Volume 116, 2014, pp. 162–172DOI: 10.1650/CONDOR-13-114.1

RESEARCH ARTICLE

Stopover ecology of American Golden-Plovers (Pluvialis dominica) inMidwestern agricultural fields

Kirk W. Stodola,1,2* Benjamin J. O’Neal,3 Mark G. Alessi,2 Jill L. Deppe,4 Tyson R. Dallas,1,2 Tara A.Beveroth,2 Thomas J. Benson,2 and Michael P. Ward1,2

1 Department of Natural Resources and Environmental Sciences, University of Illinois, Urbana, Illinois, USA2 Illinois Natural History Survey, Prairie Research Institute, University of Illinois, Champaign, Illinois, USA3 Franklin College, Franklin, Indiana, USA4 Department of Biological Sciences, Eastern Illinois University, Charleston, Illinois, USA* Corresponding author: kstodola@illinois.edu

Received September 18, 2013; Accepted December 5, 2013; Published Febuary 12, 2014

ABSTRACTStopover locations represent critical habitat in the life cycle of migratory birds and the alteration of this habitat canprofoundly influence a population. American Golden-Plovers (Pluvialis dominica) migrate though the MidwesternUnited States each spring, where most natural habitat has been converted to row crop agriculture. We investigated thestopover ecology of the golden-plover in the agricultural matrix of east-central Illinois and west-central Indianabetween 2008 and 2012. We found that golden-plovers remained in the region for ~45 days and individuals spent onaverage 24 days in the area before departing to the northwest. During a period of peak migration, golden-ploverspreferred fields with standing water and, to a lesser extent, soybean fields. Over the 45-day stopover duration, golden-plovers moved extensively (shown by a dynamic occupancy model and area used estimation), with some evidence fortilled fields becoming unoccupied at greater rates than untilled fields. The tendency to use fields with standing waterand the movement of individuals from tilled fields suggests that food accessibility, rather than food abundance, islikely a critical factor associated with the prolonged stay, movement, and field type selection of golden-plovers. Foodaccessibility is important to the golden-plover because they undergo molt into breeding plumage in the region andmust refuel for the next leg of their migration. The Midwest is a key stopover location for American Golden-Plovers andpromoting foraging conditions by manipulating the drainage of agricultural fields, via the temporary blockage of draintiles, should be a management focus.

Keywords: agriculture, migration, molt, shorebird, stopover ecology

Ecologıa de las paradas migratorias de Pluvialis dominica en campos agrıcolas del Medio Oeste de losEstados Unidos

RESUMENLos sitios de parada migratoria representan habitats crıticos en el ciclo de vida de las aves migratorias y la alteracion deestos habitats podrıa influir profundamente en una poblacion. Pluvialis dominica migra cada primavera a traves delmedio oeste de los Estados Unidos, donde la mayorıa de los habitats naturales han sido convertidos en areas agrıcolaspara cultivos en surco. Investigamos la ecologıa de las paradas migratorias de Pluvialis dominica en la matriz agrıcoladel centro-oriente de Illinois y el centro-oeste de Indiana entre 2008 y 2012. Encontramos que P. dominica estapresente en la region aproximadamente por 45 dıas y que los individuos pararon en promedio por 24 dıas antes departir al noroeste. Durante el periodo pico de migracion, P. dominica prefirio los campos de cultivo con aguasestancadas y, en menor medida, los campos de soya. Durante los 45 dıas, los individuos se movieronconsiderablemente (evidenciado a partir de un modelo dinamico de ocupacion y de la estimacion del area usada)y existe alguna evidencia de que los campos cultivados se desocupan a tasas mayores que los no cultivados. Creemosque la tendencia de usar los campos con aguas estancadas y el movimiento de los individuos desde los camposcultivados sugieren que la accesibilidad al alimento, y no su abundancia, es probablemente un factor crıtico asociadocon la estadıa prolongada, el movimiento y la seleccion del tipo de campo en P. dominica. Dicha accesibilidad alalimento es importante para P. dominica debido a que los individuos mudan a su plumaje reproductivo en esa region ydeben reabastecerse para la siguiente etapa de su migracion. El medio oeste de los Estados Unidos es una localidadclave de parada migratoria para P. dominica y el manejo de los campos agrıcolas deberıa enfocarse en la manipulaciondel drenaje de los mismos por medio del bloqueo temporal de los canales de desague para promover las buenascondiciones de alimentacion para esta especie.

Palabras clave: agricultura, ave playera, ecologıa de escalas migratorias, migracion, muda

Q 2014 Cooper Ornithological Society. ISSN 0004-8038, electronic ISSN 1938-5129Direct all requests to reproduce journal content to the Central Ornithology Publication Office at aoucospubs@gmail.com

INTRODUCTION

Stopover locations are a critical component of a migratory

bird’s annual cycle and the loss or alteration of stopover

habitat can negatively impact a species’ population (Skagen

2006). Choosing high quality stopover sites could greatly

enhance the condition of migrating individuals, because

the majority of time spent during migration is at stopover

locations (Hedenstrom and Alerstam 1997). Ideally,

stopover sites provide safe (i.e. refuge from predators)

access to ample food so that individuals can refuel for

continued migration (Zwarts et al. 1990, Atkinson et al.

2007) and possibly undergo molt (Holmgren et al. 1993,

Lindstrom et al. 2010). Locations with these conditions are

neither widespread nor well known. However, where they

occur, thousands of migrating individuals may congregate

(Botton et al. 1994, Andres and Browne 1998, Andrei et al.

2006), which can represent a large percentage of the global

population for some species (Jehl 1988, Jorgensen et al.

2008). Given their importance in a migratory species’

annual cycle, it is imperative that stopover sites are

identified and the aspects that make them valuable

determined.

The flyway through the Midwestern United States is an

important pathway for many long-distance migratory

shorebirds (Colwell 2010). Historically, the region con-

tained large expanses of grasslands with wet meadows and

marshes (Prince 1997) that millions of migrating shore-

birds depended on during migration (Skagen et al. 1999,Skagen and Yackel Adams 2010). However, much of the

natural landscape has been lost as most of the native

prairie has been converted to agriculture (Samson and

Knopf 1994). For instance, in Illinois, less than 1% of the

original 21 million acres of prairie still exists (Herkert et al.

1993), with most of the land converted to row crop

agriculture, specifically corn and soybeans (Walk et al.

2010). The loss of the native wetlands and prairies

throughout the Midwest may be a contributing factor in

the population declines of many migrating shorebirds

(Skagen 2006).

The American Golden-Plover (Pluvialis dominica,

hereafter golden-plover) is a long-distance migratory

shorebird that undertakes one of the longest migrations

in the world, traveling from its wintering grounds in

southern South America to its breeding grounds in the

tundra of northern North America (Johnson and Connors

2010). The yearly migration follows an elliptical pattern,

with a September to November southward migration along

an offshore Atlantic Ocean route followed by a mid-

continental northward return from February to April

(Johnson and Connors 2010). Individuals typically arrive

on their breeding grounds in northwestern Canada to

northern Alaska in mid-May to mid-June (Johnson and

Connors 2010). During the northward migration, large

congregations of golden-plovers have been reported in the

Midwest (Braile 1999, Johnson and Connors 2010), with

eBird observations from this region beginning in late

March and continuing until the start of May (Sullivan et al.

2009). In the Midwest, the species is most often found in

agricultural fields (Braile 1999), a habitat that is highly

disturbed and not the traditional stopover habitat for the

species.

How the conversion of stopover habitat has affected

golden-plovers is unclear. The golden-plover must now

rely on agricultural fields during its northbound migration

(Braile 1999, Johnson and Connors 2010) because little

natural habitat exists. The loss of natural habitat may have

been a contributing factor in the dramatic decline of the

historic population of the species (Clay et al. 2010),

although there is no direct evidence for this. The current

population status of the golden-plover is also somewhat

tenuous: The species is listed as a ‘‘Species of High

Concern’’ in Canada, a species in greatest need of

conservation in Illinois, a species of conservation concern

in Indiana, and is approaching the population decline

thresholds of the IUCN Red List (Birdlife International

2008).While a multitude of factors can influence shorebird

populations (Colwell 2010, Sutherland et al. 2012) themigration period is particularly critical (Skagen and Knopf

1993, Drent et al. 2006), and alteration of stopover habitat

can have deleterious consequences (Skagen 2006). How-

ever, very little is known about the utilization of the

modern agricultural landscape by golden-plovers, and

improved knowledge of stopover areas is a priority for

future research (Potter et al. 2007, Johnson and Connors

2010).

We investigated the stopover ecology of golden-plovers

to address several basic questions associated with the time

they spend in east-central Illinois and west-central Indiana.

Specifically, we estimated stopover duration of individuals

and the window of time for which the species is present in

our study area. We also investigated the migratory

behavior of arriving and departing individuals, determining

whether the species migrates primarily by day or at night,

whether migration departures and arrivals are synchro-

nous, and the average departure direction of migrating

individuals. We examined the mass of individuals (a

measure of body condition) and whether individuals were

molting during their stopover in the agricultural fields of

Illinois. Finally, we studied the habitats (i.e. agricultural

fields) used by golden-plovers during stopover to deter-

mine whether specific fields were preferred based on

agricultural crop and tillage practices and investigated

whether food availability differed among field types. With

these data we synthesize the factors that may be most

limiting for golden-plovers stopping over in agricultural

fields of the Midwest and suggest some management

strategies for improving stopover habitat.

The Condor: Ornithological Applications 116:162–172, Q 2014 Cooper Ornithological Society

K. W. Stodola, B. J. O’Neal, M. G. Alessi, et al. Golden-plover stopover ecology 163

METHODS

Study AreaWe investigated golden-plover stopover ecology between

2008 and 2012. From 2008 to 2010 we investigated habitat

selection at 6 townships (93 km2 in area) located in east-

central Illinois and west-central Indiana, USA. Between

2009 and 2012 we supplemented the surveys with a

targeted radio-telemetry approach that investigated move-

ment, stopover duration, departure times, and departure

direction of golden-plovers in 2 specific regions of east-

central Illinois. One survey region was located east of

Arcola, Illinois (Douglas County, 398410N, 888180W) and

the other survey region was located south of Allerton,

Illinois (Vermillion County, 39854 0N, 87856 0W), each

survey region encompassing approximately 75 km2.

Agricultural fields in our study area are typically 0.8 km

3 0.8 km fields planted with corn or soybeans. Little

natural vegetation remains, with row crops being planted

to within a meter of roads and with little delineation

between fields.

TelemetryWe captured golden-plovers from 2009 to 2012 using

handheld nets and a Wilsternet (Koopman and Hulscher

1979). We placed 1.2-g transmitters on 24 golden-plovers

over the 4 years of our study (3 in 2009, 5 in 2010, 5 in

2011, and 11 in 2012).We used automated radio-telemetry,

specifically an Automated Recording Unit (ARU; JDJC

Corporation, Fisher, Illinois, USA), from 2010 to 2012,

following the methods of Ward and Raim (2011) and Ward

et al. (2013). The ARU collected data from 6 antennas

distributed 608 apart and allowed us to gather daily

information regarding detection and to estimate when and

in what direction an individual migrated. We used this

information to calculate the stopover duration of golden-

plovers in the region. We categorized migratory departures

from the study areas as being initiated during daylight

(nautical twilight sunrise–sunset, ~05:10–20:40) or at

night (~20:40–05:10). The ARU was positioned ~35 km to

the northwest of the study areas in 2010 and 2011 and

within the Allerton study area in 2012. We also attempted

to locate all birds every day or every other day using a

vehicle-mounted antenna and traditional telemetry. We

estimated the area used by golden-plovers during the

stopover period in our region using the manual tracking

data and the ‘adehabitat’ package in R (Calenge 2006).

Least-squares cross-validation was used to estimate the

smoothing parameters.

Stopover DurationWe estimated stopover duration by blending 2 estimation

approaches in a novel way. First, we estimated stopover

duration postcapture using a mark–recapture framework

described by Schaub et al. (2001), which provided an

estimate of stopover duration postcapture based on

observed capture histories while accounting for imperfect

detection. We were unable to calculate stopover duration

precapture using the methods described by Schaub et al.

(2001) because our use of transmitters changed the

detection probability pre- and post-capture. Second, we

estimated stopover duration prior to known departure

using encounter sampling (Otis et al. 1993), which

provided an estimate of stopover duration assuming the

time of departure was known. However, encounter

sampling alone was not a sufficient means of estimating

stopover duration because detection of golden-plovers was

less than 1, which violates the known-fate requirement.

Therefore, we reasoned that total stopover duration could

be estimated from the addition of stopover duration

predeparture (Otis et al. 1993) and stopover duration

postcapture (Schaub et al. 2001) minus the observed (or

naıve) time of residence.

Habitat SurveysWe collected information on the habitat used by golden-

plovers during their stopover at 2 different temporal scales.

Between 2008 and 2010 we conducted habitat surveys

during peak migration to investigate agricultural field type

preference during this critical period. We supplemented

this information by assessing field type preference over the

entire stopover duration in 2012.

Peak migration habitat selection. We surveyed for

golden-plovers at the end of April over the 3-year period

between 2008 and 2010. Specifically, we conducted our

surveys on April 19 and 20, 2008, April 25, 2009, and April

24, 2010, which represented one survey per field per year.

We surveyed near the end of April because golden-plovers

are typically most abundant around this time period when

migrating through our study area, based on reported

observations by local birders (Sullivan et al. 2009). We

conducted our surveys in 6 townships in east-central

Illinois and west-central Indiana. We utilized the township

unit to standardize our surveys and chose 30 random

locations within each township, located at the midpoints of

each east–west section road. Consequently, the centers of

our sampling locations were at least 1.6 km apart. At each

sampling location, we conducted a 5-min visual search of

the 4 fields in the intermediate directions (NW, NE, SW,

SE) and recorded the presence of golden-plovers.

We collected information on the type of agricultural

field (corn, soybean, or other) and type of tillage practice

employed (intensive tillage: largely bare ground; conser-

vation tillage: .30% soil surface covered with crop residue;

and stubble: basal parts remaining) on each field surveyed.

The high-intensity agriculture of the landscape led to 6

unique field classifications (corn stubble, corn conserva-

tion, corn tilled, soy stubble, soy tilled, and other). We also

The Condor: Ornithological Applications 116:162–172, Q 2014 Cooper Ornithological Society

164 Golden-plover stopover ecology K. W. Stodola, B. J. O’Neal, M. G. Alessi, et al.

classified the moisture level of each field as dry, wet, or

standing water present (.1 m2 pool of water).

Season-long habitat assessment. In 2012 we conduct-

ed a season-long assessment of the agricultural fields used

by golden-plovers in the Arcola and Allerton survey

regions. We utilized the north–south, east–west layout of

the county roads to thoroughly survey both focal areas. We

drove at a rate of ,35 km hr�1, scanning the surrounding

fields for congregations of golden-plovers. We noted the

agricultural crop (soybeans, corn, other) and the tillage

practice employed (tilled, untilled) in each quarter section

(0.8 km 3 0.8 km—the most common field size) as well as

the presence or absence of golden-plovers. In total, we

surveyed 160 fields in the Arcola region and 161 in the

Allerton region (we excluded areas with high amounts

[.50%] of development, as these areas never contained

golden-plovers and were too infrequent to warrant

inclusion in our analyses). Each survey was concluded by

resurveying an east–west or north–south road that had

been previously surveyed 2–3 hr prior, which allowed us to

resample approximately 20% of all fields. We conducted 8

surveys, separated by 5–6 days on average, in each location

between the end of March and the beginning of May. This

time period coincided with the arrival and subsequent

departure of golden-plovers in the area.

Molting BehaviorWe monitored the plumage of golden-plovers during our

habitat surveys in 2012 to determine whether golden-

plovers used their stopover time in central Illinois to molt

into breeding plumage. We recorded the proportion of

each congregation that was in 3 classes of plumage: winter

plumage (,10% breeding plumage), transitional plumage

(10–90% breeding plumage), and breeding plumage (.90%

breeding plumage).

Food Availability and MassWe determined whether agricultural fields differed in

earthworm abundance and biomass. We sampled earth-

worms because golden-plovers are known to eat them

(Bent 1929, Wishart et al. 1981, Braile 1999, K. W. Stodola

personal observation) and preliminary surveys suggested

that there were few other forage items available. We used a

chemical method of earthworm extraction that has been

shown to outperform hand sorting (Zaborski 2003). We

sampled 36 different fields in 2011 and 2012, gathering 1–

6 samples per field. Each sample consisted of a 0.5 m2

patch of soil to which we added 10 L of extraction solution.

The extraction solution consisted of allyl isothiocyanate

diluted to a concentration of 100 mg L�1 with isopropanol

and water (Zaborski 2003). Finally, we weighed 10

individuals (3 in 2009, 3 in 2011, and 3 in 2012) using a

spring-loaded Pesola scale.

Statistical AnalysesWe calculated the amount of time for which golden-

plovers were present after capture by modeling apparent

survival (Lebreton et al. 1992), which is a good approx-

imation of permanent emigration if mortality is assumed

to be zero (we detected no mortality). We estimated

stopover duration postcapture using the Live Recaptures

extensions in Program MARK (White and Burnham 1999).

We fit 6 different models describing daily survival. Daily

survival was modeled as constant within and among years,

as a function of year, and as a function of date within year.

Detection was modeled as either constant among years or

as a function of year. We estimated stopover duration

predeparture using the methods of Otis et al. (1993) and

program DISTANCE (Thomas et al. 2010). Specifically, we

calculated strip width (i.e. stopover duration; Lehnen and

Krementz 2005) by fitting the probability density function

using 3 different models: uniform key with a cosine

adjustment; uniform key with a simple polynomial

adjustment; and half-normal key with a cosine adjustment.

We compared candidate models of postcapture and

predeparture stopover duration using second-order

Akaike’s information criterion adjusted for small sample

size (AICc; Burnham and Anderson 2002) and providemodel-averaged estimates where appropriate. Finally, we

used 100,000 Monte-Carlo simulations to calculate

confidence intervals around our estimate of stopover

duration, because each estimate (predeparture, observed,

postcapture) was derived from different distributions.

We investigated the frequency with which fields were

used by golden-plovers and how they varied based on our

categorical predictors prior to analyzing habitat selection

during peak migration. We failed to observe any golden-

plovers in the field classification ‘‘other’’ (n ¼ 120) and

deleted this group of fields from the analysis. We used

logistic regression to investigate the influence that

agricultural crop, tillage practice, and soil moisture had

on golden-plover observations in a field. We focused on

presence or absence of golden-plover congregations

because less than 6% of fields were occupied. Inclusion

of abundance therefore provided little additional informa-

tion concerning field use by golden-plovers. A Hosmer-

Lemeshow (Hosmer and Lemeshow 1989) goodness-of-fit

test on a model that included year, township, soil type, soil

moisture, and field character failed to indicate any lack of

fit of the data (P ¼ 0.19). However, a plot of the deviance

residuals (Pierce and Schafer 1986) indicated dependence

among points nested within townships. Consequently, we

incorporated both township and location nested within

township as random effects, while agricultural crop, tillage

practice, and soil moisture were treated as fixed effects.

The dispersion parameter (Pearson chi-square/degrees of

freedom) from this model failed to indicate any over-

dispersion of the data (c , 1).

The Condor: Ornithological Applications 116:162–172, Q 2014 Cooper Ornithological Society

K. W. Stodola, B. J. O’Neal, M. G. Alessi, et al. Golden-plover stopover ecology 165

We evaluated the relative fit of 8 models that included

agricultural crop, tillage practice, soil moisture, combina-

tions of these predictors, and the interaction between

agricultural crop and tillage practice. We compared

competing models using second-order Akaike’s informa-

tion criterion adjusted for small sample size (AICc;

Burnham and Anderson 2002). We report log-odds ratios

for the parameter estimates from the best-fitting model to

facilitate comparisons.

We assessed season-long habitat preferences in 2012

using a dynamic occupancy model (MacKenzie et al. 2003).

We first modeled how detection of golden-plovers was

related to agricultural crop and tillage practice while

keeping occupancy constant. We then modeled how

colonization (arrival) and extinction (departure) were

related to survey region, agricultural crop, and tillage

practice while using the best-fitting detection model. We

kept initial occupancy constant since we began our surveys

at the beginning of the year when fields were unoccupied.

We fit models using the function colext in the ‘unmarked’

package in R (Fiske and Chandler 2011) and compared

model performance using AICc.

Finally, we investigated the pattern of molt over the course

of the stopover duration using logistic regression on the

percentage of a congregation classified as primarily in winter

plumage and classified as primarily in breeding plumage.We

analyzed the proportion of golden-plovers that departed

during the day in relation to total daylight hours using a chi-

square test. We evaluated earthworm abundance using a

zero-inflated Poissonmodel with agricultural field (crop type

and tillage practice) and survey region as fixed effects and

field as a random effect. Earthworm biomass was log-

transformed prior to analysis andweused a generalized linear

model with a normal distribution and identity link, including

survey regions as fixed effects and field as a random effect.

RESULTS

Stopover Duration

American Golden-Plovers were present in agricultural

fields in east-central Illinois for ~45 days, with detections

beginning at the end of March and lasting until mid-May

(Figure 1). Median capture date was April 19, which was

the approximate midpoint of when golden-plovers were

present in our region (Figure 1). Minimum stopover

duration (days from radio-transmitter attachment to last

detection) averaged 11.4 6 1.6 SE days. The longest

observed stopover was 29 days and the shortest was 2 days.

The best-fitting model describing stopover duration

postcapture included detection as a function of year and

time to departure as constant within and among years

(Table 1). Mean detection estimates ranged from 0.95 in

2009 when only 3 golden-plovers were tracked to 0.50 in

2012 when we tracked 11 golden-plovers; mean estimates

in 2010 and 2011 were 0.60 and 0.64 respectively. The

estimate from the best-fitting model suggested that

golden-plovers remained in the region for 14.2 days (95%

CI ¼ 9.0–22.5 days) after being captured. Stopover

duration prior to departure using program DISTANCE

and encounter sampling suggested a stopover duration of

20.4 days (95% CI ¼ 14.6–28.4 days). Total stopover

duration estimated by combining the 2 approaches was

23.7 days (95% CI ¼ 17.5–33.1 days).

Habitat Use: Peak MigrationWe observed golden-plovers on ~5% of fields (93 of 1,966)

over the 3 years of the study, irrespective of township or

FIGURE 1. Phenology of American Golden-Plover presence incentral Illinois, USA, as demonstrated by the largest count ofAmerican Golden-Plovers detected during each week of the2012 stopover period. The start of week 1 corresponds to March30.

TABLE 1. Comparison of candidate models describing stopoverduration postcapture (i.e. daily survival rate; Schaub et al. 2001)of American Golden-Plovers in Illinois, USA, in 2012. Stopoverduration postcapture (/) was modeled as either constant (.), oras a function of year or date. Detection (p) was modeled aseither constant (.) or as a function of year. Models were rankedbased on Akaike’s information criterion corrected for smallsample sizes (AICc) and weights of evidence (wi). K is the numberof model parameters and DAICc is the difference in AICc from thetop model.

Model K Deviance DAICc wi

/(.) p(Year)a 5 294.4 0.0 0.70/(Year) p(Year) 8 289.5 1.8 0.30/(.) p(.) 2 311.7 11.0 0.00/(Year) p(.) 5 307.4 13.0 0.00/(Date) p(Year) 50 261.4 115.8 0.00/(Date) p(.) 47 278.1 118.0 0.00

a The AICc value of the best-fitting model was 334.8.

The Condor: Ornithological Applications 116:162–172, Q 2014 Cooper Ornithological Society

166 Golden-plover stopover ecology K. W. Stodola, B. J. O’Neal, M. G. Alessi, et al.

location within township. We observed the highest

percentage of fields occupied by golden-plovers in 2010

(5.6%) and the lowest percentage in 2009 (3.6%). We had a

high count of 2,992 individuals in one township in 2010.

The best-fitting model explaining golden-plover observa-

tions, which received 70% of the weight of evidence (Table

2), included crop type and soil moisture. Although there

was a high amount of model uncertainty, the importance

of tillage practice appeared to be much lower than that of

crop type (models with only those predictors had DAICc¼16). In addition, the additive models that included tillage

practice with crop type improved model performance

approximately as much as random data. Consequently, we

believe there is strong evidence that crop type and soil

moisture are the most important predictors of golden-

plover observations.

Golden-plovers were more likely to be observed in fields

with standing water in comparison with fields that were

‘‘wet’’ or ‘‘dry.’’ Specifically, golden-plovers were 11 (95%

CI¼ 3–30) and 22 (95% CI¼ 7–71) times more likely to be

found in fields with standing water than in wet and dry

fields respectively. Golden-plovers were also 5.2 (95% CI¼2.7–9.9) times more likely to be observed in soybean than

in corn fields.

Habitat Use: Season-long

We found little evidence that detection of golden-plovers

differed by agricultural crop (AICc ¼ 728.1) or tillage

practice (AICc ¼ 733.6) in comparison with the constant

detection model (AICc ¼ 726.5). Detection of golden-

plovers was 0.50 (95% CI ¼ 0.33–0.66). The best-fitting

occupancy model included colonization as a function of

survey region and extinction as a function of tillage

practice (Table 3). Initial site occupancy was low. We

estimated that 5% (95% CI ¼ 2–10%) of fields were

occupied, irrespective of survey region. During stopover,

fields near Allerton, Illinois, were on average 5.6 (95% CI¼2.8–11.7) times more likely to have golden-plovers use

them if they were previously unused in comparison with

fields near Arcola, Illinois. However, the probability of an

unused field becoming used was still relatively low for both

areas: ~2% (95% CI¼0–3%) near Arcola and ~9% (95% CI

¼ 5–13%) near Allerton. The probability of a used field

becoming unoccupied between surveys was most depen-

dent on field tillage practice (Table 3). Although the

evidence was somewhat equivocal and our estimation

imprecise, a tilled field with golden-plovers was 7.3 (95%

CI ¼ 0.8–67.0) times more likely to become unoccupied

than an untilled field. Most fields with golden-plovers

would not have golden-plovers on a subsequent survey

with 71% (95% CI ¼ 47–87%) of untilled fields and 88%

(95% CI ¼ 45–98%) of tilled fields becoming unoccupied

between surveys.

The high turnover among occupied sites indicated

substantial movement by golden-plovers. This movement

was illustrated by the area used estimates for the 4

individuals for which we had sufficient information, which

ranged from 1,706 ha to 56,034 ha, with a median area of

9,962 ha (Figure 2).

Migration Behavior

Departure from the study area was synchronous within a

year. Five of the 11 radio-tagged birds left on April 25,

2012, with another 3 individuals leaving within 4 days of

that date. In 2011, 4 of the 5 birds with transmitters left

TABLE 2. Comparison of logistic regression models relatingAmerican Golden-Plover observations at 6 townships in centralIllinois and Indiana, USA, to the field characteristics ofagricultural crop (CROP), tillage practice (TILL), and soil moisture(MOISTURE). Surveys were conducted at the end of April in 2008,2009, and 2010. Year was included as a fixed effect in all models,and township and specific location within each township wereincluded as random effects. Models were ranked based onAkaike’s information criterion corrected for small sample sizes(AICc) and weights of evidence (wi). K is the number of modelparameters and DAICc is the difference in AICc from the topmodel.

Model K Deviance DAICc wi

Year þ CROP þ MOISTUREa 8 597.6 0.0 0.70Year þ (CROP 3 TILL) þ MOISTURE 11 597.6 3.0 0.16Year þ CROP þ TILL þ MOISTURE 10 596.8 3.3 0.14Year þ TILL þ MOISTURE 9 610.6 14.9 0.00Year þ CROP 6 623.2 21.6 0.00Year þ CROP þ TILL 8 622.8 25.2 0.00Year þ (CROP 3 TILL) 9 621.4 25.8 0.00Year þ TILL 7 637.6 38.0 0.00

a The AICc value of the best-fitting model was 613.6.

TABLE 3. Comparison of dynamic occupancy models relatingAmerican Golden-Plover observations to survey region (SITE),agricultural crop (CROP), and tillage practice (TILL). Constantdetection was the best-fitting detection model and thereforedetection was held constant. Models were ranked based onAkaike’s information criterion (AIC) and weights of evidence (wi).K is the number of model parameters, DAIC is the difference inAIC from the top model, w is initial occupancy, e is extinctionprobability, c is colonization probability, and p is detectionprobability.

Model K Deviance DAIC wi

w(.) e(TILL) c(SITE) p(.)a 6 682.0 0.0 0.57w(.) e(.) c(SITE) p(.) 5 686.2 2.2 0.19w(.) e(SITE) c(SITE) p(.) 6 684.5 2.5 0.17w(.) e(CROP) c(SITE) p(.) 6 686.0 4.0 0.08w(.) e(.) c(TILL) p(.) 5 714.1 30.1 0.00w(.) e(.) c(.) p(.) 4 718.5 32.5 0.00w(.) e(.) c(CROP) p(.) 5 718.4 34.4 0.00

a The AIC value of the best-fitting model was 694.0.

The Condor: Ornithological Applications 116:162–172, Q 2014 Cooper Ornithological Society

K. W. Stodola, B. J. O’Neal, M. G. Alessi, et al. Golden-plover stopover ecology 167

within 4 days of May 13, while in 2010, 4 of the 5 birds

with transmitters left within 2 days of May 8. We

determined that 12 birds departed and migrated in a

northwesterly direction (3008 6 2.38 SE), one departed

almost due north (3558), and the other individual left the

study area heading to the northeast (408). Nearly every

individual (13 of 14) initiated migration during daylight

hours ( v2 ¼ 4.89, P ¼ 0.03).

Molting Behavior

Our study area appears to be an important area for molt:

The percentage of birds in breeding plumage changed

from 0% early in the season to nearly 100% later in the

season (Figure 3). On several occasions we noted that an

individual that had been caught and radio-tagged in basic

plumage was seen molting into alternate plumage and in

some cases was observed in full alternate plumage before it

departed.

Food Availability and Mass

We found no difference in earthworm abundance or

biomass related to year (abundance: v2 ¼ 0.10, P ¼ 0.76;

biomass: v2¼ 1.50, P¼ 0.22), region (abundance: v2¼ 2.42,

P ¼ 0.12; biomass: v2 ¼ 2.89, P ¼ 0.09) or crop type

(abundance: v2 ¼ 0.63, P ¼ 0.73; biomass: v2 ¼ 1.59, P ¼0.45). Earthworm abundance was highly variable both

within a field and among fields (Figure 4). There was also a

large amount of variability in the mass of golden-plovers

among years. Average mass (range) was 158 g (147–173 g)

in 2009, 170 g (151–190 g) in 2010, and 226 g (200–238 g)

in 2012.

DISCUSSION

American Golden-Plovers make an extended stopover in

the Midwestern agricultural fields of our study area. We

used a novel approach to estimate stopover duration by

FIGURE 2. Area used estimates for 4 American Golden-Plovers with radio transmitters during spring migration in 2009, 2010, and2011 in central Illinois, USA. Different polygons represent different individuals with sufficient information to estimate area used, anddemonstrate that American Golden-Plovers use extremely large areas during their stopover, ranging from 1,706 ha to 56,034 ha. Thedark box in the inset map of Illinois indicates the approximate location of our study region.

The Condor: Ornithological Applications 116:162–172, Q 2014 Cooper Ornithological Society

168 Golden-plover stopover ecology K. W. Stodola, B. J. O’Neal, M. G. Alessi, et al.

combining 2 analytical methods (Otis et al. 1993, Schaub

et al. 2001). We took this approach because our data did

not meet the assumptions of each method independently,

and we believe that more quantitative development of such

approaches would be useful. Using this combined analysis,

we found that golden-plovers stayed in our region for

approximately 24 days. The extended time that golden-

plovers spend in Midwestern agricultural fields highlights

the importance of the area, since golden-plovers engage in

one of the longest migrations in the world and spend a

considerable portion of the year doing so. The species

spends less than 60 days on the breeding grounds in the

tundra (Johnson and Connors 2010), so it was surprising

that individuals spent such an extended period in the row

crop agricultural fields of east-central Illinois. Row crops

are not traditionally considered good wildlife habitat, yet

they are the dominant land cover across the modern

Midwestern landscape. Thus, golden-plovers may be using

row crop fields out of necessity and not because of quality.

The enormous area that golden-plovers traverse during

their stopover period may illustrate their constant search

for high-quality sites within this nontraditional habitat.

Golden-plovers exhibited an overwhelming tendency to

use fields with standing water in comparison with dry and

wet fields during the period of peak migration. Fields with

standing water are highly ephemeral in our region because

they quickly drain due to the ubiquitous use of drain tile

(perforated plastic tubes buried in fields to facilitate the

removal of excess water from a field), yet golden-plovers

actively sought them out. Fields with standing water are

likely used by golden-plovers because soil moisture

influences the availability of earthworms at the soil surface

(Gerard 1967). Earthworms are a preferred food source for

FIGURE 3. Proportion of individual American Golden-Plovers(mean and 95% confidence interval) in winter (,10% breedingplumage) and breeding (.90% breeding plumage) plumage inDouglas and Vermillion County, Illinois, USA, between March 27and May 11, 2012.

FIGURE 4. Earthworms, which are a prominent food item in the diet of migrating American Golden-Plovers through the Midwest,detected per field grouped by agricultural crop. We found no earthworms in untilled corn. Sampling effort varied among fields (1–6samples per field). Error bars represent 1 standard error.

The Condor: Ornithological Applications 116:162–172, Q 2014 Cooper Ornithological Society

K. W. Stodola, B. J. O’Neal, M. G. Alessi, et al. Golden-plover stopover ecology 169

migrating golden-plovers (Wishart et al. 1981, Braile 1999).

Our sampling suggested little difference among agricul-

tural fields with regard to earthworm abundance, which is

not unexpected given the homogeneity of the landscape

and frequent crop rotations. However, we were sampling

earthworms below the soil surface (Zaborski 2003), which

may not be accessible to golden-plovers. Therefore,

golden-plovers may seek out fields with standing water

where earthworms are more available and closer to the soil

surface.

Golden-plovers also preferred fields that had been

planted in soybeans the previous year during the period

of peak migration. Golden-plovers forage at greater rates

in tilled soybean fields in comparison with other habitats

(Braile 1999) and other researchers have also noted that

golden-plovers prefer soybean fields (Erickson 1992, Braile

1999). Although our sampling failed to indicate any

difference among fields in terms of earthworm abundance,

fields that have been permanently planted in soybeans

have greater abundance and biomass of earthworms

compared with fields that are permanently planted in corn

(Mackay and Kladivko 1985). However, because nearly all

agricultural fields in our area rotate soybean and corn on a

yearly basis, crop rotation likely diminishes any effect ofcrop type on earthworm abundance. Thus, we may have

observed golden-plovers in soybean fields because soybean

fields are often tilled earlier in the year to allow spring

planting of corn. Tilling makes earthworms immediately

accessible to golden-plovers as moist soil is brought to the

surface. Anecdotally, we would observe golden-plovers

flying between freshly plowed fields where the soil had

recently been turned. Local farmers would also relate

similar stories of large congregations of birds flying in

behind their plows. Birds would forage in these fields for a

day or two and then move on.

Our dynamic occupancy analysis and home range

estimates demonstrate how much golden-plovers are

moving around the landscape and the importance of

various habitat types. Very few fields are used by golden-

plovers at any one time, with most fields becoming unused

at later time periods. As we alluded to previously, the

ephemeral nature of fields with standing water probably

drives this pattern. Although 2012 was an extremely dry

year and no fields had standing water, fields that had just

been tilled had wet soil. These fields would quickly dry out

(within 1–2 days), which probably contributed to our

observation that tillage practices influenced the temporal

utilization of a field by golden-plovers. Furthermore, we

only observed golden-plovers in a small proportion of

untilled fields, yet these fields may represent an important

habitat component. Every morning we observed thousands

of golden-plovers leaving from a few select fields with

standing corn stubble and at night they were observed

returning, indicating a disproportionate and important use

of this habitat type during the night. Fields with standing

corn stubble provide one of the few places with remaining

vegetation cover, which may offer the best protection from

nocturnal predators, although more research is necessary.

Migrating golden-plovers utilized our study area in large

numbers, even though they were constantly moving in

search of high-quality sites and food. We observed nearly

4,000 individuals during one survey in one township,

although the true number is likely higher. A minimum of

4,000 individuals represents nearly 2% of the estimated

global population (Morrison et al. 2006) stopping over in a

relatively small region of east-central Illinois, signifying the

importance of the region. The ability to gain mass during

stopover is a sign of high-quality habitat (Zwarts et al.

1990, Gudmundsson et al. 1991) and is exhibited in other

closely related species occupying high-quality areas

(Johnson et al. 1989, Serra et al. 1999). Of the birds we

caught during our study, most were at least as heavy as has

been observed on the breeding grounds (Johnson and

Connors 2010), indicating that birds were not emaciated.

However, no data exist for condition of golden-plovers

during migration so we have little with which to compare.

High fuel intake is necessary for migrating individuals

because this is one of the most energetically demanding

times during their annual cycle. We observed golden-

plovers undergoing a near-complete molt of their body

feathers from basic to alternate plumage during their

stopover period in east-central Illinois. Molt is anenergetically demanding process for birds (Payne 1972),

and golden-plovers must both molt and refuel for the next

leg of their migration. Golden-plovers predominantly left

during the day and, based on the direction of departing

golden-plovers (~3008), we estimated that individuals

would travel another 3,500 km to reach their breeding

grounds. Using a tracking vehicle, we followed one

migrating golden-plover from east-central Illinois to

central Iowa before we lost the bird; these data suggest

that this individual migrated at a rate of 56 km hr�1 on

average. If birds averaged 56 km hr�1, it would require an

additional 62 hr of flight to reach the breeding grounds.

Arrival at the breeding grounds by late May would require

many individuals to migrate 3,500 km in ~2 weeks. Even if

these individuals stopped to refuel and rest, they would not

be able to spend the same amount of time as they did in

our study area. Therefore, the time they spend in our

region appears to be critical.

The agricultural fields of the Midwestern United States

present an interesting opportunity for the conservation of

migrating American Golden-Plovers. As a whole the

region is likely to be composed of lower-quality habitats

than were historically present; however, the area remains

an important stopover area. The closely related Eurasian

Golden-Plover also exhibits a similar predisposition

towards using intensively farmed lands and the species

The Condor: Ornithological Applications 116:162–172, Q 2014 Cooper Ornithological Society

170 Golden-plover stopover ecology K. W. Stodola, B. J. O’Neal, M. G. Alessi, et al.

performs quite well in this habitat (Lindstrom et al. 2010).

Although row crop agriculture may not be optimal habitat

for migrating plovers, because they move extensively while

stopping over in our region, targeted conservation actions

could be successful in improving agricultural habitat for

plovers.

Agriculture throughout the Midwest has been made

possible by artificial surface and subsurface draining of

fields (Pavelis 1987). The use of drain tiles for subsurface

draining is exceptionally prevalent in Illinois and Indiana,

where nearly 7 million hectares of cropland are artificially

drained (Sugg 2007). However, draining only needs to take

place prior to the planting of fields, which typically occurs

in early May for corn and late May for soybeans in Illinois

and Indiana (U.S. Department of Agriculture 2010). Most

golden-plovers will have left this region before corn, and

especially soybeans, would need to be planted. Conse-

quently, temporarily blocking drain tiles to promote the

flooding of fields during the late winter and early spring

could provide valuable foraging locations, while minimiz-

ing costs to agriculture. The propensity of golden-plovers

to utilize flooded fields suggests that these areas could

provide stable foraging locations and reduce the energetic

costs of searching for food, thereby increasing the quality

of stopover habitat in Illinois and Indiana.

ACKNOWLEDGMENTS

We thank Dave Enstrom, Misty Barron, Arlo Raim, BenNeece, Amber Wingert, Jake Hennig, Matt McKim-Louder,Megan Bales, Kelly VanBeek, Addison Demanes, John Bender,and the University of Illinois Wildlife Society for assistance inthe field. We are grateful to the Illinois Department of NaturalResources, Illinois Ornithological Society, and U.S. Fish andWildlife Service, Division of Migratory Birds for funding thisproject. We are also appreciative of the many landowners whoallowed us to capture and monitor birds on their property.

LITERATURE CITED

Andrei, A. E., L. M. Smith, D. A. Haukos, and J. G. Surles (2006).Community composition and migration chronology ofshorebirds using the saline lakes of the Southern GreatPlains, USA. Journal of Field Ornithology 77:372–383.

Andres, B. A., and B. T. Browne (1998). Spring migration ofshorebirds on the Yakutat Forelands, Alaska. Wilson Bulletin110:326–331.

Atkinson, P. W., A. J. Baker, K. A. Bennett, N. A. Clark, J. A. Clark, K.B. Cole, A. Dekinga, A. Dey, S. Gillings, P. M. Gonzalez, K.Kalasz, C. D. T. Minton, et al. (2007). Rates of mass gain andenergy deposition in Red Knot on their final spring stagingsite is both time- and condition-dependent. Journal ofApplied Ecology 44:885–895.

Bent, A. C. (1929). Life histories of North American shorebirds.Part 2. U.S. National Museum Bulletin 146. U.S. GovernmentPrinting Office, Washington, D.C., USA.

Birdlife International (2008). Species factsheet: Pluvialis dominica.http://www.birdlife.org/datazone/speciesfactsheet.php?id¼3113

Botton, M. L., R. E. Loveland, and T. R. Jacobsen (1994). Siteselection by migratory shorebirds in Delaware Bay, and itsrelationship to beach characteristics and abundance ofhorseshoe crab (Limulus polyphemius) eggs. Auk 111:605–616.

Braile, T. M. (1999). Migration studies of shorebirds in west-central Indiana. M.S. thesis, Purdue University, West Lafayette,IN, USA.

Burnham, K. P., and D. R. Anderson (2002). Model Selection andMultimodel Inference: A Practical Information-theoreticApproach. 2nd edition. Springer-Verlag, New York, NY, USA.

Calenge, C. (2006). The package adehabitat for the R software: Atool for the analysis of space and habitat use by animals.Ecological Modelling 197:516–519.

Clay, R. P., A. J. Lesterhuis, and O. Johnson (2010). Conservationplan for the American Golden-Plover (Pluvialis dominica).Version 1.1. Manomet Center for Conservation Sciences,Manomet, MA, USA.

Colwell, M. A. (2010). Shorebird Ecology, Conservation, andManagement. University of California Press, Berkeley, CA,USA.

Drent, R. J., A. D. Fox, and J. Stahl (2006). Travelling to breed.Journal of Ornithology 147:122–134.

Erickson, H. T. (1992). Spring migration of Lesser Golden-Ploverin Benton County, Indiana. Indiana Audubon Quarterly 70:87–92.

Fiske, I., and R. B. Chandler (2011). unmarked: an R package forfitting hierarchical models of wildlife occurrence andabundance. Journal of Statistical Software 43:1–23.

Gerard, B. M. (1967). Factors affecting earthworms in pastures.Journal of Animal Ecology 36:235–252.

Gudmundsson, G. A., A. Lindstrom, and T. Alerstam (1991).Optimal fat loads and long-distance flights by migratingKnots Calidris canutus, Sanderlings C. alba and TurnstonesArenaria interpres. Ibis 133:140–152.

Hedenstrom, A., and T. Alerstam (1997). Optimum fuel loads inmigratory birds: Distinguishing between time and energyminimization. Journal of Theoretical Biology 189:227–234.

Herkert, J. R., R. E. Szafoni, V. M. Kleen, and J. E. Schegman (1993).Habitat establishment, enhancement and management forforest and grassland birds in Illinois. Natural HeritageTechnical Publication 1. Division of Natural Heritage, IllinoisDepartment of Conservation, Springfield, IL, USA. 20 pp.

Holmgren, N., H. Ellegren, and J. Pettersson (1993). Theadaptation of molt pattern in migratory Dunlins Calidrisalpina. Ornis Scandinavica 24:21–27.

Hosmer, D. W., and S. Lemeshow (1989). Applied LogisticRegression. John Wiley and Sons, New York, NY, USA.

Jehl, J. R., Jr. (1988). Biology of the Eared Grebe and Wilson’sPhalarope in the nonbreeding season: A study of adaptationsto saline lakes. Studies in Avian Biology 12. CooperOrnithological Society.

Johnson, O. W., and P. G. Connors (2010). American Golden-Plover (Pluvialis dominica). The Birds of North America Online201. http://bna.birds.cornell.edu/bna/species/201

Johnson, O. W., M. L. Morton, P. L. Bruner, and P. M. Johnson(1989). Fat cyclicity, predicted migratory flight ranges, andfeatures of wintering behavior in Pacific Golden-Plovers.Condor 91:156–177.

The Condor: Ornithological Applications 116:162–172, Q 2014 Cooper Ornithological Society

K. W. Stodola, B. J. O’Neal, M. G. Alessi, et al. Golden-plover stopover ecology 171

Jorgensen, J. G., J. P. McCarty, and L. L. Wolfenbarger (2008).Buff-breasted Sandpiper density and numbers during migra-tory stopover in the Rainwater Basin, Nebraska. Condor 110:63–69.

Koopman, K., and J. B. Hulscher (1979). Catching waders with a‘‘Wilsternet’’. Wader Study Group Bulletin 26:10–12.

Lebreton, J. D., K. P. Burnham, J. Clobert, and D. R. Anderson(1992). Modeling survival and testing biological hypothesesusing marked animals: A unified approach with case studies.Ecological Monographs 62:67–118.

Lehnen, S. E., and D. G. Krementz (2005). Turnover rates of fall-migrating Pectoral Sandpipers in the lower MississippiAlluvial Valley. Journal of Wildlife Management 69:671–680.

Lindstrom, A., J. Danhardt, M. Green, R. H. G. Klaassen, and P.Olsson (2010). Can intensively farmed arable land befavourable for birds during migration? The case of theEurasian Golden Plover Pluvialis apricaria. Journal of AvianBiology 41:154–162.

Mackay, A. D., and E. J. Kladivko (1985). Earthworms and rate ofbreakdown of soybean and maize residues in soil. SoilBiology and Biochemistry 17:851–857.

MacKenzie, D. I., J. D. Nichols, J. E. Hines, M. G. Knutson, and A. B.Franklin (2003). Estimating site occupancy, colonization, andlocal extinction when a species is detected imperfectly.Ecology 84:2200–2207.

Morrison, R. I. G., B. J. McCaffery, R. E. Gill, S. K. Skagen, S. L.Jones, G. W. Page, C. L. Gratto-Trevor, and B. A. Andres (2006).Population estimates of North American shorebirds, 2006.Wader Study Group Bulletin 111:67–85.

Otis, D. L., L. L. McDonald, and M. A. Evans (1993). Parameterestimation in encounter sampling surveys. Journal of WildlifeManagement 57:543–548.

Pavelis, G. A. (Editor) (1987). Farm drainage in the United States:History, status, and prospectus. USDA Publication 1455.Economic Research Service, Washington, D.C., USA.

Payne, R. B. (1972). Mechanisms and control of molt. In AvianBiology. Volume 2 (D. S. Farner, J. R. King, and K. C. Parkes,Editors). Academic Press, New York, NY, USA. pp. 103–155.

Pierce, D. A., and D. W. Schafer (1986). Residuals in generalizedlinear-models. Journal of the American Statistical Association81:977–986.

Potter, B. A., R. J. Gates, G. J. Soulliere, R. P. Russell, D. A. Granfors,and D. N. Ewert (2007). Upper Mississippi River and GreatLakes Region Joint Venture Shorebird Habitat ConservationStrategy. U.S. Fish and Wildlife Service, Fort Snelling, MN,USA.

Prince, H. (1997). Wetlands of the American Midwest. TheUniversity of Chicago Press, Chicago, IL, USA.

Samson, F., and F. Knopf (1994). Prairie conservation in NorthAmerica. Bioscience 44:418–421.

Schaub, M., R. Pradel, L. Jenni, and J. D. Lebreton (2001).Migrating birds stop over longer than usually thought: Animproved capture-recapture analysis. Ecology 82:852–859.

Serra, L., D. A. Whitelaw, A. J. Tree, and L. G. Underhill (1999).Moult, mass and migration of Grey Plovers Pluvialissquatarola wintering in South Africa. Ardea 87:71–81.

Skagen, S. K. (2006). Migration stopovers and the conservation ofArctic-breeding Calidridine sandpipers. Auk 123:313–322.

Skagen, S. K., and F. L. Knopf (1993). Toward conservation ofmidcontinental shorebird migrations. Conservation Biology 7:533–541.

Skagen, S. K., and A. A. Yackel Adams (2010). Are there optimaldensities for prairie birds? Condor 112:8–14.

Skagen, S. K., P. B. Sharpe, R. G. Waltermire, and M. B. Dillon(1999). Biogeographical profiles of shorebird migration inmidcontinental North America. U.S. Geological Survey, FortCollins, CO, USA.

Sugg, Z. (2007). Assessing U.S. farm drainage: Can GIS lead tobetter estimates of subsurface drainage extent? WorldResources Institute, Washington D.C., USA.

Sullivan, B. L., C. L. Wood, M. J. Iliff, R. E. Bonney, D. Fink, and S.Kelling (2009). eBird: A citizen-based bird observationnetwork in the biological sciences. Biological Conservation142:2282–2292.

Sutherland, W. J., J. A. Alves, T. Amano, C. H. Chang, N. C.Davidson, C. M. Finlayson, J. A. Gill, R. E. Gill, P. M. Gonzalez, T.G. Gunnarsson, D. Kleijn, C. J. Spray, et al. (2012). A horizonscanning assessment of current and potential future threatsto migratory shorebirds. Ibis 154:663–679.

Thomas, L., S. T. Buckland, E. A. Rexstad, J. L. Laake, S. Strindberg,S. L. Hedley, J. R. B. Bishop, T. A. Marques, and K. P. Burnham(2010). Distance software: design and analysis of distancesampling surveys for estimating population size. Journal ofApplied Ecology 47:5–14.

U.S. Department of Agriculture (2010). Field Crops: UsualPlanting and Harvesting Dates. Agricultural Handbook 628.USDA, National Agricultural Statistics Service.

Walk, J. W., M. P. Ward, T. J. Benson, J. L. Deppe, S. A. Lischka, S.D. Bailey, and J. D. Brawn (2010). Illinois Birds: A Century ofChange. Illinois Natural History Survey Special Publication 31.Illinois Natural History Survey, Champaign, IL, USA.

Ward, M. P., and A. Raim (2011). The fly-and-social foraginghypothesis for diurnal migration: Why American Crowsmigrate during the day. Behavioral Ecology and Sociobiology65:1411–1418.

Ward, M. P., J. H. Sperry, and P. J. Weatherhead (In press).Evaluation of automated radio telemetry for quantifyingmovements and home ranges of snakes. Journal ofHerpetology.

White, G. C., and K. P. Burnham (1999). Program MARK: Survivalestimation from populations of marked animals. Bird Study46(Supplement):120–138.

Wishart, R. A., P. J. Caldwell, and S. G. Sealy (1981). Feeding andsocial behavior of some migrant shorebirds in southernManitoba. The Canadian Field Naturalist 95:183–185.

Zaborski, E. R. (2003). Allyl isothiocyanate: An alternativechemical expellant for sampling earthworms. Applied SoilEcology 22:87–95.

Zwarts, L., B. J. Ens, M. Kersten, and T. Piersma (1990). Moult,mass, and flight range of waders ready to take off for long-distance migrations. Ardea 78:339–364.

The Condor: Ornithological Applications 116:162–172, Q 2014 Cooper Ornithological Society

172 Golden-plover stopover ecology K. W. Stodola, B. J. O’Neal, M. G. Alessi, et al.