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NOCTURNAL HABITAT SELECTION OF
WINTERING SURF SCOTERS
IN THE SALISH SEA

by
Lindsey Hamilton

A Thesis
Submitted in partial fulfillment
of the requirements for the degree
Master of Environmental Studies
The Evergreen State College
September 2015

©2015 by Lindsey Hamilton. All rights reserved.

This Thesis for the Master of Environmental Studies Degree
by
Lindsey Hamilton

has been approved for
The Evergreen State College
by

________________________
Dr. Dina Roberts
Member of the Faculty

________________________
Date

ABSTRACT
Nocturnal Habitat Selection of Wintering Surf Scoters in the Salish Sea
Lindsey Hamilton
Marine bird surveys are primarily conducted during diurnal periods, thus our
understanding of their ecology and distribution is biased; our understanding of Surf
Scoter (Melanitta perspicillata) distribution is no different. Diurnal data currently guides
conservation and management decisions regarding this declining species. Our research
objectives were to 1) determine nocturnal use areas of the Salish Sea and associated
habitat characteristics, 2) determine influencing factors of selection of nocturnal use, and
3) develop predictive models to estimate likely nocturnal use areas across the Salish Sea
and assess vulnerabilities to potential oils spills or increased shipping traffic. We used
existing Surf Scoter Platform Terminal Transmitter (PTT) data, provided by The
Washington Department of Fish and Wildlife (WDFW) and various spatial layers in a
GIS to identify habitat characteristics of nocturnal locations and to measure distances
traveled between diurnal foraging and nocturnal resting areas. Results indicated that
Scoters will travel an average of 3,967 m from diurnal foraging areas to habitats that are
farther from shore and greater in water depth, and that these habitats are limited by tidal
currents and vessel traffic. We implemented a use versus pseudo-non-use resource
selection design, using logistic regression, and Akaike’s information criterion (AIC) to
create a predictive model for nocturnal Scoter presence in the Salish Sea. Our resulting
model identified distance to shore, water depth, tidal current and vessel traffic as strong
predictors of nocturnal presence. Determining marine nocturnal use habitat characteristics
fills an important data gap in understanding the winter ecology of Surf Scoters. Our
results provide guidance for better management of over-wintering seabirds in the Salish
Sea and inform oil spill response preparedness efforts.

TABLE OF CONTENTS
List of Figures…………………………………………………………………......……v
List of Tables……………………………………………………………………..……..vi
Acknowledgements………………………………………………………….………….vii
Chapter 1 Literature Review………………………………………………………......1
Introduction………………………………………………………………………….…...1
The Salish Sea……………………………………………………………………….…...4
Components of the Salish Sea………………………………………………....…5
Surf Scoter Ecology…………………………………………………………………...…6
Scoter Diet……………………………………………………………………….8
Surf Scoters in the Salish Sea……………………………………………………………10
Status……………………………………………………………………...….….11
Nocturnal Behavior………………………………………………………………………15
Conservation Implications……………………………………………………………….17
Oil Transportation in the Salish Sea……………………………………………………..19
Justification for Research……………………………………………………………......22
Chapter 2 Data Preparation…………………………………………………………...25
Introduction…………………………………………………………………………...…25
Methods……………………………………………………………………………...…..26
Nocturnal vs. Diurnal Designation………………………………………………26
Data Filtering…………………………………………………………………….26
Controlling for Twilight Movement……………………………………………..29
Excluding Flightless Moult Periods……………………………………………..30
Removing Autocorrelation……………………………………………………....31
Results and Summary……………………………………………………………………31
Chapter 3 Manuscript………………………………………………………………….32
Introduction…………………………………………………………………………...…32
Methods………………………………………………………………………………….36
Study Area……………………………………………………………………….36
Data Sets…………………………………………………………………………37
Spatial Analysis………………………………………………………………….42
Statistical Analysis……………………………………………………………….43
Results……………………………………………………………………………………44
Distances Traveled From Diurnal Foraging Areas………………………………44
Habitat Selection…………………………………………………………………45
Discussion………………………………………………………………………………..48
References……………………………………………………………………………….54
Appendix………………………………………………………………………………...59

iv

LIST OF FIGURES
Figure 1. Salish Sea study area and recognized regions…………………………………37
Figure 2. Nocturnal locations of 34 Surf Scoters in the Salish Sea study area used for
habitat selection analysis and modeling……………………..…………………………..39
Figure 3. Average distance traveled by Surf Scoters between diurnal and nocturnal
locations within a 24 hour period within the Salish Sea (2003-2007)…………………..45
Figure 4. Map displaying nocturnal locations of 34 Surf Scoters (2003-2007) as well as
tanker vessel traffic density from 2011………………………………………………….52
Figure 5. A sample of results from three different Douglas Argos filters (KEEP_LC=1,
KEEP_LC=2 and MAXREDUN=1km, KEEP_LC=1) applied to Surf Scoter location
data, Used for visual comparisons, in order to choose the filter that balances accuracy and
retention of data………………………………………………………………………….64
Figure 6. Flow chart of data preparation strategy……………………………………….65

v

LIST OF TABLES
Table 1. Habitat covariates used in univariate and logistic regression analysis to assess
habitat selection of wintering Surf Scoters in the Salish Sea 2003-2007. Each covariate
was measured from various vector and raster spatial layers. Additional information for
spatial layers is listed in Table 8…………………………………………………………41
Table 2. Log transformed mean distance (in meters for all columns) traveled by Surf
Scoters between diurnal and nocturnal locations within the Salish Sea (2003-2007). Each
measurement is the shortest strait line distance between a set of diurnal and nocturnal
points within an Argos duty cycle. If land features separated the two locations, the
shortest distance around that feature was measured……………………..……...……….44
Table 3. Mean comparisons between use (n = 1,064) and pseudo-non-use (n = 5,002)
nocturnal Surf Scoter locations for 5 habitat variables within the Salish Sea 2003-2007.
Differences evaluated with t-tests (JMP®, Version 11. SAS Institute Inc., Cary, NC,
1989-2007)…………………………………………………..………………………...…46
Table 4. Importance weight of each habitat covariate, calculated from confidence set
(models with Δi >10) of candidate models from best-fitting logistic-regression used to
predict nocturnal Surf Scoter presence from habitat characteristics in the Salish Sea,
2003-2007…………………………………………………..………………………...….46
Table 5. Resulting confidence set (models with Δi >10) of candidate models from bestfitting logistic-regression used to predict nocturnal Surf Scoter presence from habitat
characteristics in the Salish Sea, 2003-2007. The 4 models listed are the Models with the
lowest AICc and the highest weights (wi) are the most supported models……..……..…47
Table 6. The strategy used to upload all Surf Scoter Argos and additional parameter data
into Movebank, using existing Movebank attributes. The Field Name column lists all
parameters in the original Surf Scoter data, including sun angle variables. The Movebank
attributes were used to filter the data with the Douglas-Argos Algorithm and were
manually re-labeled to original field names after exported from Movebank………....…59
Table 7. Comparisons of the percent of locations retained through three different
Douglas-Argos Algorithm filters. Percentages are averages of nine different bird location
data sets. Standard deviations result from averaged percent of locations retained of the
22 location sets……………………………………………………………………….…..62
Table 8. Spatial layers used to determine Surf Scoter habitat selection in the Salish Sea
and source information…………………………………………………………………..63

vi

ACKNOWLEDGEMENTS
I would like to thank Joseph Evenson from the Washington Department of Fish and
Wildlife for inspiring, guiding and supporting my work in exploring nocturnal habitats of
Surf Scoters in the Salish Sea, and for providing the data set necessary to do so. A special
thanks to my reader Dina Roberts who provided ceaseless support, guidance and
encouragement above and beyond her responsibilities as faculty. I also want to thank
Cliff Rice of Washington Department of Fish and Wildlife for invaluable guidance on
research design and data analysis, and Mike Ruth, adjunct faculty of The Evergreen State
College, for GIS support. A special thanks to the inspiring and supportive community of
The Evergreen State College Master of Environmental Studies Program and my 2013
cohort. A final thanks to my family and friends for their consolation and understanding,
and especially to my husband who has sacrificed and provided enduring support
throughout this venture.

vii

CHAPTER ONE
LITERATURE REVIEW
Introduction
Declining marine bird populations are well documented globally, with numerous
factors contributing to the problem (Bower 2009). Threats such as bycatch, pollution,
overfishing, hunting, energy production, invasive species and human disturbance (Croxall
et al. 2012) impact marine bird populations both on their breeding grounds and during the
non-breeding periods of their life cycles. Estimated population declines of 69.7%
between 1950 and 2010 have been reported for seabirds worldwide, with the greatest
declines observed in wide ranging pelagic species (Paleczny et al. 2015). Studies of
seabird populations in the Salish Sea of Western Washington, USA and coastal British
Columbia, Canada show no exemption from these trends. Long term monitoring of
seabird species in the region have revealed steady population declines in Surf, Whitewing and Black Scoters since the 1970s (Washington Department of Fish and Wildlife
Waterfowl Section 2013).
The Surf Scoter, Melanitta perspicillata, is historically one of the most abundant
over-wintering residents in the Puget Sound. The northern Salish Sea waters of the Strait
of Georgia provide critical migration stop-over sites for a subset of the Puget Sound
population. As Scoters are one of the least studied of all North American duck species
(Sea Duck Joint Venture 2003), recent observed declines have prompted research focused
on resource use, recruitment, local distributions and movements between breeding and

1

wintering ranges in order to document the ecology of their annual life stages (J. Evenson
personal communication).
Surf Scoters rely on the Salish Sea ecosystem for moulting, over wintering and
preparing for spring migration and breeding (Pearson 2013). Nearshore habitats provide
the food resources that allow them to meet their high energetic requirements during short
photo periods and cold temperatures of winter. Three Scoter species (White-winged,
Surf, Black) have a high degree of dependency on the Puget Sound for survival, which
prompted their selection as the wintering marine bird indicator species group in the Puget
Sound in 2013. In preparation for migration to inland breeding grounds to boreal lakes of
Canada, the Puget Sound population also often overlaps with other Pacific Flyway
populations to feed on herring spawn in critical habitats in along northern Puget Sound,
Canada and Alaska shorelines (Washington Department of Fish and Wildlife Waterfowl
Section 2013).
Due to low recruitment rates, adult Scoter survival is critical for sustaining
populations (Washington Department of Fish and Wildlife Waterfowl Section 2013).
Therefore, non-breeding marine habitats play a large role in population growth or decline.
In the Salish Sea, Scoters face high energetic demands due to long periods of cold,
blustery weather and shorter day lengths, limiting their ability to meet caloric
requirements (De La Cruz et al. 2014). A deficiency in resources can affect Scoter health
beyond the overwinter period in marine waters. During moult, the overall health of birds
affects the extent and quality of feather replacement, and moulting trade-offs may have
consequences on future reproduction (Pearson 2013). Spring food sources are important

2

for building energy reserves for spring migration and the breeding season (Lok et al.
2008).
Beyond providing critical habitat for diverse wildlife, of which Scoters are an
important component, the vast marine and estuarine habitats of the Salish Sea are
economically and culturally important to human populations in both the United States
and Canada. Sea ducks are experiencing the impacts of urban and residential
development, shoreline armoring, water and sediment contamination, changes in food
web dynamics, and non-native and invasive species (Gaydos and Pearson 2011). The
availability of a critical food source, shellfish, is complicated simultaneously by a
thriving aquaculture industry and increasing ocean acidification. In addition, high
densities of oil shipment traffic put them at risk of oiling through catastrophic events and
chronic discharges.
Information on seasonal distribution and habitat associations for Surf Scoters is
limited. It is unknown whether winter habitats are limiting and this has hindered the
ability to develop strategies that would effectively protect essential habitats (Sea Duck
Joint Venture Management Board 2014). For obvious reasons, bird surveys are mainly
conducted during daylight hours; therefore current Surf Scoter distribution and use data
lacks any nocturnal component. Nocturnal resting areas and their associated habitats
have not been determined for Surf Scoters (J. Evenson, personal communication).
Identifying new use areas may reveal unknown vulnerabilities, such as an increased risk
of oil contamination. Washington’s Geographic Response Plans (GRPs) facilitate
immediate and efficient oil spill response in the Puget Sound area and do not currently
account for nocturnal habitats. In addition, research on nocturnal habitat use may
3

demonstrate a greater need for studying nocturnal distributions of marine birds in general.
Nocturnal marine bird data is limited to foraging activities, and migration events. A data
gap exists for nocturnal habitat use in general across the globe. This study fills this gap
in the understanding of Surf Scoter ecology and behavior, which can provide additional
insight to better manage and protect Scoter species.

The Salish Sea
The Salish Sea, a 16,925 km2 inland sea, stretches from south Puget Sound near
Olympia, Washington, USA, north to the Campbell River of southwestern British
Columbia (BC), Canada. The Salish Sea encompasses the Strait of Juan de Fuca, the
Strait of Georgia and the Puget Sound. These major bodies of water and the hundreds of
rivers that flow into them form a large estuary system that is one of the most biologically
productive marine ecosystems worldwide (About the Strait 2015). The Salish Sea holds
20 globally significant Important Bird Areas for 25 species that exist within its
boundaries (Crewe et al. 2012).
The Salish Sea’s abundant resources have also attracted a growing human
population. The Georgia Basin and Puget Sound region combined, support a population
of over seven million people (Crewe et al. 2012) and this number is estimated to increase
to nine million by the year 2025 (US EPA 2014). The Strait of Georgia is the heart of
British Columbia and the coastlines and waters of the Puget Sound provide an important
part of the United States and Pacific Northwest economy and culture (Fresh et al. 2011).
The Salish Sea supports fishing, recreation, shellfish aquaculture, transportation, cement
4

plants, restaurants and logging operations. Its deep harbors, natural resources, location
and position along the Pacific Rim have also made it an important center for global trade.
For example, it is home to Canada’s biggest port and provides a route for 135 million
tons of cargo a year (About the Strait 2015). Since the beginning of European settlement
there have been significant changes to nearshore ecosystems including a dramatic loss of
river delta area and shoreline, elimination of coastal embayments, modifications to
beaches and bluffs, and loss of tidal wetlands (Fresh et al. 2011). The United States and
Canada have unilateral and bilateral efforts underway to improve the health of the Salish
Sea marine ecosystem since the 1980s (Gaydos and Pearson 2011).
Components of the Salish Sea
The Strait of Georgia, a semi-enclosed inland sea, lies between the mainland of
British Columbia and Vancouver Island. It is ~200 km long by 30 km wide with channel
depths up to 400 meters. The Strait has limited connection to the open Pacific Ocean
from the north, except for a series of long and narrow passages, and a larger oceanic
influence through the Haro Strait, the San Juan Island channels and the Strait of Juan de
Fuca to the southwest. The Fraser River provides significant freshwater inflows in the
summer, bringing rich silt from 850 miles of river and 20 million ha of British Columbia
terrain (About the Strait 2015). It is a significant influence, forming highly stratified
surface layers throughout the strait’s water column (Pawlowicz et al. 2003). It is known
for having one of the largest salmon runs in North America and for providing a vital stop
over area for birds from three continents (About the Strait 2015).

5

The Puget Sound portion of the Salish Sea consists of the enclosed waters from
Deception Pass to Olympia, Washington, including Hood Canal and Admiralty Inlet.
The Puget Sound is the second largest estuary in the United States, containing over 8,000
square miles of marine waters and estuarine environments, 2,500 miles of coastline and a
watershed of more than 8.3 million acres (Fresh et al. 2011). It supports an abundance of
terrestrial, freshwater, estuarine and marine species, habitats and ecosystems (Fresh et al.
2011). Nearshore habitat provides for many different communities at the base of a
complex estuary and saltwater food chain, and is one reason why the Puget Sound
ecosystem is recognized as providing critical habitat for many breeding, migrating and
non-breeding birds. Nearshore habitat reaches from the tops of shoreline bluffs and
extends through offshore water, ending at the disphotic zone (Lyons 2013). These highly
productive habitats include bluffs, beaches, mudflats, kelp, eelgrass beds, salt marshes,
gravel spits and estuaries (Diefenderfer et al. 2009). They also maintain abundant
concentrations of shellfish, marine mammals and Pacific salmonids. Estuary and
nearshore habitat also provides ecosystem goods and services to the human communities
that inhabit western Washington (Fresh et al. 2011), such as food, recreation, clean water,
flood control, and carbon sequestration (Lyons 2013).

Surf Scoter Ecology
Surf Scoters (Anserformes:Anatidae) are sea ducks, which are marine-dwelling
diving birds, and one of the least studied duck species in North America (Fresh et al.
2011). Adult males are distinctive in appearance, with bright, bulbous, multi-colored

6

bills, and distinctive white patches on their forehead and nape and black plumage. They
breed at low densities exclusively in North American interior wetlands of the Boreal
forest. They over-winter at lower latitudes in marine nearshore environments. On the
west coast their distribution reaches as far south as Baja Mexico and on the east coast as
far south as Virginia. It is in these shallow coastal waters where they spend the majority
of their annual life cycle (Sea Duck Joint Venture 2003). Abundance estimates are poor
and their breeding range is incompletely surveyed. The best estimate available is
700,000 birds in North America, with an estimated 225,000 residing along the Pacific
Flyway (Sea Duck Joint Venture 2015). Also, as these ducks are game species, harvest
estimates indicate 25,000-30,000 Surf Scoters are taken annually in the US and Canada
(Sea Duck Joint Venture 2003).
Knowledge of populations, distribution, and basic ecology of Surf Scoters lag
behind that of most other duck species (Washington Department of Fish and Wildlife
Waterfowl Section 2013). Gaps in the knowledge of their ecology and behavior have
become evident. This realization as well as the declining status of the species has
captured the attention of managers and researchers. Between 2003 and 2006 The
Washington Department of Fish and Wildlife implanted satellite Platform Terminal
Transmitters (PPT’s) and VHF radio transmitters into White-winged and Surf Scoters.
During the same time period, researchers in Baja California Mexico, San Francisco Bay
and the Strait of Georgia in British Columbia also tagged and tracked Surf Scoters with
satellite PTT’s and VHF transmitters. The objectives of this collaborative study were to
document use and fidelity to winter and spring foraging areas, migration routes, moulting
areas, breeding range and nocturnal resting areas.
7

When examining annual migration movements of Surf Scoters researchers
documented that while there is a distinct wintering Puget Sound population, the
distribution often overlaps with other wintering populations along the greater Pacific
Flyway during non-winter times of the year, and that members of other populations
utilize the Puget Sound for some extent of time (Nysewander et al. 2006). Preliminary
results for the Puget Sound wintering Surf Scoter population suggests high site fidelity to
Washington’s marine waters, heavy use of Washington marine waters for spring staging,
with a smaller portion utilizing the Strait of Georgia and southeast Alaska during spring.
Breeding locations occurred in the north and east sections of the breeding range (De La
Cruz et al. 2014), and female moulting locations concentrated in Washington State and
Southeast Alaska, while males moulted north of Southeast Alaska with a few documented
south of Washington (Washington Department of Fish and Wildlife Waterfowl Section
2013).
Scoter Diet
Surf Scoters feed on a variety of invertebrates, but most commonly utilize clams
mussels and herring eggs in the marine environment (Buchanan 2006). In marine
habitats, most food available in late summer has low energy density and requires a high
consumption rate (Systad et al. 2000). During fall moulting periods, Surf Scoters offset
increased energy needs by selecting smaller non-molluscan invertebrates, which are high
in energy content (Tschaekofske 2010). Mussels are more abundant and accessible in
early winter, causing this food source to be depleted quickly (Kirk et al. 2008). Herring
eggs are a high energy, lipid rich food source that is available in late summer and early
spring (Lok et al. 2008). A portion of the Puget Sound population feeds on herring eggs
8

in the spring (Buchanan 2006), and this is thought to be important for building energy
reserves for spring migration and the breeding season (Lok et al. 2008). Prey
characteristics dictate space requirements of all predators where high resource availability
allows individuals to meet energy requirements within smaller areas, whereas low
resource availability requires larger areas (Kirk et al. 2008). This is demonstrated well by
Surf Scoter’s seasonally changing diurnal winter distributions.
Sea ducks modify foraging behavior and efforts throughout the non-breeding season
in response to day length and prey availability. At higher latitudes day length decreases
drastically in the winter, and few individual Surf Scoters will actively engage in foraging
during nocturnal hours (Lewis et al. 2005). Decreased available forage time combined
with lower air and water temperature and increased wind and waves results in increased
energy costs in all wintering waterfowl (Systad et al. 2000). Sea ducks are generally
associated with sub tidal zones over sand-mud, cobble, and rocky substrates (Kirk et al.
2008) in nearshore waters less than 60 feet deep and in eelgrass habitat (Buchanan 2006).
They utilize rocky intertidal shores that provide easily accessible prey, such as mussels
and will also feed on clams, which are buried in soft bottom intertidal flats at lower
densities. In late winter and early spring Surf Scoters are found at localized herring
spawning sites and often follow spawning events in a northward progression (Buchanan
2006).
Patchy spatial distribution, rapid depletion of prey species and temporary availability
has been shown to increase movement probability and home ranges for predators (Kirk
et al. 2008). Seasonal Surf Scoter population distributions along the Pacific flyway vary
seasonally with local food source characteristics. In San Francisco Bay, wintering
9

Scoters moved greater distances and used larger areas during December and January.
Conversely they moved shorter distances in February and March while using restricted
areas (De La Cruz et al. 2014). Surf Scoters in Baynes Sound, British Columbia moved
nearly ten times as far during spring herring spawning than in winter (Lok et al. 2008).
In the Puget Sound, WDFW data suggests that Scoters that are exposed to herring spawn
during the winter, will follow it northward in the spring, and those that are not exposed
are less likely to forage on herring for migration (J. Evenson personal communication).
Such research has aimed to identify habitat availability and limitations to explore patterns
of habitat use. These findings can be used to identify mechanisms that drive observed
and predicted diurnal spatial and temporal trends in sea duck populations (Faulkner
2013).

Surf Scoters in the Salish Sea
The Salish Sea is recognized as an important nesting and migration site for marine
birds (Bower 2009) of the Pacific Flyway, providing habitat to over 70 species
(Washington Department of Fish and Wildlife Waterfowl Section 2013). Despite this
importance, relatively few studies of local marine bird populations have been conducted
(Bower 2009). Prior to the 1970s, marine bird abundance and distribution information
came from anecdotal accounts and Christmas Bird Counts. The United States and
Canada have since increased efforts to document Surf Scoter population trends (Vermeer
1981, Wahl et al. 1981, Badzinski et al. 2008, Crewe et al. 2012, Washington Department
of Fish and Wildlife Waterfowl Section 2013). Since 1994, scientists have collected

10

population trend data through Winter Surveys within inland waters of Washington State,
however these trend surveys did not discern mechanisms for population declines.
Status
Pacific Surf Scoters reside in Washington waters throughout the year (J. Evenson
personal communication), however the majority of the population spends summer months
at northern breeding grounds across Canada’s Boreal and return to the Puget Sound to
over-winter. Their breeding distribution is concentrated in Saskatchewan, Nunavut,
Alberta and Northwest Territories of Canada (Buchanan 2006, Washington Department
of Fish and Wildlife Waterfowl Section 2013). Males initiate migration to marine areas
in early to mid-July, and most birds arrive by September. Scoters are the most abundant
group of non-breeding marine birds in the Puget Sound during fall, winter and early
spring (De La Cruz et al. 2009, Pearson 2013).
Marine birds are commonly used as indicator species for monitoring the health of
ecosystems (Bower 2009) because they forage over large geographic areas and feed at
multiple trophic levels. They also demonstrate subtle and sometimes dramatic responses
to aquatic productivity and environmental changes, providing early warnings of
ecosystem change (Mallory et al. 2010). In this way, they are a leading indicator of
ecosystem attributes like biodiversity. A decline in marine bird populations may indicate
a decline in overall biodiversity (Pearson 2013). At the same time, most resident
migratory and nearshore diving birds can act as lagging indicators. Harvey and authors
(2012) identified trophic interactions that are most important to the overall structure of
the central Puget Sound food web. Their study found that bird biomass on the water can

11

act as a lagging proxy for the biomass for functional groups across the food web (Pearson
2013).
Scoters, as a group of species, have been chosen by the Puget Sound Partnership
as indicator species for the over-wintering marine bird community (Washington
Department of Fish and Wildlife Waterfowl Section 2013). Of the wintering marine
birds in the Puget Sound, Scoters are the most dependent. They utilize the estuary widely
for fall moulting, over-wintering and spring staging. This dependency is attributed to
abundant bivalve prey and the highly productive eelgrass beds in the area. Eelgrass beds
provide important habitat and nursery areas for shellfish such as bivalves (Mumford
2007). Also, female Pacific Herring adhere their eggs to eelgrass during spring spawning
events (Small et al. 2005). Eelgrass habitats, as well as rocky shores and shallow
mudflats are a major contributor to food sources that allow Scoters to spend all of fall,
winter and spring in the Puget Sound. Site fidelity to wintering locations makes it
difficult to move to different areas in response to degradation of critical habitat
(Johannessen and McCarter 2010). During moult, the overall health of birds affects the
extent and quality of feather replacement, and moulting trade-offs may have
consequences on future reproduction (Pearson 2013). Therefore, the habitat quality of
the Puget Sound can have consequences beyond its borders for these bird’s survival.
In response to an oil spill in the Strait of Juan de Fuca in 1978-79, the Department
of Commerce and the Environmental Protection Agency funded a large scale survey of
marine birds in northern Puget Sound, called the Marine Ecosystems Analysis (MESA)
program (Wahl et al. 1981). These surveys included land-based point counts, ferry-based
and aerial transect surveys north of Admiralty Inlet, including only the southern portion
12

the Strait of Juan de Fuca and excluding the Puget Sound itself . The Washington
Department of Fish and Wildlife (WDFW) has been monitoring sea duck populations in
the Puget Sound since 1993 as part of the Puget Sound Ecosystem Monitoring Program
(PSEMP). They have been conducting December and January aerial surveys since 1993
(Washington Department of Fish and Wildlife Waterfowl Section 2013). These annual
surveys consist of transects that cover nearshore and offshore open water habitat
throughout the Puget Sound and Strait of Juan de Fuca (Essington et al. 2011). The
2013-2015 WDFW/PSEMP wintering population index of scoter species has declined
49% since 1994-96 (3-year average population index = 107,214). Comparisons between
the 2013-15 WDFW/PSEMP data and MESA population estimates suggest a 76% decline
in wintering scoters in the inner marine waters of Washington since 1978-79. These
population estimates represent all scoter species combined, including White-winged,
Surf, and Black scoters. WDFW data suggests that Surf Scoters comprise 80% of all
wintering scoters in Washington (Washington Department of Fish and Wildlife
Waterfowl Section 2013, J. Evenson personal communication).
Scoters initiate movements towards northern breeding habitat in April (J. Evenson
personal communication). Migratory stop-over sites are also critical habitats for Surf
Scoters, and for the Puget Sound population, these are largely located within the Strait of
Georgia. While Surf Scoters feed along shallow rocky shores for mussels and clams
during the fall and winter, and herring eggs in the spring (Badzinski et al. 2008), the
Strait supports higher densities of Surf Scoters in the spring. Portions of the Scoter
population and other species that winter across the Salish Sea move north to the Strait of
Georgia, as they follow the succession of herring spawn events on their way to northern
13

breeding grounds. Large flocks will typically spend about two weeks at Ganges Harbour,
the west coast of Vancouver Island, (Vermeer 1981) Iona Island, Englishman River
estuary, Nanoose Bay and Deep bay in March and April (Badzinski et al. 2008). Aerial
and boat surveys conducted during January-March of 1978 counted scoters along 2700
km of coastline. These surveys documented an increase in Surf Scoter population from
200,000 in January and February to 650,000 in March when they became the most
numerous marine birds (Vermeer 1981).
Before the 1990s, the only British Columbia Surf Scoter population trend data
available came from Christmas bird counts. These found a 2.4% decline in the Surf
Scoter population from 1959 and 1988. In 1999, Bird Studies Canada (BSC)
administered the British Columbia Coastal Waterbird Survey (BCCWS) with support
from Environmental Canada - Canadian Wildlife Service. This is a citizen science survey
to meet the long term monitoring needs for waterbirds in the area. From September to
April, volunteers conduct complete counts of all visible birds at designated survey sites
(Badzinski et al. 2008). The BCCWS continues to be the only long-term monitoring
program for nonbreeding waterbird population trends in British Columbia. The survey
did not detect any changes in Surf Scoter populations from 1999 to 2011 along the BC
coast. This suggests a steady Surf Scoter population within this time period (Crewe et al.
2012).

14

Nocturnal Behavior
While most research on seabirds explores their diurnal ecology, a few initial
studies have begun to investigate whether sea ducks will forage outside of daylight hours,
especially during short days in winter when energy requirements are high. For example,
eiders were found to arrive and depart from feeding areas at lower light intensities as day
length decreased in Norway. Other studies have suggested that certain levels of light are
required to initiate morning flights in Canadian Geese, and Wood Ducks (Systad et al.
2000). Scoters have been found to forage during daylight hours only with the exception
of a few individuals documented foraging at night (Systad et al. 2000).
Lewis and authors (2005) investigated nocturnal foraging behaviors of Surf and
White-winged scoters to assess daylight restrictions on foraging time. They monitored
radio telemetry signal losses during foraging dives. In order to consider surface foraging
they looked at differences in diurnal verses nocturnal Surf Scoter distributions in relation
to shallow foraging areas. They found that 70% of Surf Scoters were found in intertidal
areas during daylight hours compared to just 5% at night. The majority of the population
was found in sub tidal waters at night. Mean individual location distance from shore was
231 m for diurnal positions and 704 m for nocturnal positions. This not only precluded
the possibility of significant nocturnal foraging, but also revealed a difference in
distributions and habitat use within a 24 hour period. Lewis and authors (2005)
hypothesized that Scoters choose not to forage at night due to unprofitable nocturnal
foraging, predation risk and/or visual constraints. Without the aid of visual predator
recognition nocturnally active predators such as river otters and mink may lower
energetic advantages of nocturnal foraging. Beyond these few studies, there is no
15

literature focused on distribution or behaviors of sea ducks during nocturnal resting
hours.
Re-examining satellite telemetry data originally collected to document annual life
stages of Surf Scoters, Washington Department of Fish and Wildlife noticed a similar
pattern to the findings of Lewis and colleagues (2005). Preliminary analysis prior to this
study suggests that during nocturnal hours Surf Scoters in the Puget Sound are
characterized by the following: 1. Birds are found in more open and exposed water, in
much higher densities, and within 24 km of foraging areas, 2. They can be seen gathering
in large mixed species flocks of up to 200 birds in open water areas in the evening (J.
Evenson personal communication), 3. Based on observations, there may be primary and
secondary preferences for these areas in each of the sub regions of western Washington
marine waters (Sea Duck Joint Venture Management Board 2014).
Winter in the Puget Sound is characterized by shorter days coupled with colder
weather, stronger wind and stronger currents. This decreases feeding time available to
Scoters, and increases energetic demands. During this time of year the birds can spend
more time resting than foraging and this rest may be critical for conserving energy (De
La Cruz et al. 2014). This is important as Scoters are k-selected species or long lived
with low reproductive rates. Surveys conducted in the Puget Sound from 2008-10
indicated an average juvenile percentage of 8.3%, which suggests their populations are
sensitive to adult female survival (Washington Department of Fish and Wildlife
Waterfowl Section 2013). Therefore, quality of wintering area habitat is vital to stable
population growth (De La Cruz et al. 2014). Gaining a better understanding of Surf
Scoter winter habitat use, both diurnally and nocturnally, is therefore necessary to
16

understanding population ecology during this critical stage in a complex migratory life
cycle.

Conservation Implications
In Washington, six million people reside along the shorelines of the Puget Sound,
dramatically altering habitat utilized by sea ducks. With the region’s population growing
at about 20% each decade (Washington Department of Fish and Wildlife Waterfowl
Section 2013) the Surf Scoter population will face increasing pressures from many
angles. Sea ducks are experiencing the increasing burdens of urban and residential
development, shoreline armoring, water and sediment contamination, changes in food
web dynamics and non-native and invasive species (Gaydos and Pearson 2011). Two
significant anthropogenic pressures directly affect critical sea duck prey species in the
Puget Sound with uncertain implications.
Washington State has an extensive and expanding shellfish industry. Washington
waters produce the largest amount of cultured shellfish in the nation, comprised of
oysters, clams, mussels and geoducks (Puget Sound Partnership (PSP) 2012). Research
has shown aquaculture to modify habitat chemically, biologically and physically,
producing indirect consequences on ecosystem processes. These effects may cascade to
higher trophic levels and influence epibenthic predators like sea ducks. However, recent
research has demonstrated that Surf Scoters have a positive correlation to medium level
shellfish cultivation in the South Puget Sound (Faulkner 2013). Aquaculture operations
often provide an addition food source that is a profitable resource to predators. Further
17

complicating this relationship is the onset of climate change and the resulting ocean
acidification that has received a great deal of attention in the past five years. Mollusks
are one of the most sensitive marine animals to ocean acidification in their larval and
juvenile stages (Kroeker et al. 2013) and Washington aquaculture has experienced this
first hand. Exposure of early life stages to high pH waters may represent a bottleneck for
their populations (Kroeker et al. 2013). Low recruitment in shellfish farms may suggest
struggling wild populations. Both increased aquaculture and ocean acidification may
influence current and future Scoter distributions.
Sea duck populations are also affected by hunting during their winter stay in
Washington. Classified as a game species in the United States and Canada, Scoters are
managed under state, federal and migratory waterfowl regulations cooperatively through
the Pacific Flyway Council. Documented declines in Surf Scoter populations have
resulted in a shorter hunting season and incrementally reduced bag limits since 1998.
Surf Scoter take was historically, and is currently minimal in British Columbia. The
current bag limit for Scoters is two per day in Washington. Reductions in bag limits
combined with increased migratory bird hunting fees are perhaps responsible for the 51%
decline in Washington State Scoter harvest between 2007 and 2009. Between 2010 and
2012 it was estimated that 3.7% of the total Scoter population was harvested. Low
productivity and high site fidelity lends limited capability to compensate for hunting
mortality through increased recruitment or increased survival outside of the hunting
season. Harvest is therefore considered completely additive to natural mortality
(Washington Department of Fish and Wildlife Waterfowl Section 2013).

18

Oil Transportation in the Salish Sea
The Strait of Juan de Fuca serves as the entrance to United States and Canadian
ports for approximately 10,000 deep draft vessels annually. Oil tankers transport crude
oil from Valdez Alaska to Puget Sound refineries through the Strait of Juan de Fuca, as
part of the Trans-Alaska Pipeline System. Crude and refined oil products are exported
from the Port of Vancouver, Canada, via Juan de Fuca, Georgia and Haro Straits. Also,
natural gas condensates are imported through the Channel of Caamano Sound to the
Methanex Marine Terminal in Kitimat (EnviroEmerg Consulting Services 2008).
Vessels that travel the Salish Sea must traverse diverse water passages. Some are broad
and deep and some are narrow with swift currents, which can be navigationally
challenging. Tug and barge movements, ferry operations, and fishing and recreational
vessels also contribute to internal transit traffic (Dorp and Merrick 2014). British
Columbia’s waters support an average of 410,303 vessels a year, of which 2,739 are
tankers carrying liquid oil in bulk (EnviroEmerg Consulting Services 2008). Washington
State waters see about 230,000 transits annually, including nearly 8,000 deep draft vessel
movements (Dorp and Merrick 2014). Along with this vessel traffic comes potential
hazards for all human and natural communities in the Salish Sea. Past major oil spills in
the Salish Sea occurred in Port Angeles in 1985, Guemes Channel in 1988, Anacortes in
1994 and English Bay this year. Current complex and dynamic vessel traffic continues to
place the area at risk for large spills. Oil spills are also a growing public concern due to
proposed marine terminal developments, which would intensify marine traffic (Dorp and
Merrick 2014).

19

More than 15 billion gallons of oil are shipped through Washington State waters
annually (Washington Department of Ecology and Puget Sound Partnership 2011). In
response to the risks associated with this traffic, Washington State has created a series of
Geographic Response Plans (GRPs) to facilitate immediate and efficient oil spill response
in the Puget Sound area. These plans prioritize response actions during the critical hours
immediately following an oil spill, to protect vulnerable resources. They consist of predesignated potential “oil spill origins” placed where spills are most likely to occur. A
prioritized table of actions is listed for each of those points. When a trajectory for oil
movement is available, plans are modified accordingly. Additionally, wildlife maps
outline marine mammal haulouts, sensitive species nesting locations and bird
concentration areas.
Each regional GRP outlines specific responses to protect designated sensitive
areas. However, the general strategy is the same across all regions of the Puget Sound.
The top priority is to control and contain the oil at its source. Second, is to prevent oil
from reaching sheltered shorelines such as coves and harbors by placing physical barriers
across narrow inlets. Lastly, responses specific to wildlife protection are implemented,
including restricting fly zones and in some cases hazing. Flight restrictions are placed
over these areas to limit wildlife disturbance and injury. Hazing involves using visual
and sound devices, personnel, vessels and aircraft to drive wildlife out of contaminated
areas. These plans currently protect nearshore habitats that are critical for Surf Scoters.
However, nocturnal resting areas for sea birds are not considered.
Many small oil spills go undetected or unreported when they are 1000L or less.
These small-scale oil discharges are more widely distributed and happen more frequently,
20

causing a greater ecological impact and can have cumulative effects on seabird
populations (O’Hara et al. 2009). Seabirds spend long periods at sea, which puts them at
great risk of oil spills (Votier et al. 2005). Oiled birds have been found along shorelines
in areas where dense seabird concentrations overlap with heavy vessel traffic across the
globe. Oil in very small amounts can be lethal to a seabird (Wiese and Robertson 2004).
In cases outside of large catastrophic spills, oil found on bird carcasses is usually of the
heavy fuel type commonly found in bilges of large tanker, cargo and container ships.
These beached birds are often the only sign of illegally spilled or chronically leaking oil
in marine waters (Wiese and Robertson 2004). The Coastal Observation and Seabird
Survey Team (COASST) is a citizen science team that counts and documents beached
dead birds. Counts for the Port Townsend Marine Science Center beach have found low
numbers of oiled birds each year since 2007, averaging about 2.3 per year, 15.5 percent
of all birds found. No other Puget Sound beach that is surveyed has documented any
oiled birds. (COASST : Beached Bird Patterns 2015). These types of surveys only detect
small percentages of birds affected by oil in an area. Only a small fraction that perish at
sea will make it to shore, as most are scavenged, sink, or drift away from shore. In
addition, the individuals that do reach the shore may not be detected during surveys
because of scavengers, or because they are covered by beach substrate through wave
action (Wiese and Robertson 2004). Therefore, oiled birds found at the Port Townsend
Marine Science Center beach indicate the possibility that intentional illegal and/or
accidental oil discharging is currently happening in the Puget Sound in unknown
amounts. Increasing vessel traffic would increase the potential of chronic oil discharges,

21

and therefore increase hazards to those populations of birds that utilize habitat near high
traffic areas.

Justification for Research
Surf Scoters have been designated as a symbol of health for the Puget Sound and
are of great regional and international interest. Their strong dependency on various
resources through changing seasons and life stages creates opportunities for learning
about the complex web of interactions between natural processes and human
modifications that shape Salish Sea waters. In order to effectively maintain a viable and
abundant population of Surf Scoters in the Puget Sound a full picture of their annual
movements and life stage requirements as they travel between habitats associated with
breeding and wintering ranges is essential. Adult survival is critical for sustaining this
population and identifying limitations and resource availability in their marine locations
will play an important role in understanding factors responsible for their decline. One
component of this story that is missing is their nocturnal distribution and habitat
requirements.
As most all marine bird surveys are conducted during hours of daylight,
distribution data is biased towards this factor; our understanding of Surf Scoter
distribution is no different. Diurnal data is what currently guides conservation and
management decisions regarding this declining species. Determining nocturnal use area
habitat characteristics that are selected for fills an important data gap that addresses the

22

ecology and conservation of Surf Scoters. Revealing unknown distributions may also
expose unknown risks associated with human activity.
In addition, known nocturnal distributions have the potential to positively influence
oil spill response planning. Oil spills have the potential to cause significant damage to
local marine bird populations if they occur around a primary nocturnal resting area.
Numerous oil refineries and shipping channels are situated at or near common
aggregations of Surf Scoters (Buchanan 2006). If an incident were to happen near a
resting area, a relatively small spill could impact the same number of birds as a larger
spill would (J. Evenson personal communication). Outside of catastrophic events, small
but chronic oil discharges could be a significant cause of mortality among birds that
congregate near heavy oil shipment routes regularly. With increases in oil transportation
through the Salish Sea looming in the near future, identifying critical use areas would
allow them to be taken into consideration during the planning and approval process.
Most marine bird surveys are conducted during daylight hours and are often the
sole information that is relied on to draw conclusions about distribution and habitat use.
In addition, it is common for researchers to create utilization distributions from satellite
data without differentiating between day and night locations. Satellite data tends to
collect more, and higher quality data during nocturnal hours when animals are more
stationary and abstaining from diving behavior (J. Evenson personal communication).
This can create a picture of resource use and animal behavior that is inaccurate. It
identifies a need to consider nocturnal distribution when assessing the conservation needs
of marine bird species in general. It may also reveal disparities in our knowledge and
conservations efforts of the Salish Sea region. For example, the Puget Sound Vital Sign
23

program currently focuses solely on the nearshore environment (Pearson 2013).
Recognizing the importance for open water habitats for Scoters and other birds may
demonstrate the advantage of broadening the scope of such efforts.

24

CHAPTER 2
DATA PREPERATION
Introduction
Satellite telemetry is a common tool used to study animals that migrate long
distances as it allows them to be tracked remotely and systematically for extended
periods of time (Hoenner et al. 2012). Argos provides year-round worldwide coverage
with several polar orbiting satellites. Perceived Doppler shifts (messages) are recorded
from platform terminal transmitters (PTTs) when a satellite passes overhead to collect a
location (Douglas et al. 2012). The Argos data used in this study was originally collected
to document spring staging, summer nesting and fall molting grounds of Puget Sound
Surf Scoters. During the winters of 2003-2006 the Washington Department of Fish and
Wildlife implanted Surf Scoters with PTTs. The resulting data set contains locations
spanning from two months to over two years from 34 birds. These birds were
documented traveling from the Puget Sound, as far north as the north coast of Alaska and
as far east as the Northwest Territories in Alberta, Canada (Washington Department of
Fish and Wildlife Waterfowl Section 2013).
Non-telemetry marine bird surveys conducted at the Puget Sound and Strait of
Georgia regional scale are conducted during the day and do not capture nocturnal
behavior. As satellite telemetry data collects locations throughout a 24 hour period, it
provides a unique opportunity to examine nocturnal distributions and associated habitats.
However, this data collection method was not designed for examining location and
movement at the scale of our study area, or for distinguishing between photo periods. In

25

order to ensure that our final data set accurately represented nocturnal locations, several
treatments were applied to filter and correct the raw Argos data.

Methods
Nocturnal vs. Diurnal Designation
In order to distinguish between diurnal and nocturnal locations, Sunrise, sunset
and solar positions were calculated using equations based on Astronomical Algorithms
by Jean Meeus (US Department of Commerce 2015). A spreadsheet provided by NOAA
(http://www.esrl.noaa.gov/gmd/grad/solcalc/calcdetails.html) was modified and
integrated into the existing Scoter data to determine solar elevation for each location. To
ensure accuracy, all solar elevations were corrected for atmospheric refraction. Diurnal
and nocturnal locations were determined by whether the sun was above or below the
horizon respectively, at the date and time of each specific location.
Data Filtering
Animal tracking PTTs suffer from degrading effects, such as high speed
movement, impaired visibility, and temperature changes. Sea ducks such as Surf Scoters
also hinder accurate readings when diving for food. This can result in a prevalence of
low-quality locations, and the resulting errors have important implications for the data’s
use in conservation. Each location is assigned a location quality class (LC) which
coincides with an accuracy estimate based on the least-squares method of location
derivation. LCs 3, 2, 1 and 0 result from four or more messages and have an estimated
error radius of <250m, 250-500m, 500-1500m and >1500m respectively. Locations that
26

are derived from less than four messages have no estimated error and are assigned LCs of
A and B. All invalid locations are assigned an LC of Z. Depending on the intended use
of this kind of location data, filtering is usually necessary to remove implausible locations
(Douglas et al. 2012).
For the purposes of habitat assessment in the Salish Sea area, the Surf Scoter
Argos location data were validated with the Douglas-Argos Filter Algorithm within the
Movebank platform (movebank.org). Movebank is a free online infrastructure that
supports animal tracking data storage, sharing, and analyzing. When uploading
customized tabular data into Movebank, attributes cannot be changed or added. In order
to upload our additional sun angle parameters together with the Argos location data we
concatenated many of our fields and used surrogate attributes as listed in Table 6 of the
Appendix.
We utilized the Douglas-Argos filtering method to remove implausible auxiliary
Doppler locations. The Douglas-Argos filter provides three filters that can be used
individually or in different combinations of increasing complexity (Douglas et al. 2012).
This filter procedure allows a user to choose which LCs they would like to remove from
the data set (KEEP_LC). The two other filters assess the plausibility of each location
using the distance between consecutive locations and/or rates and bearings among
consecutive movement vectors. The distance filter in particular operates on the
assumption that significant location errors rarely occur in the same geographic locality in
succession. It searches for spatiotemporal redundancy within a locality threshold, or
maximum redundancy, (MAXREDUN) that is defined by the user. Maximum

27

redundancy will retain locations that have temporally near-consecutive locations that are
spatially within the MAXREDUN variable regardless of LC (Douglas 2006).
In order ensure that we used the most accurate data possible, while also retaining
a robust number of locations for analysis, we evaluated three different options for
filtering our data utilizing the KEEP_LC and MAXREDUN variables. We organized
locations from each of the 34 birds into location sets. These sets were comprised of
consecutive locations that anecdotally seemed to define use areas between larger
migration movements. We then selected nine different birds, that in combination had
locations that represented the variety of sizes of these use areas, and the different
geographical regions within the Salish Sea. This resulted in 22 location sets from the
South Sound, Birch Bay, Bainbridge Island, Strait of Georgia, Boundary Bay, Saratoga
Passage, Padilla Bay, Admiralty Inlet, Rosario Strait, and the Washington coast regions.
We applied the following filtering strategies to each individual bird distribution
separately: 1). KEEP_LC = 2, 2). KEEP_LC = 2 and MAXREDUN = 1 km, and 3).
KEEP_LC = 1. The results of the different treatments were compared by visual
inspection (example of visual comparison in Figure 5, Appendix) and by calculating the
percentage of locations that were retained with each filter (Table 7, Appendix).
When using the KEEP_LC=2 filter alone, we can assume that all locations have
an error less than 500m. When looking at the resulting point distributions, locations over
land were minimized and the distributions appeared more unified. However, we
sacrificed all but 37% of our data. Incorporating the MAXREDUN=1km allowed for
locations with a lower LC class to be retained if they were consecutively redundant. This
introduced unknown errors into our locations, but allowed us to keep 56% of our data.
28

Compared to the KEEP_LC=2 filter, the resulting distributions looked similar, with a few
more locations falling over land, and a slightly wider spread. The KEEP_LC=1 filter
allowed for errors up to 1500m but did not retain a greater amount of locations than the
KEEP_LC=2, MAXREDUN=1km filter. Also, visual comparisons showed a less unified
distribution compared to the KEEP_LC=2 filter. Some of the narrowest channels that the
Surf Scoters utilize in the South Puget Sound region are less than 1000m wide. When
looking at locations in this area, the KEEP_LC=1 filter retained many points that fell
over land compared to the KEEP_LC=2, MAXREDUN=1km filter. Under the
assumption that it would retain an acceptable amount of data, while at the same time
keeping locations that were accurate enough for the purposes of our analysis, we chose to
apply the KEEP_LC=2, MAXREDUN=1km filter to all data sets.

Controlling for Twilight Movement
Scoters have been observed to initiate travel to diurnal foraging areas or nocturnal
resting areas during twilight hours (J. Evenson personal communication). If Surf Scoters
move to or from a nocturnal area within a twilight period at significant rates, this could
bias our nocturnal designation when operating off of solar angles below and above the
horizon alone. To assure that all nocturnal locations used are within final resting areas,
and not in transit, we excluded all points that sun angle measurements between 0 (the
horizon) and -6 degrees (civil twilight).

29

Excluding Flightless Moult Periods
Remigial moult can make up to 10 percent of an individual Scoter’s year. The
upper estimate of flightless days for a Surf Scoter during the moulting period is 47.
However, as a population the process can last over a four month period. Surf Scoters
have been documented in moult as early as the end of June, and as late as early
November. Individual moult timing can be dependent on migration timing, body
condition and breeding activity. Male Scoters typically leave breeding areas earlier in the
season and therefore can moult in early summer (late June/early July). Females will
begin migration considerably later (August/September) if they successfully hatch a
brood. Surf Scoters in the Salish Sea are known to exhibit two main moulting events in
late July and again in early September (Dickson et al. 2012).
During flightless periods, Surf Scoters are restricted from moving long distances
and this may affect access to ideal nocturnal resting habitat. Therefore, habitat
characteristics of nocturnal locations could be significantly different during moult. This
could be a very interesting comparison to make. However, for the purposes of this study,
data that fell within estimated flightless periods were excluded. In this way we can be
confident that variation within the data has not resulted from restricted movement.
Moulting period was conservatively estimated for each bird based on findings from
Dickenson and authors (2012) and by closely examining movement behavior. Each
bird’s movement was examined closely within the months of June-November, for an
abrupt decline in daily distances traveled. All consecutive locations that fell within the
following 50 days were excluded from the data set.

30

Removing Autocorrelation
One of the assumptions of resource selection methods is that each animal
relocation is independent. This assumption is often violated when relocations are close to
one another in time (Manly et al. 2007). To remove auto correlated locations, all but one
point within a duty cycle were removed. The location with the lowest solar elevation was
kept to ensure accurate representation of nocturnal use areas.

Results and Summary
We received raw Argos data from 34 Surf Scoters from Washington Department
of Fish and Wildlife. Between the 34 birds there were a total of 26,633 locations, ranging
from South Puget Sound to northern breeding areas, of which 12,361 were collected
during nocturnal periods. After filtering for location error and removing civil twilight
and moult locations we were able to collect 233 measurements of distance traveled
between diurnal and nocturnal locations. After removing all diurnal locations as well as
locations that fell over land features the final data set consisted of 1,064 nocturnal
locations within the Salish Sea study area, with representation from all of the original 34
birds. A graphical summary of our data preparation strategy can be found in the
Appendix (Figure 6).

31

CHAPTER 3
MANUSCRIPT
Introduction
Declines in marine bird populations have been documented globally, due to a
complexity of factors (Bower 2009). Of monitored seabird populations, estimated
declines of 69.7% between 1950 and 2010 worldwide have been reported, with the
greatest declines observed in populations of wide ranging pelagic species (Paleczny et al.
2015). A majority of marine birds are long-ranging migratory species that utilize
multiple ecosystems, spanning international borders. Such complex life histories hinder
comprehensive monitoring and conservation (Vilchis et al. 2015). Thus, it is a challenge
to understand how threats such as bycatch, pollution, overfishing, hunting, energy
production, invasive species and human disturbance independently and synergistically
influence population declines across terrestrial breeding areas and at over-winter sites
(Croxall et al. 2012). Sea ducks (Mergini spp.) are a prime example of this situation due
to their annual migratory cycle that requires habitats that are both remote and challenging
environments in which to conduct research.
Surf Scoters are one of the least studied of all North American duck species (Sea
Duck Joint Venture 2003). Over the last two decades, a growing awareness of sea duck
population declines has increased funding and research to understand the impacts of
various threats. In 2010, the Sea Duck Joint Venture (SDJV) in collaboration with
specialists in waterfowl management and habitat conservation identified high priority
initiatives and species for North American sea ducks. The Surf Scoter, Melanitta
perspicillata, is one of five species identified for high priority, highlighting gaps in
32

understanding of this species ecology that hinder sustainable harvest and habitat
conservation (Sea Duck Joint Venture Management Board 2014).
Prior to the 1970s, Salish Sea marine bird abundance distribution information
came from anecdotal accounts and Christmas Bird Counts. The United States and Canada
have since increased efforts to document Surf Scoter population trends (Vermeer 1981,
Wahl et al. 1981, Badzinski et al. 2008, Crewe et al. 2012, Washington Department of
Fish and Wildlife (WDFW) Waterfowl Section 2013). The Department of Commerce
and the Environmental Protection Agency funded a large scale survey of marine birds in
northern Puget Sound, called the Marine Ecosystems Analysis (MESA) program in 197879 (Wahl et al. 1981). The WDFW has been monitoring wintering sea duck populations
in the Puget Sound and Strait of Juan de Fuca since 1993 as a part of the Puget Sound
Ecosystem Monitoring Program (PSEMP) (Essington et al. 2011). The 2013-2015
WDFW/PSEMP wintering population index of scoter species has declined 49% since
1994-96 (3-year average population index = 107,214). Comparisons between the 201315 WDFW/PSEMP data and MESA population estimates suggest a 76% decline in
wintering scoters in the inner marine waters of Washington since 1978-79. These
population estimates represent all scoter species combined, including White-winged,
Surf, and Black Scoters. WDFW data suggests that Surf Scoters comprise 80% of all
wintering Scoters in Washington (WDFW Waterfowl Section 2013, J. Evenson personal
communication).
As a group, Scoters are recognized as an important indicator species for the overwintering marine bird community in the Puget Sound region (Pearson 2013). High
moulting site fidelity and their dependence on local shellfish, herring spawn and eelgrass
33

beds encompassed in this estuary (Pearson 2013) allows them to often spend all of fall,
winter and spring in the area. The Puget Sound is part of a larger, geographic area known
as the Salish Sea. The Salish Sea spans from 47.0385 to 50.1964 N and -122.2295 to 124.7949 W. The Strait of Georgia also provides important fall and spring stop-over sites
for a portion of the Puget Sound Scoter population. It supports higher densities of Surf
Scoters in the spring, as they follow the succession of herring spawn events on their way
to northern breeding grounds (Vermeer 1981).
Scoters are k-selected species that are long lived with low recruitment rates,
which suggests that their populations are sensitive to adult female survival (WDFW
Waterfowl Section 2013). Winter at northern latitudes is characterized by shorter days,
colder weather and stronger winds, resulting in decreased foraging time. Over-wintering
birds often spend more time resting than foraging and the ability to rest may be critical
for conserving energy, and survival. Therefore, an understanding of winter habitat
quality is vital to maintaining stable population growth (De La Cruz et al. 2014).
As is true for most marine bird species, all Surf Scoter surveys have been
conducted during diurnal periods, resulting in distribution data that lacks a nocturnal
component (J. Evenson, WDFW, personal communication). These data are what
currently guides conservation and management decisions in the Salish Sea. Without a
nocturnal component, our knowledge on winter habitat requirements for Surf Scoters is
incomplete and planning efforts are insufficient. Determining nocturnal use area habitat
characteristics fills an important data gap within Surf Scoter winter habitat requirements
and overall ecology. Several studies have investigated nocturnal foraging habits (Systad

34

et al. 2000) and Lewis and authors (2001) included diurnal verses nocturnal Surf Scoter
distributions within a broader study.
Expanding the known Surf Scoter distribution in the Salish Sea may reveal
unknown vulnerabilities. The Salish Sea’s location along the Pacific Rim and valuable
resources make it an economically and culturally important global trade center.
Resulting anthropogenic pressures have significantly altered habitat that is utilized by
marine birds (Fresh et al. 2011). The more than seven million residents of the Georgia
Basin and Puget Sound region (Crewe et al. 2012) share common concerns over urban
growth and its effects on their common marine waters, watersheds and migratory
flyways. For example, oil tankers transport crude oil from Valdez Alaska to Puget Sound
refineries through the Strait of Juan de Fuca, as part of the Trans-Alaska Pipeline System,
contributing to the 230,000 vessel transits (Dorp and Merrick 2014), and 15 billion
gallons of oil transport that Washington State supports annually (Washington Department
of Ecology and Puget Sound Partnership 2011). Also, crude and refined oil products are
exported from the Port of Vancouver, Canada, via Juan de Fuca, Georgia and Haro
Straits. Determining nocturnal distributions of Surf Scoters may reveal vulnerabilities to
oil contamination. Oil spills have the potential to cause significant damage to local
marine bird populations if they occur around a primary nocturnal resting area.
Additionally, Scoters’ strong site fidelity to wintering locations make it difficult to move
to different areas in response to degradation of critical habitat (Johannessen and McCarter
2010).
Recent development of high quality satellite telemetry tools provide opportunities
to document aspects of Surf Scoter ecology across complex life stages at continental and
35

global scales. Resulting research has described some aspects of Scoter marine food and
resource utilization (Kirk et al. 2007, Kirk et al. 2008, Anderson et al. 2008, Anderson et
al. 2009, Tschaekofske 2010, Anderson and Lovvorn 2011, Anderson et al. 2012, De La
Cruz et. al. 2014), diurnal distributions in marine habitats (Anderson et al. 2012),
migration routes and stop-over habitats (De La Cruz et. al. 2009, Lok et al. 2011) and
breeding (Takekawa et al. 2011).
Satellite telemetry provides high quality location data for both diurnal and
nocturnal periods. At night, sea ducks are neither traveling nor diving, allowing satellites
to collect more abundant and accurate location points. This study utilized existing
platform terminal transmitter (PTT) data from Surf Scoters that were marked in the Puget
Sound from 2003-2006 (WDFW Waterfowl Section 2013). Our research objectives were
1) determine what areas of the Salish Sea Surf Scoters utilize during nocturnal rest
periods and describe the associated habitat characteristics, 2) determine what factors
influence the selection of identified resting areas through resource selection probability
analysis, and 3) develop predictive models to estimate likely nocturnal resting areas
across the Salish Sea. In addition, we assessed vulnerabilities to potential oils spill zones
and whether or not oil spill response plans would effectively protect these important
resting areas.

Methods
Study Area
We studied nocturnal habitat selection of Surf Scoters in the Salish Sea. The
Salish Sea, a 16,925 km2 inland sea, includes the Strait of Juan de Fuca, the Strait of
36

Georgia and the Puget Sound, which is
bound by the Olympic Peninsula of

Figure 1. Salish Sea study area and
designated regions.

western Washington State, Vancouver
Island and Mainland British Columbia,
Canada. These bodies of water have
varying degrees of oceanic and freshwater
influences, and together they form a large
and biologically productive estuary. The
Salish Sea supports an abundance of
terrestrial, freshwater, estuarine and
marine species, habitats and ecosystems
(Fresh et al. 2011) and encompasses 20
globally significant “Important Bird Areas” (Crewe et al. 2012). For purposes of our
study, we designated the Salish Sea into five distinct regions: Strait of Georgia, Strait of
Juan de Fuca, North Puget Sound, Central Puget Sound and South Puget Sound (Figure
1).
Data Sets
Location (Telemetry) Data
To document spring staging, summer nesting and fall molting grounds of Surf and
White-winged Scoters, WDFW captured and equipped these two species with PTTs
during the winters of 2003-2006. Captures were conducted in Henderson Inlet, Eld Inlet,
Peale Passage, Greater Port Orchard area, Penn Cove, Oak Harbor, Kilisut Harbor and
Birch Bay, as these areas are representative of Puget Sound (WDFW 2015). Mist net
37

capture and transmitter deployment was conducted following Lok and authors (2011), De
La Cruz and authors (2009) and Wells (2011). Location data were collected via the
Argos data system (Argos, www.argos-system.org). Duty cycles for this study were
programed to transmit location data for 6-8 hours and then turn off for 48-96 hours for
the life of the transmitter or the bird. All PTT-marked Surf Scoters from the Puget Sound
capture sites were utilized for the purposes of this study, resulting in location data for 34
adult birds (25 females, 9 males). Locations used were restricted to the Salish Sea study
area boundary.
We distinguished between diurnal and nocturnal locations by calculating sunrise,
sunset and solar position using equations based on Astronomical Algorithms by Jean
Meeus (US Department of Commerce 2015). Nocturnal and diurnal locations were
determined by whether the sun was above or below the horizon at the date and time of
each specific location. However, Scoters have been observed to initiate travel to diurnal
foraging areas or nocturnal resting areas during civil twilight hours (J. Evenson personal
communication). All nocturnal locations that had solar elevations between 0 (the horizon)
and -6 degrees were excluded from analysis to account for this delayed or early
movement between habitats. The location data were then filtered with the DouglasArgos Filter Algorithm within the Movebank platform. Movebank is a free online
infrastructure that supports animal tracking data storage, sharing, and analyzing
(https://www.movebank.org/). Using Movebank, we removed implausible auxiliary
Doppler locations based on a maximum redundancy distance of 1 km. All locations with
classes of 1 and 2 were retained as they have accuracy rates of <150 m and 150-350 m
respectively, as reported by Argos. Flightless moulting periods were conservatively
38

estimated for each bird using parameters set by Dickson and authors (2012) and by
closely examining movement behavior in ArcMap. Locations during flightless moulting
periods were excluded from the data set. To remove auto correlated locations, all but one
point within a duty cycle were removed. The location with the lowest solar elevation was
kept to ensure accurate representation of nocturnal use areas. Lastly, any points that fell
over a land mass were excluded for measuring covariate habitat data. After all data
filtering and correction, we used 1,064 presence locations throughout the Puget Sound,
Strait of Georgia and Strait of Juan de Fuca for our analysis (Figure 2).
Figure 2. Nocturnal locations of 34 Surf Scoters in the Salish Sea study area used for
habitat selection analysis and modeling.

39

To determine how far Surf Scoters will travel between diurnal foraging areas and
nocturnal resting areas we measured distance traveled between diurnal and nocturnal
locations when both were present within one Argos duty cycle. In order to have the
ability to compare nocturnal habitat used to the habitat available within our study area we
created pseudo-absence locations within a polygon, modified from a shoreline data layer.
The estimation of a resource selection function assumes that the proportion of used units
is small (Braun and Wildlife Society 2005). Therefore we generated 5,100 random
locations using the Create Random Points tool in ArcMap 10.2 so that the used locations
were about 20% of all nocturnal used and pseudo-non-used locations. After the points
were generated, any locations that fell over land were deleted, resulting in a total of 5,002
pseudo-absence locations across the Puget Sound, Strait of Juan de Fuca and Strait of
Georgia.
Habitat Covariate Data
To assess nocturnal habitat characteristics, distance to shore, water depth, tidal
current, exposure of nearest shoreline and vessel traffic density were measured from
various spatial layers in a GIS (Table 1). All spatial data was managed and manipulated
within ArcMap 10.2 (ESRI 2011. ArcGIS Desktop: Release 10. Redlands, CA). The
Washington and Canada raster bathymetry layers were combined using the Mosaic tool.
The Canada shoreline exposure vector layer was made up of continuous wave exposure
measurements, while the Washington vector layer reported 6 different exposure
categories. All continuous data in the Canadian layer were reclassified into the same 6
exposure categories of very protected, protected, semi-protected, semi-exposed, exposed,
and very exposed. The two exposure layers were then combined using the Merge tool.
40

Table 1. Habitat covariates used in univariate and logistic regression analysis to assess
habitat selection of wintering Surf Scoters in the Salish Sea 2003-2007. Each covariate
was measured from various vector and raster spatial layers. Additional information for
spatial layers is listed in Table 8 of the Appendix.
Variable

Description

Range of Values
-782 – 0 meters

Water Depth
Minimum
distance to
Shore (m)

Distance from each nocturnal location to the
nearest shoreline.

0 – 15,000 meters

Tidal Current
(Root-MeanSquared)

Root-mean-square difference of total
discrepancy at each location.

0 – 1.214 RMS
Tidal Current

Exposure

The amount of wave exposure experienced by
the shoreline nearest to each nocturnal
location.

Very Protected
Protected
Semi-Protected
Semi-Exposed
Exposed
Very Exposed

Total Vessel
Traffic Density

Density of vessel traffic in 2011. Best
interpreted using a high and low density scale
and does not represent actual vessel counts.

0 – 581

Tanker Vessel
Traffic Density

Density of Tanker vessel traffic in 2011. Best
interpreted using a high and low density scale
and does not represent actual vessel counts.

0 - 19

Distance to
Potential Oil
Spill Origin
Points

Distance from each nocturnal location to the
nearest Potential Oil Spill Origin Point. Strait
of Georgia locations excluded.

50 – 57,000 m

A shoreline vector layer for Washington provided shoreline data for the Puget Sound and
Strait of Juan de Fuca. In order to encompass the entirety of the Salish Sea study area,
41

the Strait of Georgia portion was drawn by hand, following the “Oceans” basemap
provided by ArcMap. All spatial data was projected into WGS 1984 UTM Zone 10N for
analysis.
Spatial Analysis
Distance Traveled From Diurnal Foraging Areas
To determine how far Surf Scoters will travel between nocturnal and diurnal
areas, we measured the maximum distance between diurnal and nocturnal locations
within a transmitting cycle. If the only diurnal locations within that cycle fell over land
and were within 350 m (high end of estimated Argos error for LC 2 locations) of the
shoreline, they were snapped to the shoreline layer, using the Snap Tool, and used for
measurement. If they were more than 350 m from shore they were not used. The
shortest strait line distance was calculated using the Measure tool. Surf Scoters are rarely
observed flying over land during the winter period in the Salish Sea (J. Evenson, personal
communication); therefore, if a land mass fell between the two locations, the shortest
distance around that land mass was measured by hand using the Measure tool. The
distance was measured by hand 5 times and the shortest resulting distance was used.
Habitat Covariate Data
All covariate measurements were completed using tools available in ArcMap
10.2. The shortest distance from each location, to the nearest shoreline was calculated
using the Near tool. To calculate exposure, we again measured the shortest distance
between each location and the exposure vector line using the Near tool and then used the
Join Field tool to join the exposure attribute data to each of the locations. The same
42

procedure was used to extract root-mean-squared tidal current measurements from the
nearest tidal current vector point for each Scoter location. Depth and Vessel Traffic
Density at each location were calculated using the Extract Values to Points tool.
Statistical Analysis
We investigated differences in distances traveled between nocturnal and diurnal
locations between four regions within the Salish Sea with analysis of variance and means
comparisons for all pairs (ANOVA, Tukey-Kramer HSD; JMP®, Version 11. SAS
Institute Inc., Cary, NC, 1989-2007). Measurements were not calculated for the Strait of
Juan de Fuca region due to a lack of data. To better meet the normality assumption of
ANOVA, we log˗ transformed the data. To investigate characteristics of nocturnal
locations we assessed differences in five habitat covariates between used and pseudonon-used locations with t-tests (JMP®, Version 11).
We determined which habitat variables were the best predictors of nocturnal
locations using logistic regression (JMP®, Version 11) and evaluated competing models
with Akaike’s information criterion adjusted for small sample sizes and AICc weights
(Burnham and Anderson 2002). We evaluated correlations among all continuous
variables (R > 0.6; JMP®, Version 11). Using the subset approach to model selection we
developed 15 candidate models for evaluation with 4 predictor variables; minimum
distance to shore, water depth, root-mean-square tidal current, and vessel traffic density.
The resulting coefficients were used to estimate a resources selection function (RSF)
where 𝑤(𝑥) is the probability of nocturnal Surf Scoter presence.
exp(𝛽 +𝛽1 𝑋𝑖1 + 𝛽2 𝑋𝑖2 +𝛽3 𝑋𝑖3 )

0
𝑤(𝑥) = (1 −exp(𝛽

0 +𝛽1 𝑋𝑖1 + 𝛽2 𝑋𝑖2 +𝛽3 𝑋𝑖3 ))

(1)
43

Results
Distances Traveled From Diurnal Foraging Areas
Different regions of the Salish Sea are categorized by varying hydrologic and physical
features. Surf Scoters are not observed traveling over land during local movements (J.
Evenson, personal communication), suggesting land features may affect distances
traveled between nocturnal resting and diurnal foraging areas.
Table 2. Log transformed mean distance (in meters for all columns) traveled by Surf
Scoters between diurnal and nocturnal locations within the Salish Sea (2003-2007). Each
measurement is the shortest strait line distance between a set of diurnal and nocturnal
points within an Argos duty cycle. If land features separated the two locations, the
shortest distance around that feature was measured.
n

̅
𝑿

̅ (log)
𝑿

SD

Range*

All Regions

233

3967

3.44

0.38

222 – 22,979

Strait of Georgia

10

3629

3.36

0.51

222 - 8511

North Puget Sound

83

3854

3.45

0.36

222 – 19,025

Central Puget Sound

96

3218

3.36

0.35

449 – 21,174

South Puget Sound

42

6066

3.63

0.41

499 – 22,979

*Numbers from original data
From the original filtered data sets from 34 Surf Scoters, we measured 233
movements between diurnal and nocturnal locations throughout the Salish Sea (Table 2).
ANOVA revealed variation between regions (F3 = 5.22, P = 0.0017) and a post hoc
Tukey test showed a difference in mean distance traveled between the South Puget Sound
and the Central Puget Sound region (q = 2.59, P = 0.0008) (Figure 3).

44

Figure 3. Average distance traveled by Surf Scoters between
diurnal and nocturnal locations within a 24 hour period within
the Salish Sea (2003-2007).

Habitat Selection
Compared to diurnal periods, preliminary analysis of the data suggested that
during nocturnal periods, Surf Scoters utilize areas that are: 1) in more open and exposed
water, 2) at higher densities, and 3) within 24 km of foraging areas (J. Evenson, personal
communication). Five habitat variables (minimum distance to shore, water depth, rootmean-squared tidal current, exposure of nearest shoreline, and vessel traffic density) were
acquired for 1,064 nocturnal use locations and 5,002 randomly generated pseudo-non-use
locations. Means comparisons revealed differences in minimum distance to shore, rootmean-squared tidal current, exposure of nearest shoreline and vessel traffic density (Table
45

3). Surf Scoters utilize areas closer to shore, with lower tidal currents and wave
exposures and minimized vessel traffic compared to the available habitat in the Salish
Sea.
Table 3. Mean comparisons between use (n = 1,064) and pseudo-non-use (n = 5,002)
nocturnal Surf Scoter locations for 5 habitat variables within the Salish Sea 2003-2007.
Differences evaluated with t-tests (JMP®, Version 11. SAS Institute Inc., Cary, NC,
1989-2007).
Pseudo-nonuse

Use

F

Range of
nocturnal use

𝑋̅

SE

𝑋̅

SE

Min Dist to Shore (m)

1388

33

3605

49

-37.14***

2 - 8415

Water Depth (m)

-34

41

-115

1.42

42.83

˗251 - 0

Tidal Current (RMS)

0.09

0.002

0.22

0.003

-33.15***

0.01 – 0.87

Shoreline ExposureA

2.40

0.60

3.1

0.01

0.09***(U)

1-4

Vessel Traffic Density

0.22

0.02

0.54

0.03

-8.23***

0 – 9.12

Variable

A signifies that difference was evaluated with chi squared likelihood statistic.
*** signifies P < 0.0001 at 95% confidence.

Table 4. Importance weight of each habitat covariate, calculated from confidence
set (models with Δi >10) of candidate models from best-fitting logistic-regression used to
predict nocturnal Surf Scoter presence from habitat characteristics in the Salish Sea,
2003-2007.

Parameter

Importance Weight

Depth

.9997

Tidal

.9997

Vessel Traffic Density

.9143

Distance to Shore

.7717

46

Of the 15 candidate nocturnal presence prediction models, the full model best fit
the data, accounting for 71% of the AICc weight and was 3.5 times more likely to explain

Table 5. Resulting confidence set (models with Δi >10) of candidate models from bestfitting logistic-regression used to predict nocturnal Surf Scoter presence from habitat
characteristics in the Salish Sea, 2003-2007. The 4 models listed are the Models with the
lowest AICc and the highest weights (wi) are the most supported models.
Model

k

AICc

Δi

wi

Depth + MinDistShore + Tidal + Vessel

6

4440.66

0

0.7099

Depth + Tidal + Vessel

5

4443.15

2.49

0.2044

Depth + MinDistShore + Tidal

5

4445.54

4.88

0.0618

Depth + Tidal

4

4447.46

6.8

0.0236

habitat preference than the next best fitting model. This is strong but not unequivocal
(w>0.90) support for the best AICc model, so model averaging (Burnham and Anderson
2002) was conducted. We created a confidence set of candidate models (Table 5) by
excluding all models with a Δi greater than 10, as these have insufficient evidence to be
considered plausible (Burnham and Anderson 2002). Only the 4 confidence set models
were averaged as they accounted for 99% of the AICc weight. This resulted in a
regression equation (Equation 2), which was our final nocturnal presence model, for
resource selection function (Equation 1) estimation. In addition, importance weights were
calculated for all four parameters by summing Akaike weights for each of the confidence
models that contained each parameter (Table 4).

𝑝̂ = 0.1257 + 0.0131*Depth + -4.9350*Tidal + -0.0519*Vessel + -4.48E-05*MinDistShore (2)

47

Discussion

We provide a quantitative analysis that shows that Surf Scoters utilize nocturnal
habitats that differ from documented diurnal distributions. Our study found water depth,
tidal current, vessel traffic and distance to shore to be strong predictors of nocturnal
locations. We demonstrated that Scoters are resting in areas with an average depth of 34
m, an average of 1,388 m from shore, and with minimized tidal currents and vessel
traffic. We also found that they will stay within an average distance of 3,967 m of
diurnal areas, unless topography necessitates greater travel distances to reach preferred
habitat. These are novel habitat selection and use findings and expand the known Surf
Scoter distribution in the Salish Sea.
Sea ducks are widely associated with subtidal zones (Žydelis et al. 2006, Kirk et
al. 2007), most commonly in water less than 18m deep, and these diurnal distributions are
highly influenced by nearshore food sources (Buchanan 2006, Kirk et al. 2008, De La
Cruz et al. 2014). In a diurnal resource selection study conducted in San Francisco Bay,
De La Cruz and authors (2014) found water depth as a strong predictor of locations, and
that birds were 2.45% less likely to be found with each meter of increasing depth. Our
nocturnal location data has confirmed that Surf Scoters move offshore to deeper water in
comparison to diurnal foraging areas to rest at night. Our results compliment the findings
of Lewis et al (2005) who compared diurnal and nocturnal locations in order to
investigate the possibility of nocturnal foraging of Scoters. They documented mean
individual location distance from shore as 231 m for diurnal and 704 m for nocturnal
positions. As the scope of their study did not necessitate exploration at greater distances,

48

they would not have detected existing nocturnal locations at the distances that we
documented.
Our documented nocturnal use habitats are farther from shore, and in deeper
water than documented diurnal use areas, yet the opposite is true when compared to all
available habitat in the Salish Sea. This suggests that Surf Scoters balance utilizing
preferred nocturnal habitat, with minimizing distances from food resources. The longer
distances traveled in the South Puget Sound region reinforces the importance of the
characteristics that are selected. This region is almost entirely comprised of narrow
passages and inlets (Washington Department of Ecology 2003). These features hinder
straight line travel between use areas, and demonstrate that Surf Scoters will travel
greater distances to rest in more desirable habitat, such as areas over deeper water and
farther from the shoreline.
Surf Scoters are largely unaware when resting at night (J. Evenson personal
communication) and moving away from shore to gather at high densities may be a
defense strategy. Land predators such as mustelids, will feed on compromised Sea Ducks
(Anderson et al. 2012). In addition, selection for relatively low tidal currents prevents
them from moving considerable distances throughout the night. Drifting away from
diurnal food resources would increase energy requirements associated with daily
movements. Drifting closer to shore could expose them to predation.
Our specific findings on probability of selection of the four habitat covariates
should be interpreted with caution. One assumption of our study design is that the
pseudo-non-used points are actually unused. Given the ratio of our sample of individual

49

birds compared to the actual size of the Puget Sound population and the habitat available,
there is reason to believe that this assumption was violated in some locations. Also,
perception of available habitat to an animal is affected by competition, which was not
considered in this study. These findings provide a starting point for further investigation
into Surf Scoter nocturnal distributions in the Salish Sea, as well as other estuaries with
similar species and physical characteristics.
In order to effectively maintain a viable and abundant population of Surf Scoters
in the Salish Sea, a full picture of their annual movements and life stage requirements as
they travel between habitats associated with breeding and wintering ranges is essential.
Adult survival is critical for sustaining this population and identifying limitations and
resource availability in their marine locations will play an important role in understanding
factors responsible for their decline. Our study adds one component of this story that is
missing, their nocturnal distribution and habitat requirements.
Conservation Implications
Diurnal data is what currently guides conservation and management decisions
regarding Surf Scoters. As this is true for most marine birds, these findings set a
precedent for future studies regarding a variety of marine birds displaying similar
movement and behavior characteristics and identifies a need to consider nocturnal
distribution when assessing the conservation needs of marine bird species in general. For
example, the Puget Sound Vital Sign program currently focuses solely on the nearshore
environment (Pearson 2013). Recognizing the importance for open water habitats for

50

Scoters and other birds may demonstrate the advantage of broadening the scope of such
efforts.
Habitat in deeper and more open water is accompanied by threats that are
different than those Scoters face in nearshore habitats, such as vessel traffic. British
Columbia’s waters support an average of 410,303 vessels a year, of which 2,739 are
tankers carrying liquid oil in bulk (EnviroEmerg Consulting Services 2008). Washington
State waters see about 230,000 transits annually, including nearly 8,000 deep draft vessel
movements (Dorp and Merrick 2014). De La Cruz and authors (2014) found that sea
ducks did not permanently avoid ferry routes in San Francisco Bay, despite daily
displacement observed as diving, flying or swimming away in response to the boats.
They theorized that the resources in the area are too important to avoid, and that the birds
are in good enough condition to make up for the cost of the disturbance. Conversely, our
nocturnal data demonstrated a strong avoidance of high vessel traffic in the Salish Sea.
As rest periods are critical for conserving energy during winter months (De La Cruz et al.
2014), vessel disturbance would be detrimental during nocturnal periods. This suggest
the possibility of night time vessel traffic limiting available nocturnal habitat to Surf
Scoters.
In addition to vessel traffic being a direct daily disturbance, bird distributions that
are close to oil transportation are at greater risk of contamination. Many small oil spills
go undetected or unreported when they are 1000L or less. These small-scales oil
discharges are more widely distributed and happen more frequently, causing a greater
ecological impact and can have cumulative effects on seabird populations (O’Hara et al.
2009). Oiled birds have been found along shorelines in areas where dense seabird
51

concentrations overlap with heavy vessel traffic across the globe. Oil in very small
amounts can be lethal to a seabird (Wiese and Robertson 2004) and in cases outside of
large catastrophic spills, oil found on bird carcasses is usually of the heavy fuel type,
commonly found in bilges of large tanker, cargo and container ships. As more than 15
billion gallons of oil are shipped through Washington State waters annually (Washington
Department of Ecology and Puget Sound Partnership 2011), identifying nocturnal use
areas that overlap with oil vessel traffic will have significant conservation implications.
Determination of important nocturnal use areas will also improve catastrophic oil spill
response plans. For example, in response to the risks associated with oil transportation,
Washington State has created a series of Geographic Response Plans (GRPs) to facilitate
an immediate and efficient oil spill response in the Puget Sound area. These plans

Figure 4. Map displaying nocturnal
locations of 34 Surf Scoters (20032007) as well as tanker vessel
traffic density from 2011

52

prioritize actions during the critical hours immediately following an oil spill, to protect
vulnerable resources. Each regional GRP outlines specific responses to protect
designated sensitive areas. However, the general strategy is the same across all regions
of the Puget Sound. Top priorities are to control and contain the oil at its source, prevent
oil from reaching sheltered shorelines and wildlife protection through restricted fly zones
and in some cases hazing. These plans currently protect nearshore habitats that are
critical for Surf Scoters. However, nocturnal resting areas for sea birds are not
considered. As Scoters tend to gather in dense flocks with various other species (J.
Evenson personal communication) in these habitats, immediate response to these areas
would be invaluable in limiting birds affected by an oil spill.

53

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58

APPENDIX – ADDITIONAL TABLES AND FIGURES
Table 6. The strategy used to upload all Surf Scoter Argos and additional parameter data
into Movebank, using existing Movebank attributes. The Field Name column lists all
parameters in the original Surf Scoter data, including sun angle variables. The Movebank
attributes were used to filter the data with the Douglas-Argos Algorithm and were
manually re-labeled to original field names after exported from Movebank.
Field Name

From DS
or DIAG

Comment

Movebank Attribute Used

PTT

X

Tag Identifier

Tag ID

Set of location points for one
seasonal utilization distribution

Migration Stage Custom

LocSet

Concatenated with Migration Stage
Custom (LocSet(tab)LocName)

LocName

CycNum

Reporting cycle

Tag Tech. Spec.

SunCycN

Reporting cycle. Changes if the
sun position changes from
above to below the horizon, or
below to above the horizon

Concatenated with Tag Tech. Spec.
(CycNum(tab)SunCycNum)

DateUTM

X

Date Utime

Timestamp (Fixed offset from UTC,
UTC + 0)

TimeUTM

X

Time Utime

Concantenated with Date Utime

LC

X

Location Class

Argos Location Class

IQ

X

IQ

Argos quality indicator

SAT

X

Satelliet Identifier

Argos Satellite ID

Lat1

X

Reported latitude #1

Argos latitude 1

Lon1

X

Reported longitude #1

Argos longitude 1

Lat2

X

Reported latitude #2

Argos latitude 2

Lon2

X

Reported longitude #2

Argos longitude 2

NbMes

X

Number of measures

Argos Nmessages

59

NbMesG120

X

NbMesG120

Argos NbMesG120

BestLevel

X

BestLevel

Argos best level

Dur

X

Duration

Argos Pass Duration

NoPc

X

NoPc

Argos NOPC

Freq

X

Freq

Argos calculation frequency

Alt

X

Altitude

Argos altitude

TempC

Transmitter Temperature
Celsius, from algorithm applied
to sensor data

Temperature External

TempF

Transmitter Temperature
Fahrenheit, from algorithm
applied to sensor data

N/A (deleted)

Volt

Transmitter Voltage, from
algorithm applied to sensor data

Tag Voltage

Cnt

X

Cnt

GPS Satellite Count

Act

X

Act

Activity Count

BestLat

Best Latitude

Concatenated into Comments

BestLon

Best Longitude

Concatenated into Comments

TimeLoc

Local Time based on best
lat/long (geographic time)

Concatenated into Comments

TimeLocDec

Concatenated into Comments

DateLoc

Local Date based on best
lat/long (geographic date)

Concatenated into Comments

SRTLoc

Sun Rise Time (based on
lat/long/date)

Concatenated into Comments

SRTCalc
SSTLoc

Concatenated into Comments
Sun Set Time (based on
lat/long/date

Concatenated into Comments

60

Concatenated into Study Specific
Measurement

SSTCalc

SunUp

Is sun above horizon (Y/N)

Concatenated into Study Specific
Measurement

GeoTz

Concatenated into Study Specific
Measurement

BeforeSRT

Concatenated into Study Specific
Measurement

AfterSST

Concatenated into Study Specific
Measurement

TimeFromSRTDec Hr

Concatenated into Study Specific
Measurement

TimeFromSSTDecHr

Concatenated into Study Specific
Measurement

BattPerc

Estimate percent battery
remaining

Light Level

PosSel

Notes if position 1 or 2 were
selected as the best location

Habitat

JulianDay

Concatenated into Study Specific
Measurement

SolarElevationCorrecte
dForATMRefrctionDeg

Concatenated into Study Specific
Measurement

61

Table 7. Comparisons of the percent of locations retained through three different
Douglas-Argos Algorithm filters. Percentages are averages of nine different bird location
data sets. Standard deviations result from averaged percent of locations retained of the
22 location sets.
KEEP_LC=2

Average
Percent
Locations
Retained
Standard
Deviation
between
Location Sets

KEEP_LC=2,
MAXREDUN=1km

KEEP_LC=1

All Locs

Noc Locs

All Locs

Noc Locs

All Locs

Noc Locs

29.89%

37.79%

51.91%

56.25%

52.09%

61.93%

0.17

0.19

0.24

0.21

0.12

0.11

62

Table 8. Spatial layers used to determine Surf Scoter habitat selection in the Salish Sea
and source information.
Spatial Data Type

Source

Metadata Link

Washington
Bathymetry

Ocean Service, Office
for Coastal
Management (NOAA)

http://estuarinebathymetry.noaa.gov/documentat
ion/P290_B30doc.html

Canada
Bathymetry

National Geophysical
Data Center (NOAA)

file:///F:/All%20Thesis%20Stuff/Nocturnal%20
Distributions%20of%20Wintering%20Surf%20
Scoters%20Study/GIS/GIS%20Data/GIS%20La
yers/Canada%20Bathymetry/british_columbia_
3sec.htm

Washington
Shoreline
Exposure

Pacific Northwest
Environmental
Response Management
Application (NOAA)

https://erma.noaa.gov/northwest/erma.html#/x=124.21722&y=48.75354&z=7&layers=1+7332

Canada Shoreline
Exposure

BC Marine
Conservation Analysis

http://bcmca.ca/datafiles/individualfiles/bcmca_
eco_physical_exposure_marxan_metadata.htm

Tidal Current

BC Marine
Conservation Analysis

http://bcmca.ca/datafiles/individualfiles/bcmca_
eco_physical_hightidalcurrent_marxan_metadat
a.htm

Washington
Marine Shoreline

Washington
Department of Ecology

https://fortress.wa.gov/ecy/coastalatlas/tools/Ma
p.aspx

Potential Oil Spill
Origins

Washington
Department of Ecology

https://fortress.wa.gov/ecy/coastalatlas/tools/Ma
p.aspx

Vessel Traffic
Density

Office for Coastal
Management (NOAA)

http://coast.noaa.gov/dataservices/Metadata/Tra
nsformMetadata?u=http://coast.noaa.gov/data/D
ocuments/Metadata/harvest/MarineCadastre/Ves
selDensity2011.xml&f=html

Tanker Vessel
Traffic Density

Office for Coastal
Management (NOAA)

http://coast.noaa.gov/dataservices/Metadata/Tra
nsformMetadata?u=http://coast.noaa.gov/data/D
ocuments/Metadata/harvest/MarineCadastre/Tan
kerVesselDensity2011.xml&f=html
63

Figure 5. A sample of results from three different Douglas Argos filters (KEEP_LC=1,
KEEP_LC=2 and MAXREDUN=1km, KEEP_LC=1) applied to Surf Scoter location
data, Used for visual comparisons, in order to choose the filter that balances accuracy and
retention of data.

64

Figure 6. Data manipulation, preparation and analysis strategy to assess nocturnal habitat
selection of Surf Scoters in the Salish Sea 2003-2007.

Raw Argos Data

Diurnal vs. Nocturnal
Jean Meeus Algorithm

Douglas Argos Filter Algorithm
KEEP LC = 2, MAXREDUN = 1
Estimated
Flightless
Moult Locations

Diurnal to
Nocturnal Travel
Distance
ANOVA between
Salish Sea Regions

Randomly
Generated
Locations

Remove

Remove

Remove

Plausible Locations

Implausible
Locations

Civil Twilight
Locations

Diurnal and Nocturnal Data Set

Remove

Diurnal and
Land Locations

Remove

Non-Independent
Locations

Final Nocturnal Data Set

Logistic Regression
AIC Model Selection
Resource Selection Function

Nocturnal Surf Scoter Presence Probability Model

65