproductive potential, and an association factors (temperature, precipitation, etc.), absolute numbers with dynamic and early successional environments (Stearns of females successfully producing poults may be relatively 1977). Species with these life history traits are hypothe- consistent across years. However, despite increasing sized to exhibit their greatest population growth rates at population density, absolute number of successful females small population densities relative to environmental remains relatively static, and proportion of the population carrying capacity (K; Fowler 1981). This hypothesis is that experience failed nesting attempts rises. The expected supported for turkeys by Porter et al. (1990) and McGhee result is what we observed in historical trends, reduced PPH and Berkson (2007), who both found population growth to ratios concomitant with an increasing percentage of the be greatest at small densities. A correlate of this is that female population observed without broods. greatest levels of per capita recruitment would be expected Female survival rates may be linked to decreases in per to be associated with these periods of great growth capita reproductive success. Female survival rates have occurring at small densities, and historical trends in the been observed to exhibit seasonal variation, with least southeastern United States largely conform to this pattern. survival rates often associated with reproductive seasons A number of general mechanisms to explain how (Vander Haegen et al. 1988, Palmer et al. 1993, Wright et density dependent reproduction arises in populations have al. 1996). Female survival outside of reproductive seasons, been hypothesized. The concept of site dependent popula- assuming harvest (legal or illegal) is not a major cause of tion regulation (also termed the habitat heterogeneity mortality, can be quite great (e.g., Pack et al. 1999). This hypothesis) suggests that as population density increases, makes intuitive sense, as incubation and brood-rearing a progressively larger proportion of the population is forced activities leave females especially vulnerable to predation. into using poor quality breeding habitat, resulting in Conversely, females that experience early nest loss, or declines in per capita reproductive success (Rodenhouse alternatively do not attempt to nest at all, may be spared the et al. 1997, McPeek et al. 2001). Under this paradigm, 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 (2) For a given landscape, females that do not attempt to effort on survival for Rio Grande turkeys. Thus, as reproduce or those that experience early nest failure proportion of reproductively unsuccessful females increas- (i.e., do not incubate a nest or tend a brood) have es, overall population level survival rate may increase as greater annual survival rates than females that are well. reproductively active, after controlling for factors such A number of hypotheses based on assumptions we used as legal and illegal harvest. could be explicitly tested via properly designed studies. (3) Following from hypothesis 2, for a given landscape, Results of such studies would serve to better elucidate mean annual survival rate of females is positively mechanisms linking turkey reproduction and density. These correlated with population density. hypotheses include: (4) If quality nesting areas are settled first as site- (1) For any given population, female reproductive success, dependent regulation would suggest, then for a given defined as proportion of females alive at beginning of a landscape, with all else being equal, there should exist reproductive season to successfully hatch young a negative correlation between nest initiation date and (Vangilder 1992), is negatively correlated with popu- nest success when population densities are great. This lation density. 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

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 Figure 10. Mean (6 SE) survival estimates of female turkeys assumptions, we feel that attempting to draw inferences from studies conducted on radiotagged individuals during 1980s, regarding population changes based on currently available 1990s, and after 2000. References to specific studies can be harvest data is tenuous at best. found in Table 4. BBS routes are surveyed once annually, and not all routes in a given state necessarily traverse ideal or suitable occupy limited available quality habitat and, as a turkey habitat. Additionally the survey methods are not result, late nesting females will be forced into marginal specifically designed to detect turkeys. Despite these short habitat conditions. Strength of this relationship should comings, given consistency in methodology and spatial be weak when population densities are small. coverage over time, the fact that turkeys are encountered with sufficient frequency so that relative abundance Clearly, our inferences are based on data summarized estimates increased strongly suggests actual population over coarse spatial scales. In reality, a state’s turkey increases over time. Thus, the BBS appears useful for population likely consists of a number of relatively distinct detecting long-term population changes, but lack of turkey- subpopulations with varying levels of connectivity (Flem- specific survey protocols likely limits its effectiveness at ing and Porter 2007). By making inferences at the state detecting short-term population changes and its usefulness level, we are necessarily making inferences based on data as a short-term monitoring tool. sets that represent aggregations of these various subpopu- Summer brood surveys represented the most common lations. At any given point in time, it is reasonable to method of indexing productivity, but methodologies varied expect that variation exists between these populations, in widely, hindering direct comparisons across states and that some populations may be growing, whereas others are regions, and little work has been done assessing ability of stable or decreasing (e.g., Butler et al. 2015). Furthermore, such surveys to accurately index productivity. The few these populations are likely influenced to different degrees studies that have been conducted in this regard, on Rio by localized changes in factors such as habitat conditions Grande turkeys, indicated poor correlations between survey and availability, predator populations, and harvest regimes. results and reproductive parameters of radiotagged popu- When we aggregated across these populations, we neces- lations (Butler et al. 2005) and lack of statistical power sarily missed some of this localized variation. For these necessary to detect biologically meaningful changes reasons, we did not attempt to draw correlative inferences (Schwertner et al. 2003). Future work should assess ability regarding state level habitat changes and productivity of brood surveys to accurately index productivity and trends, as attempting to draw inferences between habitat measure detection probabilities in a variety of landscapes. 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, MANAGEMENT IMPLICATIONS further refining these ideas, and exploring alternative We offer that the time has come for studies specifically hypotheses as appropriate. However, we stress that such designed to elucidate mechanistic relationships between work should focus on elucidating population processes at population density and demographic processes in turkeys. scales relevant to turkey population ecology. An understanding of processes that regulate turkey Our work highlights an important issue regarding use populations, and how these processes may vary with and interpretation of methods used to survey turkey respect to population density, would allow for further demographic parameters. The lack of a universally refinement of population models. This would allow accepted and accurate tool to measure turkey population generating more precise predictive models and promote size is a problem that was first identified several decades more informed decisions regarding harvest management. ago (Mosby 1967) and is still an issue today. While Additionally, while it may not be feasible to measure commonly used, current harvest metrics are likely poor population density directly at large scales, an understanding indices of population size or density. Hunter numbers of mechanistic connections between density and demo- change through time, and the great correlation observed graphic parameters would potentially allow sufficient between hunter numbers and raw harvest estimates inferences to be drawn by tracking relationships among invalidates use of raw harvest as a meaningful index to reproduction, survival, and harvest. Accounting for density population size. Additionally, not all turkeys are available dependence is an important aspect of creating sound policy Turkey Productivity Byrne et al. 349 to meet specific management goals (Stevens et al. 2015). Byrne, M. E., and M. J. Chamberlain. 2013. Nesting ecology of wild However, given how little is presently known about turkeys in a bottomland hardwood forest. American Midland processes linking turkey population density and demo- Naturalist 170:95–110. graphic parameters, we hesitate to provide specific Byrne, M. E., M. J. Chamberlain, and F. G. Kimmel. 2011. Seasonal space use and habitat selection of female wild turkeys in a management recommendation until further research is Louisiana bottomland forest. Proceedings of the Southeastern conducted. Association of Fish and Wildlife Agencies 65:8–14. Carrete, M., J. A. Donazar, and A. Margalida. 2006. Density- dependent productivity depression in Pyrenean bearded ACKNOWLEDGMENTS vultures: implications for conservation. Ecological Applica- We thank the cooperating states of the SEWTWG tions 16:1674–1682. Collier, B. A., K. B. Melton, J. B. Hardin, N. J. Silvy, and M. J. (Alabama, Arkansas, Florida, Georgia, Kentucky, Louisi- Peterson. 2009. Impact of reproductive effort on survival of ana, Mississippi, Missouri, North Carolina, Oklahoma, Rio Grande wild turkey (Meleagris gallopavo intermedia) hens South Carolina, Tennessee, Texas, Virginia, and West in Texas. Wildlife Biology 15:370–379. Virginia) that provided access to their respective historical Dhondt, A. A., B. Kempenaers, and F. Adriaensen. 1992. Density- data, and agency personnel who provided information dependent clutch size caused by habitat heterogeneity. Journal regarding data collection and helpful thoughts and of Animal Ecology 61:643–648. comments that improved this manuscript: S. Barnett, B. Eriksen, R. E., T. W. Hughes, T. A. Brown, M. D. Akridge, K. B. Bond, A. Butler, S. Dobey, D. Godwin, J. Hardin, J. Honey, Scott, and C. S. Penner. 2015. Status and distribution of wild turkeys in the United States: 2014 status. Proceedings of the C. Hunter, J. Isabelle, K. Krantz, K. Lowery, G. Norman, National Wild Turkey Symposium 11:7–18. C. Ruth, D. Sawyer, R. Shields, J. Stafford, E. Stanford, D. Errington, P. L. 1945. Some contributions of a fifteen-year local Steffen, C. Taylor, and J. Waymire. We thank our study of the northern bobwhite to a knowledge of population cooperating state agencies and state chapters of the phenomena. Ecological Monographs 15:1–34. 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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 Bret A. Collier is an Assistant Professor in the School of Renewable population ecology, with a particular focus on animal movement Natural Resources at Louisiana State University. Bret’s research ecology, habitat use, and elucidating the links between individual focus is wildlife population dynamics and development of statistical behaviors and population level processes. He conducts research on a methods for wildlife biologists, although he has been known to wide variety of species in both marine and terrestrial ecosystems, delve into a variety of wildlife-related topics. He has been actively and has been involved in wild turkey research since 2007. 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.

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.