Wind Drift and the Use of Radar, Acoustics, and Canadian Migration Monitoring Network Methods for Monitoring Nocturnal Passerine Migration

Wind drift and the use of radar, acoustics, and Canadian Migration Monitoring Network methods for monitoring nocturnal passerine migration

MICHAEL L. PECKFORD B.Sc. Acadia University, 1999

Thesis submitted in partial fulfillment of the requirements for the Degree of Masters of Science (Biology)

Acadia University Spring Convocation 2006

This thesis by MICHAEL L. PECKFORD was defended successfully in an oral examination on 07 APRIL, 2006.

The examining committee for the thesis was:

Dr. Rick Mehta, Chair

Dr. Erica Dunn, External Reader

Dr. Dave Shutler, Internal Reader

Dr. Philip D. Taylor, Supervisor

Dr. Dan Toews, Department Head

This thesis is accepted in its present form by the Division of Research and Graduate Studies as satisfying the thesis requirements for the degree of Master of Science (Biology).

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Table of Contents

List of Tables ...... iv List of Figures...... v Abstract...... vii Acknowledgements...... viii

General Introduction ...... 1 Migration and weather ...... 1 Monitoring migration...... 6

Chapter 1. Influence of wind on autumn nocturnal avian migratory behaviour in southwestern Nova Scotia ...... 8 Introduction...... 8 Methods...... 14 Results...... 18 Discussion...... 21 Conclusions...... 26

Chapter 2. Radar, acoustics, and Canada Migration Monitoring Network methods as tools for monitoring nocturnal passerine migration ...... 37 Introduction...... 37 Methods...... 43 Results...... 50 Discussion...... 53 Conclusions and Recommendations ...... 61

General Discussion ...... 70 Monitoring implications...... 70 Population implications ...... 71 Limitations and future work...... 72

Literature Cited ...... 73

Appendix 1...... 80 Appendix 2...... 80 Appendix 3...... 90

List of Tables

Table 1-1. Numbers and proportions of migrants grouped by track bearing over the entire season...... 36

Table 1-2. Observed proportion of migrants under each wind condition minus the proportion expected if migrants ignore wind conditions. A positive value indicates more than expected migrants were detected (cells bordered by a solid line) and a negative value indicates fewer migrants than expected were detected (bordered by a dashed line), Chi- squared test, p < 0.001. NA indicates wind conditions that did not occur. Wind speed is represented in m/s...... 36

Table 2-1. Description of initial predictor variables used in all generalized linear models ...... 67

Table 2-2. Pearson’s correlation coefficients (r) and p values of comparisons between each diurnal monitoring method...... 67

Table 2-3. Pearson correlation coefficients (r) of numbers of birds detected by diurnal monitoring methods compared to numbers of birds detected by radar per hour on the previous night...... 67

List of Figures

Figure 1-1. Map of Gulf of Maine and surrounding region, indicating the study site, Bon Portage Island...... 28

Figure 1-2. Schematic of the “doughnut” area surveyed by the radar beam. From Eastwood (1967)...... 29

Figure 1-3. A bird’s track velocity (T) (direction and speed in relation to the ground) is the resultant vector of its heading velocity (H) (direction and speed in relation to the air) and the wind velocity (W). (a) Drift: Flying at a constant heading towards the goal, birds will be drifted by crosswinds. (b) Compensation: Adjusting heading during flight to maintain a constant track towards the goal. From Richardson (1991)...... 29

Figure 1-4. Nightly change in seasonal proportion of migrants. Circular histograms show the tracks during the 6 peak-volume migration nights (A = 08-09 Sep; B = 17-18 Sep; C = 21-22 Sep; D = 01-02 Oct; E = 11-12 Oct; F = 18-19 Oct) with bins representing 15°. ρ represents the directional concentration of the night (1 = all migrants within one bin, and 0 = equal distribution among bins) See Appendix 3 for mean track bearing on each peak volume night...... 30

Figure 1-5. Map of Gulf of Maine and surrounding region. All data shown are from the 6 large-volume migration nights (see Figure 1-4). Circular histogram of headings, centered on Bon Portage Island. Asterisk indicates the mean projected landfall. Lines indicate projected landfall of 32% of migrants. Map project, lines and asterisk were computed using a North American equidistant conical projection...... 31

Figure 1-6. Geometric mean numbers of tracks detected per hour over the entire season...... 32

Figure 1-7. Mean hourly tracks of migrants since sunset (a) and before sunrise (b)...... 32

Figure 1-8. Circular distribution of wind direction observed during the study. Bins represent 5.5° sections of the data. Length of each arm represents occurrence of wind direction (rho = 0.35). Wind is given in the direction the winds are flowing from...... 33

Figure 1-9. Scatter plot of migrant headings against wind direction. Slope of locally weighted regression line indicates migrants are drifting. Size of each point represents the relative number of birds (per 20-min) on a square root scale...... 34

Figure 1-10. Scatter plot of migrant headings against wind direction. Panels group data by hours since sunset. Locally weighted regression line shows change of bird heading by wind direction; slope near zero indicates migrants are drifting. Size of each point represents the relative number of birds (per 20-min), on a square root scale...... 35

Figure 2-1. Numbers of nf-calls and radar tracks recorded while both methods were simultaneously in operation. Data are natural log transformed. Line is locally weighted regression line...... 64

Figure 2-2. Scatter plot of the natural log of the numbers of birds detected on the radar after midnight against the natural log of the numbers of birds detected during the census, netting and DETs. Lines are locally weighted regression lines...... 65

Figure 2-3. Plots of hourly correlation coefficients against hours after sunset. Correlations were between census (circles), netting (squares), and DETs (triangles) data and hourly radar data during the previous (a) and following (b) night. Closed symbols indicate significant positive correlations, p < 0.05...... 66

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Abstract

Tools to monitor migrating birds such as radar and acoustics can add valuable information towards understanding migration ecology and population trend estimates. I used a modified marine radar and acoustic sensor to monitor nocturnal migration of passerines at the Atlantic Bird Observatory in southwestern Nova Scotia, Canada.

Nightly variation of migration numbers was high. High volume nights were mainly during relatively light northerly winds and consisted of a predominantly SW migratory direction, consistent with the ‘expected’ regional migratory pattern. Radar data confirmed that migrants typically employ a ‘constant-heading’ migration strategy. Also, consistent with other studies, numbers of nocturnal migrants detected by radar were significantly positively correlated with numbers of migrants detected by ground counts the following day.

These findings illustrated both the importance of a multifaceted approach to migration monitoring, and the importance of incorporating environmental data of wind conditions in interpreting ground counts at migration monitoring stations.

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Acknowledgements

First, I’d like to acknowledge John Black from Brock University for being kind enough to give his Super Decca 101 radar (a.k.a. “the beast”) and custom made acoustic sensor to this project, without which, this thesis would not have been realized. Also, thanks for John’s patience in answering my unending radar questions. I would like to thank Dave Wilson (Electro Marine Inc.) for the modifications to the radar, Dermott Kearney for supplying valuable weather data, and all the ABO volunteers that aided in data collection.

I’d like to specifically thank Trina F. and Joe N. for providing encouragement and instilling me with confidence during the early days of this endeavour. I’d also like to thank Trina and Anna C. for reviewing this manuscript.

To my peers that helped see me through what I would call the most difficult task I have ever attempted, I’d like to thank ( in order of appearance ): Katie D. for listening to me complain; Krista C. for keeping up the fight against the mighty R; Andrew T. for entertaining dreams of rock-stardom; Kim D. for being a “typical” Newfoundland girl; Greg M. for the spots during those “max” presses; a particular thank you to Tina L. for the company during the late generator refuelling nights and all the intangible support that allowed me to get through the “I can’t do this” days, without which I may not have seen this through to the finish; Sean L. and Jolene S. for endless pitchers of Raven; Shannon O. for “corrie” conversations and keeping my apartment safe while I was away; Carolyn M. for accepting the “passing of the torch”; and last but not least a thank-you to Norah L. for her support and understanding during the last months of the painful writing process.

Special thanks to my parents and sister for their love, support, and understanding of the difficulties that this thesis has caused.

Lastly, I am indebted to Phil Taylor for the countless ways in which he aided in this project, and for being the major source of knowledge that I have gained during this process. His big picture view has always kept me placing my research in the proper context.

“He learnt to communicate with birds and discovered that their conversation was fantastically boring. It was all to do with wind speed, wing spans, power-to-weight ratios and a fair bit about berries. Unfortunately, he discovered, once you have learnt birdspeak you quickly come to realize that the air is full of it the whole time, just inane bird chatter. There is no getting away from it.” ~ Douglas Adams (Life, the Universe and Everything)

General Introduction

Avian migration is a dynamic process subject to rapid evolutionary change (Rappole

1995). The ability of migrants to exploit tropical niches indicates that migration arose when tropical and subtropical species began moving north to capitalize on unexploited seasonally abundant food sources (Rappole and Jones 2002). Primitive migratory behaviour consisted of short distance migration, which subsequently evolved into longer distances (Berthold 1999). The benefits of the migratory journey must be high to offset the mortality associated with migration, which has been estimated at as much as 85% of apparent annual mortality (Alerstam et al. 2003; Sillett and Holmes 2002). In the present era, billions of passerines migrate from Central/South America to the boreal forests of

Canada and back each year. The onset and timing of that migration for most small passerines is dictated by endogenous factors and environmental cues such as change in temperature or photoperiod (Berthold 1999). Knowledge of how these environmental factors affect migrants is critical to understanding the processes underlying migration and, in turn, how these processes affect population estimates based on surveys of the numbers of migrants passing through a given area.

Migration and weather

The processes and mechanisms that drive avian migration are great importance to the study of life history and conservation of numerous passerine species. Suspected high mortality during migration has potentially large implications at population levels (Butler

2000; Sillett and Holmes 2002; Sillett et al. 2000), but the full extent of these effects are still unclear. Severe, stochastic weather events are one possible cause of high mortality

(Butler 2000). Therefore, understanding how migrants react to changing environmental conditions is an important factor in understanding population dynamics.

The interaction between migrant physiology and local weather conditions has a major role in an individual’s decision to initiate or terminate a migratory flight (Able 1973;

Åkesson and Hedenström 2000; Lack 1960; Pyle et al. 1993; Richardson 1985, 1990a,

1991). Factors such as age and physical condition of the individual can also play a major role in departure decisions. For example, young migrants behave differently towards coastlines than do older migrants (Dunn and Nol 1980; Ralph 1981) and fat individuals are more likely to migrate than non-fat individuals (Alerstam et al. 2003; Bruderer and

Liechti 1998; Demong and Emlen 1978; Fortin et al. 1999; Liechti and Bruderer 1998).

Since environmental factors can have impacts on internal conditions (e.g. fat loads), I explore here some of the key environmental factors that affect migratory decisions.

Wind conditions during migration impacts the fitness of passerines by affecting the energy costs associated with flight, through increasing flight costs of flying in adverse winds, and increasing migratory distance from being drifted off course (Desholm 2003).

Also, poor wind conditions can stall migratory flights, which can in turn affect arrival date on the breeding or wintering grounds (Drent et al. 2003; Møller 1994). Therefore, wind has been identified by many researchers as the most significant weather variable that affects migratory departure decisions (Erni et al. 2002; Richardson 1990b). Most individuals migrate with following (Richardson 1990a) and calm (< 5 m/s) (Erni et al.

2002) winds. It is generally accepted that migrants avoid nights with strong opposing

3 winds and wait for “favourable” winds before initiating a migratory flight (Richardson

1990a).

To decrease overall flight times, strong tailwinds are selected by individuals that make long migratory flights (Bruderer 1997b). Also, regardless of the length of migratory flight, significant correlations have been found between the numbers of migrants departing and strength of winds which have a large tailwind component with respect to the preferred migratory direction (Åkesson and Hedenström 2000). However, some studies have observed individuals migrating downwind regardless of the direction of the wind, even if this is not in the preferred direction (Able 1973; Gauthreaux and Able

1970). This behaviour may not be appropriate for all situations. Individuals that are confronted by a topographical barrier ( e.g. large bodies of water or deserts) may select wind directions that will not lead them towards inhospitable habitats (Erni et al. 2005;

Richardson 1990a). Irrespective of the presence of a barrier, individuals that are

“grounded” ( i.e. waiting for favourable wind conditions) will often wait only so long; if winds do not become favourable then migrants will depart even in a headwind (Åkesson and Hedenström 2000). The latter behaviour constitutes a trade off between length of time waiting and how dangerous it is to migrate in “unfavourable” winds. It is believed that migrants determine whether winds are favourable or unfavourable by visual cues

(e.g. cloud movements) or taking off and “sampling” wind conditions prior to a migratory flight (Åkesson and Hedenström 2000).

Another weather variable that can affect migration is precipitation (Demong and Emlen

1978; Richardson 1990a). The duration of precipitation is a more suitable predictor of low migration volume than the amount of precipitation per night (Erni et al. 2002). After several days of precipitation, migration is more likely to be observed (Erni et al. 2002).

Also, migrants may take off in light rain if it has been raining for many days. The onset of precipitation during migration can cause migrants to land earlier than expected

(creating a “fallout” event) or cause migrants to fly around a system of rain, resulting in individuals straying from their regular course (Richardson 1990a).

Changes and fluctuations in barometric pressure are possible important cues for migrants making departure decisions. Some species ( e.g. white-throated sparrows ( Zonotrichia albicollis )) can detect changes in barometric pressure (Muller 1976). Some studies have shown significant correlations between numbers of migrants aloft and rising barometric pressure (Richardson 1990a) but others have not (Able 1973). A concern with quantifying barometric pressure is the interconnectedness of pressure systems with other local weather variables ( e.g. wind direction and speed, temperature, precipitation, etc.)

(Lamb 1975). Migrants preferring a southwesterly migratory route migrate in high densities after the passage of low pressure systems. During fall migration, the number of days prior to the passage of cold fronts (associated with these pressure systems) can correlate with numbers of migrants aloft (Able 1973). Migrants preferring a south- southeast to southerly migratory direction often use strong northwesterly winds that occur after the passage of a cold front (Bruderer 1997b). These individuals are probably cuing

5 in to the wind direction associated with these pressure systems rather than the actual change in pressure (Erni et al. 2002).

Other variables such as temperature, cloud cover, visibility, and the lunar cycle can have moderate affects on migration. Generally, autumn migration is greater on nights with lower temperatures (Richardson 1990a). However, temperature is also strongly correlated with other variables related to migration, such as the movements of pressure systems.

Overall, cloud cover is unfavourable to migration (Demong and Emlen 1978), but is probably not enough to deter migration in itself (Richardson 1990a). Fog and visibility have a similar, unfavourable, effect on migration. Studies on the effects of lunar stage on migratory behaviour of passerines have obtained conflicting results, i.e. some studies showing no effect (Able 1973) while others show an increase in migration volume with decreasing moonlight (Nisbet and Drury 1968; Pyle et al. 1993).

Migratory passerines likely use all of these environmental cues, and the interactions among them, in some hierarchical fashion (differing between spring and autumn) to decide on which nights to depart on a migratory flight (Able 1973; Demong and Emlen

1978). Due to the complexity of this behaviour there is much to be learned concerning nocturnal migration of passerines. The importance that these environmental factors have on migration illustrates the need for further work in this field.

Monitoring migration

Some of the first attempts to quantify migration involved the counting and marking of individuals at stopover sites – places where migrants rest and feed during migration. One of the original purposes of monitoring migration was to gain insight into the migration paths of various taxa of birds by attaching standardized numbered bands to the legs of individuals and then ‘hoping’ that they would be recaptured in some other location, at some future time (Bairlein 2001). This technique has been used for the past 100 years in

North America (Wood 1945), and band recoveries have revealed migratory routes of numerous species (Brewer et al. 2000). However, information on migration pathways is still limited. We currently have some knowledge of the general pathways that many migrating passerine species take (known as “migration flyways”), but little is known about the fine scale decisions that migrants make during their migration ( e.g. flight behaviour in various wind conditions or in relation to topographical features).

An additional focus of migration monitoring is to estimate population trends. In the mid

1990’s, the Canadian Migration Monitoring Network (CMMN) was established, promoting use of standardize methods across the country to improve population trend estimates of boreal species that are not adequately covered by the Breeding Bird Survey

(BBS) (Dunn 1995). The BBS is the primary means of estimating range wide population trends of passerines in North America, but does not sample most of North America’s boreal forest habitat.

Since site-specific conditions dictate appropriate monitoring methods, methods used at each CMMN member station can not be identical (Hussell and Ralph 2005). Most member stations use standard censuses, constant-effort mist netting, and daily estimated totals (DETs) of numbers of individuals of each species detected in the study area. These methods complement one another in their abilities to detect different species of passerines, with the census detecting aerial species that are rarely netted, netting detecting secretive species, and DETs combining both methods. The combination of these techniques provides better information on the numbers and local species assemblages of migrants than one method could alone (Dunn et al. 2004; Faaborg et al. 2004; Whitman

2004). Consistency in these methods across years at each site is stressed to ensure variation in population trend estimates are not the result of changing in monitoring techniques or effort (Hussell and Ralph 2005).

In this thesis I explore various behaviours of nocturnal passerine migration, and how the numbers of migrants detected by different migration monitoring methods relate to each other. I determine how migrants react to wind displacement during migratory flight, and how nocturnal calling rates are affected by various weather variables. Also, using data collected at a Canadian Migration Monitoring station (the Atlantic Bird Observatory), I compare estimates of migrant numbers generated from ground counts (censuses, netting, and daily estimated totals) to estimates of migrants aloft (radar and acoustic sensors).

This work will add valuable insight into how migrants react to various weather conditions and give insight into the value of adding nocturnal surveys to Canadian Migration

Monitoring Network stations.

Chapter 1. Influence of wind on autumn nocturnal avian migratory behaviour in southwestern Nova Scotia

Introduction

The entire migratory journey of most passerines is composed of many short (500 –

1,000 km) nocturnal flights (Alerstam et al. 2003; Cochran and Kjos 1985; Lack 1959;

Liechti and Bruderer 1998; Wikelski et al. 2003), separated by one or more nights without flight (Cochran and Kjos 1985). Each night, an individual’s decision to migrate or not is determined by many factors, both internal (body condition, migratory urge) and external (time of year, wind/rain conditions, presence of ecological barriers, suitability of stopover site). Most species feed and rest during the day and migrate at night, taking off shortly after sunset (Åkesson et al. 1996; Alerstam et al. 2003; Richardson 1978c) and flying for 4 to 5 hours (Gauthreaux and Belser 1998).

General migratory direction is dictated in most small passerines by genetic factors

(Berthold 1999; Helbig 1991, 1992; Sutherland 1998). Through crossbreeding experiments between migrants with different migratory routes, it has been shown that the general migratory orientation for passerines is quite flexible. A significant change in the orientation direction can be detected in just two generations of crossbreeding (Berthold

1999; Berthold et al. 1992). Migratory directions have evolved according to location of breeding and wintering areas, modified by continental geographical/environmental features such as prevailing winds (Richardson 1985), and location of landforms such as mountain ranges (Sutherland 1998), rivers (Bingman et al. 1982), coastlines (Alerstam and Pettersson 1977), or other landscape features (Richardson 1978c).

Large scale migratory routes (known as “flyways”) are loosely defined regions within which birds migrate in high concentrations. The flyways in central and eastern North

America have been identified as having higher migration volume than the western flyway, and most migrants fly below 750 m (Gauthreaux et al. 2003; Lowery and

Newman 1966). Flyways exist because of a combination of topography and an innate desire among individuals to migrate in particular directions (Helbig 1991, 1992;

Sutherland 1998). Deviations from preferred migratory routes are likely the result of fine- scale decisions that depend more on various geographical, environmental, physiological conditions, and temporally specific decisions than on genetic preferences for a particular direction (Green et al. 2004; Lack 1959; Richardson 1978c). Fine scale migratory flight decisions include temporarily flying ‘off-track’ when faced with an ecological barrier

(Bruderer 2003; Bruderer and Liechti 1998; Lack 1959), coasting along leading-lines

(Alerstam 1990; Alerstam and Pettersson 1977), or flying with winds when they are blowing opposite to the desired migratory direction, a process known as reverse migration (Komenda-Zehnder et al. 2002; McLaren et al. 2000; Richardson 1978b). The complexity of the interactions among weather conditions, migratory urge, and geography means that our understanding of these fine-scale decisions is quite limited. This knowledge is of critical importance, however, if we wish to improve our ability to assess habitat needs (at stopover and refuelling sites) and determine more accurately the demographic consequences of migration for migratory species.

Optimal migration theory (Alerstam and Lindström 1990) attempts to quantify the trade- offs among energy consumption, risk of mortality, and duration of a migratory flight

(Liechti and Bruderer 1998). Wind conditions play a large role in dictating initiation of a migratory flight (Erni et al. 2002; Richardson 1990a), duration of migration, and energy consumption rates (Åkesson and Hedenström 2000; Alerstam and Lindström 1990;

Danhardt and Lindström 2001; Liechti and Bruderer 1998; Liechti et al. 1994), as well as causing mortality by blowing migrants off course. To minimize energy costs, individuals will most often postpone a migratory flight until favourable winds are available (Åkesson and Hedenström 2000; Erni et al. 2002; Liechti 1995). Favourable winds are usually defined as following and calm (Richardson 1990a). Migrants make fine-scale adjustments to wind by selecting the optimal flight altitude based on wind direction and speed

(Cochran and Kjos 1985) and altering their airspeeds (speed of the individual in relation to the surrounding air) to minimize energy loss and maximize flight range (Liechti 1995).

The maximum nightly migratory flight range (under good weather conditions) for a small passerine is believed to be approximately 1,000 km (Lack 1959). However, whether this distance is attained depends in part on a migrant’s behavioural response to its environment — for example, its response to any ecological barriers ( e.g. deserts or oceans) that it encounters en route (Liechti 1995). Ecological barriers have affected the evolution of migratory pathways of birds (Alerstam et al. 2003; Erni et al. 2005). A migrant faced with a large ecological barrier has the choice of crossing or flying around.

Crossing a barrier means that an individual takes the fastest route (the shortest distance), a strategy favoured in minimizing time spent migrating (Alerstam and Lindström 1990), but the risks involved in making this crossing may be substantial (Alerstam 1990; Diehl et al. 2003; Fortin et al. 1999). Many factors, including time of night, physiological

11 condition, migratory urge, wind condition, and orientation of the barrier edge, can influence an individual migrant’s decision at such barriers (Bruderer and Liechti 1998).

The migratory urge of an individual may sometimes override these or other factors, irrespective of the presence of a barrier. If poor weather conditions are experienced for several consecutive nights, birds may eventually choose to migrate in suboptimal wind conditions (Erni et al. 2002; Richardson 1991). Also, if migrants are not able to build fat reserves at a stopover site they may choose to migrate with low fuel loads in search of better quality habitat (Dunn 2002). An individual’s migratory urge may also change over the course of a night. Fortin et al. (1999) showed that migrants are less likely to cross large barriers during the second half of the night than they are during the first half.

Migrants may also land early in the night when approaching a large ecological barrier

(Bruderer and Liechti 1998).

The orientation of a barrier’s edge can play a role in a migrant’s decision to either cross or fly around that barrier. Migrants flying parallel to topographical features are aided in orientation and may avert barriers (Alerstam 1990; Bingman et al. 1982). Many migrants

“coast” (follow coastlines), especially when flying at low altitudes (Åkesson 1993), or when the coastline is closely inline with the migrant’s preferred migratory direction

(Alerstam and Pettersson 1977). “Coasting” avoids migration over water and can reduce the tendency to drift off course because individuals have a landmark to follow (Åkesson

1993).

Migrating passerines also use coastlines to correct for wind drift (Åkesson 1993), the tendency for wind to move a migrant in a different direction than the one in which it is attempting to fly. It is expected that for a migrant to detect that it is being drifted by the wind, it needs to have a visual reference of the earth to determine its relative motion over the ground (Åkesson 1993; Alerstam and Pettersson 1976, 1977; Lack 1959). However, there are numerous examples of passerines migrating at night, above fog or clouds, or over water, that seem to compensate for wind drift (Drury and Nisbet 1964; Evans 1966)

It has been suggested that when over water, migrants use the motion of waves relative to the land to detect wind drift (Alerstam and Pettersson 1976; Lack 1959) but there are no data to support this hypothesis. Other studies have shown that individuals only partially compensate for wind drift (Åkesson et al. 2002; Alerstam and Pettersson 1976), overcompensate (Alerstam 1979), or passively drift with the winds (Demong and Emlen

1978; Gauthreaux and Able 1970; Lack 1960; Richardson 1976). Some evidence suggests that compensation for wind drift increases throughout the night (Cochran and Kjos 1985;

Drury and Nisbet 1964). From these variable results, it is apparent that compensation for wind drift, if it exists, may be a complex species- or situation-specific behaviour.

Optimal migration theory predicts that migrants should allow drift when flying in strong, high altitude winds to maximize the distance they can travel, and should overcompensate

(fly into winds) in weaker winds at lower altitudes (Alerstam 1978a; Alerstam and

Pettersson 1977; Green et al. 2004). Migrants should also allow more drift when they are still far from their final destination, and compensate more as they near this destination.

The option chosen by a migrant is of course only sound if the migrant can ultimately land

13 at a suitable site. In the case of coastal migrants, significant drift could result in individuals being far from shore when they elect to cease a migratory flight. For example, migrants leaving Nova Scotia to cross the Gulf of Maine, where they face prevailing westerly winds that push them out over the Atlantic Ocean, would be expected either to compensate for wind drift or not fly in winds blowing in other than the preferred migratory direction. Studies of migration across the Gulf of Maine have, indeed, shown full compensation to wind drift by passerines (Drury and Nisbet 1964).

Previous work showed that during the autumn, migrants move southwest (SW), south (S), and north (N) in the Gulf of Maine region (Richardson 1972, 1990a). The most commonly observed direction is SW, while S and N movements usually occur only with

N and S winds, respectively (Richardson 1972). The ground speed of migrants in this area is between 9-13 m/s (Drury and Nisbet 1964). Passerines migrate through the area in concentrated “waves”, usually with a wave in August and another in early October. As a consequence, a large portion of a season’s migratory birds tend to be observed over a small number of nights (Drury and Keith 1962).

Since Drury and Nisbet (1964), techniques for monitoring migratory passerines have improved, primarily due to improvements in radar technology. In addition, relationships between weather and migratory patterns can be more readily assessed with better data on such features as upper level wind measurements. At the time of Drury and Nisbet (1964), it was not possible to easily collect data on the migratory tracks of individual passerines, and it was usually possible to obtain data on migration only from nights with fairly strong

14 directional movements. Drury and Nisbet were able to analyse only the general directions of tracks and approximate volumes of migrants. Data were also biased by the fact that nights with tail winds gave better estimates than nights with head winds (Drury and

Nisbet 1964). A consequence, I elected to assess the migratory patterns of passerines in the Gulf of Maine region using more current technology to expand upon the pioneering work of Richardson and Drury and Nisbet.

Specifically, my aims were to describe the general seasonal and nightly patterns of migratory behaviour, identify wind conditions that are avoided and preferred for migration and determine whether migrants compensate for wind drift during flight.

Methods

Study site

The study took place on Outer Island, Nova Scotia (43° 28’N, 65° 44’W; known locally as Bon Portage Island) (Figure 1-1) during the autumn 2003 migration season between

6 September and 31 October . Bon Portage, which houses a migration monitoring station of the Atlantic Bird Observatory (ABO), is a 365 ha, low-lying island located in southwestern Nova Scotia. It is recognized by Bird Life International as an important bird area, and during autumn migration serves as a stopover site for migrating passerines preparing to cross the Gulf of Maine (Davis 2001).

Radar data

I used a modified 1970 Decca Super 101 marine radar (see Appendix 1 for radar specifications) to collect information on the tracks of individual migratory birds. The original radar antenna was removed and replaced with a parabolic antenna, fixed at an angle of 31° from the vertical. When operational, the radar antenna rotated approximately once every 2 seconds so that the radar beam (approximately 5° in diameter (Black 2000)) swept through a doughnut-shaped area of the sky (Figure 1-2). The minimum altitude that migrants could be distinguished from ground ‘clutter’ was ~200 m; the maximum altitude at which individuals could be detected was ~1300 m.

The radar (and computer for data storage) was situated in an inland clearing near the

ABO netting area and powered by a portable gas-powered generator. Radar data collection began 30 min prior to civil sunset and continued until 30 min after civil sunrise. Each night a brief interruption in data collection was necessary to allow for refuelling of the generator. Otherwise, throughout the night, data were collected for 20 min periods followed by a 10 min period of no data collection. The radar was set to automatically turn off if a very large number of targets were detected during a short period of time indicating precipitation and to turn on again 20 min later. A custom-made processing board was obtained from John Black (originally built by T. MacDonald,

Brock University) and installed in the computer to convert and store the raw radar signal as ASCII files. Each ASCII file contained radar data from each 20-min period, consisting of data on the revolution number of the radar, and information on each returning pulse: count, azimuth, range, width and amplitude.

The radar transmits a beam consisting of a series of radiation pulses. When these pulses hit a target ( e.g. a bird flying through the radar beam), some are reflected back to, and detected by, the radar unit. Data collected by the radar unit when it detects a reflected pulse include the distance to the target, the azimuth of the target, and the strength of the returned pulse.

A series of functions were written for the R language (R Development Core Team 2005) that enabled me to combine the radar pulses together into putative single targets which were then further combined into tracks (see Appendix 2 for source code of functions). A group of pulses was determined to be two separate birds if they were at least 18 pulses apart or differed in range by more than 20 m. The tracks of a migrant’s trajectory were formed by taking each target and (initially assuming a speed of 20 m/s +/- 5 m/s)

“looking” ahead a maximum of 5 revolutions of the radar to find the next likely target generated from the same migrant. Once two targets were ‘potentially’ linked, information on their actual speed and direction were used in subsequent forward scans of the data to detect further targets that were likely from the same individual. The process was continued until no further targets were detected comprising a putative track for any given individual. A 20-min data stream was processed by first removing all long tracks (> 5 targets linked together), and then searching for shorter tracks within the remaining targets. Only migrants that contained tracks of at least 3 targets were considered for further analysis.

The direction that a migrant is determined to be moving by the radar is the migrant’s movement in relation to the ground ( i.e. its track). The direction a migrant is attempting to fly ( i.e. the direction the migrant’s head is orientated) is the migrant’s heading (Figure

1-3). The track of a migrant is the vector product of the migrant’s heading vector and the wind vector at that altitude. The heading velocity for each individual was calculated by subtracting the wind vector from the migrant’s track vector.

Weather data

Weather data were obtained from Environment Canada (Meteorological Services of

Canada, St. John’s Newfoundland). Radiosonde sounding measurements, which include wind profile (speed and direction), temperature, dew point, barometric pressure, and relative humidity, were obtained twice daily (00 z (Zulu) and 12 z) from the closest weather station, Dayton Nova Scotia (43 o 52’ N, 66 o 06’ W, 9 m a.s.l (Above Sea

Level)). During intermediate times (0300 z, 0600 z, 0900 z) radiosonde data from

Gagetown New Brunswick, Caribou Maine, and Lunenburg Nova Scotia, were used to extrapolate wind profiles for the study area. Wind profiles consisted of measurements at heights of 0, 100, 300, 500, 700, 1000, 1300, 1500 m a.g.l (Above Ground Level).

Statistical tests

As described in Green and Alerstam (2002), I tested for the occurrence of wind drift.

Taking each 20-min set of data as one case, I regressed the 20-min mean migration heading on the 20-min mean angular difference between the track and the heading (Track

− Heading). If migrants were drifting due to crosswinds from various directions, the

18 headings of the birds would remain constant and the tracks would vary (Figure 1-3a)

(Liechti 1995). Conversely, if migrants were fully compensating for wind displacement, then under different wind directions the headings of the migrants would vary and the track would remain constant (Figure 1-3b). Therefore, a regression coefficient of 0 indicates full drift, a value of 1 indicates full compensation for wind displacement, and a value between 0 and 1 indicates partial drift (Green and Alerstam 2002).

Results

General seasonal and nightly patterns

Radar data were collected on 50 of the 55 nights between 06 September and 31 October

2003. A total of 40,111 individual tracks (each consisting of at least 3 targets, or ‘hits’ of the same individual bird) were detected. Tracks had a mean height of 339 m and mean ground speed of 13.6 m/sec. The number of migrants detected nightly varied considerably

(Figure 1-4). A large proportion (45%) of the total number of tracks was detected on only

6 nights ( i.e. 12% of total nights surveyed). During these large volume nights, the general migratory direction was southwest (SW), with the exception of the night of 01-02

October (Figure 1-4). The same preference was typical over the entire season (Table 1-1).

During these 6 large volume migration nights, the mean migratory heading was 245°. If this bearing is followed on an orthodome (great circle) route, migrants would make landfall at the center of the Cape Cod peninsula, Massachusetts (Figure 1-5). Of all the migrants that were detected on these nights, if they were not drifted off course after

19 departing from Nova Scotia, 32% would make landfall at or near the Cape Cod region

(i.e. between the lines in Figure 1-5).

After a peak in the mean hourly number of migrants one hour after sunset, detections declined throughout the remainder of the night (Figure 1-6). This peak in migrants presumably represents individuals departing from southwestern Nova Scotia. The mean hourly migratory track increased slightly over the night and changed sharply at sunrise

(Figure 1-7).

Avoided and preferred wind conditions

A larger than expected proportion of migrants was detected during N and NE winds with speeds between 3-9 m/s (Table 1-2). Conversely, fewer than expected migrants were observed migrating during southerly wind greater than 3 m/s (Table 1-2). The most common wind direction observed during this study was west (W) to SW (Figure 1-8) and the most common wind speeds observed were 6-9 m/s. This shows that migrants are selecting specific “favourable” winds and avoiding most other conditions. Other studies have also shown that migrants are often selective with respect to the winds in which they choose to migrate ( e.g. Richardson 1990a).

Fewer migrants than expected were detected during nights with light (0-3 m/s) winds from the north to northwest. However, more migrants than expected were observed during nights with light (0-3 m/s) southerly to southwesterly wind (Table 1-2). During

20 these light southerly winds, the mean migratory direction was into the wind at a bearing of 267°.

Wind drift

Over the entire season, the average heading of migrants under various wind conditions was fairly consistent (Figure 1-9), indicating that the predominant heading did not change with wind direction, and that most individuals migrating past the study area used a

“constant heading” migration strategy. The slope of the estimate from the linear regression to test for the occurrence of wind drift was not significantly different from 0

(regression coefficient 0.011 ± 0.094 p = 0.91), indicating that migrants in this area are not compensating for wind drift (Figure 1-9). This is well illustrated by the night of 01-02

October. Of the 6 large volume migration nights, this was the only night without a strong directional tendency of migrants towards the SW (Figure 1-4 D) and it was the only one of the 6 nights with variable winds.

Even though migrants, overall, did not compensate for wind drift, it appears that migrants may not be drifting with the wind at all times. Examining the variation in the heading of migrants per hour since sunset shows that at 7 h from sunset, migrants may begin compensating for wind displacement (Figure 1-10). This corresponds with the time of night at which correlations between radar observations and ground counts of migrants the following day become highly significantly correlated (Table 1-3).

Discussion

General seasonal and nightly patterns

The nightly variation in numbers of migrants in this region has been described as high

(Richardson 1972). Drury and Keith (1962) reported 60% of the seasonal volume of migration has been observed on only 12 (9%) of 130 nights monitored. Similarly, I found that 6 (12%) of the 50 nights monitored accounted for 45% of the total numbers of migrants observed. Even though there is at least a small amount of migration each night, these data demonstrate that autumn migration consists of a series of waves of movement.

This punctuated pattern is likely due to different populations initiating migration at different times (Drury and Keith 1962), combined with individual migrants being selective of weather conditions for migration (Åkesson and Hedenström 2000;

Richardson 1972).

Nights with the largest numbers of migrants occurred in early and mid September, and early and mid October (see Appendix 3; Table A3-1 for probable species composition).

Species composition and timing of these movements are similar to what was observed by

Drury and Keith (1962). During these nights, the general direction of attempted flight

(i.e. the heading) was 245°, which is the “preferred” migratory direction in the region

(Richardson 1972). If these migrants were able to stay on this preferred heading, on an orthodome-like route, they would cross the Gulf of Maine and make landfall at the Cape

Cod peninsula. It is unlikely that these migrants are actually traveling on a great circle route, since this would require constant adjustment of headings, which I found evidence against. However, given that the distance across the Gulf is only ~400 km, little

22 adjustment would be needed to remain more or less on a great circle path. For example, in the calculation used to determine the destination of a migrant on an orthodome bearing of 245°, a heading adjustment was made only once, at the halfway point across the Gulf.

Similar to my findings, Drury and Nisbet (1964) observed migrants, presumably from southwestern Nova Scotia, reaching Cape Cod 4-5 h after sunset at a bearing of SW to

WSW, suggesting that migrants are leaving Nova Scotia in a broad front.

In addition to these observed ~SW movements, a few nights were dominated by reverse migrants (Table 1-1), predominantly under conditions of southerly winds. Moderate numbers of migrants were also observed moving in a SSE direction. These SSE movements were most likely composed of migrants making a transoceanic flight towards the west Indies or Bermuda (Nisbet et al. 1995; Richardson 1978a). Richardson (1972) observed similar movements at predictable times after the passage of pressure systems.

The pioneering work done by Richardson, and Nisbet and Drury did not include radar data at the level of individual migrants, and therefore were restricted to describing the migration system at a large scale ( i.e. nightly mean directions). Data collected in this study allowed for analysis on a fine temporal scale, at the individual level. Although I was not able to track individual migrants long enough to detect course changes or hesitation to cross the Gulf, I did examine the average hourly change in individual migrant tracks over the night. At sunrise, the mean flight direction changed drastically from 216° to 323°. This likely represents migrants reorienting due to the presence of the rising sun (Alerstam 1978b; Diehl et al. 2003). At dawn they may be unwilling to cross

23 the Gulf of Maine, and reorient over the water, flying back to the land and coasting, possibly looking for adequate stopover habitat (Alerstam 1978b). Richardson (1972) found no evidence to suggest coasting or hesitation to cross the Gulf by nocturnal migrants. The more fine-scale approach used here shows that migrants may hesitate to cross the Gulf during the dawn/pre-dawn hours. It is likely that the small marine radar used in this study, compared to the larger radars used by Richardson (1972), is better suited to detect these small scale directional movements.

The peak in mean numbers of migrants detected at the beginning of the night may be a reflection of migrants bunched up at the coast, resulting in larger numbers taking off in the general stopover area. Other studies that have looked at the nightly pattern of migration volume have also found a peak shortly after sunset (Alerstam 1972; Zehnder and Karlsson 2001) as well as a peak around midnight (Alerstam 1972).

Avoided and preferred wind conditions

Migration was detected during all observed wind conditions. However, some conditions appeared more “favourable” for migration than others ( i.e. more migration was observed than expected). Migrants mostly selected winds that were between NW and E and

3-9 m/s. These conditions would aid in crossing the Gulf of Maine by shortening the crossing time, and in turn reduce energy costs and risk of mortality, both of which are consistent with optimal migration theory (Alerstam and Lindström 1990). During very slow wind speeds from the W to N, fewer migrants than expected were observed. This suggests that to efficiently cross the Gulf of Maine, most migrants prefer tail winds of at

24 least 3 m/s. Many other studies have also found that migrants select wind conditions that aid in reducing migratory energy costs (Åkesson and Hedenström 2000; Cochran and

Kjos 1985; Liechti and Bruderer 1998; Richardson 1990a).

Fewer migrants than expected were observed during southerly winds (most speeds).

Much of the migration observed during southerly winds was reverse migration (defined as migratory flights opposite to the normal direction of migration). The strongest occurrence of higher migration than expected during southerly winds was in the case of very light (0-3 m/s) wind from the SW-W, and these movements were all in the normal

SW direction (267°, i.e. into the wind). Headwinds can aid in lift (Thomas and

Hedenström 1998), and at low wind speeds this may compensate for energetic costs of flying against the wind.

Wind drift

I found no evidence that migrants compensate for wind drift, suggesting instead that individuals use a constant-heading migration system ( i.e. individuals fly towards a goal and do not compensate for being displaced from that goal). This contrasts with Drury and

Nisbet (1964), who found that migrants over the Gulf were capable of detecting the magnitude and direction of wind drift and compensating for this displacement. This contradiction is of some significance, since the work of Drury and Nisbet (1964) is one of the few cases where migrants had been found to compensate for wind drift while over the ocean.

The results from my study may contradict those of Drury and Nisbet (1964) because of the use of an improved method to detect drift (Green and Alerstam 2002). Moreover, to obtain an accurate estimate of wind drift, migration data under many different wind directions is necessary (Green and Alerstam 2002), and Drury and Nisbet (1964) had poor estimates of migratory direction during tailwind conditions. Alternatively, the differing results may be because migrants in the Gulf of Maine region allow wind drift during the initiation of their Gulf crossing (the period sampled in my study) and then compensate for drift during the latter part of the crossing (the period sampled by Drury and Nisbet), once they can use the approaching coastline as a point of reference. Optimal migration theory indicates that migrants should allow wind drift early in a migratory flight to conserve energy by flying with the winds, and compensate only late in the flight to remain on course (Alerstam 1979).

The observed constant migration heading suggests that most individuals possess an innate desire to migrate in one general direction. The desired direction (~SW) probably evolved in migrants that first colonized Nova Scotia by way of a series of islands (Province of

Nova Scotia 1994) from Nova Scotia to New England during the post-glacial period

(Trotter 1909), and is reinforced or maintained by the angle of the Nova Scotia coastline

(Alerstam and Pettersson 1977). This preferred migratory direction was also observed in orientation experiments conducted at the ABO by Fitzgerald (2004); she found that the mean preferred migratory direction for yellow-rumped warblers (Dendroica coronata ) in cages (horizon exposed) was 238°, close to the mean heading of 245° from my study for nights with large volumes of migration.

The apparent compensation for drift 7 h after sunset may not indicate that individuals compensate for wind drift. It may be a function of migrants being less willing to cross the

Gulf late in the night (Bruderer and Liechti 1998), which may result in coasting, or it may indicate that migrants are starting to land during this period of the night (Fortin et al.

1999). The latter is supported by correlations between ground counts and hourly radar data for the previous night (Table 1-3). It appears that a greater proportion of migrants begin landing approximately 7 h after sunset. Therefore, migrants may not actually be compensating for wind drift, but cueing in to ground features, flying towards them, and landing. This illustrates the importance of having detailed data at a fine temporal scale to be able to differentiate true wind compensation from other behaviours.

Conclusions

As expected, the general migratory behaviour observed in this study (i.e. common direction, preferred wind conditions, and seasonal timing) was similar to that observed in previous studies ( e.g. Drury and Nisbet 1964, Richardson 1972). However, in contrast to

Drury and Nisbet (1964), I showed that migrants do not compensate for wind drift but are selective of what winds to migrate in, allowing drift with these winds. A large proportion of the migrants observed, if they remained on the same track (220°-245°), would make landfall at or near the Cape Cod peninsula. The success with which migrants cross the

Gulf of Maine and reach Cape Cod is explained by the selective nature of choosing winds that have a large tailwind component, allowing them to fly on a constant heading and even though they are drifting with the winds, still reach their goal.

The use of moderate tailwinds to shorten crossing time, the avoidance of light tailwinds and the use of light headwinds to aid in lift, are all predictable behaviours from optimal migration theory (Alerstam and Lindström 1990). These results indicate that the Gulf of

Maine may be a substantial barrier for individuals during autumn migration. This region may therefore be ideal for studying migratory flight strategies and physiological constraints placed on migrants when faced with the crossing large barriers.

A good understanding of migrant behaviour towards various topographical features and wind conditions can have implications for migration monitoring. Local topography and the fact that migrants drift with winds can affect numbers of individuals at stopover locations. Also, the use of ground counts in this study to interpret the behaviours of the migrants observed by the radar illustrates the importance of multifaceted approaches to studying nocturnal avian migration.

Figure 1-1. Map of Gulf of Maine and surrounding region, indicating the study site, Bon Portage Island.

Figure 1-2. Schematic of the “doughnut” area surveyed by the radar beam. From Eastwood (1967).

(a) Goal Direction

(b)

A B C D E F

N N N N N N = 0.31 ρ = 0.82 ρ = 0.81 ρ = 0.76 ρ ρ = 0.79 ρ = 0.72 W E W E W E W E W E W E

S S S S S S D

E A C B F

32 Averagenumber tracks of 0 5 10 15 20 25

0 2 4 6 8 10 12 Hours after sunset

Figure 1-6. Geometric mean numbers of tracks detected per hour over the entire season.

a) b) Degrees Degrees 100 150 200 250 300 350 100 150 200 250 300 350

0 2 4 6 8 10 10 8 6 4 2 0

Hours since sunset Hours before sunrise

Figure 1-7. Mean hourly tracks of migrants since sunset (a) and before sunrise (b).

W + E

300

200

100 Migrant Heading Migrant (degrees)

SWNE Wind Direction

SWNE SWNE

9 10 11 12

300 200 100 0 5 6 7 8

300 200 100 0 1 2 3 4

Migrant Heading Migrant (degrees) 300 200 100 0

SWNE SWNE Wind Direction

Table 1-1. Numbers and proportions of migrants grouped by track bearing over the entire season. Direction of Flight Number of Migrants Proportion of Migrants N – NE 1,586 3.95 NE – E 1,610 4.01 E – SE 1,511 3.77 SE – S 4,809 11.99 S – SW 12,474 31.10 SW – W 14,202 35.41 W – NW 2,353 5.87 NW – N 1,566 3.90

E-SE SE-S S-SW SW-W W-NW NW-N N-NE NE-E 0-3 NA 0.1 1.0 7.1 -0.9 -1.3 NA 1.8 3-6 -0.4 -1.0 -0.3 -0.8 -1.9 4.5 7.8 0.4 6-9 -1.5 -2.5 0.2 -4.4 -0.6 6.8 7.0 0.2 9-12 -1.6 -0.2 -1.5 -3.8 -1.2 NA NA NA 12-15 -0.7 -0.2 -1.0 -1.5 -0.5 -0.6 NA NA 15-+ -0.2 -2.2 -5.3 -2.1 NA NA NA NA

Table 1-3. Pearson’s correlation coefficients (r) (and associated p-values) from comparisons between numbers of migrants detected through ground counts and hourly numbers of individuals detected through radar counts during the previous night. Hour since sunset Number of radar tracks r p 0 2,383 0.13 0.475 1 6,914 0.32 0.030 2 4,789 0.33 0.030 3 2,952 0.25 0.094 4 3,222 0.30 0.043 5 4,834 0.37 0.012 6 3,689 0.33 0.028 7 2,839 0.50 < 0.001 8 2,307 0.57 < 0.001 9 1,963 0.49 < 0.001 10 1,332 0.58 < 0.001 11 955 0.55 < 0.001 12 445 0.15 0.511

Chapter 2. Radar, acoustics, and Canada Migration Monitoring Network methods as tools for monitoring nocturnal passerine migration

Introduction

Migrating passerines use stopover sites to refuel and rest before making further migratory flights (Mehlman et al. 2005). Migration has been studied at certain stopover sites for many years, and have contributed data on migration routes, timing, population trends, and demographics for many species. An advantage of studying migration at stopover sites is that migrants often congregate predictably in small areas. A major drawback, however, is that the migrants that stopover are an unknown sample of the total numbers that pass over the site on the prior night. If this sample is not a constant proportion of all migrants flying over, or is a non-random sample of the species composition, then the utility of migration data for estimating numbers or demographic trends in populations is diminished. This drawback can be mitigated by obtaining information on the numbers of migrants that choose not to stop over, and by using alternate means to measure species composition of over-flying migrants. This can be achieved by directly measuring migration during the night with radar and acoustic sensors.

Standard protocols for conducting ‘migration monitoring’ have been promoted (Hussell and Ralph 2005), resulting in more rigorous studies of migration at stopover sites.

Standard protocols are used by members of the Canadian Migration Monitoring Network

(CMMN), which was established with the goal of improving population trend estimates for migratory landbirds in Canada (Dunn 1995), targeting in particular the species that breed in parts of North America that are beyond the area normally monitored by other means ( e.g. BBS) (Badzinski and Francis 2000). To achieve this goal, efforts have been

38 made to coordinate migration-monitoring techniques and standardize data collection across the country. Most member stations of the CMMN use constant-effort mist netting and conduct standard censuses to obtain daily counts of migrants at stopover sites. In addition, daily estimated total numbers (DETs) of each species are generated by combining data from censuses, netting, and non-standard observations. These methods have different strengths in detecting various species at stopover sites (Hussell and Ralph

2005).

Constant-effort mist netting is useful for sampling species that spend time in lower vegetation strata, are cryptic, are skulkers, or are otherwise difficult to detect by visual and/or auditory surveys (Dunn and Ralph 2004; Rappole et al. 1993, 1998). Censuses can add information on aerial foragers, species that remain high in the canopy and rarely move down to the level of the mist-nets, as well as species in habitats that may not be effectively sampled by mist nets. Censuses may also more accurately measure the numbers of migrants that are making large movements during initial post-dawn hours

(reoriented migration; Alerstam 1978b). DETs combine data form these two techniques, eliminating duplicate counts, and also add data for individuals detected outside of the standard time in which mist netting and censuses are conducted. Combining various methods provides better information than one method could alone on the numbers and local species assemblages of migrants (Dunn et al. 2004; Faaborg et al. 2004; Whitman

2004). These methods are employed by the CMMN and most other migration monitoring station throughout North America.

Uses of migration monitoring data

The BBS provides valuable information on the population status of many species that breed in Canada. However, certain species, particular boreal breeders, are not adequately surveyed because of inaccessibility to their breeding grounds (Peterjohn and Sauer 1994).

Agreement between migration counts and BBS trends provides more confidence in population trends than does data from one method alone (Dunn et al. 1997). Comparison of population trend estimates from BBS and migration monitoring stations demonstrates that migration count data reflects population trends (Dunn 2005; Dunn et al. 1997).

However, there are still known biases associated with estimating trends from migration data. For instance, the subset of migrants that stopover is influenced by a variety of factors, both internal ( e.g. physical condition of the migrants) and external ( e.g. upper altitude weather conditions, time of night that migrants pass over). These factors are not typically measured during traditional migration monitoring but change both daily and seasonally ( e.g. Barriocanal et al. 2002; Bruderer and Liechti 1998; Drury and Keith

1962).

Alternate technologies such as radar and acoustic sampling offer some opportunities to reduce these biases. Radar can provide data on the timing, direction, and volume of migrants, while acoustic data can provide data on species composition as well as additional data on the volume of migrants during flight. These additional sources of data can then be used to obtain estimates of the proportions of overall migrants that stop over at sites ( i.e. numbers of migrants do not choose to stop and would otherwise go undetected) and how this changes with environmental conditions.

Radar

Radar has been used to monitor migrating birds since the early 1940s (Eastwood 1967) when, during WWII, mysterious targets known as “angels” were noticed on radar plan position indicator (PPI) screens (Drury et al. 1961). Initially, only large individuals could be detected individually and smaller passerines were seen only as a mass movement of radar targets (e.g. Nisbet and Drury 1968). In the past few decades, improvements in radar technology have allowed ornithologists to identify individual passerine migrants, with relatively small, cost-effective radar units.

A few studies have made direct comparisons between numbers of individuals detected by radar and by counts of migrants at stopover sites; Nisbet and Drury (1969) used moon watching to count migrating passerines, finding variability in comparisons between moon counts and counts at various migration monitoring stations. Williams et al. (1981) found a marginally significant positive correlation between radar and ground counts. More recently, Zehnder and Karlsson (2001) detected a stronger, significant positive correlation between nocturnal counts of migrating passerines and migrants captured through constant-effort netting at a stopover site. The varying strengths of correlations from previous studies indicate a need for further work to understand how ground counts relate to numbers of migrants aloft, and the influence of environmental conditions on this relationship.

Acoustic monitoring

Acoustic sensors also have the potential to be used to directly sample the volume of migration and have the advantage over radar of giving some indication of species composition. Directional microphones, sensitive to detecting high frequency sounds, have been used to record the nocturnal flight calls (nf-calls) of migrating passerines since the

1960’s (Graber 1968). During nocturnal migration, most species of passerines

(exceptions include vireos, flycatchers, and nuthatches) produce short calls, usually between 50 and 300 ms, at a frequency between 2-9 kHz (Farnsworth and Lovette 2005).

The purpose of these calls is not fully understood. During migration, passerines do not remain together in true flocks, but rather they fly in a “loose” flock formation (Hamilton

1962) and it is thought that nf-calls may serve as some sort of flocking mechanism

(Thake 1981) by helping individuals both keep together and maintain distance, so they avoid hitting one another (a type of “air traffic control”). Another hypothesized function of the calls is to increase the size of the migrating flock by attracting individuals into the flock from other flocks or the ground (Hamilton 1962). Such a strategy could increase safety (Hamilton 1962) and improve orientation (Simons 2004; Thake 1981).

Graber and Cochran (1960) found that numbers of nf-calls were not always well correlated with ground observations. A major reason for this is the variability in the calling rates of migrating passerines. It has been shown that nf-calling rates can be affected by weather conditions and are variable at both the individual and species level

(Farnsworth 2005). Without knowledge of the calling rates of the migrants being recorded, it is difficult to translate numbers of nf-calls to the numbers of individuals

42 flying over, since some individuals will pass over undetected ( i.e. due to not calling) and some will call multiple times ( i.e. thus be counted more than once). Further, there is a need for a more detailed understanding of how environmental factors can affect calling rates of nocturnal migrating passerines (Farnsworth et al. 2004).

In this paper I undertake some exploratory analyses of migratory counts, through radar and acoustic monitoring, to test how well the current CMMN methods track the actual numbers of individuals flying over migration stopover sites. I explore a data set comprising five different methods of counting migrating birds, all of which were simultaneously employed at the same location. Two of the methods enumerated individuals during their migratory flight (radar and acoustic sensors) and three enumerated individuals during daylight at a stopover site (censuses, netting, and DETs). I first test how well radar and acoustic monitoring (“nocturnal” measures of migration) compare to one another and how censuses, netting, and DETs (“diurnal” measures of migration) compare to one another. I then compare radar data to each diurnal method to test if an index of the numbers of migrants aloft is related to the numbers of migrants detected at the stopover site. Finally, I test whether environmental factors influence the number of migrants that are detected via diurnal counts and acoustic monitoring. I use the results of these comparisons to propose simple means to improve data collection at migration monitoring sites.

Methods

Study site

The study was carried out at the Atlantic Bird Observatory (ABO), situated on Bon

Portage Island (BP) (43° 28’ N, 65° 44’ W) (Figure 1-1). Migration monitoring data collection (described below) used standard count techniques (Hussell and Ralph 2005).

Data were collected on 76 of 78 days between 15 August and 31 October 2003.

Netting

Under good weather conditions (low wind, no precipitation), constant-effort mist netting was conducted daily using 15 mist nets (12 × 2.6 m, 4-panel, 36 mm extended mesh).

Mist nets were opened 30 min prior to civil sunrise and remained open for 6 h , excluding time lost to short-term closure due to bad weather. Nets were closed 8 h after civil sunrise, even if 6 h of netting has not been completed. Nets were checked every 30 min.

All birds were extracted and brought to a nearby banding laboratory where each was fitted with a uniquely numbered US Fish and Wildlife Service aluminium leg band, aged, sexed, weighed, assigned a categorical subcutaneous fat score (based on visible fat in the furculum) (Kaiser 1993; Rogers 1991), and measured for wing chord. See Pyle (1997) for a full description of techniques.

Census

A daily standard census commenced 1 h after civil sunrise, regardless of weather

(excluding extreme rain and wind). One observer walked, at a constant pace, along a prescribed trail (~2.5 km) through all major habitat types in the vicinity of the site to

44 ensure all habitats were equally represented. The duration of the census was kept constant at 90 min. All individuals heard or seen were recorded. To ensure maximum detection, personnel skilled in passerine identification (both sight and sound) conducted censuses.

Daily estimated totals (DETs)

Non-standard observations of migrants in the study area were recorded throughout the day. Each evening, once all data collection was completed, data from netting, census, and non-standard observations were combined to generate DETs of the numbers of individuals per species on the island. This was done by the same individual each day to reduce variation in estimates. DETs are calculated by adding the numbers of individuals detected by each method (censuses, mist netting, and non-standard observations) and then adjusting those numbers to give what the main observer feels is the best possible estimate of the actual numbers of new migrants in the monitored area on that day.

For this paper, I used ground count data only from those species that were believed to be non-resident, nocturnal migrants ( i.e. species that could possibly be detected by the radar or acoustic sensor). To ensure that the data best represented migrants that were newly arrived at the stopover site, only newly banded individuals were included from the netting data ( i.e. no re-trapped individuals were counted). These ‘stopover’ individuals could not be identified in the census and DET data, and therefore those counts may have been too high.

Radar

Radar provides the most detailed index of the actual numbers of individuals migrating overhead, and also provides data on their speed, direction, and altitude, and how those change through the night and under various weather conditions. However, one problem with radar data is that an unknown proportion of migrants go undetected due to two phenomena that occur as the distance between the target ( i.e. individual migrant) and the radar increases. First, targets (especially small targets) have an unknown probability of going undetected as their distance from the radar signal increases, and second, because the beam covers a large and increasing area with height, targets have a greater chance of staying in the beam longer and thus being detected as distance from the radar signal increase. Both phenomena obviously interact, but in an unknown way, which makes it particularly difficult to confidently determine exact counts of individuals flying overhead

(Bruderer 1997a). As a consequence, I consider the radar data to be an index of the volume of migration on any particular night relative to other nights. This assumes that there are no major changes in altitudinal patterns of migrant flow within or across nights, which is confirmed by a visual inspection of the data.

Protocols for radar data collection are reported in Chapter 1.

Acoustic monitoring

An acoustic sensor, consisting of a Knowles EK3028 hearing aid sensor (peak response at

5 KHz, falling off by 13 db at 800 Hz and 8.5 KHz) (Black pers. comm. 2005) powered by a 9 V battery, was situated approximately 100 m from the radar and used to record

46 nightly nf-calls made by migrating passerines (specifically warblers and sparrows). The sensor, the same as that used by Blanchard and Black (1994), was pyramidal with a beam angle of approximately 60 o and a beam width of 1,385 m at the maximum detection range of ~600 m. It was placed, with the base pointing up, ~30° from the horizontal, to prevent damage from leaking or pooling water. Acoustic data were recorded by a high fidelity video cassette recorder (VCR) onto an eight hour video tape at the slowest recording speed. The start time of acoustic monitoring varied across nights. However, most nights recording commenced at ~20 h (local time) and lasted for 6 h. The acoustic sensor could not detect nf-calls during rain.

There are fundamental differences between the acoustic sensor and the radar. For warblers, the radar detects individuals between 250 m and ~1,000 m a.g.l, whereas the acoustic sensor detects individuals between 0 and ~600 m a.g.l. Since the height of the detected nf-calls cannot be determined, the difference in the sampling area between the acoustic and radar methods cannot be corrected.

At the end of the season each tape was digitized using Tseepo (Oldbird Inc. 2005), a program that detects nf-calls between 6-10 kHz and saves them as “*.WAV” files. Each sound was saved as an individual file which was time-stamped relative to its position on the VCR tape. Due to the frequency range in which the software sampled, only nf-calls of warblers and sparrows were detected. Using the visual spectrographic analysis software

GlassOFire (Oldbird Inc. 2005), each call was compared to a reference set of nf-calls

(Evans and O’Brien 2002). Definite non-nf-calls were discarded and the remaining

47 probable calls were saved. Both Tseepo and GlassOFire were obtained from

“www.oldbird.org” on 01 December 2003.

Data analysis

Data manipulation and statistical tests were performed using R 2.2.1 (R Development

Core Team 2005)

Correlations

Pearson’s correlation tests were used to determine relationships between different monitoring methods (censuses, netting, DETs, radar, and acoustics). To obtain normal distributions, data were natural log-transformed. I compared the numbers of individuals detected by each diurnal monitoring method with the radar data on the previous and subsequent night, and compared the numbers of nf-calls to the number of radar tracks detected.

Generalized linear models

Generalized linear models were fit with appropriate families to model how environmental factors influence the number of nf-calls and individuals detected by the diurnal monitoring methods. A negative binomial family (function glm.nb from Venables and

Ripley (2002)) was used to model census, DETs, and acoustic data, to account for the

“clumpy” nature of the data that arises from flocking behaviour of migrants. A Gaussian family was used to model netting data, expressed as numbers of individuals per net hour

(nh). Saturated models were initially constructed to determine overall quality of fit

(through residual analysis). Models were then simplified using manual, stepwise backwards elimination by examining the quality of parameter estimates, and the contribution of each term to the overall fit of the model (Crawley 2002).

Diurnal measures of migration

I examined the relationship between each diurnal measure of migration and 13 potential predictor variables (Table 2-1). In each model, I first accounted for the number of tracks detected per unit of time by the radar. Because capture rates increase after nights with headwinds (Barriocanal et al. 2002), nightly mean wind direction was included in each model. Nightly mean migratory direction was included to determine if the number of individuals detected on the ground differed as a function of the general direction of migration, as it was expected that individuals about to cross a large barrier ( e.g. Gulf of

Maine) would be more prone to stopover (Zehnder and Karlsson 2001). Predictor variables representing the occurrence of rain, degree of visibility and cloud cover were all included because these variables can cause the cessation, and inhibit the initiation, of a migratory flight (Richardson 1990a). During autumn, nights with a decreasing temperature trend (or nights with low temperatures) are often nights with large volumes of migration (Graber and Cochran 1960). Two variables, change in 24 h temperature and mean daily temperature, were included to test for these effects. The phase of the moon was included, because a fuller/brighter moon can block celestial migratory cues and therefore reduce the amount of migration (Pyle et al. 1993). The predictor variable rho is a measure of the directional concentration of migration for a given night. This variable was used as an indicator of favourable conditions ( i.e. higher rho relates to “good”

49 migratory conditions). I hypothesized that the stronger the directional concentration of migrations, the better the conditions were for migration, and therefore that fewer individuals would stopover (Barriocanal et al. 2002).

To ensure that any difference between the census and netting models was not due to different species being more readily sampled by either method (see above), I reran the census model and included only species that consisted of at least 10% of the overall banding for that year. This did not change the census model, suggesting that differences were not species-based.

Acoustic monitoring

A set of environmental predictor variables based on the literature (e.g. Farnsworth 2005), along with numbers of tracks detected by the radar were selected (Table 2-1). The phase of the moon, cloud cover, and visibility were included, because decreased visual ability of a migrant can have a positive affect on the number of nf-calls produced (Farnsworth

2005). Temperature (change in mean 24 h temperature, and daily mean temperature) was included because it has been shown to be positively correlated with counts of nf-calls

(Graber and Cochran 1960). Since nf-calls are potentially used as a flocking mechanism

(Hamilton 1962), the nightly directional concentration of the migrants (rho) was used in the model. I predicted that an increase in rho would be positively related to calling rates.

Nightly mean wind directions were included because nf-calling increase in favourable winds (Graber and Cochran 1960) and nightly mean migratory direction was included to

50 test if migrants embarking on a trans-gulf flight increased calling. Migrants approaching an ecological barrier increase their nf-calling rates (Hamilton 1962).

Results

General results

A total of 1,343 migrants, comprising 67 species was banded at a rate of 0.28 birds/nh.

The census and DETs detected a total of 117 and 160 species, respectively. Radar and acoustic data were collected on 50 and 43 nights, respectively, from 06 September until

31 October. Radar detected 40,111 tracks and 3,369 usable nf-calls (see methods) were detected by the acoustic sensor.

Correlations within method types

Diurnal methods

Highly significant positive correlations were found among all diurnal methods (census, netting and DETs) (Table 2-2). This suggests that at this site, all standard CMMN methods are measuring essentially the same thing: some index of the number of migrants that stopover. The highest correlation was between the census and DETs and the lowest was between netting and census. This suggests (not surprisingly) that the DETs values are more influenced by data obtained on the census than they are by netting data.

Nocturnal methods

The numbers of nf-calls recorded was positively correlated with the numbers of migrants from the radar (r = 0.41, p = 0.009). Visually, the relationship between the two indices

51 appears to be non-linear (Figure 2-1). However, this was not well supported with the addition of a quadratic term for radar counts within a linear model relating nf-calls to radar counts (F1 = 0.78, p = 0.38).

Correlation across method types

To determine if ground counts represent migrants arriving at or leaving the stopover site,

I tested for correlations between each diurnal measure and radar data from both the previous night (migrants arriving at the stopover site) and from the following night

(migrants leaving the stopover site). Radar data and diurnal counts during the following day were significantly correlated (Table 2-3), and linear (Figure 2-2). This suggests that, at this site, radar data provide an index of the number of migrants that are entering the stopover site and not of the number of migrants that are leaving the site ( i.e. the radar is measuring stopover behaviour).

I found stronger correlations between diurnal methods and the radar data collected from the second half of the night (post-midnight) than from the first half (pre-midnight) (Table

2-3) which suggests that migrants that are stopping over at this site are from the pool of individuals flying over during the second half of the night.

To further assess the relationship between radar and diurnal methods at a finer scale, I also tested for correlations between each diurnal method and hourly radar data from the previous and following nights. Counts from diurnal methods were not correlated with any portion of radar data from the following night. However, there was a trend, of decreasing

52 correlation as time after sunset increased (Figure 2-3). Correlations between all diurnal monitoring methods and radar data from the previous night increased throughout the night (Figure 2-3). However, there was a decrease in the correlation between diurnal methods and radar data at 12 hours after sunset on the previous night (Figure 2-3).

Environmental factors affecting diurnal counts

Generalized linear models were constructed to relate the numbers of migrants detected by diurnal methods (census, netting and DETs) to environmental factors (Table 2-1). All predictor variables affected diurnal counts as predicted from the literature (Richardson

1990a), with the exception of the nightly mean migratory direction in the netting model

(Table 2-4).

The census and netting models differed in that the census model included rain as an important variable and the netting model included visibility. Visibility and cloud cover are somewhat interchangeable in that they both have similar biological effects on the migrants. The census and netting model also differed in that each contains a different measure of temperature (daily temp and 24 h change in temp). As with visibility and cloud cover, these two measures of temperature are considered to be interchangeable.

The DETs model, which depends on data from both census and netting, included variables that were unique in each of the census and netting models ( i.e. rain (present only in the census model) and cloud cover (the equivalent of the visibility variable found only in the netting model)) (Table 2-4).

Environmental factors affecting acoustic monitoring

The number of migrants detected by the radar was positively correlated with the number of nf-calls detected. Of the environmental variables, both increasing moon phase ( i.e. brighter nights) and cloud cover ( i.e. darkening skies) were negatively related to the numbers of nf-calls detected whereas the mean daily temperature was positively related.

Discussion

Correlations within methods

All diurnal methods were significantly positively, linearly, correlated, indicating that all methods were effectively quantifying the same thing: an index of numbers of migrants that are stopping over at this site. The highest correlation was between the census and

DETs and the lowest was between netting and census. This indicates that DETs are influenced more highly by data obtained on the census than they are by netting, and also underscores the effectiveness of netting data to obtain data for species not always captured in the census. A similar analysis of a longer term dataset from Long Point Bird

Observatory yielded comparable results; netting and census data were the least correlated and the DETs and census were the most correlated (Dunn et al. 2004). Dunn et al. (2004) examined these relationships at the species level and found that the method detecting the majority of individuals (census or netting) generally had the greatest effect on the DETs, illustrating the advantages of DETs. Through combining data from various diurnal methods, the DETs better represent the numbers of migrants at the stopover site.

The nocturnal methods (radar and acoustics) were also significantly, positively correlated. Although, non-significant, it appears that with increasing migrants detected by the radar a disproportionately greater number of nf-calls were detected (Figure 2-1). This suggests that acoustics may be a valuable tool in monitoring nocturnal migration, but illustrates the need for caution since there are other factors affecting the production of nf- calls (see below: “Environmental factors affecting acoustic monitoring”). Similar results have been found when comparing nf-calls to Next Generation Radar (NEXRAD) data.

Larkin et al. (2002) found a strong correlation while Farnsworth et al. (2004) found only a marginal correlation. They also stressed the importance that other factors

(environmental and internal) have on nf-call production. The benefits of acoustics in monitoring migration would be enhanced by automated identification of nf-calls to species. Considerable work is currently being done to achieve these goals ( e.g. Evans and

O'Brien 2002) and this ability should be available in the near future.

Nocturnal and diurnal method comparisons

The DETs method was most strongly correlated with radar counts of migrants during any portion of the previous night except post-midnight, in which case netting was most strongly correlated. Conversely, the netting data were very weakly correlated with radar data during the first part of the night. These findings are similar to results from

Massachusetts (Nisbet and Drury 1969; Williams et al. 1981) and Falsterbo, Sweden

(Zehnder and Karlsson 2001). Of these other studies, the most recent found the strongest correlation between ground counts and nocturnal measures of migration. This is likely due to an increase in standardization of ground count methods in recent years.

The variation in the correlations between netting data and radar data from the first part of the night and the latter part of the night may be a function of the behaviour of the migrants upon landing. Because the numbers of individuals from the census are correlated with radar data from most portions of the night, this suggests that a portion of migrants land throughout the night. Assuming that all migrants take off at sunset

(Åkesson et al. 1996; Alerstam 1972; Alerstam et al. 2003; Biebach et al. 2000), individuals landing in the latter part would have likely been migrating for a longer period of time than those stopping over during the first portion of the night. The individuals that have migrated for a longer time have expended more energy and thus are probably in greater need of replenishing fuel reserves than those that landed earlier (Chernetsov

2006). The need to replenish fuel would result in more movement and given that the probability of being netted increases with movement rate (Remsen Jr. and Good 1996), one would expect that netting counts would increase when individuals are behaving in this manner. Such an effect would not be apparent in the census data because detection of individuals with this method is not as strongly affected by movement behaviours.

It might be expected that ground counts of migrants would be positively correlated with radar data during dusk on the following night, as the radar would probably detect migrants leaving the stopover area. I did not find significant positive correlations between any diurnal monitoring methods and radar data from any portion of the following night.

However, during the following night the strongest correlation was immediately after

56 sunset. This suggests that there is variability in stopover duration and most individuals are stopping over for more than one day (Calvert et al. 2006, in review).

Correlations between ground counts and hourly radar data during the previous night increased throughout the night. This may be because migrants are increasingly more prone to land as the night progresses, a phenomenon which is compounded by the presence of a barrier (Bruderer and Liechti 1998; Fortin et al. 1999). However, there is a decrease in the correlation coefficient 12 h after sunset. For most nights, the 12 th hour from sunset coincides with the dawn hour. In Chapter 1, I showed that during the dawn hour the mean angle of migration was northerly (Figure 1-7). Because at dawn, radar data are not significantly correlated with netting or DETs, and migrants are flying from over the ocean towards the land, they may be flying several kilometres inland before landing

(Richardson 1978b) to avoid the coast (Alerstam 1978b). Migrants avoid stopping over at coastlines due to increased predation or low food availability (Alerstam 1978b), and their behaviour may differ with age of migrant, weather and moon phase (Dunn and Nol

1980).

Although netting data were the best predictors of numbers of birds detected by the radar during the latter half of the night, it appears that this relationship does not remain linear during periods when large numbers of birds are detected by the radar (Figure 2-2). This may be due to sampling bias. Standard protocols (6-h netting limit, 1-h processing time limit per individual, net checks every 0.5 h, etc.) limit how many individuals can be banded per day. Personnel availability and skill also can be a limiting factor. This

57 asymptotic sampling effect is also apparent (although not to the same degree) in the census. For example, the census is more strongly restricted in time than in distance, meaning that if the observer has reached the allotted time limit before he/she has reached the end of the census route, he/she would continue on the route but not include any further observations in the census count. This may result in a low representation of the true numbers of individuals in the area if particular habitats are not censused. This usually only occurs when there are large numbers of migrants at the stopover site, resulting in too many individuals to count in the allotted 90-min.

DETs are not simply an addition of census, netting, and non-standard observational data.

Experienced biologists, that are familiar with the site, can “adjust” the DETs to give a somewhat better representation of the numbers of migrants moving through the stopover site than can be estimated then by the census or netting alone. This is evident from the relationship between DETs and radar data (Figure 2-2). However, because of DETs are adjusted by the observer, during periods when few migrants at the stopover site it appears that the observer may increase their estimate, while when there are large numbers of migrants at the stopover site they decrease their estimate. This illustrates a possible problem with the subjectivity of the DETs method.

Environmental factors affecting diurnal counts

Models relating environmental factors to the numbers of migrants stopping over, were largely consistent with what is known about migration (Richardson 1990a). In both the census and DETs models, rain at 18 h had a negative influence on the numbers of

58 migrants detected. Rain at this time of the night ( i.e. when migration is usually initiated) inhibits the initiation of migratory flight (Erni et al. 2002; Richardson 1990a), which is reflected in low numbers of migrants observed on the ground at the stopover site the next day.

Cold autumn nights often stimulate large numbers of individuals to migrate (Richardson

1990a), which was seen in my models where increasing temperature was negatively related to the numbers of migrants detected on the ground.

An increase in cloud cover (in the DETs model), or a decrease in visibility (in the netting model) was associated with fewer migrants detected. These two variables should have similar effects on migration and thus be interchangeable in terms of the biological responses that migrants make regarding these conditions. An increase in cloud cover (or decrease in visibility) may block migratory cues, making migration more “difficult”

(Richardson 1985, 1990a).

During a night with a southerly mean migratory direction, more migrants were detected by the census and DETs compared to nights with an easterly, northerly, or westerly direction. This may be because individuals that are migrating in a southerly direction are embarking over a large body of water which promotes landing by the migrants (Bruderer and Liechti 1998). The same trend was not detected by netting data. This was probably not a function of the biology of the migrants but rather of the interaction between individuals being selective of what wind conditions in which they chose to migrate (see

Chapter 1), and netting being partially dependent on wind conditions since certain wind conditions preclude netting due to safety concerns for the migrants. At this site,

“favourable” wind conditions often coincided with conditions that were not conducive for netting. The operation of the census was less sensitive to weather. Therefore, the census estimates of numbers affected by the direction of migration is a better representation than that given by the netting model.

Rain did not explain any of the variation in the netting model. This is unexpected since rain at 18 h had an effect on the number of migrants detected by the census and DETs model.

Overall, the models testing the importance of environmental factors to numbers of individuals detected by each diurnal method were very similar. Variables in the DETs model are a compilation of those in models for census and netting. Rain and cloud cover

(analogous to visibility) were both in the DETs model, but only in the census and netting models, respectively. This is another reflection that the numbers of migrants detected by the DETs is influenced by both the census and netting methods.

Environmental factors affecting acoustic monitoring

Other studies have compared nf-calls with radar tracks by examining the timing of peaks in numbers nf-calls and density of migrants per night (e.g. Graber 1968; Lowery and

Newman 1966). The peaks in nf-calls usually coincide with when birds are landing ( i.e. during dawn hours) (Farnsworth 2005; Graber and Cochran 1960). To build on this work,

I investigated the change in nf-calling and radar tracks over the night, and not simply the peak numbers detected. In doing so I avoided sampling when peaks in nf-calling occur.

The numbers of nf-calls detected were affected by more than just the number of migrants aloft. This was expected, because nf-calls are affected by environmental factors such as cloud cover, visibility, temperature, etc. (Farnsworth 2005). Specifically, I found that increasing moonlight had a negative influence on the number of nf-calls. This is consistent with the hypothesis that nf-calls are being used to prevent midair collisions

(Hamilton 1962). An increase in light would improve visibility and thus would reduce the need for calls. Cloud cover and temperature had an opposite effect on the production of nf-calls. With increasing cloud cover, fewer calls were detected, which contradicts the above explanation regarding moon phase. Although the literature indicates that calling increases during cold autumn nights (Graber and Cochran 1960), I detected the opposite trend.

It is unclear why the cloud cover and temperature had these unexpected effects. One potential explanation may be that migrants increase their calling rate when approaching an ecological barrier (Farnsworth 2005; Hamilton 1962). The migrants in this study may have been responding to the Gulf of Maine as a barrier, and therefore reacted differently to other environmental stimuli ( e.g. cloud cover and temperature) than reported in other studies that were conducted more “inland” ( e.g. Evans and Mellinger 1999; Farnsworth et al. 2004; Graber and Cochran 1960). Future work is necessary to make comparisons between acoustic recordings at the coast and inland to help answer this question.

The nf-calling rate is an important aspect of migration that is still poorly understood

(Farnsworth et al. 2004). I demonstrate here that some environmental factors appear to influence calling rates, but in ways that are inconsistent with other studies. More research is needed to assess how nf-calling varies at the species level, and how different species respond to varying environmental conditions. New technologies where acoustic sensors can be attached to migrating passerines and recording can take place during flight are being developed (Jim Cochran, Sparrow Systems, pers. comm.). These will allow researchers to examine calling rates for various species under various conditions. Such data will improve the utility of using acoustic sensors as a migration monitoring tool.

Conclusions and Recommendations

The methods used in this study were useful for measuring the magnitude of migration.

Significant positive correlations between numbers of individuals detected by radar, acoustics, and diurnal methods (censuses, netting, and DETs) indicate that all of these methods are comparably measuring the process of nocturnal passerine migration in the area. However, while they are all significantly positively correlated, they all measure different aspects of the process. The diurnal methods measure the numbers of migrants that have chosen to land at the stopover site. This urge to land during migration can be brought on by the physiological state of the migrant, time of night, weather conditions, and topographical features such as barriers (Bruderer and Liechti 1998). The scope of this study did not allow me to distinguish the importance of these variables. However, due to the strong correlations between the ground counts and the radar data in the latter part of the night, it appears that an interaction between time of night migrants are passing over

62 and the presence of a barrier may contribute to their decision to land (Bruderer and

Liechti 1998).

The nocturnal monitoring methods (radar and acoustic) both sample numbers of migrants during flight, but do so by measuring different aspects of migration. The radar detects individual migrants and provides data on their position and movement through space and time, while the acoustic sensor measures a behavioural aspect of migration (the emission of nf-calls). This behaviour, while it appears to relate fairly closely to the numbers of migrants aloft, is also affected by various environmental factors.

I recognize that because this study took place during only one autumn migration season, at one location, recommendations that can be made here for future migration monitoring by other CMMN stations are somewhat limited. However, the finding that all methods were relatively strongly and positively correlated suggests that the monitoring efforts currently in place at CMMN stations (or specifically the ABO) provide data on the numbers of migrants moving through the local area.

This study has also shown that radar can enhance CMMN methods by measuring large- volume migrations. There may be limits to the numbers of migrants that can be

“detected” by each diurnal sampling method; these limits are less important for the nocturnal measures of migration. On nights when large volumes of migrants move through the stopover area, radar has advantages over the diurnal methods in that it is not restricted by the numbers of individuals which can be recorded. As a result, during these

63 occasions radar can provide better estimates of numbers than can ground counts. Overall, this study has shown that radar, and possibly acoustic monitoring in the future, can be used as an enhancement for current CMMN stations.

ln(nf-calls1) + 2 3 4 5 6

0 2 4 6 8 ln(radar targets + 1)

Figure 2-1. Numbers of nf-calls and radar tracks recorded while both methods were simultaneously in operation. Data are natural log transformed. Line is locally weighted regression line.

ln(DET+1) ln(census+1) ln(Banding/nh+1) 0.02 0.04 0.06 3.0 3.5 4.0 4.5 5.0 5.5 4.0 4.5 5.0 5.5 6.0 6.5 0 1 2 3 4 5 6 0 1 2 3 4 5 6 0 1 2 3 4 5 6 ln(post midnight targets/radar h + 1)

a) b) CorrelationCorrelationCorrelationCoefficient Coefficient Coefficient CorrelationCorrelationCoefficient Coefficient -0.2-0.2-0.2 0.0 0.0 0.0 0.2 0.2 0.2 0.4 0.4 0.4 0.6 0.6 0.6 -0.2-0.2-0.2 0.0 0.0 0.0 0.2 0.2 0.2 0.4 0.4 0.4 0.6 0.6 0.6

0 2 4 6 8 10 12 0 2 4 6 8 10 12

Hours After Sunset Hours After Sunset

Table 2-1. Description of initial predictor variables used in all generalized linear models Variable Type Description Tracks Continuous Number of birds detected by the radar Tracks/h Continuous Number of birds detected by the radar divided by the number of hours the radar was in operation Wind dir Categorical Nightly mean wind direction; “E” (>= 45° & < 135°), “S” (>= 135° & < 225°), “W” (>= 225° & < 315°), “N” (>= 315° & < 45°). Bird dir Categorical Nightly mean migratory direction; “E” (>= 45° & < 135°), “S” (>= 135° & < 225°), “W” (>= 225° & < 315°), or “N” (>= 315° & < 45°). Rain (18) Categorical Occurrence of rain at 18 h Rain (00) Categorical Occurrence of rain at 00 h Rain (06) Categorical Occurrence of rain at 06 h Cloud cover Pseudo- Amount of cloud cover measured on a scale from 0 continuous (no cloud cover) to10 (total cloud cover). Vis Pseudo- The distance (km) at which it is possible to see Continuous without instrumental assistance Change 24 h temp Continuous Amount of change in mean daily temperature (°C) Daily temp Continuous Mean daily Temperature (°C) Moon Pseudo- Amount of moon visible. Scale from 0 (new moon) to Continuous 1 (full moon). Rho Pseudo- Concentration of the nightly migratory direction. Scale Continuous from 0 (even scatter of direction) to 1 (all individuals migrating in the same direction)

Table 2-2. Pearson’s correlation coefficients (r) and p values of comparisons between each diurnal monitoring method. Comparison r p Birds/nh vs Census 0.46 < 0.001 Birds/nh vs DETs 0.62 < 0.001 Census vs DETs 0.73 < 0.001

Table 2-3. Pearson correlation coefficients (r) of numbers of birds detected by diurnal monitoring methods compared to numbers of birds detected by radar per hour on the previous night. Pre-midnight Entire night Post-midnight ln(variables +1) r p r p r p Birds/nh 0.09 0.57 0.31 0.046 0.46 0.002 Census 0.33 0.02 0.41 0.003 0.38 0.008 DETs 0.41 0.01 0.43 0.003 0.42 0.005

Table 2-4. Summary of generalized linear models and analysis of variance tests, showing parameter estimates (Est.), standard errors (Std. error), deviance, and degrees of freedom (DF). Census and DETs models have negative binomial distributions, netting model has a Gaussian distribution. For a description of variables see Table 2-1. Census (Theta = 5.01 (1.08)) DETs (Theta = 6.84 (1.41)) Netting Variable Est. (Std. error) Deviance DF Est. (Std. error) Deviance DF Est. (Std. error) Deviance DF Resid. NULL Dev. -- 104.29 -- -- 112.69 -- -- 0.011 -- Intercept 4.37 (0.26) -- -- 5.84 (0.23) -- -- -0.011 (0.012) -- -- ln(tracks/h + 1) 0.16 (0.056) 17.43 1 0.11 (0.047) 12.98 1 0.0035 (0.0017) 0.0010 1 Bird dir (W) -0.35 (0.16) 21.15 3 -0.30 (0.14) 22.82 3 -0.0046 (0.0049) 0.0010 3 Bird dir (N) -0.27 (0.25) -- -- -0.043 (0.22) -- -- 0.0083 (0.0085) -- -- Bird dir (E) -0.60 (0.21) -- -- -0.15 (0.18) -- -- 0.012 (0.0074) -- -- Rain (18) -0.45 (0.17) 4.14 1 -0.41 (0.14) 7.54 1 ------Cloud cover ------0.04 (0.020) 6.88 1 ------Daily temp -0.043 (0.014) 9.91 1 -0.04 (0.012) 12.04 1 ------Vis ------0.0011 (0.00042) 0.0014 1 24 h change temp ------0.0021 (0.00068) 0.0015 1

Table 2-5. Summary of generalized linear model (negative binomial distribution) and analysis of variance test with nf-call data as the response showing parameter estimates (Estimate), standard errors (Std. error), deviance and degrees of freedom (DF). These data consist of nights with strong directional migration (rho > 0.6 and number of tracks > 100). For description of variables see Table 2-1. Variable Estimate (Std. error) Deviance DF Residual NULL Deviance -- 101.71 24 Intercept 4.36 (0.53) -- -- Tracks 0.0010 (0.0042) 37.44 1 Moon -2.63 (0.59) 9.52 1 Cloud cover -0.20 (0.40) 11.07 1 Daily temp 0.13 (0.029) 17.31 1

General Discussion

Monitoring implications

Understanding the impact that environmental factors can have on the volume of nightly migration is critical in enhancing our knowledge of the processes that affect migration and, in turn, how these processes affect estimates of numbers of migrants. Currently, passerine migration is predominantly monitored through ground counts, which are dependent on migrants choosing to stopover. Since the mechanisms that cause stopover events are not measurable by current, commonly used, migration monitoring techniques, there is a need for methods capable of quantifying the numbers of migrants that do not stopover to supplement current practices. In this study I incorporated methods capable of monitoring numbers of migrants that do not stopover with currently used methods to enhance current migration monitoring practices.

I demonstrated that current diurnal methods of monitoring migration are giving fairly good estimates of numbers of migrants moving through the local area. I showed that ground counts were highly correlated with radar data from the latter part of the previous night. This indicates that established CMMN stations may be enhanced by operating radar during the early portion of the night to sample migrants that are not correlated with ground counts. Using radar in this way would quantify both migrants that do and do not stopover.

I show that the volume of migration on any given night is highly dependent on wind conditions. This may have implications for current migration monitoring stations that

71 have limited resources. These stations could use information from wind conditions to predict when it would be more advantageous to conduct ground counts, thereby maximizing the monitoring efforts.

Population implications

Passerines migrate by either drifting with the wind or compensating for wind displacement. The former is thought of as the more parsimonious of these two strategies since the latter is dependent on an individual’s ability to sense the magnitude of the wind and determine its motion relative to the ground, which is difficult over water (Alerstam and Pettersson 1976). Since the more parsimonious, evolutionary stable strategy is often passed on over evolutionary time scales (Stewart 1993), it is not surprising that migrants in this region appear to allow themselves to drift with the wind and select for winds that allow them to drift across the gulf and not be blown out over the open ocean.

In this region, individuals migrate on a mean heading which coincides with the shortest bearing to cross the Gulf of Maine, and is also in line with the Nova Scotia coastline, both of which are energy efficient and aid in orientation. This migratory strategy has resulted in the migrants in this region being dependent on wind conditions to aid them in crossing the Gulf of Maine. Migrants are forced to weigh the costs of migrating during unfavourable winds (Bruderer and Liechti 1998) against waiting for favourable winds and therefore delaying their arrival on breeding/wintering grounds (Drent et al. 2003;

Møller 1994). If the proportion of unfavourable wind conditions increases due to climate change, then migrants may be forced to make migratory flights during these suboptimal

72 conditions and be subjected to increased mortality. Furthermore, the possibility an increase in stochastic inclement weather events do to global climate change (Easterling et al. 2000; Intergovernmental Panel on Climate Change 2002), may also cause increased mortality during migration (Butler 2000).

Limitations and future work

Currently our knowledge of nocturnal migration in this region is from large-scale work conducted in the 1970s ( e.g. Richardson 1972). Therefore, information is lacking on fine- scale discussions made by migrants in this region. The scale at which this study was conducted gave some insight into fine-scale decisions that migrants make ( e.g. orientation changes during dawn). However, this study was limited in that it was conducted at only one location, and took place over one migration season. Future work using multiple radars in proximity to one another ( e.g. ~50 km apart) would give better data on the decisions migrants are making under various seasonal and environmental conditions, and how those decisions are related to topography.

In this thesis I have shown the value of using a multifaceted approach to monitoring passerine migration. While each method has advantages and disadvantages for migration monitoring, I show the importance of using multiple monitoring methods in conjunction with each other to best exploit the synergistic benefits of simultaneously using multiple techniques. Depending on site-specific factors, some combination of nocturnal and diurnal monitoring techniques is ideal to maximize the benefits of an established

Canadian Migration Monitoring Network station.

Literature Cited

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Appendix 1

Radar Specifications:

Model: Decca Super 101 Wavelength: 3.2 cm Frequency: 9415 to 9475 MHz Peak Power: 3 Kw Pulse Length: 0.08 msec (24 m) Pulse Repetition Frequency: 3000 Hz

Appendix 2

Functions written by P. Taylor and C. Leon (2004), in R language of statistical computing, to convert radar data into a usable format:

i) Function name: Doall Function purpose: Take each 20 data file, pass each to function “alltracks” and then combine all these data into one data frame. function() { #make a vector of all the files in working directory dirinfo<-list.files()

#do alltracks for each file twentymin<-lapply(dirinfo,alltracks)

#this will name each summary table the same as the file that was used to create it (night needs to have at least 3 target tracks) names(twentymin)<-as.character(dirinfo)

speed.height.bearing <- function(indata) {

#strip data so only 3 or more hit birds are left indata <- indata[indata$count>2,]

#pass the data to function “first.last” (v) to combine all the targets in each track to calculate one speed, height and bearing from each track. if(nrow(indata)!=0){indata.out<-first.last(indata)}

else if(nrow(indata)==0){indata.out<- data.frame(tracknum=NA,birdid=NA,pulse=NA,x=NA,y=NA,range=NA,width=

NA,amplitude=NA,nhits=NA,rev=NA,height=NA,radius=NA,speed=NA,bearing =NA,count=NA,pultime=NA,lagtrack=NA,new.bearing=NA,new.speed=NA,new .height=NA,new.pulse=NA)}

indata.out<-data.frame(indata.out) indata.out }#end function

#create a table of the speed, heights, and bearings of each bird that was hit at least 3 times by passing data to function “speed.height.bearing” summary<-lapply(twentymin,speed.height.bearing)

#now comnbine the components of the list to form one table summary<-do.call("rbind",summary)

#add columns for month, day and time summary$day <- as.numeric(substr(row.names(summary),3,4)) summary$time <- as.numeric(substr(row.names(summary),5,8)) summary$mon <- as.numeric(substr(row.names(summary),1,2))

#no need to have the row names anymore summary<-data.frame(summary,row.names=NULL) }#end function

ii) Function name: Alltracks Function purpose: Calls function “readrawconvert”, then repeatedly calls function “birdtracks” passing new verisions of the radar data, with previously established tracks pulled out. function(infilename) { #takes 'infilename' which is the name of the ascii file, converts it, then repeatedly calls birdtracks, passing new versions of indata, with previous track pulled out #birdtracks identifies a likely bird track within indata

#read in raw data and calculate radar parameters dataanddetails <- readrawconvert(infilename)

#select just the data from the list provided by readrawconvert indata <- dataanddetails[[1]] details <- dataanddetails[[2]]

#run through the whole file first with cutoff at 4 i <- 1 alltracks <- NULL

while (length(indata$birdid)>0) { pairs <- birdtracks(indata,revtime=details[2],pulsetime=details[3],cutoff=4) pairs$tracknum <- rep(i,length(pairs$birdid)) indata <- indata[(indata$birdid %in% pairs$birdid)==F,] alltracks <- rbind(alltracks,pairs) i <- i + 1 } trackcounts <- data.frame(table(alltracks$tracknum)) names(trackcounts) <- c("tracknum","count") alltracks <- merge(alltracks,trackcounts,by="tracknum") indata <- alltracks[alltracks$count==1,c(2:(length(alltracks)-1))] bigtracks <- alltracks[alltracks$count>1,]

#run through a subset of the file (eliminating bigtracks) now with a cutoff of 1 i <- ifelse(length(bigtracks$birdid)==0,1,max(bigtracks$tracknum)+1) alltracks <- NULL while (length(indata$birdid)>0) { pairs <- birdtracks(indata,revtime=details[2],pulsetime=details[3],cutoff=1) pairs$tracknum <- rep(i,length(pairs$birdid)) indata <- indata[(indata$birdid %in% pairs$birdid)==F,] alltracks <- rbind(alltracks,pairs) i <- i + 1 } #put the two files together if(is.null(alltracks) == F) { trackcounts <- data.frame(table(alltracks$tracknum)) names(trackcounts) <- c("tracknum","count") alltracks <- merge(alltracks,trackcounts,by="tracknum") } alltracks <- rbind(bigtracks,alltracks) alltracks$pultime <- details[3] alltracks }#end of function

iii) Function name: Readrawconvert Function purpose: Reads raw ASCII file from the radar compiles all targets. function(indataname) { #takes raw file from radar and creates a file of birdhits #set up the fixed radar parameters

radarangle <- 59 year <- 2003 rangefilter=293 ampfilter=80

#determine the number of lines in the file #this seems pretty fast, but there might be another way #this includes blank lines lastline <- length(readLines(indataname))

#I don't know if there is consistently an eof after the date, so I'll #start about 10 lines from the bottom, then read until I find the -999 lastline <- lastline-10 junk <- read.table(file=indataname,skip=lastline,nrows=1,sep=",") while (junk$V1 != -999) { junk <- read.table(file=indataname,skip=lastline,nrows=1,sep=",") lastline <- lastline+1 }

#read the pulse hits data from the file indata <- read.table(file=indataname,skip=5,nrows=lastline-6,sep=",",blank.lines.skip= FALSE,col.names=c("rev","pulse","azimuth","range","width","amplitude","junk")) indata <- indata[,1:6] #gets rid of the 'extra' variable

#read in the fourth line of the raw data file -- the line with the time information. Ignore the other lines have to assume this is the fourth line of the file -- should check that at some point start.time <- read.table(file=indataname,skip=4,nrows=1,sep=",",fill=T,col.names= c("month","day","hour","min","sec")) start.time <- paste(paste(year,start.time$month,start.time$day,sep="-")," ", paste(start.time$hour,start.time$min,start.time$sec,sep=":"),sep=" ") start.time <- as.POSIXct(start.time)

#read the endtime end.time <- read.table(file=indataname,skip=lastline,nrows=1,sep=",",fill=T,col.names= c("month","day","hour","min","sec")) end.time <- paste(paste(year,end.time$month,end.time$day,sep="-")," ", paste(end.time$hour,end.time$min,end.time$sec,sep=":"),sep=" ") end.time <- as.POSIXct(end.time)

#do overall calculations for the time that the radar ran #There are 600 revolutions per run, so should be able to calculate from that duration <- c(difftime(end.time,start.time,units="secs"))

84 numrevs <- 600 #this is fixed by the user revtime <- duration/numrevs lastpoint <- length(indata$pulse) pulsetime <- (((indata$rev[lastpoint]- 1)+(indata$azimuth[lastpoint]/360))*revtime)/indata$pulse[lastpoint] radardata <- c(numrevs,revtime,pulsetime)

#filter the data to a standard year indata <- indata[indata$range>rangefilter,] indata <- indata[indata$amplitude>ampfilter,]

#now process those data #strip out the zero azimuth (why? I forget) temp <- indata[indata$azimuth!=0,]

#set up two new variables that are the differences between successive pulses and ranges temp$pulsediff <- c(0,temp$pulse[1:length(temp$pulse)-1])-temp$pulse temp$rangediff <- c(0,temp$range[1:length(temp$range)-1])-temp$range

#create a null set of birdids temp$birdid <- rep(0,length(temp$pulse))

#call it a new bird wherever two hits are either more than 18 pulses apart, or the range is greater than 20 #the 18 is calculated in John's program -- not sure why you need to do it more than once temp$birdid[temp$pulsediff< I(-18)|abs(temp$rangediff)> 20] <- 1:length(temp$birdid[temp$pulsediff< I(-18)|abs(temp$rangediff)> 20]) birdnum<-0 temp.bird <- temp$birdid for (i in 1:length(temp.bird)) { birdnum <- max(birdnum,temp.bird[i]) temp.bird[i] <- birdnum } temp$birdid <- temp.bird

#transform azimuth and range to x & y #will use standard spherical co-ordinates for the rest of the functions #first rotate azimuth since azimuth = 0 is North, azimuth is converted to radians from degrees #first, set up a correction, if the radar wasn't positioned correctly correct <- 45 #the correction in degrees, for the radar in 2003 was 45 deg temp$azimuth <- temp$azimuth + correct

85 temp$azimuth[temp$azimuth>360] <- temp$azimuth[temp$azimuth>360] - 360 #temp$azimuth <- 2*pi - (temp$azimuth*pi/180) temp$azimuth <- rad(temp$azimuth) temp$x <- temp$range*cos(temp$azimuth) temp$y <- temp$range*sin(temp$azimuth)

#aggregate the data for each bird temp.agg <- aggregate(cbind(temp$pulse,temp$x,temp$y,temp$range,temp$width,temp$amplitude), list(temp$birdid),mean) names(temp.agg) <- c("birdid","pulse","x","y","range","width","amplitude") temp3.agg <- aggregate(temp$rev,list(temp$birdid),min) temp.agg$nhits <- c(table(temp$birdid)) temp.agg$rev <- temp3.agg$x temp.agg$height <- temp.agg$range*cos(rad(radarangle)) temp.agg$radius <- temp.agg$range*sin(rad(radarangle)) temp.agg$birdid <- as.numeric(as.character(temp.agg$birdid)) list(temp.agg,radardata) }#end function

iv) Function name: Birdtracks Function purpose: Links targets together to form each migrants track. function(birdhits,cutoff,pulsetime,revtime) { #program to select tracks from birdhits based on discussions with Carlos Leon and my initial ideas

#set initial parameters bearingsd <- 5 speed <- 20 speedsd <- 5 heightbroad <- 20 heightsd <- 20

# need to add a bit to numpulsesperrev because the birds can fly some distance before being hit again numpulsesperrev <- (revtime/pulsetime) * 1.2 maxrevsahead <- 5

#max and min speeds allowed for birds -- slower or faster objects are turfed minspeed <- 2 maxspeed <- 45

#birdtrack stores the data for a single track

#set it up initially with the first birdhit, and the guesses for speed and bearing birdtrack <- birdhits[1,] birdtrack$speed <- speed birdtrack$bearing <- 0 nonexthit <- FALSE firsttime <- TRUE

#loop around at most maxrevsahead times looking for the next likely hit while (nonexthit==F) { firstbird <- birdhits[1,]

#birdsub is the set of birds that are within a given heightrange (heightbroad) and ahead of firstbird birdsub <- birdhits[abs(birdhits$height-firstbird$height)

#select all likely second hits of firstbird within one revolution of the radar #then continue looking at more revolutions until a bird is found, or numrevs #is greater than some max (as far ahead as you want to look) secondhit <- FALSE numrevs <- 1 while ((secondhit==F)&(numrevs < maxrevsahead)) { #secondhits contains all initially likely second hits of the current first bird secondhits <- birdsub[birdsub$pulse< firstbird$pulse+numrevs* numpulsesperrev,]

if (length(secondhits$pulse)>0) #skip calculations if there are no secondhits { #now calculate the speed between point 1 (or previous point) and the rest of the points in secondhits #need to calculate speed because distance will depend on altitude refpoint <- birdtrack[length(birdtrack$x),] second.dist <- sqrt((refpoint$x-secondhits$x)^2+(refpoint$y- secondhits$y)^2) second.time <- (secondhits$pulse-refpoint$pulse)*pulsetime second.speed <- second.dist/second.time

#calculate the bearing between refpoint and the rest of the points and convert into degrees x <- secondhits$x - refpoint$x y <- secondhits$y - refpoint$y second.bearing <- atan(y/x)

second.bearing <- ifelse(x<0,second.bearing+pi,(ifelse(y<0, second.bearing +2*pi,second.bearing))) second.bearing <- second.bearing*180/pi

#calculate the probability density for the speed and bearing #note that the first time this is meaningless, so we do it differently #select the most likely second hit (best hit) if there is one #note that this must be done differently the first time through #since there may be multiple possible second hits #note also that this may happen subsequently, but is much less likely #and actually it rarely happens the first time through

if (firsttime) { speed.probdens <- ifelse(((second.speed > minspeed) & (second.speed0.1 besthit.bearing <- besthit.speed #since the bearing is irrelevant first time through firsttime <- FALSE } #end if firstime TRUE

else #begin if firsttime FALSE { speed.probdens <- dnorm(second.speed,speed,speedsd) bearing.probdens <- dnorm(second.bearing-birdtrack$bearing [length(birdtrack$bearing)],0,bearingsd) besthit.speed <- speed.probdens==max(speed.probdens)& (max(speed.probdens)>0.00005) besthit.bearing <- bearing.probdens==max(bearing.probdens)& (max(bearing.probdens)>0.00001) }

besthit <- besthit.speed==T&besthit.bearing==T

second.speed <- second.speed[besthit] second.bearing <- second.bearing[besthit] secondhits <- secondhits[besthit,]

if (length(secondhits$pulse) > 1) { #need to discriminate between possibles ... maybe by height? second.height <- secondhits$height-refpoint$height secondhits <- secondhits[which.min(abs(second.height)),]

second.speed <- second.speed[which.min(abs(second.height))] second.bearing <- second.bearing[which.min(abs(second.height))] }

if (length(secondhits$pulse) == 1) #if there is a second hit, then add the data to birdtrack { secondhits$speed <- second.speed secondhits$bearing <- second.bearing birdtrack <- rbind(birdtrack,secondhits) #adds hit to track list for a single bird birdhits <- birdhits[birdhits$birdid >= secondhits$birdid[1],] #removes the secondhit from birdhits

speed <- second.speed #reset speed estimate

secondhit <- TRUE #this is to end the while statement, because at least one second hit was found } #end if there was a second hit } #end if (skip calculations) numrevs <- numrevs + 1 } #end inner while statement nonexthit <- numrevs==maxrevsahead #if searched maxrevsahead ahead and found nothing, then end outer while } #end outer while statement if (length(birdtrack$pulse) > cutoff) {birdtrack} else {birdtrack[1,]} #if not a track of more than 4, then just return the first point (to eliminate it)

} #end function

iv) Function name: First.last Function purpose: for each track, computes one bearing, height and speed by using the first and last target in each track. The first and last target are used to account for curvature in track caused by the radar beam. function(indata) {

#dump the details that were carried from alltracks pultime<-indata$pultime

89 pultime<-pultime[1]

#remove all birds with only 1 or 2 hits indata <- indata[indata$count>2,]

#get the cooridnates of first and last hit for each bird indata$lagtrack <- c(indata$tracknum[2:length(indata$tracknum)],0) indata.last <- indata[indata$tracknum!=indata$lagtrack,] indata.first <- indata[indata$speed==20&indata$bearing==0,] x <- indata.last$x - indata.first$x y <- indata.last$y - indata.first$y

#first calculate an overall bearing bearing <- atan(y/x) bearing <- ifelse(x<0,bearing+pi,bearing) bearing <- 2*pi - bearing indata.last$new.bearing <- deg(bearing) indata.last$new.bearing <- indata.last$new.bearing %% 360

#then calculate an overall speed distance <- sqrt(x^2+y^2) #details[3] contains the time/pulse in secs birdtime <- (indata.last$pulse-indata.first$pulse) * pultime bird.speed <- distance/birdtime indata.last$new.speed <- bird.speed

#then calculate an average height indata.last$new.height <- (indata.last$height+indata.first$height)/2

#calculate an average pulse count for each bird to help determin time each bird was hit indata.last$new.pulse <- (indata.last$pulse + indata.first$pulse)/2 indata.last }#end of function

Appendix 3 Table A3-1. Abundant species observed at the Atlantic Bird Observatory the day following each peak volume night of migration. Date Number Mean track Possible species of tracks bearing 08-09 2,790 235° Black-and-white warbler ( Mniotilta varia ) September Red-breasted nuthatch ( Sitta canadensis ) Magnolia warbler ( Dendroica magnolia ) Common yellowthroat ( Geothlypis trichas ) Yellow warbler ( Dendroica petechia ) Northern waterthrush ( Seiurus noveboracensis ) American redstart ( Setophaga ruticilla ) Alder flycatcher ( Empidonax alnorum ) 17-18 2,334 228° Blackpoll warbler ( Dendroica striata ) September Song sparrow ( Melospiza melodia ) Common yellowthroat ( Geothlypis trichas ) Red-eyed vireo ( Vireo olivaceus ) Red-breasted nuthatch ( Sitta canadensis ) American goldfinch ( Carduelis tristis ) Purple finch ( Carpodacus purpureus ) American redstart ( Setophaga ruticilla ) American robin ( Trudus migratorius ) 21-22 2,566 223° Blackpoll warbler ( Dendroica striata ) September Song sparrow ( Melospiza melodia ) American goldfinch ( Carduelis tristis ) Baltimore oriole ( Icterus galbula ) Cedar waxwing ( Bombycilla cedrorum ) 01-02 4,913 225° Song sparrow ( Melospiza melodia ) October Blackpoll warbler ( Dendroica striata ) Common yellowthroat ( Geothlypis trichas ) Red-eyed vireo ( Vireo olivaceus ) 11-12 3,106 221° Cedar waxwing ( Bombycilla cedrorum ) October Yellow-rumped warbler ( Dendroica coronata ) Song sparrow ( Melospiza melodia ) Blackpoll warbler ( Dendroica striata ) Purple finch ( Carpodacus purpureus ) American goldfinch ( Carduelis tristis ) 18-19 2,245 212° Yellow-rumped warbler ( Dendroica coronata ) October Golden-crowned kinglet ( Regulus satrapa ) White-throated sparrow ( Zonotrichia albicollis ) American goldfinch ( Carduelis tristis ) Cedar waxwing ( Bombycilla cedrorum ) Purple finch ( Carpodacus purpureus )