Demographics and Habitat Selection for the Western Meadowlark (Sturnella Neglecta) in the Nebraska Sandhills

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Theses, Dissertations, and Student Research in Agronomy and Horticulture Agronomy and Horticulture Department

2009

Demographics and Habitat Selection for the Western Meadowlark (Sturnella neglecta) in the Nebraska Sandhills

Matthew D. Giovanni University of Nebraska at Lincoln

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Giovanni, Matthew D., "Demographics and Habitat Selection for the Western Meadowlark (Sturnella neglecta) in the Nebraska Sandhills" (2009). Theses, Dissertations, and Student Research in Agronomy and Horticulture. 4. https://digitalcommons.unl.edu/agronhortdiss/4

This Article is brought to you for free and open access by the Agronomy and Horticulture Department at DigitalCommons@University of Nebraska - Lincoln. It has been accepted for inclusion in Theses, Dissertations, and Student Research in Agronomy and Horticulture by an authorized administrator of DigitalCommons@University of Nebraska - Lincoln. DEMOGRAPHICS AND HABITAT SELECTION FOR THE WESTERN

MEADOWLARK (STURNELLA NEGLECTA) IN THE NEBRASKA SANDHILLS

Matthew D. Giovanni

A DISSERTATION

Presented to the Faculty of

The Graduate College at the University of Nebraska

In Partial Fulfillment of Requirements

For the Degree of Doctor of Philosophy

Major: Agronomy

(Applied Ecology)

Under the Supervision of Professors Larkin A. Powell and Walter H. Schacht

Lincoln, Nebraska

December, 2009

DEMOGRAPHICS AND HABITAT SELECTION FOR THE WESTERN

MEADOWLARK (STURNELLA NEGLECTA) IN THE NEBRASKA SANDHILLS

Matthew David Giovanni, Ph.D.

University of Nebraska, 2009

Advisors: Larkin A. Powell and Walter H. Schacht

The prairie ecosystems of the Great Plains region in North America have largely been replaced and fragmented with industrial agriculture and invasive herbaceous and woody plant species. The concurrent and large-scale suppression of wildfire and elimination of grazing by native ungulates may have further decreased the availability and quality of habitat for wildlife. Indeed, 2004 estimates indicate only 30% of historic grasslands in the Great Plains still exist, while the trends of decreased land area enrolled in the

Conservation Reserve Program and increased land area in commercial agriculture indicate continued loss of habitat. This decrease in habitat availability continues to cause population declines for most grassland bird species in North America. The Nebraska

Sandhills (Sandhills) region is the largest continuous mixed-grass prairie system remaining in North America, and is almost exclusively managed for cattle production with rotational grazing and wild-hay supplementation. The Sandhills are also, however, subject to increasing demands of natural resources while biological data for management and conservation planning is minimal at best.

The Western Meadowlark (Sturnella neglecta) is broadly distributed across the temperate grasslands of central and western North America, but long-term abundance data indicate populations are declining across Nebraska and most of the species’ breeding range. The Sandhills are a potential population source for grassland bird species breeding elsewhere in fragmented, lower-quality habitat, but information on the demographic responses of species to land management in the Sandhills is mostly nonexistent. I sampled from a population of Western Meadowlarks in the central Sandhills from 2006-

2008 to understand the relationships between breeding ecology, vegetation structure, and associated land management. Specifically, I report and discuss 1) selection of nest-site habitat by adults as a function of vegetation variables, 2) nest survival as a function of vegetation, researcher-effect, and temporal variables, 3) selection of habitat by fledglings as a function of vegetation variables, and survival of fledglings as a function of climate and temporal variables. iv TABLE OF CONTENTS

ABSTRACT...... ii

TABLE OF CONTENTS...... iv

LIST OF TABLES...... vi

LIST OF FIGURES...... vii

CHAPTER 1: HABITAT-SPECIFIC EFFECTS OF VEGETATION STRUCTURE ON

THE SELECTION OF NEST SITES BY WESTERN MEADOWLARKS (STURNELLA

NEGLECTA) IN THE NEBRASKA SANDHILLS...... 1

ABSTRACT...... 2

INTRODUCTION...... 3

METHODS...... 5

RESULTS...... 8

DISCUSSION...... 10

LITERATURE CITED...... 17

CHAPTER 2: ASSESSMENT OF MICROHABITAT, RESEARCHER, AND

TEMPORAL EFFECTS ON PREDATOR-LIMITED SURVIVAL OF WESTERN

MEADOWLARK (STURNELLA NEGLECTA) NESTS IN THE NEBRASKA

SANDHILLS...... 39

ABSTRACT...... 40

INTRODUCTION...... 41

METHODS...... 43

RESULTS...... 50

DISCUSSION...... 54 v LITERATURE CITED...... 63

CHAPTER 3: MOVEMENT, MICROHABITAT SELECTION, AND PREDATOR-

AND THERMOREGULATION-LIMITED SURVIVAL FOR WESTERN

MEADOWLARK (STURNELLA NEGLECTA) FLEDGLINGS IN THE NEBRASKA

SANDHILLS...... 86

ABSTRACT...... 87

INTRODUCTION...... 88

METHODS...... 90

RESULTS...... 95

DISCUSSION...... 97

LITERATURE CITED...... 102

vi LIST OF TABLES

Chapter 1.

Table 1. Models for probability of nest-site selection for Western Meadowlarks in the

Nebraska Sandhills, USA, 2006-2008. Model-selection parameters include the

number of parameters (K), deviance (Dev), log-likelihood (Loge(L)), Akaike

Information Criterion scores for small sample sizes (AICc), relative model

differences in AICc (Δi), and relative model weights (wi)...... 25

Table 2. Logit-scale model coefficients (β) with lower (LCL) and upper (UCL) 95%

confidence limits and standard errors (SE) for the global model of nest-site

selection probability for Western Meadowlarks in the Nebraska Sandhills, USA,

2006-2008. Asterisks indicate coefficients with confidence intervals that do not

overlap zero...... 26

Chapter 2.

Table 1. Models of daily survival probability for Western Meadowlark nests in the

Nebraska Sandhills, USA, 2006-2008. Model-selection parameters include the

number of model parameters (K), deviances (Dev), log-likelihoods (Loge(L)),

Akaike Information Criteria for small sample sizes (AICc), relative differences

(ΔAICc), and weights (wi)...... 75

Table 2. Logit-scale model coefficients (β) with lower (LCL) and upper (UCL) 95%

confidence limits and standard errors (SE) for the a priori temporal model of

daily survival for Western Meadowlark nests in the Nebraska Sandhills, USA,

2006-2008. Asterisks indicate coefficients with confidence intervals that do not

overlap zero...... 76 vii Table 3. Logit-scale model coefficients (β) with lower (LCL) and upper (UCL) 95%

confidence limits and standard errors (SE) for the a posteriori nest-site vegetation

and temporal model of daily survival for Western Meadowlark nests in the

Nebraska Sandhills, USA, 2006-2008. Asterisks indicate coefficients with

confidence intervals that do not overlap zero...... 77

Chapter 3.

Table 1. Models of daily survival probability for Western Meadowlark fledglings in the

Nebraska Sandhills, USA, 2006-2007. Model-selection parameters include

number of model parameters (K), model deviance (Dev), model log-likelihood

(Loge(L)), Akaike Information Criterion scores adjusted for small sample sizes

(AICc), relative model differences in AICc (Δi), and relative model weights

(wi)...... 111

Table 2. Models of microhabitat-selection probability for Western Meadowlark

fledglings in the Nebraska Sandhills, USA, 2006-2007. Model-selection

parameters include number of model parameters (K), model deviance (Dev),

model log-likelihood (Loge(L)), Akaike Information Criterion scores adjusted for

small sample sizes (AICc), relative model differences in AICc (Δi), and relative

model weights (wi)...... 112

viii LIST OF FIGURES

Chapter 1.

Figure 1. Litter depth and vegetation height (centimeters ± 95% confidence intervals) for

random sites according to Western Meadowlark habitat type and year in the

Nebraska Sandhills, USA, 2006-2008...... 28

Figure 2. Ground cover (± 95% confidence intervals) at random sites according to

Western Meadowlark habitat type in the Nebraska Sandhills, USA, 2006-

2008...... 29

Figure 3. Litter depth and vegetation height coefficients of variation (CV ± 95%

confidence intervals) for random sites according to Western Meadowlark habitat

type and year in the Nebraska Sandhills, USA, 2006-2008...... 30

Figure 4. Litter depth and vegetation height (centimeters ± 95% confidence intervals) for

Western Meadowlark nest sites and random sites in the Nebraska Sandhills, USA,

2006-2008...... 31

Figure 5. Ground cover (± 95% confidence intervals) for Western Meadowlark nest sites

and random sites in the Nebraska Sandhills, USA, 2006-2008...... 32

Figure 6. Litter depth and vegetation height coefficients of variation (CV ± 95%

confidence intervals) for Western Meadowlark nest sites and random sites in the

Nebraska Sandhills, USA, 2006-2008...... 33

Figure 7. Effects of heterogeneity (coefficient of variation, CV) of litter depth and

vegetation height on the probability of nest-site selection by Western

Meadowlarks in the Nebraska Sandhills, USA, 2006-2008...... 34 ix Figure 8. Effects of litter depth and vegetation height on the probability of nest-site

selection by Western Meadowlarks in the Nebraska Sandhills, USA,

2006-2008...... 35

Figure 9. Effects of sedge cover on the probability of nest-site selection by Western

Meadowlarks in lowland wet meadows in the Nebraska Sandhills, USA, 2006-

2008...... 36

Figure 10. Habitat-type specific effects of litter cover and grass cover on the probability

of nest-site selection by Western Meadowlarks in the Nebraska Sandhills, USA,

2006-2008...... 37

Figure 11. Habitat-type specific effects of forb cover on the probability of nest-site

selection by Western Meadowlarks in the Nebraska Sandhills, USA,

2006-2008...... 38

Chapter 2.

Figure 1. Proportions (± 95% confidence intervals) of clutch initiations per 0.5 month

period for Western Meadowlarks in the Nebraska Sandhills, USA, 2006-

2008...... 79

Figure 2. Proportions (± 95% confidence intervals) of clutches hatched for nests partially

predated (n = 19) and not partially predated (n = 143) for Western Meadowlarks

in the Nebraska Sandhills, USA, 2006-2008...... 80

Figure 3. Ambient temperature and nest-bowl temperature (per 0.5 h) for a Western

Meadowlark nest that failed (where temperature lines begin to converge) on 15

June in the Nebraska Sandhills, USA, 2007...... 81 x Figure 4. Distribution of nest predations according to age for Western Meadowlarks in

the Nebraska Sandhills, USA, 2006-2008...... 82

Figure 5. Quadratic effects of age on the probability of daily survival (± 95% confidence

intervals, dashed lines) for Western Meadowlark nests in the Nebraska Sandhills,

USA, 2006-2008...... 83

Figure 6. Interacting effects of nest age and ordinal day on the probability of daily

survival (± 95% confidence intervals, dashed lines) for Western Meadowlark

nests in the Nebraska Sandhills, USA, 2006-2008...... 84

Figure 7. Effects of forb cover on the probability of daily survival (± 95% confidence

intervals, dashed lines) for Western Meadowlark nests in the Nebraska Sandhills,

USA, 2006-2008...... 85

Chapter 3.

Figure 1. Maximum daily temperature (solid lines) and confirmed mortalities (vertical

dashed lines) from unknown causes for Western Meadowlark fledglings in the

Nebraska Sandhills, USA, 2006-2007...... 113

Figure 2. Frequency of confirmed mortalities and lost radio-signals according to days

post-fledging for Western Meadowlark fledglings in the Nebraska Sandhills,

USA, 2006-2007...... 114

Figure 3. Model-predicted daily survival probabilities (± 95% confidence intervals,

dashed lines) as a function of age (since hatching) for Western Meadowlark

fledglings in the Nebraska Sandhills, USA, 2006-2007...... 115 xi Figure 4. Model-predicted daily survival probabilities (± 95% confidence intervals,

dashed lines) as a function of ordinal day (season) for Western Meadowlark

fledglings in the Nebraska Sandhills, USA, 2006-2007...... 116

Figure 5. Model-predicted daily survival probabilities (± 95% confidence intervals,

dashed lines) as a function of daily maximum temperature for Western

Meadowlark fledglings in the Nebraska Sandhills, USA, 2006-2007...... 117

Figure 6. Observed and model-predicted distances (meters ± 95% confidence intervals,

dashed lines) moved from nest sites as a function of age (since hatching) for

Western Meadowlark fledglings in the Nebraska Sandhills, USA, 2006-2007.. 118

Figure 7. Observed and model-predicted distances (meters ± 95% confidence intervals,

dashed lines) moved between radio-telemetry locations as a function of age (since

hatching) for Western Meadowlark fledglings in the Nebraska Sandhills, USA,

2006-2007...... 119

Figure 8. Ground cover for random locations and locations selected by Western

Meadowlark fledglings in the Nebraska Sandhills, USA, 2006-2007...... 120

Figure 9. Maximum vegetation height, vegetation density, and litter depth for random

locations and locations selected by 25-31 day-old and ≥ 32 day-old Western

Meadowlark fledglings in the Nebraska Sandhills, USA, 2006-2007...... 121

Figure 10. Ground cover for random locations and locations selected by 25-31 day-old

and ≥ 32 day-old Western Meadowlark fledglings in the Nebraska Sandhills,

USA, 2006-2007...... 122

1 HABITAT-SPECIFIC EFFECTS OF VEGETATION STRUCTURE ON THE

SELECTION OF NEST-SITES BY WESTERN MEADOWLARKS (STURNELLA

NEGLECTA) IN THE NEBRASKA SANDHILLS

MATTHEW D. GIOVANNI, 1, 2, 3 LARKIN A. POWELL,1 AND WALTER H.

SCHACHT 2

1School of Natural Resources, University of Nebraska-Lincoln, Lincoln, Nebraska 68583

2Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln,

Nebraska 68583

[email protected]

2 ABSTRACT. ― Populations of Western Meadowlarks and other grassland bird species in North America continue to decline according to long-term monitoring data and vital rate studies. The Nebraska Sandhills region is one of North America’s largest remaining native grassland systems, but biologists and land managers largely lack the information needed to make informed decisions for habitat and population management of wildlife species. We sampled microhabitat variables at Western Meadowlark nest sites and random sites from 2006-2008 in the Nebraska Sandhills, and modeled nest-site selection as a function of vegetation structure and heterogeneity. Western Meadowlarks were more likely to select nests with increasing levels of litter, grass, forb, and sedge cover, heterogeneity of litter depth and vegetation height, and litter depth, indicating that nest sites with more vegetation cover and heterogeneity were selected relative to availability.

Selection also varied according to year and habitat type, with Western Meadowlarks more likely to select nest sites with less vegetation cover in the upland dry prairie habitat type compared to the more productive lowland wet meadow habitat type. Our results indicate that nest-site selection by Western Meadowlarks was habitat- and year-specific, highlighting the importance that variation in nest-site microhabitat availability is not only affected by livestock grazing and hay production, the two primary land uses in the

Nebraska Sandhills, but also by stochastic processes like precipitation that vary temporally. Habitat and population management decisions for grassland birds in the

Nebraska Sandhills should therefore allow for unpredictable variation in microhabitat availability by continuing to use land resources conservatively.

KEY WORDS: Western Meadowlark, Sturnella neglecta, Nebraska Sandhills, nest-site microhabitat, vegetation structure heterogeneity, habitat selection models

3 The prairie ecosystems of the Great Plains region in North America have been mostly replaced and fragmented with industrial agriculture and invasive herbaceous and woody plant species, and the historic disturbance regime of burning and grazing has been almost completely eliminated (Samson et al. 2004, Askins et al. 2007). This dramatic loss of habitat quantity and quality continues to cause population declines for most grassland bird species in North America (Peterjohn and Sauer 1999, Coppedge et al. 2001,

Scheiman et al. 2003, Grant et al. 2004, Brennan and Kuvlesky 2005, North American

Bird Conservation Initiative 2009). The Nebraska Sandhills is the largest remaining continuous mixed-grass prairie system in North America, approximating 5 million ha, and is almost exclusively managed for the production of beef cattle, with rotational grazing and wild-hay harvest as the predominant land uses (Bleed and Flowerday 1998).

The Nebraska Natural Legacy Project (NNLP, Schneider et al. 2005) is a comprehensive wildlife conservation strategy developed by the Nebraska Game and

Parks Commission in response to the U.S. Congress establishing the Wildlife

Conservation and Restoration Program and State Wildlife Grants Program. The NNLP identified key threats to conservation in the Nebraska Sandhills, which include 1) alteration of natural grazing and burning regimes, 2) wetland and wet meadow drainage,

3) spread of invasive species, 4) inter-basin water transfer, 5) lack of knowledge about the region’s biological diversity, 6) ranching economics, and 7) conversion and fragmentation by irrigated agriculture, energy development, and tree plantings (Schneider et al. 2005). Thus, the Nebraska Sandhills is a large, mostly unfragmented and native grassland system, and likely supports large populations of grassland bird species limited to fragmented and marginal habitat elsewhere. The lack of biological data in general, as

4 indicated by the NNLP, however, concurrent with increasing threats to the region’s natural resources, highlights the need for research to inform management and conservation planning.

The Western Meadowlark (Sturnella neglecta) is an iconic and conspicuous grassland generalist in central and western North America (Davis and Lanyon 2008), but

USGS Breeding Bird Survey data indicate populations are declining across most of the species’ breeding range, including in Nebraska (Sauer et al. 2008). The Nebraska

Sandhills is in the high-density center of the Western Meadowlark’s breeding range

(Sauer et al. 2008), but there is no information on habitat selection in the Nebraska

Sandhills for Western Meadowlarks or similar passerine species. Habitat-selection analysis is an important research tool for making decisions about habitat and population management because demographic responses vary with the resources made available by different land-management strategies (Strickland and McDonald 2006). Information on nest-site selection for the Western Meadowlark, however, is currently restricted to the northern Great Plains (Dieni and Jones 2003, Scheiman et al. 2003, Davis 2005, Koper and Schmiegelow 2006), and no such information exists for Western Meadowlarks or ecologically similar grassland passerines in the Nebraska Sandhills. Vegetation in the

Nebraska Sandhills is managed differently between the two primary habitat types, with rotational grazing in the upland, semi-dry prairie pastures, and wild-hay harvest in the lowland, sub-irrigated wet meadow pastures. Western Meadowlarks, however, nest continuously across the landscape but in higher densities in the wet meadows (Giovanni

2009). We therefore report habitat-specific probabilities of nest-site selection as a function of vegetation structure and associated land management, and then discuss the

5 implications for managing nesting habitat for Western Meadowlarks in the Nebraska

Sandhills.

METHODS

Study area. ― We sampled data in the central Nebraska Sandhills at the University of

Nebraska-Lincoln’s Gudmundsen Sandhills Laboratory (42° 4' N, 101° 27' W), which includes about 5,000 ha of upland prairie and about 500 ha of lowland wet meadows and stream corridor. The central Sandhills is considered semi-arid and subject to high variation in annual precipitation, receiving approximately 50 cm of precipitation annually

(Wilhite and Hubbard 1998). The topography of the central Sandhills is characterized by mostly linear dune formations averaging between 41-50 m in height, with south- and southeast-facing slopes generally having steep inclines, ranging from 20-28 degrees

(Kaul 1998, Swinehart 1998). The variation in topography and consequent soil structure, water-availability, and temperature creates distinct plant communities in the Sandhills

(Schacht et al. 2000). We categorized two primary habitat types for meadowlarks based on plant community characteristics and associated management regimes. The lowland habitat type consists of sub-irrigated, wet meadows characterized by Carex sedges, forbs, and cool-season grasses, including Melilotus sweet clovers, Alfalfa (Medicago sativa),

Poa bluegrasses, Timothy (Phleum pratense), Reedcanarygrass (Phalaris arundinacea),

Switchgrass (Panicum virgatum), Blue Grama (Bouteloua gracilis), Purple Lovegrass

(Eragrostis spectabilis), and rushes (Juncus spp.). The upland, semi-arid prairies characterized by warm-season grasses, forbs, and shrubs such as Prairie Sandreed

(Calamovilfa longifolia), Needle and Thread (Hesperostipa comata), Porcupine Grass

(Hesperostipa spartea), Little Bluestem (Schizachyrium scoparium), Sand Bluestem

6 (Andropogon gerardii), Prairie Junegrass (Koelaria macrantha), Western Ragweed

(Ambrosia psilostachya), Stiff Sunflower (Helianthus pauciflorus), Leadplant (Amorpha canescens), and Prairie Wildrose (Rosa arkansana) (Kaul 1998, Schacht et al. 2000).

The lowlands produce substantially more above-ground plant biomass (4,900 kg/ha) relative to the uplands (1,700 kg/ha) (Bauer 2004). The upland prairie pastures are managed with rotational grazing of cow-calf pairs at moderate stocking rates (range: 0.5-

1.7 animal unit months/ha) from May-October, whereas the lowlands are managed primarily for hay production, with a single annual hay harvest typically occurring in early to mid July.

Sampling.― We located Western Meadowlark (meadowlark) nests with the rope-drag method (Winter et al. 2003) and fortuitously while field sampling from May through

August in 2006, 2007, and 2008. We categorized nest and random sites into lowland and upland habitat types. Immediately following termination of nests, we characterized nest- site microhabitat within a 1-m2 sampling frame centered on the nest and at three points at the ends of 5-m radii pointing north, southwest, and southeast from the nest bowl. We measured litter depth and vegetation height with a tape at four points immediately outside of the nest structure and in the four corners of the sampling frame. We measured vegetation height by recording the height of the tallest vegetation touching a vertical tape.

We visually estimated the percentage of 1-m2 sampling frames with bare ground and cover by forbs, shrubs, sedges, grass, and litter. We characterized vegetation structure with the same sampling design at GIS-generated random points from May through

August at to obtain an index of availability for nest site microhabitat selection modeling, and we assumed that random sites were spatially independent of nest sites.

7 Statistics.― We used R v2.9 (R Development Core Team 2009) for statistical modeling.

We compared means and 95% confidence intervals (CI’s) for vegetation structure variables between 1) upland and lowland habitat types, and 2) nest sites and random sites.

We estimated heterogeneity of litter depth and vegetation height by calculating means of coefficients of variation for individual nest sites and random sites. We excluded vegetation structure variables that had at least one correlation coefficient of > 0.5 to minimize multicollinearity in our models. We modeled the probability of nest-site selection as a function of habitat-specific vegetation structure with logistic regression

(Manly et al. 2002, Keating and Cherry 2004, Davis 2005), and assumed that randomly selected sampling points did not contain nest sites. Specifically, we fit generalized linear models with binomial response distributions and logit-link functions to vegetation data from nest sites and random sites. We considered model coefficients (β’s) significant if

CI’s did not overlap with zero.

We included interactions of habitat type with each vegetation variable in our models to understand habitat-specific effects of vegetation structure on the probability of nest-site selection. Year was a secondary and lesser source of variation in vegetation structure, but models with vegetation-habitat-year interactions failed to converge so we averaged across years. Our a priori model set for nest-site selection included 1) a constant slope model, 2) seven models representing individual vegetation variables and interactions with habitat type, 3) a vegetation global model inclusive of all eight vegetation variables, 4) two models representing heterogeneity of litter depth and heterogensity of vegetation height, 5) a global heterogeneity model, and 6) a global model with all variables, for a total of 13 models. We used Akaike’s Information

8 Criterion with a second-order bias correction (AICc) and proportional model weights to select the best-fit model (Anderson 2008). We predicted probabilities of nest-site selection and CI’s by back-transforming logit-scale predictions with the inverse of the logit-link function,

exp() , 1 + exp() where x is equal to, for example, a mean selection value, and we limited our ranges of prediction with the ranges of values in our empirical data for each vegetation variable.

We predicted nest-site selection probabilities for both habitat types when interactions between habitat type and vegetation variables were significant. We report all means, model coefficients, and model predictions with 95% CI’s.

RESULTS

We sampled vegetation at 45, 63, and 71 Western Meadowlark nests, and 89, 228, and

162 random locations in 2006, 2007, and 2008, respectively, from 9 lowland wet meadow pastures (mean pasture area = 48 ± 5 ha, range = 26-66 ha) and 10 upland dry prairie pastures (mean pasture area = 234 ± 58 ha, range = 62-542 ha). Litter depth and vegetation height were greater at lowland sites relative to upland sites for all years (Fig.

1). Litter depth and vegetation height in lowland and upland sites were also greater in

2006 relative to 2007 and 2008 (Fig. 1). Averaged across years, percentage bare ground and litter cover were greater at upland sites, while percentage of shrub, sedge, forb, and grass were greater at lowland sites (Fig. 2). Litter depth heterogeneity at random sites and averaged across years was greater at upland sites (0.94 ± 0.08) compared to lowland sites (0.73 ± 0.06), and also varied among years within habitat types (Fig. 3). Vegetation height heterogeneity at random sites did not vary between lowland sites (0.32 ± 0.03) and

9 upland sites (0.36 ± 0.03), but was greater in 2006 than in 2007 and 2008 for both habitat types (Fig. 3). Litter depth at nest sites was greater relative to random sites for all years

(Fig. 4). Vegetation height was greater at nest sites relative to random sites in 2007,

2008, and averaged across years (Fig. 4). Nest sites had less bare ground and shrub cover relative to random sites, and more grass cover relative to random sites (Fig. 5). Litter depth CV in 2007 was greater at nest sites (1.06 ± 0.14) compared to random sites (0.70 ±

0.07), but was not different between nest sites and random sites in 2006, 2008, and averaged across years (Fig. 6). Vegetation height CV did not vary between nest sites and random sites for any years, but was greater in 2006 than in 2007 and 2008 for nest sites and random sites (Fig. 6).

We assessed effects of vegetation variables and made predictions from the global model for nest-site selection because it was the best fit with nearly 100% of the model weight (Table 1). Litter depth, heterogeneity of litter depth, heterogeneity of vegetation height, litter cover, grass cover, forb cover, and sedge cover positively affected the probability of nest-site selection averaged across habitat types (Table 2). The effects of litter depth, vegetation height, litter cover, grass cover, forb cover, and sedge cover also, however, interacted and varied with habitat type (Table 2, Figs. 8, 9, 10, 11). Litter depth and vegetation height positively affected the probability of nest-site selection in uplands, where litter depth is shallower and vegetation is shorter, but had relatively little influence in lowlands (Table 2, Fig. 8). Sedge cover positively affected the probability of nest-site selection in lowlands (Table. 2, Fig. 9), but sedges were relatively absent in uplands. The effects of litter cover, grass cover, and forb cover were stronger in lowlands at low and

10 moderate levels of cover, but were stronger in uplands at higher levels of cover (Figs. 10,

11).

DISCUSSION

Meadowlarks in the Sandhills selected nest sites with more vegetation cover relative to random sites. All mean values of vegetation variables that influenced nest-site selection varied between habitat types, and most effects of vegetation variables on the probability of nest-site selection interacted with habitat type, emphasizing the importance of accounting for sources of spatial variation in microhabitat use and availability. The probability of nest-site selection, on average, increased with increasing structural heterogeneity, litter depth, and cover by litter, grass, forbs, and sedges. Nest-site selection probabilities were higher in the upland habitat type compared to the lowland habitat type at equal values for vegetation variables, indicating that meadowlarks selected nest sites with relatively less vegetation cover and structural heterogeneity in the uplands where less cover is available. Conversely, meadowlarks selected higher levels of vegetation cover in the lowlands compared to the uplands at equal levels of selection probability. Previous research suggests that meadowlarks select nest sites with more cover and higher densities of live and dead vegetation relative to availability (Davis and

Lanyon 2008). Davis (2005) reported that meadowlarks selected nest sites with relatively less bare ground and more vegetation cover compared to random sites and four other sympatric grassland passerine species, and Dieni and Jones (2003) reported that meadowlarks selected nest sites with more vegetation cover compared to five other sympatric grassland passerine species and random sites. Scheiman et al. (2003) also reported less bare ground and deeper litter at nest sites relative to random sites, and

11 Granfors (1996) reported that Eastern Meadowlarks (Sturnella magna) in eastern Kansas selected nest sites with more litter cover and depth compared to random sites.

Shrub cover and vegetation height were the only vegetation parameters that did not affect nest-site selection in our study. Shrubs were present in the stream corridor sites in the lowland habitat type, and sparsely distributed throughout the upland dry prairies

(Kaul 1998, Schacht et al. 2000). With (1994) reported that nest survival for McCown’s

Longspurs (Calcarius mccownii) decreased significantly with increased shrub cover because Thirteen-lined Ground Squirrels (Spermophilus tridecemlineatus), a common nest predator of grassland passerine species (Pietz and Granfors 2000, Renfrew and Ribic

2003), focused activity around and under shrubs. Thirteen-lined Ground Squirrels are common in the upland dry prairies of the Sandhills (Freeman 1998), and were observed predating nest contents of grassland passerines (Giovanni 2009). Shrubs in the upland prairies, however, like meadowlark nests (Giovanni 2009), are sparsely distributed, potentially limiting our ability to detect any effect on nest-site selection. Managing for decreased shrub cover in grassland systems to benefit passerine populations may also conflict with management objectives for upland game species, such as Northern

Bobwhite (Colinus virginianus) and Lesser Prairie Chicken (Tympanuchus pallidicinctus), which often select shrub cover in grassland systems (Hiller and Guthrey

2005, Patten et al. 2005).

Nest densities of meadowlarks are greater in lowland wet meadows compared to upland dry prairies, likely because of uniformly greater vegetation cover in lowlands, but all nests exposed to harvesting activities failed, and hay harvest was consequently the second greatest source of mortality for nests and first-week fledglings after predation

12 (Giovanni 2009). The availability of the preferred nesting vegetation in the lowlands, however, is limited by hay harvesting, which abruptly removes most vegetation cover once per summer, typically in late June and early July. Thus, variations in the timing and frequency of harvests can affect nesting and renesting effort of meadowlarks by modifying vegetation variables that influence nest-site selection. For example, harvesting hay earlier in the season could decrease nesting effort and survival by concurring with peak nesting effort, but postponing harvest until after July decreases forage quality of hay for livestock. Increasing the frequency of harvest by adding an earlier or later harvest could have even stronger negative effects on meadowlark population growth by decreasing not only initial nesting effort during the early peak in nesting, but also renesting effort following the mid-summer harvest. Considering the low probability of nesting-period (28-day) survival for meadowlark nests at our study site (0.077, 95% CI:

0.039-0.133, n = 159 nests), the moderate probability of 28-day survival for meadowlark fledglings at our study site (0.608, 95% CI: 0.359-0.788, n = 46 fledglings) (Giovanni

2009), and the relatively short lifespan (3-5 years) of meadowlark adults (Davis and

Lanyon 2008), renesting is likely a critical component of the meadowlark’s breeding strategy and population growth.

Livstock grazing is an effective method of managing vegetation for wildlife habitat in grassland systems (Fuhlendorf and Engle 2001, Vavra 2005, Briske et al. 2008,

Derner et al. 2009). Grazing management, including variations of stocking rates and grazing systems, is an important tool for creating and maintaining the heterogeneity in vegetation structure that provides habitat for wildlife species that prefer different habitat characteristics (Fuhlendorf and Engle 2001, Vavra 2005, Kempema 2007, Derner et al.

13 2009). Meadowlarks nesting in the uplands were more likely to select nest sites with less cover than meadowlarks in the lowlands, but they still selected nest sites with more vegetation cover and structural heterogeneity compared to random sites in the uplands.

Upland dry prairie pastures in the Sandhills are typically managed with moderately stocked cow-calf herds in rotational grazing systems. Pastures in the central Sandhills managed with moderate stocking rates and rest-deferred rotational grazing systems, with longer rest periods between grazings, had greater heterogeneity in vegetation structure relative to pastures managed with short-duration rotational grazing (Kempema 2007).

Short-duration rotational grazing sytems use high stocking rates and short grazing and rest periods to achieve relatively continuous distribution of cattle across pastures, maximize forage use, and minimize the ability of cattle to preferentially select forage

(Fuhlendorf and Engle 2001, Briske et al. 2008, Derner et al. 2009). Short-duration rotational grazing sytems also, for the same reasons, tend to decrease structural heterogeneity in vegetation (Fuhlendorf and Engle 2001, Briske et al. 2008, Kempema

2007). Thus, implementing higher stocking rates and/or short-duration rotational grazing systems could decrease vegetation structural-heterogeneity and remove the already sparse vegetation cover used for nesting by meadowlarks and other grassland bird species in the uplands of the Sandhills (Kempema 2007). We therefore suggest 1) avoiding the use of high stocking rates and short-duration grazing systems, 2) using moderate stocking rates and continuous or rest-deferred rotational grazing systems, and 3) investing in experimental research to understand Sandhills-specific relationships between management strategies (grazing, haying, and burning), and habitat-selection and demographic responses by grassland bird species (e.g., Kempema 2007).

14 Our data also indicate that vegetation cover is generally less available in the uplands of the Sandhills relative to lowlands, and that vegetation structure in the uplands has relatively stronger effects on the probability of nest-site selection by meadowlarks.

Upland plant communities are also less able to tolerate drought conditions relative to lowlands, which are close to the water table, sub-irrigated, and consequently more consistently productive (Kaul 1998, Schacht et al. 2000, Bauer 2004). George et al.

(1992) observed that Western Meadowlarks nearly ceased nesting efforts during a a drought year in western North Dakota, and Kempema (2007) found a negative effect of drought on nest survival of Western Meadowlarks in the Sandhills. Thus, upland pastures should be managed even more conservatively relative to lowlands during drought years because uplands have less water available and a decreased ability to consistently produce available vegetation cover for nesting grassland birds.

Microhabitat selection studies for grassland bird species often include or at least discuss associated microclimate and survival characteristics such as thermoregulation and predation risk (With and Webb 1993, Hiller and Guthrey 2005, Patten et al. 2005,

Tieleman et al. 2008, Robertson 2009). Indeed, microhabitat selection analyses can most effectively facilitate decision-making for habitat and population management when interpreted in the context of how survival, productivity, population growth, or other demographic parameters respond. Giovanni (2009) found a positive effect of nest-site forb cover on daily nest-survival probability. Experimental research on the effects of fire on plant community composition and structure in the Sandhills has not been conducted, but Volesky and Connot (2000) observed that forb cover was greatest in the year following September burning, but decreased in the following two years. Churchwell et

15 al. (2008) reported that patch-burn management, relative to rotational graszing, increased structural heterogeneity of vegetation and the probability of nest survival for a grassland passerine species. Forb cover at nest and random sites was greater in the lowlands than at nests in the uplands at our study site, potentially suggesting that meadowlark nests in the lowlands have a higher probability of daily nest survival. The advantage of nesting in the more dense vegetation of the lowlands, however, may be offset by the annual hay production, which caused 100% nest mortality and 19% of confirmed nest failures

(Giovanni 2009).

Giovanni (2009) only detected one effect of a nest-site vegetation variable (a psotive effect of forb cover) among the numerous variables modeled. Indeed, most researchers reported few or no effects of nest-site microhabitat variables on daily nest survival for grassland bird species, especially relative to the typically overwhelming effects of temporal parameters (e.g., Howard et al. 2001, Davis 2005, Winter et al. 2005,

Kempema 2007, Reidy et al. 2009). Nonetheless, researchers should still continue investigating microhabitat-selection in order to provide the necessary habitat for grassland birds to nest. Since microhabitat variables rarely explain variation in vital rates for grassland bird species, researchers should focus survival sampling and modeling designs on macrohabitat and landscape variables such as habitat patch size or level of fragmentation (Johnson and Temple 1990, Winter and Faaborg 1999, Herkert et al. 2003,

Phillips et al. 2003, Horn et al. 2005, Skagen et al. 2005, Davis et al. 2006), prescribed burning frequency or spatial configuration (Johnson and Temple 1990, Churchwell et al.

2008), and distance to edge (Angelstam 1986, Johnson and Temple 1990, Winter et al.

2000, Vander Haegen et al. 2002, Horn et al. 2005). The Nebraska Sandhills is one of

16 North America’s largest remaining native prairie regions, and a potential population source for grassland bird species limited to fragmented and marginal habitat elsewhere.

Successful long-term strategies for management of habitat for grassland bird species in the Sandhills, however, will require research focused on the interactions of climate stochasticity and land management strategies (i.e., grazing, haying, and, potentially, burning) and the effects on habitat quality and selection, and demographic responses of grassland bird species.

ACKNOWLEDGMENTS

We thank L. Anderson, R. Byrnes, S. Groepper, B. Kramlich, B. Lawyer, J. Soper, L.

Thacker-Lynn, and J. Warner for their dedicated sampling efforts, and the staff at the

University of Nebraska-Lincoln’s (UNL) Gudmundsen Sandhills Laboratory for logistical support. We thank M. Post van der Burg and A. J. Tyre for modeling suggestions. Funding was provided by the UNL Foundation’s Burlington Northern

Water Science Endowment, Department of Agronomy and Horticulture, School of

Natural Resources, and Undergraduate Creative Activities and Research Experience

Program. We are grateful for additional support from the UNL Center for Great Plains

Studies’ Graduate Student Grant, the UNL Center for Grassland Studies’ A. W. Sampson

Graduate Fellowship in Range Science, and the Nebraska Chapter of The Nature

Conservancy’s J. E. Weaver Competitive Grant. This research was supported by Hatch

Act funds through the University of Nebraska Agricultural Research Division, Lincoln,

Nebraska.

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25 Table 1. Models for probability of nest-site selection for Western Meadowlarks in the

Nebraska Sandhills, USA, 2006-2008. Model-selection parameters include the number of parameters (K), deviance (Dev), log-likelihood (Loge(L)), Akaike Information

Criterion scores for small sample sizes (AICc), relative model differences in AICc (Δi), and relative model weights (wi).

======

Model K Dev Loge(L) AICc Δi wi

______

Global 20 364 - 182 399 0 1.00

Vegetation globala 16 505 - 253 534 135 0.00

Litter depth 4 672 - 336 680 281 0.00

Shrub cover 4 680 - 340 687 288 0.00

Heterogeneity global 6 693 - 346 704 305 0.00

Litter depth heterogeneity 4 717 - 358 724 325 0.00

Grass cover 4 725 - 362 733 334 0.00

Veg. height heterogeneity 4 732 - 366 740 341 0.00

Litter cover 4 740 - 370 748 349 0.00

Vegetation height 4 741 - 370 749 350 0.00

Forb cover 4 762 - 381 770 371 0.00

Null 1 768 - 384 770 371 0.00

Sedge cover 4 766 - 383 774 375 0.00

______a Does not include the two heterogeneity parameters.

26 Table 2. Logit-scale model coefficients (β) with lower (LCL) and upper (UCL) 95% confidence limits and standard errors (SE) for the global model of nest-site selection probability for Western Meadowlarks in the Nebraska Sandhills, USA, 2006-2008.

Asterisks indicate coefficients with confidence intervals that do not overlap zero.

======

Parameter 95% LCL β 95% UCL SE

______

Intercept (lowland) - 36.811 - 26.373* - 15.934 5.326

Litter depth heterogeneity 1.200 2.023* 2.846 0.420

Veg. height heterogeneity 3.511 5.852* 8.192 1.194

Litter depth * hab. (upland) 1.065 1.843* 2.622 0.397

Veg. height * hab. (upland) 0.005 0.074* 0.143 0.035

Litter cover * hab. (upland) - 0.258 - 0.142* - 0.025 0.059

Sedge cover * hab. (upland) - 0.624 - 0.337* - 0.050 0.146

Grass cover * hab. (upland) - 0.248 - 0.135* - 0.021 0.058

Forb cover * hab. (upland) - 0.241 - 0.117* 0.008 0.064

Litter depth 0.051 0.362* 0.673 0.159

Litter cover 0.090 0.198* 0.306 0.055

Grass cover 0.129 0.233* 0.336 0.053

Forb cover 0.140 0.248* 0.356 0.055

Sedge cover 0.122 0.230* 0.338 0.055

Vegetation height - 0.044 - 0.017 0.009 0.014

27 Shrub cover - 2.587 - 1.068 0.451 0.775

Shrub cover * hab. (upland) - 0.404 1.118 2.640 0.776

Litt. depth het. * hab. (up.) - 0.856 0.522 1.900 0.703

Veg. height het. * hab. (up.) - 4.743 - 1.429 1.886 1.691

Habitat type (upland) - 1.278 10.296 21.869 5.905

______

28 5 Lowland Upland 4 95% CI) 95%

± 3

1 Litter depth (cm depth Litter

0 2006 2007 2008 Total

60 Lowland Upland 50 95% CI) 95%

± 40

Vegetation height (cm height Vegetation 0

2006 2007 2008 Total

Fig. 1. Litter depth and vegetation height (centimeters ± 95% confidence intervals) for random sites according to Western Meadowlark habitat type and year in the Nebraska

Sandhills, USA, 2006-2008.

29 50 Lowland Upland 40 95% 95% CI)

± 30

10 Ground cover (% (% cover Ground

0 Shrub Sedge Forb Bare Litter Grass

Fig. 2. Ground cover (± 95% confidence intervals) at random sites according to Western

Meadowlark habitat type in the Nebraska Sandhills, USA, 2006-2008.

30 1.40 Lowland Upland 1.20

1.00 95% CI) 95%

± 0.80

0.60

0.40

Litter depth depth ( CV Litter 0.20

0.00 2006 2007 2008 Total

0.60 Lowland Upland 0.50 95% CI) 95%

± 0.40

0.30

0.20

0.10 Vegetation height CV ( CV height Vegetation 0.00

2006 2007 2008 Total

Fig. 3. Litter depth and vegetation height coefficients of variation (CV ± 95% confidence intervals) for random sites according to Western Meadowlark habitat type and year in the

Nebraska Sandhills, USA, 2006-2008.

31 5 Nest Random 4 95% CI) 95%

± 3

Litter depth (cm depth Litter 1

0 2006 2007 2008 Total

60 Nest Random 50 95% CI) 95%

± 40

10 Vegetation height (cm height Vegetation 0

2006 2007 2008 Total

Fig. 4. Litter depth and vegetation height (centimeters ± 95% confidence intervals) for

Western Meadowlark nest sites and random sites in the Nebraska Sandhills, USA, 2006-

2008.

32 60 Nest Random 50

95% CI) 95% 40 ±

Ground cover (% (% cover Ground 10

0 Shrub Sedge Forb Bare Litter Grass

Fig. 5. Ground cover (± 95% confidence intervals) for Western Meadowlark nest sites and random sites in the Nebraska Sandhills, USA, 2006-2008.

33 1.40 Nest Random 1.20

1.00 95% 95% CI)

± 0.80

0.60

0.40 Litter CV depth ( Litter 0.20

0.00 2006 2007 2008 Total

0.60 Nest Random 0.50 95% CI) 95%

± 0.40

0.30

0.20

0.10 Vegetation height CV ( CV height Vegetation

0.00 2006 2007 2008 Total

Fig. 6. Litter depth and vegetation height coefficients of variation (CV ± 95% confidence

intervals) for Western Meadowlark nest sites and random sites in the Nebraska

Sandhills, USA, 2006-2008.

1.00

0.80 95% CI) 95% ± 0.60

0.40

0.20 Probability of selection ( selection of Probability 0.00 0.0 0.4 0.8 1.2 1.6 2.0 2.4 2.8 Litter depth CV

1.00

0.80 95% CI) 95% ±

0.60

0.40

0.20 Probability of selection ( ofselection Probability

0.00 0.0 0.2 0.4 0.6 0.8 1.0 1.2 Vegetation height CV

Fig. 7. Effects of heterogeneity (coefficient of variation, CV) of litter depth and vegetation height on the probability of nest-site selection by Western Meadowlarks in the

Nebraska Sandhills, USA, 2006-2008.

1.00

0.80 95% CI) 95%

± Upland 0.60 Lowland 0.40

0.20 Probability of selection ( selection of Probability 0.00 0 1 2 3 4 5 6 7 8 Litter depth (cm)

1.00 Upland 0.80 95% CI) 95%

± Lowland 0.60

0.40

0.20 Probability of selection ( selection of Probability 0.00 0 10 20 30 40 50 60 70 80 90 Vegetation height (cm)

Fig. 8. Effects of litter depth and vegetation height on the probability of nest-site selection by Western Meadowlarks in the Nebraska Sandhills, USA, 2006-2008.

1.00

0.80 95% CI) 95% ± 0.60

0.40

0.20 Probability of selection ( ofselection Probability 0.00 0 6 12 18 24 30 36 42 48 54 60 66 72 Sedge cover (%)

Fig. 9. Effects of sedge cover on the probability of nest-site selection by Western

Meadowlarks in lowland wet meadows in the Nebraska Sandhills, USA, 2006-2008.

1.00 Upland 0.80 95% CI) 95%

± Lowland 0.60

0.40

0.20 Probability of selection ( selection of Probability 0.00 0 10 20 30 40 50 60 70 80 90 Litter cover (%)

1.00 Upland 0.80 95% CI) 95%

± Lowland 0.60

0.40

0.20 Probability of selection ( ofselection Probability

0.00 0 10 20 30 40 50 60 70 80 90 Grass cover (%)

Fig. 10. Habitat-type specific effects of litter cover and grass cover on the probability of nest-site selection by Western Meadowlarks in the Nebraska Sandhills, USA, 2006-2008.

1.00 Upland 0.80 95% CI) 95%

± Lowland 0.60

0.40

0.20 Probability of selection ( selection of Probability 0.00 0 10 20 30 40 50 60 70 Forb cover (%)

Fig. 11. Habitat-type specific effects of forb cover on the probability of nest-site selection by Western Meadowlarks in the Nebraska Sandhills, USA, 2006-2008.

39 ASSESSMENT OF MICROHABITAT, RESEARCHER-ACTIVITY, AND

TEMPORAL EFFECTS ON SURVIVAL OF WESTERN MEADOWLARK

(STURNELLA NEGLECTA) NESTS IN THE NEBRASKA SANDHILLS

MATTHEW D. GIOVANNI,1, 2, 4 LARKIN A. POWELL,1 WALTER H. SCHACHT,2

MAX POST VAN DER BURG,3AND ANDREW J. TYRE1

1School of Natural Resources, University of Nebraska-Lincoln, Lincoln, Nebraska 68583

2Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln,

Nebraska 68583

3Department of Ecology, Evolution, and Organismal Biology, Iowa State University,

Ames, IA 50011 USA.

[email protected]

40 ABSTRACT. ― Populations of most grassland bird species in North America continue to decline according to long-term monitoring data and vital rate studies. The Nebraska

Sandhills region the largest remaining continuous mixed-grass prairie system, and one the largest remaining native grassland systems in North America, but minimal information exists for making management decisions for grassland bird species. We present information on nest survival and productivity for Western Meadowlarks in the Nebraska

Sandhills. We directly and remotely monitored a total of 200 nests from 2006 to 2008, and found no difference in daily nest survival due to researchers directly visiting nest sites. Nest predation accounted for 72% of 140 nest failures with known fates. Forb cover at nest sites had a positive effect on daily nest survival, but researcher, temperature, precipitation, and other nest-site microhabitat effects were highly variable. Nest survival varied quadratically with age, decreased with ordinal day, and effects of age and ordinal day were interactive. Like other studies focused on the effects of nest-site microhabitat on nest survival, we only identified one influential nest-site microhabitat variable but numerous influential temporal variables, indicating that nest survival may be more effectively modeled with nest-predator related variables and habitat variables at larger spatial scales. We provide nesting ecology insights for a species of conservation concern in a relatively unstudied system, and reinforce the need for ornithologists to directly link predator dynamics with nest-site selection and survival for grassland bird species.

KEY WORDS: Western Meadowlark, Sturnella neglecta, Nebraska Sandhills, demographics, daily nest survival, nest predators, nest-site microhabitat, researcher effects, age effects, day effects

41 The prairie ecosystems of the Great Plains region in North America have been mostly replaced and fragmented with industrial agriculture and invasive herbaceous and woody plant species, and the historic disturbance regime of burning and grazing has been almost completely eliminated (Samson et al. 2004, Askins et al. 2007). This dramatic loss of habitat quantity and quality continues to cause population declines for most grassland bird species in North America (Peterjohn and Sauer 1999, Coppedge et al. 2001,

Scheiman et al. 2003, Grant et al. 2004, Brennan and Kuvlesky 2005, North American

The Nebraska Natural Legacy Project (NNLP, Schneider et al. 2005) is a comprehensive wildlife conservation strategy developed by the Nebraska Game and

Parks Commission in response to the U.S. Congress establishing the Wildlife

42 indicated by the NNLP, however, concurrent with increasing threats to the region’s natural resources, highlights the need for research to inform management and conservation planning.

The Western Meadowlark (Sturnella neglecta) is an iconic and conspicuous grassland generalist in central and western North America (Davis and Lanyon 2008).

The NNLP does not currently designate the Western Meadowlark as a high conservation priority Tier 1 or Tier 2 at-risk species (Schneider et al. 2005), but Breeding Bird Survey data indicate populations are declining across most of the species’ breeding range, including in Nebraska (Sauer et al. 2008). The Nebraska Sandhills is in the high-density center of the Western Meadowlark’s breeding range (Sauer et al. 2008), but there is only one report on nest survival from the Nebraska Sandhills for Western Meadowlarks, and sampling was limited to nests in upland prairies (Kempema 2007). Giovanni (2009) reported that Western Meadowlark nest densities were substantially higher in the lowland wet meadows. Wet meadows in the Nebraska Sandhills, however, are also the primary source of supplemental wild hay for feeding livestock during the winter, but no research to-date has investigated how hay harvesting affects nest survival of Western

Meadowlarks or other grassland passerines using wet meadows in the Sandhills.

Only a few researchers have provided information on demographic responses of

Western Meadowlarks to land management and associated habitat. Johnson and Temple

(1990) reported nest survival and brood-parasitism rates as a function of habitat patch- size, distance to wooded edge, and burn history in western Minnesota. Perhaps most closely related to our study, Kempema (2007) estimated daily nest survival for Western

Meadowlarks in the central Nebraska Sandhills, but their sampling was restricted to

43 upland dry prairies where nest densities are significantly less relative to lowland wet meadows (Giovanni 2009). Kempema (2007) did, however, detect a negative effect of drought on daily nest survival, possibly mediated by nest predators and lack of sufficient nesting cover. Koper and Schmiegelow (2006) modeled nest survival as a function of land use and habitat structure in a mixed-grass prairie system of southern Alberta, and

Dickinson et al. (1987) related nesting biology parameters to female-pairing status and time-of-season in southern Manitoba. Davis (2003, 2005) and Davis et al. (2006) have arguably provided the majority of research focused on demographics for Western

Meadowlarks, including nest-site selection and survival modeling, and estimates of nest productivity and parasitism for populations in the mixed-grass prairies of southern

Saskatchewan. Thus, most information on demographics for Western Meadowlarks is from northern Great Plains grassland systems in the Canadian Provinces.

Despite the importance of the Nebraska Sandhills as a major Great Plains prairie remnant, surprisingly little information exists for managing grassland bird species in the

Nebraska Sandhills. We report 1) information on clutch-initiation timing, nest productivity, causes of nest failure, partial nest predations, and brood parasitism by

Brown-headed Cowbirds (Molothrus ater), and 2) model-based predictions for effects of nest-site microhabitat, temporal, climate, and researcher-related variables on daily nest survival.

METHODS

Study area. ―We monitored Western Meadowlark (meadowlark) nests and quantified nest-site habitat from 2006-2008 in the central Nebraska Sandhills (Sandhills) at the

University of Nebraska-Lincoln Gudmundsen Sandhills Laboratory (42° 4' N, 101° 27'

44 W), which includes about 5,000 ha of upland prairie and about 500 ha of lowland wet meadows and stream corridor. The Sandhills is considered semi-arid and subject to high variation in annual precipitation, receiving approximately 50 cm of precipitation annually

(Wilhite and Hubbard 1998). The topography of the Sandhills is characterized by mostly linear dune formations averaging between 41-50 m in height, with south- and southeast- facing slopes generally having steep inclines, ranging from 20-28 degrees (Swinehart

1998). The variation in topography and consequent soil structure, water-availability, and temperature creates distinct plant communities in the Sandhills (Schacht et al. 2000).

Lowland, sub-irrigated wet meadows are characterized by Carex sedges, forbs, and cool- season grasses, while upland semi-arid prairies are characterized by warm-season grasses, forbs, and shrubs (see Kaul 1998, Schacht et al. 2000, and Giovanni 2009 for more detail). The upland prairie pastures are managed with rotational grazing of cow-calf pairs at moderate stocking rates (range: 0.5-1.7 animal unit months/ha) from May through

October. The wet meadows are managed primarily for hay production, with a single annual hay harvest typically occurring in early to mid July.

Nest locating and monitoring.― We located meadowlark nests with the rope-drag method (Winter et al. 2003, Giovanni 2009) and by fortuitous encounters in wet meadow pastures and upland prairie pastures from May through July in 2006, 2007, and 2008. We recorded nest locations with global positioning systems, and marked nest sites for relocation with survey flags 5 m north and south of the nest. We recorded the presence or absence of meadowlark eggs, nestlings, and adults, and Brown-headed Cowbird eggs or nestlings. We estimated the age of eggs with the candling method (Lokemoen and

45 Koford 1996), and the age of nestlings by plumage development if a nest was discovered post-hatch (Podlesak and Blem 2002, Jongsomjit et al. 2007).

We monitored nests directly and remotely to test for potential effects of researcher activity on daily nest survival. We visited nests directly every 2-3 days to record nest fate, and we monitored nests remotely by deploying temperature data-loggers to every other nest during the 2007 and 2008 breeding seasons. Temperature data-loggers can accurately determine nest status by measuring thermoregulation-mediated nest-bowl temperatures (Hartmann and Oring 2006). We programmed iButton (Maxim Integrated

Products, Inc. 2009) temperature data-loggers with the iButton Viewer software and

DS9490B USB Port Adapter (Maxim Integrated Products, Inc. 2009) to record a temperature value every 0.5 h. We affixed data-loggers to standard golf tees as stakes, and maintained a prepared set of data-loggers during field sampling for immediate deployment upon discovering a new nest. We inserted data-loggers sub-flush with the bottom of nest bowls and buffered surfaces of data-loggers with nest material to prevent egg breakage. We returned to nests to recover data-loggers on or after the date we estimated nests would fledge according to the estimated age. Thus, remotely monitored nests were visited only once while active for deployment of temperature data-loggers.

We plotted nest bowl and ambient temperatures against time to obtain nest-termination dates and survival intervals (Hartmann and Oring 2006, Weidinger 2006).

Nest-site sampling.―We characterized nest-site vegetation structure after nest termination within a 1-m2 sampling frame centered on the nest bowl and three points 5 m from the nest at the ends of radii pointing north, southwest, and southeast. We measured litter depth and maximum vegetation height at points in the four cardinal directions

46 immediately outside of the nest structure (or in the center of the sampling frame for 5-m nest points) and in the four corners of the sampling frame. We measured vegetation height by recording the height of the tallest vegetation touching a vertical tape. We visually estimated the proportion of forbs, shrubs, sedges, grass, litter, cow pie, and bare ground in a 1-m2 sampling frame. We used a visual obstruction pole (Robel et al. 1970) to estimate vegetation density around the nest bowl and 5-m points. We measured concealment of the nest bowl interior (Davis and Sealy 1998) with a visual obstruction cube graduated into nine 1-cm squares per side. We estimated nest-bowl concealment by counting the number of squares fully visible from a 1-m distance and height in the four cardinal directions and from 1 m directly above the nest. Lastly, we used data from an on-site climate monitoring station (High Plains Regional Climate Center 2009) to estimate daily averages of hourly maximum temperature and precipitation for daily nest survival modeling. We used averages for each nest survival interval per nest, and averages of the three days prior to initial nest status checks.

Statistical analysis.― We used R v2.9 (R Development Core Team 2009) for statistical modeling. We report nest productivity parameters, including 1) proportion of nests that hatched, 2) maximum number of eggs per nest pre-hatching, 3) maximum number of nestlings per nest that hatched and per nesting attempt, 5) proportion of nests that lost eggs to partial predation per nesting attempt, 6) number of eggs lost to partial predation per nest subjected to partial predation and per nesting attempt, 7) proportion of nests that hatched a clutch and produced nestlings, 8) proportion of nests <20-days old that lost nestlings to partial predation, and 9) number of nestlings lost to partial predation per nest subjected to partial predation and per nesting attempt.

47 We were able to detect egg-hatching based on nest bowl temperature data, but we were not able to determine the number of eggs hatched. Therefore, rather than assuming all eggs hatched, we excluded nests that were monitored with temperature data-loggers from the descriptive statistics for nestlings. We also excluded clutch size observations from nests that were 1) < 4-days old and monitored with temperature data-loggers, and 2)

< 4-days old and failed before the next observation, since clutch size may have continued to increase after the initial (and only) observation. We fit a generalized linear model

(with a Poisson distribution and log-link function) to the maximum number of eggs observed per nest > 4-days old because clutch size often decreases as the breeding season progresses (Dickinson et al. 1987, Davis 2003, Giocomo et al. 2008).

We report estimates of apparent nest success (number of nests that fledged ≥ 1 nestling, as confirmed with video-monitoring or radio-telemetry in 2006 and 2007, divided by the number of total nesting attempts), cause-specific nest failures, and unknown nest fates. We confirmed a nest as predated when eggs or nestlings were absent from the nest at ≤ 24-days old. Nestlings typically fledge the nest at 12-15 days of age post-hatch, and usually hatch 12-15 days after a clutch-laying period of 3-5 days (Davis and Lanyon 2008). Thus, we classified nests ≥ 25-days old as either a confirmed fledging, confirmed failure, or unknown fate. Among nests confirmed as predated, we present the frequency of nest predations categorized by estimated nest age in a histogram.

We used Tyre and Post van der Burg’s (2005) package for R v2.9 (R

Development Core Team 2009) to fit logistic-exposure nest-survival models (Shaffer

2004) and estimate the probability of daily nest survival. The logistic-exposure model is

48 a generalized linear model with a binomial response distribution for nest fate (i.e., 1 = nest is active and 0 = nest is terminated) and a modified logistic-regression link function,

() = , 1 − where θ is the probability of survival, and the exponent with t (length of observation interval or days exposed) accounts for the length of exposure intervals between nest survival observations (Shaffer 2004). Our a priori model set for daily nest survival included models with parameter groups representing 1) temporal effects (age, age2, ordinal day, age*ordinal day, and year), 2) researcher-based effects (i.e., directly monitoring nests versus remotely monitoring nests with temperature data-loggers), 3) weather effects (maximum daily temperature and precipitation), and 4) nest-site microhabitat effects, including nest-bowl concealment, vegetation density, litter depth, maximum vegetation height, cover by litter, forb, shrub, sedge, grass, and bare ground

(Dieni and Jones 2003, Davis 2005). We expected the temporal model or a model containing the temporal-parameter group to explain the most variation in nest survival

(Davis 2003, Davis 2005, Grant et al. 2005). We also expected potential negative effects of temperature and precipitation on nest survival (George et al. 1992, Kempema 2007).

Thus, our total a priori model set included a constant model, a global model, and models with individual parameter groups and all combinations of parameter groups for a total of

16 models (Table 1).

Making inferences from nest-survival models containing only management- relevant, non-temporal parameters (e.g., nest-site vegetation variables) can be difficult because competing nest-survival models containing only temporal parameters (e.g., age,

49 season, and year) are often the best fit in AIC-based model-selection methods (Howard et al. 2001, Davis 2005, Grant 2005, Winter et al. 2005, Kempema 2007, Reidy et al. 2009).

In this context, temporal parameters can be considered “nuisance” parameters relative to management-relevant parameters. Thus, including temporal parameters in a model with management-relevant parameters is important to maximize model fit, but additional variation as explained by management-relevant parameters should also be assessed. We therefore fit a daily nest-survival model approximating the best-fit model from our nest- site selection analysis (Giovanni 2009) to directly link nest-site selection with nest survival. Our a posteriori nest-survival model with nest-site vegetation variables included the temporal parameters from the a priori temporal model, in addition to nest- bowl concealment, litter depth, litter-depth heterogeneity, vegetation height, vegetation- height heterogeniety; bare ground and cover by litter, forbs, shrubs, sedges, and grass; and habitat type as a categorical variable. We report coefficients and 95% confidence intervals (CI’s) from the a posteriori nest-site vegetation model of daily nest survival to assess additional variation explained by nest-site vegetation, but we predict and report daily and 28-day nest-survival estimates from the best-fit a priori model.

We excluded nest-site microhabitat variables that had at least one correlation coefficient of > 0.5 to minimize multicollinearity in our models. We used Akaike’s

Information Criteria corrected for small sample sizes, deviances, log-likelihoods, and proportional model weights (Anderson 2008) to select the best-fit model of daily nest survival. We present 1) daily and 28-day (nesting period) nest-survival probabilities with

95% CI’s, and 2) daily nest-survival model coefficients (β’s) with 95% CI’s. We back-

50 transformed log-of-odds (logit) scale means and CI’s to probability values with the inverse logit function,

exp() , 1 + exp() where x is equal to, for example, a mean daily survival value. We used bootstrapping to estimate 28-day nest-survival probabilities and 95% CI’s by randomly drawing 10,000 logit-scale daily nest-survival values and quantiles from a normal distribution confined by the logit-scale daily nest-survival mean and SE as predicted from the top-ranked nest- survival model. We report individual means with SE’s, comparative means with 95%

CI’s, and all mean proportions with 95% CI’s for binomial proportions (Crawley 2005).

We considered model β’s significant if 95% CI’s did not overlap with zero.

RESULTS

We found 218 nests in 9 lowland wet meadow pastures (mean pasture area = 48 ± 5 ha, range = 26-66 ha) and 10 upland dry prairie pastures (mean pasture area = 234 ± 58 ha, range = 62-542 ha). We observed clutch initiations as early as 10 May and as late as 26

July, but clutch initiations likely started in March or April (Dickinson 1987, Davis and

Lanyon 2008). The 2006 breeding season was hotter and drier than in 2007 and 2008.

Clutch-initiation frequency decreased as the breeding season progressed (Fig. 1), but maximum observed clutch size/week did not (β = - 0.014 ± 0.025 95% CI). Mean maximum observed clutch size was 4.1 ± 0.1. The mean proportion of nests that hatched eggs was 0.44 ± 0.04, and maximum observed brood sizes for nesting attempts that hatched eggs (nestlings/nest hatched) and all nesting attempts (nestlings/nest) were 3.2 ±

0.1 and1.5 ± 0.1, respectively. The mean proportion of nests that lost eggs to partial predation and mean number of eggs lost from partial-clutch predations was 0.12 ± 0.03

51 and 1.4 ± 0.3, respectively. The mean proportion of nests that hatched following partial- clutch predations appeared to be lower than the mean proportion of nests that hatched without partial predations (Fig. 2), but uncertainty was high due to the small sample size of partially predated clutches. The mean proportion of nests with Brown-headed

Cowbird eggs and nestlings was 0.16 ± 0.03 and 0.02 ± 0.01, respectively. The number of Brown-headed Cowbird eggs per parasitized nest and per nesting attempt was 1.7 ± 0.1 and 0.3 ± 0.1, respectively. The number of Brown-headed Cowbird nestlings per parasitized nest was 0.2 ± 0.1. The mean proportion of parasitized meadowlark nests that either rejected Brown-headed Cowbird eggs or were partially predated was 0.25 ± 0.08, and the number of eggs rejected or partially predated per parasitized nest was 1.9 ± 0.4.

We obtained survival data for 206 nests and associated nest-site microhabitat data for 152 of those nests. We obtained survival data from 41 of the 206 nests with temperature data-loggers during the 2007 and 2008 seasons, and estimated nest termination dates by comparing nest bowl temperature with ambient temperature (Fig. 3).

We confirmed 140 nest failures from 206 nest attempts (32 ± 6% apparent nest success) but we were unable to confirm failure or fledging success for 50 (24 ± 6%) of the 206 nest fates. Among confirmed nest failures, the proportion of failures from predation (72

± 7%) was significantly greater relative to hay harvest (19 ± 6%), flooding (7 ± 4%), and trampling by cattle (2 ± 2%). Predators, haying, and flooding accounted for 57 ± 11%,

31 ± 10%, and 12 ± 7% of confirmed nest failures in the lowland wet meadow pastures, respectively, while predators accounted for 100% of confirmed nest failures in the upland dry prairie pastures. The frequency of confirmed nest predations increased as nests approached hatching age, peaked at hatching age (15-17 days), and then decreased

52 thereafter (Fig. 4). The mean age for all confirmed nest failures was 16.5 ± 0.5 days

(SE). All nests subjected to hay harvesting (n = 26) failed from cutting, raking, or baling activities.

We excluded litter cover from our nest survival models because it was correlated with forb cover (r = - 0.55), grass cover (r = - 0.54), vegetation density (r = - 0.46), and litter depth (r = 0.42). We also excluded vegetation density because it was correlated with maximum vegetation height (r = 0.46), litter cover (r = - 0.46), and forb cover (r =

0.50). The temporal model had the largest model-selection probability (wi = 0.47), and the eight models containing the temporal parameter group (age, age2, day, age*day, year) collectively accounted for 0.86 of the model set’s weight (Table 1). The temporal + researcher model was the the second best fit but the additional parameter for effects of researcher activity was highly uncertain (β = 0.098 ± 0.557 95% CI) and did not decrease model deviance or increase model log-likelihood relative to the temporal model (Table

1). The temporal + climate model was the third best fit, and maximum daily temperature from 1 May through 1 August was significantly greater in 2006 (28.5 ± 1.3 °C) compared to 2007 (26.3 ± 1.0 °C) and 2008 (25.7 ± 1.3 °C), and total precipitation was lesser in

2006 (10.2 cm) compared to 2007 (20.5 cm) and 2008 (27.5 cm). However, the additional parameters for precipitation (β = - 0.070 ± 0.254 95% CI) and temperature (β =

- 0.006 ± 0.066 95% CI) were highly uncertain and did not decrease model deviance or increase model log-likelihood relative to the temporal model (Table 1).

We therefore selected the temporal as the best fit because of the conspicuous nesting effect of the temporal parameter group within the model set. The temporal model indicated a quadratic effect of age, a negative effect of ordinal day, interactive effects of

53 age and ordinal day, a positive effect of forb cover, and a positive effect of 2008 relative to the intercept, 2006 (Table 2). Predictions from the temporal model as a function of age2 indicated daily nest-survival probability decreased after clutch initiation, minimized around hatching age, and then increased until nestlings fledged (Fig. 5). Ordinal-day had a negative effect (Table 2), indicating that nest survival decreased as the breeding season progressed. We illustrated the age*day interaction by predicting nest survival from the temporal model for the range of nest ages (1-28 days) on ordinal day 20 (21 May) and 80

(20 July), and then plotted nest survival as a function of age (Fig. 6).

Our a posteriori assessement of the nest-site vegatation + temporal model indicated that forb cover was the only nest-site vegetation coefficient with a 95% CI that did not overlap with zero, and had a positive effect on daily nest-survival probability

(Table 3, Fig. 7). Forb cover was greater at nests in the lowland wet meadows (20.4 ±

3.4%, n = 91 nests) relative to nests in the upland dry prairies (10.0 ± 2.3%, n = 68 nests).

Litter cover, although not included in our models, may have had a negative effect on daily nest survival since it was negatively correlated with forb cover (r = - 0.55). Litter cover was greater at nests in the upland dry prairies (43.3 ± 3.6%, n = 68 nests) relative to nests in the lowland wet meadows (13.6 ± 2.5%, n = 91 nests).

Predicted daily nest survival from the researcher + temporal model did not vary between nests monitored directly (0.924, 95% CI: 0.891-0.948, n = 118 nests, 2006-

2008) and nests monitored remotely with temperature data-loggers (0.931, 95% CI:

0.888-0.958, n = 41 nests, 2007-2008). Predicted daily and 28-day nest survival from the temporal model was 0.884 (95% CI: 0.836-0.919) and 0.031 (95% CI: 0.006-0.094), respectively, in 2006 (n = 42 nests), 0.926 (95% CI: 0.897-0.947) and 0.117 (95% CI:

54 0.048-0.221), respectively, in 2008 (n = 66 nests), and overall was 0.912 (95% CI: 0.890-

0.931) and 0.077 (95% CI: 0.039-0.133), respectively, for 2006-2008 (n = 159 nests).

DISCUSSION

Nest survival and productivity for Western Meadowlarks at our sampling sites in the

Nebraska Sandhills was limited primarily by predators and hay harvesting. Given the high rates of nest predation, we suspect that our estimated proportion of confirmed nest failures due to predation may even be conservative because 1) some of the nearly fledged nests that we categorized as having unknown fates were likely predated, and 2) some of the nests categorized as failed from haying activities may have been predated in the one or two days after the last nest check but before hay harvest. Predators consistently limit nest survival for Western Meadowlarks (Johnson and Temple 1990, Davis 2003,

Kempema 2007), Eastern Meadowlarks (Sturnella neglecta) (Roseberry and Klimstra

1970, Knapton 1988, Granfors et al. 1996, Herkert et al. 2003, Renfrew and Ribic 2003,

Gallagan et al. 2006, Giocomo et al. 2008), and similar grassland passerine species (With

1994, Pietz and Granfors 2000, Jehle et al. 2004, Winter et al. 2004, Skagen et al. 2005,

Yackel-Adams et al. 2007, Sandercock et al. 2008). We detected a quadratic effect of age on daily nest survival, as reported for grassland passerines by Davis (2003, 2005),

Jehle et al. (2004), Grant et al. (2005), and Churchwell et al. (2008), and a negative effect of ordinal day, as reported for grassland passerines by Dickinson et al. (1987), Jehle et al.

(2004), Davis (2005), Gallagan et al. (2006), and Sandercock et al. (2008). We also detected an interactive effect of age and ordinal day, indicating that nestlings (but not eggs) have a higher probability of survival later in the breeding season. Considering the predator-limited nature of our nest sample, we suspect these strong temporal patterns are

55 driven by seasonal variation in the abundance and activities of nest-predator species, further emphasizing the importance of understanding predator dynamics for managing populations of breeding grassland passerine species.

We found no difference in predator-limited daily nest survival for nests directly monitored (visited bi-daily) and remotely monitored with temperature data-loggers. The temporal + researcher model for daily nest survival was the second-ranked model with

21% of the model set’s weight, but we identified the categorical variable for researcher effect (i.e., direct versus remote nest-monitoring) as a “pretending variable” (Anderson

2008) because it decreased the model’s log-likelihood and increased the model’s deviance. Moreover, the researcher-effect coefficient from the temporal + researcher model was highly uncertain, and daily nest survival probability predictions from the researcher and temporal model did not vary between nests monitored directlyand nests monitored remotely with temperature data-loggers. Ornithologists have long been trying to determine if research activities, such as monitoring and measuring nest sites, affect nest survival (e.g., Evans and Wolfe 1967), but evidence is mostly lacking for grassland passerine species. Hendricks and Reinking (1994) and Skagen (1999) reported that researchers had no effect on predation rates of artificial nests in grassland systems.

Numerous researchers report no effects of research activity on survival of real nests

(Livezey 1980, MacIvor et al. 1990, Ortega et al. 1997, Lambert and Kleindorfer 2006,

Weidinger 2008), but these and most other reports are not for grassland passerine species, highlighting the need for more information on grassland passerine populations subject to predator-limited nest and fledgling survival.

56 Nest survival for grassland passerine species tends to be higher in larger, less- fragmented patches of habitat (Johnson and Temple 1990, Winter and Faaborg 1999,

Winter et al. 2000, Herkert et al. 2003, Renfrew and Ribic 2003, Horn et al. 2005, Koper and Schmiegelow 2006, Ribic et al. 2009), so we expected nest survival and productivity for Western Meadowlarks in the Nebraska Sandhills would be relatively high compared to other reported estimates but they were mostly comparable. Lanyon (1957) reported an apparent nest success (number of nests that fledged ≥ 1 nestling divided by the total number of nesting attempts) estimate of 35% (n = 62 nests) in southern Wisconsin,

Dickinson et al. (1987) reported 32% (n = 38 nests) in southern Manitoba, Kempema

(2007) reported 31% (n = 53 nests) in the central Nebraska Sandhills, and Davis (2003) reported 29.5% (n = 95 nests) in mixed-grass prairie pastures of southern Saskatchewan, all of which are comparable to our estimate of 32 ± 6% (n = 206 nests). Johnson and

Temple (1990) did not report an overall estimate of daily nest survival probability for western Minnesota, so we used their model-specific estimates to calculate a mean of

0.922 (95% CI: 0.917-0.927, n = 76 nests), which did not differ from Davis (2003) (95%

CI: 0.911-0.953), Koper and Schmiegelow (2006) (0.936, 95% CI: 0.915-0.956, n = 54 nests), Kempema (2007) (0.925, 95% CI: 0.898-0.946), or our estimate (0.924, 95% CI:

0.890-0.948). Lanyon (1957) and Dickinson et al. (1987) reported that 50% (n = 40 nests) and 46% (n = 41 nests), respectively, of confirmed nest failures were due to predation. These estimates are significantly lower than Davis’ (2003) nest-predation estimate of 64% and our estimate of 72% ± 7% (n = 140 failed nests), but the validity of comparisons is uncertain because of 1) the uncertainty in deciding whether nests old

57 enough to fledge actually fledged or were predated just before, and 2) the criteria used to identify nest predations may vary among researchers.

Unlike Lanyon (1957) and Davis (2003), we did not detect a decrease in clutch size as a function of ordinal day. Insect biomass of sweep-net samples from meadowlark nest sites increased with ordinal day (coefficient from gamma-regression model with log- link function, β = 0.041 ± 0.007 95% CI), indicating increasing availability of food and energy for adults. Lanyon (1957), Dickinson et al. (1987), and Davis (2003) reported clutch sizes of 4.83 ± 0.13 (SE), 5.10 ± 0.10 (SE), and 5.41 ± 0.14 (SE) eggs/nest, respectively, all of which were greater than our mean of 4.1 ± 0.1 (SE). Clutch-size estimates, however, are prone to under-estimation error and we consider our estimate as conservative because 1) partially completed clutches may be predated before the full clutch was laid and counted, 2) partial predations may occur during the clutch-laying process and afterwards but before initial counts, and 3) Brown-headed Cowbirds may peck eggs during parasitism events, and female Western Meadowlarks are reported to remove egg remains from nest bowls (Davis and Lanyon 2008), thus making egg remains undetectable for researchers and a source of error in estimating clutch size.

Most landowners in the Sandhills aggressively suppress wildfire, and prescribed burning is generally not used in land management plans. Fire suppression can modify plant community composition and structure, and affect populations of nest predators.

Johnson and Temple (1990), for example, observed lower rates of predation and higher rates of nest survival for Western Meadowlark nesting in more recently burned tall-grass prairies in Minnesota. Churchwell et al. (2008) reported that predator-limited nest survival for Dickcissels (Spiza americana) was higher and nest predation was lower in

58 patch-burned pastures with more structural heterogeneity relative to pastures with dormant-season burning and intensive grazing. Most of the evidence for effects of fire on nest predators for grassland birds comes from the Flint Hills tall-grass prairie system in eastern Kansas. Wilgers and Horne (2007) found evidence for increased predation attempts on artificial snakes and decreased abundance of real snakes after prescribed burns, and Wilgers et al. (2006) observed higher rates of herpetofaunal species loss following years with prairie burns. Similarly, Cavitt (2000) observed the relative abundance of a common grassland snakes species, Coluber constrictor, decrease following tall grass prairie burns. Setser and Cavitt (2002) also observed a relative abundance decrease for Coluber constrictor after spring burning, but also concluded that snakes can generally recolonize burned areas by late summer within the same growing season. Erwin and Stasiak (1979) observed direct mortalities and injuries of rodents and snakes following prescribed burns in late April and early May, but also observed substantial nest mortalities for grassland bird species. We suspect snakes were responsible for the majority of nest predations because 1) 35% (8/23) of confirmed mortalities for radio-tagged nestlings and fledglings were tracked directly to live snakes, and 2) snakes, especially Bullsnakes (Pituophis catenifer sayi), were frequently observed during routine field sampling, including four observations of Bullsnakes predating eggs or nestlings while directly on or adjacent to nests (Giovanni 2009). Furthermore,

Volesky and Connot (2000) reported increased cover by forbs and decreased cover by grass in the first year following a September wildlfire in the uplands of the Sandhills.

Cover by forbs was the only nest-site vegetation variable that explained variation in our daily nest survival models, and forb cover also explained variation in the probability of

59 nest-site selection by meadowlarks at our study site (Giovanni 2009). Thus, evidence indicating that managing grasslands with prescribed burning can limit the survival and abundance of nest predators may also hold true in the Nebraska Sandhills. However, the potential for mortality of grassland bird nests suggests that burning may be more beneficial if conducted before the peak of nesting initiation and/or prescribed in a patch- burn strategy to maximize the availability of structural heterogeneity (Churchwell et al.

2008).

Giovanni (2009) reported that litter depth, vegetation height and litter depth heterogeneity, and forb, grass, litter, and sedge cover had positive effects on the probability of nest-site selection by Western Meadowlarks. However, we detected only one significant nest-site microhabitat effect on daily nest survival: a positive effect of forb cover. Forb cover was, on average, greater at nests in the lowland wet meadow than at nests in the upland dry prairie nests (Giovanni 2009), suggesting that nests in the lowland wet meadows have higher daily nest survival. Western Meadowlarks may prefer nesting in the lowland wet meadows where nest densities are significantly higher (14.2 ±

1.1 SE nests/ha) compared to the upland dry prairies (1.5 ± 0.3 SE nests/ha), but the perceived benefits of nesting in wet meadows may be offset by the annual hay harvest, which caused 100% mortality for subject nests and accounted for 19% of confirmed nest failures (Giovanni 2009).

Livstock grazing is an effective method of managing vegetation for wildlife habitat in grassland systems (Fuhlendorf and Engle 2001, Vavra 2005, Briske et al. 2008,

Derner et al. 2009). Grazing management, including variations of stocking rates and grazing systems, is an important tool for creating and maintaining the heterogeneity in

60 vegetation structure that provides habitat for wildlife species that prefer different habitat characteristics (Fuhlendorf and Engle 2001, Vavra 2005, Kempema 2007, Derner et al.

2009). Meadowlarks nesting in the uplands were more likely to select nest sites with less cover than meadowlarks in the lowlands, but they still selected nest sites with more vegetation cover and structural heterogeneity compared to random sites in the uplands

(Giovanni 2009). Upland dry prairie pastures in the Sandhills are typically managed with moderately stocked cow-calf herds in rotational grazing systems. Pastures in the central

Sandhills managed with moderate stocking rates and rest-deferred rotational grazing systems, with longer rest periods between grazings, had greater heterogeneity in vegetation structure relative to pastures managed with short-duration rotational grazing

(Kempema 2007). Short-duration rotational grazing sytems use high stocking rates and short grazing and rest periods to achieve relatively continuous distribution of cattle across pastures, maximize forage use, and minimize the ability of cattle to preferentially select forage (Fuhlendorf and Engle 2001, Briske et al. 2008, Derner et al. 2009). Short- duration rotational grazing sytems also, for the same reasons, tend to decrease structural heterogeneity in vegetation (Fuhlendorf and Engle 2001, Briske et al. 2008, Kempema

61 management strategies (grazing, haying, and burning), and habitat-selection and demographic responses by grassland bird species (e.g., Kempema 2007).

Our inability to identify numerous effects of nest-site microhabitat on predator- limited daily nest survival seems to be common rather than exceptional for grassland passerine species, especially relative to the typically overwhelming effects of temporal and landscape-related parameters. Lusk (2003), however, reported numerous effects of nest-site microhabitat on nest survival of Lark Sparrows (Chondestes grammacus) in

Western Oklahoma, but most researchers report few or no effects of nest-site microhabitat on nest survival. Scheiman et al. (2003), modeled nest survival and nest-site selection as a function of numerous nest-site microhabitat variables for Western

Meadowlarks (and other species) in southeastern North Dakota, and detected numerous microhabitat effects on the probability of nest-site selection, like Davis (2005) and

Giovanni (2009). However, also like Davis (2005), Koper and Schmiegelow (2006), and

Giovanni (2009), Scheiman et al. (2003) only detected a single nest-site microhabitat effect on nest survival: a positive effect of nest-site cover by Leafy Spurge (Euphorbia esula), a non-native and invasive Eurasian forb. Davis (2005) also modeled the effects of numerous nest-site microhabitat variables on nest survival for Western Meadowlarks in southern Saskatchewan but detected only temporal effects. Similarly, Koper and

Schmiegelow (2006) only found one nest-site microhabitat effect on daily nest survival: a positive effect of litter depth. With (1994) concluded that shrub cover within 1 m of nests increased rates of incidental predation of McCown’s Longspur (Calcarius mccownii) nests by Thirteen-lined Ground Squirrels (Spermophilus tridecemlineatus). Winter et al.

(2005) modeled nest survival as a function of numerous microhabitat variables for three

62 grassland passerine species, and only found one positive effect of nest-site vegetation cover for one species.

Thus, researchers appear to more frequently report nest survival for grassland bird species as affected by temporal variables (i.e., age, day, and year) and habitat-related variables at spatial scales beyond the nest-site microhabitat scale, including habitat patch area, fragmentation, and distance to edge (Johnson and Temple 1990, Winter and

Faaborg 1999, Winter et al. 2000, Herkert et al. 2003, Grant et al. 2004, Horn et al. 2005,

Skagen et al. 2005, Davis et al. 2006, Koper and Schmiegelow 2006), and frequency or spatial configuration of prescribed burning (Johnson and Temple 1990, Rohrbaugh et al.

1999, Churchwell et al. 2008). The relative lack of evidence for microhabitat effects at the nest-site scale and the growing body of evidence for effects at larger spatial scales support the notion that most nest predator species operate at spatial scales larger than the nest-site microhabitat scale, and some may even incidentally or randomly predate nests while preferentially searching for other more consistently available primary prey items

(Angelstam 1986, Vickery et al. 1992, With 1994, Wilson and Cooper 1998, Cooper et al.

1999), potentially negating the expected fitness benefits of nest-site microhabitat selection. The abundance of evidence indicating predator-limited nest survival for grassland birds necessitates that ornithologists focus more on the interactions of primary nest-predator species with nest-site selection and survival for grassland birds at landscape scales (e.g., With 1994, Winter et al. 2000, Kuehl and Clark 2002, Phillips et al. 2003,

Reidy et al. 2009). The Nebraska Sandhills is one of North America’s largest remaining native prairie regions, and a potential population source for grassland bird species limited to fragmented and marginal habitat elsewhere. The development of successful long-term

63 management strategies for grassland bird species in the Nebraska Sandhills, however, will require significantly more research to understand the interactions of climate stochasticity and land management strategies and effects on demographic responses of grassland birds and nest predators at landscape levels.

ACKNOWLEDGMENTS

We thank L. Anderson, R. Byrnes, S. Groepper, B. Kramlich, B. Lawyer, J. Soper, L.

Thacker-Lynn, and J. Warner for their dedicated sampling efforts, and the staff at the

University of Nebraska-Lincoln’s (UNL) Gudmundsen Sandhills Laboratory for logistical support. Funding was provided by the UNL Foundation’s Burlington Northern

Water Science Endowment, Department of Agronomy and Horticulture, School of

Natural Resources, and Undergraduate Creative Activities and Research Experience

Program. We are grateful for additional support from the UNL Center for Great Plains

Studies’ Graduate Student Grant, the UNL Center for Grassland Studies’ A. W. Sampson

Graduate Fellowship in Range Science, and the Nebraska Chapter of The Nature

Conservancy’s J. E. Weaver Competitive Grant. This research was supported by Hatch

Act funds through the University of Nebraska Agricultural Research Division, Lincoln,

Nebraska.

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75 Table 1. Models of daily survival probability for Western Meadowlark nests in the

Nebraska Sandhills, USA, 2006-2008. Model-selection parameters include the number of model parameters (K), deviances (Dev), log-likelihoods (Loge(L)), Akaike Information

Criteria for small sample sizes (AICc), relative differences (ΔAICc), and weights (wi).

======

Model K Dev Loge(L) AICc ΔAICc wi

______

Temporal 7 596.2 - 298.1 609.3 0.0 0.47

Temporal + Researcher 8 596.0 - 298.0 611.0 1.6 0.21

Temporal + Climate 9 595.9 - 297.9 612.5 3.2 0.10

Null 1 611.2 - 305.6 613.1 3.8 0.07

Researcher 2 610.2 - 305.1 614.0 4.7 0.04

Temp. + Researcher + Clim. 10 595.7 - 297.9 614.1 4.8 0.04

Temporal + Nest 18 584.6 - 292.3 615.4 6.1 0.02

Temp. + Nest + Researcher 19 584.6 - 292.3 616.8 7.5 0.01

Climate 3 611.0 - 305.5 616.8 7.5 0.01

Climate + Researcher 4 610.0 - 305.0 617.7 8.4 0.01

Temporal + Nest + Climate 20 584.2 - 292.1 617.9 8.5 0.01

Nest 12 597.1 - 298.6 618.8 9.5 0.00

Global 21 584.2 - 292.1 619.2 9.9 0.00

Nest + Researcher 13 597.0 - 298.5 620.3 11.0 0.00

Nest + Climate 14 596.3 - 298.1 621.2 11.8 0.00

Nest + Climate + Researcher 15 596.2 - 298.1 622.6 13.3 0.00

76 Table 2. Logit-scale model coefficients (β) with lower (LCL) and upper (UCL) 95% confidence limits and standard errors (SE) for the a priori temporal model of daily survival for Western Meadowlark nests in the Nebraska Sandhills, USA, 2006-2008.

Asterisks indicate coefficients with confidence intervals that do not overlap zero.

======

Parameter 95% LCL β 95% UCL SE

______

Intercept (2006) 3.186 5.635* 8.083 1.249

Age - 0.550 - 0.327* - 0.104 0.114

Age2 0.000 0.006* 0.012 0.003

Ordinal day - 0.064 - 0.031* 0.000 0.016

Age*ordinal day 0.000 0.002* 0.004 0.001

Year (2007) - 0.171 0.323 0.817 0.252

Year (2008) 0.002 0.501* 1.000 0.255

______

77 Table 3. Logit-scale model coefficients (β) with lower (LCL) and upper (UCL) 95% confidence limits and standard errors (SE) for the a posteriori nest-site vegetation and temporal model of daily survival for Western Meadowlark nests in the Nebraska

Sandhills, USA, 2006-2008. Asterisks indicate coefficients with confidence intervals that do not overlap zero.

======

Parameter 95% LCL β 95% UCL SE

______

Intercepta 0.982 4.453* 7.924 1.771

Age - 0.540 - 0.313* - 0.086 0.116

Age2 0.000 0.005* 0.011 0.003

Ordinal day - 0.067 - 0.034* - 0.001 0.017

Age*ordinal day 0.000 0.002* 0.004 0.001

Forb cover 0.001 0.024* 0.046 0.012

Sedge cover - 0.036 - 0.003 0.030 0.017

Shrub cover - 0.074 0.004 0.081 0.040

Grass cover - 0.025 0.003 0.019 0.011

Bare ground - 0.041 - 0.006 0.028 0.018

Nest-bowl concealment - 0.216 0.025 0.266 0.123

Litter depth - 0.278 - 0.051 0.177 0.116

Vegetation height - 0.013 0.011 0.036 0.012

Litter depth heterogeneity - 0.681 - 0.172 0.337 0.260

Veg. height heterogeneity - 0.171 1.039 2.249 0.617

78 Year (2007) - 0.185 0.771 1.726 0.487

Year (2008) - 0.176 0.523 1.223 0.357

Upland habitat type - 0.212 0.590 1.392 0.409

______

79 0.35

0.30 95% 95% CI)

± 0.25

0.20

0.15

0.10

0.05 Proportion of clutch initiations ( initiations ofclutch Proportion 0.00 10-31 May 1-15 June 16-30 June 1-15 July 16-31 July

Fig. 1. Proportions (± 95% confidence intervals) of clutch initiations per 0.5 month period for Western Meadowlarks in the Nebraska Sandhills, USA, 2006-2008.

1.00 Clutches partially predated Clutches not partially predated

95% 95% CI) 0.80 ±

0.60

0.40

0.20 Proportio of clutches hatched ( hatched ofclutches Proportio 0.00 2006 2007 2008 TOTAL

Fig. 2. Proportions (± 95% confidence intervals) of clutches hatched for nests partially predated (n = 19) and not partially predated (n = 143) for Western Meadowlarks in the

Nebraska Sandhills, USA, 2006-2008.

81 40 C) o 30

20 bowl temperature ( bowl - 10 Nest Nest bowl

Ambient 0 7 8 9 10 11 12 13 14 15 16 17 18 19 20 June

Fig. 3. Ambient temperature and nest-bowl temperature (per 0.5 h) for a Western

Meadowlark nest that failed (where temperature lines begin to converge) on 15 June in the Nebraska Sandhills, USA, 2007.

82 25

5 Frequency of nest predations ofnest Frequency 0

Age (days)

Fig. 4. Frequency of nest predations according to age for Western Meadowlarks in the

Nebraska Sandhills, USA, 2006-2008.

83 1.00

0.98 95% CI) 95% ± 0.96

0.94

0.92

0.90

Daily nest survival probability ( probability survival Dailynest 0.88 1 4 7 10 13 16 19 22 25 28 Age (days)

Fig. 5. Quadratic effects of age on the probability of daily survival (± 95% confidence intervals) for Western Meadowlark nests in the Nebraska Sandhills, USA, 2006-2008.

84 1.00

95% CI) 95% 0.96 ±

0.92

0.88

0.84 Day 20 (21 May) Day 80 (20 July) Daily nest survival probability ( probability survival Dailynest 0.80 1 4 7 10 13 16 19 22 25 28 Age (days)

Fig. 6. Interacting effects of nest age and ordinal day on the probability of daily survival

(± 95% confidence intervals) for Western Meadowlark nests in the Nebraska Sandhills,

USA, 2006-2008.

85 1.00

0.95 95% CI) 95% ±

0.90

0.85

0.80 Daily nest survival probability ( probability survival Dailynest 0.75 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 Forb cover (%)

Fig. 7. Effects of forb cover on the probability of daily survival (± 95% confidence intervals) for Western Meadowlark nests in the Nebraska Sandhills, USA, 2006-2008.

86 MOVEMENT, MICROHABITAT SELECTION, AND PREDATOR- AND

THERMOREGULATION-LIMITED SURVIVAL FOR WESTERN MEADOWLARK

(STURNELLA NEGLECTA) FLEDGLINGS IN THE NEBRASKA SANDHILLS

MATTHEW D. GIOVANNI, 1, 2, 3 LARKIN A. POWELL,1 AND WALTER H.

SCHACHT 2

1School of Natural Resources, University of Nebraska-Lincoln, Lincoln, Nebraska 68583

2Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln,

Nebraska 68583

[email protected]

87 ABSTRACT. ― Populations of grassland bird species in North America continue to decline according to long-term monitoring data and vital rate studies. The Nebraska

Sandhills region is one of North America’s largest remaining native grassland systems, but minimal information exists for making management decisions for populations of grassland bird species. We radio-marked Western Meadowlark nestlings in 2006 and

2007 in the Nebraska Sandhills to model survival, movement, and habitat selection.

Confirmed fledgling mortalities were primarily due to predation. Daily survival probability increased with age and temperature but decreased as the breeding season progressed. Contrary to our expectations, fledgling survival appeared to be more limited by cold weather rather than hot. Distances moved by fledglings between locations and from nest sites increased significantly with age. Movement and dispersal increased rapidly after the first week post-fledging, and fledglings began dispersing > 0.5 km from nest sites during their fourth week post-fledging. First week fledglings tended to select locations with more ground cover, and taller and denser vegetation relative to older nestlings and random locations. Averaged for all ages, however, fledglings tended to select locations with more forb cover and bare ground, and taller vegetation relative to random locations. Habitat selection, survival, movement, and dispersal were affected by age, with first week fledglings subject to lower survival probability and fewer habitat choices because of limited mobility. Habitat management for Western Meadowlark fledglings should focus on moderate grazing and/or prescribed burning in the spring to obtain structurally diverse vegetation and to manage for small predators.

KEY WORDS: Western Meadowlark, Nebraska Sandhills, fledglings, survival, mixed effects models, predators, microhabitat selection, movement, dispersal, age

88 The prairie ecosystems of the Great Plains region in North America have been mostly replaced and fragmented with industrial agriculture and invasive herbaceous and woody plant species, and the historic disturbance regime of burning and grazing has been almost completely eliminated (Samson et al. 2004, Askins et al. 2007). This dramatic loss of habitat quantity and quality continues to cause population declines for most grassland bird species in North America (Peterjohn and Sauer 1999, Coppedge et al. 2001,

Scheiman et al. 2003, Grant et al. 2004, Brennan and Kuvlesky 2005, North American

The Nebraska Natural Legacy Project (NNLP, Schneider et al. 2005) is a comprehensive wildlife conservation strategy developed by the Nebraska Game and

Parks Commission in response to the U.S. Congress establishing the Wildlife

3) spread of invasive species, 4) inter-basin water transfer, 5) lack of knowledge about the region’s biological diversity, 6) ranching economics, and 7) conversion and fragmentation by irrigated agriculture, energy development, and tree plantings (Schneider et al. 2005). Thus, the Nebraska Sandhills is a large, mostly native and unfragmented grassland system, and likely supports large populations of grassland bird species limited to fragmented and marginal habitat elsewhere. The lack of biological data in general, as

89 indicated by the NNLP, however, concurrent with increasing threats to the region’s natural resources, highlights the need for research to inform management and conservation planning.

The Western Meadowlark (Sturnella neglecta) is an iconic and conspicuous grassland generalist in central and western North America (Davis and Lanyon 2008).

Fledglings of altricial species, relative to their nestling counterparts, have a new- found mobility that exposes them to new habitat selection decisions and different sources and rates of mortality (Powell et al. 2000, Jones and Bock 2005, White et al. 2005,

Yackel-Adams et al. 2006). The post-fledging life history stage is thus important to understand because it can provide additional management and conservation opportunities in breeding areas before dispersal. Krementz et al. (1989), Sullivan (1989), and Anders et al. (1997) were some of the earlier authors to present fledgling-specific survival models and estimates. Excepting Yackel-Adams et al.’s (2001, 2006) research on Lark

Bunting (Calamospiza meloanocorys) fledgling ecology in eastern Colorado, however, information on fledgling survival and habitat selection for grassland passerine species in the Great Plains is mostly nonexistent.

90 We present the first post-fledging survival, movement, and microhabitat selection information for the Western Meadowlark in the Nebraska Sandhills. Our specific objectives were to 1) quantify cause-specific sources of mortality, 2) model and estimate survival as a function of temporal and climatic variables, 3) model and estimate movement and dispersal as a function of age, and 4) model microhabitat selection.

METHODS

Study area. ― We sampled at wet meadow sites in the central Nebraska Sandhills

(Sandhills) at the University of Nebraska-Lincoln Gudmundsen Sandhills Laboratory

(42° 4' N, 101° 27' W), which includes about 5,000 ha of upland prairie and about 500 ha of lowland wet meadows and stream corridor. The central Sandhills is considered semi- arid, and is subject to high variation in annual precipitation, receiving approximately 50 cm of precipitation annually (Wilhite and Hubbard 1998). The topography of the central

Sandhills is characterized by mostly linear dune formations averaging between 41-50 m in height, with south- and southeast-facing slopes generally having steep inclines, ranging from 20-28 degrees (Swinehart 1998). The variation in topography and consequent soil structure, water-availability, and temperature creates distinct plant communities in the

Sandhills (Schacht et al. 2000), including lowland, sub-irrigated wet meadows characterized primarily by Carex sedges, forbs, and cool-season grasses. The wet meadows are typically managed for hay production, with a single annual hay harvest occurring in early to mid July.

Sampling.― We radio-marked Western Meadowlark (meadowlark) nestlings in 2006 and

2007 with 1.4 g radio-transmitters (Model BD-2, approximate battery life of 9 weeks,

Holohil Systems Ltd. 2009) with Rappole and Tipton’s (1994) attachment method but

91 used elastic thread. We received radio-transmissions with a 3-element Yagi directional antenna and an ATS Model 2000 Scientific Receiver (Advanced Telemetry Systems, Inc.

2009). We fitted nestlings with transmitters approximately 1-3 days before the anticipated fledging date. We assumed that radio-transmitter attachment had no effects on the survival, movement, and habitat use of fledglings (Powell et al. 1998,

Sanzenbacher et al. 2000, Suedkamp Wells et al. 2003). We tracked radio-marked fledglings once per day when possible but not during or immediately before inclement weather. We tracked nestlings to exact locations when possible to sample microhabitat variables, but had to measure approximate locations when older and flight-capable fledglings would flee upon approach. We assumed that fledgling movement in response to our sampling did not affect the subsequent location of fledglings one or more days later (i.e., locations were independent). Younger, mostly flight-incapable fledglings were left in place while sampling microhabitat variables, and we assumed that our brief presence while sampling did not affect fledgling survival or subsequent movement decisions We continued to search for lost radio signals of individual fledglings during routine tracking of active individuals until the transmitter was located or the sampling season ended.

We measured a number of microhabitat characteristics at fledgling locations and random locations within the same sampling site (wet meadow) to assess habitat selection.

We measured vegetation density with a visual obstruction pole (Robel et al. 1970). We measured vegetation height and litter depth with a tape in the center and corners of a 1-m2 sampling frame centered on fledgling locations. We measured vegetation height by recording the height of the tallest vegetation touching a vertical tape. We centered the 1-

92 m2 sampling frame on fledgling locations to measure the percent area of bare ground and cover by grass, sedge, forbs, and litter, and we calculated an overall vegetation cover estimate (percent grass + sedge + forb + litter). We measured the same microhabitat variables at points located 25 m from fledgling locations in a random direction, and also at points randomly generated with a geographic information system. Lastly, we used data from an on-site climate-monitoring station (High Plains Regional Climate Center 2009) to estimate daily averages of hourly maximum temperature and precipitation for modeling daily survival. We used averages for each fledgling survival interval per fledgling, and averages of the three days prior to the initial locations of fledglings.

Statistics.― Meadowlark fledglings are generally limited to crawling and hopping within their first week post-fledging (Davis and Lanyon 2008), and mortality for fledglings of grassland bird species is usually highest during this initial period (Kershner et al. 2004,

Yackel-Adams et al. 2006, Berkeley et al. 2007, Suedkamp Wells et al. 2007, Davis and

Fisher 2009). We therefore assumed mortalities for fledglings from which we lost radio- signals within the first week of fledging. We report proportions of 1) mortalities and unknown fates from the total fledgling sample, and 2) cause-specific mortalities from the total confirmed mortalities.

We used Tyre and Post van der Burg’s (2005) package for R v2.9 (R

Development Core Team 2009) to fit logistic-exposure models (Shaffer 2004) and estimate the probability of daily survival. The logistic exposure model is a generalized linear model with a binomial response distribution for fledgling fate (i.e., 1 = fledgling is alive and 0 = fledgling is dead or censored due to unavailability), and a modified logistic- regression link function,

93 () = , 1 − where θ is the probability of survival, and the exponent with t (length of observation interval or days exposed) accounts for the length of exposure intervals between fledgling survival observations (Shaffer 2004). We fit four a priori daily survival models, including models for 1) constant survival (null), 2) quadratic effects of age (Kershner et al. 2004, Yackel-Adams et al. 2006, Berkeley et al. 2007, Suedkamp Wells et al. 2007,

Davis and Fisher 2009) and ordinal day (Krementz et al. 1989, Naef-Daenzer et al. 2001,

Yackel-Adams 2006), 3) effects of precipitation and temperature, and 4) global effects

(all variables included). We used Akaike’s Information Criteria with a second-order bias correction for small sample sizes, deviance, log likelihoods, and proportional weights

(Anderson 2008) to select the best-fit daily survival model. We estimated 28-day survival probabilities and 95% confidence intervals (CI’s) by drawing 10,000 random, daily survival values and standard errors (SE’s) from a normal distribution confined by the daily survival mean and SE on the logit scale. We report daily and 28-day survival probabilities with 95% CI’s. We back-transformed log-of-odds (logit) scale means and

CI’s to probability values with the inverse logit function,

exp() , 1 + exp() where x is equal to, for example, a mean daily survival value on the logit-scale.

We used descriptive statistics and generalized linear mixed-effects models

(Bolker et al. 2008) to assess microhabitat selection. We report means and 95% CI’s for microhabitat variables measured at fledgling locations and at random locations generated with ArcGIS 9.1 (ESRI 2009). We used the number of fledglings as sample size to

94 calculate CI’s, rather than the number of fledgling locations, to avoid psuedoreplication and autocorrelation among individual fledglings. We compared microhabitat selection for 25-31 day-old fledglings (first week post-fledging) and ≥ 32 day-old fledglings because of the potential for age-dependent survival and movement. We used Bates and

Maechler’s (2008) lme4 package for R (R Development Core Team 2009) to fit generalized linear mixed-effects models (Bolker et al. 2008), with binomial response distributions (i.e., 1 = fledgling location and 0 = random location) and logit-scale link functions, to microhabitat data from fledgling and random locations. We matched microhabitat data from fledgling locations with microhabitat data from random locations within like years to account for annual variation in vegetation structure. We used a correlation matrix to remove predictor variables with correlation coefficients > 0.5 and reduce multicollinearity in our models. We fit eight microhabitat-selection models, including a null model, a model for each vegetation variable, and a global vegetation model. We specified individual fledglings as random effects in each model (Gillies et al.

2006, Bolker et al. 2008) to account for potential autocorrelation between locations selected by individual fledglings.

We used ArcGIS 9.1 (ESRI 2009) to estimate distances moved by fledglings 1) between sequential radio-telemetry locations, and 2) away from respective nest sites. We report movement data as frequency of occurrence to assess distributional properties, and movement means with 95% CI’s per 7-day age class. We fit generalized linear models with gamma-response distributions (for continuous and non-normal data) to the fledgling movement data. We report individual means with SE’s, comparative means with 95%

95 CI’s, proportions with 95% CI’s for binomial proportions (Crawley 2005), and estimated all model coefficients and predictions with 95% CI’s.

RESULTS

We radio-tracked 15 nestlings from 5 nests in 2006 from 29 June through 26 August, and

31 nestlings from 11 nests in 2007 from 11 June through 17 August. We radio-marked an average of 2.9 ± 1.0 nestlings/nest, and obtained 132 and 212 fledgling locations (i.e., survival intervals) in 2006 and 2007, respectively, for an average of 7.5 ± 3.1 locations/fledgling (range: 2–23). We confirmed 23 mortalities from 46 fledglings, of which 8 (35 ± 19%) were predations, 2 (9 ± 12%) were from hay harvesting, and 13 (57 ±

20%) were from unknown causes. The 13 mortalities from unknown causes tended to occur during or immediately after periods of decreasing temperature (Fig. 1), including 4 fledgling carcasses that we found completely intact (i.e., not predated or scavenged).

Seven of the eight fledglings confirmed as predated were tracked to live snakes, including six Bull Snakes (Pituophis catenifer sayi) and one Plains Garter Snake (Thamnophis radix). We found the scattered wings, legs, and radio-transmitter of the other fledgling, possibly indicating predation by a small mammal. Ten of the 46 (22 ± 12%) fledglings and 43 of the 344 (13 ± 3%) fledgling locations were exposed to hay harvesting, but only

2 of the 10 (5 ± 6%) exposed fledglings died from hay harvesting. Assuming consistent radio-transmitter attachment and functionality, the other 23 fledglings either died undetected after their first week post-fledging or dispersed from the study area.

Fledglings survived 17.0 ± 2.0 days before dying, dispersing, or no longer transmitting a radio signal. Twenty-two (96%) of 23 fledgling mortalities occurred during the first week post-fledging (5.0 ± 1.5 days post-fledging), and we lost 17 (74%) of the 23 lost

96 radio-signals during post-fledging weeks four and five (27.6 ± 1.8 days post-fledging)

(Fig. 2). The global model for daily survival probability was the best fit and accounted for 80% of the model selection weight (Table 1). The temporal model accounted for 20% of the weight (Table 1) but we assessed model parameter effects and made model predictions from the global model since all the temporal model parameters were nested in the global model. Daily survival was not constant (βintercept = -12.11 ± 7.45), increased with age (βage = 0.535 ± 0.301, βage2 = - 0.005 ± 0.003, Fig. 3) and maximum daily temperature (β = 0.141 ± 0.120, Fig. 5), decreased with ordinal day (β = - 0.027 ± 0.026,

Fig. 4), but precipitation had no detectable effect (β = - 0.019 ± 0.869). The model- predicted estimates for daily and 28-day survival probabilities were 0.982 (95% CI: 0.964

– 0.992, n = 46 fledglings) and 0.608 (95% CI: 0.359 – 0.788), respectively.

Fledglings crawled and then hopped during their first week out of the nest, followed by short and then sustained flights during their second week post-fledging. As fledglings aged, they moved greater distances from nest sites (βintercept = 2.976 ± 0.221,

βage2 = 0.001 ± 0.000, Fig. 6) and between radio-telemetry locations (βintercept = 2.864 ±

0.269, βage2 = 0.001 ± 0.000, Fig. 7). Fledglings began to disperse > 0.5 km from nest sites during their fourth week post-fledging (Fig. 6), which concurred with the period we lost most radio-signals (Fig. 1).

Overall, fledgling and random locations were not different for vegetation height

(33.4 ± 6.0 cm vs. 32.3 ± 1.6 cm), vegetation density (17.2 ± 3.8 cm vs. 16.7 ± 1.1 cm), and litter depth (1.3 ± 0.6 cm vs. 1.7 ± 0.1 cm). Fledgling locations did, however, tend to have more forb cover and less grass cover compared to random locations (Fig. 8).

Locations for first-week fledglings tended to have taller and denser vegetation compared

97 to random locations, but locations of older fledglings (≥ 32 days-old) did not (Fig. 9).

We suspect, however, that a larger sample size of older fledglings could increase the precision enough to reveal differences between younger and older fledglings for vegetation height and density (Fig. 9). Locations for first-week fledglings had less sedge and litter cover, and more forb and vegetation cover compared to random locations, while locations for older fledglings had less grass cover and more forb cover compared to random locations, and less grass and vegetation cover compared to locations for first week fledglings (Fig. 10).

We excluded vegetation density and litter cover from our microhabitat selection models because they were correlated with vegetation height (r = 0.84) and grass cover (r

= - 0.51), respectively. We attempted to include fledgling age as a continuous fixed- effect, and then a categorical fixed-effect (i.e., 1-week old and > 1-week old fledglings), to model age-specific microhabitat-selection, but age-inclusive models failed to converge due to insufficient sample size. The global model for microhabitat selection was the best fit, accounting for 100% of the proportional model weight (Table 2). Grass cover (β = -

0.486 ± 0.181), sedge cover (β = - 0.292 ± 0.184), and litter depth (β = - 0.294 ± 0.186) negatively affected the probability of microhabitat selection by fledglings, whereas vegetation height (β = 0.237 ± 0.147) and forb cover (β = 0.186 ± 0.124) had positive effects. Bare ground had a slight positive effect but the 95% CI overlapped zero (β =

0.079 ± 0.088).

DISCUSSION

Variation in movement, habitat selection, and survival of Western Meadowlark fledglings was age-specific, with most of the variation occurring between younger (first week),

98 relatively less mobile fledglings and older, more mobile fledglings capable of sustained flight. The probability of Western Meadowlark fledglings surviving their first week post- fledging was significantly lower compared to older fledglings, and like Western

Meadowlark nests (Giovanni 2009), was limited primarily by predation. Fledglings had a higher probability of daily survival compared to Western Meadowlark nests in the same study area, but a larger percentage of nest failures were due to predation (71 ± 7%) compared to fledgling mortalities from predation (35 ± 19%) (Giovanni 2009). The mobility of fledglings, unlike nestlings, also enabled them to actively avoid mortality from hay harvesting. Thus, increased mobility and sustained flight during the second week post-fledging was strongly correlated with increased survival.

There are only a few daily survival probabilities reported for fledglings of grassland passerine species. Yackel-Adams (2001) estimated a daily survival probability of 0.95 (95% CI: 0.93 – 0.97) for Lark Bunting fledglings in a short-grass prairie system in Colorado, which is lower than our estimate of 0.982 (95% CI: 0.964 – 0.992) for

Western Meadowlark fledglings. Yackel-Adams (2001) also reports a 0.367 (confidence not reported) probability of Lark Bunting fledglings surviving the first 3 weeks post- fledging, which is expectedly lower than our probability estimate of 0.689 (95% CI:

0.456 – 0.838) for Western Meadowlark fledglings surviving 3 weeks. Kershner et al.

(2004) estimated a daily survival probability of 0.90 (95% CI: 0.79 – 1.00) for Eastern

Meadowlarks (Sturnella magna) 3 days after fledging in a grassland system in Illinois, which closely approximated our daily survival probability estimate of 0.89 (95% CI: 0.82

– 0.93) for 3 days after fledging. Suedkamp Wells et al. (2007) estimated a survival probability of 0.65 (95% CI: 0.56 – 0.76) for Eastern Meadowlarks 30 days after fledging

99 in a tall-grass prairie system in southwestern Missouri, which is comparable to our estimate of 0.59 (95% CI: 0.32 – 0.76).

Ordinal day (season) negatively affected the survival probability of fledglings, which may have been an indirect effect of seasonal variation in predator activities or weather, and/or a lower level of food provisioning commitment from pre-migratory adults. These correlations emphasize the need for ornithologists to directly sample and include survival-limiting factors, such as predator densities, in survival models. Most studies of fledgling survival for predator-limited populations of grassland passerine species also found positive effects of increasing age (Kershner et al. 2004, Yackel-Adams et al. 2006, Berkeley et al. 2007, Suedkamp Wells et al. 2007, Davis and Fisher 2009).

Understanding how survival varies with age is important for habitat and population management because habitat selection, movement decisions, and the ability to evade sources of mortality are often age-dependent during transitional periods like fledging and natal dispersal.

The positive relationship between fledgling mass and survival probability is well- documented (Krementz et al. 1989, Sullivan 1989, Naef-Daenzer et al. 2001, Yackel-

Adams 2006, Suedkamp Wells et al. 2007), suggesting interactive effects of food limitation and weather exposure that can limit fledgling survival. Fledglings, unlike nestlings, need to select habitat that concurrently serves at least two primary functions: minimizing predation and maximizing thermoregulation. Confirmed Western

Meadowlark fledgling mortalities from unknown causes at our sampling sites tended to coincide with or were immediately subsequent to periods of low temperatures.

Maximum daily temperature also positively affected survival, again possibly indicating

100 that fledgling survival was limited by hypothermia and/or starvation. We recommend that similar fledgling survival studies with larger sample sizes include an age-weather interaction parameter in their survival models, since older fledglings can more independently forage and more actively select roosting locations to maximize thermoregulation.

Western Meadowlark fledglings in wet meadows have relatively less structural heterogeneity to select from compared to the forested or fragmented forest systems such as those described in Lang et al. (2002), Cohen and Lindell (2004), Berkeley et al.

(2007), and Rush and Stutchbury (2008), but we still detected patterns in microhabitat selection. Our microhabitat selection models and descriptive statistics both indicated that fledglings, averaged across ages, selected sites with less grass, litter, and sedge cover, but taller vegetation with more bare ground and forb cover. The mechanism underlying these patterns may be related to food provisioning access for adults still delivering food to younger, less mobile fledglings, and direct food availability for fully mobile and relatively independent fledglings. First-week fledglings exhibited the characteristic selection of taller and denser “escape cover” compared to older fledglings and random locations, indicating that such cover might be important for predator evasion and thermoregulation since these were the two primary causes of mortality. Small predators like snakes, however, may also be selecting denser cover to avoid predation by mesocarnivores and raptors (Wilgers and Horne 2007), thus potentially resulting in maladaptive microhabitat selection by fledglings.

Fledgling survival increased with age and temperature, and decreased as the breeding season progressed, and observed mortalities from known causes were mostly

101 due to predation. Thus, managing for Western Meadowlark fledglings and ecologically similar species in the Sandhills should focus on landscape-level habitat management with prescribed burning and grazing to directly and indirectly control (via habitat modification) densities of small and mid-sized predators. Numerous authors reported fledgling mortalities from predation by raptor species (Yackel-Adams et al. 2001, Davis and Fisher 2009), but raptor populations in the Sandhills are relatively sparse due to lack of nesting substrates (personal observation). Populations of snakes and small mammal species, however, are abundant, and can be managed with prescribed burning in mid- spring, causing direct mortality (Erwin and Stasiak 1979), indirect mortality via exposure to larger predators (Wilgers and Horne 2007), and habitat displacement (Cavitt 2000,

Setser and Cavitt 2002, Wilgers et al. 2006). Fire also promotes the growth of forbs in the uplands of the Nebraska Sandhills (Volesky and Connot 2000), and cover by forbs had positive effects on nest survival and nest-site selection for Western Meadowlarks.

Similarly, livestock grazing, the current de facto land management strategy in the

Sandhills, is an effective land management tool for creating structural heterogeneity in vegetation (Fuhlendorf and Engle 2001, Vavra 2005, Kempema 2007, Briske et al. 2008,

Derner et al. 2009) and removing excessive vegetation cover. The Sandhills is one of

North America’s largest remaining native prairie regions, and a potential population source for grassland bird species limited to fragmented and marginal habitat elsewhere.

The development of successful long-term management strategies for grassland bird species in the Nebraska Sandhills, however, will require more research on the interactions of climate stochasticity and land management strategies and effects on demographic responses by grassland birds.

102 ACKNOWLEDGMENTS

We thank L. Anderson, R. Byrnes, S. Groepper, J. Soper, L. Thacker-Lynn, and J.

Warner for their dedicated sampling efforts, and the staff at the University of Nebraska-

Lincoln’s (UNL) Gudmundsen Sandhills Laboratory for logistical support. We thank M.

Post van der Burg and A. J. Tyre for modeling suggestions. Funding was provided by the

UNL Foundation’s Burlington Northern Water Science Endowment, Department of

Agronomy and Horticulture, School of Natural Resources, and Undergraduate Creative

Activities and Research Experience Program. We are grateful for additional support from the UNL Center for Great Plains Studies’ Graduate Student Grant, the UNL Center for Grassland Studies’ A. W. Sampson Graduate Fellowship in Range Science, and the

Nebraska Chapter of The Nature Conservancy’s J. E. Weaver Competitive Grant. This research was supported by Hatch Act funds through the University of Nebraska

Agricultural Research Division, Lincoln, Nebraska.

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110

111 Table 1. Models of daily survival probability for Western Meadowlark fledglings in the

Nebraska Sandhills, USA, 2006-2007. Model-selection parameters include number of model parameters (K), model deviance (Dev), model log-likelihood (Loge(L)), Akaike

Information Criterion scores adjusted for small sample sizes (AICc), relative model differences in AICc (Δi), and relative model weights (wi).

======

Model K Dev Loge(L) AICc Δi wi

______

Global 6 137.3 - 68.6 146.8 0.0 0.796

Temporal 4 142.7 - 71.4 149.6 2.7 0.204

Constant 1 173.5 - 86.8 175.4 28.6 0.000

Climate 3 171.1 - 85.6 176.4 29.6 0.000

______

112 Table 2. Models of microhabitat-selection probability for Western Meadowlark fledglings in the Nebraska Sandhills, USA, 2006-2007. Model-selection parameters include number of model parameters (K), model deviance (Dev), model log-likelihood

(Loge(L)), Akaike Information Criterion scores adjusted for small sample sizes (AICc), relative model differences in AICc (Δi), and relative model weights (wi).

======

Model K Dev Loge(L) AICc Δi wi

______

Global 9 2161.2 - 1080.6 2166.1 0.0 1.000

Forb cover 4 2218.7 - 1109.3 2224.0 58.0 0.000

Grass cover 1 2236.0 - 1118.0 2241.0 75.0 0.000

Litter depth 3 2289.4 - 1144.7 2294.0 128.0 0.000

Bare ground 4 2295.5 - 1147.7 2300.0 134.0 0.000

Sedge cover 4 2306.9 - 1153.4 2312.0 146.0 0.000

Constant 4 2318.8 - 1159.4 2322.5 156.4 0.000

Veg. height 4 2318.7 - 1159.4 2324.0 158.0 0.000

______

113

Fig. 1. Maximum daily temperature (solid lines) and confirmed mortalities (vertical dashed lines) from unknown causes for Western Meadowlark fledglings in the Nebraska

Sandhills, USA, 2006-2007.

114

22 Mortality Lost signal 20 18 16 14 12 10 8 6

Frequency ofoccurrence Frequency 4 2 0 1-7 8-14 15-21 22-28 29-35 36-42 43-49 50-56 Days post-fledging

Fig. 2. Frequency of confirmed mortalities and lost radio-signals according to days post- fledging for Western Meadowlark fledglings in the Nebraska Sandhills, USA, 2006-2007.

115 1.00

0.98 95% CI) 95% ± 0.96

0.94

0.92 Daily survival probability ( probability survival Daily 0.90 25 30 35 40 45 50 Age (days)

Fig. 3. Model-predicted daily survival probabilities (± 95% confidence intervals, dashed lines) as a function of age (since hatching) for Western Meadowlark fledglings in the

Nebraska Sandhills, USA, 2006-2007.

116 1.00

0.98 95% CI) 95% ±

0.96

0.94

0.92 Daily survival probability ( probability survival Daily

0.90

Fig. 4. Model-predicted daily survival probabilities (± 95% confidence intervals, dashed lines) as a function of ordinal day (season) for Western Meadowlark fledglings in the

Nebraska Sandhills, USA, 2006-2007.

117

1.00

0.98 95% CI) 95% ±

0.96

0.94

0.92 Daily survival probability ( probability Dailysurvival

0.90 20 25 30 35 40 Temperature (oC)

Fig. 5. Model-predicted daily survival probabilities (± 95% confidence intervals, dashed lines) as a function of daily maximum temperature for Western Meadowlark fledglings in the Nebraska Sandhills, USA, 2006-2007.

118 2500 Observed 2000

95% CI) 95% Predicted ± 1500

1000

500 Distance from nest (m nest from Distance

0 24 31 38 45 52 59 66 73 80 Age (days)

Fig. 6. Observed and model-predicted distances (meters ± 95% confidence intervals, dashed lines) moved from nest sites as a function of age (since hatching) for Western

Meadowlark fledglings in the Nebraska Sandhills, USA, 2006-2007.

119

1250 Observed

) 1000 Predicted

95% CI 95% 750 ±

500

Movement (m Movement 250

0 24 31 38 45 52 59 66 73 80 Age (days)

Fig. 7. Observed and model-predicted distances (meters ± 95% confidence intervals, dashed lines) moved between radio-telemetry locations as a function of age (since hatching) for Western Meadowlark fledglings in the Nebraska Sandhills, USA, 2006-

2007.

120 100 Fledglings (n = 35) Random locations (n = 735) 90

70 95% CI) 95%

± 60

30 Ground cover (% (% cover Ground 20

Bare Sedge Litter Forb Grass Vegetation

Fig. 8. Ground cover for random locations and locations selected by Western

Meadowlark fledglings in the Nebraska Sandhills, USA, 2006-2007.

121 45 25-31 day-old fledglings (n = 38) 40 ≥32 day-old fledglings (n = 27) 35 Random locations (n = 735) 30 95% CI) 95%

± 25 20 15

Centimeters ( Centimeters 10 5 0 Vegetation height Vegetation density Litter depth

Fig. 9. Maximum vegetation height, vegetation density, and litter depth for random locations and locations selected by 25-31 day-old and ≥ 32 day-old Western Meadowlark fledglings in the Nebraska Sandhills, USA, 2006-2007.

122 100 25-31 day-old fledglings (n = 38) 90 ≥32 day-old fledglings (n = 27) 80 Random locations (n = 735) 70 95% CI) 95%

± 60 50 40 30

Ground cover (% cover Ground 20 10 0 Bare Sedge Litter Forb Grass Veg.

Fig. 10. Ground cover for random locations and locations selected by 25-31 day-old and

≥ 32 day-old Western Meadowlark fledglings in the Nebraska Sandhills, USA, 2006-

2007.