EVALUATING THE AMERICAN WOODCOCK SINGING-GROUND...

EVALUATING THE AMERICAN WOODCOCK SINGING-GROUND SURVEY

PROTOCOL IN ONTARIO USING ACOUSTIC MONITORING DEVICES

A Thesis Submitted to the Committee on Graduate Studies

in Partial Fulfillment of the Requirements for the Degree of Master of Science

in the Faculty of Arts and Science

TRENT UNIVERSITY

Peterborough, Ontario, Canada

(c) Copyright by Jacob Walker 2015

Environmental and Life Sciences M.Sc. Graduate Program

May 2015

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Abstract

EVALUATING THE AMERICAN WOODCOCK SINGING-GROUND SURVEY

PROTOCOL IN ONTARIO USING ACOUSTIC MONITORING DEVICES

Jacob Walker

The breeding phenology of American Woodcocks (Scolopax minor) was evaluated in

Ontario, Canada to determine if changes in dates of courtship activity have introduced negative

bias into the American Woodcock Singing-ground Survey (SGS). Long-term woodcock

phenology and climate data for Ontario were analysed using linear regression to determine if

woodcock breeding phenology has changed between 1968 and 2014. There was no significant

trend in woodcock arrival date, but arrival date was correlated with mean high temperature in

March. In 2011-2013, programmable audio-recording devices (song meters) were deployed at

known woodcock singing-grounds to determine if peaks in courtship activity coincided with

survey dates used by the SGS. Spectrogram interpretation of recordings and data analyses using

mixed-effects models indicated the SGS survey dates were still appropriate, except during the

exceptionally early spring in 2012 when courtship displays were waning in one region during the

survey window. The methods for interpretation of song meter recordings were validated by

conducting point counts adjacent to song meters deployed at singing-grounds, and at randomly

selected locations in woodcock habitat. Recommendations for the SGS protocol are included.

KEYWORDS: Scolopax minor, Singing-ground Survey, phenology, song meter, detectability.

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Acknowledgments

This study was funded by the U. S. Fish and Wildlife Service Webless Migratory

Gamebird Research Program, the Natural Sciences and Engineering Research Council of

Canada, Canadian Wildlife Service (CWS), Bird Studies Canada (BSC), and Trent University.

Guidance was provided by my supervisory committee, Erica Nol (Trent University), Ken

Abraham (Ontario Ministry of Natural Resources (OMNR)), and Joe Nocera (OMNR). Debra

Badzinski (BSC/Stantec) initiated the project, secured funding, and served as the industrial

partner supervisor on my supervisory committee. Christopher Sharp (CWS) facilitated the

distribution of song meters to volunteers across Ontario, and helped secure funding to see the

project to completion. Candace Gainer, Kristen Grittani, Elyse Howat, Carolyn Zanchetta, and

Pamela Butko interpreted many song meter files. Kevin Hannah (CWS), Lisa Venier (Canadian

Forest Service), Kathy St. Laurent (CWS), and Dean Phoenix (OMNR) generously loaned song

meters from their organizations for use in this project. Thank you to the many volunteers who

deployed song meters at singing-grounds, and who performed point counts alongside them.

Myles Falconer (BSC) performed an initial data analysis that helped guide subsequent years of

the study. Phil Taylor (Acadia University) helped to develop the project and provided guidance

in data analysis. Thanks to Ron Tozer for compiling and providing woodcock first observation

dates from Algonquin Provincial Park.

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Table of Contents

Abstract ........................................................................................................................................... ii

Acknowledgments.......................................................................................................................... iii

Table of Contents ........................................................................................................................... iv

List of Figures ................................................................................................................................ vi

List of Tables .................................................................................................................................. x

Chapter 1: Introduction and Literature Review ............................................................................. 1

Background Information on Woodcocks and the Singing-ground Survey ................................. 1

Evaluation of SGS Protocol ........................................................................................................ 4

Breeding Phenology .................................................................................................................... 6

Objectives .................................................................................................................................... 7

Figures ......................................................................................................................................... 8

Chapter 2: Evaluating the Effectiveness of Song Meters in Detection of Displaying Woodcocks 9

Abstract ....................................................................................................................................... 9

Introduction ............................................................................................................................... 10

Methods ..................................................................................................................................... 13

Point Counts at Known Singing-grounds .............................................................................. 13

Random Point Counts in Woodcock Habitat ......................................................................... 14

Results ....................................................................................................................................... 16

Point Counts at Known Singing-grounds .............................................................................. 16

Random Point Counts in Woodcock Habitat ......................................................................... 17

Discussion ................................................................................................................................. 18

Figures ....................................................................................................................................... 22

Tables ........................................................................................................................................ 25

Chapter 3: Evaluating American Woodcock Breeding Phenology and the Timing of the Singing-

ground Survey in Ontario.............................................................................................................. 27

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Abstract ..................................................................................................................................... 27

Introduction ............................................................................................................................... 28

Methods ..................................................................................................................................... 32

Study Area ............................................................................................................................. 32

Long-term Datasets ................................................................................................................ 34

Song Meter Monitoring ......................................................................................................... 35

Interpretation of Song Meter Recordings .............................................................................. 36

Song Meter Data Analysis ..................................................................................................... 39

Actual SGS Survey Dates ...................................................................................................... 42

Results ....................................................................................................................................... 42

Long-term Datasets ................................................................................................................ 42

Song Meter Monitoring ......................................................................................................... 43

Actual SGS Survey Dates ...................................................................................................... 50

Discussion ................................................................................................................................. 51

Figures ....................................................................................................................................... 57

Tables ........................................................................................................................................ 71

Chapter 4: Summary, Conclusions, and Recommendations for the SGS ..................................... 77

Summary and Conclusions ........................................................................................................ 77

Recommendations for the SGS ................................................................................................. 80

References ..................................................................................................................................... 81

Appendix A: Table of Song Meter Locations ............................................................................... 91

Appendix B: Plots of Detectability by Date for Each Song Meter .............................................. 93

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List of Figures

Figure 1.1 Map of the regional survey date windows used in the American Woodcock Singing-

ground Survey. Obtained from the Singing-ground Survey website:

https://migbirdapps.fws.gov/woodcock/training_tool_documents/SGS_date_window

_map.pdf.......................................................................................................................8

Figure 2.1 Spectrogram showing 21 woodcock peent calls recorded by a song meter, created

using Raven Pro 1.4 Interactive Sound Analysis Software. The x-axis spans one

minute, and the y-axis spans 4000 Hz………………………………..……………...22

Figure 2.2 Spectrogram showing a single woodcock flight display in its entirety, recorded by a

song meter, and plotted using Raven Pro 1.4 Interactive Sound Analysis Software.

The x-axis spans one minute, and the y-axis spans 4000 Hz..……………............…23

Figure 2.3 Linear regression model between the ratio of song meter detections to field observer

detections and date in 2014. There was no significant trend (R2=0.0137,

F1,12=0.1668, P=0.6901, N=14)……………………………………………………..24



https://migbirdapps.fws.gov/woodcock/training_tool_documents/SGS_date_window

_map.pdf………………………………………………………….............…………57


using Raven Pro 1.4 Interactive Sound Analysis Software. The x-axis spans one

minute, and the y-axis spans 4000 Hz ……………..………………………………..58

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song meter, and plotted using Raven Pro 1.4 Interactive Sound Analysis Software.

The x-axis spans one minute, and the y-axis spans 4000 Hz…………………….….59

Figure 3.4 NUM.MALES (the number of woodcocks detected at each site) averaged by date,

within each survey date window and year. A Loess smoothing curve with a span

parameter of 0.5 is fitted to the data to in each region and year to aid interpretation.

N is the number of song meters interpreted in each region and year.……………….60

Figure 3.5 DETECTION (the proportion of two-minute segments within the survey time frame

(15 to 60 minutes after sunset) in which at least one woodcock was detected by call)

for every site and date, grouped by survey date window and year. A Loess

smoothing curve with a span parameter of 0.5 is fitted to the data to in each region

and year to aid interpretation. N is the number of song meters interpreted…...……61

Figure 3.6 Linear regression model between date of first spring woodcock observation by

visitors and staff at Algonquin Provincial Park, ON, and year (R2=0.0461,

F1,43=3.126, P=0.0842). Mean first observation date was April 4 (Julian day 94)....62


visitors and staff at Algonquin Provincial Park, ON, and mean daily high temperature

in March from a weather station in North Bay, ON, 1968-2014 (R2=0.2986,

F1,41=19.73, P<0.0001) Mean first observation date was April 4 (Julian day 94)….62

Figure 3.8 Linear regression model between mean daily high temperature in March at a weather

station in North Bay, ON, and year (R2=0.0071, F1,43=1.313, P=0.2581)…………..63

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Figure 3.9 Linear regression model between annual woodcock Singing-ground Survey indices

for Ontario and mean daily high temperature in March at a weather station in North

Bay, ON, 1968-2014 (R2=-0.0080, F1,43=0.6503, P=0.424)………………………...63


for Ontario and date of first spring woodcock observation at Algonquin Provincial

Park, ON (R2=0.0091, F1,43=1.405, P=0.2424)……………………………………...64


for Ontario and year (R2=0.7982, F1,43=175, P<0.0001)……………………………64

Figure 3.12 Predicted values for the DETECTION model for each DAY within the survey

window of each REGION and YEAR. DAY was re-centered around its median

(May 3rd) for modelling. The predictor variable NUM.MALES was set at 1, and

TEMPERATURE (re-centered) was set at 0………………………………………..65

Figure 3.13 Predicted values for the √PEENTS model for each DAY within the survey window

of each REGION and YEAR. DAY was re-centered around its median (May 3rd) for

modelling. The predictor variable NUM.MALES was set at 1, and TEMPERATURE

(re-centered) was set at 0……………………………………………………………66

Figure 3.14 Predicted values for the √FLIGHTS model for each DAY within the survey window

of each REGION and YEAR. DAY was re-centered around its median (May 3rd) for

modelling. The predictor variable NUM.MALES was set at 1…………………….67

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Figure 3.15 Predicted values for the INTENSITY model for each DAY within the survey


(May 3rd) for modelling. The predictor variable NUM.MALES was set at 1, and

TEMPERATURE (re-centered) set to 0…………………………………………….68

Figure 3.16 Predicted values for the NUM.MALES model for each DAY within the survey


(May 3rd) for modelling…………………………………………………………….69

Figure 3.17 Histograms of the number of woodcock Singing-ground Survey routes conducted in

the three survey windows in Ontario 2012………………………………………….70

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List of Tables

Table 2.1 Locations, coordinates, observer names, and sample sizes for point counts conducted

immediately adjacent to song meters deployed at woodcock singing-grounds in

Ontario in 2013…………………………..………………………………………….25

Table 2.2 Table 2.2 Names, dates, and coordinates where routes of 2-minute point counts were

conducted alongside a song meter. Locations are the first stop on the route, which

was selected using satellite imagery to occur at the beginning of a stretch of

secondary road that ran through suitable woodcock habitat. Stop locations were

selected randomly along the route by stopping every 0.64 km. Routes were run

following the Singing-ground Survey protocol…...………………………………...26

Table 3.1 Dates, sample sizes, mean, and standard error of DETECTION for each region and

year, including estimates for the optimal stable woodcock courtship period as

visually determined from Figures 3.3 and 3.4, and the actual survey window dates for

the SGS. These means could not be directly tested due to overlap in optimal stable

period and survey window dates. An asterisk indicates end dates that were based on

removal of song meters from the field as opposed to dates determined from the data.

N refers to the number of song meter recordings interpreted from each time

period………………………………………………………………………………..71

Table 3.2 Coefficients of terms used in the final model selected for DETECTION as a response

variable. Central 2011 was the basis for comparison, and statistically significant

coefficients are in bold font…………………………………………………………72

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Table 3.3 Coefficients of terms used in the final model selected for √PEENTS as a response



Table 3.4 Coefficients of terms used in the final model selected for √FLIGHTS as a response



Table 3.5 Coefficients of terms used in the final model selected for INTENSITY as a response



Table 3.6 Coefficients of terms used in the final model selected for NUM.MALES as a

response variable. Central 2011 was the basis for comparison, and statistically

significant coefficients are in bold font……………………………………………...76

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Chapter 1: Introduction and Literature Review

Background Information on Woodcocks and the Singing-ground Survey

Despite their small size, preference for wet and shrubby habitat, and earthworm-

dominated diet, American Woodcocks (Scolopax minor) are a popular game species among

hunters (McAuley et al. 2013). The most recent estimate of annual woodcock harvest from the

United States Fish and Wildlife Service (USFWS) Harvest Information Program was 243,100

birds from the 2013-2014 hunting season (Cooper and Rau 2014). The 2013-2014 Canadian

Wildlife Service (CWS) General Harvest Survey estimate of woodcocks harvested in Canada

was 33,500 birds (Gendron and Smith 2014). These estimates were well below harvest estimates

from the 1970s and early 1980s, which ranged from 800,000 to 2 million per year in the U.S.,

and 100,000 per year in Canada (Tautin et al. 1983, USFWS 1990, CWS Waterfowl Committee

2013). The decline in woodcock harvest was largely explained by a corresponding decrease in

the number of hunters afield, with roughly 20,000 woodcock hunters in Canada in the 1970s

compared to current estimates of 2000-3000 woodcock hunters in 2013 (CWS Waterfowl

Committee 2013). Numbers of woodcock hunters in the U.S. have similarly declined, from an

estimated 700,000 woodcock hunters in the 1970s, to current estimates of roughly 110,000

woodcock hunters (USFWS 1990; Cooper and Rau, 2014). The most recent estimates of the

continental woodcock population were 2.2 million singing males (2008), and 3.5 million total

birds (2012) (Kelley et al. 2008, Andres et al. 2012).

Woodcock populations have been monitored since 1968 in the United States (U.S.) and

Canada with the American Woodcock Singing-ground Survey (SGS). The SGS produces an

index of singing males per route, which has declined since 1968 at a rate of 0.95 percent per year

(Cooper and Rau 2014). The woodcock population has been managed since the 1970s using two

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separate management regions (Central and Eastern) that are geographically identical to the

Eastern and Mississippi flyways (Cooper and Rau 2014). Studies based on band returns showed

little movement of individual woodcocks between regions, validating the distinction (Martin et

al. 1969, Krohn et al. 1974, Coon et al. 1977). Success rates of hunters have not been quantified

annually in either the U.S. or Canada, but one study indicated a 28-34% decline in number of

woodcocks flushed per hour in the Eastern management region between 1973 and 1984, and a

decline of 11% in the Central management region (Miner and Bart 1989). Preliminary analyses

of the SGS data from the 1970s and 1980s found a significant decrease in woodcock numbers in

the Eastern management region and no significant trend in the Central management region,

although the long term trends reported in 2014 were nearly identical between regions (-0.9% per

year in the Central region and -1.0% per year in the Eastern region) (Tautin 1986, Cooper and

Rau 2014). Currently, approximately 6-8% of the population is harvested each year, but two

studies have indicated that hunting mortality was not a substantial proportion of overall mortality

in the Eastern management region (Dwyer and Nichols 1982, McAuley et al. 2005).

During spring, woodcocks perform breeding displays at clearings or open fields known as

singing-grounds, which are typically adjacent to diurnal feeding and nesting habitats

(Blankenship 1957). Displays are performed in the morning before sunrise and again in the

evening shortly after sunset. The woodcock display consists of loud calls (referred to as

‘peents’) that are issued repeatedly by males at 2-5 second intervals while on the ground, and

flight displays. During the flight display, the male flies broad circles over the singing-ground,

spiralling high into the air before quickly plummeting back to the ground. The three outer

primaries on the woodcock wing produce a high pitched “twittering” sound when in flight, which

is audible throughout the flight display. Flight displays are typically about one minute in

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duration, and males usually peent repeatedly for several minutes between flights. The evening

courtship period continues for 30-45 minutes, but the pre-dawn period is shorter (Duke 1966).

Due to their cryptic coloration, secretive habits, and crepuscular breeding displays, woodcocks

were not frequently detected by the Breeding Bird Survey or other diurnal survey protocols,

which necessitated the development of a species-specific survey protocol for woodcocks (Sauer

et al. 2008, Sauer et al. 2014).

The SGS protocol was originally designed and implemented by Mendall and Aldous

(1943) to monitor woodcocks in Maine. The USFWS adopted the SGS shortly thereafter, and

woodcock monitoring commenced in many U.S. states in 1948, along routes of varying length

that were located in areas of high woodcock density (Kozicky et al. 1954). The SGS protocol

was further refined through intensive studies of breeding woodcock activities in Michigan and

Massachusetts, from which the current survey dates, start and end times, and suitable weather

conditions were derived (Sheldon 1953, Blankenship 1957, Goudy 1960, Duke 1966). The SGS

protocol was standardized and its coverage vastly expanded in 1968. Routes initially used were

replaced with new survey route locations that were determined by randomly-selecting ten-minute

degree blocks and locating routes on secondary roads near the centers of these blocks (Sauer and

Bortner 1991, Cooper and Rau 2014). The survey protocol has not changed since the

standardization in 1968, though the analysis of data generated by the SGS has gone through

several permutations that reflect advancements in statistical modelling. Most recently, a route

regression approach (Sauer and Bortner 1991) was used until the adoption of a hierarchical

model described in detail by Sauer et al. (2008).

SGS routes are located on secondary roads and consist of ten stops spaced at least 0.64

kilometers apart. At each stop, the observer listens for two minutes and records the number of

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woodcocks detected by peent calls. Flight displays are not recorded. Surveys commence 22

minutes after sunset, but start 15 minutes after sunset on evenings when cloud cover is greater

than 75%. All ten stops must be completed within 38 minutes of the start time. Surveys are not

conducted in strong wind, heavy precipitation, or if the air temperature is below 5o C. The area

covered by the SGS is divided into five regions based on latitude. Each of these regions is

assigned a date window of 20 or 21 days in which the survey can be conducted, and the range of

survey dates for each region is five days later than the region to its south (Figure 1.1, hereafter

‘survey windows’). The southernmost survey window ranges from April 10 to April 30, and the

northernmost survey window ranges from May 1 to May 20.

Evaluation of SGS Protocol

The SGS uses an annual population index, the number of singing males per route, to infer

trends in population size. This index is only informative if the number of males detected on each

route is a fixed proportion of the total number of woodcocks present each year. Three studies

have addressed this concern with mixed results (Whitcomb 1974, Whitcomb and Bourgeois

1974, Dwyer et al. 1988). Whitcomb and Bourgeois (1974) found a strong positive correlation

between the number of active singing-grounds and the population estimates of woodcocks at

their study site over seven years in Michigan. Dwyer et al. (1988) found no significant

correlation between the number of active singing-grounds and their population estimates based

on mark-recapture methods over five years in Maine. The study performed by Dwyer et al.

(1988) was comprehensive, however one component of their study created clear-cuts to produce

many new woodcock singing-grounds in the study area, which may have confounded the

relationship between the number of active singing-grounds and the number of males. Dwyer et

al. (1988) indicated that the proportion of non-vocal males increased with population density,

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yielding a higher ratio of males per active singing-ground not reflected by SGS stops

systematically located at each singing-ground within their study area. The presence of non-

vocal, subdominant male woodcocks at singing-grounds is well established, and the non-vocal

males are predominantly second-year (SY) birds entering their first breeding season (Sheldon

1967, Godfrey 1974, Dwyer et al. 1988). Dwyer et al. (1988) also found that males performing

at new singing-grounds created by clearcutting were predominantly SY males, whereas

traditionally used singing-grounds were dominated by males that were entering their second

breeding season or older (ASY). Both Whitcomb (1974) and Dwyer et al. (1988) reported that

male woodcocks that were captured while displaying early in the breeding season were

predominantly ASY, while woodcocks captured while displaying later in the breeding season

were predominantly SY. In both studies, SGS surveys were conducted according to the

designated survey date windows, which corresponded to the second half of the breeding season

in both locations as per the original survey design (see below). The implications of conducting

SGS routes late in the season, when most ASY woodcocks have ceased displaying, are unknown.

Dwyer et al. (1988) concluded that the SGS index would reflect long-term changes as long as the

ratio of dominant to subdominant males at singing-grounds did not change over time. Shissler

and Samuel (1987) conducted a weekly SGS route through an area with a known number of

active singing-grounds that were monitored twice weekly, and found that the resulting SGS

index was highly correlated to the known number of singing-grounds.

Other aspects of the SGS protocol have been studied to understand the relationship

between the SGS index and woodcock population size. Concerns that the roadside SGS routes

may not represent habitat availability across the landscape were addressed using land-cover data

in Minnesota and Wisconsin, and the proportion of woodcock habitat covered by survey routes

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was similar to the proportion of total woodcock habitat found in those states (Nelson 2010).

Factors affecting detection probability of woodcocks along SGS routes were studied in

Michigan, and weather variables (below thresholds at which SGS routes are cancelled), observer

differences, background noise, and habitat types all affected the detection distances of

woodcocks (Bergh 2011). In the hierarchical model used to analyse SGS data, random effects

terms explain variation due to observer and route, but there is no term for background noise

(Sauer et al. 2008). If traffic levels on rural roads used by the SGS have increased since the

survey was initiated in 1968, then detection probability of woodcocks has likely decreased

(Bergh 2011). Background noise levels are currently included (on a scale of 1-4) on the SGS

field data sheet used by observers, but these scores are not used in the analysis of SGS data

(USFWS 2014a). Similarly, detection probabilities along set routes could decrease over time

due to habitat/vegetation community succession, because sound transmission decreases with

vegetation density (Bergh 2011, Johnson 2008).

Breeding Phenology

The advancement in breeding phenology of many bird species in the northern hemisphere

has been well established, though analsyis of trends in average rates of advancement were

inconclusive (Root et al. 2003, Knudsen et al. 2011). Long-term changes in woodcock breeding

phenology could decrease detectability during the scheduled SGS date windows, if spring

woodcock breeding display dates have advanced since the establishment of the date windows in

1968. The survey date windows used by the SGS were originally designed to coincide with the

second half of the breeding season, to exclude detections of migrant woodcocks present early in

the season (Cooper and Rau 2014). If woodcocks cease courtship displays at earlier dates than

they did in the 1960s, the SGS dates may not currently coincide with peak breeding activity.

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Woodcocks showed a long-term trend of earlier spring arrival in New York and Massachusetts

(Butler 2003). Temperature has been correlated with the onset of woodcock breeding activity in

Alabama (Causey et al. 1987), but cues signalling the onset of spring migration are unknown.

Sheldon (1967) reported the timing of spring arrival dates in Massachusetts were easier to predict

than fall departure dates, and that spring arrival depended little on weather conditions. During

autumn migration, woodcock departure dates in Michigan, Wisconsin, and Minnesota were

related to a combination of cues including day-length, moon-phase, barometric pressure, and

wind direction, but not temperature (Meunier et al. 2008). There are no published data on cues

that might signal the end of the woodcock courtship period, or long-term datasets that include

yearly dates of the last courtship display in a set area.

Objectives

The primary objective of this study was to determine if an advancement in woodcock

breeding phenology has introduced negative bias into the SGS index, necessitating a change in

survey protocol. The province of Ontario, Canada was used as the study area, and woodcock

breeding activities were monitored using autonomous audio-recording devices (song meters,

Chapter 2) between 2011 and 2014. To address the primary objective, I: (1) assessed long-term

trends in woodcock breeding phenology in ON and (2) determined if the survey date windows of

the SGS were appropriate in years 2011-2013 for the three survey windows in ON. Additionally,

I compared woodcock detection rates of song meters to detection rates of human observers in the

field. To facilitate interpretation of results derived from song meter recordings, the comparison

between song meter and human detection rates is presented first in Chapter 2. The assessment of

woodcock breeding phenology as it relates to the SGS protocol follows, in Chapter 3. The final

chapter provides a summary and recommendations for the SGS protocol.

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Figures



https://migbirdapps.fws.gov/woodcock/training_tool_documents/SGS_date_window_map.pdf

9

Chapter 2: Evaluating the Effectiveness of Song Meters in Detection of Displaying

Woodcocks

Abstract

Programmable audio-recording devices (Wildlife Acoustics Song Meters, hereafter song

meter) were tested against field observers to assess potential differences in detections of

displaying American Woodcocks (Scolopax minor). Song meters deployed at woodcock

singing-grounds for a breeding phenology study were tested by conducting point counts

immediately adjacent to the song meters. Additional randomly-selected locations adhering to the

American Woodcock Singing-ground Survey protocol were surveyed from roadside listening

points adjacent to a vehicle-mounted song meter. Song meter recordings were interpreted

visually and aurally using spectrogram software. Detection rates between field observers and

song meter recording interpretation were similar at sites identified as having at least one singing

woodcock, but at randomly selected sites in suitable nesting habitat, song meter recording

interpretation missed 41% of all woodcocks detected. At sites with multiple woodcocks, the

probability of detection on song meter recordings decreased for each additional woodcock at the

site.

KEYWORDS: Scolopax minor, Singing-ground Survey, autonomous audio-recorder, song

meter, detectability, point counts.

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Introduction

Autonomous audio-recording devices are effective in monitoring populations of birds

(Haselmayer and Quinn 2000, Rempel et al. 2005, Acevedo and Villanueva-Rivera 2006, Venier

et al. 2011). In general, recording devices are used to record singing birds in breeding habitats

when: (1) study areas are large or remote and repeated samples throughout the breeding season

are desired, (2) permanent records of surveys are desired, (3) disturbance of singing birds is a

concern, (4) variation between field observers is a concern, and (5) skilled field observers are not

available (Acevedo and Villanueva-Rivera 2006, Rempel et al. 2013). The recording devices

have performed well in situations where many bird species were calling during surveys and were

used for comparison to field observation, yielding comparable numbers of species and

individuals detected when recordings were listened to once through (Haselmayer and Quinn

2000, Hobson et al. 2002, Rempel et al. 2005, Campbell and Francis 2011). Repeated listening

and analyses of spectrograms have often determined that more species were detected on the

recordings than in the field during point counts (Haselmayer and Quinn 2000, Hobson et al.

2002, Campbell and Francis 2011). Hutto and Stutzman (2009) however, found that their

recording system missed 41% of total detections made by recording system and field observer

combined, but only 10% of total detections were missed by field observers. Of the birds missed

by the recording device, 53% were attributed to distance, indicating that sensitivity was lower

than the human ear. Many different models of recording devices and microphones are currently

available, and vary by about 10% in sensitivity and their ability to detect distant bird

vocalizations (Rempel et al. 2013).

Seasonal breeding activity of American Woodcocks (Scolopax minor) was documented

throughout Ontario, Canada, using programmable audio-recording devices (Wildlife Acoustics

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Song Seters, models SM1, SM2, and SM2+, hereafter song meters) in the years 2011-2014, to

evaluate the temporal protocol used by the American Woodcock Singing-ground Survey (SGS).

The SGS is a range-wide survey performed by human observers, designed to monitor broad-scale

population trends of the American Woodcock. In Ontario, American Woodcocks give courtship

displays from about mid-March to late-May each year upon arrival on the breeding grounds, and

during these displays give both ‘peent’ calls on the ground and audible flight displays in the air.

The objective of this study was to evaluate the effectiveness of the song meters in documenting

woodcock courtship displays compared to field observation. Venier et al. (2011) found that the

Wildlife Acoustics song meters (SM1) recorded fewer bird species and individuals than either

field observations or another (human-operated) recording device, but that for presence-absence

data, the ability to leave a programmed song meter in the field overcame this deficiency over

time. Rempel et al. (2013) demonstrated that the newer SM2 outperformed the SM1 model in

terms of signal to noise ratio, and was comparable to other more expensive devices on the

market. Standardized (80 decibels at one meter from the speaker) call broadcast tests of several

bird species indicated that detection distances were similar for all of the recording devices tested,

including song meters, and that call detection and identification became impossible at distances

between 100 and 150 meters from the speaker (Rempel et al. 2013). Duke (1966) reported that

woodcocks could be heard by field surveyors at distances of up to 235 meters, though under

most ambient conditions, maximum detection distances were between 70 and 130 meters. Bergh

(2011) broadcast woodcock peents at 70-80 decibels in two land cover types, field and forest, to

compare detection rates of observers in the two habitats. The distance at which 50% of

broadcast woodcock peents were detected was 384 meters in fields, and 198 meters in forests.

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The effectiveness of song meters in detecting woodcock calls and flight displays was

tested by conducting point counts alongside a subset of the song meters deployed for detecting

the courtship displays of American Woodcock in 2013, and at randomly selected roadside

locations in suitable woodcock habitat alongside a vehicle-mounted song meter in 2013 and

2014. Based on the estimated detection distances (100-150 meters) of song meters reported by

Rempel et al. (2013) and the estimated detection distances of woodcock calls in the field (198-

384 meters) reported by Duke (1966) and Bergh (2011), I expected to find a discrepancy

between song meter and field observer detection rates.

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Methods

Point Counts at Known Singing-grounds

In spring 2013, I and four other observers conducted auditory point count surveys at eight

sites in Ontario, Canada alongside pre-positioned and programmed autonomous audio-recording

devices (Wildlife Acoustics Song Meters, models SM2 and SM2+) that were situated adjacent to

known woodcock singing-grounds to monitor seasonal courtship activity (Table 2.1). Song

meters were deployed from March 20 to June 1, 2013. Point counts were ten minutes in

duration, and were conducted during the SGS survey date window for these locations (U.S. Fish

and Wildlife Service 2014b). Point counts were conducted in the evening within the time frame

used in the SGS protocol (between 22 and 60 minutes after sunset, or between 15 and 53 minutes

after sunset on evenings with greater than 75 percent cloud cover). For each minute, the

observer recorded the number of woodcocks detected by peent call, the number of woodcocks

detected by flight display (sound), and the total number of woodcocks detected (individual

woodcocks could be detected by both peent call and flight display during the same minute, so the

total number of woodcocks was determined by distance and direction of calling birds, and by

tracking the progress of each flight display). Non-displaying woodcocks detected by sight only

were not recorded. Observers recorded verbally the start time of the point count into the song

meter. Several of the sites were sampled repeatedly throughout the breeding season, including

one that was surveyed seven times, one that was surveyed six times, and two that were surveyed

twice. Results of surveys performed at the same site on different days were averaged for each

site, since spacing of displaying woodcocks was similar between evenings.

Song meter “.wav” files from the evenings when point counts were conducted were

interpreted using Raven Pro 1.4 Interactive Sound Analysis Software (Bioacoustics Research

14

Program 2011). Spectrograms were used to visually quantify woodcock courtship activity, and

the scale of the y axis was set to 3000-7000 Hz, the frequencies where woodcock calls registered.

Woodcock peents appeared as thick vertical lines centered at ~5000 Hz on a spectrogram when

viewed at a scale of one minute per screen width (Figure 2.1). At the same scale, the flight

display sounds created by the modified outer primary feathers, showed a complex pattern of

rapid notes varying in pitch (Figure 2.2). Spectrograms were viewed in one minute increments,

starting where the announced start of the point count registered on the spectrogram of the

recording. For each one minute segment, the number of woodcocks detected by call, the number

of woodcocks detected by flight display, and the total number of woodcocks detected were

recorded. Recordings were interpreted prior to viewing field data sheets from the point counts to

eliminate any influence that knowledge of the number of woodcocks recorded by the human

observer in the field may have had on interpretation.

I compared number of woodcocks detected on the spectrograms by peent calls, flight

displays, or by either method, to those by field observer using paired t-tests with an alpha level

of 0.05. The proportion of spectrogram to field detections for flight displays was tested against

that of peent calls using a paired t-test with an alpha level of 0.05.

Random Point Counts in Woodcock Habitat

In spring 2013 and 2014, surveys were conducted following the protocol of the SGS near

Ottawa, ON. Estimating regional abundance was not of interest, so routes were pre-selected by

using satellite imagery to ensure that they passed through suitable woodcock habitat along

secondary, lightly travelled roads. Eighteen routes were each surveyed once (Table 2.2). Each

route consisted of 10 two-minute stops spaced 0.64 km apart, but fewer than 10 stops were made

on some routes when weather deteriorated or if woodcocks were not detected on several

15

consecutive stops in suitable habitat. A song meter recorded continuously through the duration

of the stop, and was placed on top of the car at head height, with the microphones pointing

towards the front and rear of the car. The observer recorded the start of each two-minute

listening period, and the number of woodcocks detected by peent call, the number detected by

flight display, and the total number present at the site.

Song meter .wav files from the SGS routes were interpreted using spectrograms in the

same method described for point counts. For each stop on the route, the interpreter recorded the

number of woodcocks detected by peent call, the number detected by flight display, and the total

number present. All .wav files were interpreted prior to viewing and entering data from field

sheets to avoid any influence that knowledge of the number of birds present in the field may

have had.

The total number of woodcocks detected per stop was calculated by using the maximum

number detected by either field observer or spectrogram interpreter, for peent calls, flight

displays, and total number detected by call and flight combined. Determining the number of

woodcocks present in the field was achieved by using distance, direction, and by tracking birds

performing flight displays. The number of woodcocks detected on spectrograms was determined

by frequency of calls (both number/min and Hz), direction (differences in dB recorded by each

microphone), and overlap of flight displays with calling birds or other flight displays.

Percentages of total detections were compared for field observer and spectrogram interpreter for

peent calls, flight displays, and total woodcocks, and tested for statistical significance using G-

tests. Proportions of total woodcock detections by the two methods were calculated separately

for stops with differing numbers of woodcocks detected, and compared using G-tests and

Fisher’s Exact Tests when sample sizes were small. The proportion of total peenting woodcocks

16

detected by spectrogram interpreter was calculated for each survey date and regressed against

Julian date to check for trends in detectability through the breeding season. Statistical tests were

performed using R using an alpha level of 0.05 (R Core Team 2013).

Results

Point Counts at Known Singing-grounds

The mean proportion of detections (number detected by song meter/number detected by

point count) for woodcocks detected by peent call was 0.878. The number of woodcocks

detected by peent call on the spectrograms was not significantly different from the number

detected by field observers (t7 = -1.646, P = 0.144). The average difference was 1.729 fewer

woodcock detections by spectrograms per 10 minute point count (95%CI = -4.213, 0.754). The

mean proportion of detections (song meter/point count) for woodcocks detected by flight display

was 0.909. The number of woodcocks detected by flight display on the spectrograms was not

significantly different from the number detected by field observers (t7 = -1.028, P = 0.338). The

average difference was 0.667 fewer woodcock detections by spectrograms per 10 minute point

count (95%CI = -4.213, 0.754). The mean proportion of detections (song meter/point count) for

the number of woodcocks detected by either peent or flight display was 0.872. The number of

woodcocks detected by either peent call or flight display on the spectrograms was not

significantly different from the number detected by field observers (t7 = -1.631, P = 0.147). The

average difference was -1.854 fewer woodcock detections by spectrograms per 10 minute point

count (95%CI = -4.542, 0.834). There was no difference in proportion of detections (song

meter/point count) between peent calls and flight displays (t7 = 0.464, P = 0.657).

17

Random Point Counts in Woodcock Habitat

A total of 143 two-minute listening stops were conducted. One hundred and forty-six

woodcocks were detected at 86 different stops by at least one of the methods, and no woodcocks

were detected in the field or on the spectrograms at 57 stops. The field observer heard 144 of

146 (98.6%) woodcocks detected by either call or flight display, while the interpretation of song

meter recordings via spectrogram detected only 86 (58.9%; G=83.06, df=1, P<0.0001). The

field observer heard 114 of 118 (96.6%) of peenting woodcocks, while spectrogram

interpretation recorded only 72 (61%; G=51.00, df=1, P<0.0001). For flight displays, the field

observer also heard significantly more than were detected during the interpretation of the

spectrograms (field observation: 79/79 (100%), spectrograms: 41/79 (59%), G = 64.92, df=1,

P<0.0001). There was no significant difference in spectrogram detection rates of peents

(72/118=61%) and flight displays (41/79=59%) (G=1.61, df=1, P=0.2051).

At 38 survey stops, a maximum of one woodcock was detected by either field observer or

spectrogram interpretation. The field observer heard 37 (97.4%) of these woodcocks, while the

spectrogram interpretation recorded 18 (47.4%) (G=24.31, df=1, P<0.0001). At another 38

survey stops, a maximum of two woodcocks were detected by either field or spectrogram

methods. When two woodcocks were displaying, the field observer detected at least one

woodcock at all 38 sites, while the spectrogram interpretation recorded at least one woodcock at

36 sites (94.7%) (G=0.53, df=1, P=0.4638). When two woodcocks were displaying, the field

observer detected both woodcocks at 37 sites (97.4%), while the song meter interpretation

recorded both woodcocks at only 13 sites (34.2%) (G=35.56, df=1, P<0.0001). At 9 sites a

maximum of three woodcocks were detected by either method. The field observer detected all

three woodcocks at each of the 9 sites. Spectrogram interpretation recorded at least one

18

woodcock at all 9 of these sites, at least two woodcocks at 6 sites (66.7%) (P=0.2059), and all

three woodcocks at only 1 site (11.1%) (P=0.0004). Thus, as the number of woodcocks present

per stop increased, the spectrogram interpretation was more likely to detect at least one

woodcock per stop, but rates of detection of additional woodcocks declined. After initially

finding such a large discrepancy between field observer and spectrogram detections, many of the

two minute segments where woodcocks were missed on the spectrogram were listened to in their

entirety using over-the-ear headphones at the maximum comfortable volume, and no additional

woodcocks were heard on the recordings that were missed on the spectrograms.

The ratio of woodcocks detected by spectrogram over those detected by field observer

did not vary as a function of date in 2014 (R2=0.0137, F1,12=0.1668, P=0.6901) (Figure 2.3).

Discussion

Point counts conducted adjacent to song meters that were deployed to monitor woodcock

courtship displays at known singing-grounds throughout the breeding season detected similar

numbers of woodcocks to interpretation of the song meter recordings. However, the sample size

was low (n=8) and hearing ability of field surveyors and distances between song meter

deployment locations may have varied so the statistical power to detect a difference may have

been compromised to fully evaluate this question. Repeated point counts at the song meter

locations refined estimates of spectrogram and field surveyor detection rates for those sites, but

were not independent because woodcocks called from the same general areas from one evening

to the next. During a single evening’s courtship display, however, individual woodcocks were

observed to call from several different locations within the same singing-ground between flights,

rather than returning to the same spot after each flight. Ten minute point counts interpreted in

19

one minute increments reflected these different calling locations (the mean ratio of song meter

detections to field detections over the ten minutes was not always one or zero). Point counts

longer than ten minutes in duration may have yielded more accurate detection ratios.

Nevertheless, no systematic pattern in the number of detections between the interpretation of

song meter recordings via spectrogram and field surveys was found, indicating that the non-

random placement of song meters close to woodcock singing-grounds was effective for

monitoring courtship displays.

Song meter recordings of randomly selected survey points in woodcock habitat indicated

that song meter sensitivity to woodcock calls was less than that of the field observer. Overall,

spectrogram interpretation of song meter recordings missed 41% of all woodcock detections,

which was the same percentage of detections missed by the recording device used by Hutto and

Stutzman (2009) to monitor multiple bird species in Montana. Hutto and Stutzman (2009)

attributed more than half (52.7%) of the missed detections to differences in detection distances

between the recording device and the human observers, while visual detections by the field

observer explained another 14.8% of missed detections, and unidentifiable calls another 10.3%.

I attribute the majority of missed woodcock detections in this study to more acute ability to

detect distant woodcock aurally by humans than by the song meter microphones. Missed

detections due to visual detection in the field or unidentifiable calls were not possible, since

visual detections of woodcocks were not recorded, and woodcock calls were easily identifiable

on spectrograms.

Detection distances of woodcock peents and flight displays were not measured in the

field for humans or song meters, because estimating woodcock calling positions was difficult in

the dark, and when calling woodcocks were approached to determine an accurate location they

20

either flew or stopped calling. Additionally, because displaying woodcocks only issued peent

calls from the same location for a brief period between performing flight displays, recordings

from various distances could not be made during that time. Similarly, it was difficult to estimate

woodcock position from distances of over 100 meters, where discrepancies between song meter

and human observer may occur (Duke 1966, Bergh 2011, Rempel et al. 2013). Measuring

woodcock detection distances in the future might be possible with two observers if one observer

were dedicated to determining accurate calling positions between flight displays and the other

positioned at listening points of varied distance to the singing-ground. Detection distances of

simulated woodcock calls using speakers were not measured because the appropriate volume in

decibels could not be determined. Bergh (2011) broadcasted woodcock calls at 70-80 decibels at

one meter from the speaker, but some woodcock calls recorded by the song meters exceeded 105

decibels at unknown distances from the song meter. For experiments using call broadcasting it

would be necessary to measure decibels of woodcock peent calls at known distances from the

recording device.

Discrepancies in woodcock detections between song meters and field observers when

multiple woodcocks were detected can also be attributed to distance, and spacing between calling

woodcocks. When single woodcocks were detected, spectrogram interpretation of song meter

recordings recorded 47.4% of the woodcocks detected. When two woodcocks were detected, the

song meter recorded at least one woodcock (which I assume to be the closest woodcock) at

94.7% of the sites, but the second woodcock at only 36% of the sites. The mean distance

between displaying woodcocks on singing-grounds from a compilation of studies was reported to

be 241 ± 117 (SD) meters (McAuley et al. 2013). If the second woodcock at a site was over 100

meters further away from the closest, then it would not likely be detected by the song meter if

21

detection distance drops off rapidly between 100 and 150 meters (Rempel et al. 2013). If

humans can detect woodcocks from 200-384 meters (Duke1966, Bergh 2011), then detection of

a second woodcock at a singing-ground is much more likely for a field observer than a song

meter. Similarly, when three woodcocks were detected at a site, the observer and song meter

were much more likely to be within range of two displaying woodcocks than three, based on a

triangular configuration of woodcock spacing, yielding a very low rate of song meter detection

for the third woodcock (11.1%).

Differential detection rates between song meters and field observers have several

implications when interpreting results from studies on woodcocks based on song meter data.

The ten minute point counts conducted in this study were adjacent to song meters that were

deployed at known woodcock singing-grounds to monitor breeding activity throughout the

breeding season. Although based on a small sample size, there was no statistical difference

between detection rates of woodcocks recorded on the song meters and those recorded by

observers in the field, suggesting that peaks in woodcock detections recorded by the song meters

reflect peaks in woodcock breeding activity at the sites monitored. Results from randomly

selected sites, however, indicate that it would be difficult to make inferences based on display

rates of any woodcocks other than the closest woodcock to the song meter because song meters

may have missed detections of more distant woodcocks that a field observer could have heard.

There was no evidence that song meter detection rates changed during the breeding season, so

any seasonal changes in courtship activity should be correlated to actual changes in woodcock

activity at singing-grounds monitored by song meters. If song meters were used for the SGS, the

indices would be biased low compared to those conducted by field observers with good hearing,

unless corrected using measured woodcock detection distances for each song meter.

22

Figures


using Raven Pro 1.4 Interactive Sound Analysis Software. The x-axis spans one minute, and the

y-axis spans 4000 Hz.

23


song meter, and plotted using Raven Pro 1.4 Interactive Sound Analysis Software. The x-axis

spans one minute, and the y-axis spans 4000 Hz.

24

Figure 2.3 Linear regression model between the ratio of song meter detections to field observer

detections and date in 2014. There was no significant trend (R2=0.0137, F1,12=0.1668, P=0.6901,

N=14).

25

Tables

Table 2.1 Locations, coordinates, observer names, and sample sizes for point counts conducted

immediately adjacent to song meters deployed at woodcock singing-grounds in Ontario in 2013.

Location name Latitude Longitude Observer N

Peterborough-Trent 44.3575 -78.2831 Chris Risley 2

Ottawa-Kettles Rd 45.1202 -75.8681 Jacob Walker 8

Ottawa-Rifle Rd 45.3426 -75.8723 Jacob Walker 6

Thunder Bay-TA 48.1061 -89.8500 Ted Armstrong 2

Guelph-G 43.5180 -80.1472 Mike Cadman 1

Guelph-Sh 43.5197 -80.1256 Mike Cadman 1

Guelph-Su 43.6839 -80.3215 Mike Cadman 1

Port Rowan-AH 42.6995 -80.4109 Audrey Heagy 1

26

Table 2.2 Names, dates, and coordinates where routes of 2-minute point counts were conducted

alongside a song meter. Locations are the first stop on the route, which was selected using

satellite imagery to occur at the beginning of a stretch of secondary road that ran through suitable

woodcock habitat. Stop locations were selected randomly along the route by stopping every 0.64

km. Routes were run following the Singing-ground Survey protocol.

Route Name Date Latitude Longitude

Torbolton Ridge Rd, Ottawa County 2013-04-23 45.4652 -76.1276

Chaffey's Lock Rd, Leeds and Grenville County 2013-04-27 44.5820 -76.3106

5th Line, Ottawa County 2013-05-09 45.4132 -75.9619

Kettles Rd, Ottawa County 2013-05-11 45.1197 -75.8684

Jock Trail, Ottawa County 2014-05-02 45.1557 -75.8926

Third Line and Moodie Dr, Ottawa County 2014-05-04 45.1762 -75.7388

Timm Dr, Ottawa County 2014-05-05 45.3260 -75.8419

Mer Bleue, Ridge Rd, Ottawa County 2014-05-06 45.3949 -75.5131

Marchurst Rd, Ottawa County 2014-05-07 45.3789 -76.0126

6th Line, Ottawa County 2014-05-09 45.4012 -75.9201

Fernbank Rd, Ottawa County 2014-05-11 45.2328 -75.9363

Grand View and Davidson Side Rds, Ottawa County 2014-05-12 45.3543 -75.8605

Spruce Ridge Rd, Ottawa County 2014-05-13 45.2490 -75.9867

Constance Lake Rd. and 2nd Line, Ottawa County 2014-05-15 45.4007 -75.9779

Hilda and Lois Rds, Ottawa County 2014-05-17 45.3621 -75.8756

Joy's Rd, Ottawa County 2014-05-20 45.1663 -75.8403

Rifle Rd, Ottawa County 2014-05-22 45.3501 -75.8749

Stonecrest Rd, Ottawa County 2014-05-25 45.3979 -76.0628

27

Chapter 3: Evaluating American Woodcock Breeding Phenology and the Timing of the

Singing-ground Survey in Ontario

Abstract

American Woodcock breeding phenology was examined in Ontario, Canada to determine

if the survey date windows used by the American Woodcock Singing-ground Survey (SGS), first

standardized for estimating woodcock numbers in 1968, correspond with current peak courtship

activity dates. Long-term datasets of first spring observation dates of woodcocks, laying date,

SGS index, and temperature were analysed to examine changes in woodcock breeding phenology

that may have occurred since 1968. There was some weak evidence that woodcock arrival date

in Ontario has advanced since 1968 by approximately 6 days, while arrival date was significantly

correlated to spring temperatures in March. There was no relationship between either arrival

date or spring temperatures and subsequent SGS indices those years, indicating that the survey

windows were still well-timed in early springs. Song meters were deployed adjacent to

woodcock singing-grounds over three breeding seasons to document current courtship activity

dates. With repeated daily measurements from song meters, linear and generalized linear mixed

effects models were fitted to the dataset to test for statistical differences in measurements of

courtship activity between years, regions, and dates. In 2012, the second warmest spring since

the survey was initiated, courtship activity significantly decreased during the survey window

dates in the northernmost region monitored by the SGS, but in the other two survey regions

(southern and central Ontario), woodcock courtship activity coincided with the survey window

dates. I conclude that the survey dates used by the SGS are still appropriate, though observers

should be strongly encouraged to conduct surveys early in the survey date windows in years with

unusually warm spring temperatures.

28

KEYWORDS: Scolopax minor, Singing-ground Survey, phenology, song meter, detectability.

Introduction

The American Woodcock (Scolopax minor; hereafter woodcock) is one of only two

shorebird species that are legally hunted as game birds in North America. Breeding woodcock

populations have been monitored annually since 1968 in the United States (U.S.) and Canada

using the Singing-ground Survey (SGS), and this index has been used to inform management

decisions on bag limits and hunting seasons in both countries. Analysis of data from the SGS

has shown a long-term decline of 0.95 percent per year in the number of singing male

woodcocks per survey route (1968-2014), although this trend has levelled in the past decade

(Cooper and Rau 2014). Estimates of woodcock recruitment from the U.S. Fish and Wildlife

Service (USFWS hereafter) Wing Collection Survey have also declined over the same time

period, indicating that the SGS index reflects a decreasing population (Cooper and Rau 2014).

These declines have been attributed to a widespread loss of early-successional habitat due to

aging of young forests, and urban and industrial development (Dwyer et al. 1983, Kelley et al.

2008). The survey protocol of the SGS has remained unchanged since its initiation in 1968,

which facilitates year-to-year comparisons, but leaves the survey susceptible to changes in

woodcock breeding phenology in response to a warming climate. The primary purpose of this

study was to evaluate the seasonal timing of the SGS, to ensure that scheduled survey dates in

Ontario still coincide with peak breeding activity.

Approximately 1,500 SGS routes, covering the core of the woodcock’s breeding range,

are monitored by volunteers in the U.S. and Canada. There are no routes south of Virginia or

north of populated areas in Ontario or Quebec (Cooper and Rau 2014). Roughly 800 routes are

29

surveyed in any given year, because routes where no woodcocks are detected are placed in

‘constant zero’ status and are not surveyed for the following five years (Cooper and Rau 2014).

The area covered by the SGS is divided into five evenly-sized regions based on latitude. Each of

these regions is assigned a date window of 20 or 21 days in which the survey can be conducted,

and the range of survey dates for each region is five days later than the region to its south

(hereafter ‘survey windows’) (Figure 3.1). The southernmost survey window ranges from April

10 to April 30, and the northernmost survey window ranges from May 1 to May 20. Five

additional days are allowed on either side of the survey window limits, with the permission of

the North American SGS Coordinator, to account for exceptionally cold or warm spring

temperatures. Participants are encouraged to conduct SGS routes at the earliest possible date

within the survey window (USFWS 2014a).

Long-term changes in woodcock breeding phenology could lead to changes in

detectability during the scheduled SGS date windows, if spring woodcock breeding display dates

have advanced since the establishment of the date windows in 1968. The survey date windows

used by the SGS were originally implemented based on studies in the 1930s-1960s (Mendall and

Aldous 1943, Sheldon 1953, Goudy 1960, Duke 1966). The survey window was planned to

coincide with a ‘stable period’ in the breeding season, that occurred once migrant woodcocks

had passed through each region, and before courtship activity had declined (Duke 1966, Cooper

and Rau 2014). Numerous studies on the phenology of both plants and animals have

documented advancements in the timing of spring events (e.g., flowering dates, arrival dates,

egg-laying dates, hatching dates), which when combined across taxa, showed an average change

of five days earlier per decade in the temperate region (Root et al. 2003). Studies restricted to

birds showed an average advancement in breeding season by six days earlier each decade (Root

30

et al. 2003). If woodcock display dates followed the same pattern, the cessation of breeding

displays could be roughly 28 days earlier now than it was when the SGS date windows were

created in 1968. A global review of bird migration dates showed spring arrival dates were

advancing an average of 2.3 days per decade (Gienapp et al. 2007). In Europe, similar studies

have indicated that first spring arrival dates of migratory species have advanced an average of

four days per decade, but the advancement of the median migration dates averaged across

species was slower at only one to two days per decade (Knudsen et al. 2011). Studies on North

American migratory birds have been less conclusive, but overall show a trend towards earlier

arrival (Butler 2003, Marra et al. 2005, Mills 2005, Murphy-Klassen et al. 2005, Miller-Rushing

et al. 2008, Knudsen et al. 2011).

In the one North American study that included spring arrival data for woodcocks, first

spring observation date data were analyzed, using bird species recorded by the Cayuga Bird Club

in New York, and the Worcester County Ornithological Society in Massachusetts (Butler 2003).

In the Cayuga Lake Basin, New York, woodcocks arrived an average of 25 days earlier in the

years 1951-1993 than they did in years 1903-1950, an approximate change of 5 days earlier per

decade. For Worcester County, Massachusetts, woodcock arrival date was regressed against

year (1932-1993) yielding a significant coefficient of 2 days earlier per decade. Studies on

breeding woodcocks have included dates of spring arrival, peaks in breeding display activity, egg

laying, hatching, and brooding from many disjunct locations and years (e.g., Mendall and Aldous

1943, Goudy 1960, Duke 1966, Sheldon 1967, Roberts and Dimmick 1978, Shissler and Samuel

1985, Causey et al. 1987, Dwyer et al. 1988, Murphy and Thompson 1993), but no additional

long-term data on woodcock breeding phenology from any location have been published.

31

Woodcocks are short distance migrants, and will winter in eastern North America as far

north as there is available unfrozen ground, though most spend the winter from Virginia south

and west through the Gulf States to central Texas (McAuley et al. 2013). It has been postulated

that the breeding phenology of short distance migrants should advance faster than that of long

distance migrants, and that phenology of short distance migrants should be more strongly

correlated with temperature than that of long distance migrants, but evidence to support this

hypothesis has been inconclusive (Knudsen et al. 2011). The onset of woodcock breeding

activities in Alabama was positively correlated to average daily temperatures in January, and

annual peak nesting date varied by up to a month between years 1974 and 1978 (Roboski and

Causey 1981, Causey et al. 1987). In the northern part of their breeding range wherever snow

cover persists through the winter, woodcocks are one of the earliest migratory birds to arrive on

the breeding grounds, and do so as soon as the snow cover becomes patchy (McAuley et al.

2013). Arrival dates of early migrating bird species were more variable in Manitoba than species

that arrived later in the season (Murphy-Klassen et al. 2005). Based on the advancement of

breeding phenology of other bird species, and the data from New York and Massachusetts on

woodcock arrival dates, it is possible that woodcock breeding phenology may have advanced to

the extent that SGS survey windows no longer coincide with peaks in courtship activity.

If discrepancies between the timing of the SGS and the peak in woodcock activity exist,

Ontario is a representative region for study. Three of the five survey date windows used in the

SGS are surveyed in Ontario (hereafter South, Central, and North). SGS data indicate that

Ontario has the second highest index of breeding woodcocks (singing males per route) of any

state or province, and shows a long-term trend similar to the entire continent (-0.90 % change per

year for Ontario, -0.95 % change per year continent-wide) (Cooper and Rau 2014).

32

The objective of this study was to determine if an advancement in woodcock breeding

phenology in Ontario has introduced negative bias into the SGS index, necessitating a change in

survey protocol. To address the primary objective, I: (1) assessed long-term trends in pre-

existing woodcock breeding phenology data in Ontario and (2) determined if the survey date

windows of the SGS were appropriate in years 2011-2013 for the three survey windows in

Ontario. A long-term dataset of woodcock first observation dates from Algonquin Provincial

Park in central Ontario was analyzed to look for evidence of earlier spring arrival and determine

if years with early woodcock arrivals were correlated with years with warm springs and or years

with low SGS indices. Autonomous audio-recording devices (song meters) were deployed at

known woodcock singing-grounds across Ontario to document seasonal peaks in courtship

activity (Rempel et al. 2013). Mixed effects models were used to compare woodcock courtship

activity during the three regional survey windows in Ontario, during the three years of the study.

Year was included in a three-way interaction with region and date, to determine if survey

window timing was appropriate in some regions and years but not others. Significant date effects

would indicate that woodcock courtship activity was not consistent throughout the survey

window in regions and/or years, depending on the level of the interaction. Finally, SGS data

were downloaded for all routes in Ontario in 2012, the year with the earliest spring woodcock

arrivals, to determine when during the survey windows routes were run in each region that year

and whether those routes corresponded to the earlier singing of American Woodcocks.

Methods

Study Area

Data on breeding woodcocks were gathered throughout the range of the species within

the Province of Ontario, Canada. Ontario encompasses just over 1 million square kilometers,

33

and includes large portions of four bird conservation regions, from south to north: Great

Lakes/St. Lawrence Plain, Boreal Hardwood Transition, Boreal Softwood Shield, and Taiga

Shield and Hudson Plains (Bird Studies Canada and NABCI 2014). Woodcocks reach the

northern limit of their breeding range in Ontario, and are found primarily in the Great Lakes/St.

Lawrence Plain and Boreal Hardwood Transition bird conservation regions within the province

(Sandilands 2007, eBird 2015). Reports of woodcocks are fewer in the Boreal Softwood Shield,

but due to lack of observer coverage and accessible roads in this remote part of the province, the

northern limit of the woodcock range is not well defined (Sandilands 2007, eBird 2015).

Courting woodcocks use clearings in boreal forest, and with substantial logging in northern

Ontario it seems likely that more woodcocks may use this region than currently documented

(Keppie et al. 1984). Estimated breeding woodcock densities based on SGS data showed high

concentrations of woodcocks in the Great Lakes, Clay Belt, and Rainy River regions of the

province (Sauer and Bortner 1991). The study area included sites across the province, located in

all three SGS survey date windows and within the area monitored by the SGS (Figure 3.1).

Woodcock courtship displays were monitored for three breeding seasons using song

meters at known woodcock singing-grounds across Ontario. Three SGS survey date windows

are used for different latitudes in Ontario and song meters were deployed in all three regions.

Song meters were shipped to volunteers in each region for deployment. Sites were chosen based

upon SGS stop data from previous years indicating consistent use of a location, or based upon

volunteer knowledge of reliable woodcock singing-grounds. All volunteers had some familiarity

with observing displaying woodcock.

The number and geographic spread of woodcock singing-grounds monitored each year

were dictated by the availability of song meters and volunteers. Once these limitations were

34

assessed, song meters were distributed evenly across the three survey window regions. In the

years 2011-2013, 39, 35 and 25 song meters were deployed, respectively (Appendix A). Song

meters were installed at the same sites from one year to the next whenever possible, but some

volunteers were not available in subsequent years and some of the sites selected did not have

displaying woodcocks. Song meters were deployed at a total of 67 different sites for at least one

breeding season.

Long-term Datasets

Long-term data representing woodcock spring arrival date (date of first observation) were

available from Algonquin Provincial Park (latitude 45.58°, longitude -78.36°), in the Central

survey date window in Ontario. First spring observation data for bird species reported by staff

and visitors have been recorded by staff at Algonquin Provincial Park since 1961 (Algonquin

Park Museum Bird Records). Woodcock first observation dates were available for years 1961-

2014, though data for 6 years were omitted due to low observer effort. Effort was otherwise

comparable between years, and primarily confined to the Highway 60 corridor through the park.

Yearly SGS index data based on the 157 routes in Ontario were obtained from the annual

American Woodcock Population Status Report, 2014 for the years 1968 to 2014 (Cooper and

Rau 2014). First observation date data prior to 1968 were omitted to match available SGS index

data. Long-term weather data were downloaded from Environment Canada (2015) from the

station: North Bay A (latitude 46.36o, longitude -79.42o), which was approximately 120 km from

the Algonquin Park visitor’s center. Monthly means for temperature, daily high temperature, and

daily low temperature were obtained for the months March-May, for years 1968-2012. Date of

first observation was regressed against year to detect trends in arrival dates over the time period.

A correlation matrix was generated to determine which of the monthly temperature

35

measurements was most strongly correlated to date of first observation, and then the relationship

between that variable and the date of first observation was examined using linear regression.

Another correlation matrix determined which spring temperature measurement was most

strongly associated with the SGS indices, and that temperature measurement was then compared

to the indices using linear regression. Year and first observation date at Algonquin Park were

also compared to the SGS indices using linear regression (Cooper and Rau 2014). A significant

negative relationship between spring temperature and SGS index or a positive relationship

between arrival date and SGS index would support the hypothesis that warmer spring

temperatures would lead to earlier breeding phenology and reduced detection rates in the SGS.

Woodcock egg dates from the Ontario nest records scheme (Ontario Nest Records

Scheme 2014) were obtained for nests that were found across the province. Although there were

339 nest records for woodcocks in Ontario, laying date was recorded only for 24 nests. Hatching

date was recorded for an additional 15 nests for which the laying date was estimated by

subtracting the average incubation time of 21 days (Mendall and Aldous 1943). Trends in laying

date over time were examined using linear regression, but the data were insufficient to draw any

conclusions.

Song Meter Monitoring

Woodcock calls and flight displays were recorded nightly during three breeding seasons

(2011-2013) at sites across Ontario using autonomous audio-recording devices (Wildlife

Acoustics Song Meters, models SM1, SM2, and SM2+, hereafter song meter). Each song meter

was programmed with the latitude and longitude for its deployment location, and scheduled to

record every evening from thirty minutes before local sunset until two hours after sunset (2.5

hours). In 2013, the duration of each recording was shortened to two hours to increase battery

36

longevity and space on memory cards, by advancing the stop time by 30 minutes. Interpretation

of song meter recordings from 2011 and 2012 indicated that there was very little woodcock

courtship activity between 90 and 120 minutes after sunset.

In 2011, the pilot year for the study, song meters were deployed April 1, and most

remained in the field until May 10. In 2012, song meters were deployed earlier in the season

(March 20) to record dates of first display and the early part of the breeding season, and were left

in the field until an average end date of May 12. In 2013, song meters were deployed March 20

until June 1.

Interpretation of Song Meter Recordings

Song meter .wav files were transcribed by five different interpreters using Raven Pro 1.4

Interactive Sound Analysis Software (Bioacoustics Research Program 2011). Interpreters were

trained with representative sample recordings that had known numbers of woodcocks, peent

calls, and flight displays. Interpreters were provided examples of recordings made when

multiple woodcocks were calling simultaneously, recordings made when woodcocks were distant

from the microphones, and recordings that demonstrated vocalizations of species that appeared

similar to woodcocks on the spectrogram. Spectrograms were viewed in one-minute increments

to rapidly quantify woodcock breeding activity on each recording. The scale of the vertical axis

of the spectrogram (Hz) was adjusted to view only sounds between 3000 and 7000 Hz, to

examine only the portion of the spectrogram where woodcock calls registered. Woodcock peents

appeared as thick vertical lines centered at ~5000 Hz on a spectrogram when viewed at a scale of

one minute per screen width (Figure 3.2). At the same scale, the flight display sounds created by

the modified outer primary feathers showed a complex pattern of rapid notes varying in pitch

(Figure 3.3). There were several other bird species detected on the recordings with calls or

37

portions of calls that appeared similar to woodcocks, most notably Eastern Meadowlark

(Sturnella magna), Gray Catbird (Dumetella carolinensis), and Song Sparrow (Melospiza

melodia). With practice, these similar calls could be easily distinguished visually from those of

woodcocks based on subtle differences in pitch and shape on the spectrogram. Small segments

of the recordings were validated by listening, especially in circumstances when there were

multiple woodcocks present, or when woodcocks were distant from the microphones. Flight

displays were sometimes heard when nothing could be detected visually on the spectrogram, but

it was easy to deduce when woodcocks were performing flights based on breaks in peenting, and

then validate them aurally. For each minute of each recording, we recorded: the number of

woodcocks detected, the number of peents detected, the number of flight displays first detected

during that minute (flight displays were about one minute in duration so the same flight could

often be seen in two consecutive minutes), and the number of woodcocks detected by flight

display only. If inclement weather (e.g., heavy rain or high wind) adversely affected the

detection of woodcocks during a recording, it was noted in the database and data from that date

were removed from subsequent analyses. A 2.5-hour recording of average woodcock display

intensity took 10-12 minutes to transcribe visually using spectrograms.

For each song meter, start times of recordings were checked against astronomical sunset

at the coordinates of deployment. Astronomical sunset was calculated at each song meter

location through NOAA’s Earth Systems Research Laboratory (NOAA 2015). If a discrepancy

was found between the start time logged by a song meter and astronomical sunset minus thirty

minutes at that location, the start time for each of the recordings made by that song meter was

corrected to thirty minutes before astronomical sunset. This procedure was done to check for

errors in the coordinates programmed into each song meter. The only method to check that the

38

time was set correctly on each song meter, was to verify the time on each song meter when it

came in from the field with AA batteries still installed (the time is reset upon battery removal).

No errors in set time were found for any song meter received.

For each site and date when a song meter recorded, the following variables were

calculated: NUM.MALES was the maximum number of woodcocks detected during the entirety

of a recording; DETECTION was the proportion of overlapping two-minute segments within the

survey time frame (15 to 60 minutes after sunset) in which at least one woodcock was detected

by peent call (DETECTION estimated probability of detection based on the SGS protocol, and

was used in all models as a binary variable consisting of the total number of detections and

failures out of 44 possible detections); PEENTS was the total number of peents detected on the

recording; FLIGHTS was the total number of flight displays detected on the recording;

INTENSITY was the total number of minutes in which woodcocks were detected on a recording

(up to the 120th minute, since 2013 recordings ended 30 minutes earlier), regardless of the survey

time frame. TEMPERATURE was the ambient air temperature recorded by an internal

thermometer within the song meter at the beginning of each recording (30 minutes before

sunset). For song meters that did not record temperature (SM1 units used at some sites in 2011)

and others with faulty thermometers (determined by crosschecking against temperatures from

other nearby song meters and weather stations), hourly temperature was obtained from nearby

weather stations for the approximate start times of the recordings (Environment Canada 2015).

TEMPERATURE was centered around the mean and rescaled by standard deviation for

statistical modelling. DAY was the Julian date at which the recording was made (DAY for the

2012 leap year was calculated by subtracting one from the Julian date for consistency between

years). DAY was re-centered around its median for statistical analyses. SITE was the unique

39

combination of song meter serial number and location. REGION was the survey date window in

which the song meter was located (South, Central, North). YEAR was the year in which the

recording was made (2011-2013).

Song Meter Data Analysis

Of the 99 song meters deployed over three years, 8 song meters recorded no woodcocks

and 8 song meters malfunctioned to the extent that the data could not be used (either poor

microphone performance or total failure). With data from the remaining 83 song meters,

NUM.MALES was averaged across SITE, and plotted by DAY within each REGION and YEAR

with an overlay of the SGS survey window dates for each region to determine migratory and

stable breeding periods. A Loess smoother was fitted to the data with a span parameter of 0.5

using the {car} package in R to aid visual interpretation (Fox and Weisman 2011, R Core Team

2013) (Figure 3.4). DETECTION was plotted by DAY for each site with an overlay of the

corresponding SGS survey window, and a Loess smoother with a span parameter of 0.5 was

fitted to each to aid visual interpretation (Appendix B). Examination of plots for DETECTION

by DAY among individual song meter sites enabled judgements on data quality for each site, and

allowed identification of sites where SGS survey windows were appropriate, sites where survey

windows did not coincide with woodcock displays, sites that were uninformative with regard to

survey window timing, and sites that were not used consistently as singing-grounds throughout

the breeding season. Based on plots of DETECTION by DAY, an additional 16 sites were

removed from further analysis due to inconsistent breeding activity at these sites, where many

evenings had no detections. DETECTION was plotted by DAY within each REGION and

YEAR using the remaining 67 sites, with overlays of the SGS survey windows for each

REGION and a Loess smoother with a span parameter of 0.5 fitted to the data within each

40

REGION and YEAR (Figure 3.5). Plots of NUM.MALES and DETECTION by day were

assessed visually using overlays of the survey windows to determine if the survey dates used in

the SGS coincided with the stable period in woodcock courtship activity in each REGION and

YEAR.

Mixed effects models were fitted to a subset of the data including only dates within the

SGS survey date window for each region, using package {lme4} in R (R Core Team 2013, Bates

et al. 2014) for five separate measures of woodcock courtship activity: DETECTION, PEENTS,

FLIGHTS, INTENSITY, and NUM.MALES. NUM.MALES was included as a predictor

variable for the other responses based on the assumption that additional males would increase the

probability of detecting at least one male, and because song meter detection data (Chapter 2)

indicated that detection of at least one woodcock increased with the total number detected on the

recording. DETECTION was fitted as a response variable using a generalized linear mixed

effects model with a binomial logit link. The full model fitted to the data was:

DETECTION ~ NUM.MALES + TEMPERATURE + YEAR*REGION*DAY + (1+DAY|SITE)

+ (1|YEAR/REGION/DAY)

Random effects included one random slope and intercept term for the effect of DAY and SITE,

and one random intercept term for the effect of DAY nested within REGION, which was nested

within YEAR. It was clear from the plots of DETECTION by DAY for each song meter that

sites not only varied in quality and rates of detection, but that breeding at some sites tapered off

earlier than at others, hence the random slope term. The random intercept term of

1|YEAR/REGION/DAY was used to model the random effects of regional weather patterns

within each region and year. Fixed effects included NUM.MALES, TEMPERATURE, YEAR,

REGION, and DAY, and interactions were fitted between YEAR, REGION, and DAY.

41

TEMPERATURE was included based on a significant correlation between temperature and

woodcocks recorded by observers (Duke 1966).

Linear mixed effects models with identical fixed and random effects terms were fitted to

the survey date window data using the square root of PEENTS, the square root of FLIGHTS, and

INTENSITY as separate response variables. NUM.MALES was fitted as a response variable

using a Generalized Linear Mixed Effects model with a Poisson distribution.

Fixed effects in all models were evaluated using likelihood ratio tests at an alpha level of

0.05, starting with the third-order interaction, and proceeding in a backwards stepwise procedure

removing insignificant model terms (Pinheiro and Bates 2000). If any fixed effects had

statistically significant parameter estimates based on z statistics at an alpha level of 0.05, or t

values >2 in the case of the linear mixed effects models, then the term was retained in the model

even if likelihood ratio tests were insignificant. Of primary interest were the effects of YEAR,

the interactions between YEAR and REGION and between YEAR and DAY, and the three-way

interaction between YEAR, REGION, and DAY. If the appropriateness of the SGS survey

window varied between years based on spring temperatures and woodcock breeding phenology,

significant fixed effects terms that included YEAR in models of response variables that

measured courtship activity would support this hypothesis. If the second order interaction that

included YEAR and DAY were significant, it would indicate that the survey windows were not

timed appropriately in some years (e.g., courtship activity was waning or increasing during the

survey window), and the signs of coefficients would indicate the direction of the differences.

Similarly, if the third-order interaction were significant it would indicate that there were

differences between REGIONS in the appropriateness of survey windows each YEAR. If the

interaction between REGION and YEAR were significant or the YEAR term alone, it would

42

indicate differences in courtship activity existed between REGIONS and/or YEARS, but neither

would directly support the hypothesis that breeding phenology led to changes in courtship

activity during the survey windows. However, because variation due to SITE was accounted for

by random effects, and variation due to the number of displaying woodcocks was accounted for

by including NUM.MALES, significant YEAR or REGION:YEAR terms would suggest that

courtship activity had already changed by the time the survey was conducted, or was changing at

the same rate in each REGION and/or YEAR. If the DAY term alone were significant, it would

indicate that the survey windows did not coincide with the stable periods of courtship display

during the years of the study.

Actual SGS Survey Dates

I downloaded raw SGS data from Ontario, and checked actual dates that SGS routes were

conducted in 2012 for each survey window, to determine if surveys were conducted early, late,

or uniformly throughout the window of survey dates (USFWS 2015). Histograms were plotted

for each region.

Results

Long-term Datasets

The mean ± SE date of first observation of woodcocks at Algonquin Provincial Park, ON

between 1968 and 2014 was April 4 ± 1.1 day. These date of first observation data showed a

non-significant negative trend (Date of first woodcock observation = -0.1432*year + 379.3110,

R2=0.0461, F1,43=3.126, P=0.0842, Figure 3.6) of advancement in date of first observation of 1.4

days per decade. Of the annual monthly temperature averages obtained from the weather station

in North Bay, ON, first observation dates of woodcocks at Algonquin Park were most strongly

correlated to mean daily high temperature in March (r=-0.56, P<0.0001). With each one degree

43

(°C) increase in mean high temperature in March, woodcocks were observed 1.65±0.3714 days

earlier at Algonquin Park (first observation date = 1.65*mean March high temperature (°C) +

94.8628, R2=0.2986, F1,41=19.73, P<0.0001, Figure 3.7). The average daily high temperature in

March at the North Bay weather station has not increased significantly between 1968 and 2014

(Mean daily high temperature (°C) = 0.0322*year – 63.6564,R2 = 0.0071, F1,43 = 1.313, P =

0.2581, Figure 3.8).

SGS indices from Ontario from 1968-2012 were also most strongly, albeit not

significantly, correlated with the means of daily high temperatures in March (r = -0.24, P =

0.1052). Mean high temperature did not significantly predict the number of woodcocks per route

(SGS index) (R2 = -0.0080, F1,43 = 0.6503, P = 0.424) (Figure 3.9). There was no significant

relationship between the SGS index and date of first observation of woodcocks at Algonquin

Park, indicating that the date of arrivals of woodcocks on the breeding grounds had little to no

effect on numbers detected by the SGS (R2=0.0091, F1,43=1.405, P=0.2424) (Figure 3.10). There

was a strong negative relationship between the SGS index and year: number of woodcocks per

route = -0.0935 * year + 193.4507 (R2=0.7982, F1,43=175, P<0.0001) (Figure 3.11). Plots of

residuals indicated there was temporal autocorellation between years.

Song Meter Monitoring

Plots of NUM.MALES by DAY for each region and year indicated differences in

woodcock breeding phenology between both years and regions (Figure 3.4). The arrival and

initiation of courtship activity occurred earliest in 2012, and latest in 2013. The duration of the

courtship period appeared abbreviated in the north compared with the other regions in both 2011

and 2013.

44

In 2011, song meters were not deployed early enough to document the onset of courtship

activity in the South and Central regions, but courtship activities were initiated approximately

April 1 in the North region. Date of first woodcock observation in Algonquin Provincial Park in

2011 was April 2, only two days earlier than the long term average of April 4. Mean daily high

temperature in March 2011 at North Bay was -0.5 °C, one degree lower than the long-term

average of 0.57 °C. There was some evidence of the presence of migrant woodcocks (increased

numbers of displaying males early in the breeding season) in the South in 2011 for the April 1-10

period, when woodcocks were still arriving in the North, but no indication of migrant woodcocks

displaying in the Central region (Figure 3.4). The Loess smoothing curves suggested that the

numbers of woodcocks displaying in both the Central and North regions in 2011 were already

decreasing during the SGS survey window, but were stable in the South.

In 2012, NUM.MALES by DAY plots indicated very early woodcock arrival and

courtship initiation dates, and despite the early deployment of song meters on March 20,

woodcocks were already actively displaying in all three regions when recordings commenced

(Figure 3.4). Woodcock date of first observation at Algonquin Provincial Park in 2012 was

March 15, the second earliest recorded since 1961. Temperature data from North Bay indicated

that 2012 was the second warmest March since 1968 (Environment Canada 2015). There was

evidence of displaying migrant woodcocks in the South until April 1, and a smaller but similarly

timed peak in numbers of displaying woodcocks in the Central region. The stable period, when

numbers of woodcocks per site did not change appreciably, was long in 2012, and timing of the

SGS survey windows appeared to fall within this period, though numbers of displaying

woodcocks may have been on the decline in the Central and North regions.

45

In 2013, NUM.MALES by DAY plots indicated a later arrival and onset of courtship

activity than in the previous two years (Figure 3.4). Woodcocks were first observed at

Algonquin Provincial Park on April 8, four days later than the long-term average date of April 4,

but within the third quartile of first observation dates. Mean daily temperature in March 2013 at

North Bay was 0 °C, only half a degree lower than the long-term average. Additional displaying

woodcocks presumed to be migrants were present in the South and Central regions until

approximately April 20, which coincided with the initiation of courtship displays in the North.

The SGS survey window occurred during the stable period in the South and Central regions, but

in the North the survey date window began while the number of displaying woodcocks was still

on the rise.

In 2011 in the South region, data from 8 song meter sites indicated that the survey

window was appropriate, and another 2 song meters were not deployed long enough to judge

whether the survey window was accurate (Appendix B). There were no sites that suggested an

inaccurate survey window. In the Central region in 2011, there were 3 sites where the survey

window was accurate, 1 where it was too late, and 2 sites where woodcocks were only detected

early in the season during the migratory period, but not through the duration of the breeding

season. In the North in 2011, most song meters were not deployed long enough into the

woodcock breeding season to determine whether the survey window was appropriate. Three of

the sites suggested that the SGS was well-timed for at least the first half of the survey window,

but another 8 sites were uninformative, and 3 only had sporadic woodcock detections during the

migratory period.

In 2012, DETECTION by DAY plots suggested that the survey window was appropriate

in the South region, where 6 song meter sites indicated the window was accurate, and only 1 site

46

demonstrated diminished courtship activity (Appendix B). Another 2 sites were only used

sporadically. In the South there was a large initial peak in DETECTION at all sites, which was

attributed to the presence of migrant woodcocks. In the Central region in 2012, 3 sites indicated

an appropriate survey window, while 1 site indicated the survey window was too late in the

season. One song meter only detected woodcocks sporadically. In the North in 2012, only 3

song meters were deployed long enough into the season to assess the survey window, which was

appropriate at 1 site. At the other 2 sites, DETECTION decreased during the survey window,

though woodcocks were still displaying. Data from an additional 10 song meters were truncated

too early in the season to be informative, and data from 2 song meters were removed from

analyses due to sporadic detections.

In 2013, DETECTION by DAY plots indicated that all 7 informative sites in the South

had appropriate survey windows, while 1 additional site was uninformative, and 1 was only used

sporadically (Appendix B). Several plots showed decreases in DETECTION during the survey

window, but there was evidence that migratory woodcocks were present right up until the April

20 start date of the survey window in the South, so the survey could not have been conducted

any earlier. Similarly, in the Central region in 2013, all 5 informative sites indicated that the

survey window was accurate. DETECTION at one site decreased during the survey window, but

if the survey were conducted any earlier, migrant woodcocks may have been detected. In the

North in 2013, the SGS survey window appeared to be timed appropriately at all 5 informative

sites, and 2 sites did not detect woodcocks consistently.

DETECTION by DAY data were combined for all sites within each REGION and

YEAR, excluding data from sites where woodcock courtship activity was sporadic (Figure 3.5).

Sites that were uninformative with regards to the survey window timing in their region were

47

included to increase the power to detect a pattern of DETECTION early in the breeding season.

Examination of these plots indicated that the survey window was appropriate in all three regions

in both 2011 and 2013, although in the North in 2011 song meters were retrieved too early to

determine DETECTION levels in the second half of the survey window. There was some

evidence that the survey window was too late in the Central and North regions in 2012. In the

South in 2013, however, the survey window was still appropriate.

Means, sample sizes, and standard errors of DETECTION within each REGION and

YEAR are displayed in Table 3.1, for both survey windows and optimal stable periods of

courtship display in each REGION and YEAR. In 2011, estimated means were comparable

between the stable period and the survey window, which overlapped in date, and the estimated

mean of DETECTION was higher during the survey window in the South and Central regions.

In 2012, the survey window was appropriate in the South, but in the Central region the mean of

DETECTION within the survey window was 22% lower than the mean during the stable period,

and in the North, the mean of DETECTION within the survey window was 19% lower than the

mean during the stable period. In 2013, the stable periods coincided with the survey windows in

the South and Central regions, but in the North the stable period began and ended 10 days later

than the survey window, and had a slightly higher mean DETECTION. Statistical tests of these

overall means were not possible due to the repeated samples from the same song meters,

differences in sample sizes from each song meter, and overlap in dates between stable periods

and survey windows.

To test for statistical differences in DETECTION probabilities among years within the

survey window dates, a generalized linear mixed effects model was fitted to the data, with

random effects for DAY|SITE and DAY within REGION within YEAR. The three-way

48

interaction between YEAR, REGION, and DAY was not significant, and was subsequently

removed from the model (χ2 = 3.7351, df = 4, P=0.4430). Removing YEAR:REGION did not

significantly change model fit, so it was dropped from the model (χ2=1.4066, df=4, P=0.8430).

Removing REGION:DAY also did not significantly change model fit, so it was dropped from the

model (χ2=1.2119, df=2, P=0.4679). Removing YEAR:DAY did not decrease model fit

significantly at an alpha of 0.05, but there was a significant parameter estimate for the DAY in

2012, so it was retained in the model (χ2=5.1093, df=2, P=0.0772) (Table 3.2). The resulting

model was:

DETECTION ~ NUM.MALES + TEMPERATURE + YEAR + REGION + DAY +

YEAR*DAY + (1+DAY|SITE) + (1|YEAR/REGION/DAY)

NUM.MALES and TEMPERATURE had highly significant positive coefficients (P<0.0001)

(Table 3.2). Coefficients of the DETECTION model are listed in Table 3.2. Predicted values

based on the DETECTION model with the YEAR:DAY interaction were plotted for each DAY

in the survey windows for all three REGIONs and YEARS, with NUM.MALES=1 and

TEMPERATURE=0 (re-centered) (Figure 3.12). Thus, the significant negative coefficient of

DAY in 2012 indicated that courtship activity was declining faster in 2012 than in the other years

of the study.

To model PEENTS, FLIGHTS, and INTENSITY as response variables, all zero values

were removed from the data. Non-zero values were square-root transformed for PEENTS and

FLIGHTS, yielding approximately normal distributions. INTENSITY did not require

transformation. For √PEENTS, the three-way interaction of YEAR:REGION:DAY was

significant ( χ2 = 12.498, df=4, P=0.01401). The fixed effects of NUM.MALES (χ2=136.77,

df=1, P<0.0001) and TEMPERATURE (χ2=9.8676, df=1, P=0.0017) were significant with

49

positive coefficients. Coefficients for fixed effects in the √PEENTS model are listed in Table

3.3 and predicted values for each YEAR based on the survey windows for each REGION and

NUM.MALES=1 and TEMPERATURE=0, are plotted in Figure 3.13. There was a significant

negative coefficient of DAY in the North in 2012, indicating that the number of PEENTS per

woodcock display was declining faster during the survey window in that year and region than in

others.

For √FLIGHTS the third order interaction of YEAR:REGION:DAY was not significant

(χ2=0.7203, df=4, P=0.9488). Of the second order interactions, REGION:DAY was not

explanatory (χ2=2.019, df=2, P=0.3646), but both YEAR:REGION (χ2=9.2017, df=4,

P=0.05625) and YEAR:DAY (χ2=4.1674, df=2, P=0.1245) showed some evidence that they

should be retained in the model, and both had parameter coefficients with t values that were >2.

NUM.MALES was again highly explanatory (χ2 =129.34, df=1, P<0.0001) but

TEMPERATURE was not significant and was removed from the model (χ2 =0.109, df=1,

P=0.7413). Coefficients of the √FLIGHTS model are shown in Table 3.4, and predicted values

for survey windows in the three REGIONs and YEARs are plotted in Figure 3.14, with

NUM.MALES=1 and TEMPERATURE=0. The overall model terms of the two way

interactions were not quite statistically significant, but individual parameter estimates indicated

that the number of flights per evening was declining during the survey window in 2012 faster

than in other years, and that numbers of flights per evening was lower in the North in both 2012

and 2013 than in other regions.

For INTENSITY, the third order interaction of YEAR:REGION:DAY was significant

(χ2=14.951, df=4, P=0.0048). NUM.MALES (χ2=123.16, df=1, P<0.0001) and

TEMPERATURE (χ2=5.0338, df=1, P=0.0249) were significant fixed effects with positive

50

coefficients (Table 3.5). Coefficients of the INTENSITY model are shown in Table 3.5, and

predicted values for the survey windows in the three REGIONs and YEARs are plotted in Figure

3.15, with NUM.MALES=1 and TEMPERATURE=0. Again, the significant parameter

coefficient was negative and for DAY in the north in 2012, indicating that the duration of

woodcock displays was declining faster during the survey window in this year and region than in

others.

When NUM.MALES was modelled as a response variable, the third order interaction of

YEAR:REGION:DAY was not significant (χ2=3.8011, df=4, P=0.4336). None of the second

order interactions were significant: YEAR:REGION (χ2=1.0305, df=4, P=0.9051), YEAR:DAY

(χ2=1.8888, df=2, P=0.3889), REGION:DAY (χ2=0.5149, df=2, P=0.7730). Of the three fixed

effects, YEAR, REGION, and DAY, only YEAR was significantly explanatory (χ2=6.543, df=2,

P=0.0397), but all three were retained in the final model. TEMPERATURE was not a

significant term and was removed (χ2=0.1744, df=1, P=0.6743). Coefficients for the fixed

effects in the NUM.MALES model are listed in Table 3.6, and predicted values for the survey

window dates in each REGION and YEAR are plotted in Figure 3.16. There was a significant

coefficient for 2012, indicating that the number of male woodcocks detected per site was lower

in 2012 than in the other years, likely due to woodcocks that stopped displaying altogether

during the survey window dates.

Actual SGS Survey Dates

Histograms of actual survey dates of SGS routes in Ontario from 2012 are plotted in

Figure 3.17. In the south, 6 of 26 routes were surveyed in the first half of the survey window. In

the central region, 14 of 38 routes were surveyed in the first half of the survey window. In the

North, 14 of 24 routes were surveyed in the first half of the survey window.

51

Discussion

Long-term woodcock spring arrival data from Algonquin Park in Central Ontario did not

demonstrate a significant departure from historical timing of breeding activities. Though not

significant at an alpha level of 0.05, date of first observation data from Algonquin Provincial

Park advanced at a rate of 1.4 days per decade, which was lower than the estimates of trends in

woodcock arrival published by Butler (2003) (2.2 and 5 days earlier/decade) and overall trends

in avian breeding phenology published by Root et al. (2003) (6 days earlier/decade). This rate of

advancement would equate to a current first observation date that is 6.6 days earlier than when

the SGS was initiated in 1968. I assume that dates of first observation for woodcocks are biased

several days late as estimators of first arrival, due to variation between years in the number of

days between the arrival of woodcocks on the singing-grounds and date of first observation, and

based on observations from song meter recordings in this study that indicated woodcock displays

are curtailed during the first few evenings at a singing-ground. Observer effort at Algonquin

Provincial Park, however, has been high for many years as some staff were avid birdwatchers

and the park is visited by regional birdwatchers intensively throughout the year, so there is no

reason to believe this bias has changed over time. No other long-term data on spring woodcock

arrival dates in Ontario were located. Dates of first observation were significantly correlated to

mean daily high temperature in March from a nearby weather station, which explained almost

30% of the variation in date of first observation. Snow cover data would likely explain

additional variation in date of first observation (Vander Haegen et al. 1993), but long-term snow

cover data were not obtained for any sites near Algonquin Provincial Park.

Although woodcock first observation dates were correlated to temperatures in March,

there was not a significant long-term trend in March daily high temperatures at the North Bay

52

weather station. The estimate of the trend was positive however, at 0.0322 °C per year, which

would equate to a change of mean high temperature in March of 1.5 °C between 1968 and 2014.

Based on the relationship between March temperature and woodcock first observation date at

Algonquin Provincial Park, the temperature would account for a difference in date of observation

of -2.28 days between 1968 and 2014.

There were no significant relationships between the annual numbers of woodcocks

detected on SGS routes in Ontario, and date of first observation or mean daily high temperature

in March at North Bay for those years. The temperature and first observation data were derived

from only one site each, which were centrally located in Ontario, but they may not have been

representative of spring temperatures and woodcock arrival dates across the province.

Monitoring current and future trends in woodcock arrival (or arrival of other bird species) will be

much more consistent given that the use of online database eBird remains stable or continues to

grow (eBird 2015). Even though there was evidence that woodcocks were arriving earlier than

they did before, and that woodcocks arrived earlier in warmer springs, neither temperature nor

arrival date had any detectable effect on the SGS index. The overall trend in SGS index by year

was negative and highly significant, suggesting that trends in woodcock phenology in relation to

survey window timing did not explain the decline in the index.

The analyses of song meter data across the 90 sites and three regions in Ontario

suggested that the three survey windows used by the SGS in Ontario were appropriate. It was

fortuitous that of the 46 years since the SGS was initiated, the second-warmest spring

temperatures in North Bay and second-earliest first spring woodcock observation date at

Algonquin Park both occurred in 2012, because if woodcock courtship dates decline earlier in

warm springs, then 2012 represents an extreme case during the time period. Even though many

53

song meters in the Central and North regions were removed from the field before the end of the

survey window that year, the data were still ample to support significant fixed effects model

terms including year or interactions with year for the number of peents, duration of nightly

woodcock displays, and the number of displaying male woodcocks at each site. Though support

of interaction terms including year was insignificant at an alpha level of 0.05 for both

detectability and number of flight displays, there were significant parameter coefficients in each

model indicating courtship activity was lower during the survey window in 2012. Estimated

coefficients for all response variables representative of courtship activity indicated that courtship

activity was declining during the survey window in the North region in 2012, or in the case of

the number of woodcocks per site, was lower in 2012 than in other years. I presume the number

of woodcocks per site in 2012 was lower because some woodcocks had ceased displaying

altogether during the survey window in that region and year. Even though woodcock courtship

activity was declining in the North in 2012, activity in the South was stable throughout the

survey window, and activity levels in the Central region were inconclusive. Furthermore,

woodcocks were still displaying during the survey window in the North in 2012, and the mean

detectability was only 19% lower than it would have been during an optimally-timed survey

window that year, and overall was comparable to mean detectability during appropriately timed

survey windows in South 2013 (Table 3.1).

Plots of detectability and number of woodcocks detected by date suggested that in 2012,

the year with the earliest spring woodcock arrivals, the period of courtship activity was longer

than in other years (Figures 3.4 and 3.5). Variation in arrival date appeared to be greater than

variation in the date at which courtship activity ceased. The length of the seasonal courtship

period appeared to be shorter in the North than in the other two regions (Figures 3.4 and 3.5). As

54

expected, there was no evidence of migrant woodcocks displaying in the North region, which

coincides with the northern edge of the woodcock breeding range.

The SGS protocol encourages observers to conduct routes early in the survey window,

and includes a provision that surveys may be conducted prior to or later than the designated

survey window in early or late springs respectively, with permission from the regional

coordinator. With this flexibility it seems that the effects of an early spring, such as in 2012,

could be moderated. In the North in 2012, 14 of 24 SGS routes were conducted in the first half

of the survey window, which would have lessened the effects of the decline in courtship activity

on the index, but there was no evidence that an effort was made to conduct the routes early in the

season due to an early spring. The mixed models with significant third order interactions

(number of peents and duration of courtship displays) suggested that in the South in 2012,

courtship activity may still have been increasing during the survey window, but in the Central

region, they predicted moderate declines during the survey window. In the mixed models

without third order interactions (detectability, number of flight displays, and number of males

detected), declines were predicted for all three regions during the survey windows. The majority

of SGS routes in the South and Central regions were conducted in the second half of their survey

windows, which in the South may not have had much effect on the index, but in the Central

region likely introduced negative bias. Again in the South and Central regions, there was no

indication that an attempt was made to conduct SGS routes early in the survey windows.

The effect of temperature on woodcock courtship activity was positive and significant for

three of the response variables in the mixed models: the probability of detection, the number of

peents per recording, and the duration of flight displays. Duke (1966) found a similar positive

relationship between temperature and woodcock display rates, but attributed the relationship to

55

differences in observer behavior rather than woodcock behavior. The number of males detected

each evening was a strongly significant explanatory variable in the mixed models for all response

variables representative of woodcock courtship activity. Due to limitations in song meter

detection capabilities with respect to the human ear (Chapter 2), differential calling rates of

additional woodcocks at each site were difficult to interpret. Increases in the other response

variables associated with the number of males detected, most notably detectability and the

duration of nightly woodcock displays, were primarily due to increased courtship activity of the

woodcock closest to the song meter, rather than additional peents and flight displays performed

by the extra woodcocks. For instance, the overall mean of detectability (the proportion of 2-

minute segments with at least one woodcock detected during the survey time frame) on

recordings where two birds were detected was 0.782, while the proportion of 2-minute segments

with two woodcocks calling was only 0.230. It was impossible to know how much of this

decreased detectability of the second bird was due to distance, as opposed to differences in

dominant and subdominant male behavior documented by several studies, where many sub-

dominant males were present at singing-grounds but only called sporadically (e.g., Hudgins et al.

1985, Dwyer et al. 1988). By far the largest limitation in the data was the unbalanced design

introduced by retrieving song meters earlier in the survey windows in some regions and years

than in others.

In the mixed models used to examine woodcock courtship displays, date as a continuous

variable contributed explanatory power through second or third order interactions in all models

except for the number of males. This suggests that one of the primary assumptions of the SGS,

that detectability and the number of birds engarged in courtship activity remain constant

throughout the survey window, is violated in some regions and years. To compensate for this

56

potential issue, models analysing SGS data should include a random effect to account for trends

in detectability by date that may vary between regions within the same year, and may vary

between years. Data from different states and provinces within the same survey window could

be used to estimate these effects for incorporation into the overall models. Studies that use SGS

index data as either response or predictor variables to make inferences on woodcock densities,

habitat selection, or any other variables (e.g., Thogmartin et al. 2007, Nelson 2010) should take

variation as a function of date into account.

57

Figures



https://migbirdapps.fws.gov/woodcock/training_tool_documents/SGS_date_window_map.pdf

58


using Raven Pro 1.4 Interactive Sound Analysis Software. The x-axis spans one minute, and the

y-axis spans 4000 Hz.

59


song meter, and plotted using Raven Pro 1.4 Interactive Sound Analysis Software. The x-axis

spans one minute, and the y-axis spans 4000 Hz.

60

Fig

ure

3.4

N

UM

.MA

LE

S (

the

num

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61

Fig

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62


visitors and staff at Algonquin Provincial Park, ON, and year (R2=0.0461, F1,43=3.126,

P=0.0842). Mean first observation date was April 4 (Julian day 94).


visitors and staff at Algonquin Provincial Park, ON, and mean daily high temperature in March

from a weather station in North Bay, ON, 1968-2014 (R2=0.2986, F1,41=19.73, P<0.0001). Mean

first observation date was April 4 (Julian day 94).

63

Figure 3.8 Linear regression model between mean daily high temperature in March at a weather

station in North Bay, ON, and year (R2=0.0071, F1,43=1.313, P=0.2581).


for Ontario and mean daily high temperature in March at a weather station in North Bay, ON,

1968-2014 (R2=-0.0080, F1,43=0.6503, P=0.424).

64


for Ontario and date of first spring woodcock observation at Algonquin Provincial Park, ON

(R2=0.0091, F1,43=1.405, P=0.2424).


for Ontario and year (R2=0.7982, F1,43=175, P<0.0001).

65

Figure 3.12 Predicted values for the DETECTION model for each DAY within the survey

window of each REGION and YEAR. DAY was re-centered around its median (May 3rd) for

modelling. The predictor variable NUM.MALES was set at 1, and TEMPERATURE (re-

centered) was set at 0.

66

Figure 3.13 Predicted values for the √PEENTS model for each DAY within the survey window

of each REGION and YEAR. DAY was re-centered around its median (May 3rd) for modelling.

The predictor variable NUM.MALES was set at 1, and TEMPERATURE (re-centered) was set

at 0.

67

Figure 3.14 Predicted values for the √FLIGHTS model for each DAY within the survey window

of each REGION and YEAR. DAY was re-centered around its median (May 3rd) for modelling.

The predictor variable NUM.MALES was set at 1.

68

Figure 3.15 Predicted values for the INTENSITY model for each DAY within the survey


modelling. The predictor variable NUM.MALES was set at 1, and TEMPERATURE (re-

centered) set to 0.

69

Figure 3.16 Predicted values for the NUM.MALES model for each DAY within the survey


modelling.

70

Figure 3.17 Histograms of the number of woodcock Singing-ground Survey routes conducted in

the three survey windows in Ontario 2012.

71

Tables

Table 3.1 Dates, sample sizes, mean, and standard error of DETECTION for each region and

year, including estimates for the optimal stable woodcock courtship period as visually

determined from Figures 3.3 and 3.4, and the actual survey window dates for the SGS. These

means could not be directly tested due to overlap in optimal stable period and survey window

dates. An asterisk indicates end dates that were based on removal of song meters from the field

as opposed to dates determined from the data. N refers to the number of song meter recordings

interpreted from each time period.

Stable Period Survey Window

Year Region Dates N Mean SE Dates N Mean SE

2011 South April 10-May 10* 226 0.746 0.013 April 20-May 10 154 0.760 0.015

Central April 10-May 10* 111 0.688 0.028 April 25-May 15 55 0.720 0.041

North April 10-May 13* 282 0.678 0.020 May 1-May 20 86 0.651 0.040

2012 South April 1-April 30 194 0.668 0.014 April 20-May 10 125 0.691 0.020

Central April 1-April 30 115 0.644 0.026 April 25-May 15 74 0.502 0.041

North April 1-April 30 334 0.718 0.016 May 1-May 20 106 0.584 0.036

2013 South Identical to survey window April 20-May 10 134 0.583 0.029

Central Identical to survey window April 25-May 15 88 0.695 0.036

North May 10-May 30 93 0.768 0.028 May 1-May 20 84 0.729 0.039

72

Table 3.2 Coefficients of terms used in the final model selected for DETECTION as a response

variable. Central 2011 was the basis for comparison, and statistically significant coefficients are

in bold font.

Parameter Estimate SE z value Pr(>|z|)

Intercept -0.4840 0.5201 -0.9300 0.3521

NUM.MALES 1.2191 0.0367 33.2000 <0.0001

TEMPERATURE 0.2087 0.0351 5.9500 <0.0001

YEAR2012 -0.4383 0.4733 -0.9300 0.3545

YEAR2013 -0.4671 0.5015 -0.9300 0.3517

REGIONNorth 0.0560 0.5488 0.1000 0.9188

REGIONSouth -0.0274 0.5305 -0.0500 0.9588

DAY 0.0498 0.0365 1.3600 0.1724

YEAR2012:DAY -0.1054 0.0497 -2.1200 0.0337

YEAR2013:DAY -0.0373 0.0505 -0.7400 0.4598

73

Table 3.3 Coefficients of terms used in the final model selected for √PEENTS as a response


in bold font.

Parameter Estimate SE t value

Intercept 11.8833 3.3905 3.505

NUM.MALES 3.7863 0.3218 11.765

TEMPERATURE 0.1046 0.0335 3.127

2012 -1.9133 5.1731 -0.370

2013 -0.1271 4.5064 -0.028

North 1.8982 3.9704 0.478

South 2.6184 4.0000 0.655

DAY -0.2957 0.2316 -1.277

2012:North 3.9405 5.9521 0.662

2013:North -2.2113 5.8788 -0.376

2012:South 1.2780 6.1608 0.207

2013:South -3.5935 5.5671 -0.645

2012:DAY 0.2131 0.3666 0.581

2013:DAY 0.3378 0.3005 1.124

North:DAY 0.4904 0.3357 1.461

South:DAY 0.4323 0.2711 1.594

2012:North:DAY -1.1096 0.4797 -2.313

2013:North:DAY -0.5287 0.4320 -1.224

2012:South:DAY -0.2541 0.4226 -0.601

2013:South:DAY -0.6118 0.3666 -1.669

74

Table 3.4 Coefficients of terms used in the final model selected for √FLIGHTS as a response


in bold font.


Intercept 3.4546 0.4555 7.584

NUM.MALES 0.7649 0.0648 11.802

2012 -1.4934 0.6785 -2.201

2013 -1.4190 0.5808 -2.443

North -1.5218 0.5267 -2.889

South -0.6733 0.5304 -1.269

DAY 0.0045 0.0155 0.290

2012:North 1.7511 0.7790 2.248

2013:North 1.7131 0.7430 2.306

2012:South 1.2284 0.8086 1.519

2013:South 0.6111 0.7220 0.846

2012:DAY -0.0423 0.0209 -2.029

2013:DAY -0.0074 0.0201 -0.369

75

Table 3.5 Coefficients of terms used in the final model selected for INTENSITY as a response


in bold font.


Intercept 22.7614 6.3985 3.557

NUM.MALES 9.4141 0.9333 10.087

TEMPERATURE 1.4281 0.6524 2.189

2012 -4.1705 9.5413 -0.437

2013 -0.5338 8.3494 -0.064

North 1.1993 7.6340 0.157

South 4.4235 7.5012 0.590

DAY -0.5960 0.6207 -0.960

2012:North 14.8848 11.3068 1.316

2013:North 5.3008 11.2293 0.472

2012:South 2.2005 11.4331 0.192

2013:South -9.5511 10.3896 -0.919

2012:DAY 0.0486 0.8853 0.055

2013:DAY 0.9273 0.7532 1.231

North:DAY 1.1064 0.9537 1.160

South:DAY 1.2359 0.7074 1.747

2012:North:DAY -2.5152 1.2361 -2.035

2013:North:DAY -1.0961 1.1359 -0.965

2012:South:DAY -0.4395 1.0155 -0.433

2013:South:DAY -1.7777 0.9011 -1.973

76

Table 3.6 Coefficients of terms used in the final model selected for NUM.MALES as a response


in bold font.

Parameter Estimate SE z value Pr(>|z|)

Intercept 0.4356 0.1141 3.817 0.0001

2012 -0.2656 0.1038 -2.558 0.0105

2013 -0.1634 0.1091 -1.498 0.1341

North 0.0714 0.1264 0.564 0.5725

South 0.0016 0.1162 0.014 0.9890

DAY -0.0087 0.0055 -1.57 0.1165

77

Chapter 4: Summary, Conclusions, and Recommendations for the SGS

Summary and Conclusions

American Woodcock breeding phenology in Ontario was examined using long-term data

to estimate changes in phenology over time, and current breeding phenology was documented by

recording courtship activity at woodcock singing-grounds with song meters. Song meters and

subsequent spectrogram interpretation were tested against the human ear to determine if

detection rates of woodcock calls were similar.

A long-term dataset from Algonquin Provincial Park in Ontario indicated that woodcock

arrival could have advanced an estimated 6.6 days since the SGS protocol was standardized in

1968, but the relationship was not significant at an alpha level of 0.05 (P=0.0842). Variation in

woodcock first observation date at Algonquin Provincial Park was associated with March

temperatures in that area. There was no long-term trend in egg-laying dates of woodcocks in

Ontario, but data were insufficient to make this determination. SGS indices in Ontario were not

correlated to spring temperature in North Bay, Ontario, or first observation dates of woodcocks

in Algonquin Provincial Park. The SGS survey date windows were appropriate in Ontario in

both 2011 and 2013, but in the exceptionally early spring of 2012, woodcock courtship activity

was waning in the latest survey date window when the survey was conducted. The latest survey

date window in the SGS appeared to be the most susceptible to variation in woodcock courtship

activity due to early or late spring thaws.

The data from this study suggest that changes in breeding phenology could not account

for more than a small fraction of the decline of number of male woodcocks per SGS route since

1968. Conversion of small farms to large-scale operations, urbanization, industrialization, and

maturation of forests have all been implicated as factors leading to the decline in the SGS

78

(Kelley et al. 2008), all of which have occurred in Ontario. The creation of clearings in forests

via clearcutting, however, has been suggested as a means of creating habitat for woodcocks

(Kelley et al. 2008). While large areas of southern Ontario have already been developed and

converted to large-scale agriculture, extensive logging in northern Ontario within the breeding

range of the woodcock could be creating suitable habitat. Much of northern Ontario is situated

outside the area covered by SGS routes. A woodcock population shift away from SGS routes

which were originally located near human population centers to more remote areas that have

been recently logged could lead to much lower indices, even if the breeding population were

stable. Woodcock density based on SGS indices should be re-mapped following the methods of

Sauer and Bortner (1991) using recent SGS data, and compared to regional land-use patterns. If

areas of high breeding woodcock density remain in the same general locations, then high priority

conservation areas for woodcocks will be identified. Shifts in areas of high woodcock density

could be explained by changes in land-use over the same time period.

Evaluation of song meters indicated that woodcock detection rates by a field observer and

song meter interpreter were similar at sites where song meters were positioned at known

woodcock singing-grounds. Woodcock detection rates by a song meter interpreter were lower

than those of a field observer at randomly selected locations in woodcock habitat. At sites with

multiple woodcocks, detection rates for each additional woodcock decreased, while the

probability of detecting at least one woodcock increased. These findings supported the use of

song meters in monitoring known woodcock singing-grounds, but suggested that detection rates

reported in Chapter 3 were biased low compared to field observation.

The use of song meters allowed for coverage of a very large area and over a long period

of time with minimal time spent in the field. Interpreting song meter recordings was labor

79

intensive. Automated spectrogram recognition and interpretation is now possible, but was not

reliable enough for the purposes of this study in quantifying detections over time. This should

soon change, as automated recognition programs are advancing rapidly (e.g., Isoperla, Twigle,

and Warblr bird song recognition apps for smartphones). Studies using song meters should

incorporate the possibility of song meter malfunction into study design, which could be related

to: microphone failure, batteries losing contact, SD cards losing contact, and failure to switch

from one SD card to another when memory is full. Eight percent of the song meters used in this

study failed entirely, while several others only recorded a fraction of the allotted dates.

Calibration and verification of song meter functionality could be achieved by broadcasting

sounds of the same volume at set distances, then making comparisons using spectrograms. This

could identify malfunctioning microphones or song meters prior to deployment. Efforts should

be made to identify detection distances for target species to be monitored by song meters, if

density estimates or comparisons to data from field observers are desired. Individual song meter

deployment locations should be tested by broadcasting sounds at known volumes and distances,

as differences in sound transmission could occur between sites. Similarly, it may be desirable to

test song meters before, during, and/or after deployment to ensure sound transmission did not

change over the course of the deployment (e.g., microphone wear). Careful consideration should

be made when selecting song meter sites to avoid areas with high background noise from

anthropogenic (e.g., roads, railroads, airports) or natural (e.g., frog ponds, areas prone to high

wind) sources.

80

Recommendations for the SGS

The survey date windows in the years of my study still coincided with peak woodcock

courtship activity, though the provision to allow SGS routes to be run before the survey date

window in years with early springs should be used. Volunteer surveyors in the SGS should be

strongly encouraged to conduct routes early in the survey window in years with exceptionally

early springs, and late in the survey window in exceptionally late springs. The progression of the

spring woodcock migration should be monitored by SGS coordinators so recommendations can

be made to surveyors to maximize detectability in each region. Woodcock spring arrival dates in

Ontario, and throughout their breeding range, can be monitored using eBird (eBird 2015).

Analysis of SGS data should incorporate terms that account for trends in woodcock detectability

by date, which may differ between regions and years.

81

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Appendix A: Table of Song Meter Locations

This appendix is a table containing the locations in Ontario of all American Woodcock singing-

grounds that were monitored by song meters in this study. Region indicates the survey date

window in which the site was situated. Within each year column are the identification numbers

of the song meters used at the corresponding locations. Some volunteers did not provide

coordinates of singing-ground locations, but rough estimates can be made from the location

name. Coordinates are displayed in as many decimal places as provided by the volunteer who

deployed the song meter.

Region Location name Latitude Longitude 2011 2012 2013

South Guelph-G 43.51803 -80.14719 SM2-30 SM2607 SM4824

Guelph-Sh 43.51969 -80.12564 SM2-29 SM2605 SM4823

Guelph-Su 43.68386 -80.32147 SM2-20 SM2621 SM4831

Ingersoll-01 43.05272 -80.54365 SM7670 SM4822

Ingersoll-02 43.01436 -80.89771 SM2-19

London-01 42.84996 -81.12087 SM2-18 SM2602 SM7159

London-02 42.954003 -81.38478 SM2609

PE County-01 43.9776 -77.11922 SM2-28 SM2582

PE County-02 43.95799 -77.10872 SM2-26 SM2622 SM7019

Port Rowan-01 42.6755 -80.53290 SM8239

Port Rowan-02 42.66656 -80.69847 SM1-09

Port Rowan-03 42.6648 -80.54779 SM1-43

Port Rowan-AH 42.6995 -80.41090 SM1-07 SM7739 SM7145

Port Rowan-TC 42.815 -80.76960 SM1-12 SM8257 SM7144

St Clair 42.526022 -82.40205 SM8263 SM4841

Central Ottawa-01 ? ? SM4835

Ottawa-02 45.0288 -75.76205 SM4829

Ottawa-Kettles 45.12019 -75.86806 SM6547

Ottawa-Rifle 45.34259 -75.87231 SM7146

Ottawa-RZ 45.693261 -76.86506 SM5047 SM4844

Pembroke-01 45.72479 -77.15391 SM2-14

Pembroke-02 45.72479 -77.15391 SM2-22 SM8245 SM5091

Peterborough-01 44.33779 -77.97387 SM2-32

Peterborough-02 44.7114 -78.07680 SM2-33

Peterborough-MG 44.35764 -78.25789 SM2-21 SM7705 SM7126

Peterborough-Jack Lake 44.70923 -78.08578 SM2-42 SM7160

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Region Location name Latitude Longitude 2011 2012 2013

Central Peterborough-LB 44.37873 -78.28608 SM7703 SM7032

Peterborough-Trent 44.35746 -78.28314 SM2-31 SM7672 SM5090

North Kenora-1 49.858027 -94.41587 SM8258

Kenora-2 49.88678 -94.394589 SM8257

Kenora-3 49.828702 -94.487833 SM7739

Sault Ste. Marie-01 ? ? SM2661










Sault Ste. Marie-11 46.47775 -84.04836 CFS 026











Sault Ste. Marie-22 46.44969 -83.88026 SM1-01



Thunder Bay-GH 48.76949 -88.69048 SM8258 SM8263

Thunder Bay-TA 48.106063 -89.85002 SM2624 SM8239

Timmins-01 48.5363 -81.41993 SM4834 SM4830

Timmins-02 48.579717 -81.01250 SM4835

Timmins-03 48.41374 -81.14760 SM2-34 SM2-55

Timmins-04 48.47813 -81.44821 SM2-16 SM2-52 SM4706

Timmins-05 ? ? SM1-08

Timmins-06 48.101832 -82.26981 SM1-44

Timmins-07 48.47557 -81.16027 SM2-23

Timmins-08 48.5678 -81.00760 SM2-24

Timmins-09 48.50127 -81.17542 SM2-25

Timmins-10 48.50086 -81.16531 SM2-21

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Appendix B: Plots of Detectability by Date for Each Song Meter

Detectability (the proportion of two-minute segments within the survey time frame, 15 to

60 minutes after sunset, in which at least one woodcock was detected by call) was plotted against

Julian date for each of the song meters that had usable recordings. Vertical dashed lines indicate

the beginning and end of the survey date window used by the SGS in the region where each song

meter was installed. Song meters are grouped by region within year, and each song meter was

assigned one of four categories: those with appropriate survey windows, those with inappropriate

survey windows, those that were uninformative as to whether or not the survey window was

appropriate for the site, and those that did not appear to be consistently used singing-grounds

(which were removed from all data analyses other than plots of numbers of displaying males by

date).

South 2011: Appropriate Survey Window

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South 2011: Uninformative

95

Central 2011: Appropriate Survey Window

Central 2011: Inappropriate Survey Window

96

Central 2011: Removed from Data Analysis

North 2011: Appropriate Survey Window

97

North 2011: Uninformative

98

North 2011: Removed from Data Analysis


99

South 2012: Inappropriate Survey Window

100

South 2012: Removed from Data Analysis


101

Central 2012: Inappropriate Survey Window



102

North 2012: Inappropriate Survey Window

North 2012: Uninformative

103


104


105

South 2013: Uninformative

South 2013: Removed from Data Analysis


106


107


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