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SPECIAL ISSUE: SPECIES INTERACTIONS, ECOLOGICAL
NETWORKS AND COMMUNITY DYNAMICS
Feather mites play a role in cleaning host feathers: New
insights from DNA metabarcoding and microscopy
Jorge Do~
na
1
|
Heather Proctor
2
|
David Serrano
3
|
Kevin P. Johnson
4
|
Arnika Oddy-van Oploo
2
|
Jose C. Huguet-Tapia
5
|
Marina S. Ascunce
5,6
|
Roger Jovani
1
1
Department of Evolutionary Ecology,
Estaci
on Biol
ogica de Do~
nana (EBD-CSIC),
Sevilla, Spain
2
Department of Biological Sciences,
University of Alberta, Edmonton, AB,
Canada
3
Department of Conservation Biology,
Estaci
on Biol
ogica de Do~
nana (EBD-CSIC),
Sevilla, Spain
4
Illinois Natural History Survey, Prairie
Research Institute, University of Illinois at
Urbana-Champaign, Champaign, Illinois
5
Department of Plant Pathology, University
of Florida, Gainesville, Florida
6
Emerging Pathogens Institute, University
of Florida, Gainesville, Florida
Correspondence
Jorge Do~
na and Roger Jovani, Department
of Evolutionary Ecology, Estaci
on Biol
ogica
de Do~
nana (EBD-CSIC), Sevilla, Spain.
Emails: jdona@ebd.csic.es and
jovani@ebd.csic.es
and
Heather Proctor, Department of Biological
Sciences, University of Alberta, Edmonton,
AB, Canada.
Email: hproctor@ualberta.ca
Funding information
Ministry of Economy and Competitiveness,
Grant/Award Number: RYC-2009-03967,
CGL2011-24466, CGL2015-69650-P, SVP-
2013-067939; Natural Sciences and
Engineering Research Council of Canada
(NSERC); Spanish Ministry of Economy and
Competitiveness, through the Severo Ochoa
Program for Centres of Excellence in R+D+I,
Grant/Award Number: SEV-2012-0262
Abstract
Parasites and other symbionts are crucial components of ecosystems, regulating
host populations and supporting food webs. However, most symbiont systems,
especially those involving commensals and mutualists, are relatively poorly under-
stood. In this study, we have investigated the nature of the symbiotic relationship
between birds and their most abundant and diverse ectosymbionts: the vane-dwell-
ing feather mites. For this purpose, we studied the diet of feather mites using two
complementary methods. First, we used light microscopy to examine the gut con-
tents of 1,300 individual feather mites representing 100 mite genera (18 families)
from 190 bird species belonging to 72 families and 19 orders. Second, we used
high-throughput sequencing (HTS) and DNA metabarcoding to determine gut con-
tents from 1,833 individual mites of 18 species inhabiting 18 bird species. Results
showed fungi and potentially bacteria as the main food resources for feather mites
(apart from potential bird uropygial gland oil). Diatoms and plant matter appeared as
rare food resources for feather mites. Importantly, we did not find any evidence of
feather mites feeding upon bird resources (e.g., blood, skin) other than potentially
uropygial gland oil. In addition, we found a high prevalence of both keratinophilic
and pathogenic fungal taxa in the feather mite species examined. Altogether, our
results shed light on the long-standing question of the nature of the relationship
between birds and their vane-dwelling feather mites, supporting previous evidence
for a commensalistic–mutualistic role of feather mites, which are revealed as likely
fungivore–microbivore–detritivore symbionts of bird feathers.
KEYWORDS
bacteria, birds, diet, DNA metabarcoding, ecological interactions, feather mites, fungi,
high-throughput sequencing, interactions, symbionts
1
|
INTRODUCTION
Symbionts (i.e., parasites, mutualists and commensalists that inti-
mately interact with their hosts; Leung & Poulin, 2008) comprise the
most diverse group of organisms on Earth (Dobson, Lafferty, Kuris,
Hechinger, & Jetz, 2008; Larsen, Miller, Rhodes, & Wiens, 2017;
Poulin & Morand, 2000, 2004). Symbionts are crucial for ecosystem
stability: they regulate host populations and support food webs,
where parasites alone are responsible for 75% of the network links
(Lafferty, Dobson, & Kuris, 2006). Thus, the study of host–symbiont
Received: 26 December 2017
|
Revised: 15 February 2018
|
Accepted: 21 March 2018
DOI: 10.1111/mec.14581
Molecular Ecology. 2018;1–16. wileyonlinelibrary.com/journal/mec ©2018 John Wiley & Sons Ltd
|
1
ecology is vital to understand many important processes, such as
emerging infectious diseases (Hoberg & Brooks, 2015), biological
invasions (Traveset & Richardson, 2014), crop pests (Hosokawa,
Kikuchi, Shimada, & Fukatsu, 2007) or the effect of climate change
upon biodiversity (Carlson et al., 2017). Historically, most efforts
have been directed to the study of parasites with direct harmful
effects on humans or livestock. Symbiont systems involving com-
mensals and mutualists are relatively poorly studied compared to
free-living organisms and host–parasite systems (Jovani, Do~
na, Labra-
dor, & Serrano, 2017).
Host–symbiont interactions rarely involve a simple one-symbiont:
one-host interaction. Rather, even without considering the interaction
of the host species with other free-living species, any host–symbiont
interaction typically involves several other species (Hopkins, Wojdak,
& Belden, 2017; Poulin, 2010). In addition, whether a particular sym-
biont species acts as a parasite, commensal or mutualist can be highly
context-dependent (i.e., the mutualism–parasitism continuum frame-
work; for example, Brown, Creed, Skelton, Rollins, & Farrell, 2012;
Cheney & C^
ot
e 2005; Newton, Fitt, Atkins, Walters, & Daniell, 2010;
Jovani et al., 2017). Thus, the study of symbionts as a whole, and not
separately according to the presumed nature of their relationships
with their hosts, is needed (Jovani, 2003; Jovani et al., 2017).
Defensive mutualisms (i.e., those in which symbionts protect
their hosts from natural enemies, which have been often perceived
as biological curiosities) have been reviewed recently following this
approach and placed into this framework (Hopkins et al., 2017).
Accordingly, defensive mutualisms, instead of being anecdotal host–
symbiont associations, have been revealed as diverse and common
associations in a wide range of plants and animal hosts from nearly
all habitats on the planet. Nonetheless, with a few exceptions, most
of the diversity of host–symbiont associations remains unexplored or
largely unstudied.
A good example of our lack of knowledge of these interactions
involves symbiotic relationships between birds and their feather
mites (Acariformes: Astigmata: Analgoidea and Pterolichoidea). These
mites are the most abundant and diverse ectosymbionts of birds.
Almost all bird species harbour species- or genus-specific feather
mites (Do~
na, Proctor, Mironov, Serrano, & Jovani, 2016; Gaud &
Atyeo, 1996; Proctor, 2003). Feather mites are highly specialized
symbionts due to their (i) life cycle (i.e., they are permanent
ectosymbionts, Dabert & Mironov, 1999; Proctor, 2003); (ii) high
host specificity (Do~
na, Proctor, Mironov, Serrano, & Jovani, 2017);
(iii) specific distribution on particular feathers and microsites on
feathers (Fern
andez-Gonz
alez, P
erez-Rodr
ıguez, de la Hera, Proctor,
&P
erez-Tris, 2015; Jovani & Serrano, 2001, 2004; Stefan et al.,
2015); and (iv) mainly vertical mode of transmission (Do~
na, Potti,
et al., 2017; Jovani, Tella, Sol, & Ventura, 2001; Mironov & Maly-
shev, 2002). However, as with many other symbionts, they are chal-
lenging to study, and this has strongly hampered our comprehension
of this system (Do~
na, Diaz-Real, et al., 2015; Proctor, 2003; Proctor
& Owens, 2000).
A long-standing question in understanding the interaction
between feather mites and birds is whether these mites feed on bird
tissues (e.g., feathers, skin, blood) or upon resources found on the
bird’s surface (e.g., algae, fungi). If they feed on bird tissues, they are
more likely to be classified as parasites (Harper, 1999; Poulin, 1991;
Thompson, Hillgarth, Leu, & McClure, 1997), while if they do not,
feather mites would more likely be commensals or even mutualists
(Blanco, Tella, & Potti, 1997; Blanco, Tella, Potti, & Baz, 2001;
Galv
an et al., 2012). Previous evidence has suggested that feather
mites could feed mainly on the uropygial gland oil of birds (Dubinin,
1951; Proctor, 2003; Walter & Proctor, 2013c). However, this oil is
a nitrogen-deficient source (Jacob & Ziswiler 1982; Proctor, 2003),
and previous evidence has shown that feather mites complement
their diet with fungi, pollen and algal particles (Blanco et al., 2001;
Dubinin, 1951; Proctor, 2003; Walter & Proctor, 2013c). Examining
thousands of slide-mounted feather mites from 26 mite species,
Dubinin (1951) found that almost all mite species had fungal spores
in their guts, most from Cladosporium,Alternaria and rust fungi.
Moreover, Blanco et al. (2001) found fungal mycelia and spores in
the guts of 53% of Pterodectes rutilus (Robin) (Proctophyllodidae) and
38% of Scutulanyssus nuntiaventris (Berlese) (Pteronyssidae) mites
from two species of swallows (Hirundinidae). Likely because of this
potential mixture of feather mite diet, a recent isotopic study (Stefan
et al., 2015) of the diet of two feather mite species produced incon-
clusive results. Interestingly, however, this study showed a strong
correlation between the isotopic carbon signatures among mites
inhabiting the same individual host, and between the carbon signa-
ture (but not the nitrogen signature) of feather mites and the blood
of their individual bird host, thus suggesting that diet could be
mainly based on shared host-associated resources, arguably preen
gland oil (Stefan et al., 2015). Thus, it remains an open question to
what extent feather mites feed on uropygial oil or also upon other
bird tissues, whether exogenous resources, such as fungi and bacte-
ria, constitute an important food resource for these mites, and which
specific taxa are eaten by feather mites.
In this study, we investigated the diet of feather mites using two
complementary methods. First, we used light microscopy to examine
feather mite gut contents under the microscope from a large sample
of feather mites from ~200 bird species. Light microscopy allows
detection of feather fragments, fungi, plant material and algae that are
refractory to the clearing and mounting media (see Materials and
methods). In a second approach, for a smaller number of vane-dwelling
mite species, we studied gut contents using high-throughput sequenc-
ing (HTS) and DNA metabarcoding. This molecular approach comple-
mented the light microscope analysis for certain potential food
resources that would not be easily recognized in the slide-mounted
specimens (e.g., bacteria, soft bird tissues) and also allowed for a
detailed analysis of fungi, bacteria and plant taxa in the mites’diet.
2
|
MATERIALS AND METHODS
2.1
|
Gut content assessment via light microscopy
For the microscopy analysis, we used previously slide-mounted mites
from the Proctor Lab collection of feather mites from around the
2
|
DO ~
NA ET AL.
world. Mites had been cleared in lactic acid and mounted in polyvi-
nyl alcohol medium (#6371A; BioQuip, Rancho Dominguez, CA,
USA). This process clears soft tissues but retains refractory material
(e.g., chitin, cellulose). Selection of mites to examine was based on
taxonomic diversity of mites and host birds, and ecological breadth
of hosts (e.g., birds from terrestrial, marine and freshwater habitats,
including predators, granivores, nectarivores, etc.). We initially exam-
ined several thousand mites using a Leica DMLB compound micro-
scope with DIC lighting. Mites with visible gut contents were
photographed at various magnifications (200, 400 and 8009)
depending on size of material in the gut. For each host bird species
included in the study, our goal was to photograph a minimum of five
individual mites from each mite genus present on the bird species. In
some cases, if there were fewer than five mites with gut contents
available for a mite genus and/or bird species, then all the available
mites that contained gut contents were photographed. Under ideal
circumstances, we would have focused on mite species rather than
genera, but particularly for tropical areas, feather mite alpha-taxon-
omy is in an early state and many species have yet to be described.
Also, for many taxa, only adult males can be readily ascribed to spe-
cies, and we wished to include nymphal and female mites in our
assessment. Mites were identified to genus using Gaud and Atyeo
(1996) with additional literature for more recently described genera
(e.g., Valim & Hernandes, 2010). In total, 1,300 individual mites rep-
resenting 100 genera (18 families) from 190 host bird species (72
families; 19 orders) were photographed.
Each morphologically unique type of gut content was given a
code, and for every individual mite, all the types of gut content pre-
sent were recorded, as well as the approximate amount of each type
of gut content. Aided by illustrations in Lacey and West (2006) and
consultation with a mycologist (T. Spribille, University of Alberta), we
then classified all unique types of gut contents as fungi, diatoms,
plant spores, “unidentifiable”and oily globules (possibly uropygial
gland oil or digestive by-products in peritrophic membranes).
Unidentifiable objects were mainly extremely small fragments or
flecks of material <5lm long (some of which could have potentially
been tiny remnants of feather barbules) (e.g., Figure S10). Oil glob-
ules were not included in the analyses, as we consider that our abil-
ity to consistently identify this material was much lower than for
other types of gut content (see an example of potential oil globs in
Figure S11).
2.2
|
Sample collection and sterilization for DNA
metabarcoding
For the DNA metabarcoding study, 1,833 individual mites of 18 mite
species from 18 passerine bird host species (34 individual birds or
infrapopulations) were sampled from birds captured with mist nets in
Andalusia (Spain) during the spring of 2015 (see Table S1, for sam-
pling details). An effort was made to collect all mites found on the
wing flight feathers from each sampled bird, using a sterile swab
impregnated with ethanol. Mites were preserved at 20°C in tubes
with 96% ethanol. In those cases in which more than one mite
species was found on an individual bird, one different sterile swab
was used for collecting each tentative mite species (according to
Do~
na et al., 2016 based on genus-specific location on bird feathers)
into different tubes.
Mites were sterilized in AllGenetics & Biology, SL (A Coru~
na,
Spain) with three ethanol washes following Andrews (2013). Each
time, tubes containing mites were agitated manually. Then, all etha-
nol was collected with the pipette using a thin pipette tip, with care-
ful visual checks to avoid removing any mites. Tubes were then
refilled with ethanol. Washed mites were then used for further anal-
yses (hereafter mite samples) and the ethanol extracted from the
first wash was used as the environmental control sample (hereafter,
external sample).
2.3
|
DNA extraction, amplification, library
construction and sequencing
DNA isolation, amplification and library preparation were carried out
at AllGenetics & Biology, SL (A Coru~
na, Spain). Genomic DNA was
extracted from each mite sample using the HotSHOT method (Truett
et al., 2000). Briefly, the ethanol from the last mite wash was evapo-
rated and a 1-M NaOH solution was added to the dried wells, incu-
bated at 95°C and neutralized with equivalent amounts of Tris–Cl.
The final extraction volume was 30 ll. A negative control that con-
tained no sample was included in every extraction round to check
for contamination during the experiments. This procedure preserves
exoskeletons for morphological identifications (see Do~
na, Diaz-Real,
et al., 2015). However, in contrast to more aggressive isolation
methods, DNA from Gram-positive bacteria, undigested diatoms and
intact fungal spores may not have been amplified. After DNA extrac-
tion, the remaining exoskeletons were separated from the buffer and
stored in 80% ethanol. External samples were extracted as follows.
The ethanol phase from the first mite wash was pipetted onto a
nitrocellulose filter (ca. 9 cm²with a pore size of 22 lm), and then,
DNA was isolated using the PowerSoil DNA isolation kit (Mobio) fol-
lowing manufacturer’s instructions. The final elution volume was
50 ll.
From each sample, a total of seven libraries were built: five from
DNA extracted from mite samples and two from the DNA extracted
from the external samples (i.e., see above for sample name defini-
tions). HTS libraries were prepared by amplifying a different molecu-
lar marker and by adding the Illumina-specific sequencing primers,
indices and adaptors. The regions amplified from mite samples were
as follows: the bacterial/archaeal 16S rRNA gene variable region 4
(515F/806R, Caporaso et al., 2012), the ITS 2 region of the fungal
rRNA operon (ITS86F/ITS4, De Beeck et al., 2014), the ITS 2 region
of plants and algae (S2F/S3R, Chen et al. 2010) and the region of
the mitochondrial COI gene of birds. To maximize the potential for
retrieving bird DNA, we used internal primers of the mitochondrial
COI gene suitable for amplifying degraded DNA (BirdF1/AvMiR1,
Kerr, Lijtmaer, Barreira, Hebert, & Tubaro, 2009). In addition, we
amplified the COI gene of feather mites (bcdF05/bcdR04, Dabert,
Ehrnsberger, & Dabert, 2008) to molecularly confirm the mite
DO ~
NA ET AL.
|
3
species identity (Do~
na, Diaz-Real, et al., 2015). Only bacterial and
fungal regions were amplified from the external samples.
Libraries were built following the recommended protocol by Illu-
mina for bacterial 16S metabarcoding, with some modifications. Simi-
lar protocols have been used by other authors (e.g., Lange et al.,
2014; Vierna, Do~
na, Vizca
ıno, Serrano, & Jovani, 2017). Briefly, the
libraries were constructed in a two-step PCR (hereafter, PCR1 and
PCR2): PCR1s were carried out in a final volume of 25 ll, containing
6.50 ll of Supreme NZYTaq Green PCR Master Mix (NZYTech),
0.5 lM of each primer and PCR-grade water up to 25 ll. Thermal
cycling conditions included an initial denaturation step at 95°C for
5 min, followed by 35 cycles of denaturation at 95°C for 30 s,
annealing at various temperatures (bacteria: 50°C; fungi: 52°C; plant:
51°C; bird: 59°C; mite: 55°C), extension at 72°C for 45 s and a final
extension step at 72°C for 10 min. PCR1 products were purified by
solid-phase reversible immobilization (SPRI) (Hawkins, O'Connor-
Morin, Roy, & Santillan, 1994), using Mag-Bind RXNPure Plus mag-
netic beads (Omega Biotek). To eliminate the primer dimers gener-
ated during PCR, we used a final bead concentration of 0.5X, thus
size selecting the high molecular weight amplicons over primer
dimers. The purified products were loaded in a 1% agarose gel
stained with GreenSafe (NZYTech) and visualized under UV light.
PCR2 was carried out using 2.5 ll of the amplified DNA from
PCR1 as a template and was performed under the same conditions
as PCR1, but only running five cycles at 60°C as the optimal anneal-
ing temperature.
A total of 31 different index combinations were used, and 40
PCR cycles were performed (Vierna et al., 2017). The resulting prod-
ucts were purified following the SPRI method as indicated above.
Likewise, the purified products were loaded in a 1% agarose gel
stained with GreenSafe (NZYTech) and visualized under UV light.
All products (a total of 238 libraries) were pooled together in 21
sets of differentially indexed samples. All pools were quantified with
Qubit
™
fluorometer (Invitrogen). We did not obtain bird DNA in any
sample and plant DNA only from two samples (see Results below).
Accordingly, all except one plant pool (i.e., the one containing the
only two samples successfully amplified, see Results below) were not
sequenced as they did not reach the minimum amount of DNA for
HTS.
All pools were sequenced by Novogene (Beijing, China) on Illu-
mina HiSeq 4000 using the PE 250 strategy (see Supporting Infor-
mation for coverage information; Table S2). Quality controls were
carried out using company in-house Perl scripts to remove contami-
nated adaptors and low-quality sequences.
2.4
|
Bioinformatic analysis
Bacterial sequences were postprocessed and classified with MOTHUR
v1.38.1 (Schloss et al., 2009) according to the MiSeq SOP (accession
date: 30 August 2016, Kozich, Westcott, Baxter, Highlander, &
Schloss, 2013). In brief, sequences were aligned and classified
against the SILVA (v123) database (Pruesse et al., 2007). Potential
mitochondrial, chloroplastidial and other nontarget sequences were
removed, and the UCHIME algorithm was used to identify and
remove chimeras (Edgar, Haas, Clemente, Quince, & Knight, 2011).
Lastly, sequences were clustered into OTUs using the cluster.split
command. Fungal sequences were processed using the PIPITS pipe-
line (Gweon et al., 2015). Briefly, this procedure extracts the ITS
subregion from reads and then assigns them taxonomically with a
trained RDP Classifier (Bengtsson-Palme et al., 2013). One mite sam-
ple containing <100 reads after preprocessing was not used for fur-
ther analyses on fungal sequences (see Table S2). Plant raw reads
were quality trimmed (sliding window of 30 bp with a minimal aver-
age Phred score of 33) using TRIMMOMATIC 0.36 (Bolger, Lohse, &
Usadel, 2014) and then clustered to OTUs at 97% using CD-HIT ver-
sion 4.5 (Fu, Niu, Zhu, Wu, & Li, 2012). Representative (centroid)
sequences were blasted using MEGABLAST against the NCBI “nr”
nonredundant nucleotide sequence collection (National Center for
Biotechnology Information: http://www.ncbi.nlm.nih.gov/).
Mite identity was molecularly confirmed in all cases using a simi-
lar pipeline to that used in Do~
na, Moreno-Garc
ıa, Criscione, Serrano,
and Jovani (2015). In brief, we used Geneious R10 (http://www.ge
neious.com, Kearse et al., 2012) plugin Sequence classifier, over a
concatenated file containing the forward and reverse reads (quality
trimmed as described above for plant libraries and with a minimum
length of 200 bp). Then, we used the recommended threshold and a
reference DNA barcode library (Do~
na, Diaz-Real, et al., 2015).
2.5
|
Statistical analysis
Differences in prevalence and morphological diversity of diet
resources (the maximum diversity retrieved for each mite sample,
that is, each mite infrapopulation; see above) from microscopy
assessments were analysed using generalized linear mixed models
(GLMM) (GLMER function from package LME4 1.1-12, Jovani & Tella,
2006; Bates, M€
achler, Bolker, & Walker, 2015). For assessing differ-
ences in prevalence, we ran a binomial GLMM considering preva-
lence (1: presence, 0: absence) as the response variable, the type of
food resource as the predictor variable and the bird infrapopulation
nested into bird species plus mite genera as random factors. For
assessing differences in morphotype diversity of fungi and diatoms,
we ran a Poisson GLMM considering morphotype diversity as the
response variable, and the same structure of predictor and random
factors. We confirmed assumptions underlying GLMMs by exploring
regression residuals for normality against Q-Q plots.
Fungal and bacterial OTUs were imported to Rand manipulated
using PHYLOSEQ R package (McMurdie & Holmes, 2013). In particular,
we studied the variance in bacterial and fungal assemblage composi-
tion among infrapopulations using a permutational multivariate anal-
ysis of variance on Bray–Curtis and Jaccard distance matrices
(PERMANOVA; adonis function from the VEGAN v2.4.1 Rpackage,
Oksanen et al., 2017). The null hypothesis was that the centroid
does not differ between host species and/or mite species (Anderson
& Walsh, 2013). This test is highly sensitive to data dispersion
(Anderson, 2001), and thus, we tested it with the multivariate homo-
geneity PERMDISP2 procedure (Anderson, 2006; betadisper function
4
|
DO ~
NA ET AL.
from VEGAN, Anderson & Walsh, 2013) with 999 permutations. Addi-
tionally, following previous approaches to overcome this statistical
issue (e.g., Brice, Pellerin, & Poulin, 2017), we explored the commu-
nity clustering with ordination analyses (principal coordinates analy-
ses, PCoA) and stacked bar plots at the infrapopulation level.
3
|
RESULTS
3.1
|
Composition and morphological diversity of
feather mites’diets assessed by microscopy
From a total of 481 infrapopulations (1,300 individual mites) belong-
ing to 190 bird species and 100 mite genera, fungal material (spores
and hyphae) was the most prevalent type of gut content (GLMM:
v²=168.73, df =2, p<.001; Figure 1) and the most morphologi-
cally diverse (GLMM: v²=442.5, df =2, p<.001; Figure 1). In addi-
tion, diatoms and plant material were also found, but in a much
lower frequency and morphotype diversity than fungi (Figure 1).
Highly similar results were found when only analysing passerines
(Figure S1 and S2), the avian order in which bird species were also
studied using DNA metabarcoding (see below). The overall predomi-
nance of fungi was widespread across the avian phylogeny (Figure 2)
and feather mite taxonomy (Table 1).
3.2
|
DNA metabarcoding of feather mites’diets
Metabarcoding results of the mite species from the genera Procto-
phyllodes Robin, 1877, Trouessartia Canestrini, 1899, Dolichodectes
Park & Atyeo, 1971, and Scutulanysuss Mironov, 1985 showed
highly congruent results with the microscopic analyses in terms of
the prevalence and diversity of food resources, while complementing
them with bacterial detection and providing taxonomic detail of the
organisms involved. We found bacterial DNA in all samples
(Table S2). The bacterial genera identified primarily belonged to the
phyla Proteobacteria, Actinobacteria and Bacteroidetes, with Pro-
teobacteria being the most frequently represented (Figure S5).
Within these phyla, we retrieved a high diversity of bacterial genera
(Figures 3, S7 and S8). Genera commonly found in soil and as envi-
ronmental “background noise”such as Sphingomonas, Acinetobacter
and Pseudomonas were the most prevalent genera (Table 2, Fig-
ures 3, S7 and S8) while typically endosymbiotic genera such as Bar-
tonella, Enterococcus and Buchnera were the most abundant when
they were present (Table 2, Figures 3, S7 and S8). PERMANOVAs
showed statistically significant differences in bacterial composition
between mite (53% variance, F=1.25, p=.006) and bird species
(52% variance, F=1.31, p=.001). Nonetheless, we found different
levels of dispersion between mite (F=7.19, p=.001) and bird
0
20
40
60
Fungi Diatoms Plants
Prevalence
0
3
6
9
Fungi Diatoms Plants
Diversity
(a) (b)
FIGURE 1 Barplot and boxplot depicting the (a) prevalence (N=481) and (b) morphological diversity (using the maximum diversity
retrieved per infrapopulation) of diet items found in the microscopy assessment of feather mite gut contents. Error lines in (a) represent
confidence intervals (95%). Blue dots in (b) represent real data points (jittered). Representative pictures of each food resource are placed
beneath the plots
DO ~
NA ET AL.
|
5
species (F=9.95, p=.001). In addition, ordinations as well as indi-
vidual stacked bar plots of bacterial profiles did not show clustering
by mite or by bird species in bacterial OTUs or genera (Figures 4
and S7). Additionally, a re-analysis excluding all bacterial OTUs found
in the external samples, that is, to exclude potential environmental
contamination coming from bacterial OTUs still remaining after mite
washes, showed almost identical results: significant differences in
bacterial composition between mite species (PERMANOVA, 51%
variance, F=1.15, p=.023) and bird species (PERMANOVA, 49%
variance, F=1.20, p=.01). Nonetheless, again, we found different
levels of dispersion between mite species (F=8.46, p=.002) and
between bird species (F=11.84, p=.001). In addition, ordination
and profile plots did not show clustering by either mite or bird spe-
cies in bacterial OTUs and genera (Figures S8 and S9).
We found fungal DNA in all infrapopulations except one
(Table S2). Overall, we retrieved a high diversity of fungal species,
Picus canus
Dryocopus javensis
Colaptes auratus
Chrysocolaptes lucidus
Sphyrapicus varius
Picoides pubescens
Picoides villosus
Asio otus
Asio flammeus
Bubo virginianus
Aegolius funereus
Surnia ulula
Pandion haliaetus
Accipiter cooperii
Accipiter gentilis
Haliaeetus leucocephalus
Elanoides forficatus
Collocalia troglodytes
Collocalia esculenta
Apus pacificus
Falco sparverius
Passer domesticus
Passer montanus
Plectrophenax nivalis
Seiurus aurocapilla
Dendroica tigrina
Dendroica petechia
Geothlypis trichas
Oporornis philadelphia
Ver mi vora peregrina
Seiurus noveboracensis
Sturnella neglecta
Icterus galbula
Euphagus cyanocephalus
Agelaius phoeniceus
Molothrus ater
Melospiza melodia
Ammodramus leconteii
Pooecetes gramineus
Junco hyemalis
Zonotrichia leucophrys
Zonotrichia albicollis
Spizella arborea
Spizella pallida
Spizella passerina
i
ma
h
tals
u
h
pol
e
M
a
n
aic
i
vod
u
lag
nari
P
Pheucticus ludovicianus
Carpodacus purpureus
Pinicola enucleator
Carduelis pinus
Loxia curvirostra
Loxia leucoptera
Carduelis flammea
Carduelis hornemanni
Carduelis tristis
Coccothraustes vespertinus
Anthus hodgsoni
Lonchura malacca
Irena cyanogastra
Chloropsis hardwickii
Dicaeum hypoleucum
Dicaeum ignipectus
Arachnothera magna
Aethopyga christinae
Regulus calendula
Bombycilla cedrorum
Bombycilla garrulus
Cyornis herioti
Myophonus caeruleus
Copsychus luzoniensis
Cyornis rufigastra
Zoothera sibirica
Zoothera citrina
Zoothera naevia
Catharus ustulatus
Catharus guttatus
Turdus migratorius
Sialia currucoides
Myadestes townsendi
Dumetella carolinensis
Sturnus vulgaris
Sitta canadensis
Sitta frontalis
Sitta carolinensis
Certhia americana
Spizixos semitorques
Hemixos castanonotus
Parad oxornis webbianus
Alcippe morrisonia
Zosterops japonicus
Leiothrix lutea
Garrulax sannio
Stachyris capitalis
Progne subis
T
achycineta bicolor
Riparia riparia
Hirundo daurica
Hirundo rustica
Aegithalos concinnus
Phylloscopus cebuensis
Orthotomus cuculatus
Eremophila alpestris
Alauda arvensis
Parus atricapillus
Vireo olivaceus
Dicrurus leucophaeus
Dicrurus balicassius
Corvus corax
Pica hudsonia
Cyanocitta cristata
Urocissa erythrorhyncha
Hypothymis helenae
Te r p siphone cinnamomea
Oriolus sagittatus
Pericrocotus ethologus
Hemipus picatus
Manorina melanocephala
Sclerurus caudacutus
Sclerurus mexicanus
Campylorhamphus trochilirostris
Conopophaga ardesiaca
Contopus cooperi
Empidonax alnorum
Empidonax minimus
Ty ra nn us ty rannus
Lepidothrix coeruleocapilla
Alisterus scapularis
Tr ichoglossus haematodus
Pezoporus wallicus
Platycercus caledonicus
Platycercus adscitus
Lathamus discolor
Neophema chrysogaster
Cacatua roseicapilla
Cuculus fugax
Cepphus grylle
Fratercula arctica
Larus delawarensis
sut
atnegr
a
sura
L
nac
x
ipips
ur
a
L
Sterna hirundo
Chlidonias niger
Bartramia longicauda
Calidris minutilla
Limnodromus griseus
Gallinago gallinago
T
ringa solitaria
Tringa flavipes
Tringa melanoleuca
Haematopus bachmani
Charadrius vociferus
Grus canadensis
Porzana carolina
Fulica americana
Podiceps grisegena
Podi ceps auritus
Phaethon aethereus
Phaethon rubricauda
Nycticorax caledonicus
Gorsachius melanolophus
Pelecanus erythrorhynchos
Hydrobates pelagicus
Bulweria bulwerii
Calonectris diomedea
Puffinus puffinus
Phalacrocorax auritus
Gavia pacifica
Gavia immer
Phapitreron leucotis
Ptilinopus superbus
Perdix perdix
Phasianus colchicus
Bonasa umbellus
Lagopus lagopus
Dendragapus canadensis
Alectura lathami
Anas discors
Anas clypeata
Anas americana
Anas platyrhynchos
Oxyura jamaicensis
Anser albifrons
Cygnus columbianus
Nothura maculosa
Rhynchotus rufescens
FIGURE 2 50% majority-rule consensus phylogenetic tree depicting the distribution of food resources retrieved by microscopic analysis of
feather mite gut contents across the phylogeny of birds. In brief, 1,000 trees were obtained from BirdTree (Jetz, Thomas, Joy, Hartmann, &
Mooers, 2012, http://birdtree.org) and summarized using SUMTREE v 4.1.0 in DENDROPYV4.1.0 (Sukumaran & Holder, 2010, 2015), following
Rubolini, Liker, Garamszegi, Møller, and Saino (2015). Rings from the centre out, brown: fungi. Mustard: diatoms. Green: plants. Most external
ring colours depict bird orders
6
|
DO ~
NA ET AL.
which was much higher in the mite samples compared to the external
samples (See Material and Methods above, Figure S5). Fungal species
retrieved from mite samples mostly belonged to the phyla Ascomycota
and Basidiomycota, with Ascomycota being the most represented (Fig-
ure S4). At the genus level, the most prevalent were Cladosporium,
Toxicocladosporium and Aureobasidium (Table 2, Figures 5 and S6).
On the other hand, Meira, Malassezia and Talaromyces were the most
abundant fungal genera when present (Table 2, Figures 5 and S6).
Interestingly, we retrieved genera for which keratinolytic activity is
known, such as Cladosporium, Acremonium, Malassezia, Penicillium
and Phoma. PERMANOVAs showed significant differences in fungal
composition between mite species (51% variance, F=1.18, p=.027)
and bird species (49% variance, F=1.21, p=.016). Nonetheless, dis-
persion analyses (see Methods) revealed different levels of dispersion
between mite species (F=9.22, p=.004) and between bird species
(F=9.36, p=.002), suggesting the need for a detailed inspection of
the within-species variance. By doing so, principal coordinates analy-
ses as well as stacked bar plots at the individual level within species
showed no apparent consistency of fungal profiles either within mite
or bird species (Figures 6 and S6).
Plant DNA was only sequenced from two infrapopulations (of 34)
from two mite species inhabiting two different bird individuals. The
first infrapopulation from which plant DNA was recovered belonged to
Proctophyllodes sylviae Gaud, 1957 from the Blackcap Sylvia atricapilla
(Linnaeus, 1758). Plant OTUs retrieved matched to Polygala teretifolia
Thunb. (99.7% pairwise similarity; grade 88.6%), Citrus clementine hort.
(two OTUs: 92.8, 98.4% pairwise similarity; grade 96.4, 99.2%), Daphne
laureola L. (94.9% pairwise similarity; grade 93.2%) and Digitalia ciliaris
(Retz.) Koeler. (96.5% pairwise similarity; grade 96%). The second
infrapopulation belonged to Trouessartia bifurcata (Trouessart, 1885)
also from a Sylvia atricapilla host, in which the single OTU retrieved
matched to Quercus sp. (95.5% pairwise similarity; grade 97.7%).
4
|
DISCUSSION
In this study, by analysing the diet of feather mites using both DNA
metabarcoding and microscopy-based methods, we investigated the
long-standing question of the nature of the interaction between birds
and feather mites. Fungi and potentially bacteria (see below) were
revealed as the main recognizable food resources for feather mites,
while diatoms and plant matter appeared as rare food resources. Simi-
larly, Dubinin (1951) examined the guts of 18,735 specimens of
Freyana spp. (Freyanidae) from waterfowl and found diatoms in only
135 of them (0.72%). Importantly, we did not find visual or DNA evi-
dence of feather mites feeding upon bird resources (e.g., blood, skin)
other than likely uropygial gland oil (see Materials and Methods), in
spite of using primers suitable for amplifying degraded bird DNA. We
observed no obvious feather filaments in our microscopy analysis, but
this and our molecular study would not have been able to identify tiny
(non-DNA-bearing) fragments of feathers, which have been occasion-
ally reported in microscopy studies. The chelicerae of vane-dwelling
feather mites do not seem capable of cutting or tearing intact feathers,
so if the tiny fragments we observed in the guts are indeed feather
fragments, they would likely be ingested along with other loose mate-
rial. In addition, we found a high prevalence of both keratinophilic and
pathogenic fungal taxa (e.g., Cladosporium,Penicillium, Al Rubaiee, Al
Murayati, Nielsen, & Møller, 2017; Friedrich, Gradi
sar, Mandin, &
Chaumont, 1999; Gunderson, 2008; Marchisio, Curetti, Cassinelli, &
Bordese, 1991; Nwadiaro, Ogbonna, Wuyep, & Adekojo, 2015) in
feather mite guts. Whether the quantities of bacteria and fungi eaten
by feather mites are enough to increase host fitness requires further
study. Altogether, our results support previous evidence on the com-
mensalistic–mutualistic role of vane-dwelling feather mites (Blanco
et al., 1997, 2001; Galv
an et al., 2012; Proctor, 2003; Walter & Proc-
tor, 2013a,b,c). Thus, vane-dwelling feather mites probably should no
longer be considered to be parasites of birds (e.g., Harper, 1999) but
rather commensalists–mutualists. This does not apply to the few taxa
of quill-dwelling feather mites that clearly feed on feather pith (e.g.,
Ascouracaridae) or those that live on or in the epidermis of the host
(e.g., Dermationidae, Epidermoptidae) (Gaud & Atyeo, 1996; Proctor,
1999). Additionally, whether uropygial gland oil constitutes an impor-
tant food resource for feather mites remains unanswered from our
data (Pap, V
ag
asi, Osv
ath, Muresßan, & Barta, 2010) and should be
studied using more sensitive methods (e.g., HPLC, histological staining
analysis). Indeed, should uropygial gland oil be beneficial for birds, a
TABLE 1 Prevalence (% of feather mite
infrapopulations) of identified food items
found in the best-sampled mite families.
Phylogenetic information was retrieved
from Klimov and O’Connor (2013)
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NA ET AL.
|
7
large number of mites feeding upon this resource might have a detri-
mental effect on host fitness (Blanco et al., 2001). However, a recent
review concluded that is not even clear how or if uropygial gland oil
affects bird fitness (Moreno-Rueda, 2017). In the light of our findings,
previous occasional documentation of unhealthy birds with high num-
bers of vane-dwelling feather mites (e.g., Atyeo & Gaud, 1979) could
be reinterpreted as birds in poor condition providing more food
resources to feather mites (e.g., fungi and bacteria, which may be
directly or indirectly related with host’health status, Blanco et al.,
2001; Soler et al., 2012). It may also be that birds in poor condition
preen less, which could in turn impact the abundance of feather mites
if they are susceptible to removal by preening activities. However, it
remains the possibility that feather mites have an effect on host fit-
ness by removing preen gland oil, by potential aerodynamic costs of
harbouring large amounts of mites and by indirect effects on host fit-
ness mediated by other ectoparasites (e.g., the occasional ingestion of
feather mites by feather lice which may indirectly increase the cost of
parasitism of feather lice).
The possibility that symbiont species might be at risk of extinc-
tion (e.g., Carlson et al., 2017; R
ozsa & Vas, 2014) suggests the need
0.00
0.25
0.50
0.75
1.00
Dolichodectes edwarsii
Dolichodectes hispanicus
Proctophyllodes cetti
Proctophyllodes clavatus
Proctophyllodes doleophyes
Proctophyllodes lusciniae
Proctophyllodes motacillae
Proctophyllodes reguli
Proctophyllodes rubeculinus
Proctophyllodes stylifer aff caeruleus
Proctophyllodes stylifer aff cristatus
Proctophyllodes stylifer aff major
Proctophyllodes stylifer aff troglodytes
Proctophyllodes sylviae
Proctophyllodes vassilevi
Scutulanyssus hirundicola
Trouessartia bifurcata
Trouessartia reguli
Trouessartia rubecula
Genus
Xenophilus
Wolbachia
Weissella
Veillonella
Varibaculum
Turicella
Tepidiphilus
Taibaiella
Streptococcus
Stenotrophomonas
Staphylococcus
Spirosoma
Sphingomonas
Sphingobium
Sphingobacterium
Sorangium
Solitalea
Solirubrobacter
Shinella
Sediminibacterium
Salinicola
Saccharopolyspora
Rothia
Roseomonas
Rickettsia
Rhodopseudomonas
Rhodococcus
Ralstonia
Pseudoxanthomonas
Pseudonocardia
Pseudomonas
Prevotella
PRD01a011B
Porphyrobacter
Pontibacter
Polynucleobacter
Planctomyces
Phreatobacter
Phenylobacterium
Perlucidibaca
Peptoniphilus
Pedobacter
Patulibacter
Paracocccus
Opitutus
Ohtaekwangia
Novosphingobium
Nocardioides
Nevskia
Neisseria
Moraxella
Microvirga
Micrococcus
Methylobacterium
Methylobacillus
Massilia
Marmoricola
Lysobacter
Limnobacter
Lactobacillus
Kocuria
Hyphomicrobium
Hymenobacter
Hydrotalea
Hydrogenophaga
hgcI_clade
Herbaspirillum
Gemella
Fusobacterium
Fructobacillus
Fluviicola
Flavobacterium
Finegoldia
Fibrella
Ezakiella
Exiguobacterium
Escherichia−Shigella
Epilithonimonas
Enterococcus
Dyadobacter
Duganella
Dietzia
Dermacoccus
Curtobacterium
Cryptosporangium
Croceicoccus
Corynebacterium_1
Corynebacterium
Cloacibacterium
Chthoniobacter
Chryseobacterium
Caulobacter
Capnocytophaga
Caldibacillus
Buchnera
Bryobacter
Brevundimonas
Brevibacterium
Bradyrhizobium
Bosea
Blastomonas
Blastococcus
Bifidobacterium
Bdellovibrio
Bartonella
Bacteroides
Bacillus
Azospirillum
Aureimonas
Arenimonas
Arcicella
Aquabacterium
Anaerococcus
Alpinimonas
Alloprevotella
Alloiococcus
Alicyclobacillus
Alcanivorax
Agaricicola
Actinomycetospora
Actinomyces
Acinetobacter
12up
Relative abundance of bacterial genera
FIGURE 3 Stacked bar plots of the bacterial genera retrieved in the molecular analyses of mite species. Low abundance taxa (<2%) were
not shown for illustrative purposes
8
|
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NA ET AL.
for a rapid integration of this knowledge into bird-related practices,
such as those in wild bird conservation programmes. Also, our results
suggest that further studies of birds in farms, zoos and the pet trade
are needed, where traditionally feather mites were viewed as para-
sites, with birds provided with treatment using acaricides (e.g., Alek-
seev, 1998; Salisch, 1989). This practice not only has the downside
of monetary expense because of the use of acaricides, but could also
result in the loss of the potential services provided by feather-clean-
ing mites, as our results suggest.
Analyses of the bacterial and fungal DNA found in the guts of
feather mites revealed a high diversity of taxa that were not struc-
tured by host or by mite species (Figures 4, 6 and S6-S9). This sug-
gests trophic opportunism of mites (da Silva, Dorrestein, & Quinn,
2015; Kent & Burtt, 2016), which may graze upon whatever food
resources might be available at the time. This opportunistic “feather-
cleaning”feeding behaviour is also supported by the large amount of
unidentifiable items we found in the guts and by the higher abun-
dance and diversity of fungi found in the mite samples in comparison
with the external samples (e.g., Figures S3 and S10). Overall, many
other species of sarcoptiform mites, including many free-living Astig-
mata, are functionally defined as fungivore–microbivore–detritivores
(e.g., Pyroglyphidae and most oribatid mites, Walter & Proctor
2013a,b), and our results also support this classification for feather
mites. In fact, our results are in large agreement with previous stud-
ies on microbes found in other mite species (Chaisiri, McGarry, Mor-
and, & Makepeace, 2015; Hubert et al., 2012), where strong
TABLE 2 Prevalence and abundance (mean; minimum–maximum) statistics from the 30 most prevalent fungal and bacterial genera retrieved
by DNA metabarcording. The three genera which were, on average, most abundant for each taxon, are asterisked and highlighted in bold.
Relative abundance was calculated as the % of sequences of the given genus in those samples where the genus was found
Fungi
Prevalence
(% of samples)
Relative abundance
(% sequences within samples) Bacteria
Prevalence
(% of samples)
Relative abundance
(% sequences within samples)
Cladosporium 63 17; 2–62 Sphingomonas 88 12; 5–33
Toxicocladosporium 63 26; 2–89 Acinetobacter 71 18; 5–66
Aureobasidium 53 26; 2–70 Pseudomonas 71 14; 5–50
Cryptococcus 42 6; 2–11 Sediminibacterium 53 10; 6–19
*Malassezia 42 31; 3–94 Brevundimonas 47 11; 6–18
Penicillium 42 11; 2–43 Escherichia–Shigella 41 7; 5–12
Rhodotorula 32 7; 2–21 Staphylococcus 41 14; 5–35
Acremonium 26 9; 2–18 Methylobacterium 35 8; 6–12
Catenulostroma 26 13; 3–37 Massilia 29 10; 6–21
Devriesia 26 7; 2–14 *Bartonella 24 42; 6–90
Erysiphe 26 23; 7–76 Blastomonas 24 9; 5–14
Pleurotus 26 8; 2–13 Streptococcus 24 10; 6–13
Alternaria 21 13; 6–18 Bradyrhizobium 18 6; 5–7
Aspergillus 21 10; 2–29 Corynebacterium_1 18 7; 7–7
Beauveria 21 8; 4–11 Lactobacillus 18 8; 6–10
Erythrobasidium 21 10; 2–22 Moraxella 18 8; 5–11
Sporobolomyces 21 5; 2–712up 12 11; 8–13
*Talaromyces 21 30; 3–98 Actinomycetospora 12 5; 5–6
Dioszegia 16 3; 3–4Bosea 12 7; 6–9
Golovinomyces 16 13; 2–26 Chryseobacterium 12 6; 6–6
*Meira 16 47; 5–73 *Enterococcus 12 40; 14–57
Phaeotheca 16 21; 18–27 Alicyclobacillus 6 12; 12–12
Pseudocercospora 16 15; 12–21 Anaerococcus 67;7–7
Stagonospora 16 11; 2–27 Aquabacterium 66;6–6
Tilletiopsis 16 6; 3–8Arcicella 66;6–6
Arthrocatena 11 3; 3–3Bacteroides 6 20; 20–20
Claviceps 11 4; 3–6*Buchnera 6 41; 41–41
Debaryomyces 11 17; 4–29 Cloacibacterium 69;9–9
Exobasidium 11 11; 9–14 Duganella 68;8–8
Farysizyma 11 8; 5–12 Dyadobacter 6 10; 10–10
DO ~
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|
9
evidence has been found for the utilization of bacteria as a food
source in free-living astigmatan species (Erban & Hubert, 2008,
2010; Hubert, Nesvorna, Kopeck
y, S
agov
a-Mare
ckov
a, & Poltronieri,
2014; Hubert et al., 2016). In these studies, microbiomes composed
of highly diverse taxa in low abundance have been interpreted as
evidence for microbivory. In contrast, microbiome profiles showing
a low diversity of highly abundant taxa are interpreted as evidence
of symbiotic or pathogenic bacterial species (Hammer, Janzen, Hall-
wachs, Jaffe, & Fierer, 2017; Hubert et al., 2016). In this way, the
prevalence–abundance patterns of the bacteria found here (Table 2)
suggest a combination of bacteria used as food resource (mostly
environmental-associated genera, which were more prevalent but
less abundant, for example,Sphingomonas and Acinetobacter;
Table 2) and of potentially symbiotic, commensalistic or pathogenic
bacteria (less prevalent but much more abundant when present, for
example,Bartonella,Enteroccocus; and the primary endosymbiont,
Buchnera; Table 2).
Lack of a stable “microbiome”across different individuals of a
given species has been found in other organisms with a nutritionally
broad diet (Shapira, 2016). In contrast, species with highly biased
diets, such as lice feeding on bird feathers (mainly keratin) or ter-
mites feeding on dead wood (mainly cellulose), typically have perma-
nent and relatively stable endosymbiotic bacteria which provide
them essential vitamins or other nutritional supplements (Puchta,
1955; Ohkuma, 2008; Perotti, Kirkness, Reed, & Braig, 2009; Boyd
et al., 2016; but see Hammer et al., 2017). Thus, our results suggest-
ing the lack of a stable microbiome at the mite species level add
support to the hypothesis of a generalist fungivore–microbivore–
Axis.2 (8.5%)
−0.25
0.00
0.25
0.50
−0.2 0.0 0.2 0.4
Axis.1 (9.5%)
Axis.2 (8.5%)
−0.2
0.0
0.2
0.4
Axis.2 (6.3%)
Dolichodectes edwarsii
Dolichodectes hispanicus
Proctophyllodes cetti
Proctophyllodes clavatus
Proctophyllodes doleophyes
Proctophyllodes lusciniae
Proctophyllodes motacillae
Proctophyllodes reguli
Proctophyllodes rubeculinus
Proctophyllodes stylifer aff caeruleus
Proctophyllodes stylifer aff cristatus
Proctophyllodes stylifer aff major
Proctophyllodes stylifer aff troglodytes
Proctophyllodes sylviae
Proctophyllodes vassilevi
Scutulanyssus hirundicola
Trouessartia bifurcata
Trouessartia reguli
Trouessartia rubecula
Mite species
−0.2
0.0
0.2
0.4
Axis.1 (6.6%)
Axis.2 (6.3%)
−0.2 0.0 0.2
Axis.1 (6.6%)
−0.2 0.0 0.2
−0.2 0.0 0.2 0.4
Axis.1 (9.5%)
−0.25
0.00
0.25
0.50
Acrocephalus arundinaceus
Acrocephalus scirpaceus
Certhia brachydactyla
Cettia cetti
Cyanistes caeruleus
Erithacus rubecula
Ficedula hypoleuca
Hippolais polyglotta
Hirundo rustica
Luscinia megarhynchos
Motacilla cinerea
Parus cristatus
Parus major
Regulus ignicapillus
Sylvia atricapilla
Sylvia borin
Sylvia melanocephala
Troglodytes troglodytes
Bird species
(a)
(c)
(b)
(d)
FIGURE 4 Principal coordinates analysis (PCoA) of bacterial communities of feather mite infrapopulations: First row, samples coloured by
mite species and (a) based on Bray–Curtis and (b) Jaccard distances, respectively; Second row, samples coloured by bird species and c) based
on Bray–Curtis and (d) Jaccard distances, respectively. OTUs counts were scaled to the smallest library following McMurdie and Holmes (2014)
and Denef, Fujimoto, Berry, and Schmidt (2016)
10
|
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NA ET AL.
detritivore diet for the feather mites reported here, instead of these
resources being taken as a by-product of a diet based mostly on
uropygial oil (Engel & Moran, 2013; Sanders et al., 2017; Shapira,
2016). In fact, in 42% of the mites in which we detected any food
resource, we did not see any oil globules (but see Materials and
Methods) also suggesting that resource intake does not depend on
oil ingestion.
A further understanding of the multilayered hologenome (i.e., to
distinguish between stable–unstable, adapted–unadapted bacterial
taxa, Shapira, 2016) through large-scale microbiome-oriented studies
will help in disentangling the role of these potentially symbiotic or
pathogenic bacteria of feather mites. Furthermore, whether feather
mites select among available food resources (fungal preferences have
been found in free-living fungivorous Astigmata, Hubert et al., 2003;
Hubert, Jarosık, Mourek, Kubatova, & Zdarkova, 2004) or do not
need to rely on bacterial symbionts requires further experimental
study. Lastly, a hypothesis of an “external-rumen”mode of feeding,
in which mites ingest predigested food (by bacteria), has been also
supported in free-living astigmatan mites (Hubert et al., 2014, 2016)
and would be also compatible with our results.
Feather mite species are relatively host-specific and (presumably)
host-specialized symbionts that appear to have relatively low levels
0.00
0.25
0.50
0.75
1.00
Dolichodectes edwarsii
Dolichodectes hispanicus
Proctophyllodes cetti
Proctophyllodes clavatus
Proctophyllodes doleophyes
Proctophyllodes lusciniae
Proctophyllodes motacillae
Proctophyllodes reguli
Proctophyllodes rubeculinus
Proctophyllodes stylifer aff caeruleus
Proctophyllodes stylifer aff cristatus
Proctophyllodes stylifer aff major
Proctophyllodes stylifer aff troglodytes
Proctophyllodes sylviae
Proctophyllodes vassilevi
Scutulanyssus hirundicola
Trouessartia bifurcata
Trouessartia reguli
Trouessartia rubecula
Relative abundance of fungal genera
Genus
Xylodon
Wallemia
Var ari a
Uwebraunia
Trichomonascus
Tremella
Toxicocladosporium
Tilletiopsis
Teratosphaeria
Sympoventuria
Stemphylium
Stagonospora
Sporobolomyces
Slimacomyces
Skeletocutis
Sistotrema
Setoseptoria
Scytalidium
Sclerostagonospora
Sarocladium
Rhodotorula
Rhinocladiella
Radulidium
Pyrenophora
Pyrenochaetopsis
Pyrenochaeta
Pseudozyma
Pseudocercospora
Protomyces
Podosphaera
Plenodomus
Phoma
Phlogicylindrium
Phialophora
Phellinus
Phaeotheca
Phaeosphaeria
Phaeomoniella
Phacidium
Phacidiella
Perenniporia
Penicillium
Parmotrema
Neoerysiphe
Mycosphaerella
Mucor
Meira
Malassezia
Lophodermium
Lophiostoma
Leucosporidium
Leucosporidiella
Leptoxyphium
Lecanicillium
Lalaria
Kondoa
Itersonilia
Immersidiscosia
Hyphopichia
Hortaea
Gymnopus
Golovinomyces
Gnomoniopsis
Geosmithia
Fusicladium
Fuscoporia
Fellomyces
Farysizyma
Exophiala
Exobasidium
Erythrobasidium
Erysiphe
Entoleuca
Dioszegia
Devriesia
Cystofilobasidium
Cystobasidium
Cylindrium
Cyathicula
Curreya
Cryptococcus
Coniosporium
Clohesyomyces
Cladosporium
Cladophialophora
Ceriporia
Ceraceomyces
Catenulostroma
Candida
Blumeria
Arthrocatena
Acremonium
FIGURE 5 Stacked bar plots of the fungal genera retrieved in the molecular analyses of mite species. Low abundance taxa (<2%) were not
shown for illustrative purposes
DO ~
NA ET AL.
|
11
of switching to new host species (Do~
na, Proctor, et al., 2017; Do~
na,
Sweet, et al., 2017; Gaud 1992; Klimov, Mironov, & O’Connor,
2017; Matthews et al., 2018). These switches mostly involve closely
related hosts, but major-host switches (e.g., between bird orders)
have been revealed as a major driver of their diversification (Do~
na,
Proctor, et al., 2017). As for many other host–symbiont systems
(Clayton, Bush, & Johnson, 2016; Nylin et al., 2017), understanding
the (co)eco-evolutionary scenario of host-switching in this host–sym-
biont system is still in its infancy. However, the likely opportunistic
diet of feather mites reported here suggests that host-switching of
feather mites would not be constrained by the extrinsic nutritional
resources available on the new host (but it may be, for example,by
feather morphology or by the bird preening efficiency; Clayton et al.,
2005). Uropygial gland oil composition, however, differs between
birds (Soini, Whittaker, Wiesler, Ketterson, & Novotny, 2013); and
whether mites are specialized to host oil is unknown, and requires
further study. Nevertheless, the fact that different bird species can
harbour contrasting (and consistent) abundances of feather mites
(Diaz-Real et al., 2014; Do~
na, Moreno-Garc
ıa, et al., 2015) suggests
that, among others factors, the abundance of food resources for
feather mites could strongly differ between bird species, but this
also needs additional research.
Overall, this study supports the hypothesis that the interaction
between birds and vane-dwelling feather mites involves commensal-
ism or mutualism, with feather mites acting as feather-cleaners of
birds. This opens the possibility of studying bird-feather mites as an
interesting case study of defensive symbiosis (Hopkins et al., 2017).
Further experimental research is needed to unravel the likely
−0.25
0.00
0.25
0.50
−0.20 0.0 0.2 −0.20 0.0 0.2
−0.20 0.0 0.2
0.4
Axis.2 (8.5%)
−0.25
0.00
0.25
0.50
−0.2 0.0 0.2 0.4
Axis.1 (9.5%)
Axis.2 (8.5%)
−0.2
0.0
0.2
0.4
Axis.2 (6.3%)
Dolichodectes edwarsii
Dolichodectes hispanicus
Proctophyllodes cetti
Proctophyllodes clavatus
Proctophyllodes doleophyes
Proctophyllodes lusciniae
Proctophyllodes motacillae
Proctophyllodes reguli
Proctophyllodes rubeculinus
Proctophyllodes stylifer aff caeruleus
Proctophyllodes stylifer aff cristatus
Proctophyllodes stylifer aff major
Proctophyllodes stylifer aff troglodytes
Proctophyllodes sylviae
Proctophyllodes vassilevi
Scutulanyssus hirundicola
Trouessartia bifurcata
Trouessartia reguli
Trouessartia rubecula
Mite species
−0.2
0.0
0.2
0.4
Axis.1 (6.6%)
Axis.2 (6.3%)
Axis.1 (9.5%) Axis.1 (6.6%)
Acrocephalus arundinaceus
Acrocephalus scirpaceus
Certhia brachydactyla
Cettia cetti
Cyanistes caeruleus
Erithacus rubecula
Ficedula hypoleuca
Hippolais polyglotta
Hirundo rustica
Luscinia megarhynchos
Motacilla cinerea
Parus cristatus
Parus major
Regulus ignicapillus
Sylvia atricapilla
Sylvia borin
Sylvia melanocephala
Troglodytes troglodytes
Bird species
(a) (b)
(d)(c)
FIGURE 6 Principal coordinates analysis (PCoA) of fungal communities of feather mite infrapopulations: First row, samples coloured by
mite species and (a) based on Bray–Curtis and (b) Jaccard distances, respectively; second row, samples coloured by bird species and (c) based
on Bray–Curtis and (d) Jaccard distances, respectively. OTUs counts were scaled to that of the smallest library following McMurdie and
Holmes (2014) and Denef et al. (2016)
12
|
DO ~
NA ET AL.
context-dependent (possibly even occasionally parasitic) relationship
between vane-dwelling feather mites and birds (Blanco et al., 2001).
In particular, future studies should investigate the following. (i) Using
appropriate and sensitive methods such as HPLC, test whether
uropygial gland oil is part of the diet of feather mites. A comparative
exploration of the diet of feather mites inhabiting birds with vestigial
uropygial gland that produce powder down would be also useful. If
uropygial oil is a large component of vane-dwelling feather mites, it
would be then important to test whether removal of the oil affects
bird fitness. (ii) Investigate whether the diet of feather mites differs
along the annual cycle of birds (e.g., migration, moult). (iii) Examine
the potential aerodynamic costs of harbouring different quantities of
feather mites. (iv) Determine effects of feather mites on host fitness
as mediated by other ectosymbionts (e.g., feather lice). (v) Test
whether an experimental increase in feather mites’abundance
increases, decreases or has no overall effect on host fitness. Lastly,
(vi) examine whether experimental variation in feather mites abun-
dance has a context-dependent (e.g., under different environmental
conditions) effect on host fitness over time.
ACKNOWLEDGEMENTS
Funding was provided by the Ministry of Economy and Competitive-
ness (Ram
on y Cajal research contract RYC-2009-03967 to RJ,
research project CGL2011-24466 to RJ, and CGL2015-69650-P to
RJ and DS) and by a Natural Sciences and Engineering Research
Council of Canada (NSERC) Undergraduate Student Research Award
to AO and HP. Also, by “A first look into feather mites diet selection
and endosymbiotic community: a metabarcoding approach”project
funded by the internal EBD proposal call “Microproyectos”financed
by the Spanish Ministry of Economy and Competitiveness, through
the Severo Ochoa Program for Centres of Excellence in R+D+I (SEV-
2012-0262). JD was supported by the Ministry of Economy and
Competitiveness (Severo Ochoa predoctoral contract SVP-2013-
067939 and by a short stay abroad fellowship). We thank Toby Spri-
bille from the University of Alberta for help with categorizing fungal
material, and many collectors of feather mite specimens from around
the world, in particular Sarah Bush and Dale Clayton (University of
Utah). Special thanks to three anonymous reviewers for their con-
structive comments and taxonomic corrections.
AUTHOR CONTRIBUTIONS
J.D., H.P., D.S., K.P., A.O., J.H., M.A. and R.J. conceived the study.
J.D., R.J., H.P. and D.S. designed the study. J.D. analysed the data
with the support of J.H., M.A. and R.J. J.D. wrote the manuscript
with the help of all authors.
DATA ACCESSIBILITY
The HiSeq raw data and the processed representative sequence files
have been deposited in Figshare (https://doi.org/10.6084/m9.figsha
re.5729277).
ORCID
Jorge Do~
na http://orcid.org/0000-0002-5075-9627
Heather Proctor http://orcid.org/0000-0002-4920-9556
David Serrano http://orcid.org/0000-0001-6205-386X
Roger Jovani http://orcid.org/0000-0002-8693-9742
REFERENCES
Al Rubaiee, Z., Al Murayati, H., Nielsen, J. T., & Møller, A. P. (2017).
Fungi, feather damage, and risk of predation. Ecology and Evolution,7,
10797–10803. https://doi.org/10.1002/ece3.3582
Alekseev, A. N. (1998). Ticks and mites as permanent and temporary bird
ectoparasites, vectors and disseminators of pathogens. Scandinavian
Society for Parasitology,8,1–8.
Anderson, M. J. (2001). A new method for non-parametric multivariate
analysis of variance. Austral Ecology,26,32–46.
Anderson, M. J. (2006). Distance-based tests for homogeneity of multi-
variate dispersions. Biometrics,62, 245–253. https://doi.org/10.1111/
j.1541-0420.2005.00440.x
Anderson, M. J., & Walsh, D. C. (2013). PERMANOVA, ANOSIM, and the
Mantel test in the face of heterogeneous dispersions: What null
hypothesis are you testing? Ecological Monographs,83, 557–574.
https://doi.org/10.1890/12-2010.1
Andrews, E. S. (2013). Analyzing arthropods for the presence of bacteria.
Current Protocols in Microbiology,28, 1E.6.1–1E.6.14. https://doi.org/
10.1002/9780471729259.mc01e06s28
Atyeo, W. T., & Gaud, J. (1979). Feather mites and their hosts. In Recent
advances in acarology II (pp. 355–361). New York, NY: Academic
Press. https://doi.org/10.1016/B978-0-12-592202-9.50049-5
Bates, D., M€
achler, M., Bolker, B., & Walker, S. (2015). Fitting linear
mixed- effects models using lme4. Journal of Statistical Software,67,
1–48.
Bengtsson-Palme, J., Ryberg, M., Hartmann, M., Branco, S., Wang, Z.,
Godhe, A., ... Nilsson, R. H. (2013). Improved software detection and
extraction of ITS1 and ITS2 from ribosomal ITS sequences of fungi
and other eukaryotes for analysis of environmental sequencing data.
Methods in Ecology and Evolution,9, 914–919.
Blanco, G., Tella, J. L., & Potti, J. (1997). Feather mites on group-living
red-billed choughs: A non-parasitic interaction? Journal of Avian Biol-
ogy,28, 197–206. https://doi.org/10.2307/3676970
Blanco, G., Tella, J. L., Potti, J., & Baz, A. (2001). Feather mites on
birds: Costs of parasitism or conditional outcomes? Journal of Avian
Biology,32, 271–274. https://doi.org/10.1111/j.0908-8857.2001.
320310.x
Bolger, A. M., Lohse, M., & Usadel, B. (2014). Trimmomatic: A flexible
trimmer for Illumina sequence data. Bioinformatics,30, 2114–2120.
https://doi.org/10.1093/bioinformatics/btu170
Boyd, B. M., Allen, J. M., Koga, R., Fukatsu, T., Sweet, A. D., Johnson, K.
P., & Reed, D. L. (2016). Two bacterial genera, Sodalis and Rickettsia,
associated with the seal louse Proechinophthirus fluctus (Phthiraptera:
Anoplura). Applied and Environmental Microbiology,82, 3185–3197.
https://doi.org/10.1128/AEM.00282-16
Brice, M. H., Pellerin, S., & Poulin, M. (2017). Does urbanization lead to
taxonomic and functional homogenization in riparian forests? Diver-
sity and Distributions,23, 828–840. https://doi.org/10.1111/ddi.
12565
Brown, B. L., Creed, R. P., Skelton, J., Rollins, M. A., & Farrell, K. J.
(2012). The fine line between mutualism and parasitism: Complex
effects in a cleaning symbiosis demonstrated by multiple field experi-
ments. Oecologia,170, 199–207.
Caporaso, J. G., Lauber, C. L., Walters, W. A., Berg-Lyons, D., Huntley, J.,
Fierer, N., ... Knight, R. (2012). Ultra-high-throughput microbial
DO ~
NA ET AL.
|
13
community analysis on the Illumina HiSeq and MiSeq platforms. ISME
Journal,6, 1621–1624. https://doi.org/10.1038/ismej.2012.8
Carlson, C. J., Burgio, K. R., Dougherty, E. R., Phillips, A. J., Bueno, V. M.,
Clements, C. F., ... Getz, W. M. (2017). Parasite biodiversity faces
extinction and redistribution in a changing climate. Science Advances,
3, e1602422. https://doi.org/10.1126/sciadv.1602422
Chaisiri, K., McGarry, J. W., Morand, S., & Makepeace, B. L. (2015). Sym-
biosis in an overlooked microcosm: A systematic review of the bacte-
rial flora of mites. Parasitology,142, 1152–1162. https://doi.org/10.
1017/S0031182015000530
Cheney, K. L., & C^
ot
e, I. M. (2005). Mutualism or parasitism?The variable
outcome of cleaning symbioses. Biology Letters,1, 162–165.
Chen, S., Yao, H., Han, J., Liu, C., Song, J., Shi, L., ... Luo, K. (2010). Vali-
dation of the ITS2 region as a novel DNA barcode for identifying
medicinal plant species. PLoS ONE,5(1), e8613.
Clayton, D. H., Bush, S. E., & Johnson, K. P. (2016). Coevolution of life on
hosts: Integrating ecology and history. Chicago, IL: University of Chi-
cago Press.
Clayton, D. H., Moyer, B. R., Bush, S. E., Jones, T. G., Gardiner, D. W.,
Rhodes, B. B., & Goller, F. (2005). Adaptive significance of avian beak
morphology for ectoparasite control. Proceedings of the Royal Society
of London B: Biological Sciences,272, 811–817. https://doi.org/10.
1098/rspb.2004.3036
da Silva, R. R., Dorrestein, P. C., & Quinn, R. A. (2015). Illuminating the
dark matter in metabolomics. Proceedings of the National Academy of
Sciences,112, 12549–12550.
Dabert, J., Ehrnsberger, R., & Dabert, M. (2008). Glaucalges tytonis sp n.
(Analgoidea, Xolalgidae) from the barn owl Tyto alba (Strigiformes,
Tytonidae): Compiling morphology with DNA barcode data for taxon
descriptions in mites (Acari). Zootaxa,1719,41–52.
Dabert, J., & Mironov, S. V. (1999). Origin and evolution of feather mites
(Astigmata). Experimental and Applied Acarology,23, 437–454.
https://doi.org/10.1023/A:1006180705101
De Beeck, M. O., Lievens, B., Busschaert, P., Declerck, S., Vangronsveld,
J., & Colpaert, J. V. (2014). Comparison and validation of some ITS
primer pairs useful for fungal metabarcoding studies. PLoS ONE,9,
e97629. https://doi.org/10.1371/journal.pone.0097629
Denef, V. J., Fujimoto, M., Berry, M. A., & Schmidt, M. L. (2016). Seasonal
succession leads to habitat-dependent differentiation in ribosomal
RNA: DNA ratios among freshwater lake bacteria. Frontiers in Microbi-
ology,7, 606. https://doi.org/10.3389/fmicb.2016.00606
Diaz-Real, J., Serrano, D., P
erez-Tris, J., Fern
andez-Gonz
alez, S., Bermejo,
A., Calleja, J. A., ... Jovani, R. (2014). Repeatability of feather mite
prevalence and intensity in passerine birds. PLoS ONE,9, e107341.
https://doi.org/10.1371/journal.pone.0107341
Dobson, A., Lafferty, K. D., Kuris, A. M., Hechinger, R. F., & Jetz, W.
(2008). Homage to linnaeus: How many parasites? How many hosts?
Proceedings of the National Academy of Sciences,105, 11482–11489.
https://doi.org/10.1073/pnas.0803232105
Do~
na, J., Diaz-Real, J., Mironov, S., Bazaga, P., Serrano, D., & Jovani, R.
(2015). DNA barcoding and minibarcoding as a powerful tool for
feather mite studies. Molecular Ecology Resources,15, 1216–1225.
Do~
na, J., Moreno-Garc
ıa, M., Criscione, C. D., Serrano, D., & Jovani, R.
(2015). Species mtDNA genetic diversity explained by infrapopula-
tion size in a host-symbiont system. Ecology and Evolution,5, 5801–
5809.
Do~
na, J., Potti, J., De la Hera, I., Blanco, G., Fr
ıas, O., & Jovani, R. (2017).
Vertical transmission in feather mites: Insights into its adaptive value.
Ecological Entomology,42, 492–499. https://doi.org/10.1111/een.
12408
Do~
na, J., Proctor, H., Mironov, S., Serrano, D., & Jovani, R. (2016). Global
associations between birds and vane-dwelling feather mites. Ecology,
97, 3242.
Do~
na, J., Proctor, H., Mironov, S., Serrano, D., & Jovani, R. (2017). Host
specificity, infrequent major host switching and the diversification of
highly host-specific symbionts: The case of vane-dwelling feather
mites. Global Ecology and Biogeography,27, 188–198.
Do~
na, J., Sweet, A. D., Johnson, K. P., Serrano, D., Mironov, S., & Jovani,
R. (2017). Cophylogenetic analyses reveal extensive host-shift specia-
tion in a highly specialized and host-specific symbiont system. Molec-
ular Phylogenetics and Evolution,115, 190–196.
Dubinin, V. B. (1951). Feather mites (Analgesoidea). Part 1. Introduction
to their study. Fauna USSR,6(5), 1–363 [in Russian].
Edgar, R. C., Haas, B. J., Clemente, J. C., Quince, C., & Knight, R. (2011).
UCHIME improves sensitivity and speed of chimera detection. Bioin-
formatics,27, 2194–2200. https://doi.org/10.1093/bioinformatics/
btr381
Engel, P., & Moran, N. A. (2013). The gut microbiota of insects–diversity
in structure and function. FEMS Microbiology Reviews,37, 699–735.
https://doi.org/10.1111/1574-6976.12025
Erban, T., & Hubert, J. (2008). Digestive function of lysozyme in synan-
thropic acaridid mites enables utilization of bacteria as a food source.
Experimental and Applied Acarology,44, 199. https://doi.org/10.1007/
s10493-008-9138-x
Erban, T., & Hubert, J. (2010). Comparative analyses of proteolytic activi-
ties in seven species of synanthropic acaridid mites. Archives of Insect
Biochemistry and Physiology,75, 187–206. https://doi.org/10.1002/
arch.20388
Fern
andez-Gonz
alez, S., P
erez-Rodr
ıguez, A., de la Hera, I., Proctor, H. C.,
&P
erez-Tris, J. (2015). Different space preferences and within-host
competition promote niche partitioning between symbiotic feather
mite species. International Journal for Parasitology,45, 655–662.
https://doi.org/10.1016/j.ijpara.2015.04.003
Friedrich, J., Gradi
sar, H., Mandin, D., & Chaumont, J. P. (1999). Screen-
ing fungi for synthesis of keratinolytic enzymes. Letters in Applied
Microbiology,28, 127–130. https://doi.org/10.1046/j.1365-2672.
1999.00485.x
Fu, L., Niu, B., Zhu, Z., Wu, S., & Li, W. (2012). CD-HIT: Acceler-
ated for clustering the next-generation sequencing data. Bioin-
formatics,28, 3150–3152. https://doi.org/10.1093/bioinformatic
s/bts565
Galv
an, I., Aguilera, E., Ati
enzar, F., Barba, E., Blanco, G., Cant
o, J. L., ...
Jovani, R. (2012). Feather mites (Acari: Astigmata) and body condition
of their avian hosts: A large correlative study. Journal of Avian Biology,
43, 273–279. https://doi.org/10.1111/j.1600-048X.2012.05686.x
Gaud, J. (1992). Acquisition d’h^
otes nouveaux par les acariens plumicoles.
Bulletin de la Soci
et
e Francßaise de Parasitologie,10,79–91.
Gaud, J., & Atyeo, W. T. (1996). Feather mites of the World (Acarina,
Astigmata): The supraspecific taxa. Annales du Musee Royale de L’Afri-
que Centrale, Sciences Zoologiques,277 1–193 (Pt. 1, text), 1–436 (Pt.
2, illustrations).
Gunderson, A. R. (2008). Feather-degrading bacteria: A new frontier in
avian and host–parasite research? The Auk,125, 972–979. https://d
oi.org/10.1525/auk.2008.91008
Gweon, H. S., Oliver, A., Taylor, J., Booth, T., Gibbs, M., Read, D. S., ...
Schonrogge, K. (2015). PIPITS: An automated pipeline for analyses of
fungal internal transcribed spacer sequences from the Illumina
sequencing platform. Methods in Ecology and Evolution,6, 973–980.
https://doi.org/10.1111/2041-210x.12399 https://doi.org/10.1111/
2041-210X.12399
Hammer, T. J., Janzen, D. H., Hallwachs, W., Jaffe, S. L., & Fierer, N.
(2017). Caterpillars lack a resident gut microbiome. Proceedings of the
National Academy of Sciences,114, 9641–9646. https://doi.org/10.
1073/pnas.1707186114
Harper, D. G. C. (1999). Feather mites, pectoral muscle condition, wing
length and plumage coloration of passerines. Animal Behavior,58,
553–562. https://doi.org/10.1006/anbe.1999.1154
Hawkins, T. L., O’Connor-Morin, T., Roy, A., & Santillan, C. (1994). DNA
purification and isolation using a solid-phase. Nucleic Acids Research,
22, 4543–4544.
14
|
DO ~
NA ET AL.
Hoberg, E. P., & Brooks, D. R. (2015). Evolution in action: Climate
change, biodiversity dynamics and emerging infectious disease. Philo-
sophical Transactions of the Royal Society B,370, 20130553. https://d
oi.org/10.1098/rstb.2013.0553
Hopkins, S. R., Wojdak, J. M., & Belden, L. K. (2017). Defensive sym-
bionts mediate host–parasite interactions at multiple scales. Trends
in Parasitology,33,53–64. https://doi.org/10.1016/j.pt.2016.10.
003
Hosokawa, T., Kikuchi, Y., Shimada, M., & Fukatsu, T. (2007). Obligate
symbiont involved in pest status of host insect. Proceedings of the
Royal Society of London B: Biological Sciences,274, 1979–1984.
https://doi.org/10.1098/rspb.2007.0620
Hubert, J., Jaros
ık, V., Mourek, J., Kubatova, A., & Zdarkova, E. (2004).
Astigmatid mite growth and fungi preference (Acari: Acaridida): Com-
parisons in laboratory experiments. Pedobiologia,48, 205–214.
https://doi.org/10.1016/j.pedobi.2003.12.005
Hubert, J., Kopeck
y, J., Perotti, M. A., Nesvorn
a, M., Braig, H. R., S
agov
a-
Mare
ckov
a, M., ... Zurek, L. (2012). Detection and identification of
species-specific bacteria associated with synanthropic mites. Micro-
bial Ecology,63, 919–928. https://doi.org/10.1007/s00248-011-
9969-6
Hubert, J., Kopecky, J., Sagova-Mareckova, M., Nesvorna, M., Zurek, L., &
Erban, T. (2016). Assessment of bacterial communities in thirteen
species of laboratory-cultured domestic mites (Acari: Acaridida). Jour-
nal of Economic Entomology,109, 1887–1896. https://doi.org/10.
1093/jee/tow089
Hubert, J., Nesvorna, M., Kopeck
y, J., S
agov
a-Mare
ckov
a, M., & Poltron-
ieri, P. (2014). Carpoglyphus lactis (Acari: Astigmata) from various
dried fruits differed in associated micro-organisms. Journal of Applied
Microbiology,118, 470–484.
Hubert, J., Stejskal, V., Kub
atov
a, A., Munzbergov
a, Z., V
a
nov
a, M., &
Zd’
arkov
a, E. (2003). Mites as selective fungal carriers in stored grain
habitats. Experimental and Applied Acarology,29,69–87. https://doi.
org/10.1023/A:1024271107703
Jacob, J., & Ziswiler, V. (1982). The uropygial gland. In D. S. Farner, J. R.
King & K. C. Parkes (Eds), Avian biology (pp. 199–324). London: Aca-
demic Press Inc.
Jetz, W., Thomas, G. H., Joy, J. B., Hartmann, K., & Mooers, A. O. (2012).
The global diversity of birds in space and time. Nature,491, 444–
448. https://doi.org/10.1038/nature11631
Jovani, R. (2003). Understanding parasite strategies. Trends in Parasitol-
ogy,19,15–16. https://doi.org/10.1016/S1471-4922(02)00007-7
Jovani, R., Do~
na, J., Labrador, M., & Serrano, D. (2017). Opening the
doors of parasitology journals to other symbionts. Trends in Parasitol-
ogy,33, 578–579. https://doi.org/10.1016/j.pt.2017.05.003
Jovani, R., & Serrano, D. (2001). Feather mites (Astigmata) avoid moulting
wing feathers of passerine birds. Animal Behaviour,62, 723–727.
https://doi.org/10.1006/anbe.2001.1814
Jovani, R., & Serrano, D. (2004). Fine-tuned distribution of feather mites
(Astigmata) on the wing of birds: the case of blackcaps Sylvia atri-
capilla.Journal of Avian Biology,35,16–20. https://doi.org/10.1111/j.
0908-8857.2004.03213.x
Jovani, R., & Tella, J. L. (2006). Parasite prevalence and sample size: Mis-
conceptions and solutions. Trends in Parasitology,22, 214–218.
https://doi.org/10.1016/j.pt.2006.02.011
Jovani, R., Tella, J. L., Sol, D., & Ventura, D. (2001). Are hippoboscid flies
a major mode of transmission of feather mites? Journal of Parasitol-
ogy,87, 1187–1189. https://doi.org/10.1645/0022-3395(2001)087
[1187:AHFAMM]2.0.CO;2
Kearse, M., Moir, R., Wilson, A., Stones-Havas, S., Cheung, M., Sturrock,
S., ... Drummond, A. (2012). Geneious Basic: An integrated and
extendable desktop software platform for the organization and analy-
sis of sequence data. Bioinformatics,28, 1647–1649. https://doi.org/
10.1093/bioinformatics/bts199
Kent, C. M., & Burtt, E. H. Jr (2016). Feather-degrading bacilli in the plu-
mage of wild birds: Prevalence and relation to feather wear. The Auk,
133, 583–592. https://doi.org/10.1642/AUK-16-39.1
Kerr, K. C., Lijtmaer, D. A., Barreira, A. S., Hebert, P. D., & Tubaro, P. L.
(2009). Probing evolutionary patterns in Neotropical birds through
DNA barcodes. PLoS ONE,4, e4379. https://doi.org/10.1371/journal.
pone.0004379
Klimov, P. B., Mironov, S., & O’Connor, B. M. (2017). Detecting ancient
codispersals and host shifts by double dating of host and parasite
phylogenies: Application in proctophyllodid feather mites associated
with passerine birds. Evolution,71, 2381–2397. https://doi.org/10.
1111/evo.13309
Klimov, P. B., & O’Connor, B. (2013). Is permanent parasitism reversible?
—Critical evidence from early evolution of house dust mites. System-
atic Biology,62, 411–423. https://doi.org/10.1093/sysbio/syt008
Kozich, J. J., Westcott, S. L., Baxter, N. T., Highlander, S. K., & Schloss, P.
D. (2013). Development of a dual-index sequencing strategy and
curation pipeline for analyzing amplicon sequence data on the MiSeq
Illumina sequencing platform. Applied and Environmental Microbiology,
79, 5112–5120. https://doi.org/10.1128/AEM.01043-13
Lacey, M. E., & West, J. S. (2006). The Air Spora: A manual for catching
and identifying airborne biological particles. Dordrecht, Netherlands:
Springer. https://doi.org/10.1007/978-0-387-30253-9
Lafferty, K. D., Dobson, A. P., & Kuris, A. M. (2006). Parasites dominate
food web links. Proceedings of the National Academy of Sciences,103,
11211–11216. https://doi.org/10.1073/pnas.0604755103
Lange, V., B€
ohme, I., Hofmann, J., Lang, K., Sauter, J., Sch€
one, B., ... Sch-
midt, A. H. (2014). Cost-efficient high-throughput HLA typing by
MiSeq amplicon sequencing. BMC Genomics,15, 63. https://doi.org/
10.1186/1471-2164-15-63
Larsen, B. B., Miller, E. C., Rhodes, M. K., & Wiens, J. J. (2017). Inordinate
fondness multiplied and redistributed: The number of species on
earth and the new pie of life. The Quarterly Review of Biology,92,
229–265. https://doi.org/10.1086/693564
Leung, T. L. F., & Poulin, R. (2008). Parasitism, commensalism, and mutu-
alism: Exploring the many shades of symbioses. Vie et Milieu,58, 107.
Marchisio, V. F., Curetti, D., Cassinelli, C., & Bordese, C. (1991). Kerati-
nolytic and keratinophilic fungi in the soils of Papua New Guinea.
Mycopathologia,115, 113–119. https://doi.org/10.1007/BF00436799
Matthews, A. E., Klimov, P. B., Proctor, H. C., Dowling, A. P., Diener, L.,
Hager, S. B., ... Boves, T. J. (2018). Cophylogenetic assessment of
New World warblers (Parulidae) and their symbiotic feather mites
(Proctophyllodidae). Journal of Avian Biology,49, jav-01580. https://
doi.org/10.1111/jav.01580.
McMurdie, P. J., & Holmes, S. (2013). phyloseq: An R package for repro-
ducible interactive analysis and graphics of microbiome census data.
PLoS ONE,8, e61217. https://doi.org/10.1371/journal.pone.0061217
McMurdie, P. J., & Holmes, S. (2014). Waste not, want not: Why rarefy-
ing microbiome data is inadmissible. PLoS Computational Biology,10,
e1003531. https://doi.org/10.1371/journal.pcbi.1003531
Mironov, S. V., & Malyshev, L. L. (2002). Dynamics of infection of Frin-
gilla coelebs chaffinch nestlings with feather mites (Acari: Analgoidea).
Parazitologiya,36, 356–374. (in Russian with English abstract).
Moreno-Rueda, G. (2017). Preen oil and bird fitness: A critical review of
the evidence. Biological Reviews,92, 2131–2143. https://doi.org/10.
1111/brv.12324
Newton, A. C., Fitt, B. D., Atkins, S. D., Walters, D. R., & Daniell, T. J.
(2010). Pathogenesis, parasitism and mutualism in the trophic space
of microbe–plant interactions. Trends in Microbiology,18, 365–373.
Nwadiaro, P., Ogbonna, A., Wuyep, P., & Adekojo, D. (2015). Kerati-
nolytic activity of Cladosporium and Trichoderma species isolated from
barbers’landfill. International Journal of Biosciences,6, 104–115.
Nylin, S., Agosta, S., Bensch, S., Boeger, W. A., Braga, M. P., Brooks, D.
R., ... Janz, N. (2017). Embracing colonizations: A new paradigm for
DO ~
NA ET AL.
|
15
species association dynamics. Trends in Ecology and Evolution,33,4–
14.
Ohkuma, M. (2008). Symbioses of flagellates and prokaryotes in the gut
of lower termites. Trends in Microbiology,16, 345–352. https://doi.
org/10.1016/j.tim.2008.04.004
Oksanen, J., Blanchet, F. G., Kindt, R., Legendre, P., O’hara, R. B., Simp-
son, G. L., ... Wagner, H. (2017). Vegan: Community ecology package.
R package version 2.4-3.
Pap, P. L., V
ag
asi, C. I., Osv
ath, G., Muresßan, C., & Barta, Z. (2010). Sea-
sonality in the uropygial gland size and feather mite abundance in
house sparrows Passer domesticus: Natural covariation and an experi-
ment. Journal of Avian Biology,41, 653–661. https://doi.org/10.1111/
j.1600-048X.2010.05146.x
Perotti, M. A., Kirkness, E. F., Reed, D. L., & Braig, H. R. (2009).
Endosymbionts of lice. In K. Bourtzis, & T. A. Miller (Eds.), Insect sym-
biosis, Vol. 3(pp. 205–219). Boca Raton, FL: CRC Press.
Poulin, R. (1991). Group-living and infestation by ectoparasites in passeri-
nes. Condor,93, 418–423. https://doi.org/10.2307/1368958
Poulin, R. (2010). Network analysis shining light on parasite ecology and
diversity. Trends in Parasitology,26, 492–498. https://doi.org/10.
1016/j.pt.2010.05.008
Poulin, R., & Morand, S. (2000). The diversity of parasites. The Quarterly
Review of Biology,75, 277–293. https://doi.org/10.1086/393500
Poulin, R., & Morand, S. (2004). Parasite biodiversity. Washington, DC:
Smithsonian Institution Press.
Proctor, H. C. (1999). Gallilichus jonesi sp. n. (Acari: Ascouracaridae): A
new species of feather mite from the quills of the Australian brush-
turkey (Aves: Megapodiidae). Austral Entomology,38,77–84.
https://doi.org/10.1046/j.1440-6055.1999.00088.x
Proctor, H. C. (2003). Feather mites (Acari: Astigmata): Ecology, behavior,
and evolution. Annual Review of Entomology,48, 185–209. https://d
oi.org/10.1146/annurev.ento.48.091801.112725
Proctor, H. C., & Owens, I. (2000). Mites and birds: Diversity, parasitism
and coevolution. Trends in Ecology and Evolution,15, 358–364.
https://doi.org/10.1016/S0169-5347(00)01924-8
Pruesse, E., Quast, C., Knittel, K., Fuchs, B. M., Ludwig, W., Peplies, J., &
Glockne, F. O. (2007). SILVA: A comprehensive online resource for
quality checked and aligned ribosomal RNA sequence data compati-
ble with ARB. Nucleic Acids Research,35, 7188–7196. https://doi.
org/10.1093/nar/gkm864
Puchta, O. (1955). Experimentelle untersuchungen uber die bedeutung
der symbiose der kleiderlaus Pediculus vestimenti Burm. Z Para-
sitenkd,17,1.
R
ozsa, L., & Vas, Z. (2014). Co-extinct and critically co-endangered spe-
cies of parasitic lice, and conservation-induced extinction: Should lice
be reintroduced to their hosts? Oryx,49, 107–110.
Rubolini, D., Liker, A., Garamszegi, L. Z., Møller, A. P., & Saino, N. (2015).
Using the BirdTree. org website to obtain robust phylogenies for
avian comparative studies: A primer. Current Zoology,61, 959–965.
Salisch, H. (1989). Recent developments in the chemotherapy of parasitic
infections of poultry. World’s Poultry Science Journal,45, 115–124.
https://doi.org/10.1079/WPS19890009
Sanders, J. G., Łukasik, P., Frederickson, M. E., Russell, J. A., Koga, R.,
Knight, R., & Pierce, N. E. (2017). Dramatic differences in gut bacte-
rial densities correlate with diet and habitat in rainforest ants. Integra-
tive and Comparative Biology,57, 705–722. https://doi.org/10.1093/
icb/icx088
Schloss, P. D., Westcott, S. L., Ryabin, T., Hall, J. R., Hartmann, M., Hollister,
E. B., ... Weber, C. F. (2009). Introducing mothur: Open-source, plat-
form-independent, community-supported software for describing and
comparing microbial communities. Applied and Environmental Microbiol-
ogy,75, 7537–7541. https://doi.org/10.1128/AEM.01541-09
Shapira, M. (2016). Gut microbiotas and host evolution: Scaling up sym-
biosis. Trends in Ecology and Evolution,31, 539–549. https://doi.org/
10.1016/j.tree.2016.03.006
Soini, H. A., Whittaker, D. J., Wiesler, D., Ketterson, E. D., & Novotny, M.
V. (2013). Chemosignaling diversity in songbirds: Chromatographic
profiling of preen oil volatiles in different species. Journal of Chro-
matography A,1317, 186–192. https://doi.org/10.1016/j.chroma.
2013.08.006
Soler, J. J., Peralta-S
anchez, J. M., Mart
ın-Platero, A. M., Mart
ın-Vivaldi,
M., Mart
ınez-Bueno, M., & Møller, A. P. (2012). The evolution of size
of the uropygial gland: Mutualistic feather mites and uropygial secre-
tion reduce bacterial loads of eggshells and hatching failures of Euro-
pean birds. Journal of Evolutionary Biology,25, 1779–1791. https://d
oi.org/10.1111/j.1420-9101.2012.02561.x
Stefan, L. M., G
omez-D
ıaz, E., Elguero, E., Proctor, H. C., McCoy, K. D., &
Gonz
alez-Sol
ıs, J. (2015). Niche partitioning of feather mites within a
seabird host, Calonectris borealis.PLoS ONE,10, e0144728. https://d
oi.org/10.1371/journal.pone.0144728
Sukumaran, J., & Holder, M. T. (2010). DendroPy: A Python library for
phylogenetic computing. Bioinformatics,26, 1569–1571. https://doi.
org/10.1093/bioinformatics/btq228
Sukumaran, J., & Holder, M. T. (2015). SumTrees: Phylogenetic tree sum-
marization. 4.0.0. Retrieved from https://github.com/jeetsukumaran/
DendroPy
Thompson, C. W., Hillgarth, N., Leu, M., & McClure, H. E. (1997). High
parasite load in house finches (Carpodacus mexicanus) is correlated
with reduced expression of a sexually selected trait. The American
Naturalist,149, 270–294. https://doi.org/10.1086/285990
Traveset, A., & Richardson, D. M. (2014). Mutualistic interactions and
biological invasions. Annual Review of Ecology, Evolution, and System-
atics,45,89–113. https://doi.org/10.1146/annurev-ecolsys-1202 13-
091857
Truett, G. E., Heeger, P., Mynatt, R. L., Truett, A. A., Walker, J. A., & Warman,
M. L. (2000). Preparation of PCR- quality mouse genomic DNA with hot
sodium hydroxide and tris (HotSHOT). BioTechniques,29,52–54.
Valim, M. P., & Hernandes, F. A. (2010). A systematic review of feather
mites of the Pterodectes generic complex (Acari: Proctophyllodidae)
with redescriptions of species described by Vladim
ır
Cern
y. Acarina,
18,3–35.
Vierna, J., Do~
na, J., Vizca
ıno, A., Serrano, D., & Jovani, R. (2017). PCR
cycles above routine numbers do not compromise high-throughput
DNA barcoding results. Genome,60, 868–873. https://doi.org/10.
1139/gen-2017-0081
Walter, D. E., & Proctor, H. C. (2013a). Systematic and morphological
survey. Mites: Ecology, evolution & behaviour (pp. 39–68). Dordrecht,
the Netherlands: Springer.
Walter, D. E., & Proctor, H. C. (2013b). Mites in soil and litter systems. In
Mites: Ecology, evolution & behaviour (pp. 161–228). Dordrecht, the
Netherlands: Springer.
Walter, D. E., & Proctor, H. C. (2013c). Animals as habitats. In Mites: Ecol-
ogy, evolution & behaviour (pp. 341–422). Dordrecht, the Netherlands:
Springer.
SUPPORTING INFORMATION
Additional supporting information may be found online in the
Supporting Information section at the end of the article.
How to cite this article: Do~
na J, Proctor H, Serrano D, et al.
Feather mites play a role in cleaning host feathers: New
insights from DNA metabarcoding and microscopy. Mol Ecol.
2018;00:1–16. https://doi.org/10.1111/mec.14581
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DO ~
NA ET AL.
