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Soundscape Ecology: The Science of Sound in the Landscape

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This article presents a unifying theory of soundscape ecology, which brings the idea of the soundscape—the collection of sounds that emanate from landscapes—into a research and application focus. Our conceptual framework of soundscape ecology is based on the causes and consequences of biological (biophony), geophysical (geophony), and human-produced (anthrophony) sounds. We argue that soundscape ecology shares many parallels with landscape ecology, and it should therefore be considered a branch of this maturing field. We propose a research agenda for soundscape ecology that includes six areas: (1) measurement and analytical challenges, (2) spatial-temporal dynamics, (3) soundscape linkage to environmental covariates, (4) human impacts on the soundscape, (5) soundscape impacts on humans, and (6) soundscape impacts on ecosystems. We present case studies that illustrate different approaches to understanding soundscape dynamics. Because soundscapes are our auditory link to nature, we also argue for their protection, using the knowledge of how sounds are produced by the environment and humans.
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BioScience March2011•Vol.61•No.3 AmericanInstituteofBiologicalSciences
Soundscape Ecology
Biophony Anthrophony Geophony
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Soundscape Ecology: The Science
of Sound in the Landscape
Bryan C. Pijanowski, Luis j. ViLLanueVa-riVera, sarah L. Dumyahn, aLmo Farina, Bernie L. krause,
Brian m. naPoLetano, stuart h. GaGe, anD naDia Pieretti
This article presents a unifying theory of soundscape ecology, which brings the idea of the soundscape—the collection of sounds that emanate
from landscapes—into a research and application focus. Our conceptual framework of soundscape ecology is based on the causes and conse-
quences of biological (biophony), geophysical (geophony), and human-produced (anthrophony) sounds. We argue that soundscape ecology shares
many parallels with landscape ecology, and it should therefore be considered a branch of this maturing field. We propose a research agenda for
soundscape ecology that includes six areas: (1) measurement and analytical challenges, (2) spatial-temporal dynamics, (3) soundscape linkage to
environmental covariates, (4) human impacts on the soundscape, (5) soundscape impacts on humans, and (6) soundscape impacts on ecosystems.
We present case studies that illustrate different approaches to understanding soundscape dynamics. Because soundscapes are our auditory link to
nature, we also argue for their protection, using the knowledge of how sounds are produced by the environment and humans.
Keywords: soundscapes, bioacoustics, biophony, nature deficit disorder, dawn and dusk chorus
The purpose of this article is to present a new field of study
called soundscape ecology, emphasizing the ecological char-
acteristics of sounds and their spatial-temporal patterns as
they emerge from landscapes. We believe that soundscape
ecology shares considerable parallels with landscape ecology
(Forman and Godron 1981, Urban et al. 1987, Turner 1989,
Turner et al. 2001, Farina 2006), because processes occurring
within landscapes can be tightly linked to and reflected in
patterns of sounds in landscapes.
To illustrate the main themes of this relatively unexplored
field, we introduce new terms and a conceptual framework
for soundscape ecology, summarize what is known about
sounds in the environment, and present overviews of four
case studies that quantify soundscape dynamics. We con-
clude with an argument for the need to conserve natural
soundscapes. This article also represents an innovation in
presentation; we introduce sound recordings as an inte-
gral component of the article. All acoustic recordings used
in this article as single demonstrations and many others
used in our analyses may be accessed online in two places:
(1) by reading the full-text version of this article online (dx.
doi.org/10.1525/bio.2011.61.3.6); and (2) at our own self-
hosted site (www.purdue.edu/soundscapes/bioscience), which
features additional Web tools for learning.
What is soundscape ecology?
The term “soundscape” has been used by a variety of dis-
ciplines to describe the relationship between a landscape
and the composition of its sound. The work of Southworth
(1969) exemplifies one of the first uses of the term in the
literature. Southworth was interested in urban soundscapes;
Sounds are a perpetual and dynamic property of all
landscapes. The sounds of vocalizing and stridulating
animals and the non-
biological sounds of
running water and
rustling wind ema-
nate from natural
landscapes. Urban
landscapes, in con-
trast, are dominated
by human-produced
sounds radiating from a variety of sources, such as machines,
sirens, and the friction of tires rotating on pavement (Bar-
ber et al. 2010). Since Rachel Carson’s seminal work, Silent
Spring (1962), nature’s sounds have been inextricably linked
to environmental quality. Because sound is a fundamental
property of nature and because it can be drastically affected
by a variety of human activities, it is indeed surprising
that sound has not become a more universally appreciated
measure of a coupled natural–human system (Liu et al.
2007). To date, no coherent theory regarding the ecological
significance of all sounds emanating from a landscape exists.
Fortunately, new technologies such as automated record-
ing devices (e.g., Acevedo and Villanueva-Rivera 2006), the
existence of inexpensive storage capabilities, developments
in acoustic data processing (e.g., Sueur et al. 2008, Trifa et al
2008), and theories of related ecological disciplines such as
landscape ecology (Forman and Godron 1981, Urban et al.
1987, Turner 1989, Turner et al. 2001, Farina 2006) have
advanced sufficiently to allow research on the ecological
significance of sounds in landscapes to progress.
BioScience 61: 203–216. ISSN 0006-3568, electronic ISSN 1525-3244. © 2011 by American Institute of Biological Sciences. All rights reserved. Request
permission to photocopy or reproduce article content at the University of California Press’s Rights and Permissions Web site at www.ucpressjournals.com/
reprintinfo.asp. doi:10.1525/bio.2011.61.3.6
This article contains
sound files that may be
accessed by reading the full-
text version of this article
online at dx.doi.org/10.1525/
bio.2011.61.3.6.
204 BioScience • March 2011 / Vol. 61 No. 3 www.biosciencemag.org
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in particular, his work addressed how the sounds of the
built environment enhanced people’s perception of space
and their relationship to the activities occurring within cit-
ies. As a result, the first mention of soundscapes appears
in urban planning literature. Nearly a decade later, Schafer
(1977) recognized that sounds are ecological properties
of landscapes, referring to soundscapes as “the acoustical
characteristics of an area that reflect natural processes.” His
primary interest was in characterizing
natural sounds that could be used to
compose music. Krause (1987) later
attempted to describe the complex ar-
rangement of biological sounds and
other ambient sounds occurring at a
site, and introduced the terms “bio-
phony” to describe the composition
of sounds created by organisms and
“geophony” to describe nonbiological
ambient sounds of wind, rain, thunder,
and so on. We extend this taxonomy of
sounds to include “anthrophony”—those caused by humans.
Soundscape ecology thus can be described by our working
definition as all sounds, those of biophony, geophony, and
anthrophony, emanating from a given landscape to create
unique acoustical patterns across a variety of spatial and
temporal scales.
At the onset, we wish to separate other acoustic studies
from what we believe is a unique field of acoustics presented
here. To our knowledge, soundscape ecology has not been
used in the literature to describe a field of ecology. Acoustic
ecology, as introduced by Schafer (1977) and Truax (1999),
is seen as complementary to traditional ecological concepts
rather than situated within them. Broadly interdisciplinary,
acoustic ecology studies the relationships and interactions
among humans and sounds in an environment, includ-
ing musical orchestrations, aural awareness, and acoustic
design (Schafer 1977, Truax 1999). Acoustic ecology largely
emphasizes human-centered
inquiry rather than the larger
socioecological systems approach
taken here.
Bioacoustics (Fletcher 2007)
is another related research
area that we distinguish from
soundscape ecology. The study
of animal communication is a
rich and mature field, spanning
behavior, life-history theory, and
the physics of sound production
by animals. However, a major-
ity of these studies focus on a
single species or a comparison
of species. Our presentation
of soundscape ecology focuses
mostly on macro or community
acoustics. We are interested in the
composition of all sounds heard at a location that are bio-
logical, geological, or anthropogenic. Another rich area of
acoustics research has focused on noise in the environment.
Primarily in the field of engineering, significant research
has addressed the physics of sound (e.g., Hartmann 1997),
and new methods have been employed to calculate noise
produced from planes and automobiles across large regions
(Miller 2008).
Conceptual framework for
soundscape ecology
Since its conception, landscape ecol-
ogy has focused on the interaction of
pattern and ecological processes across
large spatial regions (Urban et al. 1987,
Turner 1989, Turner et al. 2001, Farina
2006). Many of the basic principles
of soundscape ecology are common
to those of landscape ecology. These
include the assignment of a sound-
scape to a geographic context, the identification of anthro-
pogenic and biological processes and spectral and temporal
patterns in the soundscape, how disturbance alters patterns
and processes across scales, the emphasis on interactions be-
tween biological and anthropogenic factors, how organisms
perceive spatial configuration in landscapes, and the need to
develop tools to quantify pattern.
Our general conceptual framework (figure 1) bases
soundscape ecology on the same foundations as landscape
ecology and draws from areas of coupled natural–human
systems (Liu et al. 2007), with natural and human systems
interacting to form spatial-temporal patterning of sound
in landscapes. Humans transform landscapes (Lambin and
Geist 2006) through land-use and land-cover change (fig-
ure 1, arrow 1), and these human modifications of the land
interact with a variety of biophysical features (e.g., terrain,
soils) to produce heterogeneity in spatial structure across
Figure 1. Conceptual framework for soundscape ecology.
“Over increasingly large areas of the United
States, spring now comes unheralded by the
return of the birds, and the early mornings
are strangely silent where once they were
filled with the beauty of bird song.”
— Rachel Carson, Silent Spring (1962)
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the landscape (Farina 2006). Landscape structure in turn
(figure 1, arrow 2) influences the distribution and abun-
dance of species and their interactions at multiple spatial
and temporal scales (MacArthur and MacArthur 1961).
Landforms (e.g., valleys, rivers) also provide for types of
geophysical motion patterns, especially those that make
frequent sounds such as water and wind. Furthermore, cli-
mate (figure 1, arrow 3) controls the distribution of species
(Currie 1991) in conjunction with the timing of specific
life-history events (e.g., breeding or the emergence of noisy
insects; e.g., Brown et al. 1999, Beebee 2002, Ahola et al.
2004). Climate (arrow 3) also influences geophonic sounds.
The natural components of biophony and geophony (both
as arrow 4) at any given location and time contribute to the
observed soundscape. Human activities produce sounds
(anthrophony) as well (arrow 5). Biophony, geophony, and
anthrophony (arrows 4 and 5) integrate to create the com-
plete soundscape. What occurs in the soundscape can feed
back to natural processes (arrow 6); for example, animal
vocalizations masked by human-generated noise may alter
population or community dynamics such as predator-prey
relationships (Barber et al. 2009).
Our conceptual framework for soundscapes also empha-
sizes two unidirectional components between humans and
soundscapes (figure 1, arrows 5 and 7); such feedbacks
characterize coupled natural–human systems (Liu et al.
2007). In the direction of humans to soundscapes (arrow 5),
anthropogenic sounds often permeate natural landscapes.
Unwanted sound, or noise, is a common issue in cities glob-
ally, and the problem has spread to more rural and remote
areas with the expansion of motorized transportation net-
works (Wrightson 2000). As such, many policies have been
enacted to control noise. For example, the importance of
sounds in national parks was identified early on with the
increasing volume of motorized recreation (National Parks
Overflight Act of 1987). The National Park Service (NPS)
formally recognizes soundscapes as a park resource, and
that the organization should “restore to the natural condi-
tion wherever possible those park soundscapes that have
become degraded by unnatural sounds (noise), and will
protect natural soundscapes from unacceptable impacts”
(NPS 2006, p. 56).
In the opposing direction, soundscapes can influence
human well-being (figure 1, arrow 7). As with other natu-
ral resources, natural and unique soundscapes have many
associated human ideals, such as cultural, sense of place,
recreational, therapeutic, educational, research, artistic, and
aesthetic values. Many of these values foster a conservation
ethic by directly influencing people’s ability to connect with
the natural world (Rolston 1988). Indeed, the NPS recognizes
the importance of healthy soundscapes for positive park vis-
itor experiences (Miller 2008). Natural sounds engage one of
our senses and provide information about our surroundings.
Wilson (1999) suggested that the natural world is the most
information-rich environment that humans can experience,
and we believe that some of the important information con-
veyed is through sound. In contrast, urban soundscapes are
described as containing little acoustic information (Schafer
1977), reinforcing a growing disconnect between humans
and nature (Louv 2008). Therefore, the sounds of an envi-
ronment should not be something that we try to block out,
but rather something that we value.
Figure 2. Spectrogram of an 11-second recording of the dawn chorus at the La Selva Biological Station, Costa Rica. Birds and
insects are creating a variety of sounds from 1 kilohertz (kHz) to some even above 12 kHz. There is considerable biophonic
activity between 4 and 6 kHz, with the loudest sounds occurring 1 second into this recording. Crickets are stridulating at 4.7, 5.3,
and 6.0 kHz. Note that raindrops falling from the tropical canopy can be heard (sounds below 2 kHz), an example of geophony.
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Sound in the environment
There are many ways to quantify sounds in the environment.
Measuring sound. Soundscapes can be measured using
automated digital recording systems. Digital acoustic
recorders store the timing and intensity (or power) of
the sounds detected by microphones, which allows signal
processors to reconstruct the frequency distribution of signal
intensity over time. Intensity is most commonly recorded
as dB (decibels), although digital recorders store amplitude
in dBFS (or decibels full scale); the peak is assigned a value
of dBFS = 0, and all other values scale on the basis of the bit
value of the recording. Humans tend to interpret frequency
as pitch (although the relationship is not one to one) and ide-
ally can detect signals with frequencies ranging from 20 hertz
(Hz) to 20 kilohertz (kHz). Many digital sound recorders
sample at 44.1 kHz with a 16-bit depth, which is CD quality,
and store the data as uncompressed WAVE (or WAV) files.
Figure 2 shows a visual representation, called a spectro-
gram, of a 10-second recording from the La Selva Biologi-
cal Reserve in Costa Rica (for reference, listen to sound file
1). This spectrogram contains three dimensions of sound:
(1) time, along the x axis; (2) frequency, represented along the
y axis; and (3) energy, also called amplitude, normally color
coded or plotted on the z axis. Reading a spectrogram, also
called a sonogram, is done in the same way that one reads
sheet music: Notes are arranged linearly through time with
higher frequencies (or pitch) at the top of the musical staff.
Biophysical models of sound transmission. Biologists have
invested significant effort into understanding animal
communication, and their findings offer insight into the
soundscape’s role in ecological communities. Much of the
research into animal acoustic communication (e.g., Marten
et al. 1977) has utilized the Sender-Propagation-Receiver
(SPR) model to describe the three primary elements of
information propagation: (1) the sender’s biophysical char-
acteristics and the intent of its message, (2) the role of the
physical environment in shaping the signal, and (3) the
perception and interpretation of the signal by its recipient
(figure 3a). The sender encodes a string of information
into a sound signal that is composed of certain physical
factors, including the signal’s (a) frequency, (b) energy or
amplitude, (c) directionality, and (d) the point (or points,
if the sender is in motion) of origin. The propagation of
the signal depends both on the medium through which it
passes (air, water, solid media, etc.) and on the arrange-
ment of reflective and absorptive surfaces of that medium
(e.g., vegetation, buildings, and water bodies). Finally, the
signal the receiver interprets will be further influenced by
that receiver’s hearing range and its ability to translate the
signal back into information (Forrest 1994). Although most
organisms cannot actively control which sound signals they
receive, selection pressures can adjust the configuration of
their auditory organs to optimize their ability to detect con-
specific signals (Dooling et al. 1992).
A multisource model is illustrated in figure 3b. Note that
sounds from birds and amphibians may be interfered with by
wind, rushing water, or potentially noise created by humans
(Ryan and Brenowitz 1985). The integration of all these sig-
nals, natural and human, makes up the soundscape. Note also
that an acoustic sensor array could be employed to record
sounds at multiple locations; sound waves could then be con-
ceptualized as an acoustic field that changes with time.
Relevant ecological hypotheses. Two complementary
hypotheses, the morphological adaptation hypothesis
(MAH) and the acoustic adaptation hypothesis (AAH),
describe how ecological feedback mechanisms give rise
to changes in animal signals, whereas the acoustic niche
hypothesis (ANH) describes how these feedback mecha-
nisms lead to the complex arrangement of signals in the
soundscape. The MAH focuses on the sender, and posits
that an organism’s physical attributes, such as its body
size, the length of its trachea, and the structure of its
beak, influence what sorts of sound signals an organism
can produce (e.g., Bennet-Clark 1998). A larger bird with
a longer trachea, such as a heron or a goose, will usually
produce sounds at lower frequencies than a smaller
bird with a shorter trachea, such as a thrush or a finch.
Figure 3. Sound transmission models for (a) single and
(b) multiple sources of sound.
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The AAH (e.g., Daniel and Blumstein 1998) focuses on
interactions between the sender and the medium, and
proposes that certain groups of organisms will adjust the
attributes of their sounds to maximize their propagation
(Morton 1975). Support for the AAH has been mixed;
some researchers found no correlation between signal
composition and habitat (Daniel and Blumstein 1998),
whereas others (e.g., Brown et al. 1995) found evidence
that the acoustic properties of an environment can influ-
ence the evolution of vocalizations.
In his formulation of the ANH, Krause (1987) pointed
out that both the morphological and the behavioral
adaptations described by the MAH and the AAH can also
be triggered by interspecific interference when organ-
isms’ calls contain similar frequency and timing features.
After repeatedly observing complex arrangements of
nonoverlapping signals in his recordings of soundscapes
in multiple habitat types, Krause (1987) postulated that
such interspecific competition for auditory space would
prompt organisms to adjust their signals to exploit vacant
niches in the auditory spectrum to minimize spectral or
temporal overlaps in interspecific vocalizations. Ficken
and colleagues (1974), for instance, observed that least fly-
catchers (Empidonax minimus) at Lake Itasca, Minnesota,
would insert their shorter songs between the longer songs
of red-eyed vireos (Vireo olivaceus) when the two species
shared the same habitat. An important prediction that
follows from this hypothesis is that less-disturbed habi-
tats with unaltered species assemblages will exhibit higher
levels of coordination between interspecific vocalizations
than more heavily disturbed habitats, in which species
assemblages were recently altered. Likewise, invasive spe-
cies could create biophonic disturbances, thereby altering
natural acoustic partitioning (figure 4, sound files 2–4).
Finally, Farina and Belgrano’s (2006) eco-field hypothesis
can be used to describe the soundscape from the receiver’s
perspective as a carrier of meaning. This hypothesis
proposes that an organism uses the signs it identifies in
Figure 4. Spectrograms of two endemic birds, Turdus merula (a) and Sylvia atricapilla (b), and the nonendemic,
invasive Leiothrix lutea (c). Note that L. lutea and T. merula have overlapping frequencies in their songs, especially
around 2 kilohertz (kHz), which are the loudest parts of their calls. Sylvia atricapilla sings at higher frequencies that are
potentially masked by L. lutea, which has many parts of its song in high frequencies (> 6 kHz) and with clear modulation
patterns. Leiothrix lutea has more behavioral overlap with S. atricapilla than it does with T. merula.
208 BioScience • March 2011 / Vol. 61 No. 3 www.biosciencemag.org
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the soundscape to construct a cognitive template that it
then uses to match particular spatial configurations with
life functions such as food, water, and shelter.
What produces sound? The urban environment generally con-
tains sounds with considerably different spectral and tem-
poral properties from those produced by living organisms.
Urban landscapes are saturated with signals that carry little
or no intentional information and are regarded as unwanted
noise by many people. These signals emanate from vehicles
(e.g., motors and road noise) and stationary machines (e.g.,
air conditioners; sound file 5). Most of these sounds occur at
low acoustic frequencies (less than 4 kHz).
The geophysical environment produces a variety of in situ,
contextual ambient sounds. Familiar such sounds are wind,
rain, and running water, the frequencies of which occur
between 100 Hz and 1 kHz with little rain, or between
100 Hz to 8 kHz during windy or moderate to heavy rain.
Geophony varies seasonally and diurnally.
Among terrestrial organisms, vertebrates and certain
groups of insects produce the most sound. The most audible
insects are crickets, katydids, grasshoppers, and cicadas.
Insects produce sounds most strongly around 3 to 4 kHz and
6 to 8 kHz, either through stridulation (crickets and katydids)
or by vibrating a rigid membrane (cicadas). Stridulation is
created by insects by rubbing body parts together. Insects
call during the day (cicadas), at night (crickets), or both
(some cicadas). Additionally, songs from many insects pos-
sess a certain periodicity. For example, sounds from crickets
are composed of pulses and chirps produced at precise inter-
vals, and crickets are well known for having chirp rates that
are strongly influenced by temperature (Walker 1962). Other
cyclical patterns of sound production in insects throughout
the year relate to the phenological life cycle of the species.
Annual cicadas (Tibicen spp.), for instance, will sing dur-
ing hot days, late in the summer after they emerge from
the ground, with the timing of emergence being a function
of accumulated heating degree days (Williams and Simon
1995). Sounds produced from wing beats from flies, bees,
and wasps could contribute significantly to the soundscapes
if these insects are present in large numbers.
Amphibians such as frogs and toads rely primarily on
vocalizations to attract mates (Gerhardt 1994). In the northern
temperate regions of eastern North America, spring peepers
(Pseudacris crucifer) are common singers at night in wetlands
and ponds. Calls are intense during the breeding seasons, which
extend from late winter (February) to early spring (May) in the
northern United States and from late fall (October) to early
spring (March) in more southern locations. Frequencies of
frog and toad choruses range from 2 to 5 kHz.
Almost all birds use sound to attract mates, defend ter-
ritories, sound alarms, and communicate other types of
information. Many of the passerines are especially known
for producing elaborate songs (Kroodsma 2005). Most songs
and calls produced by birds occur in the 2 to 6 kHz range.
The acoustic frequency of a bird’s song relates to its body
size (large-bodied birds produce sounds as low as 1 kHz) and
habitat type and structure; for example, some tropical birds
use protracted pure tones in environments with persistent
geophonic sounds of wind and rain, and some vocalizations
reach frequencies in the 10 to 12 kHz range (Kerry Rabenold,
Department of Biological Sciences, Purdue University, West
Lafayette, Indiana, personal communication, 5 October 2010).
A variety of terrestrial mammals also produce sounds
(McComb and Reby 2005). Groups that are frequent con-
tributors of sound produced in landscapes include primates
(e.g., monkeys, baboons), elephants, canines (e.g., wolves
and coyotes), rodents (e.g., squirrels, chipmunks), and
felines (e.g., lions), among others. Bats generally produce
two types of sound; the first, referred to as “echolocation,
is emitted as ultrasonic frequencies (above human hearing
ability) and is used to locate prey. The second, communica-
tion calls, are more readily audible to humans and are used
to identify individuals.
Recently, considerable evidence has emerged showing
that anthrophony can influence animal communication
in a variety of ways. For example, American robins (Tu r -
dus migratorius) shift the timing of their singing in urban
environments to the night (Fuller et al. 2007). In song spar-
rows (Melospiza melodia), the lowest-frequency notes were
higher in environments with high ambient noise (Wood
and Yezerinac 2006). Brumm (2004) found that free-
ranging nightingales (Luscinia megarhynchos) in noisier
environments sing more loudly than those in quieter envi-
ronments, and Slabbekoorn and Peet (2003) determined
that the great tit (Parus major) sings at higher pitches in
urban noise conditions.
Rhythms of nature. The sounds of nature contain numer-
ous rhythms or cycles. Many recognized temporal cycles
of communication occur in terrestrial animals, the most
well studied being those of birds, amphibians, and insects.
Collectively, we refer to these periodic acoustic patterns
as “the rhythms of nature. Most songbirds are known to
begin singing at the same time each year (Saunders 1947),
and these birds sing most intensely early in the morning
(Kacelnik and Krebs 1982) and late evening (referred to as
the dawn and dusk chorus, respectively). Dawn chorus in
birds is thought to occur when individuals, arriving back to
their territory, use songs to advertise their presence (Staicer
et al. 1996). This circadian pattern of singing in birds, the
timing of which is largely affected by weather and climatic
conditions, strongly correlates with sunrise and sunset and
becomes more pronounced with the onset of breeding and
migration.
A research agenda for soundscape ecology
We believe that we are now well poised to place sound-
scape ecology into a more research and application focus.
Research is needed in several new areas, organized around
the following main themes: measurement and quantifica-
tion, spatial-temporal dynamics, environmental covariates,
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human impacts on soundscapes, soundscape impacts on
humans, and soundscape impacts on wildlife.
Theme 1: Improve the measurement and quantification of
sounds. Acoustic sensors are needed that can automate the
recording of sounds, that are inexpensive, and that can be
placed in large networks in hostile environments. Research is
required that can automatically differentiate all sounds ema-
nating from landscapes. For example, researchers need tools
that can classify biological, geophysical, and anthropogenic
sources of sounds. Scientists also need a better understand-
ing of how these sources of sounds differ in their composi-
tion. How do anthrophonic sounds differ in composition
(acoustic frequency, time interval) from biophonic sounds?
Is the presence of certain kinds of sounds indicative of a
healthy or deteriorating landscape? In situ measurements of
biodiversity need to be compared with soundscape measures
to determine how well vocal organisms provide a proxy for
biodiversity in general. Research in this area can also advance
our ability to use soundscape measures for natural resource
management and biological conservation.
Theme 2: Improve our understanding of spatial-temporal dynamics
across different scales. Research is needed on how soundscapes
vary with landscape patterns and processes (figure 1, arrows
1 and 2). How do soundscapes differ with land-use patterns?
Comparisons of soundscape dynamics should be made of
various natural ecosystems around the world but also across
areas that differ in the amount of human disturbance within
an ecosystem. Vertebrate species richness has been shown
to vary with vegetation structure (canopy height, density).
Is soundscape diversity greatest where vegetation structure
is most complex? More research is needed that attempts to
characterize the different types of the temporal patterns of
soundscapes. How do soundscapes vary over different time
frames (seconds, minutes, hours, diurnally, annually) (figure
1, arrows 4 and 5) in different landscapes? How are the dawn
and dusk choruses affected by human activities?
Theme 3: Improve our understanding of how important environ-
mental covariates impact sound. Biophonic and geophonic
sounds very likely vary according to many environmental
factors, such as weather, plant phenology, and elevation.
Specific research is needed on how soundscapes vary by
temperature (air, soil, and water), solar radiation, lunar
radiation, relative humidity, heating degree days, and mois-
ture budgets (figure 1, arrow 4). Knowledge of these covari-
ates will be necessary as researchers attempt to understand
how human activity impacts natural soundscape dynamics.
Studies on how geophonic sounds of wind, running water,
and rain affect biophonic patterns will help us to understand
the plasticity of biological communication as it relates to
human-generated sounds.
Theme 4: Assess the impact of soundscapes on wildlife. There is a
need for more research on how certain soundscape qualities
(e.g., noise, ambient sounds like running water and wind)
affect individual wildlife species and populations (figure 1,
arrow 6). Research is required on the ways anthrophony affects
wildlife behavior, such as breeding, predator-prey relation-
ships, and physiology. As soundscape patterns such as signal
composition, sound diversity, and temporal cycles change,
what are the impacts to species’ life-history patterns?
Theme 5: Assess the impacts of humans on soundscapes. Humans
create many objects that produce sounds (figure 1, arrow
5). How do engines, road noises, bells, sirens, and other
machines affect soundscape composition? As new technolo-
gies emerge, how do these affect the soundscape? What poli-
cies are needed to protect soundscapes in various settings
such as national parks or our cities and neighborhoods?
How can land-use planners and policymakers determine
future soundscapes?
Theme 6: Assess soundscape impacts on humans. Humans
are surrounded by sounds that emanate from the environ-
ment and these sensory connections to nature are from the
soundscape (figure 1, arrow 7). Research is needed on how
natural sounds influence the development of individuals’
sense of place, place attachment, and connection to nature.
More specifically, how do human demographic variables
such as culture, place of residence, or age affect the strength
of human values associated with soundscapes? What factors
affect human (in)tolerance of soundscape changes, espe-
cially where those changes increase noise?
Soundscape ecology case studies
We present four case studies that illustrate various aspects of
soundscape ecology. These studies also exemplify the kinds
of research that can be conducted across the six research
themes posed above. The first case study, which is not a sepa-
rate study in itself as are the three others, represents selected
recordings from the massive Krause 40-year-old soundscape
archive. Krause, a musician and recording engineer, has
recorded natural sounds for use in the entertainment indus-
try. The second focuses on characterizing the “rhythms of
nature” in midlatitude landscapes that vary across a human
disturbance gradient. A third study, conducted in Sequoia
National Park in the United States, attempts to determine
whether organisms are partitioning their sounds and the
extent to which geophonic sounds, such as rivers and wind,
interfere with animal communication. The final study, con-
ducted in montane forests in Tuscany, Italy, centers on map-
ping dynamic soundscapes.
Krause ambient sounds soundscape archive. We use sev-
eral field recordings that are part of the massive Krause
soundscape archive to illustrate how sounds reflect cer-
tain characteristics of landscapes and the organisms that
live within them. A 1-minute-28-second recording of a
tropical forest in Madagascar in 1996 (sound file 6) rep-
resents an excellent example of the ANH, exemplifying
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the human ear but are known to occur continuously in ant
colonies. Recent research (Hickling and Brown 2000) has also
shown that only sounds produced in a near field on the order
of 100 millimeters or less are detected by ants, and ambient
sounds produced farther away are ignored.
Sounds produced by many organisms may also reflect the
animals complex social structure. The recording (sound file
10) of gray wolves (Canis lupus) in Canada’s Algonquin Pro-
vincial Park in 2008 captures the vocalizations of wolves as
the normal foreground biophony progresses. This recording
may also elicit a strong sense of wildness, triggering many
human senses and values. The entire context of wolves howl-
ing among the tapestry of boreal sounds can be a memorable
experience (sensu Fisher 1998), emphasizing the importance
of our auditory connection with nature.
Tippecanoe Rhythms of Nature study. Several of this article’s
authors (LJV, BCP, and BMN) conducted a yearlong study to
measure near-continuous sounds in a variety of landscapes
in northwestern Tippecanoe County, Indiana (see online
supplementary material at www.jstor.org/stable/10.1525/
bio.2011.61.3.6), in order to characterize different rhythms
of nature and the impacts of humans on them. We deployed
automated Wildlife Acoustics Songmeters in eight locations
that varied in land-use characteristics, spanning old growth
forest to agricultural fields (figure 5). The proportion of
that sounds produced by animals are separated in space,
time, and frequency. Here, dozens of birds vocalize with
little frequency or temporal overlap. One bird (probably a
sickle-billed vanga, Falculea palliata) produces four rapid
calls followed by a brief pause at 1 kHz, much below the
frequency of other bird vocalizations. This recording
most likely represents some of the greatest acoustic niche
separation in the world.
The nighttime recording of organisms producing sounds in
a bai in the Central African Republic (sound file 7) illustrates
how unique landscapes can create unique soundscapes. Here,
the normal synchronous production of nighttime sounds by
insects and frogs is interwoven with the loud trumpeting, bel-
lowing, and grunting of forest elephants (Loxodonta cyclotis).
A bai is a special landscape where forest elephants go (areas
have been cleared by elephants) because of the high salt con-
tent of the mud surrounding ponds created by groundwater
upwelling; thus, landscape structure and the specific animals
occupying these areas can create a unique soundscape.
A recording (sound file 8) of the dawn chorus in
Zimbabwe illustrates not only the complexity of sounds
produced in the morning but also animals’ use of special
landforms to propagate calls. The first minute contains
a typical chorusing of about 30 different species of birds
(see supplementary online materials at www.jstor.org/
stable/10.1525/bio.2011.61.3.6). At 1:13 into this record-
ing, however, baboons (Papio
cynocephalus) begin to bark.
Note how the echo decay of the
baboons (> 4 seconds) differs
from the echo decay of the birds
(approximately one-third of a
second), such as the black-eyed
bulbul (Pycnonotus barbatus) in
the dry forest. The landform is
thus exploited by these animals
to propagate their voices. Many
animals, such as African lions
(Panthera leo), forest and plains
elephants, and hyenas, choose
the time and place to make their
voices echo.
Wiens and Milne (1989), among
others, have emphasized the need
to understand landscapes from
the perspective of the size of an
organism; they found that from a
beetle’s point of view, the very fine
structure of a landscape influences
movement patterns. Additionally,
many insects produce sounds that
aid in breeding or communica-
tion that may not be audible to
humans or to other organisms in
the landscape. Ant stridulations
(sound file 9) are not audible to
Figure 5. Land-use and land-cover composition within 100 meters of each acoustic
recorder. Land-use and -cover data were from the 30-meter 2001 National Land
Cover Database, classified into major land-use and -cover types. Martell Forest
is a secondary forest owned by Purdue University, the Wildlife Area is a wetland
surrounded by 10- to 15-year-old trees, Ross Reserve is an old growth forest also
owned by Purdue University. FNR Farm and McCormick Woods are two mixed
use sites; the former is an abandoned orchard and McCormick Woods is a small
(40-hectare) forest stand surrounded by residential urban development.
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a yearlong study to determine whether (a) sounds from
animals occurred with any acoustic niche separation, and
(b) geophony affected biophony patterns. A forest riparian
zone (near a relatively noisy stream), an oak savanna, a dry
savanna chaparral (with high winds), and an old-growth
forest site were monitored daily at dawn, midday, dusk,
and midnight (for 60 minutes during the period of Sep-
tember 2001 through October 2002) using digital acoustic
recorders (see supplementary online materials for details).
Randomly selected 11.5-second segments were analyzed by
urban and agriculture within 100 meters of the recorder
was used as a measure of human disturbance. We collected
and analyzed more than 34,000 15-minute recordings.
We were also interested in applying metrics traditionally
used by ecologists, such as diversity, evenness, richness,
and dominance. To accomplish this, we discretized the
spectrogram into 10 frequency bands and calculated the
amount of sound occurring in each band. We used these
values to calculate (a) diversity (using Shannon’s index) and
(b) evenness (using the Gini coefficient). We also deter-
mined the most dominant fre-
quency band occurring in each
15-minute recording. The total
amount of acoustic activity in
each recording was used as a sur-
rogate for sound sources, which
in some cases will be correlated
to species richness. These metrics
were examined across landscapes
and over two time periods.
Activity, diversity, and even-
ness were greatest for the natural
landscapes (forests and wetlands),
and all values decreased as human
disturbance increased (figure 6).
A plot of mean monthly Shan-
non’s diversity index values by
site (figure 7a) shows that a peak
in entropy occurs during the late
summer. Late summer sound-
scapes are composed of birds
and insects (mostly cicadas and
crickets). Comparing these same
sites across time of day (figure 7b)
aggregated from May through
September, a 7:00 a.m. (i.e., dawn
chorus) and 10:00 p.m. peak (i.e.,
dusk chorus) are evident in all
but the agricultural sites. Night-
time entropy values are twice
that of midday values in all sites
except the cornfield site. Sound
files 11–34 contain a full day of
recording from our wetland site.
In May, all sites were dominated by
low-frequency sounds (figure 8),
but by late summer (August and
September) bands 3 through 8
became prominent, especially in
natural landscapes.
Sequoia National Park acoustic niche
hypothesis study. Four relatively
pristine habitats located in the
Sequoia National Park were selected
by BK and SHG (see figure 9) for
Figure 6. Annual average values for (a) total activity, (b) frequency band diversity,
and (c) frequency band evenness in the Tippecanoe Rhythms of Nature Study.
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site possessed the greatest diversity of sounds, from flies
(200 Hz) to birds (8.7 kHz from one unidentified bird).
Finally, in the old-growth site (Crescent Meadow), animals
produced sounds from 200 Hz (flies) to around 9 kHz
(birds); about 82% of the spectrogram was occupied by
vocalizations. Frogs chorused between 600 Hz to 2 kHz,
just below the acoustic frequency of the robin, which sings
in the 2 to 3.3 kHz range. Sound files 35–38 contain sample
dawn chorus recordings from this study. The amount
of acoustic activity for each site (figure 9a) shows that
the Buckeye Flats site contained more than 10 times the
amount of acoustic activity, mostly from the geophonic
sounds of the stream. Within each site (figure 9b), acoustic
activity was highly variable over a season; fall, in half of the
cases, was the most acoustically active season (see sample
sound files 35–38).
Mapping the soundscapes in the Tuscany study. A two-month
study was conducted from June to July of 2008 in a second-
ary montane beech forest in the Italian Apennine National
Park, located along the northern slopes of Mount. La Nuda.
The study was conducted to determine how spatially variable
soundscapes are in a relatively homogenous forest. Twenty
digital recorders (Handy Recorder, H4) were placed in a
5 × 4 grid with 100-meter spacing. Eleven three-hour record-
ings (0600 to 0900) were collected under ideal meteorological
conditions. Approximately 13 species of birds, such as the
European robin (Erithacus rubecula), the chaffinch (Fringilla
coelebs), and the blackcap (Sylvia atricapilla), vocalize in this
forest (sample in sound file 39). An acoustic complexity index
(see supplementary online materials) was used to quantify
spectral complexity, and interpolation software was used to
create soundscape maps.
Data from the acoustic recorders were used to construct
soundtopes (Farina 2006)—a three-dimensional map of
acoustic complexity (y axis) plotted across the landscape
(plotted across the x and z axes). The 11 daily soundscape
maps for this landscape (figure 10) indicate that large
interseasonal changes of the soundscape occur. We antici-
pated that the soundscape maps would be similar through-
out the year, reflecting static territorial boundaries. The
breeding period of every species has a different phenological
time and for each time requires specific resources (food,
shelter, singing spots, etc.), these resources are spatially and
temporally variable as well. The soundtope shifts across the
environment consequently.
Summary of case studies. The above case studies illustrate
various ways that data can be collected, analyzed, and
interpreted. These case studies highlight many of the
research themes described above. The Krause archive
demonstrates the complex composition of a community
of organism vocalizations, the interaction of landscape
features and sound propagation, and the importance of an
organism’s perception of scale in the landscape in which it
lives. The Tippecanoe study shows that temporal patterns
examining spectrograms and listening to the recordings.
A total of 190 spectrograms were produced, and the vocal
niches in these spectrograms were analyzed (a) qualita-
tively, by describing biophonic and geophonic patterns;
and (b) quantitatively, by calculating the acoustic activity
occurring at each site.
The vocalizations of American robins and the American
dippers (Cinclus mexicanus) in the riparian zone (Buckeye
Flats) location were evident, with frequencies of songs
occurring in a manner that avoided masking by the nearby
noisy stream. Insects produced sounds that were higher in
pitch than birds, demonstrating niche partitioning. Only
57% of the spectrograms contained sounds. Within the
oak savanna site (Sycamore Creek), vocalizations by birds
ranged from 500 Hz (mourning dove, Zenaida macroura)
to more than 20 kHz (unidentified bird); approximately
94% of the spectrogram was occupied by at least one vocal
organism. The dry savanna chaparral (Shepard’s Saddle)
Figure 7. Temporal cycles of frequency band diversity
plotted by (a) month and (b) hour. (a) Monthly average
frequency band diversity (Shannon’s) as it differs between
sites and (b) hourly average frequency band diversity
(Shannon’s) as it differs between sites.
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of soundscapes exhibit strong dawn and dusk chorus
peaks that diminish with increasing human disturbance
on the landscape. The Sequoia study attempts to quantify
the effects of geophony on biophonic patterns, and shows
that animals that communicate in each habitat do so at
different frequencies to avoid overlap. Lastly, the Tuscany
soundscape mapping study illustrates that soundtopes
constructed from acoustic arrays could be used to quantify
the spatial dynamics of soundscapes.
The way forward
The study of soundscapes can yield valuable information
about the dynamics of a variety of landscapes. Given that
technological advances are occurring rapidly and theories
about the interplay of patterns and processes occurring
within landscapes are maturing, we believe that soundscape
ecology can enhance our understanding of how humans
affect ecosystems. Indeed, we are at a critical juncture in our
history, and there is a need for transformative approaches
that help us to more thoroughly elucidate how humans
affect our planet (Vitousek et al. 1997, Chapin et al. 2000).
At present, there is a renewed interest in studying eco-
systems at large, continental scales. Automated acoustic
recordings could provide a means to collect information
at fine temporal resolutions (Porter et al. 2005). Initiatives
such as the National Science Foundation’s NEON (National
Ecological Observatory Network) project are being built to
study ecosystems at subcontinental scales (Keller et al. 2008).
Furthermore, recordings made today will become tomor-
row’s “acoustic fossils,possibly preserving the only evidence
we have of ecosystems that may vanish in the future because
of a lack of desire or ability to protect them.
We also argue that society should value natural sound-
scapes as it does other aspects of nature. Soundscapes rep-
resent the heritage of our planet’s acoustic biodiversity,
and reflect Earth’s natural assemblage of organisms—
soundscapes are an ecosystem service (MA 2005) that
provides cultural and other services. Natural sounds
are our auditory link to nature, and the trends toward
increasing society’s “nature deficient disorder” (Louv
2008) are likely to continue as we replace natural sounds
with those made by humans. This research reflects again
on Rachel Carson’s call made in Silent Spring, in which she
highlighted the dangers of pesticides and their potential
threat to wildlife and the environment. The unintended
silencing of organisms by a myriad of human activities
provides yet another indication of our impact on the
planet’s ecosystems.
Figure 8. Frequency band dominance summarized for four months of the Tippecanoe study.
214 BioScience • March 2011 / Vol. 61 No. 3 www.biosciencemag.org
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Figure 9. Summary of the Sequoia National Park study. Acoustic activity averages for each site and by season. Note that
the Buckeye Flats location (a) contains greater acoustic activity, a result of the nearby rapid flowing stream that produced
considerable geophonic sounds. The inset (b) graphs the same data but with Buckeye Flats removed. These values (b)
reflect mostly biophony. Sycamore Creek contained the greatest acoustic activity of these three. The fall contains the
greatest activity although there was no consistent pattern across sites. Photos of each landscape are provided in (c).
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Articles
Alex Pijanowski, Burak Pekin, and Christian Perry. Jarrod
Doucette assisted with the graphics.
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... An important direction in the ecology of soundscape is to explore the distribution of sound in landscape patterns and the factors influencing it and focus on ecosystem processes and the impact of human activities on biodiversity [10,11]. The soundscape consists of biophony, geophony, and anthrophony. ...
... The soundscape consists of biophony, geophony, and anthrophony. Krause et al. [12] defined biophony and geophony as a collection of biotic and abiotic sounds (wind, rain, thunder, etc.), respectively, while Pijanowski et al. [11] expanded the soundscape categories by proposing the category of anthrophony, defining it as sounds produced directly or indirectly by humans. Soundscape properties vary according to geographic location, vegetation composition and structure, and time [13]. ...
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The use of passive acoustic monitoring (PAM) can compensate for the shortcomings of traditional survey methods on spatial and temporal scales and achieve all-weather and wide-scale assessment and prediction of environmental dynamics. Assessing the impact of human activities on biodiversity by analyzing the characteristics of acoustic scenes in the environment is a frontier hotspot in urban forestry. However, with the accumulation of monitoring data, the selection and parameter setting of the deep learning model greatly affect the content and efficiency of sound scene classification. This study compared and evaluated the performance of different deep learning models for acoustic scene classification based on the recorded sound data from Guangzhou urban forest. There are seven categories of acoustic scenes for classification: human sound, insect sound, bird sound, bird–human sound, insect–human sound, bird–insect sound, and silence. A dataset containing seven acoustic scenes was constructed, with 1000 samples for each scene. The requirements of the deep learning models on the training data volume and training epochs in the acoustic scene classification were evaluated through several sets of comparison experiments, and it was found that the models were able to achieve satisfactory accuracy when the training sample data volume for a single category was 600 and the training epochs were 100. To evaluate the generalization performance of different models to new data, a small test dataset was constructed, and multiple trained models were used to make predictions on the test dataset. All experimental results showed that the DenseNet_BC_34 model performs best among the comparison models, with an overall accuracy of 93.81% for the seven acoustic scenes on the validation dataset. This study provides practical experience for the application of deep learning techniques in urban sound monitoring and provides new perspectives and technical support for further exploring the relationship between human activities and biodiversity.
... However, studies have also questioned the generalization of soundscape diversity representing biodiversity (Fairbrass et al., 2017;Gasc et al., 2015;Ross et al., 2021). Despite the debate, researches focusing on the island biogeography of soundscape diversity are still limited (Lomolino et al., 2015;Pijanowski, 2011;Robert et al., 2019). ...
... The rich habitat types thus provide a variety of ambient sounds on islands (e.g. tree salsa and wind; Pijanowski, 2011;, which will increase the soundscape richness. Second, on large islands, diverse habitats make the acoustic spaces more redundant, and increased interspecific competition drives the differentiation of acoustic niches within bird assemblages (Kohn & Walsh, 1994;Ricklefs & Lovette, 1999;Robert et al., 2019). ...
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The equilibrium theory of island biogeography predicts the positive species–area relationship and the negative species–isolation relationship, resulting in higher species richness on large and close islands. Unlike species richness, soundscape diversity integrates sound from various sources (e.g. biophony, geophony and anthrophony). However, how soundscape diversity varies with island area and isolation still needs to be tested. Here, we explored the island biogeography of bird soundscapes and the determinants of island attributes in shaping bird diversity and soundscape diversity. Thousand Island Lake, Zhejiang, China. Birds. We recorded avian soundscapes by audio recorders and censused bird diversity by line transects on 20 land‐bridge islands. We calculated four acoustic indices (acoustic complexity index, bioacoustic index, acoustic evenness index and acoustic entropy index) to assess acoustic richness, evenness and heterogeneity to explore the soundscape diversity of birds. We used multiple linear regressions, spatial autoregressions and piecewise structural equation models to examine the relationships between bird richness and acoustic diversity, and island attributes. We found positive diversity–area relationships for avian soundscapes. Larger islands had more vocal species and higher habitat diversity, which led to an increment in the richness and unevenness of avian soundscapes on large islands. Acoustic evenness decreased with increasing isolation (distance to the mainland). Soundscapes on large islands are more diverse than those on small islands. Rich acoustic assemblages and heterogeneous habitats promote increased soundscape diversity on islands. Conversely, the lack of vocal contributors, resulting in a decrement in the communication of acoustic signals, can create a lower soundscape diversity on small and remote islands. Our study emphasizes the necessity of examining both species and habitat diversity in island biogeography for better understanding the underlying mechanisms determining biological soundscapes on islands.
... Aquatic ecosystems are composed of a combination of sounds produced by animals (biophonies), physical agents (geophonies) and human activities (anthropophonies) which defines the soundscape (Pijanowski et al., 2011). These particular mixtures of sounds reflect the ecological pattern and processes of specific aquatic environments (Matsinos et al., 2008;Ceraulo et al., 2018). ...
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Introduction Maternal care in marine decapods involves eggs caring in the brood compartment until the larvae hatch. This behavior mainly allows embryo mass oxygen supply, ensuring healthy embryonic development. The present study aimed to analyze the effect of different sound sources (anthropogenic and biologic) and their temporal patterns (low and high rate: 1 min of the sound stimulus + 5 min of silence and 1 min of the sound stimulus + 1 min of silence, respectively) on the maternal care of the key crab species, Neohelice granulata . Methods In the laboratory, three acoustic stimuli were played back: an artificial white noise (10 Hz – 20 kHz), and two sounds obtained from the crabs´ natural habitat, motorboat passages and biological signals from a crabs’ predator fish. Three behavioral variables were quantified: still position, and two maternal care behaviors: abdominal flapping and chelae probing. Results Results demonstrated that the high rate anthropogenic stimuli, white noise and motorboat, affected all behavioral variables, increasing the still position and diminishing the maternal care behaviors. Otherwise, the predatory stimulus did not affect the still position although diminished the maternal care behaviors (high rate). Discussion The different behavioral response depending on the sound stimuli may indicate that crabs distinguish sound sources. The anthropogenic noise is suggested to cause distraction that is linked to the increased still position, while the predator stimulus would be associated with an alert behavior not affecting the locomotion behavior. The sound stimuli effect on the maternal care behavior revealed a negative effect that potentially could affect offspring survival. This is important considering the ecosystem engineering function of the studied key crab species. The reduction of the noise emission pattern rate is suggested as a mitigation action to diminish sound impact effects in the crab’s natural habitat. The study contributes the first to assessing the effect of different sound sources on the maternal care behavior of a crustacean species.
... The concept of soundscape (or acoustic space) has been described and defined in different ways, especially in the context of landscape ecology (Pijanowski et al. 2011;Truax, Barrett 2011;Farina 2014). The soundscape can be understood as the result of a combination of sound sources and sound dynamics of different origin: geo-environmental, or geophonies (e.g., wind, moving water, thunder, volcanic eruption); biological or biophonies (e.g., sounds made by living beings); anthropic/technological or anthropophonies/ technophonies (e.g., industrial noises, music, urban or air traffic) . ...
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This contribution is the result of experimenting with methodologies linked to the understanding of soundscapes in the context of medieval monasteries. In our specific area, the approach was not focused on the cognitive concept itself so much as the perception of the spirituality of the ringing of bells. The premise was to understand whether, and how, the bells functioned as a soundmark for controlling the territory. The case studies examined are the Abbey of Farneta, the Monastery of Camaldoli and the Abbey of San Fedele, today located in the Province of Arezzo: the three religious structures differ in their historical development, geographical position, and economy. However, what links them (and what links the great majority of medieval monastic complexes) is a tight control of their territory for production purposes, in order to guarantee the profitability of their material heritage. Analyses of the diffusion of the sound of the bells was compared (and integrated) with visibility and catchment analyses. The aim was to understand whether this type of analytical approach could contribute to the definition of a monastery’s ‘catchment area’. The data that emerged describe a complex economic landscape in which identified anomalies at settlement level can be worth analyzing and trying to understand.
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In recent years, passive acoustic monitoring (PAM) has become increasingly popular. Many acoustic indices (AIs) have been proposed for rapid biodiversity assessment (RBA), however, most acoustic indices have been reported to be susceptible to abiotic sounds such as wind or rain noise when biotic sound is masked, which greatly limits the application of these acoustic indices. In this work, in order to take an insight into the influence mechanism of signal-to-noise ratio (SNR) on acoustic indices, four most commonly used acoustic indices, i.e., the bioacoustic index (BIO), the acoustic diversity index (ADI), the acoustic evenness index (AEI), and the acoustic complexity index (ACI), were investigated using controlled computational experiments with field recordings collected in a suburban park in Xuzhou, China, in which bird vocalizations were employed as typical biotic sounds. In the experiments, different signal-to-noise ratio conditions were obtained by varying biotic sound intensities while keeping the background noise fixed. Experimental results showed that three indices (acoustic diversity index, acoustic complexity index, and bioacoustic index) decreased while the trend of acoustic evenness index was in the opposite direction as signal-to-noise ratio declined, which was owing to several factors summarized as follows. Firstly, as for acoustic diversity index and acoustic evenness index, the peak value in the spectrogram will no longer correspond to the biotic sounds of interest when signal-to-noise ratio decreases to a certain extent, leading to erroneous results of the proportion of sound occurring in each frequency band. Secondly, in bioacoustic index calculation, the accumulation of the difference between the sound level within each frequency band and the minimum sound level will drop dramatically with reduced biotic sound intensities. Finally, the acoustic complexity index calculation result relies on the ratio between total differences among all adjacent frames and the total sum of all frames within each temporal step and frequency bin in the spectrogram. With signal-to-noise ratio decreasing, the biotic components contribution in both the total differences and the total sum presents a complex impact on the final acoustic complexity index value. This work is helpful to more comprehensively interpret the values of the above acoustic indices in a real-world environment and promote the applications of passive acoustic monitoring in rapid biodiversity assessment.
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Marine noise is an emerging pollutant inducing a variety of negative impacts on many animal taxa, including fish. Fish population persistence and dynamics rely on the supply of early life stages, which are often very sensitive to disturbance. Impacts of marine noise pollution (MNP) on juvenile fish have rarely been investigated in temperate regions. This is particularly true for the Mediterranean Sea, which is considered as an MNP hotspot due to intensive maritime traffic. In this study, we investigate the relationship between MNP related to boat traffic and (i) assemblage structure and (ii) the density of juvenile fishes (post-settlers at different stages) belonging to the Sparidae family. We quantified MNP produced by boating at four coastal locations in the French Riviera (NW Mediterranean Sea) by linearly combining five variables into a ‘noise index’ (NI): (i) boat visitation, (ii) number of boat passages/hour, (iii) the instantaneous underwater noise levels of passing boats, (iv) continuous boat underwater noise levels and (v) duration of exposure to boat noise. Then, using the NI, we identified an MNP gradient. By using juvenile fish visual censuses (running a total of 1488 counts), we found that (i) the assemblage structure and (ii) the density patterns of three fish species (i.e., Diplodus sargus, D. puntazzo, D. vulgaris) changed along the MNP gradient. Specifically, the density of early D. sargus post-settlers was negatively related to MNP, while late post-settler densities of D. puntazzo and, less evidently, D. vulgaris tended to decrease more rapidly with decreasing MNP. Our findings suggest the following potential impacts of MNP on juvenile sparids related to coastal boat traffic: (i) idiosyncratic effects on density depending on the species and the developmental stage (early vs. late post-settlers); (ii) negative effects on recruitment, due to possible alteration of late post-settlement movement patterns.
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Acoustic signals must be transmitted from a signaller to a receiver during which time they become modified. The acoustic adaptation hypothesis suggests that selection should shape the structure of long-distance signals to maximize transmission through different habitats. A specific prediction of the acoustic adaptation hypothesis is that long-distance signals of animals in their native habitat are expected to change less during transmission than non-native signals within that habitat. This prediction was tested using the alarm calls of four species of marmots that live in acoustically different habitats and produce species-specific, long-distance alarm vocalizations: yellow-bellied marmot, Marmota flaviventris; Olympic marmot, M. olympus; hoary marmot, M. caligata; and woodchuck, M. monax. By doing so, we evaluated the relative importance the acoustic environment plays on selecting for divergent marmot alarm calls. Representative alarm calls of the four species were broadcast and rerecorded in each species' habitat at four distances from a source. Rerecorded, and therefore degraded alarm calls, were compared to undegraded calls using spectrogram correlation. If each species' alarm call was transmitted with less overall degradation in its own environment, a significant interaction between species' habitat and species' call type would be expected. Transmission fidelity at each of four distances was treated as a multivariate response and differences among habitat and call type were tested in a two-way MANOVA. Although significant overall differences in the transmission properties of the habitats were found, and significant overall differences in the transmission properties of the call types were found, there was no significant interaction between habitat and call type. Thus, the evidence did not support the acoustic adaptation hypothesis for these marmot species. Factors other than maximizing long-distance transmission through the environment may be important in the evolution of species-specific marmot alarm calls. (C) 1998 The Association for the Study of Animal Behaviour.