![Results from BirdNET App data case studies
BirdNET App submissions of White-throated Sparrow (A) and Yellowhammer (B) songs were manually sorted into distinct dialects, which reflected the geographically distinct dialects reported by [4] and [5], respectively. Common Crane observations corresponded with the species’ known migration routes from the Iberian Peninsula to northern Europe and, to a lesser extent, across the Po River Valley in northern Italy (C). Base maps were provided by the University of Minnesota (https://conservancy.umn.edu/handle/11299/227302).](https://www.researchgate.net/profile/Connor-Wood-4/publication/361615995/figure/fig2/AS:11431281275486908@1725413205660/Results-from-BirdNET-App-data-case-studies-BirdNET-App-submissions-of-White-throated_Q320.jpg)
Access to this full-text is provided by PLOS.
Content available from PLOS Biology
This content is subject to copyright.
COMMUNITY PAGE
The machine learning–powered BirdNET App
reduces barriers to global bird research by
enabling citizen science participation
Connor M. WoodID*, Stefan Kahl, Ashakur Rahaman, Holger Klinck
K. Lisa Yang Center for Conservation Bioacoustics, Cornell Lab of Ornithology, Cornell University, Ithaca,
New York, United States of America
*cmw289@cornell.edu
The BirdNET App, a free bird sound identification app for Android
and iOS that includes over 3,000 bird species, reduces barriers to citi-
zen science while generating tens of millions of bird observations
globally that can be used to replicate known patterns in avian
ecology.
The ubiquity of smartphones combined with the power of new machine learning algorithms
presents an opportunity to promote positive interactions between humans and birds and thus
create new possibilities for avian research. We present the BirdNET App, a free program capa-
ble of identifying over 3,000 bird species by sound alone. The BirdNET App allows users to
record audio on a smartphone and transmit that audio and anonymized metadata to the Bird-
NET server, and a bird species identification is provided with a qualitative confidence score.
The raw audio, quantitative confidence score, and metadata (date, time, and location) are
saved on the server for subsequent research usage; all observations are anonymized, and no
user data are stored. This analytical workflow was inspired in part by the Pl@ntNet app, which
combines crowdsourcing and machine learning to engage users in plant identification [1]. The
BirdNET App relies on 2 components: the species identification algorithm and the user
interface.
Briefly, the BirdNET algorithm is a deep convolutional neural network. The 2018 to 2020
version could identify 984 North American and European bird species; an early 2021 update
enabled it to identify over 3,000 species from around the world. For a full technical description
of the algorithm, see [2]. The interface was built around a real-time spectrogram from which
users actively select snippets of sound for analysis, a feedback button allowing users to indicate
whether a prediction was correct or not, and a basic data portal allowing users to view their
own observations and commonly observed species in their area (Fig 1). The app interface sup-
ports 13 languages, with species names translated into an additional 12 languages. Our under-
lying philosophy was that consistently high prediction accuracy with global coverage and a
simple, rewarding user interface would help us achieve the sustained use that would be neces-
sary for research applications and most enjoyable for users.
To test whether the app could indeed remove barriers to citizen science—something that is
often suggested but rarely tested [3]—we compared usage of the BirdNET App to that of eBird.
PLOS Biology | https://doi.org/10.1371/journal.pbio.3001670 June 28, 2022 1 / 6
a1111111111
a1111111111
a1111111111
a1111111111
a1111111111
OPEN ACCESS
Citation: Wood CM, Kahl S, Rahaman A, Klinck H
(2022) The machine learning–powered BirdNET
App reduces barriers to global bird research by
enabling citizen science participation. PLoS Biol
20(6): e3001670. https://doi.org/10.1371/journal.
pbio.3001670
Published: June 28, 2022
Copyright: ©2022 Wood et al. This is an open
access article distributed under the terms of the
Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.
Data Availability Statement: The data used in the
four case studies (two song dialects and two
migrations, one of each in North America and in
Europe) are publicly available (https://doi.org/10.
5281/zenodo.6484061). BirdNET App data will be
publicly available once it has been compiled in a
shareable format; until then, we welcome inquiries
from researchers interested in using BirdNET App
data (email: ccb-birdnet@cornell.edu).
Funding: Funding was provided by Jake Holshuh,
the Arthur Vining Davis Foundation, the European
Union, the European Social Fund for Germany, the
Cornell Lab of Ornithology, the German Federal
Ministry of Education and Research in the program
of Entrepreneurial Regions InnoProfileTransfer in
the project group localizIT (funding code
03IPT608X), and Chemnitz Technical University. All
Both programs enable citizen scientists to submit bird observations for research purposes, but
eBird, a pioneering project initiated in 2003, is designed for intermediate/expert birders who
can submit checklists of birds they have identified, while the BirdNET app can be used by
beginners who have no knowledge of birds. In 2020, the BirdNET App engaged >1.1 million
participants compared to 317,792 eBird participants (eBird: An online database of bird distri-
bution and abundance. Ithaca, New York: Cornell Lab of Ornithology; 2021). This difference
in participation does not imply that one is “better” than the other; instead, it reflects conscious
Fig 1. Images of the app user interface (iOS version, American English) and the distribution of submissions. A
spectrogram (A) visualizes environmental sounds in real time by flowing right to left and is paused when the user
selects a snippet of sound for analysis (B). After that snippet is analyzed by the BirdNET server, a qualitative species
identification is provided (C), and the user has the option of indicating whether that identification is correct (D).
BirdNET App users made 31 million submissions in 2020, and even after stringent quality filters were applied, 5.8
million observations remained and achieved near-total coverage of North America and Europe (E).Image credit:C.
Wood; base maps were provided by the University of Minnesota (https://conservancy.umn.edu/handle/11299/227302).
https://doi.org/10.1371/journal.pbio.3001670.g001
PLOS BIOLOGY
PLOS Biology | https://doi.org/10.1371/journal.pbio.3001670 June 28, 2022 2 / 6
funding was secured by HK and SK. The funders
had no role in study design, data collection and
analysis, decision to publish, or preparation of the
manuscript.
Competing interests: The authors have declared
that no competing interests exist. SK developed the
app but has made it freely available and receives no
financial benefit for downloads, submissions, or
other usage.
design choices. By catering to a smaller pool of more advanced users, eBird can generate
high-quality data (e.g., abundance and nondetections). By removing the need for preexisting
bird identification skills and specialized equipment like binoculars, the BirdNET App vastly
expands the number of potential citizen science participants, although it generates more sim-
plistic presence-only data. These paradigms are complementary, and, ideally, synergistic, with
an exchange of data and participants leading to improved research tools and conservation
outcomes.
By reducing the barrier to entry of citizen science bird monitoring, we rapidly amassed 5.8
million high-quality bird observations across North America and Europe (Fig 1E). We then
conducted 4 case studies to test whether app data could be used to replicate known patterns in
avian ecology and thus serve as a reliable research resource. The case studies were selected
based on the availability of results we could attempt to reproduce (e.g., song dialect mapping
has been somewhat limited at broad scales because of data limitations) and to encompass a
range of phenomena (e.g., simple versus complex songs and the migration of small versus
large populations). First, we manually classified 1,392 White-throated Sparrow (Zonotrichia
albicollis) song observations as ending in a doublet or triplet phrase. While [4] reported a uni-
form west-to-east expansion of the White-throated Sparrow’s doublet song, we found that the
triplet has persisted across the western portion of the sparrow’s range and that the doublet
phrase has penetrated to the far eastern edge of the range (Fig 2A). The improved detail we
achieved is likely a function of sample size: The 1,392 songs we analyzed were collected in just
3 months, whereas the 1,785 songs analyzed by [4] were amassed over 70 years. Second, we
manually classified 4,466 Yellowhammer (Emberiza citrinella) song observations as B- or X-
type and found, consistent with [5], that the X-type song was dominant in Germany and
southeastern England, while the B-type song was dominant in Poland and eastern Europe (Fig
2B). Third, we mapped 2,690 Brown Thrasher (Toxostoma rufum) observations and found
that the app data accurately reflected the known seasonal and migratory ranges of this species
in eastern North America (BirdLife International and Handbook of the Birds of the World
[2019] Bird species distribution maps of the world. Version 2019.1. Available from: http://
datazone.birdlife.org/species/requestdis). Yet, the Brown Thrasher has an estimated popula-
tion of 6,100,000 [6], and its migration is essentially a massive wave of birds, and we wanted a
more difficult test. Therefore, fourth, we mapped 1,700 observations of the Common Crane
(Grus grus), which has a migratory population of approximately 250,000 that flies from the Ibe-
rian Peninsula and northern Africa to northern Europe (BirdLife International. 2020. Species
factsheet: Grus grus). Once again, we were able to accurately map this migration, and there
were even observations from a newly described flyway across the Po River plain in northern
Italy used by just a few thousand individuals [7] (Fig 2C). The success of these 4 case studies
suggests that the BirdNET App can generate reliable data suitable for novel inquiries in bird
research. The data used in these case studies are publicly available (https://doi.org/10.5281/
zenodo.6484061).
By creating an app that was easy to use and produced satisfying results, we were able to cre-
ate a user community of over 2 million people globally and thus rapidly accrue tens of millions
of bird observations across 6 continents that can yield accurate assessments of avian ecology.
The defining characteristic of machine learning–powered nature apps like BirdNET is that dig-
ital media are converted to biological observations by the algorithm, rather than human
observers. Consequently, potential biases such as species-specific classification performance
will be uniform and directly measurable, as opposed to variable and uncertain, as occurs when
users of unknown skill are identifying species. We have shown that eliminating the need for
species identification skills and equipment removes a potentially significant barrier to citizen
science participation and that the resulting data can be used to generate accurate results.
PLOS BIOLOGY
PLOS Biology | https://doi.org/10.1371/journal.pbio.3001670 June 28, 2022 3 / 6
Fig 2. Results from BirdNET App data case studies. BirdNET App submissions of White-throated Sparrow (A) and
Yellowhammer (B) songs were manually sorted into distinct dialects, which reflected the geographically distinct
dialects reported by [4] and [5], respectively. Common Crane observations corresponded with the species’ known
migration routes from the Iberian Peninsula to northern Europe and, to a lesser extent, across the Po River Valley in
northern Italy (C). Base maps were provided by the University of Minnesota (https://conservancy.umn.edu/handle/
11299/227302).
https://doi.org/10.1371/journal.pbio.3001670.g002
PLOS BIOLOGY
PLOS Biology | https://doi.org/10.1371/journal.pbio.3001670 June 28, 2022 4 / 6
Pl@ntNet app data have been used to support conservation initiatives in Europe and Africa
[8], and we believe BirdNET App data have similar potential. Expanded research applications
will depend on a thorough understanding of the biases inherent in the bird observations. Vari-
ation in vocal activity across seasons and among species, user motivations, cell phone coverage,
ambient noise among habitats, and species-specific classification accuracy, as well as digital
divides such as the wealth needed to own a smartphone will all be manifested in BirdNET App
data [3]. Nonetheless, the 4 case studies we presented reveal that app data can already provide
accurate results.
A forthcoming app user data analysis portal will allow users to analyze their own observa-
tions in greater detail than the current species list and “explore your area” features, an
improvement that will enable users to pursue their own research questions. Adding an
optional social networking component to the app could be transformative. The ability to con-
nect with friends could foster friendly competition that would likely increase submission rates.
More importantly, pooling results across multiple users would enable community-driven proj-
ects to document local bird diversity to promote ecotourism, defend lands against resource
extraction, or educational uses. An optional “point count mode” with which users could sub-
mit 3- to 5-minute continuous soundscape recordings would allow communities to document
an estimated 5% to 15% of the local bird species per week even with minimal participation [9].
Moreover, these acoustic point counts would yield analytically valuable nondetections of the
remaining species.
At the outset of this project, we envisioned networks of app users interacting with birds,
with their own bird observations, with each other, and with researchers. In just 3 years, over 2
million users from over 100 countries have generated over 40 million submissions. Critically,
these submissions are not the final product: The BirdNET App enables top-down and, soon,
bottom-up research on avian ecology at continental scales. We welcome inquiries from
researchers interested in using BirdNET App data (email: ccb-birdnet@cornell.edu). Raw
observations are not yet publicly available due to the substantial challenges to hosting tens of
millions of observations and audio files; sharing observations only (i.e., prediction scores and
metadata but no audio) is simpler but prevents validation of observations and thus leaves the
data vulnerable to misinterpretation. Detailed data usage guidelines that will facilitate wide-
spread open-access data sharing are forthcoming.
The removal of barriers is the most important aspect of machine learning–powered nature
apps like BirdNET. People do not need knowledgeable mentors or specialized equipment,
which is often expensive, to identify the species around them, they simply need a smartphone.
Challenges remain, as classification accuracy will never be perfect and gaps—both geographic
and phylogenetic—in coverage will persist. However, greater accessibility means that more
people have the opportunity to engage more deeply with the nonhuman world, which can
potentially improve both their physical and mental health [10]. Highly accessible tools can also
foster participation in other citizen science programs, such as eBird. At a societal level, making
nature experiences more accessible may help make people more attuned to—and invested in—
environmental conditions [11]. From a research perspective, greater accessibility means that
more data can be collected more rapidly than if the citizen science participant pool were lim-
ited to wealthier and already-skilled volunteers. We hope that the BirdNET App and similar
projects will enable new opportunities for avian research and conservation.
Acknowledgments
We are grateful to all the BirdNET App users, especially those whose feedback continues to
shape the project. The BirdNET algorithm could not exist without the thousands of skilled
PLOS BIOLOGY
PLOS Biology | https://doi.org/10.1371/journal.pbio.3001670 June 28, 2022 5 / 6
volunteers who contributed millions of recordings to the Macaulay Library and Xeno-canto.
We thank Amir Dadkhah for coding the iOS version and Dan Salisbury for validating app sub-
missions. Jan Kocna, A
´lvaro Vega Hidalgo, Auguste Bonnin, Margherita Silvestri, Luuk van
der Duim, Ricardo Brioschi Lyra, Vera Fink, Volodymyr Pyrih, Konstantin Lisnyak, and
Dainius Kučinskas translated the app.
References
1. Affouard A, Goeau H, Bonnet P, Lombardo JC, Joly A. Pl@ntNet app in the era of deep learning. In:
ICLR: International Conference on Learning Representations. 2017. p. 6.
2. Kahl S, Wood CM, Eibl M, Klinck H. BirdNET: A deep learning solution for avian diversity monitoring.
Eco Inform. 2021; 61:101236. https://doi.org/10.1016/j.ecoinf.2021.101236
3. Andrachuk M, Marschke M, Hings C, Armitage D. Smartphone technologies supporting community-
based environmental monitoring and implementation: a systematic scoping review. Biol Conserv. 2019;
237:430–42.
4. Otter KA, Mckenna A, LaZerte SE, Ramsay SM. Continent-wide Shifts in Song Dialects of White-
Throated Sparrows. Curr Biol. 2020; 30:1–5. https://doi.org/10.1016/j.cub.2020.05.084
5. Petruskova
´T, Diblı
´kova
´L, Pipek P, Frauendorf E, Procha
´zka P, Petrusek A. A review of the distribution
of Yellowhammer (Emberiza citrinella) dialects in Europe reveals the lack of a clear macrogeographic
pattern. J Ornithol. 2015; 156(1):263–73.
6. Rosenberg KV, Kennedy JA, Dettmers R, Ford RP, Reynolds D, Alexander JD, et al. Partners in Flight
Landbird Conservation Plan: 2016 Revision for Canada and Continental United States. Partners in
Flight Science Committee; 2016 p. 119.
7. Mingozzi T, Storino P, Venuto G, Alessandria G, Arcamone E, Urso S, et al. Autumn Migration of Com-
mon Cranes Grus grus Through the Italian Peninsula: New Vs. Historical Flyways and Their Meteoro-
logical Correlates. Acta Ornithol. 2013 Jun; 48(2):165–77.
8. Bonnet P, Joly A, Faton JM, Brown S, Kimiti D, Deneu B, et al. How citizen scientists contribute to moni-
tor protected areas thanks to automatic plant identification tools. Ecol Solut Evid. 2020; 1(2):e12023.
9. Wood CM, Kahl S, Chaon P, Peery MZ, Klinck H. Survey coverage, recording duration and community
composition affect observed species richness in passive acoustic surveys. Methods Ecol Evol 2021
May; 12(5):885–96.
10. Bratman GN, Anderson CB, Berman MG, Cochran B, de Vries S, Flanders J, et al. Nature and mental
health: An ecosystem service perspective. Sci Adv. 2019 Jul 1; 5(7):eaax0903. https://doi.org/10.1126/
sciadv.aax0903 PMID: 31355340
11. Soga M, Gaston KJ. Shifting baseline syndrome: causes, consequences, and implications. Front Ecol
Environ. 2018; 16(4):222–30.
PLOS BIOLOGY
PLOS Biology | https://doi.org/10.1371/journal.pbio.3001670 June 28, 2022 6 / 6
Available via license: CC BY 4.0
Content may be subject to copyright.
Content uploaded by Connor M Wood
Author content
All content in this area was uploaded by Connor M Wood on Jul 25, 2022
Content may be subject to copyright.
