Faculty of Science
THE IMPACTS OF URBANIZATION ON THE BACTERIAL COMMUNITIES OF
MOUNTAIN CHICKADEES (POECILE GAMBELI)
2020 | COLTON ROBERT ALEXANDER STEPHENS

B.Sc. Honours thesis – Biology

THE IMPACTS OF URBANIZATION ON THE BACTERIAL COMMUNITIES OF
MOUNTAIN CHICKADEES (POECILE GAMBELI)

by

COLTON ROBERT ALEXANDER STEPHENS

A THESIS SUBMITTED IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF

BACHELOR OF SCIENCE (HONS.)
in the
DEPARTMENT OF BIOLOGICAL SCIENCES
(General Biology)

This thesis has been accepted as conforming to the required standards by:
Matt Reudink (Ph.D.), Thesis Supervisor, Dept. Biological Sciences
Jonathan Van Hamme (Ph.D.), Co-supervisor, Dept. Biological Sciences
Eric Bottos (Ph.D.), Co-supervisor, Dept. Biological Sciences
Mark Rakobowchuk (Ph.D.), External examiner, Dept. Biological Sciences
Dated this 18th day of December, 2019, in Kamloops, British Columbia, Canada

© Colton Stephens, 2019

ABSTRACT
Urbanization is a global process with numerous consequences on wildlife, including the
amplification of certain infectious diseases. No studies have yet determined if this pattern exists
across all detectable pathogenic bacteria, and few have investigated how bacterial communities
change across urban to rural/native habitat gradients. We used whole-community sequencing of
partial 16S rRNA gene amplicons to examine relationships between urban and rural bacterial
communities found on Mountain Chickadee (Poecile gambeli) feathers and nests in Kamloops,
British Columbia, Canada. Between urban and rural sites, we observed mostly similar abundances
of major bacterial phyla, and dominant genera with pathogenic members, on both bird feathers and
their nests. However, urban habitats tended to increase the richness of both bacterial communities
and potential pathogens on birds, and accounted for some of the differences in bacterial occurrence
between urban and rural environments. Similarities in bacterial communities between nests and
their occupants indicated some degree of transmission occurred between them, or that shared
environments result in similar community assemblages. We predicted habitat using potential
pathogen occurrence with a 90% success rate for feather bacteria, and a 72.2% success rate for
nest bacteria, indicating an influence of urban environments on potential pathogen presence. Our
findings show that urban environments result in significant differences between urban and rural
bacteria associated with Mountain Chickadees, with potential indications towards diverging
disease dynamics across urban and rural gradients.

Thesis Supervisor: Associate Professor Matt Reudink (Ph.D.)

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ACKNOWLEDGEMENTS
I am very thankful to the numerous people who contributed many hours towards this
project and who provided mentorship, without which, this would never have been possible. This
includes my supervisors Matt Reudink, Jonathan Van Hamme, and Eric Bottos, as well as all those
who assisted me in the lab and field. I am also grateful for all the support from my friends and
family who helped me through the struggles of balancing this project with my personal life, for
the TRU Biological Sciences Department and all that I’ve learned from them, and for NSERC who
provided funding to support this project. Finally, thank you to the many chickadees in Kamloops
for tolerating our work, who have contributed towards our understanding of the potential
consequences of urbanization on wildlife.

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TABLE OF CONTENTS
ABSTRACT.................................................................................................................................... ii
ACKNOWLEDGEMENTS ........................................................................................................... iii
LIST OF FIGURES ........................................................................................................................ v
LIST OF TABLES .......................................................................................................................... v
INTRODUCTION .......................................................................................................................... 1
MATERIALS AND METHODS .................................................................................................... 5
Mountain Chickadee nest and feather sampling ......................................................................... 5
Estimating urbanization............................................................................................................... 7
DNA extraction, amplification, and sequencing ......................................................................... 7
Statistical analyses....................................................................................................................... 9
Ethics Statement ........................................................................................................................ 12
RESULTS ..................................................................................................................................... 12
Community composition and abundance of potential pathogens.............................................. 12
Bacteria dissimilarity and modelling ........................................................................................ 16
Discriminant functions analysis ................................................................................................ 19
DISCUSSION ............................................................................................................................... 20
Community composition of feathers and nests ......................................................................... 20
Effects of urbanization on mountain chickadees and their nests .............................................. 23
Differences in urban classifications .......................................................................................... 25
Detection of potential avian pathogens using community filtering .......................................... 11
Recommendations for future work ............................................................................................ 26
Opportunities in urban ecology ..................................................Error! Bookmark not defined.
LITERATURE CITED ................................................................................................................. 28
SUPPLEMENTAL DATA ........................................................................................................... 35
Urbanization Index .................................................................................................................... 35
Project Data and R Code ........................................................................................................... 35

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LIST OF FIGURES
Figure 1. Overview of nest boxes monitored for use by Mountain Chickadees (Poecile gambeli)
in Kamloops, British Columbia, Canada. Nest boxes were located in Kenna Cartwright Park
(red), in suburban Kamloops neighborhoods (green), or at Thompson Rivers University (blue). . 6
Figure 2. Community phylum-level composition for bacteria found on Mountain Chickadee
(Poecile gambeli) feathers and nesting material in urban and rural areas. OTU abundances were
grouped by phylum and samples were merged based on environment sampled from. Phylum that
contributed to less than 1% of each sample’s abundance were grouped together as Other. ......... 13
Figure 3. Abundance of the five most dominant genera containing pathogenic members found on
Mountain Chickadee (Poecile gambeli) A) feathers and B) nests in urban and rural environments.
....................................................................................................................................................... 15
Figure 4. Principle coordinates analyses (PCoAs) of Bray-Curtis distances between urban and
rural feather bacterial communities, nest bacterial communities, potential pathogens on feathers,
and potential pathogens on nests. Path lines on feather PCoAs show samples taken from the same
site (birds sampled at the same nest box at any point in the season). ........................................... 17

LIST OF TABLES
Table 1. Student’s t-test scores between urban and rural environments, and between feathers and
nests, for bacterial communities and genera with pathogenic members from Mountain Chickadee
(Poecile gambeli). ......................................................................................................................... 14
Table 2. PERMANOVA results of Bray-Curtis distances for bacterial communities and genera
with pathogenic members from Mountain Chickadee (Poecile gambeli) feathers and nests, with
urban metrics as included variables. ..............................................Error! Bookmark not defined.
Table 3. Linear mixed modeling for bacterial diversity metrics predicted by age of birds, sex,
body size, and habitat sampled from (urban or rural). .................................................................. 19
Table 4. Linear mixed modeling for community metrics between nest and feather bacteria. ..... 19

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INTRODUCTION
Over 50,000 species assessed by the International Union for Conservation of Nature
(IUCN) are threatened by activities associated with urbanization. These activities include
residential and commercial development (McKinney, 2002), and construction and use of
transportation and service corridors (Coffin, 2007). Additional threats result from natural system
modifications (Coyner et al., 2002; Coffin, 2007; Grimm et al., 2008; Wilson and Chakraborty,
2013), pollution (McKinney, 2002; Longcore and Rich, 2004; Gaston et al., 2013; Wilson and
Chakraborty, 2013; Luther et al., 2016), climate change (Colbert et al., 2018), severe weather (Paul
et al., 2018; Zhang et al., 2018), and human intrusions on wildlife (Hadidian, 2015; Larson et al.,
2016; Liu et al., 2016). Urbanization is a global threat to biodiversity, with decreases in species
richness of over 50% for many plants and animals at urban cores (McKinney, 2002). This reduction
in diversity is caused by several factors including land-use change, which replaces natural habitats
with pavement and buildings (McKinney, 2002). The vegetation in these areas is degraded or less
abundant due to removal of native species through trampling, erosion, mowing, pollution, and
other human-related activities, and the invasion of non-native species, both intentionally and
unintentionally introduced (McKinney, 2002). Remnant habitat in urban areas is fragmented (Liu
et al., 2016; Colbert et al., 2018), especially near the urban core as land is replaced with artificial
surfaces and structures, managed vegetation, and bare ground (McKinney, 2002). Urbanization
directly leads to bird mortality through collisions with glass windows and tall buildings, and
through consumption of the toxic materials that appear in cities (Burton and Doblar, 2004).
Domestic urban animals also prey on native wildlife, with, for example, house cats killing 1.3 to
4.0 billion birds annually in the United States (Felis catus; Loss et al., 2013). Aronson et al. (2014)

1

found that native bird density decreased by a mean of 92% in urban areas for 147 cities worldwide,
when compared to the density of these species found in non-urban environments.
Although many species have not adapted to urban environments, some may tolerate, or
even thrive in these areas (McKinney, 2002). Species are classified as urban avoiders when they
rarely appear in cities, urban utilizers when they occur in cities but rely on surrounding natural
habitat, and urban dwellers when they live in urban areas with no reliance on traditional natural
environments (Fischer et al., 2015). Moderate disturbance, as seen in suburban habitats, may lead
to increased biodiversity (intermediate disturbance hypothesis; McKinney, 2002). This increase in
biodiversity often results from the introduction (accidental or purposeful) of non-native species,
species which may provide ecological resources for wildlife; for example, fruit and seed-bearing
plants. Non-traditional resources exist in suburban habitats, including bird feeders and refuse
generated by human activities, both of which increase food availability for urban-tolerant species.
Despite these beneficial resources provided, urbanization can increase exposure of wildlife
to infectious diseases (Bradley and Altizer, 2007). Urban species loss has been linked to increased
prevalence of malaria, schistosomiasis, Lyme disease, hantavirus pulmonary syndrome, and West
Nile virus (Pongsiri et al., 2009). Densification of pathogens, vectors, and hosts in urban
environments can result in increased disease transmission due to the concentration of infected
individuals around food sources, such as garbage piles and bird feeders (Bradley and Altizer,
2007). Arthropod vectors carrying diseases such as Lyme disease may propagate in cities, as
reduced seasonality resulting from the heat island effect may increase their success during mild
winters (Lindgren and Gustafson, 2001). Urban environments also promote the spread of
infectious diseases in wildlife as stress from interspecies competition, or heavy metal and organic
contaminant pollution, may have deleterious effects on immunity (Bradley and Altizer, 2007). This

2

amplification of infectious diseases may place populations at risk of local extinction, especially
when other compounding factors already threaten them.
Increased wildlife disease rates in urban areas may also place human populations at risk.
By establishing human-wildlife interfaces, wildlife diseases can transmit to humans resulting in
zoonotic infections (Daszak, 2000; Hassell et al., 2017). These infections are often severe
(Akritidis, 2011; Christou, 2011; Pappas, 2011), resulting in epidemics (Breithaupt, 2003). For
example, all major influenza epidemics in the past century were zoonotic (Alexander and Brown,
2000; Smith et al., 2009), including the 1918 Spanish Flu (Taubenberger et al., 2005), the most
lethal pandemic in recent history (Taubenberger and Morens, 2006). Birds contribute to the spread
of human diseases due to the great distances avian communities disperse and migrate (Smith et al.,
1996; Brinkerhoff et al., 2011). Controlling pathogen abundance in urban birds may, therefore,
reduce disease emergence events in human, and other animal, populations.
Several studies highlight how urban areas increase pathogen abundance in wild birds. For
example, stagnant waters and nutrient-rich runoff in cities can promote mosquito abundance,
leading to proliferation of West Nile virus, as has been observed in both Chicago (Hamer et al.,
2012) and Atlanta (Bradley et al., 2008). Pathogenic Yersinia spp., some of which are causative
agents of the bubonic plague, were more prevalent in House Sparrows (Passer domesticus) in
urban environments than rural environments in Flanders, Belgium (Rouffaer et al., 2017).
Recently, both Dhondt et al. (2007) and Adelman et al. (2015) found that feeders may facilitate
the transmission of Mycoplasma gallisepticum, the causative agent of mycoplasmal conjunctivitis.
Feeders may also lead to increased avian pox infections, conjunctivitis, cloacal infections, and
fungal skin disease in birds (Wilcoxen et al., 2015). However, not all bird species will respond

3

equally to urban-amplified diseases, as differences in feeding behavior and ecology partly
determine their risk (Morishita et al., 1999).
Here we examine if urbanization is associated with the occurrence and relative abundance
of pathogenic bacteria on birds and their nests in the south-central interior of British Columbia,
Canada, a region currently experiencing increased population growth and expansion of urban and
suburban environments (Environmental Reporting BC, 2018). Some avian species, such as
Mountain Chickadees (Poecile gambeli), readily nest and breed in urban and suburban habitats
and do not experience detrimental breeding success when nesting in urban environments (Marini
et al., 2017a, 2017b). However, it is not known if urbanization may result in reduced adult survival
if urban nesters experience greater pathogen exposure.
Mountain Chickadees are small songbirds found in the coniferous forests of western North
America (McCallum et al., 1999). They often nest in tree cavities created by other species, and
have been observed to use artificial nest boxes in both natural forests and urban environments
(Marini et al., 2017a, 2017b). During the non-breeding season, urban chickadees regularly visit
bird feeders, which may act as vectors for pathogen transfer. During the breeding season however,
feeder visits are uncommon, but pathogen transfer may occur between these birds and their local
environment during foraging activities and nest building. Because the physical habitat (e.g.,
buildings, roads, artificial lawns, ornamental non-native plants) differs drastically between these
rural and urban habitats, we predict that chickadees living in urban environments will be exposed
to different bacterial communities and pathogens than rural chickadees.
Advances in DNA sequencing and nucleic acid detection have allowed for studies on
microbial communities, pathogen detection, and quantification. In the context of avian species,
published reports include those examining feather-degrading bacteria (Lee et al., 2015; Fellahi et

4

al., 2016) and intestinal microbiota (Fuirst et al., 2018; Teyssier et al., 2018), with few studies on
microbiomes of bird feathers and their nests (van Veelen et al., 2017; Fuirst et al., 2018; Musitelli
et al., 2018; Javůrková et al., 2019). Studies on bacterial communities in cities have found
differences in community composition of soil bacteria across urban sites (Reese et al., 2016), and
Razzauti et al. (2015) used 16S rRNA gene sequencing to identify potentially zoonotic bacteria in
wildlife. While the studies discussed previously have reported links between specific diseases and
urbanization, none to our knowledge have examined linkages between urbanization and total
detectable bacterial pathogens found on rural and urban birds or their nests. In this study, we apply
high-throughput 16S rRNA gene sequencing of DNA extracted from Mountain Chickadee nests
and tail feathers to examine the relationship between urbanization and the presence of detectable
pathogenic bacteria living on and near these birds.
MATERIALS AND METHODS
Mountain Chickadee nest and feather sampling
We collected feather samples and nest swabs during the 2018 chickadee breeding season
(May to August) in Kamloops, British Columbia, Canada (50°40.23′ N, 120°23.86′ W; Figure 1).
Kamloops is a city of approximately 90,000 people, situated in the Interior of British Columbia.
We monitored chickadees nesting in 18 nest boxes in suburban Kamloops neighborhoods, 7 boxes
at Thompson Rivers University, and 18 nest boxes in Kenna Cartwright park (50°40.232′ N,
120°23.855′ W). Kenna Cartwright park is located in a suburban area next to the Trans-Canada
Highway, and is an 800-ha protected area composed of Douglas-fir and Ponderosa pine (Pinus
ponderosa) forests, mixed with shrub-steppe grassland dominated by big sagebrush (Artemisia
tridentata) and common rabbitbrush (Ericameria nauseosa). Some walking paths and singlevehicle roads break up the natural setting of the park while the remaining area is largely natural.

5

All boxes were elevated 2 m from the ground with at least 150 m between boxes. Nest boxes were
composed of either PVC pipe, pressure-treated wood, or natural logs.

Figure 1. Overview of nest boxes monitored for use by Mountain Chickadees (Poecile gambeli)
in Kamloops, British Columbia, Canada. Nest boxes were located in Kenna Cartwright Park (red),
in suburban Kamloops neighborhoods (green), or at Thompson Rivers University (blue).
During the nesting period, we captured chickadees from their nest boxes after donning
sterile gloves. We measured the tarsus length, banded, weighed, and sexed each bird. One tail
feather from each adult was collected and placed into a sterile centrifuge tube, frozen immediately
on dry-ice, transported to the laboratory, and stored at -80°C until processing. At the conclusion
of the nesting season, we used sterile 100% cotton swabs to sample the entire top layer of the nest
bedding material from each nest box. These were stored and transported under the same conditions

6

as feather samples. All nest boxes were sprayed on all surfaces with a 10% (v/v) bleach solution
at the end of the breeding season.
Estimating urbanization
We estimated urbanization for the 75 m radius area surrounding each nest box according
to LaZerte et al. (2017), an area roughly equivalent to an average territory (following Hajdasz et
al., 2019). We converted GPS waypoint data for the 75 m radius area of each nest box into KML
files using R v3.4.3 (R Core Team, 2017) and the maptools package v0.9-5 (Bivand et al., 2019),
then converted these KML files into aerial images using Google Earth v7.3.2.5776 (Google Earth
Development Team, 2019). GIMP software v2.10.8 (The GIMP Development Team, 2019)
allowed us to edit each image to categorize urban structures (buildings, pavement, human-made
structures, and compacted or disturbed earth), deciduous vegetation, coniferous vegetation, natural
grasses, and maintained lawns. Using R and the vegan package v2.5-4 (Oksanen et al., 2019), we
performed a principal component analysis (PCA) to generate a continuous habitat index. The first
principal component accounted for 67% of the total variation in habitat cover and was used to
generate an urbanization index for each nest box. Natural features (coniferous trees and natural
grasses) resulted in larger values while more urban features (deciduous trees in Kamloops,
maintained lawns, buildings, compacted soils, human-made structures, and pavement) resulted in
smaller values.
DNA extraction, amplification, and sequencing
After protocol optimization, we extracted total DNA from feathers (31) and nest swabs
(18) using the DNeasy PowerLyzer PowerSoil Kit (Qiagen, Hilden, Germany) following the
manufacturer instructions, with some modifications to improve DNA extraction efficiency.
Specifically, we first cut away half of each swab tip, and each feather every centimeter along the

7

rachis, and inserted segments into individual tubes. We added bead and lysis buffers to these tubes,
being sure that all samples were submerged in buffer. We added an additional incubation step at
65°C for 10 minutes to increase bacterial cell lysis. After incubation, we immediately proceeded
with bead beating using a Vortex Genie 2 with a 24-tube platform adapter (Qiagen). The
manufacturer’s protocol was then followed up to the elution step. PCR of the V4 hypervariable
region of the bacterial 16S rRNA gene was completed on an S1000 thermocycler (BioRad,
Hercules, USA). We prepared 20 µl reaction mixtures by combining 10 µL 2X GoTaq Green
Master Mix (Promega, Madison, USA), 1 µL of each forward and reverse primer (341F
5′TACGGGAGGCAGCAG and 806R 5′GGACTACVSGGGTATCTAAT), 5 µL DNA extract,
and 3 µl PCR-grade water. Thermocycling conditions consisted of initial denaturation at 95°C for
4 minutes, followed by 35 cycles of denaturation at 95°C for 30 seconds, primer annealing at 55°C
for 45 seconds, and at 72°C for 2 minutes, followed by final extension at 72°C for 5 minutes. A
1.5% agarose gel was exposed to 90 V for 35 minutes in TAE buffer, after our first round of PCR,
and revealed amplicons of approximately 400 bp. These amplicons were cleaned using Agencourt
AMPure XP beads (Beckman-Coulter, Brea, USA) prior to a second round of PCR with adaptor
and

Ion

Xpress

barcoded

primers

(341F

5′

CCATCTCATCCCTGCGTGTCTCCGACTCAG[barcode]TACGGGAGGCAGCAG and 806R
5′CCACTACGCCTCCGCTTTCCTCTCTATGGGCAGTCGGTGATGGACTACVSGGGTAT
CTAAT). We used the same reaction volume and component amounts for this second round of
PCR. The thermocycler program was also identical to that used for first round PCR, except with
an annealing temperature of 65°C. A second magnetic bead clean-up was then completed, followed
by gel extraction using the MicroElute® Gel Extraction Kit (Omega Bio-tek, Georgia, USA) to
remove non-target DNA fragments smaller than 100 bp. We quantified all DNA extracts and first-

8

round PCR products using the Quant-iT dsDNA HS Assay Kit (Thermo Fisher Scientific,
Waltham, USA) on an ABI QuantStudio 3 Real-Time PCR System (Thermo Fisher Scientific).
We diluted our cleaned, second-round PCR amplicons to equimolar amounts based on dilution
factors obtained through qPCR with an Ion Library Quantitation Kit (Thermo Fisher Scientific).
We then prepared these pools for sequencing using an Ion 520TM & Ion 530TM Kit-Chef on an Ion
Chef system, and sequenced them on an Ion S5 XL using 400 bp chemistry (Thermo Fisher
Scientific). Data was processed using AMPtk (Palmer et al., 2018) for quality filtering, OTU
clustering at 97% sequence identity, and to assign taxonomy: 47 samples met the read target
number of >10,000 reads, grouping to 3325 OTUs.
Statistical analyses
We used Phyloseq v1.30.0 (McMurdie and Holmes, 2013) to rarefy 16S rRNA gene
sequence reads to an even depth across samples (based on the lowest sequence read number in the
dataset), to obtain even coverage for analyses. Bacterial genera containing species pathogenic to
wild birds (Willis and Wilkie, 1999; Stephens and Hampson, 2001; Hubálek, 2004; KilonzoNthenge et al., 2008; Tsiodras et al., 2008; Benskin et al., 2009) and clinical pathogens in people
(using genera from the 16Spath 2.0 database; Teng et al., 2014) were subsampled from the
community dataset to create a new dataset containing only genera with pathogenic members. We
filtered pathogens at the genus level as the V4 region of the 16S rRNA gene is not accurate for
identifying many pathogenic bacteria at the species level (Chakravorty et al., 2007). Feathers and
nest swabs were analyzed as independent datasets. After rarefying our data, subsampling
pathogens, and separating feathers and swabs, we were left with four datasets: 1) bacterial
communities on feathers, 2) bacterial communities on nest swabs, 3) genera with pathogenic
members from feathers, and 4) genera with pathogenic members from nest swabs.

9

We used Phyloseq and Vegan v2.5-6 (Oksanen et al., 2019) in RStudio v1.2.5019 (RStudio
Team, 2015) to estimate richness and Shannon-Wiener alpha diversity of bacterial communities
and pathogenic genera from feathers and nest swabs. For beta diversity, we first log 10(x+1)
transformed abundances to reduce skew in the sequence data. To visualize similarity among
samples, we generated principle coordinates analysis ordinations based on Bray-Curtis distances.
We performed PERMANOVA on these distances with urban metrics (urban or rural category, and
urbanization index) to test the influence of urbanization on community dissimilarity. In addition
to diversity estimates, we graphically compared community composition between urban and rural
birds and nest boxes using Phyloseq, Dplyr v0.8.3 (Wickham et al., 2019b), and GGplot2 v3.2.1
(Wickham et al., 2019a). We merged samples based on urban or rural classification according to
mean values, then generated stacked relative abundance graphs of phylum-level community
composition. We also merged urban and rural samples from our pathogen datasets to graphically
compare abundance of the four most common genera (with >50 sequence reads after merging).
Student’s t-tests were used to compare differences in phylum or genera abundance for bacterial
communities or pathogenic genera, respectively, between feathers, swabs, and habitat (urban or
rural).
We constructed a series of linear mixed models to examine the factors that predicted
bacterial community diversity and abundance on chickadee feathers. In separate models, we used
the following metrics as response variables: Community Richness, Pathogen Richness, Pathogen
Abundance, Community Shannon-Wiener Diversity Index, and Pathogen Shannon-Wiener
Diversity Index. For each response variable, we included fixed effects of Habitat (urban or rural),
Age, Sex, and Body Size (PC1) and a random effect of Box ID. An identical model was run using
the fixed effect of our urbanization index in lieu of Habitat (urban or rural category). We then used

10

a stepwise backward procedure to eliminate non-significant (p < 0.05) effects and arrive at a final
model that best predicted the microbial community metric. For nest swabs, we constructed a series
of t-tests with Community Richness, Pathogen Richness, Pathogen Abundance, Community
Shannon-Wiener Diversity Index, or Pathogen Shannon-Wiener Diversity Index as the response
variable and Urban or Rural Habitat as the independent categorical variable. Next, for each
microbial community metric, we examined correlations between a) nest swabs and bird feathers
using a linear mixed model with feather microbial community as the response variable, nest feather
community as the fixed effect, and Box ID as the random effect and b) males and females at the
same nest using Pearson correlation. Finally, to ask whether we could predict the habitat the birds
occupied (urban vs. rural) based on microbial community composition, we used Discriminant
Functions Analysis of all OTUs from genera with pathogenic members.
Detection of potential avian pathogens using community filtering
In an effort to survey total bacterial pathogens an urban environment, we filtered genera
with pathogenic members from our original community data. This resulted in a dataset of 206
OTUs belonging to genera including members pathogenic to either humans, birds, or both. We
further filtered this data to only include potential avian pathogens, identifying 75 OTUs belonging
to

Pseudomonas,

Escherichia/Shigella,

Clostridium,

Enterococcus,

Streptococcus,

Staphylococcus, Rickettsia, Mycobacterium, Nocardia, Erysipelothrix, and Campylobacter. We
did not detect any potential avian pathogens from the genera Borrelia, Chlamydia, Coxiella,
Ehrlichia, Enterobacter, Haemophilus, Klebsiella, Listeria, Mycoplasma, Pasteurella, Riemerella,
Salmonella, Vibrio, or Yersinia. Although we were only able to broadly categorize these bacteria
as potential pathogens, advances in the accuracy of sequencing platforms and pipelines to obtain

11

species- and strain-level accuracy, such as through amplicon sequence variants (Fuirst et al., 2018),
may allow for absolute pathogen detection in the future.
Ethics Statement
This research was carried out under Thompson Rivers University Animal Care and Use
Protocol no. 100846, University of Northern British Columbia Animal Care and Use Protocol no.
2014–06, and under Canadian Wildlife Service collection permit no. 22806.
RESULTS
Community composition and abundance of potential pathogens
Community composition was highly similar at the phylum level for feather and nest
bacteria in urban and rural environments (Figure 2). Proteobacteria was the dominant phylum
observed for all categories. Actinobacteria, Bacteroidetes, Cyanobacteria/Chloroplast, and
Firmicutes together composed approximately half of each community in different proportions.
Candidatus Saccharibacteria, unassigned phyla, and phyla composing less than 1% of each sample
(Other), were the least abundant phyla across all feathers, swabs, and environments.
The relative abundance of bacterial phyla found on feathers was not significantly different
between urban and rural environments (all p > 0.05; Table 1). However, there were more
Actinobacteria on rural nests than urban nests (p = 0.05). No differences in abundance were
observed for all other phyla from nests (all p > 0.05). When comparing phylum-level composition
between feathers and swabs, there was no difference in relative abundance for any phylum (all p
> 0.05).

12

Figure 2. Community phylum-level composition for bacteria found on Mountain Chickadee
(Poecile gambeli) feathers and nesting material in urban and rural areas. OTU abundances were
grouped by phylum and samples were merged based on environment sampled from. Phyla that
contributed to less than 1% abundance were grouped together as Other.

13

Table 1. Student’s t-test scores between urban and rural environments, and between feathers and
nests, for bacterial communities and genera with pathogenic members from Mountain Chickadee
(Poecile gambeli).
Scale
Bacterial
Communities

Phylum / Genus
Actinobacteria
Bacteroidetes
Candidatus Saccharibacteria
Cyanobacteria/Chloroplast
Firmicutes
Proteobacteria
Other
Unassigned
Pathogen Genera
Clostridium sensu stricto
Clostridium XI
Enterococcus
Escherichia/Shigella
Pseudomonas
*Indicates significance

Feathers (p = )
Urban vs. Rural
0.19
0.91
0.08
0.44
0.70
0.60
0.40
0.21
0.79
0.62
0.84
0.85
0.95

Nests ( p = )
Urban vs. Rural
0.05*
0.09
0.42
0.06
0.83
0.40
0.25
0.82
0.38
0.18
0.86
0.45
0.57

Feathers
vs. Swabs
(p = )
0.69
0.13
0.94
0.38
0.55
0.06
0.27
0.39
0.73
0.02*
0.05*
0.02*
0.82

We detected 206 OTUs grouped to genera with pathogenic members. When examining
only these genera, we found no differences in abundance between urban and rural environments
for both feather and nest bacteria (all p > 0.05; Table 1). The five most abundant genera with
pathogenic members were Clostridium sensu stricto, Clostridium XI, Enterococcus,
Escherichia/Shigella, and the most dominant genus, Pseudomonas (Figure 3). Comparisons
between nests and feathers for these genera showed significant differences in Clostridium XI (p =
0.02), Enterococcus (p = 0.05), and Escherichia/Shigella (p = 0.02) abundance, but no changes in
Clostridium sensu stricto (p = 0.73) or Pseudomonas (p = 0.82).

14

Figure 3. Abundance of the five most dominant genera containing pathogenic members found on
Mountain Chickadee (Poecile gambeli) A) feathers and B) nests in urban and rural environments.

15

Bacteria dissimilarity and modelling
We used PCoAs and PERMANOVA analyses to test influences of urban metrics (urban or
rural, urbanization index) on differences in bacterial communities and potential pathogen presence.
Bacteria found on feathers and nests appeared to be more similar when found in the same
environment (urban or rural; Figure 4; Table 2). Urban or rural habitat explained some of the
differences in bacterial communities on feathers (R2 = 0.06; p = 0.05), nests (R2 = 0.11; p = 0.001),
and potential pathogens from nests (R2 = 0.11; p = 0.003). However, this effect was not observed
for potential pathogens found on feathers (R2 = 0.05; p = 0.12). Urban bacterial communities and
potential pathogens also appeared to show greater variation within groups than rural communities
(Figure 4). Our urbanization index only explained some of the differences in nest bacterial
communities (R2 = 0.10; p = 0.009), and was not significant for feather communities (R2 = 0.05;
p = 0.11), feather pathogens (R2 = 0.05; p = 0.11), or nest pathogens (R2 = 0.07; p = 0.22).

16

Figure 4. Principle coordinates analyses (PCoAs) of Bray-Curtis distances between urban and
rural feather bacterial communities, nest bacterial communities, potential pathogens on feathers,
and potential pathogens on nests. Path lines on feather PCoAs show samples taken from the same
site (birds sampled at the same nest box at any point in the season).

17

Table 2. PERMANOVA results of Bray-Curtis distances for bacterial communities and genera
with pathogenic members from Mountain Chickadee (Poecile gambeli) feathers and nests, with
urban metrics as included variables.
Urban Metric
Urban or Rural

Urbanization Index

Community
Feather Community
Nest Community
Feather Pathogens
Nest Pathogens
Feather Community
Nest Community
Feather Pathogens
Nest Pathogens

R2
0.06
0.11
0.05
0.11
0.05
0.10
0.05
0.07

p=
0.05*
0.001***
0.12
0.003**
0.11
0.009**
0.11
0.22

*Indicates significance

When we asked which factors best predicted microbial community composition, Habitat
(urban vs. rural) was the only factor that explained Community Richness (p = 0.04; Table 3) and
Pathogen Richness (p = 0.03; Table 3), both of which were positively related to urbanization. None
of the factors in our model were associated with variation in Pathogen Abundance, Community
Shannon-Wiener Diversity Index, or Pathogen Shannon-Wiener Diversity Index. For nest swabs,
we found no differences between urban and rural habitats for any of the microbial community
metrics (all p > 0.14). When using urbanization index as our Habitat fixed effect, Pathogen
Richness was also associated for feathers (p = 0.04). No other metrics from feather or swabs were
explained by urbanization index (all p > 0.05). When we examined the relationship between
microbial communities on nest swabs and feathers, we found no relationships with Community
Richness, Pathogen Richness, or Community Shannon-Wiener Diversity Index (Table 4);
however, we did find positive relationships with Pathogen Abundance and Pathogen ShannonWiener Diversity Index (Table 4). When we examined relationships between male and female
feather microbial community composition at the same nest, only pathogen abundance was
correlated (r12 = 0.59, p = 0.04; all other p > 0.34).

18

Table 3. Final best fit models from a linear model predicting bacterial diversity metrics based on
age of birds, sex, body size, and habitat sampled from (urban or rural).
Habitat Metric
Urban or Rural

Urbanization Index

Community Metric

Final Model

DF

F

p

Community Richness

Habitat

1, 15.91

4.88

0.04*

Pathogen Richness

Habitat

1, 16.41

5.34

0.03*

Pathogen Abundance

-

-

-

-

Community Shannon-Wiener

-

-

-

-

Pathogen Shannon-Wiener

-

-

-

-

Community Richness

-

-

-

-

Pathogen Richness

Habitat

1, 15.65

5.01

0.04*

Pathogen Abundance

-

-

-

-

Community Shannon-Wiener

-

-

-

-

Pathogen Shannon-Wiener

-

-

-

-

*Indicates significance

Table 4. Relationships between bacterial community metrics on feathers and swabs.
Metric

DF

F

p

Community Richness

1, 16.46

0.47

0.50

Pathogen Richness

1, 18.7

0.25

0.62

Pathogen Abundance

1, 14.13

7.64

0.02*

Community Shannon-Wiener

1, 18.92

0.71

0.41

Pathogen Shannon-Wiener

1, 12.96

7.90

0.01*

*Indicates significance

Discriminant functions analysis
Discriminant functions analysis of bird feathers using 30 OTUs belonging to genera with
pathogenic members performed with a 90% success rate; 1/12 rural birds were misclassified as
urban and 2/18 urban birds were misclassified as rural (Entropy r2 = 0.44). For nest swabs, the
discriminant functions analysis was less successful, with a 72.2% success rate; 2/8 rural nests were
misclassified as urban and 3/10 rural nests were misclassified as urban.

19

DISCUSSION
Urban landscapes have myriad effects on the wildlife living in or near them, and change
ecosystems on a macroscopic scale. Here we demonstrate how urbanization also influences the
bacterial communities and potential pathogens found on Mountain Chickadees and their nests.
Numerous studies and reviews have highlighted how urbanization can amplify infectious diseases
in wildlife (Coyner et al., 2002; Bradley and Altizer, 2007; Bradley et al., 2008; Pongsiri et al.,
2009; Hamer et al., 2012; Wilcoxen et al., 2015; Rouffaer et al., 2017; Tian et al., 2018). However,
no studies have attempted to characterize global changes in all detectable pathogens across
between urban and rural habitats. A few studies have also found differences in intestinal/faecal
microbiota across urban environments in birds (Fuirst et al., 2018; Teyssier et al., 2018), but none
have questioned how urbanization changes the external microbial communities of avian species.
Other researchers have further reported on urbanization influencing microbial communities in soils
(Reese et al., 2016; Wang et al., 2017), water bodies (Wang et al., 2011; Drury et al., 2013; Newton
and McLellan, 2015; Hosen et al., 2017), plants (Bartlewicz et al., 2016), and as a result, microbial
ecosystem functions (Wang et al., 2011, 2017). Taken together these findings show that urban
landscapes not only alter ecosystems on a larger, visible scale, but also change the characteristics
of microbial communities at smaller ecosystem scales.
Community composition of feathers and nests
With the recent advancement of microbiome research, researchers have begun
characterizing the effects and relationships of entire microbial communities in their environments.
This has created numerous opportunities to examine bacterial community relationships with
wildlife. In this study, we contribute to the growing body of literature characterizing avian
microbiomes, which highlight the similarities and differences between them. Bacterial
communities on Mountain Chickadee feathers and nests were dominated by the phyla
20

Proteobacteria, Actinobacteria, Firmicutes, and Bacteroidetes. This is similar to the findings of
Javůrková et al. (2019) who showed these 4 phyla were consistently abundant in 7 passerine
species from the Czech Republic. These dominant phyla have also been observed in similar
proportions on non-passerine birds such as Herring Gulls (Larus argentatus) in the mid-Atlantic
coastal US (Fuirst et al., 2018), and Woodlarks (Lullula arborea) and Skylarks (Alauda arvensis)
in the Netherlands (van Veelen et al., 2017). The consistent occurrence of these dominant phyla
across avian species could indicate some conserved elements of avian microbial communities.
However, variation in geography and diet of these species likely result in differences between their
microbiomes. This might explain why we detected Candidatus Saccharibacteria and
Cyanobacteria/Choloroplast as some of our major phyla while other studies did not. Additionally,
changes in avian microbiomes due to seasonality may be significant and demands further
investigation. For example, House Sparrows (Passer domesticus) in the autumn and winter from
Belgium have vastly different microbiomes than any of the species discussed, which may result
from seasonal differences in community assemblage (Teyssier et al., 2018).
During the breeding period, cavity nesting birds like Mountain Chickadees spend much of
this time living in their nest alongside their mate. This may indicate that bacterial communities
between bird pairs, and their nests, may be similar as contact between them likely results in
transmission between these elements.
When we compared the bacterial phyla found on feathers and nests, there were no
differences in abundance between them. Similarly, when comparing the abundance of the
dominant potential pathogens on nests and birds, we found no changes in Clostridium sensu stricto
or Pseudomonas, but did find differences for Escherichia/Shigella, Enterococcus, and Clostridium
XI. We also found that the abundance and diversity between feathers and nests for all potential

21

pathogens was related. However, community or potential pathogen richness, and community
diversity, was not related between feathers and nests. This shows Mountain Chickadees share
much of their bacteria communities with the nests they live in, although some differences do exist.
This similarity may result from increased transmission between these elements (Brandl et al., 2014;
van Veelen et al., 2017), or as a consequence of living in the same environment. Other studies have
found similar microbial community assemblages between larks and their nests (van Veelen et al.,
2017), and between some Reed Warbler (Acrocephalus scirpaceus) nest components (Brandl et
al., 2014), which supports this pattern across different avian species. The differences we did
observe between feathers and nests may indicate that not all bacteria transmit between nests and
birds, or that small but significant differences exist between these environments that change the
occurrence of some bacteria.
When we examined differences in bacterial communities between male and female birds,
we found the abundance of potential pathogens was connected, but not the diversity or richness of
microbial communities and potential pathogens. As no studies (to our knowledge) have compared
microbiomes of bird pairs during the breeding season, we can only speculate as to why these results
occurred. We suggest that direct contact between these birds may result in similarities for their
microbiomes, while differences between these communities could result from the allocation of
tasks during the breeding season. Male chickadees spend much of the early breeding season outside
the nest foraging food for the young, while the females spend most of their time incubating eggs
and nestlings (McCallum et al., 1999). This could result in male birds being exposed to different
bacteria and environmental conditions outside the nest box, which may explain some of the
differences observed between male and female microbiomes.

22

Effects of urbanization on mountain chickadees and their nests
Urbanization is a global threat to biodiversity and ecosystems due to the alterations they
make to native habitats. Although we are aware of some of these changes in animal and plant
populations, few studies have examined if urbanization alters microbial communities. If changes
in bacteria occur across urban landscapes, then birds living in these areas may have different
bacterial assemblages and possibly be exposed to different pathogenic species.
We first determined if urbanization changes the microbial communities of Mountain
Chickadees and their nests. Using broad scale investigations on phylum composition and
community dissimilarity, combined with linear mixed modelling of community diversity and
richness, we found that urbanization does influence some aspects of bacterial community
assembly. We found no changes in the relative abundance of dominant phyla between urban and
rural environments, except for an increase in Actinobacteria in rural nests. This contrasts with the
findings of Fuirst et al. (2018), who observed significant differences between phyla and families
of intestinal bacteria found in Herring Gulls (Larus argentatus) across urban environments of the
mid-Atlantic coastal US. However, differences resulting from geography, ecology of our study
species, and type of urban environments sampled, may account for these contradictions. Although
no effect was found for the relative abundance of dominant phyla, urbanization was related to a
slight increase in community richness, but not diversity, for bird feather microbiomes. Teyssier et
al. (2018) found an opposite effect for gut microbiomes in House Sparrows (Passer domesticus)
from Belgium, with urbanization decreasing the richness of these communities. Finally, we found
that urbanization explained some (5-10%) of the differences in bacterial community composition
on both feathers and nests. This influence of urbanization on community dissimilarity was also
found for cloacal microbiomes in House Sparrows by Teyssier et al. (2018). These results show

23

that, at least for Mountain Chickadees, urbanization does not drastically alter community
composition at a phylum level for bacteria on feathers or nests. However, urbanization does
increase species richness slightly for bacterial communities on chickadee feathers, and accounts
for some of the differences between communities across these gradients. These effects may be
similar for other species and urban environments, although we are unable to determine if
inconsistencies between our findings and other studies resulted from differences in experimental
design and methodology, or from the ecology of the study species.
Next, we examined effects of urbanization on bacterial genera with pathogenic members,
which we broadly grouped as potential pathogens. We carried out comparisons between
urbanization on the abundance of dominant potential pathogens, as well as their richness, diversity,
and the total abundance of all potential pathogens grouped together. Furthermore, we used these
potential pathogens to predict the urban or rural environments from which they originated using
discriminant functions analysis. From these analyses we found that urbanization alters the
prevalence of some potential pathogens found on birds and their nests. When looking at the five
most dominant potential pathogens, we found no differences in their relative abundance between
urban and rural environments. Urban or rural habitat also had no effect on the abundance of all
potential pathogens grouped together, or the diversity of potential pathogens. However, we did see
that urbanization was tied to an increased richness of potential pathogens on birds, but not nests.
Urbanization also accounted for 11% of the differences between potential pathogens on nests, but
not feathers. Finally, our discriminant functions analysis could predict the urban or rural
environment with 90% success using potential pathogens on birds and 72.2% success using
potential pathogens on nests. Similar to our findings with bacterial communities, urbanization does
not determine the abundance of the most common potential pathogens or the abundance of all

24

potential pathogens grouped together. However, when differences exist in the prevalence of these
organisms, urbanization does account for some of these differences. Our discriminant functions
analysis specifically shows that certain members of these genera may associate with urban
landscapes, and our models revealed an increased number of potentially pathogenic species found
on birds in urban areas. Whether these bacteria are confirmed pathogens and pose a significant risk
to urban birds is beyond the ability of our current sequencing methods.
Differences in urban classifications
We classified urbanization in two ways, as a binomial variable (urban/rural) or as a
continuous variable using an urbanization index. This index is useful for assigning an unbiased
and objective measurement of urbanization. Unfortunately, these indexes do not yet account for
all aspects of urban environments that a broad urban or rural classification inherently includes,
which could explain why we found stronger effects from urbanization when considering a broader
urban/rural classification. Our index is based on the territory size of the chickadee species being
studied here, but urban influences from outside home ranges, like heat islands and changes in air
quality (McKinney, 2002; Wilson and Chakraborty, 2013; Aronson et al., 2014) may contribute to
what makes these areas urban. Both Rouffaer et al. (2017) and Teyssier et al. (2018) also showed
this problem of scaling when studying House Sparrows, as landscape and home-range scales to
classify urbanization resulted in different relationships with community metrics. As a result, we
concluded that the use of our urbanization index was not suitable for this study, and instead found
the results of an urban or rural category to be more robust and inclusive of the many nuances of
urban environments.

25

Recommendations for future work
Based on our results, we have identified some recommendations for future studies.
Researchers should account for differences in both bacterial communities and pathogens across
seasons, as some known pathogens, such as Mycoplasma gallisepticum, change in abundance
depending on the season (Hartup et al., 2001; Hosseini et al., 2004). As nest swabs were taken at
the end of the breeding season, while feathers were sampled roughly at the midpoint of the season,
we cannot rule out that the differences observed between feathers and nest did not result from
differences in sampling period. We therefore suggest sampling in similar timeframes when
comparing feather and nest microbiota to minimize temporal influences on communities. Studies
could also investigate effects of urbanization on multiple bird species across environments to better
understand how ecology and geography influence these relationships. For detecting all pathogenic
bacteria from a community, approaches that utilize multiple regions of the 16S rRNA gene
(particularly the V2, V3 and V6 regions; Chakravorty et al., 2007) may allow for a more complete
view of bacterial pathogens in environmental samples. Finally, approaches that survey nonbacterial pathogens would only further increase our understanding of the role of urbanization in
the spread of wildlife diseases.
As cities continue to expand, research that identifies the environmental roles of these novel
ecosystems will become increasingly important. Systematic poor land-use planning and overly
permissive development regulations has allowed unregulated urban sprawl without a full
understanding of its environmental impacts (Wilson and Chakraborty, 2013). We do however
know that urban areas have substantial consequences on biodiversity and wildlife (McKinney,
2002; Aronson et al., 2014). Yet, some rare species now inhabit cities allowing new opportunities
for wildlife conservation because of urbanization (McKinney, 2002). It is these opportunities and

26

unknowns of urban systems that demand more attention in future research, in particular the role of
urbanization and its impacts on disease in wildlife.

27

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SUPPLEMENTAL DATA
Urbanization Index
Urbanization Index code available at https://github.com/steffilazerte/urbanization-index,
following the updated methods in the Read-me file.
Project Data and R Code
All code and data for this project is made freely available on Google Drive at
https://drive.google.com/open?id=1yLDF8NEwlKw22vBeF-xEpZDZjfWRV5V7

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