UNIVERSITY OF FLORIDA THESIS OR DISSERTATION FORMATTING TEMPLATEEFFECTS OF HABITAT AND SOCIAL COMPLEXITY ON THE VOCAL COMPLEXITY OF BIRDS IN FAMILY PARIDAE
By
SARAH MOHAMMED OBAID
A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE
UNIVERSITY OF FLORIDA
© 2017 Sarah Obaid
To my mother, sisters, and brothers for their continuous support and patience during my studies.
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ACKNOWLEDGMENTS
I wish to acknowledge Kuwait Institute for Scientific Research, and Kuwait
Culture Office of the Kuwait Embassy for support and funding through the award of a full
scholarship and NSF-IOS grant # 1353308 to KE Sieving for research support. I thank
the Department of Wildlife Ecology and Conservation at the University of Florida for
facilitating my study and research in a variety of ways during the two fruitful years of my
Master of Science Degree. I wish to express my deep appreciation to Dr. Kathryn
Sieving for her continuous support and encouragement and to my committee members,
Scott Robinson and Lyn Branch. I also want to thank my colleagues, Harrison Jones,
Peter Monte and Lauren Diaz for obtaining many of the field recordings used in this
study. Sincere appreciation must also go to Jeffrey Lucas, Todd Freeberg, and Rajeev
Pillay, as they helped with note and call nomenclature and some of the data used in this
study.
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1 INTRODUCTION .................................................................................................... 10
2 EFFECTS OF HABITAT AND SOCIAL COMPLEXITY ON THE VOCAL COMPLEXITY OF BIRDS IN FAMILY PARIDAE .................................................... 14
Research Design and Objectives ............................................................................ 14 Research Hypotheses and Predictions ................................................................... 16 Methods .................................................................................................................. 22
Study Species: Florida Paridae ........................................................................ 22 Vocalizations of the Paridae ............................................................................. 24 Ecological Significance of the Paridae ............................................................. 26 Study Sites and Forest Characteristics ............................................................ 27 Data collection .................................................................................................. 28
Automated recording units ......................................................................... 28 Naturalistic recordings and flock composition ............................................ 28
Data processing and sample design ................................................................ 29 Call classification .............................................................................................. 30
Data Analysis .......................................................................................................... 31 Objective 1 ....................................................................................................... 31
Call accumulation curves ........................................................................... 31 Rarefaction analysis ................................................................................... 31 Vocal similarity between sample methods and sites .................................. 33
Objective 2 ....................................................................................................... 34 Social and vocal complexity in flocks ......................................................... 34
Results .................................................................................................................... 35 Vocal complexity (call richness) ....................................................................... 35 Call similarity .................................................................................................... 36 Social and vocal complexity in flocks ............................................................... 37
3 DISCUSSION ......................................................................................................... 60
Variations in Call Richness ..................................................................................... 61 Direct and indirect habitat factors influencing vocal complexity in Paridae ....... 61 Method related ................................................................................................. 65
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LIST OF REFERENCES ............................................................................................... 70
Table page 2-1 Research hypotheses and sub-hypotheses, their predictions and rationale
tested in this study. ............................................................................................. 38
2-2 Call types produced by TUTI and CACH in each site, and by each recording method. .............................................................................................................. 40
2-3 Summary of calls scored in naturalistic and automated units recording ............. 41
2-4 The Similarity and Dissimilarity values and Similarity indices compared in the two study sites. ................................................................................................... 42
2-5 The GLM results of the predictor variables on call diversity (Chao2). ................. 43
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LIST OF FIGURES
Figure page 1-1 The association between fluid social dynamics and the learning of call-
production ........................................................................................................... 13
2-3 Flow charts of research approach ...................................................................... 45
2-4 Study site locations and aerial photos indicating the 1 Km square centers of study areas where birds were observed and recorded. ...................................... 46
2-5 Call scoring nomenclature for chick-a-dee calls of (a) TUTI and (b) CACH. ....... 47
2-6 Rarefaction curves of the 30 flock samples from naturalistic recordings shows that OSBS has marginally significantly more call diversity than SF (TUTI and CACH calls pooled). .......................................................................... 48
2-7 Rarefaction curves of the 28 samples from SM3 recordings .............................. 49
2-8 TUTI vocal complexity from naturalistic recordings. ........................................... 50
2-9 TUTI vocal complexity in SM3 recordings. ......................................................... 51
2-10 CACH vocal complexity from naturalistic recordings .......................................... 52
2-11 CACH vocal complexity in SM3 recordings ........................................................ 53
2-12 Total TUTI call richness in both sites.. ................................................................ 54
2-13 Total CACH call richness in both sites. ............................................................... 55
2-14 The Variable importance values on Chao2 as a result from the regression tree analysis. ...................................................................................................... 56
2-15 Comparison of the call diversity of the study species using Chao2 according to the call classes given by species (methods pooled) ....................................... 57
2-16 Scatterplot showing the relation of Chao2 estimation with the maximum number of TUTI detected in a flock. .................................................................... 58
2-17 Scatterplot showing the relation between the Chao2 estimate and the number of heterospecific species found in a flock. ............................................. 59
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Abstract of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Science
EFFECTS OF HABITAT AND SOCIAL COMPLEXITY ON THE VOCAL COMPLEXITY
OF BIRDS IN FAMILY PARIDAE
By
Habitat structure and associated animal community complexity are thought to
influence vocal production in birds via three major influences: (1) effects on the focal
species’ social system, (2) on the species’ perception of risks and resources; and via (3)
direct effects of the habitat structure on the species’ acoustic signal properties. I
address whether these factors may drive the vocal complexity of two bird species in
family Paridae, Tufted titmouse (TUTI), Baeolophus bicolor, and Carolina chickadee
(CACH), Poecile carolinensis, in two contrasting habitats in North-central Florida, mesic
mixed hardwoods and open pine woodlands. I obtained recordings of their calls and
flock observational data over three winter seasons during October to January of 2015-
17 and scored note and call richness as a proxy for vocal complexity. I detected higher
richness in vocal production (via rarefaction analysis) in the vocal repertoire of TUTI (vs
CACH) and in both species in open pine (vs dense hardwoods). Using flock
observational data, I detected positive effects of the number of species and individual
titmice in a flock on call richness produced by chickadees and titmice leading the flock
(generalized linear models). Results confirm that habitat structure and flock social
organization are likely determinants vocal complexity in family Paridae.
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CHAPTER 1 INTRODUCTION
Insects, birds, mammals, amphibians and fish are readily identifiable by their
vocalizations, and analysis of animal bioacoustics increasingly provides insights
concerning the biology of natural communities. For example, species’ richness and
population densities can be estimated from acoustic data, providing information on
animal community structure that is useful in environmental monitoring (Gasc et al.,
2016). In turn, bioacoustics methods lend themselves to assessment of a variety of
human impacts on animal communities including noise pollution, habitat degradation,
human activity, and climate change. For example, anthropogenic noise (e.g., highways)
can degrade animal communities by influencing habitat selection and critical inter- and
intra-species communication (Grade and Sieving, 2016), and the magnitude of climate
change effects on seasonality can be detected in altered phenology of spring-time
breeding calls (birds, frogs; Gasc et al., 2016).
The vocalizations of some species are more complex (more types of notes and
calls) than other species, and with greater complexity comes a larger capacity for
encoding information about the environment. For example, most frogs have only one or
two calls, and their absence indicates a lack of breeding or absence from the system
(Garcia-Rutledge and Narins, 2001). But, many species of birds and mammals have
complex vocalizations that vary with different situations of interest to conservation. For
example, tufted titmice (Baeolophus bicolor) have different calls for different levels of
predation threat (Sieving et al. 2010), Diana monkeys (Cercopithecus diana) identify
different specific predators with their calls (Uster and Zuberbühler, 2001). This kind of
contextual vocalization provides information about more than the calling species, but
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also other species in the community (Kroodsma,1977; Blumstein and Armitage, 1997;
McComb and Semple, 2005). Understanding how to interpret and ‘de-code’ information
in the calls of vocally complex species is therefore of interest to conservation as an
additional source of information about animal community change and overall
environmental health (Joo et al., 2007).
Vocal complexity can be simply defined as the combination of richness in the
numbers of call and note types and in the ways in which these building blocks can be
combined and their patterns of production varied (e.g., syntax, notes per call, calls per
minute, etc.; Bouchet et al., 2013). Syntax is the variation of the composition and
possible order of the different elements of the vocal signals (Clucas et al., 2004).
Diversity in production patterns of calls may be reflected in the ways vocal signals are
used by social group members toward each other or in response to other species or
environmental phenomena (Krams et al., 2012). The degrees of vocal complexity can
vary among species, and have strong relationships with sociality and evolutionary traits
(Sewall, 2015; Figure 1). Basically, selection pressures related to living in social groups
influence species-level differences in communicative sophistication. For example, an
extreme adaptation to social living in animals is exhibited in the concept of a ‘signature
whistle’; a call that encodes an individual’s unique identity (akin to persons’ names in
human language). Such calls have been discovered in some of the most vocally
complex taxa (whales and birds; Tyack, 2000; Janik et al., 2006). A more pervasive
feature of animal vocal complexity observed in even minimally social taxa is ‘contextual
specificity’, or uniquely structured call sets that vary in fine acoustic structure in reliable
and repeatable patterns with different situations that individuals perceive or experience
12
(Templeton et al., 2005). This occurs widely among animals (mammals and birds
including primates, pigs, horses, sciurids, birds and others) in the context of predation
risk, stress, and emotive calls (Altenmüller et al., 2013). For example, Sieving et al.
(2010) identified 5 distinct levels of predation threat (from none to imminent attack)
encoded in the structure of tufted titmouse ‘chick-a-dee’ calls; each call set was
associated with a specific type of predator representing different attack threats,
presented at close range to a live titmouse. Additionally, they found a parallel change in
the acoustic parameters of simple contact notes that also allowed discrimination among
the five experimental treatments. Beyond discriminating among specific threats and
varying social situations (Freeberg and Krams, 2015), bird call production measures can
vary with degree of urbanization (Kight and Swaddle, 2015; LaZerte et al., 2016) and
other anthropogenic disturbances (Pillay, 2016; Gasc et al., 2016) reflective of habitat
structure. In sum, common vocally complex species produce copious information that
identifies specific situations and large-scale influences that they experience, providing
numerous opportunities for acoustic monitoring to contribute to environmental
management.
In order to develop acoustic monitoring systems that can correctly relate
variations in vocal production by animals to ecological parameters of interest in
environmental management (e.g., Pillay 2016), deeper exploration of natural variations
in vocal production measures are needed. In this thesis, I undertook an analysis of
acoustic production by two commonly occurring and highly vocally complex species of
birds in the Eastern United States in order to determine how richness of their vocal
signals varies with habitat-related physical and biotic factors.
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Figure 1-1. The association between fluid social dynamics and the learning of call-
production is visually illustrated using existing phylogenies from birds and mammals (Hackett et al. 2008; Meredith et al. 2011). Taxa in bold have fluid social dynamics and call production is learned. These taxa are hypothesized to require a high vocal complexity (Copied from Figure 1 in Sewall 2015).
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CHAPTER 2 EFFECTS OF HABITAT AND SOCIAL COMPLEXITY ON THE VOCAL COMPLEXITY
OF BIRDS IN FAMILY PARIDAE
I focused my study on natural habitat-related differences in the vocal complexity
(richness and similarity indicators) of two widely distributed habitat generalist bird
species. I focused on birds in the Paridae family (chickadees, tits and titmice) as they
live in a diversity of habitats and exhibit extremely high levels of vocal complexity
(Hailman et al., 1985; Hailman and Ficken, 1986; Ficken et al., 1994; Freeberg and
Lucas, 2012). The habitat generality of the species and the enormous flexibility in their
vocal system (Freeberg, 2006; Freeberg, 2008; Krams et al., 2012; Sewall, 2015)
provides an opportunity to determine whether the birds’ vocal production varies in
identifiable ways with major shifts in habitat occupied by the birds. A number of factors
can potentially influence natural variations in bird vocalizations among different habitats
(Nicholls and Goldizen, 2006; Briefer et al., 2010). Parids are woodland associates, so I
focused on two major woodland configurations accessible in North-central Florida that
are readily occupied by the local species of parids; xeric pine and mesic hardwood
forests (Myers and Ewel, 1990). Tufted titmice (Baeolophus bicolor) and Carolina
chickadees (Poecile carolinensis) occupy these habitats and are the focal species for
this study.
Research Design and Objectives
My overall goal in this thesis is to identify major factors that may drive the vocal
complexity of two parid (short for Paridae) species (Tufted titmouse, TUTI, Baeolophus
bicolor and Carolina Chickadee, CACH, Poecile carolinensis) in two contrasting habitats
in North-central Florida. In this study, I used the term vocal complexity interchangeably
with richness of call and note types. That is, the higher the number of call and note
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types the higher the vocal complexity is deemed to be (calls are comprised of intentional
strings of notes). To address my goal, I undertook two specific objectives.
Objective 1: I undertook a comparative study of the overall diversity and
complexity in the vocalizations of TUTI and CACH, both in family Paridae, living in 2
very distinct habitats, upland pinelands and mesic hardwood forest of North-central
Florida. I obtained recordings by two methods throughout large study areas in the two-
contrasting forest habitats: via (a) hand recording by observers of wild flocks of parids in
winter months (naturalistic recordings) and (b) remote recordings in the study areas
made with multiple automated recording units left in sample sites during one month in
the winter of 2105-16. Using data from both sources and an ‘authoritative library’ of
known calls and notes produced by the two species (Lucas, Freeberg, and Sieving,
unpublished) I scored (defined, identified, and named) all unique vocalizations within
sampled recordings and then quantitatively compared richness of vocalizations
produced by the two species in the two habitats using statistical methods that account
for incomplete sampling effort.
Objective 2: I sought to address how social complexity in mixed species flocks in
winter influences the vocal complexity (call richness) produced by the two species, and
in particular, calls by the dominant leader species, the tufted titmouse. To do this, I used
only the naturalistic recordings made while following individual flocks and observational
data collected simultaneously on flock social complexity, including species richness and
relative abundance. (Remotely recorded data could not be used for this analysis
because birds were not observed during the times they were recorded). I then tested for
correlated variations in social and vocal complexity.
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Objective 1: The overall hypothesis is that the dramatically different vegetative
structure (basal area, canopy cover, tree species dominance, etc.) of upland pine
versus mesic hardwood hammock should strongly influence the vocal production
metrics of the parid species that occupy the two habitats. Pine forests of the SE Coastal
Plain have fewer species of woody plants and significantly lower basal area than mesic
hardwoods (Myers and Ewel, 1990). Influences of habitat structure on vocal behavior
can be direct or indirect, and several of each may apply to this system. Habitat
structure can have direct effects (1) on the species’ acoustic signal properties and (2)
on the species’ perception and interactions, and indirect effects via (3) habitat-based
filtering of species and populations that, in turn, can influence the focal species’ social
and vocal system.
First, sound properties interact with environmental properties (e.g., vegetative
obstructions) and this determines how far animal signals may propagate and how they
may be altered acoustically. Both factors, in turn, can influence a signaler’s ability to
reach intended receivers with the intended signal intact (e.g., Röhr and Junca, 2013;
Aubin et al., 2014). Therefore, habitat structure exerts selection on which signals a
species may rely on as well as the properties of the signals used. For example, birds in
dense vegetation are constrained to use songs with lower frequencies than they do in
more open habitats to minimize degradation (Boncoraglio and Saino, 2007). Higher
frequency calls have shorter wave lengths that are easily scattered by obstacles
(Proppe et al., 2010). The songs of CACH and TUTI used in territory advertisement are
in the lower of frequencies used by birds (3-7 kHz). However, many parid calls
signifying predation and conflict related situational specificity range into the very high
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frequency ranges of birds (8-11 kHz) that do not travel very far in dense, woody
vegetation. Therefore, in an analysis of overall vocal richness in pine versus hardwood
forest (habitats occupied by parids but with very different physiognomy and tree density
/ basal area; KE Sieving unpublished data), I predict that both chickadees and titmice
will use fewer types of calls in hardwood forests than in pine, because of the physical
constraints on their calls. This could be a direct ‘filtering’ effect caused by reduced used
of call types that transmit poorly in the denser habitat.
Another direct effect of habitat structure on parid call production relates to habitat
visibility. When the efficiency of visual communications decreases, because of long
distance between individuals within or among social groups, these individuals tend to
depend more on vocal communication (Perez et al., 2015). Both parid species occur
year-round in pairs and usually have grown offspring with them as well (Farley et al.,
2008). Therefore, because of the large distances between trees where different family
members may be foraging apart, a greater frequency and nuance of communication to
maintain social cohesion may be needed. Additionally, high visibility may allow more
predators (either perched or flying) to be spotted by hyper-vigilant titmice, which will
generate a greater frequency of the most complex of parid call types - namely chick-a-
dee calls. By far the greatest source of variation in the entire repertoire of chickadees
and titmice lies in the chick-a-dee calls (Lucas and Freeberg, 2007; Freeberg, 2008),
because these calls can vary from 2 or 3 notes per call up to 60 or 100 or more notes
per call. Chick-a-dee calls encode a great deal of situational specificity encompassing
widely varying social and anti-predator contexts (Lucas and Freeberg, 2007; Sieving et
al., 2010). Each new note added or subtracted or substituted for another note type at
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any position would be recognized as a different call class in our data set (see below).
Therefore, greater visibility in pine should be reflected in greater call diversity in pine, for
both species.
An indirect effect relates to the common phenomenon of vocal mimicry in birds.
There are two kinds of vocal mimicry - within and between species, and in the broad
sense; it can include deceitful exchanges among individuals of same or different
species (Wilson, 1975; Dalziell et al., 2015). Mimetic signaling systems involve three
characters: ‘the mimic’, which simulates the signal properties of a second individual, ‘the
model’, and ‘the receiver’ (‘signal-receiver’, Vane-Wright, 1980; ‘dupe’, Pasteur, 1982;
Dalziell et al., 2015) or target of the mimed signal. Vocal mimicry, when it occurs,
functions to enhance signal (usually song) continuity. Interspecific vocal mimicry in birds
commonly occurs in the context of competitive exclusion. Where heterospecifics
compete for similar resources (e.g., Laiolo, 2012), vocal mimicry can lower interspecific
aggression between species. This is important when the model is a larger and more
aggressive species than the mimic; models will be less likely to attack others of its own
species where the calls of a smaller species (if its calls are undisguised) may invite
aggression by the larger species (e.g., European tit species; Wright et al., 2005; Kelley
et al., 2008). In contrast, species that participate in multi-species flocks can attract flock
participants using cues that mimic the sounds of a speciose flock (Goodale and
Kotogama, 2006). In both cases, therefore, the presence of abundant species within a
given habitat may generate interspecific vocal mimicry. In intraspecific vocal mimicry,
sometimes called vocal imitation or appropriation, signal continuity may function in
various ways (Dobkin, 1979). Intraspecific vocal copying is normally associated with
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socially networked individuals or those in close physical proximity (Baker, 1993; Balsby
et al., 2012). Therefore, because members of Paridae mimic calls of other parids
(Dalziell et al., 2015), and both species (and many others) occur at significantly higher
densities in hardwood than in pine in Florida (KE Sieving unpublished data), inter- and
intra-specific mimicry should help lessen vocal richness of parids living in hardwood.
Another indirect effect of habitat structure on call production features derives
from the propensity of parids and other species to call when they move (e.g., flight calls;
Farnsworth, 2005). Social cohesion of a flock gives the benefits of grouping, and
enhances mobility (Boinski and Garber, 2000; Conradt and Roper, 2010; Petit, 2011)
and typically requires vocal mechanisms to communicate participant movements
(Boinski, 2000; Bousquet et al., 2010; Petit and Bon, 2010; Fischer and Zinner, 2011).
Movement vocalizations have several functions. First, for coordination of time and
directions of movement (Boinski, 1993; Boinski and Campbell, 1995; Leca et al., 2003).
Second, to cue the assembly of the flock during roosting, foraging, and during predator
encounters (Arnold and Wilkinson, 2011). Third, the flight calls that are produced while
moving from one place to another (birds: Chaves-Campos, 2011; Fernández-Juricic and
Martella, 2000; cetaceans: Jensen et al., 2011; primates: Stewart and Harcourt, 1994;
Trillmich et al., 2004). Chickadees produce more ‘C’ notes and fewer ‘D’ notes in their
calls when they are in flight compared with when they call upon perching (Freeberg and
Mahurin, 2013). Therefore, the more often chickadees (and titmice) have to move, the
more likely that the overall vocal richness will be enhanced by a greater number of flight
and other social cohesion calls. During winter flocking seasons (September to March in
Florida), parid-led flocks move differently in the two forest habitats I studied; they move
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more often and faster over longer intervals in open habitats than in dense hardwood
(Contreras and Sieving, 2011; unpublished data) presumably because of the lower
basal area and tree density. Therefore, by this mechanism as well as the others
discussed above, under Objective 1 I predict a greater overall call richness for both
species of parids in pine than in hardwood forest.
Objective 2: Concerning the effect of social complexity on vocal complexity, both
the number of conspecifics and heterospecifics travelling with a flock in winter may
influence this relationship in my study. First, the number of TUTI and CACH within a
flock is variable and can influence the vocal complexity of calls by either species. In the
chickadee, conspecific group size influences the complexity of the “chick-a-dee” call,
which is used in group social cohesion. Individuals in larger social groups use calls with
greater information than did individuals in smaller social groups (Freeberg and Harvey,
2008) and in one study, call frequency of flock members increased with flock size and
species richness (Murdoch, 2012). Based on this, I predicted that under Objective 2,
flocks with more CACH and more TUTI will have greater call richness for each species,
respectively.
No information currently exists concerning the potential effects of heterospecific
species richness on parid vocal production but here I present two competing
hypotheses leading to two mutually exclusive predictions; greater flock species diversity
will have no effect on parid vocal richness or it will increase it. Parids in the South East
of USA function as passive nuclear species that do not join other birds, but rather are
joined by heterospecifics (Contreras and Sieving, 2011). Heterospecifics join parid
flocks during winter, when food is hard to find and predators are hungry and abundant
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(Contreras and Sieving, 2011), because of the utility of the information produced by
parids for mitigating risk and enhancing foraging efficiency (Cords, 1990; Sasvari, 1992;
Dall et al., 2005; Peake, 2005; Koboroff and Kaplan, 2006). This pattern suggests a
potentially parasitic relationship between satellites and the nuclear species (Goodale
and Beauchamp, 2010; Contreras and Sieving, 2011) however, because while benefits
to the satellite species seem clear, benefits to the nuclear species that provides
vigilance and information have not been identified clearly (but see Cimprich and Grubb,
1994). Therefore, while the presence of family nearby (kin selection) adequately
explains generally high vocal production and complexity by parids (Sewall, 2015), no
specific work to date suggests whether the number or species richness of heterospecific
flock participants should elicit greater vocal richness by participating parids. If the
dilution and shared vigilance is valuable enough to the nuclear species, then there could
be selection for parids to call in order to intensify the benefits of greater flock cohesion
(Murdoch, 2012). Under this scenario, then greater flock complexity may lead to greater
vocal richness by the parid species. Alternatively, if parids have increased conflicts with
heterospecifics in larger flocks, this could also increase the number of call types (e.g.,
Hino, 2005). In sum, no effect of heterospecific richness in flocks is a likely outcome if
parids have no reason to communicate with satellite species. But if I do detect it, this will
be a significant discovery because it suggests that even passive nuclear species may
solicit heterospecific participation and, in turn, that heterospecifics positively influence
nuclear species. Alternatively, the latter result could signify negative costs of conflicts
with heterospecifics. By characterizing the call classes responsible for greater vocal
richness, should it occur, the two contrasting causes (solicitation or conflict) could be
22
discerned. However, this task is beyond the scope of the current study. Under Objective
1, the research design provides limited power to discriminate among sub-hypotheses
(due to N=1 of each site type), but does allow confirmation (or rejection) of the overall
hypothesis. Objective 2 sub-hypotheses are explored using comparisons across a
range of flock sizes (Table 1).
Methods
Study Species: Florida Paridae
The Tufted Titmouse (TUTI) weighs around 30 g on average and is abundant in a
variety of woodland habitats throughout its range. It is a highly vocal species, uttering
characteristic contact, “chick-a-dee”, and a variety of other common calls most people
know, in addition to its ‘peter-peter-peter' songs (Pieplow, 2007). In Florida, they are
widespread in dense and open woodlands dominated by pine and hardwood and can be
found also in scrub, except for the southern-most end of the peninsula and the Florida
Keys (Myers and Ewel, 1990; Ritchison et al., 2015). The Carolina Chickadee (CACH)
is subordinate socially to the Tufted Titmouse, weighing on average around 12 g, and
loosely associated with titmice in winter. The CACH is the southeastern-most chickadee
species among the members of the family Paridae in the genus Poecile. CACH is
resident in the south to the Gulf Coast and extends far south, and TUTI is resident in
central Florida (Florida Fish and Wildlife Conservation Commission, 2003). Both Florida
parids constantly communicate vocally and visually with members of their core family
group (whether a mated pair, or family group) and with rivals throughout the year
(Mostrom et al., 2002). Population density of the two species varies (unpublished data)
between the two major forest types (pine Parid densities are 0.5 - 0.7 times the density
in hardwood; unpublished data) and the forest physiognomy influences the birds’
movement behavior (Contreras and Sieving, 2011).
The social organization of parids species changes from a female and male pair
defending a territory during the breeding season to two or more pairs or family groups of
parids forming a flock and defending a winter territory from other such flocks in the fall
and winter months (Ekman, 1989; Smith,1991; Mostrom et al., 2002). As a family,
parids can be a nuclear species in heterospecific, mixed species flocks during winter.
This is a result of being a family of birds that have vocalization traits that enable them to
give alarm calls and other kinds of calls that ensure the cohesion and protection of the
flock (Contreras and Sieving, 2011). Living in this kind of social groups brings benefits,
such as an enhanced ability to detect and respond to predators and food resources
(Wilson, 1975; Krause and Ruxton, 2002). These benefits often depend on vocal
signaling (Hauser, 1996; Bradbury and Vehrencamp, 2011). In many species, vocal
signals aid social cohesion as individuals move in their environment, often out of visual
contact. That’s because there is a greater need for individual discrimination based on
communicative signals, or for conveying a broader diversity of messages about
individual behavioral tendencies and environmental contexts, or for expressing a wider
range of emotional and motivational states, or some combination of these three needs
(Freeberg at al., 2012). In many ways, communicative abilities of parid species have
become a model system for the behavioral ecology of social information in animal
communities.
Tufted titmice and Carolina chickadees are ‘nuclear' species, often joined in
winter flocks by a variety of ‘satellite' species (Farley et al., 2008). As a ‘passive nuclear'
24
species in wintertime foraging flocks, titmice influence the paths that foraging flocks
follow via passive leadership; satellites join them, not vice versa (Contreras and Sieving,
2011). At all times of the year, the TUTI is a socially dominant bird species among
passerines in its size range and can be aggressive within flocks to other birds (Cimprich
and Grubb, 1994) and toward potential predators during mobbing. More species mob
(are attracted) to the alarm calls of TUTI than to CACH or other passerines in local
forests (Sieving et al., 2004; Langham et al., 2006; unpublished data). The calls and
visual signals that titmice produce when mobbing or alarm-calling to aerial predators on
the wing provide specific information about the presence, location, activities, and level
of danger represented by a given species of predator to local passerines (Sieving et al.,
2010; Hetrick and Sieving, 2012) and this information is used by a wide array of prey
species including conspecifics to minimize predation risk via behavior changes that
minimize exposure to attack (Sieving et al., 2004; Schmidt et al., 2008; Hetrick and
Sieving, 2012).
Vocalizations of the Paridae
Major vocalizations of the Paridae include (1) contact calls, (2) “chick-a-dee” calls
(many variants exist and are given across a variety of situations), (3) aerial alarm calls
(Z-note strings), (4) territorial advertisement songs (‘peter-peter’ calls), (5) aggressive
calls (seet series) and (6) nestling/fledgling food-begging calls (Pieplow, 2007). The
basic organization of these groups of parids vocalizations is highly conserved within the
family across the Holarctic range of the family (Hailman, 1989). And specifically, the
organization of the ‘chick-a-dee’ call (chick note section followed by a D note section) is
highly conserved, though its structure (numbers and kinds of notes per call and acoustic
25
fine structure of the notes in the calls) is extremely flexible within a species (Smith,
1972; Sieving et al., 2010) reflecting different behavioral states or types of information.
For examples, duration of D notes is variable and has been correlated with approach
(longer Dees) and escape or withdrawal (shorter Dees; Kelley, 1988). Note types have
been variously identified and classified but common nomenclature includes High See,
High Tee, Chick, Dee (Smith, 1972) or A, B, C, D, Z (Hailman, 1989, Hailman and
Griswold, 1996). Underway is an exhaustive and authoritative nomenclature that so far
has more than quadrupled the number of previously defined notes and will provide a
systematic naming system (Lucas, Freeberg, and Sieving; unpublished data).
Based on experimental studies using predator or competitor presentations in
aviaries and in the wild, and on descriptive studies of call variations correlated with
situational changes (food calls), we know that parids calls reliably shift in structure
between functionally different situations. For example, the ‘chick-a-dee” call is altered in
the number and relative proportion of notes in the chick and dee sections to precisely
match the caller’s perception of attack risk from a predator (Templeton et al., 2005;
Hetrick and Sieving, 2012; Courter and Ritchison, 2010). Titmice and chickadees
appear to vary the characteristics of ‘chick-a-dee' calls and the rate at which D notes are
uttered to convey information to conspecifics about predator size and threat. Smaller,
higher-threat predators, like the Eastern Screech-Owl (Megascops asio) elicited ‘chick-
a-dee' calls with more D notes (and D notes uttered at a higher rate) than larger, lower-
threat predators like the Red-tailed Hawk (Buteo jamaicensis) (Courter and Ritchison,
2010). Also, note length and spacing within both chick-a-dee calls and Z-note strings
change with greater or lesser perception of risk (Sieving et al., 2010). Z-note calls have
26
been called ‘flying-predator' calls because they are often given when an aerial predator,
such as a hawk, is observed nearby (seet calls in Sieving et al., 2010). In addition, when
birds perceive an increase in risk or agitation, they switch from soft to hard chip notes
that are uttered in between the overt social signals (Z-strings and chick-a-dee calls;
Sieving et al., 2010). Song is usually a series of repeated whistled syllables, each
composed of one or two notes “peter-peter-peter;” (Bent, 1946; Schroeder and Wiley,
1983).
Ecological Significance of the Paridae
This situationally referential vocal system in parids is likely responsible for their
important role in facilitation and organization of woodland birds across the Holarctic
avian community. They act as nuclear species in winter and nonbreeding mixed-species
forest flocks (Dolby and Grubb, 1998; Dolby and Grubb, 2000; Sieving et al., 2004;
Farley et al., 2008). Nuclear species are the ones that behavior and vocalization
enhancing the cohesion and formation of a flock. Other members of the flock are called
satellite species (Moynihan, 1962; Morse, 1970; Farley et al., 2008). They can play an
important role as the community informant, among parids themselves, or to other
heterospecific species in the flock (Schmidt et al., 2010; Hetrick and Sieving, 2012), due
to the high proportion of Holarctic species living in sympatry with parids that respond
appropriately to their anti-predator calls (Langham et al., 2006). Chickadees can
response to the messages that are given by the titmouse, especially the alarmed Z
notes (Hetrick and Sieving, 2012). The information conveyed by parids is used by
potentially very many other species that live sympatrically with parids across the
Holarctic region (Langham et al., 2006; Sieving et al., 2010; Jones, 2016). Additionally,
27
given the constant production of parid calls, the species’ high abundance and tolerance
of a wide range of disturbance regimes, human interpretation of parids signals may
provide important tools for ecosystem management. Ultimately, if links can be made
between acoustic cues in parid vocalizations and factors that degrade habitats, this can
help predict how the entire community of parid-eavesdropping species may respond to
such changes (Seppänen at al., 2007; Jones, 2016). But the first step is to inventory
parid vocalizations across heterogeneous habitats and social situations.
Study Sites and Forest Characteristics
The pine study site was located at Ordway-Swisher Biological Station,
29°40'57.12"N, 82° 0'27.19"W (Putnam County FL) and the hardwood site was located
in San Felasco State Preserve, 29°43'37.26"N, 82°25'57.14"W (Alachua County FL;
See Figure 4). Quantitative data suggest a variety of specific differences between the
forest habitats in the study areas: basal area, canopy cover, parid density, and parid
vocalization degradation with distance are all significantly higher in hardwood (KE
Sieving, unpublished data). Therefore, in analysis I summarize all the differences using
a simple categorical representation of the two forest types in which calls and social data
were collected on multiple flocks per site. Site differences we know about can be
summarized as follows: (1) Titmice and Chickadees on our sites occur at higher
densities in hardwood than in pine (nearly twice as much; Jones and Sieving,
unpublished data) and intraspecific territory boundaries in pine are often not adjacent,
but they are in hardwood. (2) Basal area of trees and number of trees per ha is much
higher in hardwood than in pine, as is tree species richness and understory density
(measured using density board technique; Sieving, unpublished data). Finally, (3) sound
28
transmission of vocalizations extends further in pine than in hardwood (e.g., sounds
emitted travel significantly further before the signal to noise ratio declines to zero in
pine; Lucas, Sieving and Freeberg, unpublished data).
Data collection
Automated recording units
Data collected from October 2015 to January 2016, during the Winter seasons. In
each site, four automated units (Wildlife Acoustics SM3 sound meters) were placed in 1
square kilometer area (Figure 4) at least 250 m apart. Each SM3 was cable locked to a
tree with a diameter less than the width of the microphones on the sound meter so as
not to obstruct the devices external microphones. The units were hung between 5 and 6
feet high where no foliage movement could interfere with the recording. Machines were
programmed using the Wildlife Acoustic Song Meter Configuration Utility 3.2.4. The
program instructed each machine to record for one half hour on the hour (every other
half hour) starting at 06:00 and ending at 17:00; the result being 6 total hours of
recordings per day. Each device ran with 4 rechargeable D batteries allowing for
between one and two weeks of power Each unit was left from between 2 to 4 weeks
from the start of recording before replacing with charged batteries and blank cards and
being started again. This allowed for at least several days of recording per month.
Naturalistic recordings and flock composition
Data on free-living flocks were collected for three winter seasons: from October
to January 2015, 2016, and 2017. Birds were determined to be part of a mixed-species
flock if at least one parid occurred with at least one bird of another species and all birds
were moving together within 25 m of their nearest neighbour (Hutto, 1987; Latta and
Wunderle, 1996; Rodewald and Brittingham, 2002). Also, each bird included was with
29
the flock for at least 5 min (Latta and Wunderle, 1996; Maldonado-Coelho and Marini,
2000). All individuals meeting these criteria were recorded. I followed flocks until the
birds were lost (Farley et al., 2008), or until I stopped getting any additional vocal data.
Usually there were two observers, 1 for recording and 1 for identifying birds. The
recording devices used included a Sennheiser ME 66 directional microphones and a
Marantz PMD 66 recorder. To avoid double sampling, flock observers searched and
encountered flocks in locations at least 100 m from any other initial recording made, and
flocks both in and outside of the main study areas (1 Km squares; see Figure 4) were
sampled. Recordings were made by myself and 3 others (see Acknowledgments).
Data processing and sample design
I planned on pooling data both SM3’s and naturalistic recordings, therefore the
sample units had to be comparable. Vocalizations taken from a set of parids (one
species or the other) calling within one winter flock or group is the natural sampling unit.
For naturalistic recordings, flocks were easy to distinguish and recordings of each flock
were, on average, 15 minutes long. Therefore, to construct an equivalent sampling unit
for the SM3 data, I used 15-minute segments from different SM3 units sampled from
different times of the day that had parid vocalizations present. Assuming that different
flocks pass the units within a few minutes and get recorded while they are within 5o
meters of the units, 15 minutes represents a ‘flock equivalent’ sample. All SM3 data
used in this analysis was first shredded into 5-minute clips, then student volunteers
manually sorted clips to identify those with parid vocalizations, then vocalizations in
these clips were scored by Dr. Rajeev Pillay (using the method described in Figure 5,
and below). I used the scored data from 3 consecutives 5-min clips containing parid
calls from the SM3 data to represent a flock-equivalent (sample unit).
All naturalistic and SM3 ‘flock equivalent’ samples were visualized in Adobe
Audition while all notes and calls were scored following the systematic characterization
described below. All vocalization scores were recorded in computerized data sheets
with species and individual counts for each flock Naturalistic recordings only) along with
flock ID, habitat, date, etc.
Call classification
To classify the parids’ calls, I followed certain steps. For chick-a-dee calls, I
identified the introductory note, which can be A, B, C, Z for TUTI, and A, B, C, E, Z, for
CACH. Secondly, I counted the number of D notes or F notes (titmice only – F notes are
frequency-modulated D notes). Then, I named the chick-a-dee call according to the
number and sequence of the introductory notes, and to the length of the dee section.
For non-chick-a-dee calls and notes, I classified the call according to the sequence, and
repetition of the notes in the call (syntax) and note types. I used overall and peak
frequency, duration and shapes (on a sonogram) to identify each note. For example, A
note in TUTI starts with high peak, a short horizontal section and then a descending tail.
A Z note is straight with, sometimes, short tails (see Figure 5). Short gaps in multi-note
calls are indicated by no space between scored notes in the D-sequence, or for
unusually long gaps an underscore _ is used. Frequency modulation of notes and note
duration were considered in characterization of notes. A ffrequency modulated D note
will be called F note (TUTI only). If the note duration is < 0.2 sec, it is called short, with
letter ‘s’ after it (ex. Ds). A Long note has > 0.2 sec duration, and two letters ‘ln’ signifies
a long note (ex. Dln or Fln). Hybrid notes are indicated with a hyphen (-), for example A-
D or A-F are common hybrids. Conventions for recognizing these duration and
31
different situations are derived primarily from data collected in experiments with
chickadees and titmice (Sieving et al., 2010; Hetrick and Sieving, 2012).
Some of the notes produced by parids are not associated with the chick-a-dee
call. These are U and T notes, which can form a call by themselves or with other notes.
Data Analysis
Objective 1
In order to test for differences in call richness, I treated call classes like species
and subjected data to typical analyses used in community ecological approaches.
Call accumulation curves
First, I had to assess whether sampling effort was sufficient to achieve a
complete accumulation of the call richness produced by these species in the different
habitats by the sampling methods used. I plotted accumulation curves and assessed
whether the rate of unique call accumulation leveled off at zero or not; in other words,
did I record all the calls the birds could produce. Accumulation curves generally did not
level off, therefore, I expected to have to estimate the total vocal diversity using
rarefaction.
Rarefaction analysis
Since accumulation curves did not level off, I needed to estimate the total
richness of unique note and call types in each of my two populations of birds using
rarefaction. Rarefaction provides a method of comparison between different
communities, whereby each community is "rarefied" back to an equal number of
sampled specimens (Heck et al., 1975; Foote, 1992; Colwell and Coddington, 1994). It
32
estimates the species richness, and for the purpose of this study, I am considering each
call as a “species”.
Using EstimateS I entered the data files with identifiers representing the two
sites, the two different species and the two sampling methods. Data files included the
call counts for each class of note and call in every flock/flock equivalent. I extrapolated
all sample sets to 300 samples beyond the number actually sampled. This step was to
estimate the number of calls that would be found by taking that many more samples
from my sites, based on the rate of accumulation evident in the actual sample set
(Colwell et al., 2004). The software produced rarefaction curves with confidence
intervals that provided the basis for testing for significant differences in vocal complexity
among different sets of data. The computation of "open" unconditional confidence
intervals for rarefaction and extrapolation assumes that some species in the
assemblage sampled remain undetected, when all individuals or sampling units are
pooled. An estimator of asymptotic richness is used to assess this assumption. This
estimator is Chao2 (Colwell, 2013) and it uses the observed number of species in a
sample, combined with the number of species appearing in only one and two samples
(Colwell and Coddington, 1994). To assess whether asymptotic Chao2 estimates of call
richness were significantly different, I identified whether or not the 84% confidence
intervals around the estimated call richness overlapped or not. I chose to use this
percentage as suggested by MacGregor-Fors and Payton (2013) because their analysis
showed that, for data like mine, 84% confidence intervals mimics differences typical of a
0.05 α value.
Vocal similarity between sample methods and sites
In addition to overall number (richness) of call types in our data (by site and
method), I tested whether unique sets of calls were being produced by the parids in
both sites. For this purpose, I need to quantify similarity indices for the parid’s call
diversity in each site. I did this in two ways. (1) The similarity/dissimilarity measures
evaluate the relative distinctness of assemblages of calls, without identifying specific
sets of calls, based on proportions of shared or unique call classes. First, I calculated a
simple similarity/dissimilarity coefficient (for each species separately) comparing the
distinctness of calls in (either the two sites or methods) using the following equations,
where a = the total number of calls present in both sites (or methods); b = the number of
calls present only in site 1 (or collected by one method); and c = the number of calls
present only in site 2 (or the other method; Jaccard, 1908). Similarity = a/(a+b+c);
Dissimilarity = 1- similarity. Second, I calculated Jaccard, Sorenson, and Bray-Curtis
similarities based on the raw call accumulation data (Magurran, 2004). Abundance-
based Jaccard and Sørensen indices, as computed in EstimateS (Colwell, 2013) on raw
call accumulation data weigh the importance of the species being present in both
samples being compared, even if not detected in one or both samples. This approach
minimizes negative bias that plagues traditional similarity indices, especially with
incomplete sampling of rich communities such as here (Chao et al., 2005; Magurran,
1998; Magurran, 2004). Similarity analysis indicates whether, in essence, birds in
different habitats or recorded via different methods, use different (or unique) sets of
calls.
34
Social and vocal complexity in flocks
To determine if the species richness and abundance of birds following titmice
and chickadees in winter foraging flocks affected the vocal output of the two parids, I
conducted a 2-stage analysis. (1) I examined all the potentially influential flock and
environmental variables for their influence on the Chao2 estimates of vocal complexity
(call richness) produced during each bout of recording a naturalistic flock using
regression tree analysis. Regression trees identify important correlates with a response
variable using a machine learning algorithm to define and apply splitting criteria to parse
the data set into successively homogeneous subsets. They do not impose strict
assumptions on types or distributions of predictor variables and are useful, therefore, for
screening large sets of variables, including selecting the best variables to use from sets
of collinear variables (De'ath and Fabricius, 2000). The flock factors screened in the
regression tree analysis for their relationship with Chao2 included sites, maximum
number of TUTI, maximum number of CACH, total Parid individuals, heterospecific
species number, and number of heterospecific individuals. (2) Following the regression
tree analysis, I submitted variables with importance values above 40% (Breiman et al.,
1993) for inclusion in a generalized linear model to test for the significance of different
variables. Prior to running a general linear model, I centered and scaled all important
predictor variables identified by the regression tree. Then, I tested for normality of the
Chao2 estimates. Normal-probability plots determined that Chao2 was normally
distributed, therefore I assumed a normal distribution in the GLM with a log-link function.
Analyses were conducted in Statistica (version 13.2).
35
Results
I scored 16877 calls from the naturalistic recordings from 30 flocks (16 flocks
from OSBS, and 14 flocks from SF), 9235 calls in OSBS, and 7642 calls in SF. From
the SM3 recordings, I scored 4316 notes and calls, 1651 from OSBS, and 2665 from
SF, from 26 flocks (11 flocks from OSBS, and 15 flocks from SF). I detected a total of
1259 types of calls and notes for TUTI (for Naturalistic recordings, 620 in OSBS, and
319 in SF; for SM3 recordings, 145 in OSBS and 175 in SF) and 231 for CACH (For
Naturalistic recordings, 59 in OSBS, and 146 in SF; for SM3 recordings, 12 in OSBS
and 14 in SF (see Table 2).
Vocal complexity (call richness)
Using rarefaction analysis, I could assess the degree of variation in call richness
between species, sites (forest types), and methods of data collection by examining the
overlap of the lower and upper 84% confidence intervals around the Chao2 estimates
after extrapolation was complete. If confidence intervals surrounding the estimated call
richness curves did not overlap at all, then they were deemed significantly different. If
one (upper or lower) half of an interval overlapped with the adjacent interval (but not the
richness curve), I counted the differences between the curves as marginally significant.
If one or both confidence intervals overlapped with one or both estimated curves, then
the two curves were deemed not different.
I summarized the data in the following ways. First, I pooled all calls of both
species to compare the total vocal complexity between the two types of forest for each
method of data collection (Figures 6 and 7). Birds in pine (OSBS) collectively produced
more call diversity with naturalistic recordings (marginally significant), while SM3
recordings indicated that parids in hardwood and pine produce similar diversity (not
36
significantly different, but overlap was asymmetrical suggesting that with more sampling
at OSBS, differences would be at least marginally significant). Separating the two
species and then comparing call richness across sites for each method, I found that in
naturalist recordings, TUTI call diversity was significantly higher in OSBS flocks than in
SF flocks (Figure 8) and the opposite pattern was seen in SM3 data, TUTI call diversity
was higher in SF (but differences were marginally significant; Figure 9). For CACH, call
richness was not different in the two sites by either data collection method (Figures 10
and 11).
For more power in assessing the habitat effect, I lumped the data from the two
methods together. Then, I used these new set to produce rarefaction curves of each
species to compare their call diversity in each habitat. In these curves, I can see clearly
that TUTI have higher call richness in OSBS than SF. The difference here is significant
as the 84% CI do not overlap. For CACH, birds in pines (OSBS) also produced more
call diversity than in hardwood (SF), but not significantly more, as the 84% CI is clearly
overlapping (Figures 12 and 13). A summary of call classes, of unique calls, shared
calls, and Uniqueness percentage for parids recorded in each site and by the two
methods, are listed in table 3.
Call similarity
Jaccard and Sørensen indices are based on the presence/absence of two
randomly chosen individuals (or here a call count), one from each of two samples
(sites), both belong to species (call type) shared by both samples (Magurran 2004). The
estimators for these indices consider the contribution to the true value of this probability
made by species (call types) present at both sites, but not detected in one or both
samples (Chao et al., 2005).
37
The similarity coefficients results did not show any differences in call richness
between the two species, and methods in the two sites. I calculated the similarity
indices (Jaccard, Sorenson, and Bray-Curtis), for the calls obtained from naturalistic
recordings, and the results showed that TUTI calls have higher similarity values. While
the similarity indices calculations for the calls obtained from SM3 recordings showed
that CACH calls have higher similarity values. All the similarity indices including
Jaccard, Sorenson, and Bray-Curtis are shown in table 4.
Social and vocal complexity in flocks
Regression tree analysis identified several flock characterisitcs as having a
strong relationship with Chao2 (figure 14). The generalized linear model testing for the
effects of these factors identified 4 of them as significant (table 5). Besides species and
site (TUTI produced significantly more call types than CACH, and OSBS birds more
than SF birds; Figure 15), thenumber of titmice and heterospecific species in a flock
were also correlated with call richness (Figures 16 and 17).
38
Table 2-1. Research hypotheses and sub-hypotheses, their predictions and rationale tested in this study. Objective 1: Habitat type determines parid vocal complexity. Hypothesis Prediction Reasoning
SH1: Habitat vegetative complexity can directly constrain call classes used by parids
Hardwood habitats will have constricted vocal richness
Transmission of calls is negatively correlated with vegetation density therefore individuals may be constricted in use of full range of frequencies, note and call types in dense habitats
SH2: Habitat complexity affects movement call frequency and structure and, in turn, parid vocal complexity
Hardwood habitats will have constricted vocal richness
Higher density of vegetation increases food density for parids and decreases distances between foraging sites; movement (and associated calls) will be less frequent over shorter distances in hardwood.
SH3: Habitat complexity alters perception of risk in parids and, in turn, affects parid vocal complexity
Hardwood habitats will have constricted vocal richness
Denser hardwoods provide greater cover for escape; therefore, perception of predation risk, and anti-predator calls (among the most complex of parid calls), will be infrequent in dense hardwood.
SH4: Habitat causes population density and vocal mimicry variation in parids and, in turn, affects vocal complexity
The proportion of unique calls will decrease in hardwood
In denser forest (hardwood), parids are more abundant and territories are closer together, affording greater opportunity for vocal mimicry.
39
Table 2-1. Continued Objective 2: Social Complexity determines vocal complexity in Paridae Hypothesis Prediction Reasoning
SH5: Parid
with each other (familial, competitive and
facilitative relationships) then each parid
individual will require a greater range of
calls if the other species is present and if
the number of parid individuals increases.
SH6: Non-parid
parids, suggesting a parasitic relationship,
and parids may not interact actively with
any of them.
increases, parids may have to interact just
because of the proximity.*
* Objective 2 is designed to discriminate between a) and b)
40
Table 2-2. Call types produced by TUTI and CACH in each site, and by each recording method. Call Species OSBS SF Total TUTI Call types Naturalistic recordings 620 319 939 SM3 recordings 145 175 320 CACH Call types Naturalistic recordings 59 146 205 SM3 recordings 12 14 26
41
Table 2-3. Summary of calls scored in naturalistic and automated units recording results, showing total number of call classes, number of unique calls produced in each site, shared calls (both sites), Uniqueness percentage (unique call classes – shared call classes/total call classes X 100), and total call counts in each site.
Naturalistic Recordings
Automated Recording Units (SM3)
percentage
42
Table 2-4. The Similarity and Dissimilarity values and Similarity indices compared in the two study sites. Method Similarity Dissimilarity Shared
call types Jaccard Sorensen Bray-
Curtis Naturalistic 0.59 0.40 95 0.84 0.91 0.77
TUTI 0.60 0.40 80 0.87 0.93 0.79 CACH 0.58 0.42 22 0.48 0.64 0.13
SM3 0.61 0.39 38 0.85 0.92 0.55 TUTI 0.60 0.40 32 0.78 0.85 0.45
CACH 0.63 0.37 5 0.92 0.96 0.77
43
Table 2-5. The GLM results of the predictor variables on call diversity (Chao2). Effect Level of
effect Column Estimate SE Wald Stat. Lower CL
95% Upper CL 95%
P-value
Intercept 1 5.62 0.09 3706 5.44 5.80 0.0000 Species T 2 0.33 0.09 12 0.14 0.51 0.0005 Species B 3 0.54 0.09 34 0.36 0.73 0.0000 Site OSBS 4 0.20 0.06 13 0.09 0.31 0.0003 Max. # TUTI 5 0.20 0.04 24 0.13 0.29 0.0000 # het. sp. 6 0.10 0.04 5 0.01 0.18 0.0310
T = TUTI, B = Both species; CACH was reference group.
44
45
Figure 2-3. Flow charts of research approach. Approach starts with sample collection
using two methods; 1) Automated recording units, 2) Naturalistic recording. Next, Scoring the data. This is followed by the data analysis.
46
Figure 2-4. Study site locations and aerial photos indicating the 1 Km square centers of
study areas where birds were observed and recorded. Hardwood forests (left) were sampled in San Felasco Hammock State Preserve (SF), and pine habitats (right) were sampled in Ordway Swisher Biological Station (OS). Labeled positions on the aerial photos indicate locations of automated recording units (SM3 recorders). The blue cross indicates the location of the city of Gainesville, FL.
47
Figure 2-5. Call scoring nomenclature for chick-a-dee calls of (a) TUTI and (b) CACH. In
each panel, the Y-axis measures frequency (kHz) and the X-axis measures time (sec). Representative calls are coded using note designations (capital letters), D note durations for TUTI are indicated by lower case letters (long = ln, short = s; see text) and for CACH D notes they are either rapid (denoted by ‘r’ or normal. Unusually long gap lengths between notes are designated with underscores (_) and notes without any gap (hybrid notes) are indicated with a dash (-).
48
Figure 2-6. Rarefaction curves of the 30 flock samples from naturalistic recordings
shows that OSBS has marginally significantly (84% CI are slightly overlapping) more call diversity than SF (TUTI and CACH calls pooled). The data were extrapolated to 300 points beyond the sample number (indicated by the vertical black line).
49
Figure 2-7. Rarefaction curves of the 28 samples from SM3 recordings suggests that
birds at SF may have more call diversity than at OSBS (TUTI and CACH calls pooled). However, significance of this difference cannot be assessed clearly because the confidence envelopes are asymmetric (SF estimate has a wider CI), suggesting sampling effort was insufficient to resolve the relationship. The data were extrapolated to 300 points beyond the sample number (indicated by the vertical black line).
50
Figure 2-8. TUTI vocal complexity from naturalistic recordings is significantly different
between sites; higher at OSBS than at SF (no overlapping in 84% confidence intervals). The data were extrapolated to 300 points beyond the sample number (indicated by the vertical black line).
51
Figure 2-9. In SM3 recordings, TUTI vocal complexity appears to be marginally higher in
SF than in OSBS (84% intervals are overlapping each other but not estimates; barely). The data were extrapolated to 300 points beyond the sample number (indicated by the vertical black line).
52
Figure 2-10. CACH vocal complexity from naturalistic recordings is not significantly
different between sites (overlapping 84% confidence intervals). The paucity of CACH data from the naturalistic recordings prevents estimation of vocal complexity differences. The data were extrapolated to 300 points beyond the sample number (indicated by the vertical black line).
53
Figure 2-11. In SM3 recordings, CACH vocal complexity is not discernably different at
the two sites (but few calls were recorded overall for this species; hence the very large 84% CI’s). The data were extrapolated to 300 points beyond the sample number (indicated by the vertical black line).
54
Figure 2-12. Total TUTI call richness in both sites. The calls obtained from the two
methods were lumped in this graph. Call richness of TUTI calls in OSBS was significantly higher than SF. The data were extrapolated to 300 points beyond the sample number (indicated by the vertical black line).
55
Figure 2-13. Total CACH call richness in both sites. The calls obtained from the two
methods were lumped in this graph. The results were not significant. The data were extrapolated to 300 points beyond the sample number (indicated by the vertical black line).
56
)
Figure 2-14. The Variable importance values on Chao2 as a result from the regression
tree analysis. I chose the variables with more than 0.4 importance, went through further GLM analysis. Max TUTI= maximum numbers of TUTI in the flock, Species= the call giver species, Total parids counts= the total number of parids individuals in the flock, Site= the study site where the flock in found, Het Sp.#= the number of heterospecific species, Het Indv#= the total number of heterospecific species individuals in the flock, Max CACH= maximum number of CACH individuals in the flock.
57
Site: OSBS Site: SF
Figure 2-15. Comparison of the call diversity of the study species using Chao2
according to the call classes given by species (methods pooled). Call classes given by TUTI (T) and calls produced by both species (B) are significantly richer in number at OSBS. The same trend is observed with just CACH call classes. Error bars represent plus/minus 1 SE.(Standard Error).
58
-1 0 1 2 3 4 5 6 7 8 9
Max # TUTI Detected
y = 73.9792 + 69.7168*x r2 = 0.1336
Figure 2-16. Scatterplot showing the relation of Chao2 estimation with the maximum
.
# of Heterospecific Species in Flock
-200
0
200
400
600
800
1000
e y = 193.0353 + 27.1377*x r2 = 0.0575
Figure 2-17. Scatterplot showing the relation between the Chao2 estimate and the
number of heterospecific species found in a flock. An increase in the number of heterospecifics increases weakly with Chao2.
60
CHAPTER 3 DISCUSSION
In this study, I detected intraspecific variations in total call richness related to
gross distinctions in habitat type for two members of the vocally complex Paridae. My
work is unique because previous analyses summarizing vocal complexity have focused
on inter-specific comparisons, mainly of song complexity (Kroodsma, 1982; Brenowitz
and Arnold, 1986; Nelson et al., 1995; Brown and Farabaugh,1997; Sewall 2015), with
some attention to variation in overall call diversity across the range of single species
(Freeberg et al., 2012; Krams et al., 2012). From a different perspective, ecological
acousticians have become interested in biophonic variation across landscapes and
habitats, but primarily in relation to its use as an indicator of species richness; not the
effects of variations in vocal production within one or more species (Gasc et al., 2016).
While there has long been interest in environmental factors that select for intraspecific
variation in various bioacoustics features of animal vocal production (again, mainly
song), especially bird song and other species’ sexual or territorial advertisement calls,
this interest has not included total repertoire size (e.g., Morton, 1977; Brenowitz and
Beecher, 2005). As Gasc et al., (2016) indicate, major advances in soundscape ecology
include exploration of the functional variation in calls of vocally complex bird species, as
indicators of ecosystem change, health, and biodiversity integrity. Here I discuss the
potential causes and implications of differences I detected in vocal richness by habitat,
methodology used to collect the vocalizations, and between the two species (Objective
1), as well as what our flock data suggest concerning the influence of flock social
structure on vocal production by the nuclear species (TUTI; Objective 2). I note that
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more calls are needed from a wider array of habitat and social conditions before I can
conclude that the patterns I detected here generally hold.
Variations in Call Richness
Direct and indirect habitat factors influencing vocal complexity in Paridae
A variety of important ecological factors varies between open pinelands and
dense hardwoods in Florida, occupied by both of our study species that may relate to
the differences in vocal production we observed. Numerous bird-relevant forest metrics
are dramatically different between the two habits. Tree density (basal area), canopy
foliage and understory woody vegetation density are all higher in hardwood forest and
light levels, visibility, and sound transmission are lower in hardwood forest (Sieving,
unpublished data). Bird responses in the study areas, where I collected vocalizations,
include a lower density of parids in pine than hardwood habitats. Census data from the
two study areas indicates that both species exist at half the density in pine as in
hardwood. Territories are widely spaced apart in OSBS, but are adjacent and smaller in
diameter in SF (Jones and Sieving, unpublished data). This is a general pattern with
woodland avian communities (e.g., Dickson et al., 1995), and has consequences for
mixed species flock structure (Morse, 1970) as well as overall population density and
associated calling behavior (see below). In addition, the direct effects of denser
vegetation on vocal production including frequency alterations and changes in usage of
different calls (Ey and Fischer, 2009). Increased density of trees and other resources for
parids in hardwood forest should, as predicted, restrict vocal richness in hardwood
compared to open pinelands. An additional source of variation in intra-specific vocal
richness derives from the recording methods used.
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I note that to discriminate among various direct and indirect effects of habitat
variation on estimates of call richness (e.g., Table 1) will require a detailed analysis of
the functional traits of call classes the birds used. For example, long range call types for
communicating over distances (e.g., songs and mobbing calls) should be most
influenced by acoustic adaptation and potentially interspecific vocal mimicry and shorter
range social calls (chips some chick-a-dee call formations) may be more affected by
intraspecific vocal appropriation. A first step would be to assess which types of call
classes, and their functions, are unique as well as shared by birds of each species living
in pine versus hardwood. Such an analysis was beyond the scope of this study but
would be possible with more data from a wider array of conditions.
Vocal mimicry within and between species could well explain the lower call
richness in the hardwood populations; it characterizes many bird communities, has
been documented in parids, and generally increases at higher population densities
(e.g., Laiolo, 2011; Delgado and Penteriani, 2007). Interspecific vocal mimicry as
defined by Dalziell et al. (2015) has been linked to various possible selective factors,
though little confirmatory data exists. Though beyond the scope of this study, vocal
mimicry could be examined with data like ours using an analysis of the specific call
classes that are shared versus unique, and in hardwood versus pine; but OSBS had
more types of calls overall (estimated for the more frequently sampled TUTI, pooled
data), and a greater mean proportion of unique calls for both species (Figure 15).
Species in Paridae in general would make very good subjects for in depth studies of
vocal mimicry for a variety of reasons. (1) Parids obtain and respond to social
information from vocal (Hetrick and Sieving, 2012) and behavioral (Jaakkonen et al.,
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2014) cues of con- and hetero-specifics. (2) Parids share the same overall organization
of their vocal repertoires (Hailman, 1989); therefore, mimicry would provide less of a
physical challenge. Indeed, Carolina chickadees and tufted titmice share a number of
calls that are indistinguishable between the two species (chip notes, including hard and
soft seet notes and Z notes and calls; Lucas, Freeberg, and Sieving, unpublished). (3)
Parid species usually occur in sympatry; from 2-5 species often live together in the
same habitats across the Holarctic (Harrap and Quinn, 1995). (4) Parids are both
competitive (Jaakkonen et al., 2014) and facilitative (Sieving et al., 2004) in their
interactions with one another where they do co-occur providing numerous hypotheses
to test. Only further work could discriminate among mimicry-related causes of similarity
in the call sets of the focal species in hardwood. Higher population density in hardwood
is likely to facilitate all factors thought to be involved in intraspecific ‘vocal appropriation’,
a type of cultural transmission, due to the larger number of nearby tutors (Dobkin, 1979;
Tchernichovski and Marcus, 2014). These factors include vocal copying of adjacent
territory occupants by juveniles while learning song, dialect matching by all members of
a population (Marler and Tamura, 1964), and acoustic matching of subordinate
vocalizations to social dominants (e.g., Lemasson et al., 2016). Conversely, all of these
mechanisms would accentuate the differences in acoustic signature of different family
groups and flocks of parids that were isolated by distance in sparse populations
(Gammon, 2007).
Another explanation for greater call richness in pine may have to do with the
diversity of calls related to habitat visibility. In Ordway open pine habitat, the visibility is
higher, which may mean that parids will encounter or detect a higher proportion of
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actively hunting raptors and, in turn, produce more alarms (Chick-a dee and Z note
strings; Sieving et al., 2010). Alarm calls provide the greatest source of variation in the
entire repertoire of chickadees and titmice (Lucas and Freeberg, 2007; Freeberg, 2008)
because of the highly variable note diversity and overall number per call that can occur
across predator encounter contexts (Hailman, 1989; Templeton et al., 2005; Sieving et
al. 2010; Hetrick and Sieving 2012). Moreover, many prey species including parids
exhibit a heightened state of vigilance and more anti-predator behaviors in habitats with
limited escape cover (Whittingham and Evans, 2004; Sieving et al., 2004). Therefore,
we might predict that with further study, a higher number of relatively higher risk calls
will be present among the unique call classes produced by parids in pinelands.
Finally, flock movement differences could contribute to differences in call
richness between the two habitats. Parids habitually call when initiating and ending
flights and these calls can vary with different social and environmental conditions
(Farnsworth, 2005; Freeberg, 2008). In a study of radio-tagged TUTI at OSBS, they
moved from tree to tree more often and moved faster over longer distance intervals in
pine stands than hardwood stands at OSBS (Contreras and Sieving 2011; Contreras
unpublished data). A high rate of inter-patch movement at OSBS (and flight call
production) could be driven by the lower basal area and scarcer invertebrate foods
available on pine trees than hardwoods in Florida in winter. Therefore, we might predict
that with further study, a higher number and richness of classes of flight calls will be
present among the unique call classes produced by parids in pinelands.
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Naturalistic recordings detected a greater percentage of the overall diversity of
call types than our automated recording units did (SM3s). Because observers followed
flocks, recordings made in this manner naturally had a high number of calls per unit
time. Flocks passing an SM3 unit, on the other hand, might only be present close
enough to be recorded by a stationary unit for less than 5 minutes at a time. Moreover,
the units will collect calls from passing flocks but they also collect calls from individual
birds passing to and from flocks and the two situations would both be treated as ‘flock
equivalents’ in our sampling scheme. Calls from lone birds could certainly bias SM3
data used in the way we did here because call richness is generated primarily by parid
social group interactions (Freeberg et al., 2012; Krams et al., 2012). Based on the call
class numbers listed for each species and site and methodology (Table 2), it is clear
that the low density of both species at OSBS hampered sampling acquisition for both
methods, but most especially for SM3 sampling and for CACH. It is also more likely in
the low-density populations at OSBS that in both sampling methods, there is a higher
likelihood of re-sampling the same flocks than in the very densely populated hardwood
sites. While such under sampling at OSBS weakens the overall power of our habitat
comparisons, I think it is unlikely that the direction of difference (higher OSBS vocal
richness) is misidentified because when methods are pooled, OSBS call classes are
markedly higher at OSBS for both species (estimated total, Figures 12 and 13; mean
estimated, Figure 15). Finally, it is possible that naturalistic recordings bias the kinds of
calls produced because of human presence. When we follow flocks, we wait a few
minutes before beginning recordings to allow the birds to acclimate to our presence. It is
likely, however, that human presence biases the types of calls given (for example,
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toward anxious loud seet or other high-risk calls; Sieving et al., 2010) or it may cause a
higher rate of movement and number of inter-patch flights (and flight calls). Human
activity and presence may also decrease flock coherence or depress foraging (e.g.,
Yasué, 2005) and alter species interactions or other functions along with call production
patterns. More work is needed to fully assess these potential sources of bias.
Flock social and vocal complexity
We detected significant effects of parid species presence, the number of titmice,
and the number of heterospecific satellites on vocal richness in the naturalistic
recordings (Table 5), though we note the estimates were small. But if real, these
patterns make sense in the following ways. TUTI are dominant to CACH and are also
much more attractive to heterospecifics as nuclear species than CACH. Therefore, we
recorded more flocks with TUTI present than CACH present – therefore a sampling bias
could be present. But more likely is the effect of parid groups size. TUTI nearly always
occur in groups of 3 or more in winter whereas CACH are often in pairs (Hetrick and
Sieving, 2012). The presence of a juvenile offspring is very likely to spur greater vocal
production in either parid, but will occur more often in TUTI in our study system. And
when both species are present there are interspecific interactions between them,
especially aggression (Cimprich and Grubb, 1994), which should inflate call richness.
The number of TUTI in a flock also positively influenced the call richness and this
could be influenced by a variety of factors. (1) As mentioned, the addition of youg of the
year would likely greatly increase vocal production between 2 and 3 TUTI in a flock. (2)
Assuming that if there are more than 3 TUTI, there likely is more than one nuclear
family group of TUTI present in flock, inter-family conflict and other interactions should
certainly inflate call richness with social calls of various kinds. (3) This effect of TUTI
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number could be modified by the nature of the dominance hierarchy in play, whether
one or more families of TUTI were present. In aviary studies of TUTI and CACH
(Sieving, unpublished data) and in field studies of other parid species (Krams, 2000;
Hino, 2005), both egalitarian and despotic hierarchies can be observed within (and
between) parid species in mixed species flocks. Vocal production appears to be
significantly higher in egalitarian flocks, in part, because a higher proportion of social
unit members vocalize. Apparently, however, despotic hierarchies in parid flocks are by
far the most common condition when it has been characterized (Thompson, 1983;
Koivula and Orell, 1988; Hay, 2003). Therefore, of the three factors, the increased vocal
richness with more TUTI is most likely attributable to the presence of young, and at
higher numbers than 3, both more dominant individuals signaling and increased conflict
between nuclear units. While we could count individual parids present, our sampling
methods did not allow for dominance data, but would certainly be useful in future
research concerning vocal production in parid groups.
Finally, I detected a weak signal of heterospecific species richness on parid vocal
production in flocks. As the number of satellite species increased, so did the call
richness across flocks (Table 5; Figure 17). If this pattern is real, then it suggests that
TUTI may be communicating with non-parid participants in addition to kin, which is
unexpected considering previous work. TUTI have been characterized as passive
nuclear species that do not join other birds, but rather are always joined by
heterospecifics (Contreras and Sieving, 2011). We hypothesize that this is because
heterospecific species utilize the valuable (context-specific) information produced by the
TUTI (Sasvari, 1992; Sieving et al., 2004; 2010). Heterospecifics use and benefit from
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the nuclear species alarm calls by a process named interceptive eavesdropping (Dall et
al., 2005; Peake, 2005; Koboroff and Kaplan, 2006), but little work has addressed
whether TUTI in particular, and nuclear species in general, gain substantive fitness
benefits from satellite participation. The most likely case is that being a passive nuclear
species defines a parasitic relationship with satellites (Goodale and Beauchamp, 2010).
If this scenario holds, TUTI may have no compelling