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ARTICLE
Bird population declines and species turnover are
changing the acoustic properties of spring
soundscapes
C. A. Morrison1, A. Auniņš 2,3, Z. Benkő4,5, L. Brotons6,7,8, T. Chodkiewicz 9,10, P. Chylarecki 9,
V. Escandell11, D. P. Eskildsen12, A. Gamero13, S. Herrando 7,14, F. Jiguet 15, J. A. Kålås 16, J. Kamp17,18,
A. Klvaňová 13, P. Kmecl 19, A. Lehikoinen 20, Å. Lindström 21, C. Moshøj 12, D. G. Noble22,
I. J. Øien 23, J-Y. Paquet 24, J. Reif25,26, T. Sattler 27, B. S. Seaman 28, N. Teufelbauer 28,
S. Trautmann 18, C. A. M. van Turnhout29,30,P.Vořišek 13,26 & S. J. Butler 1✉
Natural sounds, and bird song in particular, play a key role in building and maintaining our
connection with nature, but widespread declines in bird populations mean that the acoustic
properties of natural soundscapes may be changing. Using data-driven reconstructions of
soundscapes in lieu of historical recordings, here we quantify changes in soundscape char-
acteristics at more than 200,000 sites across North America and Europe. We integrate
citizen science bird monitoring data with recordings of individual species to reveal a pervasive
loss of acoustic diversity and intensity of soundscapes across both continents over the past
25 years, driven by changes in species richness and abundance. These results suggest that
one of the fundamental pathways through which humans engage with nature is in chronic
decline, with potentially widespread implications for human health and well-being.
https://doi.org/10.1038/s41467-021-26488-1 OPEN
1School of Biological Sciences, University of East Anglia, Norwich, UK. 2Faculty of Biology, University of Latvia, Jelgavas iela 1, Riga LV-1004, Latvia. 3Latvian
Ornithological Society, Skolas iela 3, Riga LV-1010, Latvia. 4Romanian Ornithological Society/BirdLife Romania, Cluj-Napoca, Romania. 5Evolutionary Ecology
Group, Hungarian Department of Biology and Ecology, Babeș-Bolyai University, Cluj-Napoca, Romania. 6InForest JRU (CTFC-CREAF), Solsona 25280, Spain.
7CREAF, Cerdanyola del Vallès, 08193 Barcelona, Spain. 8CSIC, Cerdanyola del Vallès, 08193 Barcelona, Spain. 9Museum and Institute of Zoology, Polish
Academy of Sciences, Wilcza 64, 00-679 Warszawa, Poland. 10 Polish Society for the Protection of Birds (OTOP), ul. Odrowaza 24, 05-270 Marki, Poland.
11 Sociedad Española de Ornitología (SEO/BirdLife), Madrid, Spain. 12 Dansk Ornitologisk Forening, BirdLife Denmark, Vesterbrogade 138-140, DK-1620
København V, Denmark. 13 European Bird Census Council-Czech Society for Ornithology, Na Bělidle 34, 15000 Prague 5, Czechia. 14 European Bird Census
Council–Catalan Ornithological Institute, Natural History Museum of Barcelona, Plaça Leonardo da Vinci 4-5, 08019 Barcelona, Catalonia, Spain. 15 Centre
d’Ecologie et des Sciences de la Conservation, UMR7204 MNHN-CNRS-SU, Paris, France. 16 Norwegian Institute for Nature Research,
P.O. Box 5685Torgarden, NO-7485 Trondheim, Norway. 17 University of Göttingen, Department of Conservation Science, Bürgerstr. 50, 37073
Göttingen, Germany. 18 Dachverband Deutscher Avifaunisten (DDA), An den Speichern 2, 48157 Münster, Germany. 19 DOPPS - BirdLife Slovenia, Tržaška
cesta 2, SI-1000 Ljubljana, Slovenia. 20 Finnish Museum of Natural History, FI-00014 University of Helsinki, P.O. Box 17 Helsinki, Finland. 21 Biodiversity Unit,
Department of Biology, Lund University, Ecology Building, S-223 62 Lund, Sweden. 22 British Trust for Ornithology, The Nunnery, Thetford, Norfolk IP24 2PU,
UK. 23 NOF-BirdLife Norway, Sandgata 30 B, NO-7012 Trondheim, Norway. 24 Natagora, Département Études, Traverse des Muses 1, B-5000
Namur, Belgium. 25 Institute for Environmental Studies, Faculty of Science, Charles University in Prague, Prague, Czechia. 26 Department of Zoology and
Laboratory of Ornithology, Faculty of Science, Palacký University Olomouc, 17 Listopadu 50, 771 43 Olomouc, Czechia. 27Swiss Ornithological Institute,
Seerose 1, 6204 Sempach, Switzerland. 28 BirdLife Österreich, Museumsplatz 1/10/8, A-1070 Wien, Austria. 29 Sovon Dutch Centre for Field Ornithology,
P.O. Box 6521, 6503 GA Nijmegen, Netherlands. 30 Department of Animal Ecology and Ecophysiology, Institute for Water and Wetland Research, Radboud
University, P.O. Box 9010, 6500 GL Nijmegen, Netherlands. ✉email: simon.j.butler@uea.ac.uk
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Over half the world’s population now live in cities1. Rapid
urbanisation, along with increasingly sedentary lifestyles
associated with a rise in electronic media, changing social
norms, and shifting perceptions around outside play2–4, are
reducing people’s opportunities for direct contact with the natural
environment. This so-called extinction of experience5is driving a
growing human-nature disconnect, with negative impacts on
physical health, cognitive ability and psychological well-being6–10.
The COVID-19 pandemic has highlighted this issue, both in
terms of the detrimental impacts on mental health due to local
and national lockdowns imposed by governments and the wide-
spread recognition of the benefits of engaging with nature during
this period11,12. Global biodiversity loss13 is also likely to be
driving a dilution of experience, whereby the quality of those
interactions with nature which do still occur is also being
reduced14 but we do not yet know the extent of such changes.
Sound confers a sense of place and is a key pathway for
engaging with, and benefitting from, nature15. Indeed, since
Rachel Carson’s (1962) classic book “Silent Spring”, nature’s
sounds have been inextricably linked to perceptions of environ-
mental quality16, and the maintenance of natural soundscape
integrity is increasingly being incorporated into conservation
policy and action17. Birds are a major contributor to natural
soundscapes18 and bird song, and song diversity in particular,
plays an important role in defining the quality of nature
experiences15,19–21. Widespread reductions in both avian
abundance22 and species richness23, alongside increased biotic
homogenisation24, are therefore likely to be impacting the
acoustic properties of natural soundscapes and potentially redu-
cing the quality of nature contact experiences25. Indeed, given
that people predominantly hear, rather than see, birds26,27,
reductions in the quality of natural soundscapes are likely to be
the mechanism through which the impact of ongoing population
declines is most keenly felt by the general public. However, the
relationship between changes in avian community structure and
the acoustic properties of natural soundscapes is nuanced and
non-linear28—the loss of a warbler species with a rich, complex
song is likely to have a greater impact on soundscape char-
acteristics than the loss of a raucous corvid or gull species, but
this will depend on how many, and which, other species are
present. The implications of biodiversity loss for local soundscape
characteristics therefore cannot be directly predicted from count
data alone.
Here we combine annual systematic bird count data from
North American Breeding Bird Survey (NA-BBS) and Pan-
European Common Bird Monitoring Scheme (PECBMS) sites
with recordings of individual bird species, downloaded from an
online database (www.xeno-canto.org), to reconstruct historical
soundscapes at over 200,000 locations across the two continents
over the past 25 years. Taking the first species listed in a site-year
count data file, a 25 s sound file for that species was inserted at a
random time point in an initially empty 5 min sound file. Play-
back volume was randomly sampled from a uniform distribution
to represent varying proximity of individual birds to the surveyor.
This process was repeated as many times as there were indivi-
duals of the first species counted, and then for all individuals of all
other species in that site-year count data file, to build a single,
composite representation of the local soundscape for the year
when those count data were collected. This process was repeated
for all site-year count data files, so that separate soundscapes were
constructed for every site in every year it was surveyed. We
employed a systematic protocol for soundscape construction,
applying the same rules for translating survey data into sounds-
cape contribution across all species, because data on vocalisation
frequency (how often an individual vocalises) and duration (how
long each vocalisation event lasts) are not available for most
species included in our analyses. However, while standardised in
length, the 25 s sound files used to represent an individual of a
given species did comprise interspersed periods of vocalisation
and silence, and therefore captured the inherent variation in song
or call structure and pattern of delivery between species to some
extent.
The acoustic characteristics of these reconstructed soundscapes
were then quantified using four indices designed to capture the
distribution of acoustic energy across frequencies and time29 and
to reflect the richness (Acoustic Diversity Index: ADI30), evenness
(Acoustic Evenness Index: AEI30), amplitude (Bioacoustic Index:
BI31) and heterogeneity (Acoustic Entropy: H32) of each
soundscape. These acoustic indices are broadly correlated with
avian species richness and abundance30–33 but are fundamentally
driven by song complexity and diversity across contributing
species. They therefore describe the key factors predicted to
underpin public perceptions of the quality of their nature
experiences15,19–21, with lower values of ADI, BI and H, and
higher values of AEI, reflecting reduced acoustic diversity and
intensity. These indices respond in a similar way when applied to
constructed soundscapes generated from simulated communities
varying in species richness and abundance, with both increasing
abundance and species richness leading to increases in ADI, BI
and H and a decrease in AEI (Figs. 1,2; Tables 1,2). These
relationships are not linear, with the rate of increase in BI and H
with increasing abundance lower at higher species richness
(Fig. 2; Table 2) and each index becoming less sensitive to
changes in community structure as soundscapes become more
saturated.
Acoustic indices have been used to explore diel and annual
patterns in soundscape structure34,35 and to characterise differ-
ences in soundscapes across habitats and landscapes33,36,37.
However, evidence of changes in soundscape characteristics over
longer time periods is currently lacking because of a scarcity in
historical soundscape recordings. By reconstructing soundscapes
from large-scale bird monitoring datasets and archived recordings
of individual species, both predominantly generated by citizen
scientists, we are able to explore changes in soundscape quality
at sites across North America and Europe over recent decades.
We reveal a chronic deterioration in soundscape quality, defined
as a reduction in acoustic diversity and/or intensity, across
both continents. Our analyses suggest that changes in the com-
position, diversity and abundance of bird communities are all
likely to have contributed to this. Ongoing declines in bird
populations13,22 are expected to cause further reductions in
soundscape quality and, by extension, a continued dilution of the
nature contact experience.
Results and discussion
We identify patterns of significant and broadly parallel declines in
ADI, BI and H across both continents since the late 1990s, and a
significant increase in AEI in North America over the same
period (Fig. 3; Table 3). These changes suggest that natural
soundscapes have, overall, become both more homogeneous and
quieter. Within these general patterns of reduced soundscape
quality, there was substantial site-level variation, with local
declines and increases in all four indices occurring across each
continent (Fig. 4, Supplementary Fig. 1), while larger-scale geo-
graphical patterns in the rates of change in each index are also
evident (Supplementary Tables 1, 2). For example, reductions in
acoustic diversity (signalled by decreases in ADI and increases in
AEI) have been greatest in the North and West of both continents
(Supplementary Figs. 2a–d, 3a–d), while soundscape intensity, as
measured by BI, has declined most in more northern and eastern
areas of North America but shows no spatial pattern across
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Fig. 1 Responses of acoustic indices to changes in abundance in simulated communities. The association between the Acoustic Diversity Index, ADI
(a,b), Acoustic Evenness Index, AEI (c,d), Bioacoustic Index, BI (e,f) and Acoustic Entropy, H (g,h) of constructed soundscapes and the number of
individuals of a single species contributing to that soundscape for North American (left column) and European (right column) species. Shaded areas
indicate ±1 standard error.
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Europe (Supplementary Figs. 2e,f, 3e,f). In contrast, while H has
also decreased more in eastern North America, it has also
decreased slightly more in the south than in the north (Supple-
mentary Fig. 2g,h). In Europe, H has decreased in northern and
western areas but increased slightly towards the south and east
(Supplementary Fig. 3g,h).
Local soundscape dynamics are likely to be underpinned
by multiple and interacting processes, operating at regional,
biome and local levels, which influence species richness
and abundance38,39, taxonomic, functional and phylogenetic
diversity40,41, and the rate and direction of change in community
composition22,42–45. Overall, there has been a significant decline
Fig. 2 Responses of acoustic indices to changes in simulated community structure. The association between the Acoustic Diversity Index, ADI (a,b),
Acoustic Evenness Index, AEI (c,d), Bioacoustic Index, BI (e,f) and Acoustic Entropy, H (g,h) of constructed soundscapes and the number of individuals
and species contributing to that soundscape for North American (left column) and European (right column) species. Colours indicate the number of
species in the community.
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in both the total number of species and individuals counted
during NA-BBS surveys and in the total number of individuals
counted during PECBMS surveys over the past 25 years (Sup-
plementary Fig. 4; Supplementary Table 3). Importantly, there
were strong positive relationships between site-level trends in
ADI, BI and H and site-level trends in both species richness and
the total number of individuals counted, with equivalent negative
relationships for site-level trends in AEI (Table 4); sites that have
experienced greater declines in either total abundance and/or
species richness also show greater declines in acoustic diversity
and intensity while sites, where total abundance and/or species
richness has increased, tend to show increases in these char-
acteristics (Fig. 5).
There were generally strong correlations in the trends of the
four acoustic indices at each site, positive between ADI, H and BI,
and negative between AEI and the other three (Supplementary
Table 4). However, these patterns were not universal, with all
potential combinations of increases or decreases in each index
observed (Supplementary Fig. 5). Furthermore, there was sub-
stantial variation in the scale of change in a given acoustic index
for any given change in species richness or abundance (Fig. 5).
Thus, while soundscape dynamics are fundamentally driven by
changes in community structure, shifts in soundscape char-
acteristics arising from changes in species composition and/or
abundance over time are both multi-dimensional and context-
dependent; measures of acoustic richness, evenness, amplitude
and heterogeneity respond independently according to both
initial community structure and how the call and song char-
acteristics of constituent species compare28. Additional analyses
are needed to understand the drivers of both this local site-level
variation and the broader geographic patterns in soundscape
dynamics, as well as the specificinfluence of changes in the
abundance or occurrence of individual species.
While predominately driven by community composition, it is
important to recognise that the acoustic properties of recon-
structed soundscapes could be influenced by methodological
decisions applied during the construction process. For example,
the ratio of individual sound file duration to total soundscape
length will influence the degree of overlap between the calls and
songs of individuals, while the probability distribution from
Table 1 Results of GLMs of the association between the acoustic properties of reconstructed soundscapes and the number of
individuals (1–10) of a single species present in a simulated community and contributing to that soundscape.
North America Europe
Fixed effects Estimate (SE) tpEstimate (SE) tp
(a) ADI (Intercept) 1.7880 (0.0021) 832.12 <0.001 1.7880 (0.0073) 246.01 <0.001
Log(Individuals) 0.0270 (0.0009) 29.49 <0.001 0.0269 (0.0031) 8.72 <0.001
(b) AEI (Intercept) 0.4836 (0.0011) 456.74 <0.001 0.4836 (0.0033) 146.15 <0.001
Log(Individuals) −0.0106 (0.0005) −23.45 <0.001 −0.0106 (0.0014) −7.50 <0.001
(c) BI (Intercept) 103.52 (0.65) 160.19 <0.001 103.5215 (0.2495) 414.89 <0.001
Log(Individuals) 2.46 (0.28) 8.93 <0.001 2.4600 (0.1063) 23.14 <0.001
(d) H (Intercept) 0.6255 (0.0047) 133.1 <0.001 0.6255 (0.0014) 445.80 <0.001
Log(Individuals) 0.0389 (0.0020) 19.4 <0.001 0.03891 (0.0006) 64.98 <0.001
(a) Acoustic Diversity Index (ADI), (b) Acoustic Evenness Index (AEI), (c) Bioacoustic Index (BI) and (d) Acoustic Entropy (H). Separate models for North American (left column) and European (right
column) species presented.
Table 2 Results of GLMs of the association between the acoustic properties of reconstructed soundscapes and the number of
individuals (1–10) and species (2,3,4,5,10,20,50) present in a simulated community and contributing to that soundscape.
Fixed effects North America Europe
Estimate (SE) tpEstimate (SE) tp
(a) ADI (Intercept) 1.866 (0.011) 175.12 <0.001 1.856 (0.008) 222.38 <0.001
Log(Individuals) 0.034 (0.006) 5.35 <0.001 0.028 (0.005) 5.66 <0.001
Log(Species) 0.056 (0.005) 11.85 <0.001 0.047 (0.004) 12.57 <0.001
Log(Individuals)
*Log(species)
−0.007 (0.003) −2.61 0.011 −0.005 (0.002) −2.13 0.037
(b) AEI (Intercept) 0.461 (0.003) 148.60 <0.001 0.472 (0.002) 203.68 <0.001
Log(Individuals) −0.012 (0.001) −8.30 <0.001 −0.012 (0.001) −10.88 <0.001
Log(Species) −0.029 (0.001) −29.67 <0.001 −0.027 (0.001) −37.18 <0.001
(c) BI (Intercept) 107.09 (0.662) 161.84 <0.001 104.53 (0.651) 160.46 <0.001
Log(Individuals) 3.913 (0.398) 9.83 <0.001 4.367 (0.392) 11.15 <0.001
Log(Species) 4.299 (0.293) 14.69 <0.001 4.49 (0.288) 15.59 <0.001
Log(Individuals)
*Log(species)
−1.103 (0.176) −6.27 <0.001 −1.170 (0.173) −6.75 <0.001
(d) H (Intercept) 0.639 (0.006) 102.04 <0.001 0.643 (0.006) 108.93 <0.001
Log(Individuals) 0.058 (0.004) 15.33 <0.001 0.056 (0.004) 15.77 <0.001
Log(Species) 0.049 (0.003) 18.05 <0.001 0.050 (0.003) 18.39 <0.001
Log(Individuals)
*Log(species)
−0.014 (0.002) −8.59 <0.001 −0.014 (0.002) −8.73 <0.001
(a) Acoustic Diversity Index (ADI), (b) Acoustic Evenness Index (AEI), (c) Bioacoustic Index (BI) and (d) Acoustic Entropy (H). Separate models for North American (left column) and European (right
column) species presented. Only significant interactions are retained.
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which playback volume is sampled will determine the relative
proportion of near and far individuals in the soundscape. As a
consequence, these methodological decisions influence the dis-
tribution of acoustic energy within each reconstructed sounds-
cape and thus the absolute values of each acoustic metric29.To
explore the implications of these decisions for detecting changes
in soundscape characteristics over time, we constructed sounds-
capes for 1000 simulated communities containing ten randomly
selected species that each declined from 10 to 5 individuals over a
6-year period. For each community, we constructed soundscapes
using four alternative approaches that altered the ratio of indi-
vidual sound file duration to total soundscape length or the
Fig. 3 Temporal trends in acoustic indices. Predicted annual variation in Acoustic Diversity Index, ADI (a,b), Acoustic Evenness Index, AEI (c,d),
Bioacoustic Index, BI (e,f) and Acoustic Entropy, H (g,h) in North America (left column) between 1996 and 2017 and in Europe (right column) between
1998 and 2018. Blue (North America) and green (Europe) lines show the predicted trends from GLMMs (Table 3); shaded areas indicate 95% confidence
intervals. Points show predicted annual values from GLMMs with the identical structure as those in Table 3 but with year fitted as a categorical rather than
a continuous variable, vertical lines indicate the 95% confidence intervals. Annual values of each acoustic index were standardised at the site-level prior to
analyses.
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proportion of near to far individuals. While the methodological
decisions applied during soundscape construction influenced the
absolute values of the four acoustic indices for a given commu-
nity, it did not influence the relative impact of changes in com-
munity composition on the acoustic indices (Supplementary
Fig. 6). Given our focus here is on temporal trends in soundscape
characteristics, rather than absolute values of each acoustic index,
and that our analyses are based on changes in standardised site-
level measures, we believe the temporal and spatial patterns in
soundscape characteristics reported here are robust to the
soundscape construction rules applied.
Natural soundscapes are under ever-increasing pressure from
global biodiversity loss and our results reveal a chronic dete-
rioration in soundscape quality across North America and Europe
over recent decades. Although we focus here on birds as the main
contributors to natural soundscapes, it is likely that the reduction
in quality has been even greater, given parallel declines in many
other taxonomic groups that contribute to soundscapes46,47.
Furthermore, pervasive increases in anthropogenic noise48 and
other sensory pollutants49 are also diluting the nature contact
experience. For example, as well as directly impacting human
behaviour and well-being50, noise pollution impairs our capacity
to perceive natural sounds51 and can limit the acoustic diversity
of soundscapes by constraining the bandwidth within which birds
sing52,53.
A scarcity of historical recordings means any assessment of
changes in natural soundscape characteristics over longer time
periods is vulnerable to the impacts of shifting baseline
syndrome54, as future soundscapes can only be compared to
the potentially already degraded soundscapes of today. Recon-
structing soundscapes from species’records and count data
avoids this problem and allows changes in local soundscape
characteristics to be explored at spatial scales not possible using
field recordings. This approach could also be used to forecast
future soundscapes based on projected species’range shifts under
environmental change scenarios. However, we strongly advocate
for the increased collection and systematic curation of sounds-
cape field recordings from across habitats and environmental
gradients to capture all facets of soundscape dynamics, such as
changes in anthropogenic noise and vocalisation behaviour across
taxonomic groups, not currently integrated into our reconstruc-
tions. The rapid increase in autonomous sound recording tools
and their widespread use could be harnessed both to launch
standardized soundscape monitoring schemes, and to collect
soundscape recordings in less structured citizen science
databases55. Such recordings could also be used to derive the
vocalisation frequency and duration data needed to further
enhance soundscape reconstructions by encoding species-specific
insertion criteria in place of the systematic protocol (one indivi-
dual equals one 25 s sound file) currently applied across all
species.
Although visual, auditory, and olfactory senses are all impor-
tant modalities characterising the nature contact experience19,20,
sound is a defining feature15. Our analyses of reconstructed
soundscapes reveal previously undocumented changes in the
acoustic properties of soundscapes across North America and
Europe over the past few decades that signal a reduction in
soundscape quality and imply an ongoing dilution of experience
associated with nature interactions. While we expect these
changes to be evident throughout the year, they are likely to
be most pronounced during spring, when birds are most vocally
active. Better understanding of exposure to changes in sounds-
cape quality, by mapping them onto spatial patterns of human
population density and locations at which nature is accessed, and
of the specific soundscape characteristics that support and
enhance the nature contact experience15, is now needed to fully
appreciate the implications for health and well-being56. Reduced
nature connectedness may also be contributing to the global
environmental crisis, as there is evidence it can lead to reductions
in pro-environmental behaviour5,57,58. The potential for declining
soundscape quality to contribute to a negative feedback loop,
whereby a decline in the quality of nature contact experiences
leads to reduced advocacy and financial support for conservation
actions, and thus to further environmental degradation7, must
also be recognised and addressed. Conservation policy and action
need to ensure the protection and recovery of high-quality natural
soundscapes to prevent chronic, pervasive deterioration and
associated impacts on nature connectedness and health and well-
being.
Methods
Bird data.North America: we used annual bird count data collated under the
North American Breeding Bird Survey (NA-BBS: https://www.pwrc.usgs.gov/bbs/)
from 1996 to 2017. NA-BBS survey routes, consisting of 50 survey points (hereafter
sites) evenly distributed over ~24.5 miles, are distributed across the United States
and Canada and are usually surveyed in June. At each site, skilled volunteers
conduct a three-minute point count, recording all birds seen or heard within a
400-m radius59.
Table 3 Results of GLMMs of the variation in the acoustic properties of soundscapes.
Fixed effects North America Europe
Estimate (SE) χ2DF pEstimate (SE) χ2DF p
(a) ADI Latitude 0.00002 (0.00088) 0.03 1 0.856 −0.0004 (0.0005) 0.78 1 0.376
Longitude 0.00002 (0.00035) 0.19 1 0.659 0.0005 (0.0003) 3.57 1 0.059
Year −0.00198 (0.00068) 8.46 1 0.004 −0.0073 (0.0030) 5.98 1 0.014
(b) AEI Latitude −0.00001 (0.00009) 0.01 1 0.949 −0.0005 (0.0005) 1.25 1 0.263
Longitude −0.00009 (0.00003) 0.08 1 0.782 −0.0005 (0.0003) 2.84 1 0.092
Year 0.00144 (0.00057) 6.47 1 0.011 0.0053 (0.0030) 3.08 1 0.079
(c) BI Latitude 0.00004 (0.00009) 0.16 1 0.691 −0.0002 (0.0005) 0.25 1 0.621
Longitude 0.00002 (0.00003) 0.44 1 0.508 0.0004 (0.0003) 2.26 1 0.132
Year −0.00217 (0.00088) 6.08 1 0.014 −0.0056 (0.0021) 6.91 1 0.009
(d) H Latitude 0.00010 (0.00009) 1.38 1 0.239 −0.0004 (0.0005) 0.86 1 0.353
Longitude 0.00006 (0.00004) 3.19 1 0.074 0.0006 (0.0003) 4.65 1 0.031
Year −0.00625 (0.00175) 12.79 1 <0.001 −0.0095 (0.0030) 10.01 1 0.002
Annual values for each acoustic index were standardised at the site-level prior to analyses.
(a) Acoustic Diversity Index (ADI), (b) Acoustic Evenness Index (AEI), (c) Bioacoustic Index (BI) and (d) Acoustic Entropy (H) in 202737 NA-BBS sites across North America between 1996 and 2017
and in 16524 PECBMS sites across Europe between 1998 and 2018.
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Europe: we used annual bird count data from 23 survey schemes across
22 countries collated under the Pan-European Common Bird Monitoring Scheme
(PECBMS: https://pecbms.info) from 1998 to 2018. In each scheme, skilled
volunteers carry out either line transects, point counts or territory mapping at
survey sites during the breeding season and record all birds encountered60
(Supplementary Table 5); while methods vary between survey schemes, they are
consistent within schemes across the time period included here.
Where count data were reported for subspecies, these were aggregated to species
level and any records of hybrid species or specifying genus only were removed. The
longitude and latitude of each survey site (just the first site of each NA-BBS survey
route) were also provided by NA-BBS and PECBMS. Not all sites were surveyed in
every year and only sites surveyed at least three times during the defined time
period were included in analyses. Note that similar results were found when
restricting data to sites surveyed in at least 10 years during the defined period.
Sound recordings. Sound files for all species detected on NA-BBS and PECBMS
surveys were downloaded from Xeno Canto, an online database of sound
recordings of wild birds from around the world (http://www.xeno-canto.org).
Specifically, we identified all files longer than 30 s, with associ ated metadata cate-
gorising them as high quality (category “A”) and as either “song”,“call”or
“drumming”types; sound files whose type category including the term “wingbeat”,
“flap”,“begging”,“alarm”or “night”types were excluded. Sound files downloaded
for NA-BBS species were restricted to those recorded in North America and those
from PECBMS to recordings made in Europe. If no sound files met these
requirements for a given species, we downloaded all files of shorter duration for
that species that met the quality and type criteria and stitched repeats of these
together to produce files longer than 30 s. Where more than 50 sound files for a
given species met our criteria for inclusion, a random selection of 50 was taken for
use in subsequent analyses. We used multiple sound files for each species to
capture, where possible, between-individual variation in song and call structure,
with the sound file(s) for inclusion in specific soundscapes randomly subsampled
from this set. If no sound files for a species were available, the sites where that
species was detected were removed from subsequent analyses; this represented
<1.5% NA-BBS sites and <3.5% PECBMS sites. Each downloaded sound file was
then standardised to ensure consistent sampling rate, duration and volume. Each
file was clipped to the first 27.5 s, with the first 2.5 s of this then removed to
produce a 25 s recording. These sound files varied in the quantity of vocalisation
they contained according to the song and call characteristics of the focal species.
Thus, some included 25 s of continuous song while others included just a single,
short burst of sound. The sampling rate was set to 44.1 kHz, and each file nor-
malised with a −6 dB gain before being saved as a mono mp3 output.
It is important to recognise that the sound recordings used here are taken in the
wild and thus inevitably contain some background noise in addition to
vocalisations of the target species, and that this may influence the acoustic
properties of the constructed soundscapes to some extent. To minimise this, we
selected only Quality “A”recordings and clipped out 25 s from the beginning of
each of these for use in soundscape construction, on the assumption that the
named focal species will be more dominant in these recordings and that it is most
likely to be vocalising towards the beginning of a submitted recording.
Furthermore, any background noise is expected to be both random in acoustic
structure and randomly distributed across the sound files of species considered
here; we see no plausible reason why, for example, the field recordings of increasing
or declining species would be more or less likely to contain background noise. Our
systematic approach to soundscape construction and our analyses of trends in
standardised site-level acoustic metrics also limits the potential of background
noise to cause directional bias in the results reported and, if anything, it is expected
to have reduced our ability to detect changes in soundscape characteristics.
In total, count data were available for 202,737 sites and 620 species in North
America, with a mean ± SE of 15.62 ± 0.6 sound files available per species. For
Europe, count data were available for 16,524 sites and 447 species, with
21.05 ± 0.9 sound files per species.
Soundscape reconstruction. This is described in detail in the main text.
Soundscape characteristics. Four acoustic indices were used to explore changes
in the acoustic properties of reconstructed soundscapes. The Acoustic Diversity
Index (ADI) uses the Shannon–Wiener index to estimate acoustic diversity,
dividing spectrograms into frequency bands and calculating the proportion of each
band occupied by sounds above a set amplitude threshold30. Higher values
represent a more even distribution of sound across frequencies and are associated
with increased species richness. The Acoustic Evenness Index (AEI) uses a similar
approach, dividing spectrograms into frequency bands but using the Gini coeffi-
cient to measure the evenness of sound distribution across them30. It is therefore
negatively related to ADI, with higher values representing a greater unevenness
between frequency bands, suggesting dominance by fewer species. Increases in
abundance are expected to have less impact on ADI and AEI than increases in
species richness as the songs of individuals from the same species will broadly
occupy the same frequency space. The Bioacoustic Index (BI) measures variation in
amplitude across a range of frequencies by calculating the dB spectrum across
frequencies and quantifying the area under the curve31. BI is expected to increase
with both increases in abundance and species richness. Total Acoustic Entropy (H)
is defined as the product of spatial and temporal entropies and quantifies variation
in amplitude across frequency bands and time using Shannon–Wiener index32.It
increases with both species richness and abundance following a logarithmic
model28,32. As soundscapes become saturated, the influence of additional species
and/or individuals on BI and H is expected to decrease. Default settings were used
Fig. 4 Spatial variation in acoustic index trends. Mean site-level trend in
Acoustic Diversity Index, ADI (a,b), Acoustic Evenness Index, AEI (c,d),
Bioacoustic Index, BI (e,f) and Acoustic Entropy, H (g,h) in 1354 1°x1° grid
squares across North America between 1996 and 2017 and 715 1°x1° grid
squares across Europe between 1998 and 2018. Colours indicate the size
and direction of trend in each acoustic index (yellow—improving
soundscape quality; blue—declining soundscape quality); note that the
colour scheme is reversed for AEI, as positive trends are taken to represent
a reduction in soundscape quality for this index. Site-level trends are
derived from changes in standardised annual values of each acoustic index.
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for each acoustic index except BI, where the maximum frequency was set to
22,050 Hz.
We initially generated soundscapes for a series of simulated communities to
confirm that the acoustic indices respond as expected when calculated from
artificial soundscapes. Firstly, we calculated ADI, AEI, BI and H for soundscapes
derived from communities comprising 1 to 10, 20, 30, 40 or 50 individuals of each
species in turn. Given the randomised selection of sound files, insertion point and
playback volume, we iterated this process 1000 times for each species-abundance
combination. Next, we constructed communities containing 2, 3, 4, 5, 10, 20 or
50 species, with 1–10 individuals of each species present, i.e. 70 communities in
total. We iterated this process 100 times for each species richness-abundance
combination, randomly selecting species for inclusion from the NA-BBS species
pool, and a further 100 times, randomly selecting species from the PECBMS species
pool. Again, the four acoustic indices were calculated for each soundscape
produced.
Annual soundscapes for each NA-BBS and PECBMS site were constructed from
each site-year count file and the four acoustic indices were calculated for each.
Given the randomised selection of the specific sound file, insertion point, and
playback volume used to represent each individual during the construction of each
soundscape, this process was iterated five times, with each acoustic index averaged
across these five site-year iterations for use in subsequent analyses. For all
PECBMS sites and for the first site of each NA-BBS route, the soundscape
generated from the fifth iteration was saved as an .mp3 file. All sound file
processing and soundscape construction was undertaken using Sound eXchange
programme (SoX: http://sox.sourceforge.net/) and acoustic indices were calculated
using R packages ‘seewave’32,‘soundecology’61 and ‘tuneR’62 in R v3.5.163.
Finally, we tested the sensitivity of soundscape characteristics to key parameters
imposed during construction. While predominately driven by community
composition, the acoustic properties of constructed soundscapes could also be
influenced by rules that influence the degree of the overlap between individual
sound files and their amplitude. First, we generated a community of 10 randomly
selected European bird species and specified declines in each species from 10 to five
individuals over a 6-year period. For each year, we then constructed four
soundscapes and extracted the associated acoustic indices for each. The first
soundscape type was built using the methods described above. The second was
built by inserting sound files into a 3-min soundscape, to increase the degree of
overlap, while the third was built by inserting sound files into a 10-min soundscape
to decrease the degree of overlap. Finally, we reverted to a 5-min soundscape but
randomly sampled playback volume for each sound file from a half-normal
distribution. This increased the relative proportion of distant vocalisations and may
be more representative of point count data, where the area surveyed increases with
increasing distance; though note this is likely to be offset by reduced detectability at
greater distances. This process was iterated for 1000 randomly sampled
communities of 10 species.
Statistical analyses
Response of acoustic indices to changes in community structure. To confirm that
acoustic indices respond to changes in species richness and abundance, we fitted
General Linear Models (GLMs) to outputs for the simulated single and multi-
species communities. In each model, the mean acoustic index across all iterations
was fitted as the response variable. For the single-species communities, the log
number of individuals was fitted as the explanatory variable and for the multi-
species communities, the log number of individuals, log number of species and
their interaction were fitted as explanatory variables. Separate models were fitted to
the North American and European data and for each acoustic index in turn.
Site-level changes in acoustic indices. We standardised each acoustic index within
each site (by subtracting the mean site-level measure from the annual value and
dividing by the site-level standard deviation64) prior to analysis to account for any
potential differences in detectability or observer effects between sites, differing
sampling protocols across survey schemes, and for initial community structure. In
all analyses, separate models were constructed for North American (204,813 sites
on 4197 routes spanning 22 years) and European data (16,524 sites spanning
21 years), and for each acoustic index in turn. To explore large-scale temporal
trends while accounting for any geographic differences in acoustic characteristics,
we fitted Gaussian General Linear Mixed Models (GLMMs) via the R package
‘lme4’65. Standardised annual site-level values for each acoustic index were fitted as
the response variable, with latitude, longitude and year (continuous) as fixed
effects. To account for non-independence of soundscapes from the same site,
random effects of site and year were included in all models, along with route and
state (North America models, Eq. (1a)) or country (Europe models, Eq. (1b)). To
assess the importance of fixed effects, we performed a likelihood ratio test by
comparing models with and without a particular term, reporting the χ2value and
associated significance. Spatial autocorrelation of modelled residuals was examined
by Moran’s I, separately for each year, using the package ‘ape’66. While significant
spatial autocorrelation was found, the sizes of the estimates were negligible (Sup-
plementary Table 6) and therefore this is subsequently ignored. To explicitly
explore how temporal trends in the acoustic properties of reconstructed sounds-
capes varied geographically, we refitted the models described above, including
latitude*year and longitude*year interaction terms. To visualise the large-scale
annual variation in acoustic properties we refitted these models with year included
as a categorial rather than a continuous variable, with predictions from these
models providing continent-level annual estimates for each acoustic index (Fig. 3).
Table 4 Results of GLMMs of the association between site-level trends in acoustic indices and site-level trends in the total
number of individuals and species in (i) 202737 NA-BBS sites across North America between 1996 and 2017 and (ii) 16524
PECBMS sites across Europe between 1998 and 2018, and the proportion of 1000 bootstrapped models reporting significant
effects for each term (p< 0.05).
Fixed effects Estimate (SE) χ2DF pProportion significant
(p< 0.05)
(i) North America
(a) ADI Individuals 0.109 (0.001) 310.08 1 <0.001 1
Species 0.578 (0.008) 4955.960 1 <0.001 1
(b) AEI Individuals −0.053 (0.006) 72.46 1 <0.001 1
Species −0.581 (0.008) 4964.17 1 <0.001 1
(c) BI Individuals 0.421 (0.006) 51.9.51 1 <0.001 1
Species 0.615 (0.008) 6267.72 1 <0.001 1
Individuals*species 0.059 (0.147) 15.975 1 <0.001 0.65
(d) H Individuals 0.910 (0.005) 32156.51 1 <0.001 1
Species 0.662 (0.007) 9766.56 1 <0.001 1
Individuals*species −0.058 (0.013) 20.99 1 <0.001 0.67
(ii) Europe
(a) ADI Individuals 0.388 (0.029) 173.51 1 <0.001 1
Species 0.657 (0.046) 206.45 1 <0.001 1
(b) AEI Individuals −0.398 (0.029) 182.80 1 <0.001 0.98
Species −0.681 (0.046) 224.51 1 <0.001 1
Individuals*species 0.188 (0.086) 4.80 1 0.028 0.33
(c) BI Individuals 0.074 (0.029) 6.25 1 <0.001 0.98
Species 0.947 (0.050) 424.93 1 <0.001 1
(d) H Individuals 0.410 (0.029) 199.76 1 <0.001 1
Species 1.095 (0.045) 590.19 1 <0.001 1
(a) Acoustic Diversity Index (ADI), (b) Acoustic Evenness Index (AEI), (c) Bioacoustic Index (BI) and (d) Acoustic Entropy (H). Only significant interactions are retained. Site-level trends are derived
from changes in standardised annual values of each acoustic index, total number of species, and total number of individuals.
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To explore the relationships between site-level trends in each acoustic index, we
fitted GLMs with the standardised annual values for each index as the response
variable and year (continuous) as the explanatory variable (Eq. (2)). This resulted
in an independent estimate of the rate of change in each acoustic index at each site.
For all six possible pairwise comparisons between acoustic indices, we used
Pearson’s correlation coefficients to estimate the magnitude of the association
between their site-level trends. All statistical analyses were carried out in R v3.5.163.
Standardised acoustic indexi;tβ0þβ1Latitudeiþβ2Longitudeiþβ3Yeart
þα1iSiteiþα2tYeartþα3jStatejþα4kRoute þεi;t
ð1aÞ
α1iN0;σ2
α1
α2tN0;σ2
α2
α3jN0;σ2
α3
α4kN0;σ2
α4
εi;tNð0;σε2Þ
where i=site, t=year, j=state, k=route
Standardised acoustic indexi;tβ0þβ1Latitudeiþβ2Longitudeiþβ3Yeart
þα1iSiteiþα2tYeartþα3jCountryjþεi;t
ð1bÞ
α1iN0;σ2
α1
α2tN0;σ2
α2
α3jN0;σ2
α3
εi;tNð0;σε2Þ
where i=site, t=year, j=country
Standardised acoustic indextβ0þβ1Yeartþεtð2Þ
εtNð0;σε2Þ
where t=year
To explore large-scale temporal trends in the total number of individuals and
species recorded on NA-BBS and PECBMS surveys, we fitted two additional
GLMMs. Standardised annual site-level values of the total number of (a)
individuals or (b) species were fitted as response variables, with latitude, longitude
and year (continuous) as fixed effects. To account for non-independence in
community structure from the same site, random effects of site and year were
included in all models, along with route and state (North America models) or
country (Europe models). Model structures were therefore equivalent to those set
out in Eqs. (1a) and (1b), albeit with different dependent variables. We then refitted
these models including year as a categorial rather than a continuous variable to
visualise the large-scale annual variation, and used predictions from these models
to provide continent-level annual estimates for total abundance and species
richness (Supplementary Fig. 5).
To explore the site-level relationships between trends in total number of
individuals, total number of species and acoustic indices, we first fitted GLMs with
either the standardised total number of (a) individuals or (b) species as response
variables and year (continuous) as the explanatory variable at each site. These models
were therefore equivalent in structure to that described in Eq. (2)andresultedin
independent estimates of the rates of change in the total number of individuals and
species at each site. We then fitted separate GLMMs for each acoustic index, in each
continent, in turn with site-level trend in acoustic index as the response variable and
site-level trends in the total number of individuals and the total number of species and
their interaction as fixed effects. State was included as a random effect in the North
American models and country as a random effect in the European models. To
incorporate the error associated with site-level trend estimates we used a
bootstrapping procedure in our assessment of the significance of the modelled effects.
We generated 1000 new estimates for each variable (site-level trend in: acoustic index,
total number of individuals and total number of species) by randomly sampling from
a normal distribution with a mean equal to the site-level trend and standard deviation
equal to the standard error of the site-level trend. The GLMMs were then fitted over
each of the 1000 datasets separately. We present the results of a final model carried
out on the original site-level estimates, as well as the proportion of times each fixed
Fig. 5 Trends in community structure and soundscape characteristics.
The association between site-level in trends in the total number of species
and the total number of individuals in 202737 NA-BBS sites across North
America (left column) and in 16524 PECBMS sites across Europe (right
column). Colours indicate site-level trends in Acoustic Diversity Index, ADI
(a,b), Acoustic Evenness Index, AEI (c,d) Bioacoustic Index, BI (e,f) and
Acoustic Entropy, H (g,h) (yellow—improving soundscape quality; blue—
declining soundscape quality); note the colour scheme is reversed for AEI,
as positive trends are taken to represent a reduction in soundscape quality
for this metric. Site-level trends are derived from changes in standardised
annual values of each acoustic index, total number of species, and total
number of individuals.
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effect included in the final model was significant across the 1000 bootstrapped
datasets (p< 0.05). Non-significant interaction terms were removed from the models.
Reporting summary. Further information on research design is available in the Nature
Research Reporting Summary linked to this article.
Data availability
The North American bird monitoring data are available directly from U.S. Geological
Survey (https://www.pwrc.usgs.gov/bbs/) and the European bird monitoring are
available, on request, from PECBMS (https://pecbms.info/). Sound recordings were
downloaded from Xeno Canto (http://www.xeno-canto.org). Acoustic indices for
soundscapes constructed from simulated communities, site-level acoustic index data for
reconstructed soundscapes for NA-BBS and PECBMS sites, and source data for all figures
are available from the Open Science Framework under accession code: https://osf.io/
jyuxk/ (ref. 67).
Code availability
R code for soundscape construction, extraction of acoustic indices, statistical analyses
and figure construction are available from the Open Science Framework under accession
code: https://osf.io/jyuxk/ (ref. 67).
Received: 14 April 2021; Accepted: 24 September 2021;
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Acknowledgements
We thank the thousands of volunteer citizen-scientists who contributed to the long-term
bird-monitoring programmes in North America and Europe, the institutions that
manage these programmes, and those funding these activities. The Norwegian Envir-
onment Agency finances the Norwegian common breeding bird monitoring and the
Swedish Bird Survey is supported by the Swedish Environmental Protection Agency and
carried out in collaboration with all 21 regional county boards. It acts within the fra-
mework of the strategic research environment Biodiversity and Ecosystem Services in a
Changing Climate (BECC). J.R. was supported by Charles University in Prague (project
no. PRIMUS/17/SCI/16). We are also grateful to the many sound recorders that have
submitted files to the Xeno Canto collection (www.xeno-canto.org). This work was
supported by Natural Environment Research Council grant NE/T007/354/1. We thank
L. Spurgin for advice on analysis; and J. Gill and J. Sauer for helpful discussions and
comments on the manuscript. Soundscape reconstruction was carried out on the High-
Performance Computing Cluster supported by the Research and Specialist Computing
Support Service (RSCSS) at the University of East Anglia.
Author contributions
S.J.B. conceived the study and constructed the soundscapes. S.J.B. drafted the manuscript
with significant contributions from C.A.M. C.A.M. performed the data analyses and
prepared the figures. A.A., Z.B., L.B., T.C., P.C., V.E., D.P.E., A.G., S.H., F.J., J.A.K., J.K.,
A.K., P.K., A.L., Å.L., C.M., D.G.N., I.J.Ø., J-Y.P., J.R., T.S., B.S.S., N.T., S.T., C.A.M.v.T.
and P.V. contributed to collection and collation of PECBMS data. All authors reviewed
and approved the final manuscript.
Competing interests
The authors declare no competing interests.
Additional information
Supplementary information The online version contains supplementary material
available at https://doi.org/10.1038/s41467-021-26488-1.
Correspondence and requests for materials should be addressed to S. J. Butler.
Peer review information Nature Communications thanks Richard Fuller, Christos
Mammides, and the other, anonymous, reviewer(s) for their contribution to the peer
review of this work. Peer reviewer reports are available.
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