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Citizen science enlists the public in collecting large quantities of data across an array of habitats and locations over long spans of time. Citizen science projects have been remarkably successful in advancing scientific knowledge, and contributions from citizen scientists now provide a vast quantity of data about species occurrence and distribution around the world. Most citizen science projects also strive to help participants learn about the organisms they are observing and to experience the process by which scientific investigations are conducted. Developing and implementing public data-collection projects that yield both scientific and educational outcomes requires significant effort. This article describes the model for building and operating citizen science projects that has evolved at the Cornell Lab of Ornithology over the past two decades. We hope that our model will inform the fields of biodiversity monitoring, biological research, and science education while providing a window into the culture of citizen science.
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Roundtable
S
tudying large-scale patterns in nature requires a vast
amount of data to be collected across an array of loca-
tions and habitats over spans of years or even decades. One
way to obtain such data is through citizen science, a research
technique that enlists the public in gathering scientific in-
formation (Bhattacharjee 2005). Large-scale projects can
engage participants in continental or even global data-
gathering networks. Pooled data can be analyzed to illumi-
nate population trends, range changes, and shifts in
phenologies. Results can be published in the scientific liter-
ature and used to inform population management decisions.
The Cornell Lab of Ornithology (CLO),home base for the
authors of this article, has welcomed public participation in
its research for decades. Today, CLO operates numerous cit-
izen science projects of various sizes, each designed to answer
scientific questions while helping the public learn about birds
and the process of science (www.cornellcitizenscience.org;
table 1). In the past two decades, CLOs projects have engaged
thousands of individuals in collecting and submitting data on
bird observations,reading about project findings, visualizing
data through Web-based graphs and maps, and even analyz-
ing data themselves. Collectively, the projects gather tens of
millions of observations each year.
Citizen science projects have been remarkably successful in
advancing scientific knowledge.Recent publications using data
collected by CLO projects have examined how bird popula-
tions change in distribution over time and space (Wells et al.
1998, Hochachka et al. 1999, Cooper et al. 2007, Bonter and
Harvey 2008, Bonter et al. 2009); how breeding success is
affected by environmental change (Rosenberg et al. 1999a,
Hames et al. 2002a); how emerging infectious diseases spread
through wild animal populations (Hochachka and Dhondt
2000, Hartup et al. 2001, Altizer et al. 2004, Hochachka et al.
2004, Dhondt et al. 2005); how acid rain affects bird popu-
lations (Hames et al. 2002b); how seasonal clutch-size vari-
ation is affected by latitude (Cooper et al. 2005a, 2005b,
2006); and how databases can be mined and models
constructed to discover patterns and processes in ecological
systems (Caruana et al. 2006, Hochachka et al. 2007, Fink and
Hochachka 2009, Kelling et al. 2009).
The CLO citizen science projects also strive to help par-
ticipants learn about birds and experience the process by
which scientific investigations are conducted. Evaluations
have shown that in addition to learning science facts (Brossard
et al. 2005, Trumbull et al. 2005), some participants have
used appropriate scientific processes and principles when
making decisions about experimental design (Trumbull et al.
2000). Individuals also have increased the numbers of days that
they watched birds and recorded information about them
after participating in a project (Thompson and Bonney 2007).
Citizen Science: A Developing Tool
for Expanding Science Knowledge
and Scientific Literacy
RICK BONNEY, CAREN B. COOPER, JANIS DICKINSON, STEVE KELLING, TINA PHILLIPS,
KENNETH V. ROSENBERG, AND JENNIFER SHIRK
Citizen science enlists the public in collecting large quantities of data across an array of habitats and locations over long spans of time. Citizen science
projects have been remarkably successful in advancing scientific knowledge, and contributions from citizen scientists now provide a vast quantity of
data about species occurrence and distribution around the world. Most citizen science projects also strive to help participants learn about the
organisms they are observing and to experience the process by which scientific investigations are conducted. Developing and implementing public
data-collection projects that yield both scientific and educational outcomes requires significant effort. This article describes the model for building
and operating citizen science projects that has evolved at the Cornell Lab of Ornithology over the past two decades. We hope that our model will
inform the fields of biodiversity monitoring, biological research, and science education while providing a window into the culture of citizen science.
Keywords: citizen science, public participation in research, public scientific literacy
www.biosciencemag.org December 2009 / Vol. 59 No. 11 BioScience 977
BioScience 59: 977–984. ISSN 0006-3568, electronic ISSN 1525-3244. © 2009 by American Institute of Biological Sciences. All rights reserved. Request
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Still other participants have demonstrated that they can use
online data tools to answer a variety of questions about bird
distribution and abundance, such as when certain species
were present in their area, where rare birds were seen,and how
populations change over time (Bonney 2007).
Developing and implementing public participation projects
that yield both scientific and educational outcomes requires
careful planning. This article describes the model for build-
ing and operating citizen science projects that has evolved at
CLO over the past two decades. We hope that our model
will inform the fields of biodiversity monitoring, biological
research,and science education while providing a window into
the culture of citizen science.
Citizen science project design
Public participation in scientific research is not new. Light-
house keepers began collecting data about bird strikes as
long ago as 1880; the National Weather Service Cooperative
Observer Program began in 1890; and the National Audubon
Society started its annual Christmas Bird Count in 1900
(Droege 2007). Throughout the 20th century, thousands of
public volunteers participated in projects to monitor water
quality, document the distribution of breeding birds, and
scour the night skies for new stars and galaxies. The current
concept of citizen science, however, with its integration of
explicit and tested protocols for collecting data, vetting of data
by professional biologists,and inclusion of specific and mea-
surable goals for public education, has evolved primarily
over the past two decades (Bonney 2007, Cohn 2008).
Citizen science projects at the CLO are driven by a re-
search question or monitoring agenda that fits within the or-
ganizations science or conservation mission. Projects range
from focused studies, such as the House Finch (Carpodacus
mexicanus) Disease Survey, which engages a few hundred
participants in watching feeders for signs of avian conjunc-
tivitis, to monitoring projects like eBird, which collects more
than 1 million bird observations each month from a legion
of birdwatchers around the world (table 1). In all CLO
projects, participants are asked to follow specific protocols,
collect data about birds and their environments, and submit
the data to the CLOs database. The protocols are provided
in field-tested instruction booklets or on Web pages and are
enhanced by educational supports such as posters, identifi-
cation guides, and CDs or online multimedia presentations.
Each project is maintained by at least one full-time staff per-
son whose tasks include responding to participants questions.
Once data are entered into the database, anyone with Inter-
net access can explore the information using a variety of
data-visualization techniques. Explorations can be species
based (Where do northern cardinals [Cardinalis cardinalis]
occur?), place based (What species will I see if I visit a certain
national wildlife refuge?), temporal (Have house finch pop-
ulations declined in recent years?), or a combination of these
(What are the average clutch sizes for tree swallows
[Tachycineta bicolor] in California and NewYork?). The data-
visualization tools are used by thousands of project partici-
pants each month to see how their contributions relate to those
of others. The tools are also used by scientists, land managers,
and conservationists who search for patterns of species
occurrence and changes in abundance over time. Results of
analyses are presented on the CLO Web site (http://birds.cornell.
edu); in the CLO’s newsletter, BirdScope; and in a variety of
scientific publications, including numerous peer-reviewed
journals.
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978 BioScience December 2009 / Vol. 59 No. 11 www.biosciencemag.org
Table 1. Current Cornell Laboratory of Ornithology citizen science projects.
Project name Description
e
Bird Online checklist program documenting the presence or absence of all species of North American birds in all locations
a
t all times of year
Celebrate Urban Birds! Simplified version of eBird focusing on 16 species common to urban areas
P
roject FeederWatch Winter-long survey of birds that visit feeders in backyards, nature centers, community areas, and other locales
throughout North America
N
estWatch Breeding-season survey of the location, habitat, and number of eggs, young, and fledglings for all species that nest
i
n North America
C
amClickr Year-long online project that enables participants to “sort and tag breeding behaviors from millions of images
a
rchived from nest cams located across North America
G
reat Backyard Bird Count Annual (February) four-day count of birds in backyards and neighborhoods across North America
Birds in Forested Landscapes Breeding-season study of the relationships between habitat and breeding success of forest birds throughout North
America
Project PigeonWatch Year-round survey of the color morphs and courtship behaviors of pigeons breeding in cities throughout North
America
House Finch Disease Survey Survey of house finches and American goldfinches showing symptoms of conjunctivitis throughout North America
B
irdSleuth Middle-school curriculum that develops skills of independent inquiry based on the Cornell Ornithological Laboratory’s
c
itizen science projects
Note: More information about each of these projects can be found at www.cornellcitizenscience.org/.
All of the data contributed to CLO citizen science databases
are provided by the public and are available at no charge to
anyone, amateur or professional, for any noncommercial
use.Maintenance and security are provided by database man-
a
gers housed within the CLO’s information science depart-
ment. Raw data are available either from individual project
Web sites or through the Avian Knowledge Network
(www.avianknowledge.net).
Citizen science program model
The CLO’s model for developing and implementing a citizen
science project has been worked out over time by a group of
individuals with expertise in education, population biology,
conservation biology, information science, computational
statistics, and program evaluation. We have found that proj-
ects whose developers follow this model can simultaneously
fulfill their goals of recruitment, research, conservation, and
education (box 1).
Choose a scientific question. Citizen science is particularly
helpful to investigators who are interested in answering
questions that have a large spatial or temporal scope. For
example,two questions that have been addressed by CLO proj-
ects are,“What are the patterns of irruption in winter finch
populations? (Project FeederWatch) and “How do clutch
sizes of eastern bluebirds (Sialia sialis) vary with latitude?”
(NestWatch). When choosing questions, project developers
must consider that most participants will be amateur
observers. Thus, questions for which data collection relies on
basic skills, such as counting a few species of birds at feeders
or determining the number of eggs in a nest, are more
appropriate than questions that require higher levels of skill
or knowledge, such as determining the level of courtship
intensity of a breeding pair of birds. Projects demanding
high skill levels from participants can be successfully devel-
oped, but they require significant participant training and
support materials such as training videos.
Monitoring studies designed to detect patterns of species
occurrence over time or space are especially well suited for
citizen science. Broadscale surveys gather tremendous quan-
tities of data that can be used to explore trends in species
occurrence across broad geographic landscapes (e.g., Robbins
et al. 1989, Hochachka et al.2007),water quality across water-
sheds (e.g., EPA 2006), or trends in population interactions
over time (e.g.,Cooper et al. 2007). But citizen science can in-
volve complex designs and even experiments, which provide
e
xcellent teaching opportunities. For example, the CLOs
Seed Preference Test involved thousands of participants who
examined food preferences of birds by providing different
types of seeds in a continent-wide experiment (Trumbull et
al. 2000). And participants in the Birds in Forested Landscapes
project are required to select survey sites, describe site habi-
tats in detail, and use playbacks of recorded songs and calls
to locate and map breeding birds.
Because complicated projects tend to attract fewer partic-
ipants, project designers who wish to reach large audiences
need to keep projects simple. However, even simple projects
can address complicated questions by recruiting a subset of
participants into more complex tasks. For example, when
clutch-size data from NestWatch yielded discoveries about
geographic trends in incubation period and hatching failure
that required study outside the scope of the original proto-
col, project staff launched a new study for which partici-
pants installed data loggers to record time and temperature
inside bluebird nests. Similarly, when the Birds in Forested
Landscapes project revealed a relationship between declining
forest birds and acid rain (Hames et al. 2002b), researchers
devised a supplemental protocol to measure the availability
of calcium-rich prey in leaf litter at hundreds of study sites.
Form a team of scientists, educators, technologists, and evalu-
ators.
A successful citizen science project requires a develop-
ment team comprising multiple disciplines. A researcher is
required to ensure the projects scientific integrity, to de-
velop protocols that will lead to the collection of quality data,
and to analyze and publish the data after they are collected.
An educator is required to explain the project’s importance
and significance to participants, to pilot- and field-test
protocols with potential participants, to develop clear and
comprehensive project support materials, and to ensure
appropriate participant feedback.A computational statistician
or information scientist is needed to develop both the data-
base infrastructure and the technology required to receive,
archive, analyze, visualize, and disseminate project data and
results.An evaluator is needed first to ensure that the project
begins with measurable objectives, and second to gather
data to assess project success based on those objectives, both
during and after project implementation.
Small groups or organizations that do not have internal
access to all disciplines can partner with other organizations
or adapt national citizen science projects for use at local or
regional scales. Projects and collaborators can be located at
CLO’s citizen science toolkit Web site, www.citizenscience.org.
Develop, test, and refine protocols, data forms, and educational
support materials.
Data quality is a critical issue for any
citizen science project.Ensuring that the public can collect and
submit accurate data depends on three things: providing
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www.biosciencemag.org December 2009 / Vol. 59 No. 11 BioScience 979
1. Choose a scientific question.
2. Form a scientist/educator/technologist/evaluator team.
3. Develop, test, and refine protocols, data forms, and
educational support materials.
4. Recruit participants.
5. Train participants.
6. Accept, edit, and display data.
7. Analyze and interpret data.
8. Disseminate results.
9. Measure outcomes.
Box 1. Model for developing a citizen science project.
clear data collection protocols, providing simple and logical
data forms, and providing support for participants to un-
derstand how to follow the protocols and submit their in-
formation. Even with these safeguards in place, we have
d
iscovered that certain concepts require special attention.
These involve issues of bias—a tendency to overreport cer-
tain species and to underreport others—and a general re-
luctance of observers to enter data when they see only
common birds or no birds at all.
Protocols. Citizen science data are gathered through pro-
tocols that specify when, where, and how data should be
collected. Protocols must define a formal design or action plan
for data collection that will allow observations made by mul-
tiple participants in many locations to be combined for analy-
sis (University of Washington Health Services 2000).Protocols
used for citizen science should be easy to perform, explain-
able in a clear and straightforward manner, and engaging for
volunteer participants.
Pilot-testing protocols with naive audiences is crucial and
is most valuable when directed at a wide swath of potential
participants. For example, CLO project designers have tested
draft protocols with local bird clubs, school groups, and
youth leaders by accompanying participants in the field and
observing them as they collect and submit data. CLO staff have
also tested protocols at distant locations by collecting feedback
online, usually from CLO members or from individuals who
have participated in previous projects. When protocols prove
to be confusing or overly complicated, they can be simplified,
clarified, or otherwise modified until the participants can
follow them with ease. For example, when developing the
House Finch Disease Survey, CLO staff realized that partici-
pants were more likely to report the presence rather than
the absence of the malady. To overcome this problem,they pro-
duced new educational materials explaining that reporting
negative data (no diseased birds seen) is just as important
as reporting positive” data (eye disease present).
Data forms. Designing data forms that are easy to under-
stand and fill in is best done in conjunction with protocol de-
sign. Quality data forms mirror project protocols and help to
prepare data for analysis. For instance, eBird data forms ask
participants to note if they are reporting all of the species
that they observed in a given time at a given place. This in-
formation allows analysts to determine if an unrecorded
species was not detected in an area or simply wasnt reported,
critical knowledge for scientists who are analyzing species
presence or absence. Online data forms also can ensure that
all essential information is provided by preventing participants
from proceeding with data entry until all of the required fields
are filled.
Errors resulting from misidentified species can be a major
issue for citizen science because many cryptic, unusual, or
similar species can be confused (Kelling 2008). Online data
forms can help with this problem by filtering anomalous
records before they enter the database. Records that do not fit
within the limits of the filter can be flagged for further review.
For example, in eBird and Project FeederWatch, if an observer
enters data that are outside the filter limits—such as a species
outside its range, or an unusually large number of individuals
in a given area—a friendly message asks the user to double-
check the entry. If the observer is confident about the obser-
v
ation, theerror message can be overridden, but the record
remains flagged until it is reviewed by a regional editor.
Flagged records are not used in data visualizations nor made
available for analysis until confirmed. Experts in species
distribution and abundance continually adjust the filters to
reflect the current understanding of species occurrence. Thus
online data entry and subsequent vetting allow errors to be
caught before they enter the database, while enabling project
participants to visualize project data as quickly as they are
submitted (Sullivan et al. 2009).
Educational materials.A variety of materials can be offered
to support participant understanding and satisfactory
completion of project protocols. Support materials include
identification guides, posters, manuals, videos, podcasts,
newsletters, and FAQs (frequently asked questions) that
discuss the challenges in making observations or in filling out
data forms. For example, CLOs NestWatch provides an
interactive quiz based on the Nest Monitoring Code of Conduct.
Users who answer all questions correctly can download a
personalized nest-monitor certificate. The desire to be certi-
fied encourages volunteers to read and understand the code
of conduct while allowing project administrators to track
the progress of individual users each time they take the quiz.
Online forums offer additional learning opportunities.
For example, NestWatch operates several forums that allow
participants to ask and answer questions related to breeding
biology, data protocols, and online data entry and retrieval.
Because these technologies are relatively new, their impact on
learning outcomes has yet to be fully determined.
Recruit participants. Recruiting participants can be very
simple or extremely challenging, depending on a projects goals
and audience. If a project has been developed for the general
public, participants can be recruited by a variety of tech-
niques, such as press releases, listservs, direct mailings,
advertisements, public service announcements, magazine
and newspaper articles, brochures, fliers, and presentations,
including posters and workshops at conferences of potential
participants or their leaders.
If a project has been developed for specific audiences, such
as youth groups, then recruitment materials should be targeted
to those audiences. However, recruiting defined audiences can
be challenging without partnering. For example, youth groups
such as scouts or Boys and Girls Clubs typically have unique
objectives, agendas, projects, and methods of presenting ma-
terials. Simply offering project support materials, such as
leaders guides, to individual groups or teachers rarely leads
to project adoption.
However, deliberate partnering during the course of
project development can yield projects that do meld with
existing programs. For example, the CLO has adapted eBird
for use in middle schools by developing a standards- and
980 BioScience December 2009 / Vol. 59 No. 11 www.biosciencemag.org
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inquiry-based curriculum, BirdSleuth, which was developed
over three years with extensive input from more than 100
middle-school teachers across North America.Because these
teachers helped to develop, pilot, and field-test the curricu-
l
um, it covers subject matter (e.g., diversity, adaptation, and
graphing skills) that teachers can easily integrate into their
lessons.
Train participants. Providing participants with the support they
require to digest project materials and gain confidence in
their data-collection skills is critical. Early CLO projects em-
ployed a printed research kit consisting of project instructions,
background reading,and support materials such as bird iden-
tification posters, CDs of bird sounds, and instructions on
building birdhouses and feeders. Such support information
is still provided to project participants, but is usually
delivered over the Internet through downloads and project
videos. Individual participants must take responsibility
for reading and studying project materials and for calling
or e-mailing for help if they are confused.
Projects that are carried out by groups provide further
opportunities for training, because project staff can provide
guidance and information to group leaders. Regional projects
can hold training workshops, and large-scale projects can
hold regional workshops in partnership with project collab-
orators. For example, in CLO’s Celebrate Urban Birds! and
NestWatch projects, training workshops have been held at
partner sites such as science museums and youth centers.
Accept, edit, and display data. Whether a project employs
paper or electronic data forms, all of the information must
be accepted, edited, and made available for analysis, not only
by professional scientists but also by the public. Indeed,
allowing and encouraging participants to manipulate and
study project data is one of the most educational features of
citizen science.
Current CLO projects allow participants to view a diverse
set of graphs, maps, histograms, and other visualizations that
immediately show how their data are being used. The projects
also supply personal data-management tools such as those that
create sophisticated birding life lists or compare informa-
tion on breeding success in nest boxes from one year to the
next. Such tools have been popular with project participants
and have increased project participation. In April 2006, the
eBird Web site was upgraded with new features that allow
participants to track their own observations and to explore
how their reports compare with others.Immediately after these
features were implemented, the number of individuals sub-
mitting data nearly tripled (Sullivan et al. 2009).
Analyze and interpret data. Citizen science projects tend to pro-
duce coarse data sets that can present significant challenges
for analysis and interpretation. Fortunately, the large size of
most citizen science data sets creates a favorable signal-to-noise
ratio, yielding strong patterns that are easy to interpret.In ad-
dition, researchers working with large data sets can develop
criteria for identifying data that contain systematic errors, such
as species misidentifications or misinterpretations of proto-
cols, and can omit such data from analysis without compro-
mising the goals of the project. For example, techniques have
r
ecently been developed to estimate detectability in observa-
tional data and to incorporate differences in detectability
(e.g.,between different observers) into data analysis (Fink and
Hochachka 2009). Also, if analysis techniques and constraints
are determined during protocol development, potential biases
or errors can be minimized as data are being collected.
Because of difficulties inherent in estimating and control-
ling for detectability, citizen science data are often more suit-
able for computing indices of relative abundance than
estimates of absolute abundance. Also, because observation
points do not always constitute a random or stratified sam-
ple, making inferences beyond the actual data points may be
difficult. However, presenting valid assumptions about the
presence of systematic errors or sampling biases can facilitate
geographical comparisons. For example, because estimating
an actual number of nest attempts requires observations of
banded individuals and a way to estimate detectability,Cooper
and colleagues (2005a, 2005b) computed the relative number
of nest attempts by eastern bluebirds for comparisons across
latitudes.And Fink and Hochachka (2009) developed new ana-
lytical techniques that allow more accurate representations of
species occurrence and relative abundance, as well as regional
and temporal comparisons.An example of the way in which
data from eBird can be used to describe seasonal patterns of
relative abundance for the eastern phoebe (Sayornis phoebe),
a common Neotropical migrant, is provided in figure 1.
Often citizen science data will show general phenomena
or patterns that must be examined further with smaller, more
focused studies. Combining multiple data sets can illustrate
fine-resolution, small-scale results in the context of large-
scale patterns. In addition, large-scale data sets obtained
through citizen science can be leveraged with sensor- or
professional-based, large-scale data sets that can be inter-
polated. Hames and colleagues (2002b) combined data
collected by Birds in Forested Landscapes participants on
the presence and absence of breeding wood thrush (Hylocichla
mustelina) in forest fragments with data interpolated from
US Geological Survey and NOAA (National Oceanic and
Atmospheric Administration) to investigate the synergistic
effects of acid rain and habitat fragmentation.Also, inferences
made from large-scale coarse patterns detected with citizen
science data are stronger when complemented with small-
scale, fine-grained studies. For example, patterns of the
spread of eye disease across house finch populations spawned
an intense investigation of disease transmission using
captive experiments, local field studies of banded individ-
uals, and modeling specifically aimed at understanding the
patterns observed across North America (Dhondt et al.
1998, 2006, Hochachka and Dhondt 2000).
Disseminate results. Results from CLO citizen science projects
have appeared in a range of scientific journals including
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Ibis, Ecology, Conservation Biology, Journal of Avian Biology,
Journal of Animal Ecology, and Proceedings of the National
Academy of Sciences. In addition, many projects publish
technical reports to disseminate results to target audiences. For
example, Project Tanager and the Birds in Forested Landscapes
project resulted in a series of forest management guidelines
intended for public and private landowners (Rosenberg et al.
1999b, 2003). More recently, results of citizen science mon-
itoring projects have been used to develop online decision-
support tools for policymakers and land managers through
regional data nodes of the Avian Knowledge Network (www.
avianknowledge.net).
Results are also published for the public through project
Web sites and through the CLO’s quarterly newsletter,
BirdScope. In addition, results frequently are reported in
popular literature such as newspapers, magazines, and news-
letters published by a variety of organizations ranging from
bird clubs to state and national biodiversity conservation
organizations. Such publications are important not only
for general interest but also for showing the public how
fellow citizens are contributing to science and, we hope,
for motivating new individuals to participate themselves.
Measure impacts. A final step in the citizen science model
involves measuring project outputs and outcomes to ensure
that both scientific and educational objectives have been
met. If they have, publications can elaborate these successes
for others to use as models. If they have not, evaluations
can illuminate how to improve the project or how to design
better projects in the future.
Outputs and outcomes can be gauged in many ways. Some
measures reflect greater knowledge in scientific fields, some
reflect improved scientific literacy among the public, and
some reflect both.
Measures of scientific contribution. Measuring contri-
butions to science is reasonably straightforward. Possible
measures include the following:
Numbers of papers published in peer-reviewed journals
Numbers of citations of results
Numbers of researchers publishing citizen science
research papers
Numbers and sizes of grants received for citizen science
research
Size and quality of citizen science databases
982 BioScience December 2009 / Vol. 59 No. 11 www.biosciencemag.org
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Figure 1. Seasonal patterns of relative abundance for eastern phoebe, a common Neotropical migrant. These
surfaces are estimated using eBird traveling counts under five miles long, collected from 2004 to 2007.
The data are fit with bagged decision-tree models. To account for habitat selectivity, remotely sensed habitat
information compiled at a 15-by-15-kilometer scale is included in the analysis. Variation in detection rates
is modeled as a function of both the effort spent watching birds and the length of the traveling count, and
variation in availability for detection is modeled as a function of the observation time of day and date.
The model is used to produce daily abundance estimates that take local habitat characteristics into
account while controlling for variation in detection rates. Seasonal abundance estimates are computed
as an average of daily abundance estimates.
Numbers of graduate theses completed using citizen
science data
Frequency of media exposure of results
S
cientific literacy outcomes. Measuring improvement
in public scientific literacy is more challenging.Among pos-
sible measures are the following:
Duration of involvement by project participants
Numbers of participant visits to project Web sites
Improved participant understanding of science content
Enhanced participant understanding of science process
Better participant attitudes toward science
Improved participant skills for conducting science
Increased participant interest in science as a career
Measurement methods can include pre- and postproject
surveys for project participants, examinations of e-mail and
listserv messages from project participants, surveys of self-
reported knowledge gains among participants, focus groups,
and in-depth interviews. Such techniques require an under-
standing of research methodology in the social sciences
(Bonney et al. 2009).
Costs. An effective citizen science program requires staff
dedicated to direct and manage project development; par-
ticipant support; and data collection, analysis, and curation.
Such a program can be costly; CLOs current citizen science
budget exceeds $1 million each year. Since 1992, the projects
have been supported largely by grants, including several from
the National Science Foundation (NSF), primarily from its
education and informatics programs. Except for Feeder-
Watch and BirdSleuth, the CLO does not currently charge
anyone to participate in its projects or to use its data; thus,
sustaining long-term projects is an ongoing challenge.
However, considering the quantity of high-quality data that
citizen science projects are able to collect once the infra-
structure for a project is created, the citizen science model
is cost-effective over the long term.
Also, new projects can build on previously developed
efforts; several organizations are now adapting eBird tech-
nology to collect new types of data. Moreover, many open-
source technologies can be modified for specific projects.
Google Maps, for example, can be customized and integrated
into Web sites at little expense.
Conclusion
We believe that the full potential for citizen science is just
beginning to be understood. As one step in moving the field
forward, the CLO hosted an invitational conference on
citizen science project development and implementation
from 20 to 23 June 2007, in Ithaca, NewYork. The conference,
sponsored by the NSF, brought together 54 citizen science
practitioners and evaluators from across North America who
discussed various models of public participation in research
and showcased projects in a variety of disciplines. Discussions
revealed that most citizen science projects developed so far
have been in disciplines that historically have embraced
volunteer involvement: ornithology, paleontology, astron-
omy, and atmospheric sciences. But many other fields,
such as botany and herpetology, are beginning to develop
successful projects as well. The full conference proceedings,
and additional information about designing, implementing,
and evaluating citizen science projects, can be found at www.
citizenscience.org.
Participants agreed that the field of citizen science is ripe
for development. However, as citizen science efforts grow in
scope and level of public involvement, the need for innova-
tive tools in database management,scientific analysis,and ed-
ucational research will be greater. For example, networking
technologies and distributed database solutions will be im-
perative, as will computationally efficient geospatial analysis
and imaging techniques. Innovative and rigorous statistical
analysis methods will be required to handle the massive
amounts of monitoring data that will be collected across vast
geographic scales. Systems for ensuring high-quality data
through interactive technological and educational techniques
will have to be developed. Research on the best ways for peo-
ple to learn through the citizen science process, and on how
that process may differ among different cultures and lan-
guages, also will be needed. To fulfill these requirements, ex-
pertise from a diversity of science, education, engineering,and
other fields must be harnessed in a collaborative, integrated
research effort.
Acknowledgments
Many individuals beyond the authors of this article have
contributed extensively to the development of the citizen
science program at the Cornell Lab of Ornithology and the
techniques described in this article, including Paul Allen,
David Bonter, Greg Butcher, Andre Dhondt, Erica Dunn,
Jennifer Fee, Daniel Fink, John Fitzpatrick, Tom Fredericks,
Jeff Gerbracht, Stefan Hames, Wesley Hochachka, Marshall
Iliff, Tim Lenz,Tim Levatich, Karen Purcell, Ron Rohrbaugh,
Roger Slothower, Brian Sullivan, Diane Tessaglia-Hymes,
and Christopher Wood. Development of the CLO citizen
science program has been supported by the National
Science Foundation (NSF) under grants ESI-9155700,
ESI-9550541, ESI-9627280, ESI-0087760, ESI-0125633,
ESI-0242666, ESI-0296149, ITR-0427914, ESI-0540185,
DBI-0542868, ESI-0610363, IIS-0612031, and DUE-0734857.
Any opinions, findings, and conclusions or recommenda-
tions expressed in this manuscript are those of the authors and
do not necessarily reflect the views of the NSF.Additional fund-
ing has come from the Robert F. and Marilyn H. Schumann
Foundation,the Park Foundation, the Leon Levy Foundation,
and the Wolf Creek Foundation.
www.biosciencemag.org December 2009 / Vol. 59 No. 11 BioScience 983
Roundtable
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984 BioScience December 2009 / Vol. 59 No. 11 www.biosciencemag.org
Roundtable
... Los proyectos de ciencia ciudadana más comunes se sitúan en disciplinas de las ciencias naturales como la ecología y la biología de la conservación, siendo frecuente encontrar proyectos que impulsan el monitoreo ciudadano de especies, ecosistemas y recursos naturales (Bonney et al., 2009). Seguramente por lo anterior, la visión de la ciencia ciudadana que predomina no es muy crítica con respecto a la propia participación ciudadana. ...
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We studied the effects of habitat fragmentation on four species of North American tanagers ( Piranga spp.) at 1107 study sites in 1995. Volunteer researchers used the standardized protocol of Project Tanager to select sites, census for breeding tanagers, and measure habitat and landscape features on a continental scale. In a principal components analysis, we used measures of forest patch size, distance to nearest other forest patches (isolation), percent forest cover and length of linear edge in a 1000-ha landscape, and elevation. Our results showed that three independent habitat gradients affected tanager occurence: (1) overall amount of fragmentation; (2) forest configuration (the amount of edge in a landscape); and (3) elevation. Multiple logistic regression indicated significant variation among the tanager species in how habitat gradients affected tanager occurrence, presence of parasitic Brown-headed Cowbirds ( Molothrus ater), and various nest predators. Scarlet, Western, and Summer Tanagers ( P. olivacea, P. ludoviciana, and P. rubra, respectively) responded similarly to the fragmentation gradient, with the probability of finding breeding tanagers dropping below 0.50 in highly fragmented sites. Response to the other habitat gradients was more variable, and effects on cowbirds and predators were usually opposite those of breeding tanagers (i.e., they were more common at highly fragmented sites). Response to habitat gradients also varied significantly among four regions within the range of Scarlet Tanagers. The strongest response to fragmentation, by breeding tanagers as well as by cowbirds and nest predators, was in the largely deforested Midwest and Atlantic Coast regions. In the heavily forested Northeast region, fragmentation had a significant but less severe effect on breeding tanagers. Effects were not significant in the Northern Forest region, where tanager abundance was low. Results of our continent-wide study suggest that (1) forest fragmentation can be described similarly across most regions of North America; (2) three widespread tanager species exhibit clear and similar negative responses to habitat fragmentation; (3) local sensitivity to fragmentation varies geographically and may be lower in regions with greater overall forest cover; and (4) results from single-species or local studies cannot be extrapolated to other species or regions. Conservation strategies to reverse the declines of forest bird populations will require knowledge of habitat requirements across the entire ranges of widespread species, as well as how demographic and landscape factors interact to create population sources and sinks. Resumen: En 1995 estudiamos los efectos de la fragmentación del hábitat en cuatro especies de tángaras de Norteamérica ( Piranga spp.) en 1107 sitios. Investigadores voluntarios utilizaron el protocolo estandarizado del “Proyecto Tángara” para seleccionar sitios, censar tángaras reproductoras y evaluar características del hábitat y del paisaje en una escala continental. En un análisis de componentes principales utilizamos medidas del tamaño del fragmento del bosque, la distancia a fragmentos cercanos (aislamiento), el porcentaje de cobertura y la longitud del borde lineal en un paisaje de 1000 ha y la altitud. Los resultados indican que la ocurrencia de tángaras fue afectada por tres gradientes de hábitat independientes: (1) fragmentación total; (2) configuración del bosque; (3) altitud. La regresión logística múltiple indicó variación significativa en el efecto de los gradientes de hábitat sobre la ocurrencia de tángaras, la presencia de tordos parásitos ( Molothrus ater) y varios depredadores de nidos. Las tángaras rojinegra, aliblanca y roja ( P. olivacea, P. ludoviciana y P. rubra respectivamente) tuvieron respuestas similares al gradiente de fragmentación, la probabilidad de encontrar tángaras reproductoras en sitios muy fragmentados fue menor a 0.50. La respuesta a los demás gradientes de hábitat fue variable, los efectos de tordos y depredadores generalmente fueron opuestos a los de tángaras reproductoras (i.e., fueron más comunes en sitios muy fragmentados). La respuesta a los gradientes de hábitat también varió significativamente entre cuatro regiones dentro del rango de distribución de la tángara roja. La respuesta más fuerte de tángaras reproductoras y tordos y depredadores de nidos a la fragmentación ocurrió en las muy deforestadas regiones Medio Oeste y Costa del Atlántico. La fragmentación tuvo un efecto significativo, pero menos severo, sobre tángaras reproductoras en la boscosa región Noreste. Los efectos no fueron significativos en la región Norte, en donde la abundancia de tángaras fue baja. Los resultados de nuestro estudio continental sugieren que (1) la fragmentación de bosques puede describirse de manera similar en la mayoría de las regiones de Norteaméria; (2) tres especies de tángaras con amplia distribución presentan respuestas negativas evidentes y similares a la fragmentación del hábitat; (3) la sensibilidad local a la fragmentación varía geográficamente y puede ser menor en regiones con una mayor cobertura forestal general; (4) los resultados de estudios locales o en una especie no pueden ser extrapolados a otras regiones ni especies. Las estrategias de conservación enfocadas a revertir la declinación de poblaciones de aves en los bosques requieren del conocimiento de las necesidades de hábitat en todo el rango de especies con amplia distribución, así de cómo interactuan los factores demográficos y del paisaje para la creación de poblaciones fuente y vertedero.
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