To my family and Rosemary, LLC - University of Floridaufdcimages.uflib.ufl.edu/UF/E0/04/34/05/00001/olson_j.pdfattempted to understand wild turkey hen nest site selection and habitat

FLORIDA WILD TURKEY NEST SITE SELECTION AND NEST SUCCESS ACROSS MULTIPLE SCALES

By

JOHN M. OLSON

A THESIS PRESENTED TO THE GRADUATE SCHOOL

OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF

MASTER OF SCIENCE

UNIVERSITY OF FLORIDA

2011

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© 2011 John M. Olson

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To my family and Rosemary, LLC

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ACKNOWLEDGMENTS

I would like to extend the sincerest of thanks to my parents, family, and fiancé, who

have always supported me and pushed me to do more. I would also like to thank Dr.

William Giuliano, Dr. Holly Ober, Dr. Emma Willcox, and John Denton for their guidance

and support; Mitchell Blake and the technicians who assisted in data collection for their

parts in this project; and the University of Florida and the Florida Fish and Wildlife

Conservation Commission for providing the financial and technical support necessary to

the project.

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TABLE OF CONTENTS page

ACKNOWLEDGMENTS .................................................................................................. 4

LIST OF TABLES ............................................................................................................ 6

LIST OF FIGURES .......................................................................................................... 7

ABSTRACT ..................................................................................................................... 8

CHAPTER

1 INTRODUCTION .................................................................................................... 10

Study Objectives ..................................................................................................... 12 Study Sites .............................................................................................................. 12

2 METHODS .............................................................................................................. 14

Data Collection ....................................................................................................... 14 Analysis .................................................................................................................. 19

3 RESULTS ............................................................................................................... 28

Selection ................................................................................................................. 28

Success .................................................................................................................. 30

4 DISCUSSION ......................................................................................................... 57

Selection ................................................................................................................. 57 Success .................................................................................................................. 61

LIST OF REFERENCES ............................................................................................... 66

BIOGRAPHICAL SKETCH ............................................................................................ 71

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LIST OF TABLES

Table page 2-1 Variable names, abbreviations, and definitions used in a priori models to

predict nest habitat selection and success at microhabitat and patch levels ...... 22

2-2 Variable categories, names, abbreviations, and definitions used in a priori models to predict nest habitat selection and success at the landscape level ..... 23

3-1 Ranked models used to predict nest habitat selection at the microhabitat level ........................................................................................................................... 33

3-2 Parameter estimates, 95% confidence intervals (CI), and odds ratios (OR) for most supported models for selection at the microhabitat level ........................... 34

3-3 Ranked models used to predict nest habitat selection at the microhabitat level ........................................................................................................................... 35

3-4 Parameter estimates, 95% confidence intervals (CI), and odds ratios (OR) for most supported models for selection at the patch level ...................................... 36

3-5 Ranked models used to predict nest habitat selection at the landscape level .... 37

3-6 Parameter estimates, 95% confidence intervals (CI), and odds ratios (OR) for most supported models for selection at the landscape level .............................. 43

3-7 Most supported a priori model(s) from each variable category predicting nest habitat selection at the landscape level .............................................................. 44

3-8 Ranked models used to predict nest success at the microhabitat level .............. 45

3-9 Parameter estimates, 95% confidence intervals (CI), and odds ratios (OR) for most supported models for success at the microhabitat level ............................ 46

3-10 Ranked models used to predict nest success at the patch level......................... 47

3-11 Parameter estimates, 95% confidence intervals (CI), and odds ratios (OR) for most supported models for success at the patch level ....................................... 48

3-12 Ranked models used to predict nest success at the landscape level ................. 49

3-13 Parameter estimates, 95% confidence intervals (CI), and odds ratios (OR) for most supported models for success at the landscape level ................................ 55

3-14 Most supported a priori model(s) from each variable category predicting nest success at the landscape level ........................................................................... 56

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LIST OF FIGURES

Figure page 2-1 Schematic of vegetation sampling plot for microhabitat level ............................. 26

2-2 Schematic of vegetation sampling plot for patch level ........................................ 27

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Abstract of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Science

FLORIDA WILD TURKEY NEST SITE SELECTION

AND NEST SUCCESS ACROSS MULTIPLE SCALES

By

John M. Olson

August 2011

Chair: William M. Giuliano Major: Wildlife Ecology and Conservation Landscapes and land-use practices in Florida continue to change and possibly

degrade the quality of habitat available to Florida wild turkey hens (Meleagris gallopavo

osceola) with respect to their nest site selection and subsequent success. This study

attempted to understand wild turkey hen nest site selection and habitat effects on

success at the microhabitat, patch, and landscape levels using logistic regression and

AIC model selection at two sites in southern Florida, 2008-2010. Hens selected nest sites

in dense vegetation (e.g., saw palmetto; Serenoa repens) that provided lateral and

vertical cover for concealment at the microhabitat level (i.e., area within 7 m of the nest

bowl), while selecting for a more open habitat at the patch level (i.e., 0.25 ha area

surrounding the nest). This presumably allowed hens to survey the area for predators

prior to ingress or egress, while also providing concealment. At the landscape level, hens

continued this trend, selecting for areas characterized by patchy, dense, hardy

vegetation, increasing possible nest locations, while allowing access to forage locations

and brood rearing habitat. Areas in which vegetation was managed (i.e., areas burned or

roller-chopped) were avoided. Successful hens (i.e., hatching of ≥1 egg) selected for

lower basal area and dense saw palmetto cover at the microhabitat level and more open

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habitat at the patch level. At the landscape level, nest success was associated with a

greater distance from habitat edges and areas burned 0.5-2 years prior, which may have

decreased the probability of predation by locating nests in the center of habitat patches,

away from edge corridors. Overall, it appears that a combination of treatments, both

prescribed burning and roller-chopping, may best benefit Florida wild turkey hens by

creating a mosaic habitat characterized by patches of dense vegetation within an open

landscape.

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CHAPTER 1 INTRODUCTION

In Florida, changing land-use practices may degrade or destroy native habitats,

through urban development, road construction, fragmentation, conversion to agriculture,

fire exclusion, invasive species, and changes in natural disturbance regimes. Of

particular importance is change in disturbance regimes such as the frequency and timing

of fire. In Florida’s native plant communities, fire suppression, reduced fire frequency,

and a switch to dormant season burning has led to the proliferation and ultimate

dominance of woody shrubs, which, in high densities, degrades habitat quality for many

species dependent upon early successional habitats with more open understories. In

much of the native rangeland and forest remaining in Florida, saw palmetto has become

the dominant understory component due to changes in fire regimes (Tanner and Marion

1990). This has resulted in a reduction of native grasses, herbaceous plants, and shrubs

beneficial to wildlife as food and cover (Tanner et al. 1986). These conditions may also

make some habitat unusable or inaccessible by forming barriers to wildlife movement.

In recent years, many managers have sought to mitigate the proliferation and

abundance of problematic woody shrub species. This type of management typically

involves habitat restoration through treatments such as prescribed fire and

roller-chopping. These two treatments aid in opening the understory, allowing

herbaceous plants and other vegetation of value to Florida’s wildlife to proliferate (Willcox

and Giuliano 2010). Research has shown that these practices can improve habitat

quality for many species, including several species of critical concern for the state of

Florida such as the gopher tortoise (Gopherus polyphemus) and red-cockaded

woodpecker (Picoides borealis), and popular game species such as northern bobwhite

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(Colinus virginianus). On many public and private lands, managers have implemented

habitat restoration specifically designed to benefit northern bobwhite. However, whether

this type of management benefits Florida wild turkey (Meleagris gallopavo osceola) hens,

their nest site selection, or their nesting success has yet to be determined.

Little is known about Florida wild turkey nest habitat selection and its effects upon

nest success. Williams and Austin (1988), Williams (1991), and Dickson (1992)

characterized Florida wild turkey nests. They reported Florida wild turkey hens select

areas in transition zones between palmetto prairie and oak scrub, where they could

conceal themselves. Williams and Austin (1988) reported that hens favored saw

palmetto, specifically palmetto ecotones, which concurs with Dickson’s (1992) accounts

that hens nesting in dense vegetation were less likely to flush, reducing detection

probability. However, much of this is only anecdotal evidence. Additionally, although it is

presently unknown whether prescribed burning and roller-chopping benefit nesting

Florida wild turkeys, these treatments may provide complex vegetation structure

preferred by nesting hens (Badyaev 1995).

Several projects have examined habitat selection of other wild turkey subspecies

found in the United States, particularly the eastern subspecies (Meleagris gallopavo

silvestris), but are equivocal. Research indicated that eastern wild turkey hens in the

Southeast selected for denser understories and more open midstories for nesting, though

higher levels of visual obstruction due to lateral cover at the nest was the most important

factor in selection (Godfrey and Norman 2001). Others have found that hens selected

against bottomland hardwoods in favor of pine (Pinus spp.) stands, and that nests located

in areas with less lateral cover, closer to roads and edges, and in forested habitats were

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more successful than those that were not (Seiss et al. 1990). Badyaev (1995) found that

eastern wild turkey hens preferred cover types that had lower overstory densities and

fewer trees of all classes, and successful nests better concealed incubating hens by

having denser lateral and vertical cover while having lower densities of large trees. Seiss

et al. (1990) found no selection for burn age by nesting hens, but Hon et al. (1978) found

hens in Georgia selected for recently burned areas, while Exum et al. (1987) found hens

in Alabama selected areas not recently burned.

Research suggests nest success as the most important factor affecting wild turkey

population growth and ultimate size (Seiss et al. 1990, Roberts and Porter 1996), habitat

often drives nest success, and habitat selection is a hierarchical process where birds

select features at different scales (Johnson 1980, Lazarus and Porter 1985, Thogmartin

1999). Therefore, to better manage the unique Florida wild turkey subspecies, further

information is needed to understand habitat determinants of nest success and how

management practices such as roller-chopping and prescribed burning affect Florida wild

turkey hen nest site selection and success.

Study Objectives

My objectives for this project were to: 1) determine what nest site characteristics

influence hens’ nest site selection at different spatial scales, 2) discern how habitat

affects nest success, and 3) evaluate how nest site selection relates to success.

Study Sites

I conducted this study on two sites in south-central Florida from 2008-2010. The

first site was Three Lakes Wildlife Management Area (WMA), located in Osceola County,

Florida. Data collection was limited to the 6,273 ha Quail Enhancement Area, where

managers conducted frequent prescribed burning and roller-chopping. Three Lakes

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WMA consists primarily of pine flatwoods, though there are also intermingled hammocks,

swamps, and wet and dry prairies (Florida Natural Areas Inventory 2010). The state of

Florida owns Three Lakes WMA, and the Florida Fish and Wildlife Conservation

Commission (FWC) executes management and allows the public to hunt the property for

white-tailed deer (Odocoileus virginianus), feral hog (Sus scrofa), northern bobwhite,

small game, and wild turkey.

The second site was Longino Ranch, located in Sarasota County, Florida. Longino

Ranch encompasses approximately 4,040 ha, with 2,020 ha used for the production of

cattle, sod, and citrus. The remaining 2,020 ha are in pine flatwoods, wet and dry prairies,

and oak-cabbage palm hammocks (Florida Natural Areas Inventory 2010). Longino

Ranch conducts prescribed burns and roller-chopping, but not on the scale of Three

Lakes WMA. Longino ranch historically managed solely for timber, but now manages for

both timber and cattle. The ranch managers operate deer, feral hog, and wild turkey

hunts for the owning family.

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CHAPTER 2 METHODS

Data Collection

I prepared capture sites (n = 20-35/year; January-February) within the Quail

Enhancement Area on Three Lakes WMA and within the boundaries of Longino Ranch. I

baited each capture site with cracked corn or three-grain scratch feed, and prepared

rocket-nets at sites only after confirming use by female turkeys through the presence of

tracks and excrement. I used rocket nets to capture turkeys from January to early March

each year from 2008-2010 on both study sites (Bailey et al. 1980). Upon firing nets, I

secured captured turkeys, and subsequently placed each into cardboard boxes

specifically designed to contain wild turkeys. Then, I fitted each captured hen with

standard numbered metal leg bands and backpack-style radio transmitters with mortality

switches (ATS transmitters, model A1540, 69-80 grams [not including harness material]:

weighing <3.5% of birds body weight). I aged, weighed, and administered a dose of

vitamin E to each captured hen in an attempt to offset the stress associated with capture.

Finally, I released turkeys within 45 minutes of capture at the capture location.

I located radioed hens remotely by triangulation (White and Garrot 1990) using radio

receivers and hand-held three element Yagi antennae from pre-established telemetry

stations (n ~ 100-250 depending upon site and year) using the peak method (Fuller et al.

2005). To locate radioed hens, I recorded one azimuth from ≥3 distinct telemetry stations

within 15 minutes to reduce error associated with long-distance movements of the

radioed hen (Fuller et al. 2005). I entered recorded azimuths into the program Location of

a Signal (LOAS; Ecological Software Solutions 2010) to map the estimated location of

tracked hens and generate error polygons. I located hens ≥3 times weekly from early

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March until July 15th each year 2008-2010. When observations indicated that a hen had

initiated a nest and begun incubation (i.e., was found repeatedly in the same location), I

recorded a nesting attempt if it could be confirmed by homing in on the nesting hen

(Tirpak et al. 2006). I monitored active nests daily via telemetry, and when the hen was

away from her nest, confirmed the status of the nest visually, careful to keep disturbance

to a minimum in nesting areas. I considered nests that hatched ≥1 egg successful (Tirpak

et al. 2006). Once a nest fate had been determined, I measured habitat characteristics at

multiple scales at nest sites and random locations.

To characterize the microhabitat (i.e., the area within 7 m of the nest bowl), I

measured lateral cover (i.e., horizontal visual obstruction), vegetation cover, shrub

height, basal area, and tree stem density in a 7 m radius plot centered on the nest bowl

(Table 1, Figure 1). I measured basal area of hardwood, coniferous, and palm species

separately from the center using a standard 10-BAF prism (Higgins et al. 2005). Only

trees that measured >11.43 cm (4.5 in) diameter-at-breast-height (dbh) were considered

(Sparks et al. 2002). Stem counts of tree species >2.54 cm (1 in) dbh within the plot were

tallied as hardwood, conifer, or palm species. I measured lateral cover by visually

estimating total cover (%) of a 36 cm x 90 cm cover board placed at three equally spaced

points along the perimeter of the 7 m plot (Higgins et al. 2005). To classify cover

densities, I recorded estimates in one of six cover classes (i.e., 1 = 0-3%, 2 = ≥4-12%, 3 =

≥13-25%, 4 = ≥26-50%, 5 = ≥51-75%, 6 = ≥76-100%). When estimating cover

obstruction, I viewed the cover board from the center point of plots at standing height (1.7

m), and used the mean of readings taken for analysis. I measured vegetation cover and

shrub height below 1.5 m using three 7 m transects radiating from the plot center (Krebs

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1999). Canopy cover of saw palmetto below 1.5 m was estimated by line-intercept

divided by the total length of transects, expressed as a percentage (Higgins et al. 2005).

To determine intercepts, I ignored intercepts <1 cm, while small openings <20 cm within

individual plants or gaps <10 cm between individual plants were included as cover. On

each transect, I recorded species and height of the tallest shrub (up to 150 cm) of the

tallest point of shrub intercept (including shrubs and trees <2.54 cm dbh; Higgins et al.

2005). For a simple estimate of total cover of all shrubs except saw palmetto, I summed

the individual shrub estimates on all three transects for analysis. After recording the

characteristics at the nest site, I recorded characteristics in a plot located a random

distance and direction from the nest within the same habitat patch.

To characterize vegetation at the patch level (i.e., area 0.25 ha around the nest

bowl), I recorded vegetation characteristics in a circular plot 28 m in radius centered on

the nest bowl (Figure 2). I measured lateral cover, vegetation cover, shrub height, basal

area, and tree stem density (Table 1). Point-centered habitat characteristics were

measured in four 7 m radius circular plots, one centered on the nest site and three

adjacent plots equally spaced around the center plot at 21 m, one on each of three

transects run outwards at 120º (Krebs 1999), using the methods described for the

microhabitat. I measured vegetation cover and shrub height below 1.5 m using three 28

m transects radiating from the center plot at the same angles as the adjacent plots.

Canopy cover of saw palmetto below 1.5 m was estimated by line-intercept methods,

calculated as the accumulated length intercepted by living and standing dead parts of

palmetto divided by the total length of transects, expressed as a percentage (Higgins et

al. 2005). In 7 m intervals along each transect, I recorded the height (up to 150 cm) and

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species of the tallest point of shrub intercept (including shrubs and trees <2.54 cm dbh;

Higgins et al. 2005). I averaged all shrub measurements for analysis.

To characterize nesting habitat at the landscape level, I used 95% fixed kernel

home ranges generated for radioed hens using the Home Range Tools extension in

ArcGIS (Environmental Systems Research Institute 2009; Rodgers et al. 2007) to obtain

the median home range size for each study site and year. I censored hens with <30

locations. To define the study areas by site and year, I used the Create Minimum Convex

Polygons extension in Hawth’s Analysis Tools (Beyer 2004; Schad 2009) to create

minimum convex polygons around all hen locations at each study site as generated by

LOAS with an estimated error of <10 ha. To delineate habitat cover types, I downloaded

and imported Florida Natural Areas Inventory (FNAI) Cooperative Land Cover Map

shapefiles (Florida Natural Areas Inventory 2010) into ArcGIS. I also downloaded United

States Geological Survey (USGS) orthophoto quadrangles to create shapefiles

delineating landscape features such as roads and water features that were absent from

the FNAI shapefiles.

I projected the nest sites discovered during the three years of the project into

ArcGIS and buffered each with a circular buffer equivalent to median home range size of

birds for each site and year to establish landscape level use areas (Tirpak et al. 2010). To

establish availability, I divided the study area size for each site and year by the median

home range size for that site and year. This provided the number of home ranges that

could fit into each study area. To obtain random points and establish availability, I

arbitrarily multiplied the number of home ranges that could fit into each study area by five

to increase sample and study area coverage. I used Hawth’s Analysis Tools to generate

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random points within each study area according to this formula and buffered each with a

circular buffer equal in area to the median home range size for each site and year (Tirpak

et al. 2006). I created a total of 370 random points and corresponding buffers, which

ranged from 25-105 per study area per year. This method of establishing availability

provided nearly total coverage of each study area.

I developed four suites of variables (i.e., habitat, management, landscape, habitat

treatment) for landscape level analyses (Johnson’s level 2; Johnson 1980). I determined

habitat from the FNAI land cover shapefile and represented the area of each particular

habitat type found within the buffer of each nest point, both random and actual (Table 2).

I used the ArcGIS intersect function to merge actual and random buffers with the habitat

shapefile to determine the area of each habitat within each buffer. Five habitat types (i.e.,

developed, bottomland forest, successional hardwood forest, sandhill, xeric hammock)

represented less than one percent of each study area and were combined into one

category, which I labeled other. Additionally, I created new categories to combine similar

habitat types, including clearing and unimproved pasture into abandoned clearing, and

hydric hammock and mesic hammock into hammock (Table 2).

I divided management into two treatments (i.e., prescribed burning and

roller-chopping) and separated each treatment into five distinct age categories defined

as: 1) treatment application <6 months prior to nest initiation, 2) treatment application 6

months – 2 years prior to nest initiation, 3) treatment application >2 years prior to nest

initiation, 4) no record of recent management, or 5) management records

incomplete/unavailable (Williams 1991; Table 2). To establish management age for

random home ranges used in selection analyses, I used median initiation dates for the

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respective area and year. Study site managers provided records of management history

and shapefiles were created according to these records. I then intersected this layer with

buffer layers to obtain areas of different management ages within each buffer.

The landscape category contained variables dealing with the area of and distance to

road, habitat edge, and water (Table 2). I mapped together paved roads, dirt roads,

firebreaks, and paths visible from aerial photographs, reasoning that if they were large

enough to be detected with aerial photography, they were large enough to be traveled by

turkeys and therefore could affect turkey behavior. Then, I applied a 2.5 m buffer to all

roads because this most closely resembled average width of roads present within study

sites as per my field experience. I intersected these landscape attributes with nest buffers

to acquire areas of each within buffers. Finally, I used the near feature in ArcGIS to obtain

distances from each nest to the nearest feature of each variable within this suite (Table 2).

In the final landscape level variable category, I combined both habitat type and

management history to create habitat treatment variables that denoted particular

management for several habitats. Habitats included were those that site managers

targeted for management. To accomplish this, I used the identity function in ArcGIS to

combine the FNAI habitat layer and the management layer into one. I intersected this

new layer with the buffers around the nests and random points to obtain areas of habitat

with treatment histories (Table 2).

Analysis

To analyze how Florida wild turkey hens selected nest sites and how habitat

affected success, I used an information-theoretical approach and logistic regression in

SYSTAT 12.0 (SYSTAT 2007). I used case-control logistic regression to compare habitat

variables present within the vegetation plots and their associated random points at the

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microhabitat level (i.e., characteristics from the 7m radius plot centered on the nest bowl;

Table 1). For patch level (i.e., vegetation characteristics within 0.25 ha area surrounding

nest bowls; Table 1) selection analyses, I used case-control logistic regression to

compare habitat variables from nest plots and their respective random points. To quantify

selection at the landscape level (i.e., landscape attributes present within simulated

circular home ranges around nest sites and random points; Table 2), I used logistic

regression to compare the habitat present within simulated home ranges for site and year

to habitat within equally sized random home ranges generated across study areas

annually.

I created models featuring each individual variable present at all three levels

(Table 1, Table 2), models containing combinations of these variables, and a null model.

Based upon prior knowledge, project goals, and my own field experience, I also created a

priori models containing combinations of variables. I evaluated models using Akaike’s

Information Criterion (AICC) adjusted for small sample size (n/K<40), and considered

models with ∆AICC ≤ 2 supported (Burnham and Anderson 2002). To rank model and

variable importance, I used Akaike weights (w), and adjusted coefficients and odds ratios

of competing models (Burnham and Anderson 1998). When 95% confidence intervals for

variables within supported models overlapped with zero, I considered them to have a

weak effect on the dependent variable, and only indicate a trend. Finally, I examined both

the best model from each landscape level category (e.g., management), and also the

best models from all landscape level categories combined to determine which had the

greatest effect on wild turkey hen nest site selection.

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I used logistic regression to compare habitat of successful and unsuccessful nests

at the microhabitat level (i.e., characteristics from the 7m radius plot centered on the nest

bowl; Table 1), patch level (i.e., characteristics from the 0.25 ha area around each nest;

Table 1), and landscape level (i.e., landscape attributes present within simulated circular

home ranges around nest sites; Table 2). I used methods as listed above for selection

analyses to determine important factors influencing to nest success.

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Table 2-1. Variable names, abbreviations, and their definitions used in a priori models to predict nest habitat selection and success at microhabitat and patch levels for Florida wild turkey hens in south Florida, 2008-2010.

Variable Abbreviation Description

Conifer basal area BAC Conifer basal area m2/ha Hardwood basal area BAH Hardwood basal area m2/ha Palm basal area BAP Palm basal area m2/ha Total basal area BAT Total basal area m2/ha Conifer stems STC Conifer stems no./ha Hardwood stems STH Hardwood stems no./ha Palm stems STP Palm stems no./ha Total stems STT Total stems no./ha Saw palmetto density SD Saw palmetto density % Visual obstruction VO Visual obstruction % Shrub height SHT Shrub height cm

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Table 2-2. Variable categories, names, abbreviations, and their definitions used in a priori models to predict nest habitat selection and success at the landscape level for Florida wild turkey hens in south Florida, 2008-2010.

Variable Category Variable Abbreviation Description

Habitat Abandoned clearing A Ha of abandoned clearing Agriculture Ag Ha of agriculture Basin swamp BS Ha of basin swamp Baygall BG Ha of baygall Bottomland forest BF Ha of bottomland forest Clearing C Ha of clearing Depression marsh DM Ha of depression marsh Dome swamp DS Ha of dome swamp Dry prairie DP Ha of dry prairie Hammock H Ha of hammock Hydric hammock HH Ha of hydric hammock Improved pasture IP Ha of improved pasture Mesic flatwoods MF Ha of mesic flatwoods Mesic hammock MH Ha of mesic hammock Other O Ha of other Pine plantation PP Ha of pine plantation Sand hill SH Ha of sand hill Scrub S Ha of scrub Scrubby flatwoods SF Ha of scrubby flatwoods Shrub bog SB Ha of shrub bog Successional

hardwood forest SHF Ha of successional hardwoods forest

Unimproved pasture UP Ha of unimproved pasture Upland hardwood

forest UHF Ha of upland hardwood forest

Wet flatwoods WF Ha of wet flatwoods Wet prairie WP Ha of wet prairie Xeric hammock X Ha of xeric hammock Landscape Distance to edge DEDGE Distance to nearest habitat edge m Distance to roads DROAD Distance to nearest road m Distance to nearest

edge DRD_DEDGE Distance to nearest habitat edge or

road m Distance to water DWATER Distance to nearest water body m Road ROAD Total amount of road ha Edge EDGE Amount of habitat edge ha Edge total RD_EDGE Total amount of habitat edge and roads

ha Water WATER Total amount of water ha Management Burn1 B1 Ha of area burned <6 months Burn2 B2 Ha of area burned between 6 months

and 2 years Burn3 B3 Ha of area burned >2 years Burn4 B4 Ha of area with no recent burn history Burn5 B5 Ha of area with incomplete burn history Chop1 C1 Ha of area roller chopped <6 months Chop2 C2 Ha of area roller chopped between 6

months and 2 years Chop3 C3 Ha of area roller chopped >2 years Chop4 C4 Ha of area with no recent roller chopping

history

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Table 2-2. Continued. Variable Category Variable Abbreviation Description

Habitat Treatment Chop5 C5 Ha of area with incomplete roller chopping history

Dry prairie1 DP1 Ha of dry prairie burned <6 months Dry prairie2 DP2 Ha of dry prairie burned 6 months - 2

years Dry prairie3 DP3 Ha of dry prairie burned >2 years Dry prairie4 DP4 Ha of dry prairie with no recent burn

history Dry prairie5 DP5 Ha of dry prairie with incomplete burn

history Mesic flatwoods1 MF1 Ha of mesic flatwoods burned <6 months Mesic flatwoods2 MF2 Ha of mesic flatwoods burned 6 months -

2 years Mesic flatwoods3 MF3 Ha of mesic flatwoods burned >2 years Mesic flatwoods4 MF4 Ha of mesic flatwoods with no recent

burn history Mesic flatwoods5 MF5 Ha of mesic flatwoods with incomplete

burn history Pine plantation1 PP1 Ha of pine plantation burned <6 months Pine plantation2 PP2 Ha of pine plantation burned 6 months -

2 years Pine plantation3 PP3 Ha of pine plantation burned >2 years Pine plantation4 PP4 Ha of pine plantation with no recent burn

history Pine plantation5 PP5 Ha of pine plantation with incomplete

burn history Scrubby flatwoods1 SF1 Ha of scrubby flatwoods burned <6

months Scrubby flatwoods2 SF2 Ha of scrubby flatwoods burned 6

months - 2 years Scrubby flatwoods3 SF3 Ha of scrubby flatwoods burned >2 years Scrubby flatwoods4 SF4 Ha of scrubby flatwoods with no recent

burn history Scrubby flatwoods5 SF5 Ha of scrubby flatwoods with incomplete

burn history Wet flatwoods1 WF1 Ha of wet flatwoods burned <6 months Wet flatwoods2 WF2 Ha of wet flatwoods burned 6 months - 2

years Wet flatwoods3 WF3 Ha of wet flatwoods burned >2 years Wet flatwoods4 WF4 Ha of wet flatwoods with no recent burn

history Wet flatwoods5 WF5 Ha of wet flatwoods with no recent burn Wet prairie1 WP1 Ha of wet prairie burned <6 months Wet prairie2 WP2 Ha of wet prairie burned 6 months - 2

years Wet prairie3 WP3 Ha of wet prairie burned >2 years Wet prairie4 WP4 Ha of wet prairie with no recent burn

history Wet prairie5 WP5 Ha of wet prairie with incomplete burn

history Chop_dry prairie1 DPC1 Ha of dry prairie roller chopped <6

months Chop_dry prairie2 DPC2 Ha of dry prairie roller chopped 6 months

- 2 years

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Table 2-2. Continued. Variable Category Variable Abbreviation Description

Chop_dry prairie3 DPC3 Ha of dry prairie roller chopped >2 years Chop_dry prairie4 DPC4 Ha of dry prairie with no recent roller

chopping history Chop_dry prairie5 DPC5 Ha of dry prairie with incomplete roller

chopping history Chop_mesic

flatwoods1 MFC1 Ha of mesic flatwoods <6 months

Chop_mesic flatwoods2

MFC2 Ha of mesic flatwoods roller chopped 6 months - 2 years


MFC3 Ha of mesic flatwoods roller chopped >2 years


MFC4 Ha of mesic flatwoods with no recent roller chopping history


MFC5 Ha of mesic flatwoods with incomplete roller chopping history

Chop_scrubby flatwoods1

SFC1 Ha of scrubby flatwoods <6 months


SFC2 Ha of scrubby flatwoods roller chopped 6 months - 2 years


SFC3 Ha of scrubby flatwoods roller chopped >2 years


SFC4 Ha of scrubby flatwoods with no recent roller chopping history


SFC5 Ha of scrubby flatwoods with incomplete roller chopping history

Chop_wet flatwoods1 WFC1 Ha of wet flatwoods <6 months Chop_wet flatwoods2 WFC2 Ha of wet flatwoods roller chopped 6

months - 2 years Chop_wet flatwoods3 WFC3 Ha of wet flatwoods roller chopped >2

years Chop_wet flatwoods4 WFC4 Ha of wet flatwoods with no recent roller

chopping history Chop_wet flatwoods5 WFC5 Ha of wet flatwoods with incomplete

roller chopping history Chop_wet flatwoods2 WFC2 Ha of wet flatwoods roller chopped 6

months - 2 years

26

Figure 2-1. Schematic of vegetation sampling plot used to record vegetation characteristics and measurements to predict nest habitat selection and success at the microhabitat level for Florida wild turkey hens in south Florida, USA, 2008-2010.

27

Figure 2-2. Schematic of vegetation sampling plot used to record vegetation

characteristics and measurements to predict nest habitat selection and success at the patch level for Florida wild turkey hens in south Florida, USA, 2008-2010.

28

CHAPTER 3 RESULTS

During the three years of study, I captured and radioed 142 hens on the two study

sites. I discovered 67 nests, 27 of which were successful. At Three Lakes WMA, I

discovered 8, 8, and 14 nests in 2008, 2009, and 2010, respectively. I found 10, 10, and

17 nests in 2008, 2009, and 2010, respectively, at Longino Ranch. The leading cause of

nest failure was depredation (n = 24), though nests also failed due to predation of the hen

on the nest (n = 8) and abandonment (total n = 7; due to habitat management n = 3, due to

observer interference n = 1, unknown cause n = 3). Habitat management efforts

accounted for three cases of nest abandonment through prescribed fire (n = 2) and

logging (n = 1). One nest was established nearing the study’s terminus and not monitored

to fate. I censored this nest and nests failing due to management or observer interference

(n = 5) from both selection and success analyses because no data regarding vegetation

characteristics could be recorded (i.e., vegetation characteristics hens selected were

destroyed, or at minimum, radically changed after a prescribed fire) and these nests failed

due to artificial causes not dependent upon hens’ selection decisions.

Selection

At the microhabitat level of selection, I found three supported models (Table 3). The

most supported model contained palm and conifer stem density and saw palmetto

density. Saw palmetto density was the only variable for which the 95% confidence

interval for the parameter estimate did not overlap with zero and indicated that hens

selected nest sites with a greater amount saw palmetto (Table 4). The other models

indicated that turkeys also selected for a greater density of palm stems. Trends

29

suggested that hens selected against increasing conifer and hardwood stem densities

(Table 4).

Six models were supported at the patch level, with the most supported model

containing palm and hardwood stem densities (Table 5); however all 95% confidence

intervals for parameter estimates overlapped with zero which limited interpretation (Table

6). Trends indicated that while hens selected higher densities of palm stems, they also

selected for more open areas, manifested by lower hardwood, conifer, and total stem and

saw palmetto densities, and lower levels of visual obstruction.

The habitat category for landscape level selection contained two supported models,

with the best model containing the habitat types agriculture, dry prairie, mesic flatwoods,

and wet flatwoods (Table 7). Agriculture, dry prairie, and mesic flatwoods had 95%

confidence intervals of parameter estimates not containing zero, and suggested that hens

selected for greater amounts of each; while trends indicated that hens also selected for

scrubby flatwoods and wet flatwoods (Table 8).

In the landscape category, there were two supported models (Table 7). The best

model contained the variables distance to road and distance to water. Both had 95%

confidence intervals of estimates that did not overlap with zero, and suggested that hens

selected sites further from roads and water (Table 8). Trends indicated that turkeys also

selected sites that were located nearer to habitat edges (Table 8).

There were six supported models at the landscape level of selection in the

management category (Table 7). The best model contained the variables denoting areas

burned 0.5-2 years ago, unburned, and unchopped; though only the 95% confidence

interval of the estimate for the unchopped did not overlap with zero (Table 8). This

30

suggested that hens selected for areas that had not received any roller-chopping

application. Trends for other parameter estimates indicated that hens selected against

sites burned >6 months prior, but selected for sites chopped >6 months prior (Table 8).

The habitat treatment category had two supported models, and the most supported

model contained unburned dry prairie, scrubby flatwoods, and mesic flatwoods, mesic

flatwoods burned 0.5-2 years ago, mesic flatwoods chopped 0.5-2 years ago, and

unchopped mesic flatwoods (Table 7). All parameters had confidence intervals

containing zero except unchopped mesic flatwoods, which suggested that hens selected

for greater amounts of this habitat treatment type (Table 8). Trends suggested that hens

selected for unburned scrubby flatwoods, unchopped and mesic flatwoods chopped 0.5-2

years ago, and against unburned dry prairie and mesic flatwoods and mesic flatwoods

burned 0.5-2 years ago (Table 8).

When I compared the best models from each of the landscape level categories, only

models from the management category were supported (Table 9), with the models

containing burned 0.5-2 years ago, unburned, and unchopped variables. Additionally, I

found that only the unchopped parameter had a 95% confidence interval of the estimate

that did not overlap with zero (Table 8, Table 9), suggesting that hens selected areas with

greater amounts of unchopped vegetation. Trends indicated hens selected for areas

unburned or chopped >6 months ago, while avoiding burns >6 months old.

Success

At the microhabitat level, nine models examining habitat differences between

successful and unsuccessful nests were supported (Table 10). The most supported

model contained only total basal area, which had a 95% confidence interval not

overlapping zero and indicated that successful nests were associated with a lower total

31

basal area than unsuccessful nests (Table 11). Other supported models suggested that

nest success was associated with a lower conifer basal area and higher saw palmetto

density. Additionally, trends indicated that hens selecting areas with greater visual

obstruction, hardwood basal area, and lower palm, conifer, and total stem density, and

hardwood and conifer basal area were more likely to succeed (Table 11).

Patch level nest success had three supported models (Table 12). The most

supported model included only palm basal area, but parameters within all models had

95% confidence intervals that overlapped with zero, limiting interpretation (Table 13).

Trends indicated that successful nests had greater palm stem density and lower total and

palm basal area than unsuccessful nests (Table 13).

The habitat category at the landscape level had four supported models, with the

most supported model containing scrubby flatwoods and wet flatwoods, but all parameter

95% confidence intervals contained zero, limiting interpretation (Table 14, Table 15).

Trends suggested that when compared with unsuccessful nests, successful nests were

more often associated with scrubby flatwoods, and less often with wet flatwoods and dry

prairie (Table 15).

In the landscape category, the null model had the most support, though there were

eight other supported models. As the null model had the most support, all results in this

category must be interpreted very conservatively. All parameters had 95% confidence

intervals overlapping zero, but trends suggested that successful nests were located

further from roads, habitat edge, and water than unsuccessful nests, while having more

area of each within the home range (Table 15).

32

Within the management category, I found five models supported at landscape level

for success (Table 14). Both parameters within the most supported model had 95%

confidence intervals that did not overlap with zero, and suggested that nests in areas that

contained more burns 0.5-2 years old and fewer chops 0.5-2 years old were more likely to

succeed (Table 15). All variables present in other models had 95% confidence intervals

that overlapped with zero, but trends suggested that successful nests had more burns

0.5-2 years old, but less area chopped >6 months ago and not chopped, and burns <6

months old and unburned (Table 15).

The habitat treatment category of landscape level nest success had two supported

models, with the most supported model containing unburned dry prairie and mesic

flatwoods chopped 0.5-2 years ago (Table 14). All parameters in both models had 95%

confidence intervals that overlapped with zero, but trends indicated that successful nests

were more often associated with unburned dry prairie, and less with dry prairie burned

0.5-2 years ago and mesic flatwoods chopped 0.5-2 years ago when compared to

unsuccessful nests (Table 15).

When I compared results among categories at the landscape level, six models had

support (Table 16). These models came from the habitat treatment, management, and

habitat categories. The most supported model contained unburned dry prairie and mesic

flatwoods chopped 0.5-2 years, but estimates of both parameters had 95% confidence

intervals that overlapped with zero. Of the supported models, only two parameters had

95% confidence intervals not containing zero, and suggested that hens selecting areas

with more burns and chops of age 0.5-2 years had greater success than those not

associated with these treatments (Table 15).

33

Table 3-1. A priori models, number of variables (K), second-order Akaike’s Information Criterion corrected for small sample size (AICc), distance from the lowest AICc (ΔAICc), and model weights (wi) used to predict nest habitat selection at the microhabitat level for Florida wild turkey hens in south Florida, 2008-2010, USA.

Model K AICC ∆AICC wi

STP,STC,SD 3 59.18 0.00 0.31 STP,STH,SD 3 59.29 0.11 0.29 STP,SD 2 59.52 0.34 0.26 BAT,STT,SD 3 62.01 2.83 0.07 BAT,STT,SD,VO 4 63.75 4.57 0.03 STC,STH,SD 3 65.07 5.89 0.02 STC,SD 2 66.11 6.92 0.01 STC,BAH,SD 3 67.66 8.48 4.42E-03 BAT,SD 2 68.11 8.93 3.52E-03 STT,SD 2 68.18 9.00 3.40E-03 BAC,BAH,SD,VO,STC,STH 6 71.41 12.22 6.79E-04 SD,VO 2 70.83 11.65 9.04E-04 SD 1 71.98 12.79 5.10E-04 BAC,BAH,SD 3 73.04 13.86 3.00E-04 BAC,SD 2 74.12 14.93 1.75E-04 BAC,STH,SD 3 74.47 15.29 1.47E-04 BAT,STT 2 74.90 15.72 1.18E-04 STP,STT 2 75.86 16.68 7.30E-05 BAT,STT,VO 3 77.07 17.89 3.99E-05 STP,STC,STH 3 78.09 18.90 2.41E-05 STT 1 77.97 18.79 2.54E-05 STC 1 78.95 19.76 1.56E-05 STP,STC 2 79.70 20.52 1.07E-05 BAC,STC 2 79.85 20.67 9.95E-06 STT,VO 2 80.10 20.92 8.78E-06 STP,STH 2 80.58 21.40 6.90E-06 BAT 1 80.65 21.47 6.67E-06 BAH,STH 2 81.34 22.16 4.73E-06 STH 1 81.30 22.11 4.83E-06 STP 1 81.32 22.13 4.78E-06 STP,BAH 2 81.67 22.48 4.02E-06 STP,BAT 2 81.71 22.52 3.94E-06 BAC,BAH,STC,STH,SD,VO 6 83.14 23.96 1.92E-06 SHT 1 81.83 22.65 3.69E-06 BAT,BAC 2 82.01 22.83 3.38E-06 BAT,VO 2 82.10 22.92 3.23E-06 BAT,BAH 2 82.16 22.98 3.13E-06 BAH 1 82.05 22.87 3.31E-06 BAP 1 82.27 23.09 2.96E-06 STP,BAC 2 82.70 23.52 2.39E-06 BAC,STH 2 82.97 23.78 2.10E-06 VO 1 82.83 23.64 2.25E-06 STP,VO 2 83.16 23.97 1.91E-06 BAC 1 83.17 23.99 1.89E-06 STP,BAP 2 83.40 24.21 1.69E-06 BAC,BAH 2 83.57 24.39 1.55E-06 NULL 0 83.79 24.68 1.34E-06

34

Table 3-2. Parameter estimates, 95% confidence intervals (CI), and odds ratios (OR) for variables used in supported a priori models to predict nest habitat selection at the microhabitat level of Florida wild turkey hens in south Florida, USA, 2008-2010.

95% CI

Model Variable Estimate Lower Upper OR

STP,STC,SD STP 0.191 -0.178 0.560 1.210 STC -0.014 -0.034 0.005 0.986 SD 0.066 0.027 0.106 1.069 STP,STH,SD STP 0.293 0.050 0.535 1.340 STH -0.010 -0.025 0.005 0.990 SD 0.065 0.026 0.104 1.067 STP,SD STP 0.279 0.029 0.529 1.321 SD 0.065 0.027 0.104 1.067

35

Table 3-3. A priori models, number of variables (K), second-order Akaike’s Information Criterion corrected for small sample size (AICc), distance from the lowest AICc (ΔAICc), and model weights (wi) used to predict habitat selection at the patch level for Florida wild turkey hens in south Florida, 2008-2010, USA.


STP,STH 2 70.63 0.00 0.19 STP,STT 2 70.76 0.13 0.18 STP,STH,SD 3 71.75 1.13 0.11 STP,VO 2 72.02 1.39 0.09 STP 1 72.12 1.49 0.09 STP,STC,STH 3 72.57 1.94 0.07 STP,BAC 2 72.75 2.12 0.07 STP,BAT 2 73.15 2.52 0.05 STP,BAP 2 73.78 3.16 0.04 STP,SD 2 74.22 3.59 0.03 STP,STC 2 74.23 3.60 0.03 STP,BAH 2 74.25 3.63 0.03 STP,STC,SD 3 76.29 5.66 0.01 STH 1 79.74 9.12 1.97E-03 BAC,STH,SD 3 80.70 10.08 1.22E-03 BAH,STH 2 80.99 10.36 1.06E-03 STC,STH,SD 3 81.48 10.86 8.26E-04 BAC,STH 2 81.72 11.10 7.32E-04 STT 1 82.04 11.42 6.25E-04 BAP 1 82.31 11.68 5.47E-04 BAT 1 82.39 11.77 5.25E-04 BAT,STT 2 82.62 12.00 4.68E-04 VO 1 82.95 12.33 3.96E-04 BAT,VO 2 83.52 12.90 2.98E-04 STT,SD 2 83.53 12.91 2.96E-04 SD 1 83.54 12.91 2.96E-04 SHT 1 83.56 12.94 2.92E-04 BAC 1 83.61 12.98 2.85E-04 BAT,STT,SD 3 83.65 13.02 2.80E-04 BAH 1 83.70 13.07 2.73E-04 STC 1 83.72 13.09 2.71E-04 STT,VO 2 83.74 13.11 2.68E-04 NULL 1 83.86 13.24 2.52E-04 BAT,SD 2 83.87 13.24 2.51E-04 BAT,STT,VO 3 84.33 13.70 1.99E-04 BAT,BAC 2 84.39 13.77 1.93E-04 BAT,BAH 2 84.50 13.88 1.83E-04 STC,SD 2 84.94 14.32 1.46E-04 SD,VO 2 85.10 14.47 1.36E-04 BAC,SD 2 85.21 14.58 1.28E-04 BAC,BAH 2 85.55 14.92 1.08E-04 BAC,STC 2 85.61 14.98 1.50E-04 BAT,STT,SD,VO 4 85.95 15.33 8.84E-05 BAC,BAH,STC,STH,SD,VO 6 86.28 15.65 7.51E-05 BAC,BAH,SD,VO,STC,STH 6 86.28 15.65 7.51E-05 STC,BAH,SD 3 86.97 16.34 5.32E-05 BAC,BAH,SD 3 87.23 16.60 4.67E-05

36

Table 3-4. Parameter estimates, 95% confidence intervals (CI), and odds ratios (OR) for variables used in supported a priori models to predict nest habitat selection at the patch level of Florida wild turkey hens in south Florida, USA, 2008-2010.

95% CI


STP,STH STP 0.045 -0.020 0.110 1.046 STH -0.001 -0.003 0.000 0.999 STP,STT STP 0.048 -0.019 0.115 1.049 STT -0.001 -0.003 0.000 0.999 STO,SH,SD STP 0.043 -0.022 0.108 1.044 STH -175.000 -0.004 0.000 0.998 SD -0.008 -0.022 0.007 0.993 STP,VO STP 0.055 -0.019 0.130 1.057 VO -0.016 -0.039 0.006 0.984 STP STP 0.047 -0.018 0.112 1.048 STP,STC,STH STP 0.046 -0.020 0.112 1.047 STC -0.001 -0.003 0.002 0.999 STH -0.002 -0.003 0.000 0.998

37

Table 3-5. A priori models, number of variables (K), second-order Akaike’s Information Criterion corrected for small sample size (AICc), distance from the lowest AICc (ΔAICc), and model weights (wi) used to predict habitat selection at the landscape level Florida wild turkey hens in south Florida, 2008-2010, USA.

Category Model K AICC ∆AICC wi

Habitat AG,DP,MF,WF 4 205.69 0.00 0.39 AG,DP,MF,WF,SF 5 206.58 0.89 0.25 AF,DP,MF,WF,SF,DS 6 208.32 2.63 0.11 AG,DP,DS,MF,SF,WF 6 208.32 2.63 0.11 DS,MF,SF,WF,AG 5 208.32 2.63 0.11 AG,DP,MF 3 211.76 6.07 0.02 AG,DP,DS,MF,SF,BS 6 213.26 7.57 0.01 AG,DP,DS,MF,SF 5 213.44 7.76 0.01 MF,DP,SF,WF 4 217.38 11.69 1.14E-03 DP,DS,MF,SF,WF 5 219.39 13.70 4.16E-04 AG,MF 2 219.77 14.08 3.44E-04 MF,SF,WF 3 220.53 14.85 2.37E-04 SF,S,SH,MF,WF,DP 6 221.27 15.58 1.62E-04 MF,WF 2 223.07 17.39 6.59E-05 MF,DP 2 224.27 18.59 3.62E-05 MF,SF,DP 3 225.37 19.69 2.09E-05 DS,DP,MF 3 226.20 20.51 1.38E-05 SF,DS,DP,MF 4 227.38 21.70 7.64E-06 DS,MF 2 228.66 22.98 4.03E-06 SF,S,SH,MF,DP 5 229.17 23.48 3.13E-06 MF 1 231.33 25.65 1.06E-06 SF,S,SH,MF 4 231.96 26.27 7.76E-07 SF,DS 2 239.47 33.78 1.81E-08 DP,WF,SF 3 240.50 34.81 1.08E-08 SF,DS,DP 3 240.93 35.24 8.75E-09 DS 1 242.48 36.79 4.03E-09 DS,DP 2 242.79 37.10 3.45E-09 BS 1 247.48 41.80 3.30E-10 DP,WF 2 247.72 42.03 2.94E-10 SF,WF 2 248.55 42.87 1.93E-10 AG,DP 2 251.07 45.39 5.48E-11 AG,WF 2 253.01 47.32 2.08E-11 O 1 256.46 50.77 3.71E-12 WP 1 256.58 50.89 3.50E-12 DP,SF 2 257.90 52.21 1.80E-12 DM 1 264.18 58.49 7.82E-14 SF 1 266.68 60.99 2.24E-14 WF 1 270.19 64.50 3.88E-15 DP 1 273.56 67.87 7.19E-16 PP 1 275.12 69.43 3.29E-16 AG 1 280.37 74.69 2.38E-17 IP 1 280.76 75.08 1.96E-17 H 1 281.34 75.66 1.46E-17 MH 1 283.30 77.62 5.50E-18 SB 1 283.35 77.67 5.36E-18 UHF 1 284.39 78.71 3.19E-18 BG 1 284.55 78.87 2.94E-18 A 1 284.97 79.28 2.39E-18 S 1 287.16 81.47 8.00E-19

38

Table 3-5. Continued. Category Model K AICC ∆AICC wi

Habitat BF 1 287.43 81.74 7.00E-19 UP 1 288.25 82.56 4.64E-19 X 1 291.19 85.50 1.07E-19 C 1 292.81 87.13 4.74E-20 HH 1 294.59 88.91 1.94E-20 SH 1 295.72 90.04 1.10E-20 O 1 297.58 91.90 4.36E-21 SHF 1 301.29 95.60 6.83E-22 NULL 0 303.44 97.75 2.33E-22 Landscape DROAD,DWATER 2 182.59 0.00 0.69 DROAD,DEDGE,DWATER 3 184.59 2.00 0.26 RD_EDGE,DROAD 2 189.99 7.40 0.02 EDGE,DROAD 2 190.10 7.50 0.02 DRD_DEDGE,WATER 2 191.07 8.48 0.01 ROAD,DROAD 2 193.24 10.64 3.38E-03 ROAD,EDGE,DROAD,DEDGE 4 193.98 11.39 2.33E-03 RD_EDGE,DWATER 2 194.33 11.74 1.96E-03 DRD_DEDGE,EDGE 2 196.53 13.93 6.53E-04 RD_EDGE,DRD_DEDGE 2 196.59 14.00 6.30E-04 DROAD,DEDGE 2 198.20 15.61 2.82E-04 DROAD 1 199.96 17.37 1.17E-04 ROAD,DWATER 2 202.15 19.56 3.92E-05 RD_EDGE 1 202.34 19.75 3.56E-05 EDGE 1 202.77 20.18 2.88E-05 RD_EDGE,DEDGE 2 203.07 20.48 2.48E-05 EDGE,DEDGE 2 203.25 20.65 2.27E-05 DRD_DEDGE,ROAD 2 203.79 21.20 1.72E-05 DWATER 1 204.00 21.41 1.55E-05 RD_EDGE,WATER 2 204.35 21.76 1.30E-05 ROAD,DEDGE 2 213.40 30.81 1.41E-07 ROAD 1 216.03 33.43 3.80E-08 ROAD,WATER 2 217.93 35.34 1.47E-08 DRD_DEDGE 1 218.23 35.63 1.26E-08 DEDGE 1 237.60 55.00 7.87E-13 WATER 1 284.25 101.66 5.82E-23 NULL 0 303.44 120.85 3.97E-27 Management B2,B4,C4 3 170.87 0.00 0.17 B2,C4 2 171.36 0.50 0.15 B2,B3,B4,C4 4 171.89 1.03 0.11 B2,B4,C2,C4 4 172.15 1.28 0.09 B2,B3,C4 3 172.25 1.38 0.09 B2,B3,B4,C2,C3,C4 6 172.48 1.62 0.08 B1,B2,C4 3 173.35 2.48 0.05 B3,B4,C3,C4 4 173.57 2.71 0.04 B1,B2,B3,B4,C3,C4 6 173.77 2.90 0.04 B4,C4 2 173.62 2.76 0.04 B1,B2,B3,B4,C4 5 173.85 2.99 0.04 B3,B4,C4 3 174.20 3.33 0.03 C4 1 174.64 3.78 0.03 B3,C4 2 174.79 3.92 0.02 B1,B4,C4 3 175.63 4.76 0.02 B1,C4 2 176.64 5.78 0.01 C1,C2,C3,C4 4 179.85 8.99 1.94E-03

39


Management C1,C2,C3,C4,C5 5 181.72 10.85 7.65E-04 B1,B2,B3,B4,B5 5 201.78 30.91 3.37E-08 B1,B2,B3,B4 4 213.67 42.80 8.82E-11 B1,B2,B3,B4,C3 5 215.42 44.55 3.68E-11 B2,B3,B4,C3 4 222.26 51.40 2.00E-12 B1,B2,C3 3 236.23 65.37 1.11E-15 B1,B2,B3 3 236.37 65.51 1.04E-15 B5 1 238.41 67.54 3.74E-16 B1,B2,C1,C2 4 239.16 68.29 2.57E-16 B1,B2,B3,C1,C2,C3 6 240.92 70.05 1.10E-16 B4,C2 2 241.35 70.48 8.61E-17 B3,B4,C3 3 243.60 72.73 2.79E-17 B2,C3 2 246.93 76.06 5.28E-18 B2,B3,C3 3 247.08 76.22 4.89E-18 B4,C1 2 248.66 77.80 2.22E-18 B4,C3 2 249.77 78.90 1.28E-18 B1,C2 2 250.30 79.44 9.79E-19 B2 1 252.17 81.31 3.84E-19 B2,C1 2 253.31 82.44 2.17E-19 B2,C2 2 253.97 83.10 1.56E-19 B4 1 256.51 85.65 4.38E-20 B1 1 263.20 92.34 1.55E-21 B1,C1 2 263.46 92.60 1.36E-21 B1,C3 2 263.70 92.84 1.20E-21 B3,C2 2 265.02 94.15 6.24E-22 C1,C2,C3 3 268.55 97.68 1.07E-22 C2 1 277.89 107.03 9.98E-25 B3 1 279.02 108.15 5.69E-25 B3,C3 2 279.06 108.19 5.58E-25 B3,C1 2 279.58 108.72 4.29E-25 C3 1 290.82 119.95 1.56E-27 C1 1 290.91 120.05 1.48E-27 C5 1 299.39 128.53 2.14E-29 NULL 0 303.44 132.57 2.83E-030 Habitat Treatment DP4,SF4,MF2,MF4,MFC4,MFC2 6 180.61 0.00 0.27 DP4,SF4,MF2,MF4,MFC4 5 182.31 1.70 0.12 MFC4,DP2,DP4,SF4 4 183.31 2.70 0.07 DP2,DP4,SF4,MF2,MF4,MFC4 6 184.30 3.68 0.04 MFC4 1 184.33 3.71 0.04 DP3,MFC4 2 184.61 4.00 0.04 MFC2,MFC4,DP2,DP4,SF4 5 184.82 4.21 0.03 DP3,DP4,MFC4 3 185.47 4.85 0.02 MFC2,MFC4 2 185.83 5.21 0.02 DP4,MFC4 2 185.83 5.22 0.02 DP3,MFC2,MFC4 3 186.10 5.49 0.02 MF3,DP2,DP3,DP4,MFC4 5 186.15 5.54 0.02 MF2,MF3,MF4,MFC2,MFC3,MFC4 6 186.24 5.62 0.02 DP2,DP3,DP4,MFC4 4 186.27 5.66 0.02 MFC3,MFC4 2 186.27 5.66 0.02 MF2,MF3,MFC3,MFC4 4 186.31 5.70 0.02

40


Habitat Treatment DP2,MFC4 2 186.35 5.73 0.02 MF2,MF3,MFC2,MFC4 4 186.51 5.89 0.01 MFC2,MFC4,DP2,DP4,SF2,SF4 5 186.57 5.96 0.01 DP3,MFC3,MFC4 3 186.62 6.00 0.01 MF2,MF3,MFC4 3 186.62 6.01 0.01 MF2,MF3,DP3,DP4,MFC2,MFC4 6 186.72 6.10 0.01 DP3,DP4,MFC2,MFC4 4 186.99 6.38 0.01 MF2,MF3,MF4,MFC2,MFC4 5 187.26 6.65 0.01 MF2,MF3,DP2,DP3,DP4,MFC4 6 187.33 6.71 0.01 DP4,MFC2,MFC4 3 187.36 6.75 0.01 DP2,DP4,MFC4 3 187.46 6.85 0.01 DP3,DP4,MFC3,MFC4 4 187.50 6.88 0.01 MFC2,MFC3,MFC4 3 187.78 7.16 0.01 DP4,MFC3,MFC4 3 187.80 7.19 0.01 DP2,MFC2,MFC4 3 187.84 7.23 0.01 MF1,MF2,MF3,MFC3,MFC4 5 187.89 7.28 0.01 DP2,DP3,DP4,MFC2,MFC4 5 188.10 7.49 0.01 DP3,MFC2,MFC3,MFC4 4 188.12 7.50 0.01 DP4,MF2,MF4,MFC4,MFC2 5 188.23 7.61 0.01 DP2,MFC3,MFC4 3 188.30 7.69 0.01 DP2,DP3,DP4,MFC3,MFC4 5 188.30 7.69 0.01 DP3,DP4,MFC2,MFC3,MFC4 5 189.03 8.42 4.04E-03 DP2,DP4,MFC2,MFC4 4 189.16 8.55 3.79E-03 DP4,MFC2,MFC3,MFC4 4 189.34 8.73 3.46E-03 DP2,DP4,MFC3,MFC4 4 189.42 8.81 3.33E-03 DP2,MFC2,MFC3,MFC4 4 189.80 9.19 2.75E-03 DP2,DP3,DP4,MFC2,MFC3,MFC4 6 190.14 9.53 2.32E-03 DP2,DP4,MFC2,MFC3,MFC4 5 191.14 10.52 1.41E-03 MFC2,MFC4,DP2,DP4,SF2 5 191.16 10.54 1.40E-03 SFC4 1 221.65 41.04 3.34E-10 MF1,MF2,MF3,MF4 4 224.41 43.80 8.38E-11 MF2,MF3,MF4 3 227.22 46.61 2.06E-11 WPC4 1 228.11 47.49 1.32E-11 DP2,DP4,SF4,MF2,MF4 5 228.26 47.64 1.23E-11 DP2,DP4,SF4,WF4,MF2,MF4 6 228.86 48.24 9.08E-12 PPC4 1 229.09 48.47 8.09E-12 MF2,MF4 6 229.59 48.98 6.29E-12 MF2,MF3,DP3 3 232.36 51.75 1.58E-12 MF2,MF3 2 233.55 52.93 8.70E-13 MF1,MF2,MF3,MFC2,MFC3 5 233.99 53.38 6.97E-13 MF2,MF3,DP2,DP3 4 234.28 53.66 6.05E-13 MF2,MF3,DP3,DP4 4 234.35 53.74 5.83E-13 MF2,MF3,MFC2 3 234.47 53.85 5.50E-13 MF2,MF3,MFC3 3 234.58 53.97 5.19E-13 MF2,MF3,DP4 3 235.25 54.64 3.71E-13 MF2,MF3,DP2 3 235.43 54.82 3.39E-13 MF2,MF3,DP2,DP3,DP4 5 236.32 55.71 2.17E-13 MF2 1 237.25 56.64 1.36E-13 MF2,MF3,DP2,DP4 4 237.29 56.67 1.34E-13 DPC4 1 239.39 58.77 4.69E-14 MFC2,DP2,DP4,SF4 4 250.52 69.91 1.79E-16 DP3,MFC2,MFC3 3 254.32 73.70 2.69E-17

41


Habitat Treatment DP3,DP4,MFC2,MFC3 4 256.24 75.63 1.01E-17 MF3,DP2,DP3,DP4.MFC2 5 256.74 76.12 8.02E-18 DP2,DP3,DP4,MFC2,MFC3 5 258.07 77.45 4.13E-18 DP3,MFC2 2 259.56 78.95 1.95E-18 MFC2,MFC3 2 259.57 78.96 1.94E-18 DP4,MFC2,MFC3 3 260.97 80.36 9.66E-19 DP2,MFC2,MFC3 3 261.10 80.48 9.07E-19 DP3,DP4,MFC2 3 261.46 80.85 7.56E-19 DP2,DP3,DP4,MFC2 4 263.42 82.80 2.84E-19 MFC2 1 266.28 85.67 6.79E-20 DP4,MFC2 2 267.56 86.95 3.58E-20 MF3,MF4 2 267.85 87.24 3.09E-20 DP2,MFC2 2 267.95 87.33 2.95E-20 DP2,DP4,MFC2 3 269.59 88.97 1.30E-20 PP2 1 272.56 91.95 2.94E-21 MF1 1 276.72 96.10 3.68E-22 DP2,DP4,SF4,WF4,DP3 5 277.10 96.48 3.04E-22 WF5 1 279.10 98.49 1.11E-22 WP2 1 279.37 98.75 9.79E-23 MF3 1 280.78 100.17 4.82E-23 SF4 1 283.27 102.65 1.39E-23 DP1,DP2,DP3,DP4 4 285.07 104.45 5.66E-24 DP2,DP4,SF4,WF4 4 285.15 104.53 5.44E-24 SFC2 1 285.57 104.95 4.41E-24 DP2,DP3,DP4,MFC3 4 285.62 105.00 4.30E-24 MF4 1 285.95 105.34 3.63E-24 DP1 1 286.03 105.42 3.49E-24 DP3,MFC3 2 286.90 106.29 2.260E-24 DP3,DP4,MFC3 3 288.11 107.50 1.23E-24 WF2 1 288.85 108.23 8.54E-25 MF5 1 288.85 108.23 8.54E-25 WP3 1 288.87 108.26 8.43E-25 SF1 1 290.26 109.65 4.21E-25 WFC4 1 290.28 109.67 4.17E-25 DP2,MFC3 2 290.49 109.87 3.76E-25 PP5 1 290.65 110.03 3.47E-25 PPC5 1 291.99 111.37 1.78E-25 DP2,DP4,MFC3 3 292.48 111.87 1.39E-25 WP4 1 292.53 111.91 1.36E-25 WF1 1 292.97 112.36 1.09E-25 DM4 1 293.42 112.80 8.69E-26 PP1 1 293.68 113.07 7.61E-26 DP2,DP3 2 293.84 113.23 7.02E-26 MFC3 1 294.02 113.40 6.44E-26 PP3 1 294.27 113.65 5.69E-26 DP4,MFC3 2 294.34 113.72 5.49E-26 PPC2 1 294.83 114.21 4.30E-26 DP2,DP3,DP4 3 295.51 114.89 3.06E-26 DP3 1 296.25 115.63 2.11E-26 DP3,DP4 2 297.36 116.74 1.21E-26

42

Table 3-5. Continued. 1 304.62 124.00 3.21E-28

Category Model K AICC ∆AICC wi

Habitat Treatment SF2 1 299.19 118.57 4.86E-27 SFC1 1 300.06 119.44 3.14E-27 SF3 1 300.31 119.69 2.77E-27 WFC5 1 300.44 119.82 2.60E-27 DPC1 1 301.47 120.86 1.54E-27 WF3 1 302.02 121.40 1.18E-27 WP5 1 302.91 122.30 7.53E-28 DP2 1 303.02 122.41 7.14E-28 WPC2 1 303.95 123.33 4.49E-28 DPC5 1 304.00 123.38 4.38E-28 WF4 1 304.68 124.07 3.11E-28 SFC5 1 304.71 124.10 3.06E-28 DP2,DP4 2 305.04 124.42 2.60E-28 SFC3 1 306.01 125.40 1.60E-28 MFC1 1 306.13 125.52 1.50E-28 WPC1 1 306.14 125.52 1.50E-28 NULL 1 306.22 125.61 1.44E-28 DP4 1 306.28 125.66 1.40E-28 WPC3 1 306.45 125.84 1.28E-28 MH4 1 306.73 126.12 1.11E-28 SF5 1 306.84 126.22 1.06E-28 PPC3 1 307.84 127.23 6.41E-29 MFC5 1 307.99 127.37 5.96E-29 WPC5 1 308.22 127.61 5.30E-29 DPC2 1 308.22 127.61 5.30E-29

43

Table 3-6. Parameter estimates, 95% confidence intervals (CI), and odds ratios (OR) for variables used in supported a priori models to predict nest habitat selection at the landscape level of Florida wild turkey hens in south Florida, USA, 2008-2010.

95% CI

Category Model Variable Estimate Lower Upper OR

Habitat AG,DP,MF,WF AG 0.017 0.005 0.030 0.017 DP 0.016 0.003 0.028 0.016 MF 0.005 0.003 0.006 0.005 WF 0.188 -0.006 0.382 0.188 AG,DP,MF,WF,SF AG 0.018 0.005 0.030 1.018 DP 0.013 0.000 0.026 1.013 MF 0.004 0.002 0.006 1.004 WF 0.185 -0.008 0.378 1.203 SF 0.027 -0.027 0.081 1.027 Management B2,B4,C4 B2 -0.003 -0.006 0.000 0.997 B4 0.002 -0.001 0.005 1.002 C4 0.008 0.006 0.011 1.008 B2,C4 B2 -0.003 -0.006 0.000 0.997 C4 0.009 0.007 0.011 1.009 B2,B3,B4,C4 B2 -0.003 -0.006 0.000 0.997 B3 -0.003 -0.008 0.002 0.997 B4 0.002 -0.001 0.005 1.002 C4 0.009 0.006 0.011 1.009 B2,B4,C2,C4 B2 -0.004 -0.007 0.000 0.996 B4 0.002 -0.001 0.005 1.002 C2 0.006 -0.008 0.021 1.006 C4 0.008 0.006 0.011 1.008 B2,B3,C4 B2 -0.003 -0.006 0.000 0.997 B3 -0.003 -0.008 0.002 0.997 C4 0.009 0.007 0.012 1.009 B2,B3,B4,C2,C3,C4 B2 -0.004 -0.007 0.000 0.996 B3 -0.006 -0.012 0.000 0.994 B4 0.002 -0.001 0.005 1.002 C2 0.009 -0.007 0.025 1.009 C3 0.013 -0.004 0.029 1.013 C4 0.009 0.006 0.012 1.009 Landscape DROAD,DWATER DROAD 0.008 0.004 0.012 1.008 DWATER 0.001 0.000 0.001 1.001 DROAD,DEDGE,

DWATER DROAD 0.008 0.004 0.012 1.008

DEDGE -0.001 -0.008 0.006 0.999 DWATER 0.001 0.000 0.001 1.001 Habitat Treatment

DP4,SF4,MF2,MF4, MFC4, MFC2

DP4 -0.022 -0.096 0.051 0.978

SF4 6.791E+06

-5.620E+11 5.620E+11 N/A

MF2 -0.007 -0.015 0.000 0.993 MF4 -0.010 -0.020 0.001 0.990 MFC4 0.021 0.013 0.028 1.021 MFC2 0.023 -0.002 0.049 1.023 DP4,SF4,MF2,MF4,

MFC4 DP4 -0.022 -0.096 0.051 0.978

SF4 6.783E+06

-5.360E+11 5.360E+011

N/A

44

Table 3-6. Continued. 95% CI


Habitat Treatment

DP4,SF4,MF2,MF4, MFC4 MF2 -0.003 -0.008 0.002 0.997

MF4 -0.008 -0.018 0.002 0.992 MFC4 0.020 0.013 0.027 1.020

Table 3-7. Best a priori model(s) from each variable category predicting nest habitat

selection at the landscape level of Florida wild turkey hens in south Florida, USA, 2008-2010.

Category Model K AICc ΔAICc wi

Management B2,B4,C4 3 170.87 0.00 0.26 Management B2,C4 2 171.36 0.50 0.20 Management B2,B3,B4,C4 4 171.89 1.03 0.15 Management B2,B4,C2,C4 4 172.15 1.28 0.14 Management B2,B3,C4 3 172.25 1.38 0.13 Management B2,B3,B4,C2,C3,C4 6 172.48 1.62 0.12 Habitat Treatment DP4,SF4,MF2,MF4,MFC4,MFC2 6 180.61 9.75 0.00 Habitat Treatment DP4,SF4,MF2,MF4,MFC4 5 182.31 11.45 0.00 Landscape DROAD,DWATER 2 182.59 11.73 0.00 Landscape DROAD,DEDGE,DWATER 3 184.59 13.72 0.00 Habitat AG,DP,MF,WF 4 205.69 34.82 0.00 Habitat AG,DP,MF,WF,SF 5 206.58 35.71 0.00

45

Table 3-8. A priori models, number of variables (K), second-order Akaike’s Information Criterion corrected for small sample size (AICc), distance from the lowest AICc (ΔAICc), and model weights (wi) used to predict nest success at the microhabitat level for Florida wild turkey hens in south Florida, 2008-2010, USA.


BAT 1 82.66 0.00 0.09 BAC,SD 2 82.95 0.30 0.08 BAT,SD 2 83.04 0.38 0.07 STP,BAC 2 83.19 0.53 0.07 STP,BAT 2 83.44 0.78 0.06 BAT,STT 2 84.01 1.35 0.04 STP,STC 2 84.15 1.50 0.04 BAT,BAH 2 84.48 1.82 0.04 BAT,BAC 2 84.64 1.99 0.03 BAT,VO 2 84.69 2.04 0.03 BAC 1 84.85 2.20 0.03 STP,STC,SD 3 84.90 2.25 0.03 BAT,STT,SD 3 85.04 2.38 0.03 BAC,BAH,SD 3 85.04 2.38 0.03 BAC,STH,SD 3 85.16 2.51 0.02 STC,SD 2 85.22 2.56 0.02 STP 1 85.43 2.78 0.02 BAC,BAH 2 85.79 3.13 0.02 BAC,STC 2 85.89 3.24 0.02 STC 1 85.94 3.28 0.02 STP,STT 2 86.01 3.35 0.02 STT 1 86.01 3.35 0.02 STP,STC,STH 3 86.09 3.43 0.02 BAT,STT,VO 3 86.20 3.54 0.01 BAC,STH 2 86.27 3.61 0.01 BAP 1 86.82 4.16 0.01 NULL 0 86.92 4.26 0.01 STP,BAP 2 87.05 4.39 0.01 STP,SD 2 87.07 4.41 0.01 BAT,STT,SD,VO 4 87.30 4.64 0.01 STC,BAH,SD 3 87.36 4.70 0.01 STC,STH,SD 3 87.41 4.76 0.01 STP,VO 2 87.47 4.81 0.01 STP,STH 2 87.51 4.86 0.01 STP,BAH 2 87.56 4.90 0.01 SD 1 87.56 4.90 0.01 STT,SD 2 87.71 5.05 0.01 STT,VO 2 88.13 5.47 0.01 BAH 1 88.27 5.61 0.01 STH 1 88.83 6.17 3.98E-03 VO 1 88.89 6.23 3.86E-03 SHT 1 88.98 6.33 3.69E-03 STP,STH,SD 3 89.27 6.61 3.20E-03 BAH,SD 2 89.52 6.86 2.82E-03 STH,SD 2 89.66 7.01 2.62E-03 SD,VO 2 89.69 7.03 2.59E-03 BAC,BAH,STC,STH,SD,VO 6 90.31 7.66 1.89E-03 BAH,STH 2 90.40 7.74 1.82E-03

46

Table 3-8. Continued. Model K AICC ∆AICC wi

SD,STH,BAH 3 91.56 8.90 1.01E-03 BAH,SD,VO 3 91.73 9.07 9.36E-04 STH,SD,VO 3 91.87 9.21 8.71E-04 SD,STH,BAH,VO 4 93.85 11.19 3.24E-04

Table 3-9. Parameter estimates, 95% confidence intervals (CI), and odds ratios (OR)

for variables used in supported a priori models to predict nest success at the microhabitat level of Florida wild turkey hens in south Florida, USA, 2008-2010.

95% CI


BAT BAT -0.105 -0.021 -0.004 0.900 BAC,SD BAC -0.151 -0.285 -0.016 0.860 SD 0.016 0.000 0.031 1.016 BAT,SD BAT -0.115 -0.220 -0.009 0.892 SD 0.010 -0.005 0.025 1.010 STP,BAC STP -0.606 -6.737E+06 6.737E+06 0.546 BAC -0.109 -0.225 0.006 0.896 STP,BAT STP -0.582 -9.157E+06 9.157E+06 0.559 BAT -0.098 -0.202 0.006 0.906 BAT,STT BAT -0.093 -0.195 0.009 0.912 STT -0.001 -0.003 0.001 0.999 STP,STC STP -0.727 -3.594E+08 3.594E+08 0.484 STC -0.004 -0.009 0.001 0.996 BAT,BAH BAT -0.121 -0.239 -0.003 0.886 BAH 0.616 -1.469 2.702 1.852 BAT,BAC BAT -0.072 -0.229 0.070 0.924 BAC -0.037 -0.220 0.145 0.963

47

Table 3-10. A priori models, number of variables (K), second-order Akaike’s Information Criterion corrected for small sample size (AICc), distance from the lowest AICc (ΔAICc), and model weights (wi) used to predict nest success at the patch level for Florida wild turkey hens in south Florida, 2008-2010, USA.


BAP 1 83.63 0.00 0.16 BAT 1 85.44 1.81 0.07 STP,BAP 2 85.62 1.99 0.06 BAC,STH 2 86.11 2.48 0.05 BAT,BAH 2 86.31 2.68 0.04 BAC 1 86.65 3.02 0.03 BAT,VO 2 86.71 3.08 0.03 BAT,SD 2 86.80 3.17 0.03 NULL 0 86.92 3.28 0.03 BAT,STT 2 87.06 3.43 0.03 BAT,BAC 2 87.27 3.64 0.03 BAT,STT,VO 3 87.33 3.70 0.03 STP,BAT 2 87.41 3.78 0.02 STT,VO 2 87.43 3.80 0.02 STH 1 87.48 3.85 0.02 STP,BAC 2 87.68 4.04 0.02 VO 1 87.70 4.07 0.02 BAC,STH,SD 2 87.96 4.33 0.02 BAC,SD 2 88.08 4.45 0.02 STT 1 88.18 4.55 0.02 BAT,STT,SD 3 88.26 4.63 0.02 STP 1 88.39 4.76 0.02 SD 1 88.53 4.90 0.01 BAC,BAH 2 88.56 4.93 0.01 BAC,STC 2 88.72 5.09 0.01 STP,VO 2 88.77 5.14 0.01 STC 1 88.92 5.29 0.01 SHT 1 88.94 5.30 0.01 BAH 1 88.97 5.34 0.01 BAT,STT,SD,VO 3 89.17 5.54 0.01 STP,STH 2 89.39 5.76 0.01 STH,SD 2 89.52 5.89 0.01 BAH,STH 2 89.60 5.97 0.01 STT,SD 2 89.69 6.06 0.01 SD,VO 2 89.80 6.17 0.01 BAC,BAH,SD 3 89.94 6.31 0.01 STP,SD 2 90.14 6.51 0.01 STP,STT 2 90.15 6.52 0.01 STH,SD,VO 3 90.33 6.70 0.01 STP,STC 2 90.50 6.87 0.01 STP,BAH 2 90.52 6.89 0.01 BAH,SD 2 90.64 7.01 4.89E-03 STC,SD 2 90.66 7.02 4.85E-03 STP,STH,SD 3 91.50 7.87 3.18E-03 STP,STC,STH 3 91.60 7.97 3.03E-03 STC,STH,SD 3 91.72 8.08 2.85E-03 SD,STH,BAH 3 91.72 8.09 2.84E-03 BAH,SD,VO 3 91.98 8.34 2.50E-03 STP,STC,SD 3 92.33 8.70 2.10E-03

48

Table 3-10. Continued.


SD,STH,BAH,VO 4 92.61 8.98 1.82E-03 STC,BAH,SD 3 92.84 9.21 1.62E-03 BAC,BAH,STC,STH,SD,VO 6 94.43 10.80 7.33E-04

Table 3-11. Parameter estimates, 95% confidence intervals (CI), and odds ratios (OR) for variables used in supported a priori models to predict nest success at the patch level of Florida wild turkey hens in south Florida, USA, 2008-2010.

95% CI


BAP BAP -1.551 -3.750 0.648 0.212 BAT BAT -0.109 -0.237 0.020 0.897 STP,BAP STP 0.002 -0.007 0.010 1.002 BAP -1.571 -3.720 0.578 0.208

49

Table 3-12. A priori models, number of variables (K), second-order Akaike’s Information Criterion corrected for small sample size (AICc), distance from the lowest AICc (ΔAICc), and model weights (wi) used to predict nest success at the landscape level for Florida wild turkey hens in south Florida, 2008-2010, USA.

Category Model K AICC ∆ AICC wi

Habitat SF,WF 2 38.70 0.00 0.14 SF 1 39.34 0.64 0.10 DP,WF,SF 3 39.61 0.90 0.09 DP,SF 2 40.17 1.47 0.07 IP 1 40.72 2.02 0.05 SB 1 40.72 2.02 0.05 DP 1 40.78 2.08 0.05 MF,SF,WF 3 40.90 2.20 0.05 SF,DS 2 41.17 2.47 0.04 MF,DP,SF,WF 4 41.73 3.03 0.03 SF,DS,DP 3 42.01 3.31 0.03 UP 1 42.11 3.41 0.02 MF,SF,DP 3 42.31 3.61 0.02 DS,DP 2 42.50 3.80 0.02 DP,WF 2 42.56 3.86 0.02 PP 1 42.64 3.94 0.02 AG,DP 2 42.90 4.20 0.02 MF,DP 2 42.92 4.22 0.02 DS,MF,SF,WF,AG 5 43.17 4.47 0.01 DS,DP,MF 3 43.33 4.63 0.01 DS,MF 2 43.39 4.68 0.01 A 1 43.43 4.73 0.01 AG,DP,MF,WF,SF 5 43.77 5.07 0.01 DP,DS,MF,SF,WF 5 43.80 5.10 0.01 SF,DS,DP,MF 4 43.90 5.19 0.01 DS 1 43.90 5.20 0.01 AG,DP,MF 3 45.11 6.41 0.01 AF,DP,MF,WF,SF,DS 6 45.15 6.44 0.01 AG,DP,DS,MF,SF,WF 6 45.15 6.44 0.01 SF,S,SH,MF 4 45.19 6.49 0.01 DS,MF,SF,WF,BG,DM 6 45.51 6.81 4.53E-03 NULL 0 45.59 6.89 4.36E-03 SF,S,SH,MF,WF,DP 5 45.72 7.02 4.08E-03 AG,DP,DS,MF,SF 5 45.72 7.02 4.08E-03 BG 1 45.75 7.05 4.01E-03 X 1 46.09 7.39 3.39E-03 S 1 46.13 7.43 3.32E-03 SH 1 46.27 7.57 3.10E-03 AG,DP,DS,MF,SF,BS 5 46.33 7.63 3.01E-03 SF,S,SH,MF,DP 5 46.46 7.76 2.82E-03 MH 1 46.47 7.76 2.81E-03 DM 1 46.98 8.28 2.17E-03 AG,DP,MF,WF 4 47.01 8.30 2.14E-03 C 1 47.08 8.38 2.06E-03 WP 1 47.09 8.39 2.05E-03 BS 1 47.09 8.39 2.05E-03 UHF 1 47.14 8.43 2.01E-03 MF 1 47.47 8.76 1.70E-03 WF 1 47.54 8.84 1.64E-03

50

Table 3-12. Continued. Category Model K AICC ∆ AICC wi

Habitat HH 1 47.58 8.87 1.61E-03 AG 1 47.64 8.93 1.56E-03 BF 1 47.66 8.95 1.55E-03 H 1 47.66 8.95 1.55E-03 SHF 1 47.66 8.95 1.55E-03 MF,WF 2 49.56 10.86 5.98E-04 AG,MF 2 49.60 10.90 5.86E-04 AG,WF 2 49.67 10.97 5.67E-04 H 1 52.12 13.42 1.66E-04 O 1 52.89 14.19 1.13E-04 Landscape NULL 0 45.59 0.00 0.09 DWATER 1 45.93 0.35 0.08 ROAD 1 45.98 0.39 0.07 DEDGE 1 46.48 0.89 0.06 RD_EDGE 1 46.65 1.06 0.05 EDGE 1 46.77 1.18 0.05 DROAD 1 46.91 1.32 0.05 WATER 1 47.02 1.44 0.04 DRD_DEDGE,ROAD 2 47.40 1.81 0.04 DRD_DEDGE 1 47.48 1.89 0.04 DRD_DEDGE,DWATER 2 47.76 2.17 0.03 ROAD,DWATER 2 47.87 2.28 0.03 ROAD,DROAD 2 47.97 2.38 0.03 RD_EDGE,DWATER 2 48.06 2.47 0.03 DROAD,DWATER 2 48.07 2.48 0.03 ROAD,WATER 2 48.08 2.49 0.03 ROAD,DEDGE 2 48.09 2.50 0.03 RD_EDGE,DEDGE 2 48.50 2.91 0.02 RD_EDGE,DRD_DEDGE 2 48.53 2.94 0.02 EDGE,DEDGE 2 48.54 2.95 0.02 DROAD,DEDGE 2 48.55 2.96 0.02 RD_EDGE,WATER 2 48.65 3.06 0.02 DRD_DEDGE,EDGE 2 48.73 3.15 0.02 RD_EDGE,DROAD 2 48.78 3.19 0.02 EDGE,DROAD 2 48.88 3.30 0.02 ROAD,EDGE,WATER 3 48.98 3.39 0.02 ROAD,EDGE,DWATER 3 48.98 3.39 0.02 RD_EDGE,DRD_DEDGE,DWATER 3 49.72 4.13 0.01 DROAD,DEDGE,DWATER 3 50.06 4.47 0.01 DROAD,DEDGE,WATER 3 50.55 4.97 0.01 RD_EDGE,DRD_DEDGE,WATER 3 50.67 5.08 0.01 ROAD,EDGE,DROAD,DEDGE 4 51.25 5.66 0.01 Management B2,C2 2 37.53 0.00 0.23 B2,C2,C3 3 38.32 0.79 0.15 B2,C2,C4 3 38.83 1.30 0.12 B2,B4,C2 3 39.28 1.76 0.10 B1,B2,C2 3 39.44 1.92 0.10 B2,B3,B4,C2 4 40.48 2.95 0.05 B1,B2,B3,C2 4 40.69 3.17 0.05 B2,B4,C2,C4 4 40.77 3.24 0.05 B1,B2,C1,C2 4 41.42 3.89 0.03 B1,B2,B3,B4,B5 5 42.18 4.66 0.02 B2,B3,B4,C2,C3,C4 6 43.76 6.24 0.01

51


Management B2 1 43.85 6.33 0.01 B1,B2,B3,C1,C2,C3 6 44.25 6.72 0.01 B1,B2,B3 3 44.73 7.20 0.01 C5 1 44.84 7.31 0.01 B2,B3,C3 3 45.44 7.92 4.39E-03 NULL 0 45.59 8.06 4.08E-03 B2,C1 2 45.60 8.08 4.06E-03 B2,C4 2 45.67 8.15 3.92E-03 B2,C3 2 45.95 8.43 3.40E-03 B2,B3,C4 3 45.99 8.47 3.34E-03 B1 1 46.56 9.03 2.52E-03 C1 1 46.84 9.31 2.18E-03 B1,B2,B3,B4 4 46.93 9.41 2.08E-03 C2 1 46.98 9.46 2.03E-03 B2,B4,C4 3 47.00 9.47 2.01E-03 B2,B4,C4 3 47.00 9.47 2.01E-03 B5 1 47.17 9.64 1.85E-03 B3 1 47.36 9.83 1.68E-03 B1,B2,C4 3 47.40 9.88 1.65E-03 B4 1 47.43 9.90 1.62E-03 C3 1 47.54 10.01 1.54E-03 C4 1 47.64 10.11 1.46E-03 B2,B3,B4,C3 4 47.71 10.18 1.41E-03 B2,B3,B4,C3 4 47.71 10.18 1.41E-03 B3,C1 2 47.77 10.24 1.37E-03 B2,C3,C4 3 47.86 10.34 1.30E-03 B1,C2 2 47.94 10.41 1.26E-03 B2,B3,B4,C4 4 47.94 10.42 1.25E-03 B1,B2,C3 3 47.98 10.46 1.23E-03 B4,C4 2 48.06 10.53 1.18E-03 B1,C4 2 48.26 10.73 1.07E-03 C1,C2,C3,C4,C5 5 48.38 10.85 1.01E-03 B1,B2,B3,B4,C3 5 48.53 11.00 9.39E-04 B1,C1 2 48.53 11.01 9.38E-04 B1,C3 2 48.68 11.16 8.69E-04 B4,C1 2 48.78 11.25 8.29E-04 B3,C3 2 48.83 11.30 8.08E-04 B4,C2 2 48.84 11.32 8.04E-04 B1,B2,B3,B4,C4 5 48.98 11.45 7.51E-04 B3,B4 2 49.06 11.53 7.21E-04 B3,C2 2 49.08 11.55 7.14E-04 B1,B4,C4 3 49.33 11.80 6.30E-04 B1,B3,C2 3 49.33 11.80 6.29E-04 B3,C4 2 49.48 11.96 5.82E-04 B4,C3 2 49.50 11.98 5.77E-04 B3,B4,C4 3 50.15 12.62 4.18E-04 C1,C2,C3 3 50.50 12.98 3.50E-04 B3,B4,C3 3 50.65 13.13 3.25E-04 B3,B4,C2 3 50.90 13.38 2.86E-04 B3,B4,C3,C4 4 52.19 14.67 1.50E-04 C1,C2,C3,C4 4 52.79 15.26 1.11E-04

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Habitat Treatment DP4,MFC2 2 37.20 0.00 0.15 DP2,DP4,MFC2 3 38.27 1.07 0.09 DP4,MFC2,MFC3 3 39.30 2.10 0.05 DP4,MFC2,MFC4 3 39.39 2.19 0.05 DP3,DP4,MFC2 3 39.41 2.21 0.05 DP4 1 39.51 2.30 0.05 DP4,MF3,MF2,MFC2 4 40.36 3.16 0.03 DP2,DP4 2 40.46 3.25 0.03 DP2,DP4,MFC2,MFC3 4 40.48 3.27 0.03 DP2,DP4,MFC2,MFC4 4 40.51 3.30 0.03 DP2,DP3,DP4,MFC2 4 40.56 3.36 0.03 MFC2,DP2,DP4,SF4 4 40.56 3.36 0.03 DP4,MFC3 2 41.54 4.33 0.02 DP4,MFC2,MFC3,MFC4 4 41.56 4.36 0.02 DP3,DP4,MFC2,MFC3 4 41.59 4.39 0.02 DP4,MFC4 2 41.60 4.40 0.02 DP3,DP4 2 41.65 4.44 0.02 DP3,DP4,MFC2,MFC4 4 41.68 4.48 0.02 DP4,MF3,MF2,MFC2,MFC4 5 42.02 4.82 0.01 DP2,DP4,MFC4 3 42.53 5.32 0.01 DPC4 1 42.54 5.33 0.01 DP2,DP4,MFC3 3 42.59 5.39 0.01 DP2,DP3,DP4 3 42.67 5.46 0.01 DP4,MF3,MF2,MFC2,SF4 5 42.73 5.53 0.01 DP2,DP4,MFC2,MFC3,MFC4 5 42.78 5.58 0.01 MF3,DP2,DP3,DP4.MFC2 5 42.80 5.59 0.01 DP2,DP3,DP4,MFC2,MFC3 5 42.85 5.64 0.01 DP2,DP3,DP4,MFC2,MFC4 5 42.88 5.67 0.01 MFC2,MFC4,DP2,DP4,SF4 5 42.88 5.67 0.01 MF2,MF3,DP4 3 43.10 5.89 0.01 MF5 1 43.22 6.01 0.01 DP5 1 43.29 6.09 0.01 MFC2,MFC4,DP2,DP4,SF2 5 43.51 6.31 0.01 DP2,DP4,SF4,WF4 4 43.57 6.37 0.01 DP4,MF3,MF2,MFC2,SF4,WF4 6 43.66 6.45 0.01 DP4,MFC3,MFC4 3 43.69 6.49 0.01 DP3,DP4,MFC3 3 43.75 6.54 0.01 DP3,DP4,MFC4 3 43.81 6.61 0.01 DP4,SF3,MFC4 3 43.81 6.61 0.01 DP3,DP4,MFC2,MFC3,MFC4 5 43.93 6.73 0.01 MF2,MF3,DP2,DP4 4 44.33 7.12 4.28E-03 DP2,DP4,MFC3,MFC4 4 44.72 7.52 3.51E-03 DP2,DP3,DP4,MFC4 4 44.82 7.61 3.35E-03 MFC4,DP2,DP4,SF4 4 44.82 7.61 3.35E-03 DP2,DP3,DP4,MFC3 4 44.88 7.68 3.24E-03 DP1,DP2,DP3,DP4 4 44.95 7.74 3.14E-03 WP5 1 44.99 7.78 3.07E-03 PPC2 1 45.08 7.88 2.93E-03 DP2,DP3,DP4,MFC2,MFC3,MFC4 6 45.24 8.03 2.72E-03 PP2 1 45.27 8.06 2.67E-03 DPC2 1 45.38 8.18 2.52E-03 MF2,MF3,DP3,DP4 4 45.38 8.18 2.52E-03 AG3 1 45.43 8.22 2.47E-03

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Habitat Treatment SFC1 1 45.49 8.29 2.39E-03 WFC4 1 45.49 8.29 2.39E-03 DPC5 1 45.62 8.42 2.24E-03 DP2,DP4,SF4,WF4,DP3 5 45.94 8.74 1.91E-03 MFC2,MFC4,DP2,DP4,SF2,SF4 6 45.97 8.76 1.88E-03 DP3,DP4,MFC3,MFC4 4 45.98 8.78 1.87E-03 PP4 1 46.03 8.82 1.83E-03 SFC2 1 46.07 8.86 1.79E-03 DP2 1 46.20 8.99 1.68E-03 NULL 1 46.36 9.15 1.55E-03 MF2,MF3,DP2,DP3,DP4 5 46.70 9.49 1.30E-03 WPC5 1 46.79 9.58 1.25E-03 WF3 1 46.88 9.68 1.19E-03 MF4 1 46.97 9.76 1.14E-03 DP1 1 46.97 9.77 1.14E-03 DP3 1 46.97 9.77 1.14E-03 WF4 1 46.97 9.77 1.14E-03 PPC3 1 46.97 9.77 1.14E-03 SFC3 1 46.97 9.77 1.14E-03 DP2,DP3,DP4,MFC3,MFC4 5 47.09 9.89 1.07E-03 WP3 1 47.11 9.91 1.06E-03 MF3,DP2,DP3,DP4,MFC4 5 47.14 9.93 1.05E-03 MFC4 1 47.18 9.98 1.02E-03 DP2,MFC4 2 47.19 9.99 1.02E-03 PPC5 1 47.28 10.08 9.76E-04 SF1 1 47.30 10.09 9.70E-04 DP2,MFC2 2 47.43 10.23 9.07E-04 PP5 1 47.45 10.25 8.98E-04 PPC4 1 47.45 10.25 8.98E-04 SFC4 1 47.63 10.43 8.21E-04 WP2 1 47.67 10.46 8.05E-04 MFC2 1 47.72 10.52 7.83E-04 SF3 1 47.76 10.56 7.68E-04 SF2 1 47.81 10.61 7.49E-04 WP4 1 47.81 10.61 7.49E-04 WPC4 1 47.83 10.62 7.45E-04 DP2,MFC3 2 47.85 10.65 7.34E-04 DP3,MFC4 2 47.93 10.73 7.07E-04 WP1 1 47.97 10.77 6.93E-04 MF1 1 48.04 10.84 6.67E-04 DP2,MFC2,MFC4 3 48.05 10.84 6.66E-04 MFC3 1 48.05 10.85 6.66E-04 DP2,DP3 1 48.12 10.92 6.42E-04 MF2 1 48.13 10.92 6.40E-04 SFC5 1 48.15 10.95 6.33E-04 WPC2 1 48.18 10.98 6.24E-04 WPC3 1 48.20 10.99 6.18E-04 MFC5 1 48.22 11.02 6.11E-04 WPC1 1 48.23 11.03 6.07E-04 MFC2,MFC4 2 48.25 11.04 6.03E-04 WF2 1 48.30 11.09 5.88E-04

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Habitat Treatment WF5 1 48.30 11.10 5.87E-04 MF3 1 48.30 11.10 5.86E-04 PP3 1 48.32 11.12 5.80E-04 MFC1 1 48.36 11.15 5.71E-04 SF4 1 48.36 11.15 5.71E-04 DPC3 1 48.36 11.15 5.71E-04 WFC5 1 48.36 11.15 5.71E-04 DP3,MFC2 2 48.48 11.27 5.38E-04 MF2,MF4 2 48.67 11.46 4.89E-04 DP3,MFC3 2 48.80 11.60 4.57E-04 DP2,MFC2,MFC3 3 49.04 11.84 4.05E-04 DP2,MFC3,MFC4 3 49.07 11.86 4.00E-04 DP3,MFC2,MFC4 3 49.07 11.87 3.99E-04 MF3,MF4 2 49.10 11.90 3.93E-04 MFC3,MFC4 2 49.12 11.92 3.89E-04 MF2,MF3,DP2,DP3,DP4.MFC4 6 49.15 11.94 3.84E-04 MF2,MF3,DP2 3 49.87 12.67 2.67E-04 DP2,MFC2,MFC3,MFC4 4 49.91 12.70 2.63E-04 DP3,MFC3,MFC4 3 49.95 12.74 2.57E-04 MF2,MF3 2 49.96 12.76 2.56E-04 MFC2,MFC3,MFC4 3 50.23 13.03 2.23E-04 DP3,MFC2,MFC3 3 50.32 13.12 2.13E-04 MF2,MF3,MFC4 3 50.42 13.21 2.04E-04 MF2,MF3,MF4 3 50.70 13.50 1.76E-04 MF2,MF3,DP3 3 50.78 13.58 1.70E-04 DP3,MFC2,MFC3,MFC4 4 51.13 13.93 1.42E-04 MF1,MF2,MF3,MFC4 4 51.51 14.31 1.17E-04 MF1,MF2,MF3,MF4 4 51.92 14.72 9.61E-05 MF2,MF3,MFC3 3 52.00 14.79 9.24E-05 MF2,MF3,DP2,DP3 4 52.10 14.89 8.80E-05 MF2,MF3,MFC3,MFC4 4 52.56 15.36 6.98E-05 MF1,MF2,MF3,MFC3,MFC4 5 53.79 16.58 3.78E-05 MF1,MF2,MF3,MFC3,MFC4 5 53.85 16.65 3.66E-05 MF1,MF2,MF3,MFC2,MFC3 5 55.27 18.07 1.80E-05

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Table 3-13. Parameter estimates, 95% confidence intervals (CI), and odds ratios (OR) for variables used in supported a priori models to predict nest success at the landscape level of Florida wild turkey hens in south Florida, USA, 2008-2010.

95% CI


Habitat SF,WF SF 14.814 -4.132E+09 4.132E+09 2.710e+06 WF -25.996 -2.238E+10 2.238E+10 0.000 DP,WF,SF DP -0.126 -0.397 0.145 0.882 WF -30.377 -7.599E+10 7.599E+10 0.000 SF 17.405 -3.238E+09 3.238E+09 3.620E+21 SF SF 14.804 -4.071E+09 4.071E+09 2.690E+06 DP,SF DP -0.126 -0.397 0.145 0.882 SF 17.405 -3.237E+09 3.237E+09 3.620E+07 Management B2,C2 B2 0.009 0.001 0.017 1.009 C2 -0.048 -0.093 -0.003 0.953 B2,C2,C3 B2 0.013 0.000 0.026 1.013 C2 -0.061 -0.119 -0.003 0.941 C3 -0.025 -0.067 0.017 0.975 B2,C2,C4 B2 0.011 0.000 0.021 0.011 C2 -0.053 -0.103 -0.003 -0.053 C4 -0.002 -0.006 0.002 -0.002 B2,B4,C2 B2 1.010 0.001 0.020 1.010 B4 -0.002 -0.008 0.004 0.998 C2 -0.052 -0.099 -0.004 0.950 B1,B2,C2 B1 -0.006 -0.027 0.015 0.994 B2 0.011 0.000 0.022 1.011 C2 -0.053 -0.102 -0.003 0.949 Landscape NULL NULL 0.000 0.000 0.000 0.000 DWATER DWATER 0.000 0.000 0.001 1.000 ROAD ROAD 0.060 -0.033 0.152 1.062 DEDGE DEDGE 0.005 -0.005 0.014 1.005 RD_EDGE RD_EDGE 0.008 -0.008 0.023 1.008 EDGE EDGE 0.009 -0.010 0.027 1.009 DROAD DROAD 0.003 -0.004 0.010 1.003 WATER WATER 0.108 -0.174 0.391 1.114 DRD_DEDGE,

ROAD DRD_DEDGE -0.009 -0.029 0.012 0.991

ROAD 0.105 -0.038 0.248 1.111 DRD_DEDGE DRD_DEDGE 0.003 -0.011 0.016 1.003 Habitat Treatment

DP4,MFC2 DP4 1.369 -0.879 3.616 13.786

MFC2 -0.001 -0.014 0.011 0.872 DP2,DP4,MFC2 DP2 -0.090 -0.302 0.121 0.914 DP4 3.228 -0.554 7.011 25.240 MFC2 -0.103 -0.351 0.146 0.903

56

Table 3-14. Best a priori model(s) from each variable category predicting nest success at the landscape level of Florida wild turkey hens in south Florida, USA, 2008-2010.

Category Model K AICc ΔAICc wi

Habitat Treatment DP4,MFC2 2 37.20 0.00 0.18 Treatment B2,C2 2 37.53 0.32 0.15 Habitat Treatment DP2,DP4,MFC2 3 38.27 1.07 0.11 Treatment B2,C2,C3 3 38.32 1.11 0.10 Habitat SF,WF 2 38.70 1.50 0.08 Treatment B2,C2,C4 3 38.83 1.63 0.08 Habitat SF 1 39.34 2.13 0.06 Management B1,B2,C2 3 39.44 2.24 0.06 Habitat DP,WF,SF 3 39.61 2.40 0.05 Habitat DP,SF 2 40.17 2.96 0.04 NULL NULL 0 45.59 8.38 0.00 Landscape DWATER 1 45.93 8.73 0.00 Landscape ROAD 1 45.98 8.77 0.00 Landscape DEDGE 1 46.48 9.27 0.00 Landscape RD_EDGE 1 46.65 9.44 0.00 Landscape EDGE 1 46.77 9.57 0.00 Landscape DROAD 1 46.91 9.70 0.00 Landscape WATER 1 47.02 9.82 0.00 Landscape DRD_DEDGE,ROAD 2 47.40 10.20 0.00 Landscape DRD_DEDGE 1 47.48 10.28 0.00

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CHAPTER 4 DISCUSSION

Selection

Florida wild turkey hen microhabitat selection was for the presence of dense lateral

cover in the form of shrubs, which has been widely documented by many, including Day

et al. (1991) with eastern wild turkeys. This manifested not in increased visual

obstruction, but in saw palmetto density. Hens selected for higher levels of saw palmetto

density most likely to aid in concealing incubating hens as saw palmetto can provide

significant lateral cover. Saw palmetto also provides hens with cover overhead, which

reduces the probability of detection by avian predators such as the red-tailed hawk (Buteo

jamaicensis; Lehman et al. 2002, Nguyen et al. 2004). It may also function to shade hens,

decreasing nest temperatures by keeping sun off hens’ backs while incubating. Findings

for other wild turkey subspecies (e.g., Meleagris gallopavo silvestris, Meleagris gallopavo

merriami, Meleagris gallopavo intermedia) corroborate the importance or presence of

dense cover featuring low shrubs and slash at nest sites (Logan 1973, Lutz and Crawford

1987, Schmutz et al. 1989, Eichler and Whiting 2004, Shields and Flake 2004, Palmer et

al. 1996). Other researchers have also observed heavy vegetation and saw palmetto at

Florida wild turkey nests (e.g., Williams et al. 1968, Williams 1991, Dickson 1992).

Additionally, hens preferred areas with higher densities of palm stems, which can

also provide a great degree of lateral and overhead cover through standing vegetation

and litter, decreasing predator efficiency by providing visual, auditory, and olfactory

obstruction at nest sites (Bowman and Harris 1980, Redmond et al. 1982, Crabtree et al.

1989, Badyaev 1995, Shields and Flake 2004). Dense cover such as this may also

provide greater numbers of locations where hens could establish nests, decreasing

58

predator efficiency by making predators search in more areas to find nests. Similar

findings include those of Thomas and Litvaitis (1993), who reported that eastern wild

turkey hens selected for more stems near the nest site.

At the patch level, hens selected for higher densities of palm stems, but trends

showed selection for more open areas with fewer stems overall, lower densities of saw

palmetto, and lower levels of visual obstruction. The dense cover selected for nest sites

may conceal hens well, but it does not allow hens to move easily through it. Therefore,

hens may choose to have more open habitat adjacent to nest sites for easy ingress and

egress. Other research has found similar results for nesting hens, suggesting that they

prefer to be concealed on the nest, while being able to survey the area for threats in

coming from and going to the nest (Logan 1973, Speake et al. 1975).

Additionally, though species such as saw palmetto afford dense understory cover to

conceal hens while nesting, it inhibits growth of other vegetation used as forage by wild

turkeys (Williams and Austin 1988, Willcox and Giuliano 2010). By selecting for patches

of dense cover within more open habitats, hens may be selecting for areas with more food

potential nearby. When they must leave to water and feed, they do not have to travel far,

and while foraging, they can readily see threats around them due to the lower levels of

lateral and overhead cover. Foraging in more open habitat such as this decreases the

risk of predation (Williams 1991). Moreover, when hens are successful, newly hatched

poults do not have to travel far to reach areas suitable for forage and movement (Lazarus

and Porter 1985, Haegen et al. 1991).

Trends evident at the finer scales continued at the landscape level. Birds typically

selected for areas characterized by patchy dense and rank vegetation in more open

59

habitats. Hens selected against areas that had been recently treated with either

prescribed fire or roller-chopping. Burned areas were especially avoided, possibly

because of the clean nature of the landscape after a burn, in addition to the typical

magnitude of the burns at Three Lakes WMA. These tended to be very large-scale,

leaving behind sizeable open areas avoided by turkeys. Though no data concerning the

Florida wild turkey’s nest site selection regarding these parameters has been garnered to

date, these findings concur with those of Exum et al. (1987) in Alabama; however, Sisson

et al. (1990) and Eichler and Whiting (2004) found eastern wild turkey hens selected nest

sites in upland pine stands burned on a three to five-year basis. Seiss et al. (1990) found

no selection based upon burn regime for eastern wild turkey nest site selection. In

addition to areas unburned and unchopped, hens selected areas not recently chopped

(0.5-2 years and >2 years). This suggests that roller-chopping may be a more suitable

management technique for turkeys in reference to nest site selection, possibly because it

can produce landscapes less open and clean than does prescribed fire, affording

incubating hens concealment within an open landscape (Willcox and Giuliano 2010).

Hens also selected sites further from roads and water, possibly because these

landscape features provide corridors for movement and foraging for many potential nest

predators (Gates and Gysel 1978, Dickson 1992). Hens selected nest sites closer to

habitat edges, which also provide corridors for movement of predators. However, this

may be attributed to hens’ selection of dense cover at finer scales for the nest

site. Habitats are typically denser near edges, so hens probably selected for areas

nearer habitat edges because of their preference for lateral and overhead cover at the

nest bowl. While Florida wild turkey hen nest site proximity to habitat edges has not been

60

examined, Seiss et al. (1990) and Swanson et al. (1996) both reported eastern wild turkey

hens selected nest sites within sixty meters of habitat edge, while Porter (1978) found that

nearly 75% of eastern wild turkey nests were located within edge habitat. Further,

Thogmartin (1999) reported that although most captured eastern wild turkey hens

selected nest sites in edge habitat, adult hens chose less edge habitat than expected,

indicating that wild turkeys may learn edge avoidance as they mature.

Continuing the trends found at finer scales, hens selected for agriculture, dry prairie,

and flatwoods at the landscape level. These habitat types feature more open

understories, while providing scrubby patches of shrubs and dense vegetation used for

escape and nesting, which allow hens to detect approaching predators without impeding

escape paths (Day et al. 1991, Lopez et al. 1997). The agricultural habitat selected was

primarily citrus groves, in which grasses grew tall, particularly near the bases of trees,

affording cover and forage potential while also allowing hens improved visibility and

movement. Others, such as Lopez (1997) and Shields and Flake (2004), have reported

nests adjacent to protective barriers such as the tree bases present in the citrus grove

and other habitats. Obstruction such as the bases of trees gives hens a 180º protective

barrier against predation and a clear line of escape (Williams 2006). In dry prairie,

grasses may provide a structure that can be very beneficial for nesting birds. Bunch

grasses abound and allow hens to conceal themselves and their broods, but also allow

for the easy movement of poults and provide areas for bugging and foraging (Lazarus and

Porter 1985, Metzler and Speake 1985, Day et al. 1991, Dickson 1992). Hens are also

able to stand up at the nest bowl and survey the surrounding area for predators before

leaving the nest in these habitats. Saw palmetto can also grow in dry prairie, and in areas

61

not dominated by it, saw palmetto grows in patches, affording heavy cover in a relatively

open habitat, why may explain why hens selected these areas while searching for nesting

areas. The same is true of flatwoods, be they mesic, scrubby, or wet. They present open

landscapes with varying densities of woody vegetation, allowing turkeys to select at

multiple scales.

In summary, trends for selection remained consistent through all spatial

scales. Hens selected for dense cover at the nest bowl, while selecting for more open

habitats at the patch level. This trend remained at the landscape, with hens selecting for

scrubby, patchy habitats that were open with some small, intermingled clumps of more

dense cover within them. This selection provided concealment at the nest site and

habitat suitable for foraging and brooding nearby.

Success

Turkey nests failed for several different reasons, but the most significant causes of

failure were nest depredation or predation of the incubating hen. Many have cited

depredation or predation of the hen as the leading causes of failure for wild turkey nests

and a limiting factor on population size (Williams 1991, Roberts and Porter 1996,

Thogmartin 1999). Others such as Seiss et al. (1990), Badyaev (1995), and Thogmartin

(1999) have reported habitat effects on nest success, and have even suggested that

depredation may influence selection of nest sites by causing avoidance of certain habitats

or habitat features. However, no such data exists for Florida wild turkeys.

At the patch level, when compared to unsuccessful nests, successful nests had

lower densities of hardwood, total stems, and saw palmetto and decreased visual

obstruction, as reported with eastern wild turkeys by Badyaev (1995). Hens that selected

for these indicators of more open habitats at the patch level were more likely to succeed,

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possibly because high basal areas manifested themselves in habitat edges. Trees in

edge habitat may function as den sites for some potential nest predators such as snakes,

opossums, and raccoons, proximity to which may decrease nest success (Williams et al.

1980, Dickson 1992, Frey and Conover 2006). Additionally, open habitat selected at the

patch level allows hens to see around the nest, quickly access forage locations, or

escape danger.

At the landscape level, hens selected scrubby habitats that may have provided open

areas for movement (i.e., scrubby flatwoods) or open habitats with scrubby patches (i.e.,

dry prairie) at the landscape level, while using dense cover for nesting at the microhabitat.

Hens also selected for habitat edge at the landscape level, which provided dense cover at

the microhabitat that functioned to incubating hens’ benefit. However, by selecting nest

sites closer to habitat edge (characterized by denser understory cover and higher basal

areas), hens may have exposed their nests to higher numbers of predators (Gates and

Gysel 1978, Wilcove 1985).

I found that habitat edges may function as somewhat of an ecological trap because

hens selected nest sites closer to habitat edges, presumably for the benefit gained in

concealment at the microhabitat level, yet success rose as distance to habitat edge,

roads, and water increased. Landscape category success results must be interpreted

conservatively, but trends, as well as habitat characteristics, indicated that edge habitat

may have decreased the likelihood of nest success. Potential nest predators use these

features such as edge, roads, and water not only as residences, but also as travel and

forage corridors (Gates and Gysel 1978). So turkeys searching for dense cover in more

open habitat types (i.e., dry prairie, etc.) may have selected for the edge of habitat where

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vegetation becomes more dense. However, these edges also mean predators were

more likely to come upon the nest, resulting in elevated levels of nest predation (Gates

and Gysel 1978, Wilcove 1985, Niemuth and Boyce 1997). For this reason, hens that

selected areas with lower basal areas and areas further from roads, habitat edges, and

water were more likely to be successful; presumably because hens selected these areas

with dense cover close to the center of habitat patches, not near habitat edge

(Thogmartin 1999). Thogmartin (1999) had similar findings, though Seiss et al. (1990)

found that successful nests were located closer to features such as habitat edge and

roads.

Another factor associated with selection of edge habitat may have been hens’

selection of dense vegetation (e.g., saw palmetto) for nest sites at the microhabitat level,

as observed by Williams et al. (1968) and Williams and Austin (1988) in Florida wild

turkey hens, and Badyaev (1995) and Lazarus and Porter (1985) researching other

subspecies. In Florida, saw palmetto has become a dominant component of the

understory, providing dense cover selected for by nesting hens. Because it is readily

available and conceals hens, it may increase the probability of nest success. Saw

palmetto exists both in the interior of habitat patches and at habitat edges, but its

presence in the center of habitat patches may allow Florida wild turkey hens to select

dense cover further from edge habitat, aiding success.

At the landscape level, selection of habitat and habitat treatment type was not

associated with success. Successful nests were associated with greater areas burned

0.5-2 years ago, which hens selected against, and with smaller areas of 0.5-2 year-old

chops, which hens selected. Sisson et al. (1990) reported that eastern wild turkey hens

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selected for areas with similar burn histories, however, Eichler and Whiting (2004)

reported that eastern wild turkey hens in Texas avoided burns 0.5-2 years old. Ultimately,

it appears a management strategy providing many different features through different

treatment applications and ages may be best for wild turkeys in Florida. Smaller scale

patchwork burning and chopping programs will create a mosaic of habitat, allowing hens

to seek out patches of dense cover not available to them when large burn and chop units

are used. Though large tracts dominated by saw palmetto may not benefit turkeys, small

patches of dense shrubs such as saw palmetto does benefit the Florida wild turkey hen in

her selection of a nest site and the probability of its success. This type of management

may also decrease hen predation and increase brood survival because it provides more

open habitat available for foraging and brood-rearing near dense patches of brush where

nests may be located.

In summary, Florida wild turkey hens seemed to select nesting sites based on

features that would decrease the probability of detection by predators. These features

provided enhanced concealment and decreased predator efficiency by increasing

possible nesting locations, as reported for eastern wild turkeys (Badyaev 1995).

However, hens selected for areas closer to edge habitat, presumably for the increase in

cover available near habitat edges, although predation may sometimes increase within

edge habitat (Gates and Gysel 1978, Niemuth and Boyce 1997). Habitat managers may

be successful in mitigating depredation through treatments to habitat edge or by allowing

and even encouraging the growth and persistence of small clumps of dense vegetation

located throughout habitat patches. If edge is made less appealing to nesting hens, they

may choose to nest in vegetation clumps more internally located in habitat patches. This

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may be accomplished using prescribed fire and roller-chopping along the edges of

habitats, reducing the dense vegetation characteristics of ecotones selected by nesting

Florida wild turkey hens. Additionally, hens selected for untreated areas and for areas

roller-chopped >6 months prior, but they experienced more success in areas burned

0.5-2 years prior. A combination of these treatments may satisfy needs for nest site

selection, while simultaneously benefitting success rates. In some areas of Florida where

saw palmetto dominates, shrub removal may be necessary. However, this study

demonstrates its benefits to wild turkey hens’ nest site selection and success. Clumps of

saw palmetto should be allowed to remain throughout habitat patches to aid in concealing

incubating hens and increasing the area that predators must search to find nests, while

kept in low enough densities that turkeys can easily move through the area while

foraging.

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BIOGRAPHICAL SKETCH

Johnny Olson was raised in Thomasville, Georgia and the surrounding Red Hills

Region. He has worked as a trapper, hired hand, and tractor driver on South Georgia

quail plantations and as a technician tracking quail and capturing broods at Tall Timbers

Research Station. Upon graduating from Furman University with a Bachelor of the Arts

degree in history in 2009, he moved to Gainesville, Florida to attend the University of

Florida for his master’s degree in wildlife ecology and conservation, researching Florida

wild turkeys. He intends to further his education by continuing at the University for his

PhD, studying white-tailed deer and coyotes in North Florida and South Georgia. He

enjoys hunting, reloading, archery, fishing, fly tying, and being outdoors.

Documents

To my family and Rosemary, LLC - University of Floridaufdcimages.uflib.ufl.edu/UF/E0/04/34/05/00001/olson_j.pdfattempted to understand wild turkey hen nest site selection and habitat