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Wintering and breeding distributions of
Black Oystercatchers (Haematopus bachmani):
Long-term trends and the influence of climate
by
Seth Gaborko Bennett
B.Sc., (Hons.), Memorial University of Newfoundland, 2013
Thesis Submitted in Partial Fulfillment of the
Requirements for the Degree of
Master of Science
in the
Department of Biological Sciences
Faculty of Science
© Seth G. Bennett 2018
SIMON FRASER UNIVERSITY
Summer 2018
Copyright in this work rests with the author. Please ensure that any reproduction or re-use is done in accordance with the relevant national copyright legislation.
ii
Approval
Name: Seth Gaborko Bennett
Degree: Master of Science
Title: Wintering and breeding distributions of Black Oystercatchers (Haematopus bachmani): Long-term trends and the influence of climate
Examining Committee: Chair: Jim Mattsson Associate Professor
David Green Senior Supervisor Professor
Mark Drever Supervisor Migratory Bird Biologist Canadian Wildlife Service
Daniel Esler Supervisor Research Wildlife Biologist United States Geological Survey
Ronald Ydenberg Supervisor Professor
Stephanie Hazlitt External Examiner Head, State of Environment Reporting Analysis, Reporting, and Knowledge Services Government of British Columbia
Date Defended/Approved: August 3, 2018
iii
Abstract
Black Oystercatchers (Haematopus bachmani) are argued to be at risk from global
climate change as rising sea levels could threaten their coastal habitat. However,
population estimates have doubled to ~15 000 since 1994. This has been attributed to
improvements in survey methods rather than to population trends, which remain
uncertain. I assessed trends and climatic influences on winter abundance (Christmas
Bird Counts, 1975/1976 – 2015/2016) and numbers of breeding pairs (British Columbia
breeding surveys, 1962 – 2014). Winter counts were stable or increasing across the
species' range. Numbers of breeding pairs were stable in British Columbia, but were
lower following the warm phase than the cool phase of the El Niño Southern Oscillation.
Although new challenges may arise as the climate continues to change, Black
Oystercatcher populations appear resilient to current environmental and anthropogenic
challenges.
Keywords: Aleutian low; bottom-up; carry-over effects; El Niño Southern Oscillation;
intertidal predator; partial migration
iv
Dedication
I dedicate this thesis to the birds.
v
Acknowledgements
First of all, my supervisor, David Green, guided, supported, and accommodated
me with seemingly endless patience. David provided so much valuable feedback on the
project, manuscript, and defence presentation, and helped me make sense of the model
outputs. The rest of my committee, Mark Drever, Dan Esler, and Ron Ydenberg, and my
examiner, Stephanie Hazlitt, provided valuable feedback, support, and encouragement.
They were also very accommodating particularly while trying to find a defence date that
worked for everybody at the end of the summer. Thanks also to Jim Mattsson, for taking
the time to act as chair for my defence.
Thanks to everyone in the Centre for Wildlife Ecology, particularly Sarah
Thompson, who extracted and prepared the temperature data used in Chapter 2. Philina
English helped me with R code for the AIC tables and graphs. Cailin Xu helped me with
the Christmas Bird Count data. David Hope, Richard Johnston, and Simon Valdez gave
me useful feedback on my defence presentation.
At the Canadian Wildlife Service, Mark Drever (again) supported me and helped
with the breeding data and R code used in Chapter 3. Holly Middleton compiled the
breeding data used in Chapter 3. Mark Hipfner supplied some updated data and useful
context for the oystercatcher surveys on Triangle Island.
At the United States Geological Survey, Dan Esler (again), Tim Bowman, Jon
Brown, Brian Uher-Koch, and Megan Willie (CWE) helped me get my hands on some
oystercatchers for the very first time in Prince William Sound. At the United States
National Park Service, Brian Robinson, Sam Stark, and Jordan Green gave me a first-
hand look at Black Oystercatcher parents and chicks on their breeding grounds in
beautiful Kenai Fjords. Thanks to the oystercatchers for being such darn cool birds.
At the Dean of Science office, Steve Obadia (IT support) saved my laptop and
the files within when it crashed and I feared all might be lost. At the SFU Counselling
Centre, Susan Brook and the rest of the Thesis Support Group provided support (as
support groups should) and shared their grad school experiences and struggles.
vi
Finally, my family: Maria Adey moved to BC with me and settled down with me
here. Her support, encouragement, and patience helped get me through. My family,
Mom, Dad, and Trevor, and Maria's family, Lin, Arnold, and Emily, were supportive and
patient and hardly ever asked me if I was getting close to finishing yet.
Funding for this project came from the CWE, CWS, Dean of Graduate Studies
(Graduate Fellowships), and the Green Lab.
vii
Table of Contents
Approval.............................................................................................................................iiAbstract............................................................................................................................. iiiDedication .........................................................................................................................ivAcknowledgements........................................................................................................... vTable of Contents.............................................................................................................viiList of Tables.....................................................................................................................ixList of Figures ................................................................................................................... xList of Acronyms ...............................................................................................................xi
Chapter 1. Introduction ............................................................................................... 1References........................................................................................................................ 3
Chapter 2. Temporal trends and climate effects on the winter numbers of a partially-migratory shorebird, the Black Oystercatcher (Haematopus bachmani), throughout its range......................................................................... 6
Introduction ....................................................................................................................... 6Methods ............................................................................................................................ 8
Study species ................................................................................................................ 8Winter distribution data.................................................................................................. 8Analyses........................................................................................................................ 9
Results ............................................................................................................................ 11Temporal trends in Black Oystercatcher winter numbers............................................ 11Relationships between climate variables .................................................................... 12Effects of winter conditions on Black Oystercatcher numbers in the following winter . 12Effects of pre-migration conditions on winter distributions of Black Oystercatchers ... 12
Discussion....................................................................................................................... 13References...................................................................................................................... 16Tables ............................................................................................................................. 20Figures ............................................................................................................................ 23
Chapter 3. The influence of the El Niño Southern Oscillation on Black Oystercatcher (Haematopus bachmani) breeding numbers in British Columbia.............................................................................................................. 25
Introduction ..................................................................................................................... 25Methods .......................................................................................................................... 27
Study species .............................................................................................................. 27Breeding surveys......................................................................................................... 27Climate indices and variables...................................................................................... 28
Southern Oscillation Index and Pacific Decadal Oscillation .................................... 29North Pacific Gyre Oscillation.................................................................................. 29Coastal Sea Surface Temperature .......................................................................... 30Upwelling ................................................................................................................. 30
viii
Analyses...................................................................................................................... 31Results ............................................................................................................................ 32
Temporal trends in numbers of Black Oystercatcher breeding pairs .......................... 33Relationships between selected climate variables ...................................................... 33Climate effects on numbers of Black Oystercatcher breeding pairs............................ 33
Discussion....................................................................................................................... 34References...................................................................................................................... 37Tables ............................................................................................................................. 42Figures ............................................................................................................................ 45
Chapter 4. Conclusions............................................................................................. 48References...................................................................................................................... 51
Appendix: Population estimates for Black Oystercatchers................................... 54
ix
List of Tables
Table 2.1 Summary of Christmas Bird Count data used for analyses of trends in Black Oystercatcher winter numbers and the effects of climate from 1975/1976 to 2015/2016. The second analysis, examining carry-over effects from winter climate conditions to numbers of oystercatchers in the following winter, used a slightly reduced data set due to gaps in the available climate data. Sample sizes for the data set used in the second analysis are shown in parentheses. ........................................................ 20
Table 2.2 AIC results from model sets examining (a) trends in winter counts of Black Oystercatchers across their range from 1975/1976 to 2015/2016, (b) counts of oystercatchers in response to conditions from the previous winter, and (c) counts of oystercatchers in response to temperatures across the species' Alaska range in the late summer (July & August) and fall (September & October). Regional trend model is bolded. All models included party distance + party distance2 as a measure of effort............ 21
Table 2.3 Parameter estimates for models examining (a) regional trends in winter counts of Black Oystercatchers and (b) trends with regional effects of Aleutian Low Pressure Index (ALPI) on counts in the following winter between 1975/1976 and 2015/2016........................................................ 22
Table 3.1 Sources and details of Black Oystercatcher breeding surveys in British Columbia, including areas surveyed, time of surveys, and survey methods. Preliminary surveys were performed from the boat except on Triangle Island, and sites were then searched more thoroughly by foot. 42
Table 3.2 AIC table for analysis of (a) Trends in numbers of breeding pairs of Black Oystercatchers and (b) climate effects on numbers of breeding pairs of Black Oystercatchers in British Columbia (n = 760 records at 193 sites). Null and subregional trend models are bolded. All models assumed a negative binomial distribution and included log-transformed shore length as an offset variable and site as a random variable. ............................... 43
Table 3.3 Parameter estimates for (a) null model showing no effect of trends in numbers of breeding pairs of of Black Oystercatchers and (b) effect of the mean April Southern Oscillation Index (SOI) on numbers of breeding pairs of Black Oystercatchers counted on surveys conducted across British Columbia between 1962 and 2014.......................................................... 44
x
List of Figures
Figure 2.1 Temporal trends in the observed numbers of Black Oystercatchers on Christmas Bird Counts conducted between 1975/1976 and 2015/2016 in five regions across their range. Lines show the predicted counts in each region and shading shows 95% confidence intervals based on the top model in Table 2.2a and Table 2.2c. Parameter estimates for this model are given in Table 2.3a. Rugging shows the distribution of data in each region, with positive partial residuals across the top and negative partial residuals across the bottom. ................................................................... 23
Figure 2.2 Relationship between Aleutian Low Pressure Index (ALPI) and observed numbers of Black Oystercatchers in Christmas Bird Counts the following year. Lines show the predicted counts in each region and shading shows the 95% confidence intervals based on the top model in Table 2.2b. Parameter estimates for this model are given in Table 2.3b. Rugging shows the distribution of data in each region, with positive partial residuals across the top and negative partial residuals across the bottom................................................................................................................. 24
Figure 3.1 Sites of lighthouses in British Columbia recording daily sea surface temperatures and salinity as of 2018. The year in parentheses next to the name of each site indicates the year in which data collection began. 7 lighthouses that no longer collect data are not shown. Source: http://www.pac.dfo-mpo.gc.ca/science/oceans/data-donnees/lightstations-phares/index-eng.html (accessed 21 August 2018) ................................ 45
Figure 3.2 Numbers of breeding pairs of Black Oystercatchers at breeding sites in British Columbia from 1962 to 2014. Subregions are denoted by colour (red = Haida Gwaii, blue = Strait of Georgia & Gulf Islands, and green = west coast of Vancouver Island). ............................................................ 46
Figure 3.3 Relationship between the Southern Oscillation Index (SOI) in April and numbers of breeding pairs of Black Oystercatchers at survey sites in British Columbia between 1962 and 2014. The line shows the relationship, and the shaded area shows the 95% confidence interval from the top model in Table 3.2b. Parameter estimates for the model are given in Table 3.3b. Rugging shows the distribution of data. .................. 47
xi
List of Acronyms
ADMB Automatic Differentiation Model Builder
AIC Akaike's Information Criterion
AK Alaska (United States of America)
ALPI Aleutian Low Pressure Index
BC British Columbia (Canada)
BCN Baja California (United Mexican States)
BCSOP British Columbia Shore Station Oceanographic Program
CA California (United States of America)
CBC Christmas Bird Count
CI Confidence Interval
DDT Dichlorodiphenyltrichloroethane
ENSO El Niño Southern Oscillation
GPS Global Positioning System
IQR Interquartile Range
NCEP National Centers for Environmental Prediction
NOI Northern Oscillation Index
NPGO North Pacific Gyre Oscillation
OR Oregon (United States of America)
PDO Pacific Decadal Oscillation
PFEL Pacific Fisheries Environmental Laboratory
SD Standard Deviation
SE Standard Error
SOI Southern Oscillation Index
SST Sea Surface Temperature
USA United States of America
WA Washington (United States of America)
1
Chapter 1. Introduction
Species occupying specialist niches tend to be more threatened by climate
change than generalist species, as they are less able to adapt to changes in their
environment (Clavel et al. 2010, Gilman et al. 2010, Stefanescu et al. 2011). This pattern
has been found to apply broadly across many taxa, such as birds (Julliard et al. 2004)
and butterflies (Stefanescu et al. 2011) in Europe. For the conservation of species
threatened by climate change, it is important to understand both the environmental
influences on these species as well as anthropogenic threats that could hinder recovery
and conservation efforts (Gilman et al. 2010, Stefanescu et al. 2011). For example, the
recovery of the Bald Eagle (Haliaeetus leucocephalus) following the 1972 ban of the
pesticide DDT was successful because the primary cause of decline was identified, and
the DDT ban was combined with other threat mitigation measures (e.g. a ban on killing;
Grier 1982).
Acquiring the data necessary to identify population trends and risks can require a
lot of time and resources, especially for data collected across broad spatial scales.
Citizen science, where volunteers from the public are enlisted to perform surveys and
record observations, can be an effective way to collect this data while also engaging the
public with the natural world (Bonny et al. 2009). Citizen science has been criticized
because differences in training, experience, and effort of participants may lead to
variation in the quality and reliability of data (e.g. Galloway et al. 2006). However, many
of these issues can be mitigated through thoughtful experimental design, volunteer
training, and data validation (Bonter & Cooper 2012). As a result, particularly effective
citizen science projects such as eBird have become invaluable sources of data (e.g.
presence/absence, counts) that are widely available to many researchers at different
institutions (Sullivan et al. 2014).
The Black Oystercatcher (Haematopus bachmani) is an important intertidal
predator and indicator species for evaluating the health of the Pacific rocky intertidal
shoreline (Wootton 1992, Bergman et al. 2013, Tessler et al. 2014). This species has
been classified as "Climate Endangered" by the Audubon Society, based on predictions
2
that the species' range could be reduced by more than 50 percent by 2050 (Langham et
al. 2015). This is largely because the species' habitat is limited to the rocky intertidal
coastline, which is expected to shrink as sea levels rise. Food availability may also be a
concern as Black Oystercatchers feed mainly on molluscs with calcified shells (Tessler
et al. 2014), which are themselves threatened by ocean acidification (Fabry et al. 2008).
Black Oystercatchers nest on beaches, and are sensitive to extreme high tides (Morse et
al. 2006). Anthropogenic factors are also of concern (Warheit et al. 1984, Spiegel 2008).
Immediately following the 1989 Exxon Valdez oil spill in Prince William Sound, Alaska,
oystercatchers in affected regions suffered reductions in population and breeding
success (Andres 1997, Murphy et al. 1997). Local populations recovered quickly,
however, and these effects were no longer observed by 1993, 4 years after the spill
(Andres 1999). Though they are one of North America's least abundant shorebirds
(fewer than 18 000 individuals globally; Appendix), populations are thought to be stable
or increasing (Hazlitt 2001, Tessler et al. 2014, Meehan et al. 2018).
Despite extensive study of the Black Oystercatcher's biology, there are still
significant gaps in our knowledge of the species' ecology. Long-term, broad-scale
population data are lacking (Tessler et al. 2014, Weinstein et al. 2014, Appendix).
Consequently, there are currently no peer-reviewed analyses of population trends in
Black Oystercatchers at a global scale. Little is known about migratory behaviour in
Black Oystercatchers, as well. The species is known to be partially migratory in the
northern part of their range (Andres 1994), and limited data have been collected on
migration timing, distances, and routes (Johnson et al. 2010). However, it is not known
to what extent migration varies from year to year in terms of distance, timing, and
proportion of migrants. Finally, there has been ample research into the species' breeding
biology and behaviour (e.g. Purdy & Miller 1988, Hazlitt et al. 2002, Hipfner et al. 2012),
and there is evidence that local climate, specifically sea surface temperature (SST),
influences breeding (Hipfner & Elner 2013). That said, it is unknown if this effect is
widespread across their range, or to what extent this species is influenced by
environmental factors other than SST.
In this thesis, I will examine long-term trends in oystercatcher numbers and
explore the influence of climate on wintering strategy and breeding numbers. In Chapter
2, I use Christmas Bird Count data to identify trends in the numbers of wintering
oystercatchers across the species' range. I then examine carry-over effects of winter
3
conditions on populations from one year to the next. Finally, I examine the influence of
summer and fall climate on winter distributions to determine the effect of environment on
wintering strategy (residency or migration) in a given year. In Chapter 3, I use summer
monitoring data from across British Columbia to determine long-term trends in the
numbers of breeding Black Oystercatchers in the province. I then examine a suite of
broad- and local-scale climate variables to determine how environmental conditions
influence breeding numbers from year to year. Together, these studies provide long-term
baseline information on population and breeding trends, which are invaluable for
contextualizing future monitoring efforts. These studies also provide insight into the
nature of environmental influences on the behavioural and breeding ecology of Black
Oystercatchers. This information could have important implications if this species
becomes a target for conservation efforts.
References
ANDRES B.A. (1994) Year-round residency in northern populations of the Black Oystercatcher. U.S. Fish and Wildlife Service, Anchorage, AK
ANDRES B.A. (1997) The Exxon Valdez oil spill disrupted the breeding of Black Oystercatchers. Journal of Wildlife Management 61(4): 1322–1328
ANDRES B.A. (1999) Effects of persistent shoreline oil on breeding success and chick growth in Black Oystercatchers. Auk 116(3): 640–650
BERGMAN C.M., PATTISON J., & PRICE E. (2013) The Black Oystercatcher as a sentinel species in the recovery of the Northern Abalone: Contemporary diet of Black Oystercatchers on Haida Gwaii includes an endangered prey species. Condor 115(4): 800–807
BONNY R., COOPER C.B., DICKINSON J., KELLING S., PHILLIPS T., ROSENBERG K.V., & SHIRK J. (2009) Citizen science: A developing tool for expanding science knowledge and scientific literacy. BioScience 59(11): 977–984
BONTER D.N. & COOPER C.B. (2012) Data validation in citizen science: A case study from Project FeederWatch. Frontiers in Ecology and the Environment 10(6): 305–307
CLAVEL J., JULLIARD R., & DEVICTOR V. (2010) Worldwide decline of specialist species: Toward a global functional homogenization? Frontiers in Ecology and the Environment 9(4): 222–228
4
FABRY V.J., SEIBEL B.A., FEELY R.A., & ORR J.C. (2008) Impacts of ocean acidification on marine fauna and ecosystem processes. ICES Journal of Marine Science 65(3): 414–432
GALLOWAY A.W.E., TUDOR M.T., & VANDER HAEGEN W.M. (2006) The reliability of citizen science: A case study of Oregon White Oak stand surveys. Wildlife Society Bulletin 34(5): 1425–1429
GILMAN S.E., URBAN M.C., TEWKSBURY J., GILCHRIST G.W., & HOLT R.D. (2010) A framework for community interactions under climate change. Trends in Ecology and Evolution 25(6): 325–331
GRIER J.W. (1982) Ban of DDT and subsequent recovery of reproduction in Bald Eagles. Science 218(4578): 1232–1235
HAZLITT S.L. (2001) Black Oystercatcher population status and trends in British Columbia. Bird Trends 8: 34–36
HAZLITT S.L., YDENBERG R.C., & LANK D.B. (2002) Territory structure, parental provisioning, and chick growth in the American Black Oystercatcher. Ardea 90(2): 219–227
HIPFNER J.M. & ELNER R.W. (2013) Sea-surface temperature affects breeding density of an avian rocky intertidal predator, the Black Oystercatcher Haematopus bachmani. Journal of Experimental Marine Biology and Ecology 440: 29–34
HIPFNER J.M., MORRISON K.W., & KOUWENBERG A.-L. (2012) Biology of Black Oystercatchers breeding on Triangle Island, British Columbia, 2003–2011. Northwestern Naturalist 93: 145–153
JOHNSON M., CLARKSON P., GOLDSTEIN M.I., HAIG S.M., LANCTOT R.B., TESSLER D.F., & ZWEIFELHOFER D. (2010) Seasonal movements, winter range use, and migratory connectivity of the Black Oystercatcher. Condor 112(4): 731–743
JULLIARD R., JIGUET F., & COUVET D. (2004) Common birds facing global changes: What makes a species at risk? Global Change Biology 10(1): 148–254
LANGHAM G.M., SCHUETZ J.G., DISTLER T., SOYKAN C.U., & WILSEY C. (2015) Conservation status of North American birds in the face of future climate change. PLoS ONE 10(9): e0135350. https://doi.org/10.1371/journal.pone.0135350
MEEHAN T.D., HARVEY A.L., MICHEL N.L., LANGHAM G.M., & WEINSTEIN A. (2018) A population model exploring factors influencing Black Oystercatcher (Haematopus bachmani) population dynamics. Waterbirds 41(2): 115–221
5
MORSE J.A., POWELL A.N., & TETREAU M.D. (2006) Productivity of Black Oystercatchers: Effects of recreational disturbance in a national park. Condor 108: 623–633
MURPHY S.M., DAY R.H., WIENS J.A., & PARKER K.P. (1997) Effects of the Exxon Valdez oil spill on birds: Comparisons of pre- and post-spill surveys in Prince William Sound, Alaska. Condor 99: 299–313
PURDY M.A. & MILLER E.H. (1988) Time budget and parental behavior of breeding American Black Oystercatchers (Haematopus bachmani) in British Columbia. Canadian Journal of Zoology 66(8): 1742 – 1751
SPIEGEL C.S. (2008) Incubation patterns, parental roles, and nest survival of Black Oystercatchers (Haematopus bachmani): Influences of environmental processes and potential disturbance stimuli. MSc thesis, Oregon State University: 139p.
SULLIVAN B.L., AYCRIGG J.L., BARRY J.H., BONNY R.E., BRUNS N., COOPER C.B., DAMOULAS T., DHONDT A.A., DIETTERICH T., FARNSWORTH A., FINK D., FITZPATRICK J.W., FREDERICKS T., GERBRACHT J., GOMES C., HOCHACHKA W.M., ILIFF M.J., LAGOZE C., LA SORTE F.A., MERRIFIELD M., MORRIS W., PHILLIPS T.B., REYNOLDS M., RODEWALD A.D., ROSENBERG K.V., TRAUTMANN N.M., WIGGINS A., WINKLER D.W., WONG W.-K., WOOD C.L., YU J., & KELLING S. (2014) The eBird enterprise: An integrated approach to development and application of citizen science. Biological Conservation 169: 31–40
STEFANESCU C., CARNICER J., & PEÑUELAS J. (2011) Determinants of species richness in generalist and specialist Mediterranean butterflies: The negative synergistic forces of climate and habitat change. Ecography 34(3): 353–363
TESSLER D.F., JOHNSON J.A., ANDRES B.A., THOMAS S., & LANCTOT R.B. (2014) A global assessment of the conservation status of the Black Oystercatcher Haematopus bachmani. International Wader Studies 20: 83–96
WARHEIT K.I., LINDBERG D.R., BOEKELHEIDE R.J. (1984) Pinniped disturbance lowers reproductive success of black oystercatcher Haematopus bachmani (Aves). Marine Ecology Progress Series 17: 101–104
WEINSTEIN A., TROCKI L., LEVALLEY R., DOSTER R.H., DISTLER T., & KRIEGER K. (2014) A first population assessment of Black Oystercatcher Haematopus bachmani in California. Marine Ornithology 42: 49–56
WOOTTON T.J. (1992) Indirect effects, prey susceptibility, and habitat selection: Impacts of birds on limpets. Ecology 73(3): 981–991
6
Chapter 2. Temporal trends and climate effects on the winter numbers of a partially-migratory shorebird, the Black Oystercatcher (Haematopus bachmani), throughout its range
Introduction
Migration to high latitudes allows birds to take advantage of seasonally abundant
resources during the breeding season (Alerstam 1990, Newton 2007). Migrating
thousands of kilometres to breed is, however, energetically costly (Wikelski et al. 2003).
Birds that remain at high latitudes year-round do not incur this cost, and may benefit by
avoiding the high mortality associated with migration (Sillet & Homes 2002, Lank et al.
2003), having a competitive advantage when claiming or defending high quality breeding
territories (Kokko 2011), and being able to optimize their breeding phenology to spring
conditions in a given year (Helm et al. 2006). On the other hand, remaining at high
latitudes year-round can be risky as inclement weather may increase metabolic costs
and decrease food availability sufficiently to cause mortality (Robinson et al. 2007). In
some species, populations at higher latitudes may consist of both migratory and resident
individuals, indicating that the costs and benefits of migration and residency are finely
balanced.
The migratory strategy of an individual within a partially-migratory population may
be either genetically controlled (obligate) or condition dependent (facultative). Controlled
laboratory and field studies suggest that migration has a genetic basis (Partecke &
Gwinner 2007, Pulido 2011), and theoretical models tend to assume that migration is a
fixed genetic dimorphism that can persist if the two strategies have equal fitness
(Gauthreaux 1982, Lundberg 1987, 1988). Alternatively, migration may be conditional
with the optimal strategy for an individual being dependent on its phenotype (Swingland
& Lessells 1979). If this is the case migratory and non-migratory individuals need not
have equal fitness and the strategy of an individual may vary from year to year,
depending on individual condition, food availability, weather, or a combination of these
7
factors (Chapman et al. 2011, Pulido 2011). Empirical studies have shown that migratory
and sedentary individuals can differ in fitness (e.g. Andriaensen et al. 1993, Gillis et al.
2008), and that the proportion of migrants can vary with temperature and food availability
(Meller et al. 2016)
Oystercatchers are a group of shorebirds that display a variety of wintering
strategies: the three species that breed in the northern hemisphere all have partially
migratory populations (Eurasian Oystercatcher, Haematopus ostralegus: Ens et al. 1992;
American Oystercatcher, H. palliatus: Clay et al. 2010; and Black Oystercatcher, H.
bachmani, Andres et al. 1994). Eurasian Oystercatchers are known to be sensitive to
winter conditions that can influence both migration strategies and survival. Severe
winters can increase migration distances forcing northern populations to move further
south than usual, induce mass mortality, and increase summer mortality via carryover
effects (Camphyusen et al. 1996, Goss-Custard 1996, Duriez et al. 2012). A genetic
analysis by Van Treuren et al. (1999) found no genetic differences between migratory
and resident Eurasian Oystercatchers, suggesting that wintering strategy is most likely
facultative, not obligate, in this group.
The Black Oystercatcher of the North American Pacific coast exhibits both non-
breeding and breeding partial migration (sensu Chapman 2011). In Prince William
Sound, Alaska, residents and migrants share a breeding habitat, but approximately 75%
of the breeding population migrate south so they overwinter apart. Conversely, in British
Columbia, residents and migrants share a winter habitat but breed apart (Andres et al.
1994). Despite being a top predator of the rocky intertidal (Wootton 1992) and an
indicator species of the rocky intertidal (Clarkson & Zharikov 2007, Bergman et al. 2013,
Tessler et al. 2014), little is known about temporal trends in their numbers and winter
distributions. In this study, I used Christmas Bird Count data to (1) estimate long-term
trends in Black Oystercatcher populations across their winter range, (2) examine
whether winter conditions have a detectable impact on numbers in the following winter,
and (3) assess whether predicted winter conditions in Alaska influence the proportion of
migrants, and hence the winter distribution, of oystercatchers in the northern part of their
range.
8
Methods
Study species
The Black Oystercatcher is a long-lived shorebird that feeds mainly on molluscs
in the rocky intertidal zone. The species ranges from the Aleutian Islands in Alaska to
Baja California, Mexico. They are one of North America's least abundant shorebirds, with
estimated global numbers of roughly 12 500 – 17 500 (Appendix). Approximately 6750
are thought to breed in Alaska (Tessler et al. 2014). Some Alaskan populations are
partially migratory (Andres et al. 1994): migrants depart between mid-August and early
November and have been found to overwinter as far south as southern Vancouver
Island (Johnson et al. 2010).
Winter distribution data
Survey data were taken from the Christmas Bird Count (CBC), a citizen science
initiative coordinated by the Audubon Society (in the United States) and Bird Studies
Canada (in British Columbia). Volunteers conduct surveys in count circles with a 24 km
diameter, recording the total numbers of individuals of each bird species seen, the
number of volunteers in the party, the number of survey hours, and the distance covered
by each survey party. Counts within a circle are conducted during a 24 h period on a set
date in late December or early January. Counts are reported with party-hours and party-
distance (in either miles or km) as measures of search effort.
My analysis included circles that were surveyed on more than one occasion from
1975/1976 to 2015/2016 and had at least one recorded Black Oystercatcher sighting
during that time. I excluded surveys earlier than the winter of 1975/1976 as the number
and distribution of count circles along the Pacific Coast was relatively sparse before
then. Circles were grouped by region (province/state). Baja California (Mexico) only
contained one count circle, so it was combined with California into a single region. The
data set was reduced further when records were excluded due to a lack of available
environmental data for the circle for some years. Table 2.1 shows a summary of the data
sets used for each analysis.
9
Analyses
I created three candidate model sets to examine (1) temporal trends in the
number of wintering Black Oystercatchers counted in CBC surveys, (2) carry-over
effects from the climate in the previous winter, and (3) redistribution of oystercatchers
between regions from year to year. In each case I fitted generalised linear mixed models
including count circle as a random term. I determined the best-fitting distribution and
best measure of effort for each model set using Akaike’s Information Criteria (AIC).
Models fitted with a negative binomial distribution outperformed those with a zero-
inflated negative binomial distribution and a Poisson distribution (AICc weights: negative
binomial = 0.71, zero-inflated negative binomial = 0.29, Poisson < 0.01). Models with
quadratic distance travelled in kilometres (distance + distance2) outperformed models
with linear distance, party hours (quadratic and linear) as a measure of effort, and
models that did not include search effort (AICc weights: quadratic distance = 0.96, linear
distance = 0.02, quadratic hours = 0.01, linear hours & no effort < 0.01). Thus, all
candidate models in each of the three candidate sets assumed a negative binomial
distribution and used quadratic distance as a measure of effort.
The first candidate model set included models that examined temporal trends in
the number of wintering Black Oystercatchers counted in CBC surveys between
1975/1976 and 2015/2016. This candidate set included models with all combinations of
year (trend), region, the additive and interactive effects of these variables, and a null
model (n = 5 models).
The second candidate model set examined whether local or regional winter
conditions in one year influenced oystercatchers counted in CBC surveys in the
subsequent year. I used average mean daily temperatures during January and February
(the coldest months) in each count circle as a measure of local winter conditions. I used
the Aleutian Low Pressure Index (ALPI) as a measure of winter conditions for the whole
region. The ALPI is a measure of the intensity of winter conditions in the north Pacific
Ocean from December to March (Surrey & King 2015). This broad-scale climate pattern
is correlated with wind and storms in the northeastern Pacific (Surrey & King 2015). If
harsh winter conditions lead to increased winter mortality, or carryover effects that
influence summer mortality or productivity later in the year, I expected that harsher
winters (lower temperatures or positive ALPI values) would correspond with lower counts
10
in the following winter. This candidate model set included univariate models with either
winter temperature or ALPI alone, models that allowed the effects of ALPI or winter
temperature to vary by region, the top model from the first candidate set examining
regional differences or year trends, and versions of each of the temperature and ALPI
models that included the regional or year trends detected in the first candidate set (n = 9
models). Temperature data at each count circle were extracted from the National
Centers for Environmental Prediction (NCEP) data set (http://www.ncep.noaa.gov/,
accessed 25 June 2017) and ALPI data were retrieved from Fisheries and Oceans
Canada (http://www.pac.dfo-mpo.gc.ca/science/species-especes/climatology-ie/cori-
irco/indices/alpi_en.txt, accessed 16 September 2017).
The third candidate model set evaluated whether conditions prior to migration
influenced migration and thus the winter distribution of Black Oystercatchers across their
range. As broad-scale migration is only known to occur in the Alaskan breeding
population, I predicted that colder pre-migration temperatures in Alaska would lead to a
decrease in Black Oystercatchers counted in that region, mirrored by an increase in
oystercatchers counted in British Columbia, and possibly further south. This pattern
could occur if oystercatchers use temperatures in the late summer or fall as a cue to
predict winter temperatures or severity. Alternately, oystercatchers may migrate in
response to physiological cues, which could in turn be affected by pre-migration
temperatures. Most oystercatchers migrate between August and October (Johnson et al.
2010), so the decision to migrate or stay must be made before or during that period. I
used average mean daily temperatures in July and August to represent late summer
conditions, and in September and October to represent fall temperatures. This model set
included models with either late summer or fall temperature terms as interactions with
region in order to detect redistribution between regions across years. I also included the
top model from the first candidate set examining regional differences or year trends, and
versions of both temperature models that also included the regional or year trends
detected in the first candidate set (n = 5 models). Temperature data were extracted from
the NCEP data set (accessed 24 September 2017) on tiles that overlapped with the
Black Oystercatcher's Alaska range. The Alaska range shapefile was downloaded from
the Alaska Center for Conservation Science, University of Alaska, Anchorage
(http://akgap.uaa.alaska.edu/species-data/, accessed 24 September 2017).
11
I used Akaike's Information Criterion (AIC) to rank the models in the three
candidate model sets. All analyses were carried out in R version 2.15.1 (2012, The R
Foundation for Statistical Computing) using the glmmADMB (Fournier et al. 2012, Skaug
et al. 2015) and AICcmodavg (Mazerolle 2013) packages.
Results
There were clear differences in numbers of oystercatchers counted in CBC count
circles in Alaska and British Columbia compared to the other three regions (Table 2.1).
Alaska had the fewest count circles (7) and the lowest survey effort (median = 105.5 km,
IQR = 52.5 – 150.9 km), but the highest counts (median = 24, IQR range = 10 – 135).
Variation in counts was also much higher in Alaska than in other regions. The most
extreme variation was seen at the Kodiak count circle, where numbers ranged from 3
oystercatchers counted in 1980 to 902 counted in 2005 (median = 189, IQR 71.5 –
378.5, n = 40 records). British Columbia had a much higher number of count circles than
Alaska (34), but survey effort was fairly low (median = 171 km, IQR = 92 – 348 km).
Counts in British Columbia were high but varied less than counts in Alaska (median =
21, IQR = 6 – 45.5). Washington and Oregon were similar to each other in terms of
numbers of count circles (11 and 10), survey effort (Washington: median = 299 km, IQR
= 213 – 485.3 km; Oregon: median = 278 km, IQR = 217.8 – 378 km), and counts
(Washington: median = 10, IQR = 3 – 30; Oregon: median = 12, IQR = 6 – 22).
California & Baja California had the most count circles (40) and the highest survey effort
(median = 369 km, IQR = 233.5 – 482 km), with counts comparable to Washington and
Oregon (median = 12, IQR = 3.5 – 30).
Temporal trends in Black Oystercatcher winter numbers
The top model in the candidate set examining temporal trends in the numbers of
Black Oystercatchers counted on CBC surveys indicated that temporal trends varied by
region (AICc weight > 0.99; Table 2.2a & 2.3a). This model predicted increases in
oystercatchers counted in Alaska and British Columbia between 1975/1976 and
2015/2016. Counts in Washington, and California were stable or slightly increasing, and
counts in Oregon remained stable over this period (Figure 2.1).
12
Relationships between climate variables
Regional mean average daily temperatures (± SD) from January to February at
CBC count circles surveyed from 1975/1976 to 2015/2016 were as follows: Alaska =
1.45 ± 1.70 ºC, British Columbia = 2.18 ± 2.68 ºC, Washington = -0.02 ± 2.05 ºC, Oregon
= 4.58 ± 1.20 ºC, and California & Baja California = 9.60 ± 2.02 ºC. ALPI values ranged
from -3.96 to +6.69 between 1975/1976 and 2015/2016 with a mean value of +0.71 ±
2.16 SD. There was no annual trend in ALPI during that period (β = 0.01 ± 0.03 SE,
adjusted R2 = 0.02, F1,40 = 0.22, p = 0.64). Mean average daily temperatures across the
Black Oystercatcher's Alaska range were: late summer (July & August) = 9.66 ± 0.71 ºC,
fall (September & October) = 6.17 ± 0.72 ºC, winter (January & February) = -1.33 ± 1.17
ºC. Temperatures in the late summer and fall did not predict winter temperatures
(summer: β = 0.13 ± 0.27 SE, adjusted R2 = 0.02, F1,40 = 0.23, p = 0.64; fall: β = 0.25 ±
0.26, adjusted R2 = 0.003, F1,40 = 0.89, p = 0.35). Alaska temperatures in the late
summer and fall also failed to predict ALPI of the following winter (summer: adjusted R2
= 0.02, F1,39 = 0.26, p = 0.61; fall: adjusted R2 = 0.02, F1,39 = 0.02, p = 0.89).
Effects of winter conditions on Black Oystercatcher numbers in the following winter
I found some evidence to suggest that winter conditions in one year can affect
Black Oystercatcher winter numbers in the following year. The top model included both a
region × year interaction term, as well as a region × ALPI interaction term (AICc weight =
0.48; Table 2.2b, Table 2.3b), and received approximately 2.5× as much support as the
model with a region × year interaction only (AICc weight = 0.19; Table 2.2b). Counter to
expectations, counts in Alaska increased in years following deeper Aleutian Lows
(positive ALPI values) than years following milder Aleutian Lows (negative ALPI values).
ALPI showed little to no effect on counts in other regions (Fig. 2.2).
Effects of pre-migration conditions on winter distributions of Black Oystercatchers
I found no evidence that temperatures in Alaska prior to migration influenced the
distribution of Black Oystercatchers during the winter. The models that included either
the late summer or fall temperature × region interaction, which would indicate that
13
environmentally driven variation in migration influenced the subsequent distribution of
wintering Black Oystercatchers, received substantially less support than the simpler
model that only allowed temporal trends in the counts to vary by region (AICc weight =
0.89; Table 2.2c, Table 2.1a).
Discussion
My analysis of CBC counts conducted across the Black Oystercatcher's entire
range from Alaska to Mexico indicates that their global numbers are likely increasing.
The number of wintering oystercatchers counted in Alaska, British Columbia,
Washington, and California increased or remained stable over the last four decades,
while counts in Oregon remained stable. Estimates of the global population of Black
Oystercatchers as of 2014 (~15 000 individuals: Appendix) are higher than estimates in
1994 (7600 individuals) and 2001 (8900 individuals; Tessler et al. 2014), although these
increases are likely due to broader survey efforts (Tessler et al. 2014). The positive
temporal trends I found in Alaska and British Columbia are particularly welcome as these
regions are thought to contain more than half of the global breeding population. Andres
et al. (2012) estimated that 65% and 14% of the global breeding population are found in
Alaska and British Columbia, respectively. However, it should be noted that previous
estimates may have overestimated the relative importance of these two regions. The first
comprehensive surveys and assessment of oystercatcher populations in California by
Weinstein et al. (2014) increased the population estimate for that region by roughly six
times. The new estimate suggests that approximately 36% of the estimated global
breeding population is found in California (45% is found in Alaska and 10% is found in
British Columbia; Appendix).
Mass mortality of Eurasian Oystercatchers during severe cold spells has been
documented and attributed to increased metabolic costs combined with reduced prey
availability (Goss-Custard 1996). If Black Oystercatchers were sensitive to cold winters, I
expected to see lower counts in years following colder winters. However, I did not find
any evidence of temperature-related declines in Black Oystercatcher populations
between 1975/1976 and 2015/2016. Furthermore, beached bird surveys in the USA and
Canada also show no evidence for mass die-offs of Black Oystercatchers from 1986 to
2017 (13 dead Black Oystercatchers were found in surveys in the USA from 1998 to
2017: COASST 2017; only one dead oystercatcher was found in Canadian surveys from
14
1986 to 1997 and 2002 to 2017: Bird Studies Canada 2008). Differences in sensitivity of
Eurasian and Black Oystercatchers to winter temperatures may be due to the feeding
strategies of each species. Many Eurasian Oystercatchers inhabit mudflats and fields,
where they feed by probing the sand and soil for buried invertebrates. During prolonged
cold spells, the ground can freeze over, vastly reducing the food available to the birds.
By contrast, Black Oystercatchers feed mostly on molluscs that cling to rocks in the
rocky intertidal, where wave action usually prevents freezing over. Therefore,
oystercatchers may not experience the same drastic reductions in prey availability during
extreme cold spells.
Eurasian Oystercatchers also experience negative carry-over effects of severe
winters during the following summer (Duriez et al. 2012). If this were the case for Black
Oystercatchers, I expected to see lower counts in the years following colder
temperatures and deeper Aleutian Lows. Such decreases could signify that either (1)
mortality was higher in severe winters, which led to lower numbers in the following
winter, (2) oystercatcher recruitment decreased in years following harsh winters, or (3)
oystercatchers left the region (or, at least, the areas covered by count circles in the
region) following harsher winters. Curiously, I found the opposite relationship with ALPI
in Alaska, where deeper Aleutian Lows were followed by higher counts in the following
winter. This result could be spurious as there were only 7 count circles in Alaska and the
confidence intervals were very broad (Fig. 2.2). It is also possible that harsh winters
could drive oystercatchers to overwinter in more sheltered areas the following winter.
Small-scale redistributions within this region could bring more oystercatchers to areas
that are accessible to volunteer surveyors, making the birds more likely to be counted in
Christmas Bird Counts. It would be valuable to study whether winter conditions influence
future wintering strategies, and identify any mechanisms that drive this.
The relative fitness benefits of alternative migratory strategies likely depend on
environmental conditions that alter the costs and benefits of remaining at northern
latitudes year-round. I expected that the temperature in Alaska prior to migration could
predict the severity of winter conditions, leading to a change in migration rates.
Alternately, pre-migration conditions could influence the physiological state of
oystercatchers, affecting their ability to endure the average Alaska winter. In either of
these cases, I expected to find reduced numbers of oystercatchers counted on CBC
surveys in Alaska, and increased numbers in regions to the south, when pre-migration
15
temperatures were colder. However, I found no evidence that temperatures in Alaska in
the late summer and fall influenced the relative number of oystercatchers in Alaska and
in regions to the south. Temperatures in the late summer or fall prior to migration did not
predict temperatures the following winter, suggesting that temperature could not be used
by oystercatchers as a reliable cue to inform migratory strategies. There are also several
other plausible reasons that could explain the failure to detect temperature effects on the
winter distribution of Black Oystercatchers. First, migration may be a fixed rather than
facultative strategy in this species, although evidence from other bird species, including
other oystercatcher species (Van Treuren et al. 1999), suggests this is unlikely. Second,
increases in the proportion of migratory individuals in British Columbia or other regions
south of Alaska may not be detected if they occupy remote wintering grounds that are
not covered by CBC count circles. Weinstein et al. (2014) found that remote islands off
the coast of California are home to many more oystercatchers than was previously
thought, leading to a drastic increase in that state's estimated oystercatcher population.
These remote islands are inaccessible to most volunteer surveyors. Third, winter
temperature may not be the major selective force acting on partial migration in Black
Oystercatchers. Meller et al. (2016) found that fall temperatures explained temporal
variation in the proportion of residents in 9 partially migratory waterbird species in
Finland. By contrast, food availability best explained the proportion of residents in 18
partially migratory terrestrial songbirds. Meller et al. (2016) postulated that differences in
sensitivity of waterbirds and terrestrial birds to temperature could be related to the
greater dependence of waterbirds on non-frozen water for feeding. Black
Oystercatchers' foraging may not be constrained by temperature and freezing in the
same way, making food availability a more important selective force unrelated to
temperature. Further work linking climate, food availability and cues that could be used
to predict the migratory strategies of individuals and populations would be informative.
The results of my study indicate that Black Oystercatcher populations are
currently healthy, as it appears that populations are stable or increasing across their
range. In the northern extent of the species' range, more research is needed to identify
the triggers for migration and determine whether there is variation in proportions of
migrants and residents from year to year. Studies have found that both temperature and
wind speed impact survival in European Oystercatchers (Goss-Custard 1996), so it
would be worthwhile to test the effects of wind speed on Black Oystercatcher numbers
16
and migration. At the individual level, body condition may play a greater role than
environment in determining whether an individual will migrate or remain resident.
Therefore, it is also worth focussing on individual differences in future studies.
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Tables
Table 2.1 Summary of Christmas Bird Count data used for analyses of trends in Black Oystercatcher winter numbers and the effects of climate from 1975/1976 to 2015/2016. The second analysis, examining carry-over effects from winter climate conditions to numbers of oystercatchers in the following winter, used a slightly reduced data set due to gaps in the available climate data. Sample sizes for the data set used in the second analysis are shown in parentheses.
Region Median Count
± SE Median Survey Distance
± SE (km) # Count Circles # Records AK 24 ± 12.9 105.5 ± 6.35 7 (7) 140 (140) BC 21 ± 1.9 171 ± 10.09 34 (33) 595 (593) WA 10 ± 2.0 299 ± 13.52 11 (11) 185 (185) OR 12 ± 1.9 278 ± 7.70 10 (9) 193 (190) CA+BCN 12 ± 0.8 369 ± 6.07 40 (40) 771 (771) Total 15 ± 1.3 271.5 ± 4.62 102 (100) 1884 (1879)
21
Table 2.2 AIC results from model sets examining (a) trends in winter counts of Black Oystercatchers across their range from 1975/1976 to 2015/2016, (b) counts of oystercatchers in response to conditions from the previous winter, and (c) counts of oystercatchers in response to temperatures across the species' Alaska range in the late summer (July & August) and fall (September & October). Regional trend model is bolded. All models included party distance + party distance2 as a measure of effort.
(a) Model K AICc delta AICc weight Region × Year 14 14649.90 0.00 > 0.99 Region + Year 10 14673.98 24.07 < 0.01 Year 6 14681.40 31.50 < 0.01 Region 9 14782.98 133.07 < 0.01 Null 5 14793.17 143.27 < 0.01 (b) Model K AICc delta AICc weight Region × ALPIt-1 + Region × Year 19 14617.95 0.00 0.48 Region × Year 14 14619.79 1.84 0.19 Region × Winter Temperaturet-1 + Region × Year 19 14620.51 2.56 0.13 Winter Temperaturet-1 + Region × Year 15 14620.86 2.91 0.11 ALPIt-1 + Region × Year 15 14621.44 2.49 0.08 Region × Winter Temperaturet-1 14 14730.73 112.78 < 0.01 Region × ALPIt-1 14 14755.29 137.34 < 0.01 Winter Temperaturet-1 6 14757.50 139.56 < 0.01 ALPIt-1 6 14762.60 144.66 < 0.01 (c) Model K AICc delta AICc weight Region × Year 14 14649.90 0.00 0.89 Region × Fall TemperatureAK + Region × Year 19 14655.09 5.18 0.07 Region × Summer TemperatureAK + Region × Year 19 14655.73 5.82 0.05 Region × Fall TemperatureAK 14 14742.54 92.64 < 0.00 Region × Summer TemperatureAK 14 14775.04 125.14 < 0.00
22
Table 2.3 Parameter estimates for models examining (a) regional trends in winter counts of Black Oystercatchers and (b) trends with regional effects of Aleutian Low Pressure Index (ALPI) on counts in the following winter between 1975/1976 and 2015/2016.
(a) Count ~ Region × Year + Party Hours + Party Hours2, random effect = (1|Circle) Random term Var. S.D. Count Circle 1.19 1.09 n = 1884 records, 102 count circles Fixed terms Est. S.E. z p Intercept 2.65 0.45 5.92 < 0.01 Region AK BC -0.67 0.50 -1.35 0.18 WA -1.18 0.59 -2.00 0.05 OR -0.39 0.58 -0.66 0.51 CA + BCN -1.23 0.49 -2.50 0.01 Year 0.024 0.0070 3.49 < 0.01 Region × Year AK BC 0.0051 0.0078 0.65 0.52 WA -0.0044 0.0075 -0.59 0.55 OR -0.029 0.0084 -3.42 < 0.01 CA + BCN -0.0021 0.0093 -0.23 0.82 Party Hours (effort) 0.0020 0.00061 3.29 < 0.01 Party Hours2 (effort) -0.0000012 0.00000060 -2.09 0.04 (b) Count ~ Region × Year + Region × ALPI + Party Hours + Party Hours2, random = (1|Circle) Random term Var. S.D. Count Circle 1.22 1.10 n = 1879 records, 100 count circles Fixed terms Est. S.E. z p Intercept 2.44 0.46 5.31 < 0.01 Region AK BC -0.62 0.51 -1.22 0.22 WA -1.37 0.61 -2.27 0.02 OR -0.35 0.61 -0.58 0.56 CA + BCN -1.22 0.50 -2.43 0.02 Year 0.026 0.0071 3.66 < 0.01 Region × Year AK BC 0.0040 0.0080 0.50 0.62 WA 0.0014 0.0095 0.14 0.89 OR -0.031 0.0086 -3.58 < 0.01 CA + BCN -0.0037 0.0077 -0.48 0.63 ALPI 0.050 0.027 1.83 0.07 Region × ALPI AK BC -0.043 0.031 -1.38 0.17 WA 0.014 0.040 0.36 0.72 OR -0.093 0.036 -2.56 0.01 CA + BCN -0.055 0.030 -1.84 0.07 Party Hours (effort) -0.0019 0.00042 4.41 < 0.01 Party Hours2 (effort) 0.0000014 0.00000042 -3.30 < 0.01
23
Figures
Figure 2.1 Temporal trends in the observed numbers of Black Oystercatchers
on Christmas Bird Counts conducted between 1975/1976 and 2015/2016 in five regions across their range. Lines show the predicted counts in each region and shading shows 95% confidence intervals based on the top model in Table 2.2a and Table 2.2c. Parameter estimates for this model are given in Table 2.3a. Rugging shows the distribution of data in each region, with positive partial residuals across the top and negative partial residuals across the bottom.
Years since 1975/1976
Coun
t
0
100
200
300
10 20 30 40
AK BC
10 20 30 40
CA+BCN
OR
10 20 30 40
0
100
200
300
WA
24
Figure 2.2 Relationship between Aleutian Low Pressure Index (ALPI) and observed numbers of Black Oystercatchers in Christmas Bird Counts the following year. Lines show the predicted counts in each region and shading shows the 95% confidence intervals based on the top model in Table 2.2b. Parameter estimates for this model are given in Table 2.3b. Rugging shows the distribution of data in each region, with positive partial residuals across the top and negative partial residuals across the bottom.
Aleutian Low Pressure Index (t−1)
Coun
t
0
100
200
300
−4 −2 0 2 4 6
AK BC
−4 −2 0 2 4 6
CA+BCN
OR
−4 −2 0 2 4 6
0
100
200
300
WA
25
Chapter 3. The influence of the El Niño Southern Oscillation on Black Oystercatcher (Haematopus bachmani) breeding numbers in British Columbia
Introduction
Ocean climate has important and wide-ranging effects on marine ecosystems
(Doney et al. 2012). Top predators can experience these effects directly, or indirectly via
climate effects on the food web (e.g. Francis et al. 1998, Jaksic & Fariña 2010, Hazen et
al. 2013). Direct effects include climate-driven changes in metabolic rates and behaviour
that can influence survival and reproduction (Barber & Chavez 1983, Menge et al. 2009).
Indirect effects of climate are often mediated through bottom-up effects on primary
productivity (Guinet et al. 1998, Borstad et al. 2011). Some predators are influenced by
both direct and indirect climate effects. The Purple Sea Star (Pisaster ochraceus), a
keystone predator of the rocky intertidal, was found to drastically reduce feeding rates in
response to slight decreases in temperature within the species' thermal tolerance
(Sanford 1999). At the same time, climate effects on primary productivity influence these
same sea stars indirectly through bottom-up effects on recruitment and growth of their
mussel prey (Mytilus californianus; Menge et al. 2007, 2009).
Climate effects on predators may operate at both broad and local scales. Broad
scale oceanographic phenomena such as the El Niño Southern Oscillation (ENSO) can
have large impacts on primary productivity (Barber & Chavez 1983, Mellink 2003), the
phytoplankton community (Yoder & Kennelly 2003), and invertebrate recruitment and
settling (Menge et al. 2011). ENSO has consequently been shown to affect breeding and
foraging in marine avian predators along the North and South American Pacific coasts
(Mellink 2003, Jaksic 2004, Jaksic & Fariña 2010). Seabird species have generally been
found to suffer reduced breeding success during El Niño events (Surman & Nicholson
2009). However, the effects of El Niño were found to have a greater impact on specialist
piscivores than on more generalist feeders, as the specialists were less able to exploit
26
alternative food sources when their primary food sources became unavailable (Jaksic
2004, Jaksik & Fariña 2010).
On the other hand, local ocean conditions may have a more direct effect on
marine communities than broad-scale oceanographic phenomena. Phytoplankton
productivity and invertebrate recruitment frequently vary from site to site, and are often
only weakly or inconsistently correlated with global climate patterns (Navarrete et al.
2002, Menge et al. 2009, Borstad et al. 2011). Local conditions often better predict local
productivity and can influence species at higher trophic levels (Guinet et al. 1998, Wolf
et al. 2009, Borstad et al. 2011). For example, Wolf et al. (2009) found that local sea
surface height had a greater effect on the timing of breeding and breeding success in
Cassin's Auklets (Ptychoramphus aleuticus) than the Northern Oscillation Index (NOI), a
broad-scale composite climate index in the north Pacific.
The demography and population dynamics of predatory seabirds has been linked
to both broad (e.g. Velarde et al. 2002) and local (e.g. Borstad et al. 2011) climate. The
Black Oystercatcher (Haematopus bachmani) is a keystone species of the Pacific North
American coast that is known to influence the composition of the intertidal invertebrate
community (Wootton 1992). However, oystercatchers are also likely sensitive to changes
in the availability and quality of filter-feeding molluscs, which make up most of their diet
(Tessler et al. 2014). Hipfner & Elner (2013) found a negative correlation between local
spring sea surface temperatures and the number of breeding oystercatchers on Triangle
Island, British Columbia, which they attributed to changes in prey availability. In this
study I used long-term monitoring data from across British Columbia to (1) determine
long-term trends in numbers of breeding oystercatchers across British Columbia from
1962 to 2014, and (2) evaluate the nature and extent of broad- and local-scale climate
effects, including local sea surface temperature, on breeding numbers of oystercatchers
along the coast of British Columbia. This study sheds light on the influence of
environmental variables on the breeding biology of this species (Tessler et al. 2014).
27
Methods
Study species
The Black Oystercatcher is a long-lived shorebird that inhabits the rocky intertidal
zone from the Aleutian Islands in Alaska to Baja California, Mexico (Tessler et al. 2014).
They are one of North America's least abundant shorebirds, with an estimated global
population of approximately 12 500 – 17 500 (Appendix). Roughly 10% of the global
population (1000 – 2000 breeding individuals) is thought to breed in British Columbia
(Tessler et al. 2014, Appendix).
Breeding oystercatchers in British Columbia typically lay their initial clutch
between mid-May and early June (Hipfner et al. 2012). They can lay 1 – 2 replacement
clutches if the first clutch is lost (Tessler et al. 2014). On Triangle Island, replacement
clutches are laid as late as July 10 (Hipfner et al. 2012). Black Oystercatchers are
generally assumed to attempt to breed every year (Tessler et al. 2014). However,
Hipfner & Elner (2013) observed an individual that occupied a territory on Triangle Island
without breeding in 2010. This individual bred or attempted to breed at this site in every
other year from 2003 to 2012. Therefore, it is possible that Black Oystercatchers skip
breeding in some years (notably, Triangle Island experienced unfavourably warm April
sea surface temperatures in 2010; Hipfner & Elner 2013).
Breeding surveys
Oystercatcher breeding data were compiled from surveys performed by
numerous agencies in the spring and summer from 1962 to 2014. Survey dates ranged
from April 14 to August 25, with a median date of June 11. Surveys were typically
conducted by boat along suitable habitat, and beaches with suitable habitat were
searched on foot for evidence of breeding or territorial pairs of oystercatchers.
Exceptions to this were Triangle Island, where surveys were conducted only by foot, and
surveys from Vermeer et al. (1989), where beaches were searched by foot only if
oystercatchers were seen from the boat. Table 3.1 summarises the data sources and
specific survey methods used by the different agencies.
28
In addition to numbers of breeding pairs, observers typically recorded the number
of nests located but did not consistently record clutch sizes or other measures of
productivity. The number of pairs of oystercatchers observed in the final data set was
highly correlated with the number of nests located (r = 0.99, p < 0.0001), though 7.1% of
records had values for pairs but not nests. For my analyses, I examined variation in the
number of breeding pairs counted on surveys. Clutch sizes or numbers of chicks were
reported for 75.8% of nests in the final data set. Of those, 23.0% had no eggs or chicks.
It is possible that some pairs recorded held territories but did not attempt to breed, as
empty nest scrapes may remain on territories from previous years. However, it is likely
that some of these empty nests were surveyed before clutches were laid, or after eggs
or chicks were lost due to predation. After hatching, some chicks may not have been
detected on surveys as they are precocial and have effective camouflage. This could
further inflate the recorded numbers of pairs with empty nests. I assumed that all pairs
recorded had attempted to breed, as they were all observed displaying territorial or
breeding behaviour.
Survey sites were defined as either a single beach or a group of beaches in close
proximity that were always surveyed together. Each site was identified based on GPS
coordinates provided for each survey. ArcGIS was then used to measure shore lengths
for each site, and these shore lengths were used to approximate survey effort. I
excluded sites that were only surveyed in one year as I was interested in annual trends
in the numbers of breeding pairs. In the few cases where a site was surveyed more than
once in a given year, I used the record with the highest count. I subsequently grouped
sites into three broad subregions based on the similarities in latitude, geography, and
exposure to the open ocean: Haida Gwaii, the West Coast of Vancouver Island, and the
Strait of Georgia & Gulf Islands.
Climate indices and variables
I examined the impact of five broad- and local-scale climate variables on the
numbers of breeding pairs of Black Oystercatchers detected during surveys. I describe
these variables and their predicted effects on oystercatchers below.
29
Southern Oscillation Index and Pacific Decadal Oscillation
El Niño conditions are known to lower primary productivity and food availability
for marine predators (Mellink 2003). Spring productivity has, in turn, been linked to
breeding success of seabirds in British Columbia (Borstad et al. 2011). However, the
relationship between ENSO and the mollusc prey of oystercatchers is potentially
complex, since El Niño conditions can reduce food availability for molluscs (a negative
effect) while simultaneously increasing their metabolic rates and growth (a positive
effect; Menge et al. 2007). I used the Southern Oscillation Index (SOI), a measure of the
air pressure differential between Tahiti and Darwin, to describe variation in ENSO.
The Pacific Decadal Oscillation (PDO) is a longer-term pattern of warming and
cooling in the North Pacific Ocean that cycles over the course of decades. Warm PDO
events are thought to amplify El Niño effects, and counteract La Niña events. Likewise,
cool PDO events counteract El Niño and amplify La Niña (Gershunov & Barnett 1998). I
therefore predicted that the SOI effect would be stronger when coinciding with the
corresponding PDO phase, and weaker when coinciding with the opposing PDO phase.
SOI and PDO data were downloaded from the National Centers for
Environmental Information, National Oceanic and Atmospheric Administration (NOAA)
website (https://www.ncdc.noaa.gov/, accessed 7 January 2015).
North Pacific Gyre Oscillation
The North Pacific Gyre Oscillation (NPGO) is an index that describes changes in
intensity of the North Pacific Gyre, and is highly correlated with fluctuations in salinity,
nutrients, and chlorophyll (Di Lorenzo et al. 2008). NPGO has been identified as one of
the strongest correlates to mussel recruitment in the subtidal zone of the Pacific
Northwest Coast (Menge et al. 2009) as the stronger gyre brings more nutrients to the
coast, increasing primary productivity.
I predicted that a higher NPGO index during the time when mussels are
developing their gonads (January to April: Emmet et al. 1987) would have a positive
effect on the numbers of breeding pairs of oystercatchers the following year, as
increased gamete production could lead to higher larval recruitment and ultimately,
higher numbers of mussel prey for the oystercatchers to feed their young. I included
models with lagged effects of 1 – 3 years because oystercatchers preferentially target
30
older, larger mussels (Norton-Griffiths 1967, Wootton 1992). NPGO data were retrieved
from http://www.o3d.org/npgo/ (accessed 2 March 2016).
Coastal Sea Surface Temperature
Sea Surface Temperature (SST) influences productivity (Behrenfeld et al. 2006),
and by extension, the condition (Zwarts et al. 1991) and behaviour (Grenon & Walker
1981, Anestis et al. 2007) of intertidal invertebrates preyed on by oystercatchers. Warm
SST in the winter are associated with lower primary productivity, reducing food
availability while also increasing metabolic costs for marine invertebrates. As a result,
mussel mass is negatively correlated with winter sea surface temperatures (Zwarts et al.
1991). I therefore expected a negative relationship between SST (between November
and February) and the number of breeding oystercatchers in the following summer.
Warm water can also alter invertebrate behaviour, making them less vulnerable
to predators: mussels spend less time with their valves open (Anestis et al. 2007), and
limpets hold to rocks more strongly (Grenon & Walker 1981), in warmer waters. Hipfner
& Elner (2013) hypothesised that warmer waters in the spring could therefore create
unfavourable feeding conditions for Black Oystercatchers, explaining why there were
fewer oystercatcher nests on Triangle Island in warm years. I therefore expected a
negative relationship between April SST and the number of breeding pairs of
oystercatchers along the coast of British Columbia. SST data were taken from the British
Columbia Shore Station Oceanographic Program (BCSOP: http://www.pac.dfo-
mpo.gc.ca/ science/oceans/data-donnees/lighthouses-phares/index-eng.html, accessed
6 January 2015). I used monthly average SST data from 19 lighthouses (12 of which are
currently collecting data: Fig 3.1), averaging available data from all lighthouses within
each subregion.
Upwelling
Upwelling brings nutrient-rich water from the deep ocean to the surface,
increasing primary productivity. The increased productivity associated with upwelling
events may cause filter-feeding mussels and limpets to spend more time feeding
(Riisgård & Larsen 2015), making them more vulnerable to predation by oystercatchers.
Upwelling increases as SST drops and thus could explain the local SST effect on
variation in the number of breeding oystercatchers at Triangle Island in British Columbia
31
(Hipfner & Elner 2013). I therefore predicted that stronger upwelling in April would lead
to an increase in oystercatchers breeding in British Columbia in the summer.
Upwelling has also been linked to mussel recruitment. Menge et al. (2011) found
that upwelling was one of the best year-round predictors of mussel recruitment in the
subtidal zone. Furthermore, the year after an upwelling (and consequential mussel
recruitment event), they observed an increase in predatory sea stars and rock crabs. I
therefore predicted that average upwelling from January to April (Emmett et al. 1987)
would be related to the number of oystercatchers breeding 1, 2, or 3 years later, with
longer time lags being predicted by oystercatcher preferences for large mussels (see
North Pacific Gyre Oscillation section). Upwelling data were obtained from the Pacific
Fisheries Environmental Laboratory (PFEL: http://www.pfeg.noaa.gov/products/PFEL/
modeled/indices/upwelling/NA/data_download.html, accessed 2 February 2015).
Upwelling data are provided at a series of points that are off-coast at 3º latitude intervals.
There are three points with upwelling measurements along the coast of British Columbia:
48º N, 125º W; 51º N, 131º W; and 54º N, 134º W. I assigned the upwelling values of the
closest of these points to each breeding site. Because Vancouver Island creates a
geographical barrier between the Strait of Georgia & Gulf Islands and the points off the
coast at which upwelling was measured, I did not anticipate an upwelling effect in this
subregion. Thus, models examining the effect of upwelling included a subregion ×
upwelling interaction term.
Analyses
I created two candidate model sets to examine (1) temporal trends in the number
of breeding Black Oystercatcher pairs in British Columbia and (2) the influence of broad-
and local-scale climate variables on the number of pairs of oystercatchers detected in
breeding surveys across British Columbia. I fitted generalised linear mixed models with a
negative binomial distribution that included site as a random term and shore length as an
offset variable. Models fitted with a negative binomial distribution outperformed those
with a zero-inflated negative binomial or a Poisson distribution (AICc weights: negative
binomial = 0.74, zero-inflated negative binomial = 0.26, and Poisson = < 0.01).
The first candidate set included all univariate combinations of year (trend) and
subregion and a null model (n = 5 models). The second candidate set included 11
32
climate models with and without regionally-specific temporal effects, and the two
highest-ranked models from the first analysis (n = 23 models). I used Akaike's
Information Criterion (AIC) to rank the models in the two candidate model sets. All
analyses were carried out in R version 2.15.1 (2012, The R Foundation for Statistical
Computing) using the glmmADMB (Fournier et al. 2012, Skaug et al. 2015) and
AICcmodavg (Mazerolli 2013) packages.
Results
The data set included 190 sites monitored for 2 – 18 years (mean = 6.7 ± 5.2
SD). Haida Gwaii contained the most sites (n = 111 sites), followed by the Strait of
Georgia & Gulf Islands (n = 45 sites) and the West Coast of Vancouver Island (n = 34
sites). Survey dates ranged from 1971 – 2007 in Haida Gwaii, 1962 – 2014 on the West
Coast of Vancouver Island, and 1978 – 2011 in the Strait of Georgia & Gulf Islands.
Shore lengths were positively skewed, and varied significantly between subregions.
Haida Gwaii had the longest shore lengths (median length = 1058 m, IQR = 405 – 2211
m), followed by the West Coast of Vancouver Island (median length = 604 m, IQR = 244
– 136 m), and the Strait of Georgia & Gulf Islands (median length = 389 m, IQR = 215 –
647 m).
The numbers of, and variation in, pairs of Black Oystercatchers observed in
breeding surveys varied by subregion. The West Coast of Vancouver Island had the
most variation in breeding numbers, with a median count of 2 breeding pairs per site
(IQR = 1 – 6 pairs). Cleland Island and Triangle Island, the two sites with the consistently
highest breeding counts, were included in this subregion (Cleland Island: median count
= 39 breeding pairs, IQR = 35 – 45 breeding pairs; Triangle Island: median count = 14
breeding pairs, IQR = 11 – 15 breeding pairs). Haida Gwaii also had a median count of 2
breeding pairs per site, but variation was lower (IQR = 1 – 4 breeding pairs per site). The
Strait of Georgia & Gulf Islands had the lowest counts, with a median count of 1
breeding pair per site (IQR = 1 – 2 breeding pairs per site). The median count for the
overall data set was 2 breeding pairs per site (IQR = 1 – 4 breeding pairs per site).
33
Temporal trends in numbers of Black Oystercatcher breeding pairs
I found little to suggest that the number of breeding pairs of Black Oystercatchers
in British Columbia have changed significantly since the 1960s. The top model in the
candidate set examining temporal trends in the number of breeding pairs was the null
model (AICc weight = 0.33; Table 3.2a & 3.3a). This model received twice the support of
the 3rd-ranked model, which included year (AICc weight = 0.15; Table 3.2a). There was
some model uncertainty since the second-ranked model that included a subregion ×
year (trend) interaction received a similar level of support as the top model (AICc weight
= 0.28; Table 3.2a).
Relationships between selected climate variables
April SOI values ranged between -1.4 (mild El Niño conditions) and +1.9 (mild La
Niña conditions), and did not show a temporal trend between 1962 and 2014 (β = 0.0027
± 0.0075 SE, adjusted R2 = 0.016, F1,51 = 0.13, p = 0.72). SOI was negatively correlated
to PDO (β = -0.41 ± 0.10 SE, adjusted R2 = 0.22, F1,51 = 16.09, p = 0.00020), indicating
that warm phases of ENSO corresponded to warm phases of PDO, and cool phases of
ENSO coincided with cool phases of PDO (note that, unlike SOI, negative values
correspond to cool phases of the PDO while positive values correspond to warm phases
of the PDO). SST did not change significantly over the years when surveys were
conducted in British Columbia (Haida Gwaii: β = 0.0055 ± 0.0121 SE, adjusted R2 =
0.031, F1,25 = 0.21, p = 0.65; West Coast of Vancouver Island: β = 0.011 ± 0.006 SE,
adjusted R2 = 0.061, F1,30 = 3.02, p = 0.093; Strait of Georgia & Gulf Islands: β = -0.0094
± 0.0093 SE, adjusted R2 = 0.0019, F1,13 = 1.03, p = 0.33). SST was higher during the
warm phase of ENSO (Haida Gwaii: β = -0.43 ± 0.10 SE, adjusted R2 = 0.41, F1,25 =
19.40, p = 0.00017; West Coast of Vancouver Island: β = -0.32 ± 0.12 SE, adjusted R2 =
0.15, F1,30 = 6.65, p = 0.015; Strait of Georgia & Gulf Islands: β = -0.24 ± 0.11 SE,
adjusted R2 = 0.22, F1,13 = 5.00, p = 0.044).
Climate effects on numbers of Black Oystercatcher breeding pairs
I found evidence of ENSO effects on the numbers of breeding pairs of Black
Oystercatchers counted in surveys across British Columbia. The four top-ranked models
all included SOI as a fixed effect (Table 3.2b) and indicated that the number of breeding
34
pairs of Black Oystercatchers was related to the SOI (Table 3.3b, Fig 3.3). The top
ranked model included only the SOI term, and estimated a 30% increase in the number
of breeding oystercatcher pairs over an SOI range of -1.5 (indicative of mild El Niño
conditions) to +1.9 (mild La Niña conditions). This model received 1.7× the support of a
model that included SOI in addition to terms that allowed temporal trends in breeding
pairs to vary with subregion, 2.8× the support of a model that allowed SOI effects to be
moderated by the PDO, and 42× the support of the null model (Table 3.2b).
In contrast, I found no evidence that winter or spring sea surface temperature
influenced numbers of breeding pairs of black oystercatchers across British Columbia.
Models with the winter or April SST term alone or in combination with terms that allowed
temporal trends to vary subregionally received similar levels of support to the null model
(Table 3.2b). I also found no evidence to suggest that variation in the NPGO or upwelling
influenced the breeding propensity of oystercatchers. Models that included NPGO (with
a 1, 2, or 3 year lag) and upwelling (in April and in the winter with a 1, 2, or 3 year lag)
received less support than the null model (Table 3.2b).
Discussion
I found that the number of Black Oystercatcher pairs counted on breeding
surveys in British Columbia remained stable from 1962 to 2014. Numbers of breeding
pairs appeared to be sensitive to conditions in the spring related to the El Niño Southern
Oscillation (ENSO), with fewer pairs counted in years with El Niño conditions than in
years with La Niña conditions. Breeding in oystercatchers is limited by availability of
territories (Tessler et al. 2014), which creates an upper limit to the numbers of
oystercatchers that can breed per year in a given area. On the other hand, breeding
numbers are likely buffered on the lower end by a sizeable pool of nonbreeding birds
that fill vacated territories as they become available (as found in Eurasian
Oystercatchers: Heg et al. 2000). For these reasons, numbers of breeding pairs are
likely a better representation of the quality and availability of intertidal habitat than of
oystercatcher population size. Therefore, decreases in numbers of breeding
oystercatchers could indicate that either (1) available habitat is shrinking or becoming
unsuitable (Hazlitt 2001), (2) anthropogenic disturbance is increasing in severity
(Warheit et al. 1984, Spiegel 2008), or (3) climatic conditions are becoming unfavourable
to breeding (Hipfner & Elner 2013). I found no evidence that these threats impacted
35
breeding in Black Oystercatchers across British Columbia during the time period of the
study.
Climate conditions related to the El Niño Southern Oscillation (ENSO) have been
shown to influence foraging and breeding success in marine birds (Mellink 2003, Jacksic
& Fariña 2010). El Niño effects on seabirds are generally negative (Surman & Nicholson
2009). However, species that specialise on fish have few alternative feeding options
when their food source is impacted. As such, they tend to be more impacted by El Niño
conditions than bird species with more omnivorous diets (Jaksic 2004). Here, I show that
ENSO is associated with a modest but significant fluctuation in the numbers of breeding
Black Oystercatchers at survey sites across British Columbia. This study therefore
provides additional evidence for the effect of broad-scale climate phenomena
(specifically ENSO) on reproduction in marine birds. ENSO is modulated by the Pacific
Decadal Oscillation (PDO), as the effects of El Niño and La Niña have been found to be
more consistent when coinciding with the corresponding PDO phase (Gershunov &
Barnett 1998). Despite this, I did not find evidence that a SOI × PDO interaction had an
effect on the numbers of breeding oystercatchers in British Columbia (Table 3.2b). As
SOI and PDO were correlated in this data set (see results), the addition of a SOI × PDO
interaction term would have complicated the model without improving it enough to earn it
a higher AICc rank.
Local climate conditions act directly on populations, and therefore often have a
stronger effect on seabird biology than broad-scale climate indices (e.g. Wolf et al.
2009). Hipfner & Elner (2013) found a negative relationship between local spring SST
and breeding propensity in Black Oystercatchers at one site, which they suggested could
be due to climate effects on prey behaviour. Therefore, I expected numbers of Black
Oystercatcher breeding pairs to be higher when spring SST was cooler and upwelling
was stronger. However, I found no evidence that either SST or upwelling in the spring
influenced the number of breeding Black Oystercatchers at a broad scale across British
Columbia. ENSO is associated with warm local SST under El Niño conditions and cool
SST under La Niña conditions (Stenseth et al. 2002, and see results). However, ENSO
also influences other local climatic variables, such as precipitation (Ropelewski et al.
1986). The findings presented here suggest that oystercatchers breeding in British
Columbia are not influenced by April SST or upwelling alone, but perhaps by some
36
combination of local climate variables whose interactions are encapsulated to some
extent within ENSO (sensu Hallett et al. 2004).
Climate effects on recruitment of prey species may cause lagged effects on
predators that target mature prey (Menge et al. 2011). As oystercatchers preferentially
target larger prey (Norton-Griffiths 1967, Wootton 1992), I expected that climate
conditions favourable to mollusc recruitment would lead to an increase in prey
availability and oystercatcher breeding propensity in subsequent years. However, I found
no evidence for lagged effects of climate on oystercatcher breeding numbers. I may
have failed to detect lagged effects of NPGO and upwelling on oystercatcher breeding
for a few reasons. First, increases in prey recruitment due to climate may correspond to
reductions in prey size. Mussel populations are limited by available space, and
increased crowding due to recruitment of juvenile mussels can reduce mussel growth
(Petraitis 1995). Second, the climate effects on mollusc recruitment were found in the
subtidal zone (Menge et al. 2009), not the intertidal where oystercatchers feed.
Relationships between climate, mussel recruitment, and/or mussel growth may differ
between the two habitats (Rilov et al. 2008). While I found no overall effect of lagged
climate variables on breeding in Black Oystercatchers, there are several stages in the
life histories of molluscs and oystercatchers alike where the influence of climate may be
lagged. Future studies that examine more direct climate effects on oystercatchers or
their prey could help establish clearer relationships between environmental variables
and biological populations.
The stable wintering (Ch. 2) and breeding trends in Black Oystercatchers over
the last 40 – 50 years in British Columbia are encouraging for this species. However,
climate change is expected to introduce new threats such as sea level rise that could
greatly reduce the oystercatcher's habitat (Langham et al. 2015). The relationship
between ENSO and climate change are poorly understood (Collins et al. 2010), so it is
unclear how oystercatcher breeding will be affected in the future. It is therefore important
to continue monitoring oystercatcher populations so that changes can be detected if, and
when, they occur. The decades of survey data compiled and used in this study will
provide a valuable baseline for future monitoring efforts.
37
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42
Tables
Table 3.1 Sources and details of Black Oystercatcher breeding surveys in British Columbia, including areas surveyed, time of surveys, and survey methods. Preliminary surveys were performed from the boat except on Triangle Island, and sites were then searched more thoroughly by foot.
Study/Author Area Surveyed
What Was Surveyed
Survey Window Year(s)
When To Land
What Was Noted On Land
Vermeer et al. Nanaimo to Chain Islands, Northern GI, Race Rocks
all islands and islets
June, July
1987 if BLOY seen
presence or absence of young and nests; vegetation; nest site characteristics
Gulf Islands National Park
Nanaimo to Chain Islands, Northern GI, Race Rocks
islands and islets with suitable habitat
mid-June 2005 to 2012
always number of eggs, chicks, nests searched
Butler & Golumbia
Nanaimo to Chain Islands, Northern GI, Race Rocks
all islands and islets
mid-June 1997, 1999, 2005 to 2006
all except Vancouver to Cortes leg
number of eggs and chicks
Triangle Triangle Island
all beaches Mid-June to Aug
2003 to 2012
N/A shoreline search, nest monitoring for fate, egg count
Canadian Wildlife Service
North Coast, Haida Gwaii, Scott Islands
all beaches April to June
1980's always empty, egg, young, adult count, nest material
Gwaii Haanas National Park, Laskeek Bay
Lost Island to Alder Island, Cumshewa to Hassell
all suitable habitat
early June to July
1992 to 2012
always occupied, active, breeding evidence
Pacific Rim National Park
high density core area
all habitat in dense area
late May to early June
2000 to 2012
always nest contents, adult count
Hazlitt Nanaimo to Chain Islands, Northern GI, Race Rocks
all habitat three visits
1996 to 1998
always
RBCPM Pacific Rim Park
incidental varying 1962 to 1873
unknown nest contents, some adult behaviour
43
Table 3.2 AIC table for analysis of (a) Trends in numbers of breeding pairs of Black Oystercatchers and (b) climate effects on numbers of breeding pairs of Black Oystercatchers in British Columbia (n = 760 records at 193 sites). Null and subregional trend models are bolded. All models assumed a negative binomial distribution and included log-transformed shore length as an offset variable and site as a random variable.
(a) Model K AICc delta AICc weight Null 3 3080.01 0.00 0.33 Subregion × Year 8 3080.37 0.36 0.28 Subregion 5 3081.46 1.45 0.16 Year 4 3081.53 1.52 0.15 Subregion + Year 6 3082.89 2.88 0.08 (b) Model K AICc delta AICc weight SOIApril 4 3072.55 0.00 0.42 SOIApril + Subregion × Year 9 3073.60 1.05 0.25 SOIApril × PDOApril 6 3074.61 2.06 0.15 SOIApril × PDOApril + Subregion × Year 11 3076.57 4.02 0.06 SSTApril + Subregion × Year 9 3079.44 6.89 0.01 NPGOt-3 + Subregion × Year 9 3079.48 6.93 0.01 SSTNovember–February + Subregion × Year 9 3079.58 7.03 0.01 SSTApril 4 3079.99 7.44 0.01 Null 3 3080.01 7.46 0.01 Subregion × Year 8 3080.37 7.82 0.01 NPGOt-1 4 3080.77 8.22 0.01 NPGOt-3 4 3080.81 8.26 0.01 NPGOt-1 + Subregion × Year 9 3081.10 8.55 0.01 SSTNovember–February 4 3081.17 8.62 0.01 Subregion × Upwellingt-3 8 3081.67 9.12 <0.01 Subregion × Upwellingt-2 + Subregion × Year 11 3082.01 9.46 <0.01 NPGOt-2 4 3082.03 9.48 <0.01 Subregion × Upwellingt-3 + Subregion × Year 11 3082.09 9.54 <0.01 NPGOt-2 + Subregion × Year 9 3082.40 9.85 <0.01 Subregion × Upwellingt-2 8 3084.37 11.82 <0.01 Subregion × UpwellingApril + Subregion × Year 11 3085.85 13.30 <0.01 Subregion × Upwellingt-1 + Subregion × Year 11 3086.15 13.60 <0.01 Subregion × UpwellingApril 8 3086.35 13.80 <0.01 Subregion × Upwellingt-1 8 3086.91 14.36 <0.01
44
Table 3.3 Parameter estimates for (a) null model showing no effect of trends in numbers of breeding pairs of of Black Oystercatchers and (b) effect of the mean April Southern Oscillation Index (SOI) on numbers of breeding pairs of Black Oystercatchers counted on surveys conducted across British Columbia between 1962 and 2014.
(a) Pairs ~ 1 + offset(log(Shore Length)), random = (1|Site) Random term Var. S.D. Site 1.67 1.29 n = 760 records, 193 sites Fixed terms Est. S.E. z p Intercept -5.95 0.10 -59.10 < 0.01 (a) Pairs ~ SOI + offset(log(Shore Length)), rand. = (1|Site) Random term Var. S.D. Site 1.67 1.29 n = 760 records, 193 sites Fixed terms Est. S.E. z p Intercept -5.96 0.10 -59.30 < 0.01 SOI 0.079 0.025 3.12 < 0.01
45
Figures
Figure 3.1 Sites of lighthouses in British Columbia recording daily sea surface temperatures and salinity as of 2018. The year in parentheses next to the name of each site indicates the year in which data collection began. 7 lighthouses that no longer collect data are not shown. Source: http://www.pac.dfo-mpo.gc.ca/science/oceans/data-donnees/lightstations-phares/index-eng.html (accessed 21 August 2018)
46
Figure 3.2 Numbers of breeding pairs of Black Oystercatchers at breeding sites in British Columbia from 1962 to 2014. Subregions are denoted by colour (red = Haida Gwaii, blue = Strait of Georgia & Gulf Islands, and green = west coast of Vancouver Island).
47
Figure 3.3 Relationship between the Southern Oscillation Index (SOI) in April and numbers of breeding pairs of Black Oystercatchers at survey sites in British Columbia between 1962 and 2014. The line shows the relationship, and the shaded area shows the 95% confidence interval from the top model in Table 3.2b. Parameter estimates for the model are given in Table 3.3b. Rugging shows the distribution of data.
−1.5 −1.0 −0.5 0.0 0.5 1.0 1.5 2.0
1.0
1.2
1.4
1.6
1.8
2.0
2.2
Southern Oscillation Index
Bree
ding
Pai
r Cou
nts
48
Chapter 4. Conclusions
The Black Oystercatcher plays an important ecological role as a top intertidal
predator in some regions (Wootton 1992) and is considered an indicator species for the
rocky intertidal coast of the Pacific northwest (Clarkson & Zharikov 2007, Parks Canada
2012a, Parks Canada 2012b, Bergman et al. 2013). Given the threat that a changing
climate could pose to this species (Langham et al. 2015), it is important that populations
are monitored and the species' biology and ecology are studied to determine whether
conservation efforts are needed. In this thesis, I used Christmas Bird Count data from
1975/1976 to 2015/2016 and breeding survey data in British Columbia from 1962 to
2014 to examine long-term trends in wintering and breeding numbers of Black
Oystercatchers over time and with respect to several climate variables. In Chapter 2, I
found that numbers of wintering oystercatchers counted in Christmas Bird Count surveys
have generally increased between 1975/1976 and 2015/2016. I found some evidence
that winter conditions affect oystercatcher numbers counted in Christmas Bird Counts in
subsequent years. However, the relationship was counterintuitive: deeper Aleutian Lows
resulted in higher counts in Alaska. In Chapter 3, I found that the numbers of Black
Oystercatchers breeding in British Columbia remained generally stable from 1962 to
2014. Finally, I found that the El Niño Southern Oscillation (ENSO), a global climate
phenomenon, influenced oystercatcher breeding numbers in British Columbia during that
time. Breeding numbers increased in years with La Niña conditions (cool SST and
increased precipitation) in April and decreased in years with El Niño conditions (warm
SST, decreased precipitation; Ropelewski et al. 2002).
Black Oystercatchers are one of North America's least abundant shorebirds
(fewer than 18 000 birds; Appendix), and this small population makes it potentially
vulnerable to perturbation (Tessler et al. 2014). That said, the overall increase in Black
Oystercatchers counted in CBC surveys over time (Ch. 2) is consistent with suggestions
that populations are stable or slightly increasing (Hazlitt 2001a, Tessler et al. 2014,
Weinstein et al. 2014, Meehan et al. 2018). Improvements in survey data in California
resulted in an approximately six-fold increase in population estimates for that region
49
(Tessler et al. 2014, Weinstein et al. 2014, Appendix). Likewise, improved surveys
across the species' range could lead to further increases in population estimates in other
regions. Numbers of breeding oystercatcher pairs remained stable overall in British
Columbia between 1962 and 2014 (Ch. 3), but population trends in breeding and
nonbreeding birds are not known.
Broad-scale climate phenomena appear to influence oystercatcher winter
abundances, as well as breeding numbers. Winter numbers in Alaska and breeding
numbers in British Columbia were linked to the previous year's Aleutian Low (Ch. 2) and
April ENSO (Ch. 3), respectively. It is unclear why a deeper Aleutian Low had an
unexpected positive effect on counts in Alaska, and this may have been a spurious
result due to the small number of count circles in Alaska. It seems unlikely that breeding
would increase in Alaska in summers following deeper Aleutian Lows. Deeper Aleutian
Lows have been linked to El Niño conditions (Hoerling et al. 1997), and during the years
covered by the Christmas Bird Count data used in this study (1975/1976 to 2015/2016),
deeper Aleutian Lows (positive ALPI values; measured from December to March) tended
to be followed by warm phase ENSO conditions in the spring (negative SOI values;
April): ALPI was negatively related to mean April SOI (β = -0.16 ± 0.05 SE, adjusted R2 =
0.17, F1, 39 = 8.90, p = 0.005). Based on the relationship between ENSO and
oystercatcher breeding numbers in British Columbia (Ch. 3), I would expect fewer
breeding pairs and possibly fewer chicks in the years following winters with deeper
Aleutian Lows. This would be more likely to manifest as lower, not higher, CBC counts. I
also found no evidence of migration between regions in response to ALPI (Ch. 2), and it
seems unlikely that numbers of resident oystercatchers in Alaska would increase in the
year after a winter with a deeper Aleutian Low. If this result represents a real relationship
and not a spurious result, a more plausible explanation for this pattern is redistribution
within Alaska (see Ch. 2). Tracking and observing individual birds over multiple seasons
and years could help explain this phenomenon.
The cues for migration in Black Oystercatchers remain unclear. While I examined
a number of local and broad-scale climate variables using linear models, other variables
not examined here, such as wind speed, may influence oystercatcher feeding and
migration behaviour, as well (Goss-Custard 1996). Further studies should also examine
the effects of severe winters and extreme weather events on oystercatchers, as these
effects may not be linear. While this thesis examines the effects of environmental
50
variables on abundances and distributions of oystercatchers, physiological factors may
be equally important when it comes to migration (Camphuysen et al. 1996). Finally,
territory quality may play a role, as well. Black Oystercatchers breeding on high quality
territories have higher reproductive success (Hazlitt 2001b), and show high site fidelity
(Hazlitt & Butler 2001). Therefore, birds that control high quality territories may choose to
remain resident in order to maintain control of the site over the winter, while birds with
without territories or controlling low-quality territories may opt to migrate. Future studies
that track individual birds over multiple years would be invaluable for determining what
causes oystercatchers to migrate.
So far, Black Oystercatcher populations appear to be fairly resilient to their
environmental and anthropogenic challenges (Ch. 2, Ch. 3, Andres 1999, Morse et al.
2006). However, global climate change is expected to introduce new threats to the
species, such as sea level rise (Langham et al. 2015). Climate change is expected to
lead to deeper Aleutian Lows, resulting in more severe winters (Fyfe et al. 1999). How
this will affect Black Oystercatcher populations is uncertain. Further study is needed to
determine if ALPI-linked variables such as wind speed and frequency of storms (Surrey
& King 2015) will influence Black Oystercatcher numbers and distributions. The
relationship between climate change and ENSO is less clear (Collins et al. 2010).
Though Black Oystercatchers in British Columbia are more likely to breed in La Niña
conditions than in El Niño conditions (Ch. 3), it is unknown how this relationship could be
affected by global climate change in the future.
Given the potential impacts of a changing climate on Black Oystercatchers,
continued monitoring and further studies are needed to inform the species' conservation
needs. The Christmas Bird Count, a citizen science project, is the source of the most
widespread, long-term, and consistent survey data for Black Oystercatchers across their
range. Citizen science is invaluable for providing researchers with more data than they
would have the resources to collect otherwise. It also makes the data widely available for
analysis by many researchers around the world (Sullivan et al. 2014). The Black
Oystercatcher is a particularly suitable species for monitoring via CBC surveys, as their
distinctive appearance is virtually unmistakable, even to very novice birdwatchers.
Oystercatchers are found only on rocky beaches, which make up a small portion of most
CBC count circles. These few areas of oystercatcher habitat are likely visited fairly
consistently by CBC participants each year, so counts likely vary less with search effort
51
in Black Oystercatchers than in other species. That said, many oystercatchers are found
in remote locations that lie outside of CBC count circles or are inaccessible to CBC
volunteers (Weinstein et al. 2014). A large portion of the population is therefore not
included in the counts. The median number of Black Oystercatchers counted in
Christmas Bird Counts across the years used in this study was 1143 individuals (IQR:
944 – 1795), which only accounts for 7.6% of the estimated global population
(Appendix). Therefore, remote surveys targeted specifically at oystercatchers, such as
the census carried out in California by Weinstein et al. (2014), are important as well. The
data used in this thesis can serve as useful long-term baselines for future analyses. It is
important that monitoring efforts continue so that any future declines in Black
Oystercatchers can be recognized and mitigated.
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Appendix: Population estimates for Black Oystercatchers
Global population estimates for Black Oystercatchers have increased from 7600
(Paige & Gill 1994) to 8900 (Morrison & Gill 2001) to 10 000 (Morrison et al. 2006,
Andres et al. 2012). However, Tessler et al. (2014) remarks that the increasing
population estimates are more likely due to more extensive survey efforts than to an
increasing population. Furthermore, the majority of the surveys that these estimates are
based on are not effective at counting oystercatchers specifically (Tessler et al. 2014).
Weinstein et al. (2014) used an improved survey design that detected far more
oystercatchers, increasing the population estimate in California by approximately 6
times. The study by Weinstein et al. (2014) reveals that California is a much more
important region to Black Oystercatchers than was previously thought. It is also likely
that better surveys across the species' range could lead to higher estimates in other
regions. Table A1 summarises the most recent regional population estimates, including
the estimate by Weinstein et al. (2014).
Table A1: Regional population estimates for Black Oystercatchers during the summer across their range. Regional estimates were compiled by Tessler et al. (2014). An updated estimate for California by Weinstein et al. (2014) and an adjusted total estimate are included in parentheses.
Region Est. range Est. midpoint Sources Southwest Alaska 2000 – 3000 2500 Andres & Falxa 1995 South-Central Alaska 2500 – 3000 2750 Andres & Falxa 1995, Gill et al. 2004 Southeast Alaska 1000 – 1500 1500 Andres & Falxa 1995 Alaska (total) 5500 – 8000 6750 Andres & Falxa 1995, Gill et al. 2004 British Columbia 1000 – 2000 1500 Jehl 1985, Campbell et al. 1990 Washington 470 – 720 595 Speich & Wahl 1989, Lyons et al. 2012 Oregon 560 – 660 610 Naughton et al. 2007, Lysons et al. 2012 California 700 – 1000
(4749 – 6067) 850
(5408) Sowls et al. 1980, (Weinstein et al. 2014)
Baja California 80 80 Palacios et al. 2009 Total (Adjusted total)
8310 – 12 460 (12 359 – 17 527)
10 385 (14 943)