Global change and local solutions: Tapping the unrealized potential
of citizen science for biodiversity research
, A.K. Ettinger
, H.K. Burgess
, L.B. DeBey
, N.R. Schmidt
, H.E. Froehlich
, J. HilleRisLambers
, J. Tewksbury
, M.A. Harsch
, J.K. Parrish
Biology Department, University of Washington, Seattle, WA 98195, USA
School of Environmental and Forest Sciences, University of Washington, Seattle, WA 98195, USA
School of Aquatic and Fishery Sciences, University of Washington, Seattle, WA 98195, USA
Received 28 June 2014
Received in revised form 3 October 2014
Accepted 22 October 2014
Available online 8 December 2014
The collective impact of humans on biodiversity rivals mass extinction events deﬁning Earth’s history,
but does our large population also present opportunities to document and contend with this crisis?
We provide the ﬁrst quantitative review of biodiversity-related citizen science to determine whether data
collected by these projects can be, and are currently being, effectively used in biodiversity research. We
ﬁnd strong evidence of the potential of citizen science: within projects we sampled (n= 388), 1.3 mil-
lion volunteers participate, contributing up to $2.5 billion in-kind annually. These projects exceed most
federally-funded studies in spatial and temporal extent, and collectively they sample a breadth of taxo-
nomic diversity. However, only 12% of the 388 projects surveyed obviously provide data to peer-reviewed
scientiﬁc articles, despite the fact that a third of these projects have veriﬁable, standardized data that are
accessible online. Factors inﬂuencing publication included project spatial scale and longevity and having
publically available data, as well as one measure of scientiﬁc rigor (taxonomic identiﬁcation training).
Because of the low rate at which citizen science data reach publication, the large and growing citizen sci-
ence movement is likely only realizing a small portion of its potential impact on the scientiﬁc research
community. Strengthening connections between professional and non-professional participants in the
scientiﬁc process will enable this large data resource to be better harnessed to understand and address
global change impacts on biodiversity.
Ó2014 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-SA license
Citizen science, also referred to as community science or public
participation in scientiﬁc research, is a growing movement that
enlists the public in scientiﬁc discovery, monitoring, and experi-
mentation across a wide range of disciplines. From microbiology
(Cooper et al., 2010) to astronomy (Lintott et al., 2008), millions
of non-experts lend their time and problem-solving abilities to dis-
coveries as fundamental as new galaxy types (Cardamone et al.,
2009), and disciplinary intersections as societally relevant as
genomics and wellness proﬁling (Dove et al., 2012).
Can this burgeoning public interest in the scientiﬁc process be
fruitfully applied to the scales of inquiry needed to address global
change impacts on biodiversity (Silvertown, 2009; Devictor et al.,
2010; Danielsen et al., 2010; Hochachka et al., 2012; Bird et al.,
2014; Pimm et al., 2014)? Speciﬁcally, can citizen science be har-
nessed to attend to the ‘‘evil quintet’’ (Brook et al., 2008): climate
change, overexploitation, invasive species, land use change, and
pollution (Vitousek, 1992; Dirzo and Raven, 2003; Butchart et al.,
0006-3207/Ó2014 The Authors. Published by Elsevier Ltd.
This is an open access article under the CC BY-NC-SA license (http://creativecommons.org/licenses/by-nc-sa/3.0/).
Corresponding author. Tel.: +1 510 846 3230.
E-mail addresses: email@example.com (E.J. Theobald), firstname.lastname@example.org (A.K.
Ettinger), email@example.com (H.K. Burgess), firstname.lastname@example.org (L.B. DeBey), natal-
email@example.com (N.R. Schmidt), firstname.lastname@example.org (H.E. Froehlich), email@example.com
(C. Wagner), firstname.lastname@example.org (J. HilleRisLambers), jtewksbury@wwﬁnt.org (J. Tewks-
bury), email@example.com (M.A. Harsch), firstname.lastname@example.org (J.K. Parrish).
These authors contributed equally to this work.
Present address: Arnold Arboretum of Harvard University, Boston, MA 02131,
USA. Tel.: +1 781 296 4821.
Present address: School of Aquatic and Fishery Sciences, University of Washing-
ton, Seattle, WA 98195, USA. Tel.: +1 517 927 1659.
Present address: Luc Hoffmann Institute, WWF International, Avenue du Mont-
Blanc 27, 1196 Gland, Switzerland. Tel.: +41 22 364 9075.
Tel.: +1 706 521 1553.
Tel.: +1 206 375 3052.
Tel.: +1 206 375 4182.
Tel.: +1 612 889 9669.
Tel.: +1 206 543 7389.
Tel.: +1 253 265 6139.
Tel.: +1 206 221 5787.
Biological Conservation 181 (2015) 236–244
Contents lists available at ScienceDirect
journal homepage: www.elsevier.com/locate/biocon
2010)? Scientiﬁc interest in biodiversity and global changes affect-
ing it has increased dramatically in recent decades. However,
tracking, understanding, and ameliorating biodiversity losses
requires collecting ﬁne-grain data over regional to continental
extents and decadal time scales (Magurran et al., 2010;
Andelman, 2011; Bellard et al., 2012; Jetz et al., 2012) – a nearly
impossible task for professional scientists and resource managers
alone (Erwin and Johnson, 2000; Millennium Ecosystem
Assessment, 2005; Hochachka et al., 2012; Pimm et al., 2014).
Since public involvement and volunteerism is a tradition in many
countries (Anheier and Salamon 1999; Corporation for National
and Community Service, 2011), and can easily amount to millions,
even billions, in in-kind economic worth (Independent Sector,
2011; Bureau of Labor Statistics, United States Department of
Labor, 2012), citizen science offers a potential source of increasing
support for basic and applied science. In other words, could volun-
teer efforts from the ballooning global human population be har-
nessed to effectively contribute to biodiversity research?
Citizen science has been increasingly advocated as a means for
scientists to address large-scale data limitations (Devictor et al.,
2010; Danielsen et al., 2010; Hochachka et al., 2012; Bird et al.,
2014). Despite many published reviews and case studies on citizen
science (e.g. Bonney et al., 2009; Dickinson et al., 2010; Dickinson
et al., 2012), a quantitative analysis of biodiversity citizen science,
critical to assessing whether these projects can indeed ‘‘ﬁll the data
gap’’, is lacking. Furthermore, the extent to which scientists already
use citizen science data is unknown. To address these needs, we
performed the largest quantitative assessment of biodiversity-
focused citizen science projects to date (to our knowledge). Noting
that citizen science projects address multiple goals, from research
and monitoring to participant experiential learning and education
(Bonney et al., 2009; Dickinson et al., 2012; Bonney et al., 2014),
and acknowledging that there are other types of citizen-oriented
science that engage non-scientists (e.g. Local Ecological Knowl-
edge, Traditional Ecological Knowledge) that have the potential
be useful in ﬁlling data gaps (Huntington, 2000), we focused our
analysis on the following overarching question: How is citizen sci-
ence currently contributing to biodiversity research and what is its
potential to contribute? To address this broad question, we had
three speciﬁc queries:
(1) What is the current scope of biodiversity citizen science, in
terms of its spatial and temporal scales, diversity coverage
(including taxonomic, genetic, and functional diversity),
and economic worth of the volunteerism engaged?
(2) To what extent is citizen science already integrated into
peer-reviewed biodiversity research, and what factors inﬂu-
ence the likelihood of publication?
(3) What is the potential of citizen science for global change
research, as measured by the rate of project initiation, rela-
tive to professional interest in biodiversity science?
2. Materials and methods
2.1. Data collection
To assess the scope, and current and potential contributions of
citizen science to biodiversity research, we created a database of
biodiversity citizen science projects from around the world. We
compiled and used information found on project websites and
via Web of Science bibliographic searches. To corroborate our
results and investigate follow-up questions, we separately sur-
veyed both citizen science project managers and biodiversity
2.1.1. Web-based biodiversity citizen science database
To ﬁnd project websites, we searched seven prominent English-
language citizen science web-based clearinghouses, together list-
ing more than 500 unique projects (Table A1). To be included in
our database, projects had to match our operational deﬁnitions of
citizen science, scientiﬁc research, and biodiversity. We deﬁned
‘‘citizen science’’ as projects engaging volunteers (i.e., unpaid,
and not receiving K-12, college, or internship credit), to collect
and/or process data as part of ‘‘scientiﬁc research,’’ which we
deﬁned as collecting quantiﬁable information related to a speciﬁc
issue or question (Miller-Rushing et al., 2012). Thus, projects such
as those geared solely toward picture-sharing (e.g. Animalsand-
Earth: http://www.animalsandearth.com/en/), park operations
and maintenance (e.g. Volunteers in Parks: http://www.parks.ca.
gov/?page_id=886), or education (e.g. Monarchs in the Classroom:
http://www.monarchlab.org/mitc/) were excluded from our study.
We included only projects that collected biodiversity data, deﬁned
as the presence and/or abundance of identiﬁed taxonomic (e.g.
species, genus, family), genetic, or functional groups as well as
contextual information (e.g. collection date and location). After
resolving duplicates and removing projects that did not match
the above criteria, our database included 388 biodiversity citizen
For each project included in our database, we assessed four
overarching features: (1) project characteristics; (2) information
on the type(s), rigor, and availability of data collected, including
measures of data quality control and assurance; (3) participant
information; and (4) number of peer-reviewed scientiﬁc publica-
tions resulting from each project’s data. Data obtained from project
websites were collected independently by two co-authors, and dis-
crepancies were resolved through group discussion and consensus.
In total, our database included 45 ﬁelds (for metadata, see Table A2
and Appendix A); in this paper we focus on a subset (15 ﬁelds) of
the total dataset, which we enumerate below.
Project characteristics included in the present analysis: head-
quarter type (governmental agency, academic institution, nongov-
ernmental organization, a partnership between the above
mentioned institution types, or ‘‘other’’ institution, including indi-
vidual people, or private institutions like zoos or museums); ﬁrst
year of activity; last year of activity (if the project was no longer
active); project goal as stated on the project website, which we sub-
sequently coded as education/outreach, data/research, or both;
project mission as stated on the project website, which we subse-
quently coded as including one or more of the following global
change drivers: invasion, climate change, and land-use change;
and number of peer-reviewed articles in the scientiﬁc journal litera-
ture containing project data (attained by citations of published
work on project web sites, and through Web of Science searches
of topic ﬁelds using the project name).
Data information included: the spatial scale over which sam-
pling was conducted (linear distance between the farthest sam-
pling points in the dataset, binned into four log-scale categories);
system within which data were collected (one or more of: terres-
trial, marine, freshwater); biodiversity dimension (one or more of:
taxonomic, genetic, functional; where the latter was included only
if traits were measured for focal organisms); taxonomic group(s)
considered; amount of data available online in some form, either
raw or summarized (all, some, or none); training method for taxon
identiﬁcation (none, or one or more of the following: in-person,
electronic/web including downloadable pdfs, other, or some com-
bination); training method for sampling protocol (same list as for
taxon identiﬁcation training); degree that sampling was standard-
ized for time- and/or distance-based sampling effort (all, some, or
none); and amount of veriﬁable data as deﬁned by one or more
E.J. Theobald et al. / Biological Conservation 181 (2015) 236–244 237
speciﬁc veriﬁcation techniques: specimen collected, photograph
taken, expert present (all, some, or none).
Participant information included: cumulative number of partici-
pants in the project, number of participants involved in the most
recent year, and actual or estimated hours contributed per volunteer
The database we assembled is, to our knowledge, the largest
publicly available database to comprehensively catalog aspects of
effort, scale, and rigor of citizen science projects. Nonetheless, it
should be acknowledged that our dataset is not a random sampling
of global citizen science, as projects were found using English-lan-
guage web-based clearinghouses. For instance, while project head-
quarters in our database span six continents, 89% of projects were
located in North America (Fig. A1). Our data collection methods
may also have excluded many smaller scale and/or ad hoc projects
without a web presence.
We sent follow-up surveys to managers of all extant projects in
our database that listed contact information on project websites
(329 projects, IRB approval number 43438). In total, we received
125 responses (38% response rate). The survey asked 32 questions,
including binomial (yes/no), multiple choice (inclusive and exclu-
sive), and open response questions, all pertaining to their citizen
science project. In this study, we use information from six ques-
tions: year founded, current and cumulative participant numbers,
data availability to scientists, and two questions regarding the
use of project data for ‘‘published peer-reviewed articles in the sci-
entiﬁc journal literature’’ (the ﬁrst is binomial, and the second con-
sists of exclusive multiple choice answers of the binned number of
publications from zero upwards). Questions used in this analysis
can be found in the Supplemental Materials.
We also surveyed biodiversity scientists to solicit information
about their own biodiversity research, as well as their awareness
and perceptions of biodiversity citizen science (IRB approval num-
ber 43438). We identiﬁed biodiversity scientists as corresponding
authors of any study with ‘‘biodiversity’’ in the topic ﬁelds (title,
keywords, or abstract), and with a working email address, as iden-
tiﬁed by Web of Science; n= 3148 (search performed on May 5,
2012). In total, we received 423 responses (13% response rate, less
than a third of the response rate of project managers). The survey
asked 25 questions, including binomial (yes/no), multiple choice
(inclusive and exclusive), and open response questions. For this
study, we used information from two questions: taxon group(s)
studied, and the maximum linear distance between sampling sites
within a single research project. Once again, questions used in this
analysis can be found in the Supplemental Materials.
Surveys were designed to be relatively short, to be salient to the
respondents (e.g. either pertaining to their own project or citizen
science as a whole), and were sent from an email address afﬁliated
with an academic institution (i.e. containing the sufﬁx ‘‘.edu’’).
Additionally, we sent follow-up emails reminding respondents of
the survey and requesting responses. Each of these characteristics
are recommended to increase email survey response rate (Sheehan,
2.2.1. Current scope of biodiversity citizen science
Spatial and temporal extent: We used the above database to
assess the spatial and temporal extent of each project. To examine
whether larger spatial and temporal extents were associated with
higher participant numbers, we ﬁt separate Poisson regressions
with log-link. The response variables were cumulative or current
participation (omitting projects that do not report these values),
and the explanatory variables were log of project spatial extent,
or project longevity (a composite from ﬁrst and most recent year).
These regressions were performed in R version 3.0.2.
Taxonomic and system breadth: To assess the taxonomic breadth
of citizen science, we categorized each project as monitoring one or
more of the following taxonomic groups: Protozoa, Bacteria, Fungi,
Plants, Invertebrates (including Orthoptera, Lepidoptera, Hyme-
noptera, Diptera, Coleoptera, Myriapoda, Crustacea, Chelicerata,
Echinodermata, Annelida, Mollusca, Cnidaria), Fishes, Amphibians,
Reptiles, Birds, Mammals. Using chi-square tests, we compared the
number of citizen science projects that collect data within each of
these groups to estimates of: (1) globally identiﬁed species in each
of these groups (Groombridge and Jenkins, 2002; Chapman, 2009),
and (2) mainstream biodiversity science interest in these groups as
proxied by research projects conducted among scientists answer-
ing our survey. Using chi-square tests, we also compared the distri-
bution of ecosystem types studied by citizen science projects
(marine, including estuarine, freshwater, and/or terrestrial) to the
distribution of these ecosystem types on Earth (ChartsBin
statistics collector team, 2010). All chi-square tests were per-
formed in R version 3.0.2.
Economic worth: To estimate the economic worth of volunteer-
ism in biodiversity citizen science, we ﬁrst estimated the number
of volunteers participating in citizen science annually from 1930
to 2012. Since less than half of projects in our database (191 out
of 388) reported the number of participants online (either cumula-
tively or for the most recent year), we augmented this dataset with
data provided by project managers. If managers reported a range,
we used the minimum value for conservative estimates. To esti-
mate the total annual volunteers across all projects, we assumed
the same average participation levels for the non-reporting pro-
jects, and created a range of participation using both our web
and project manager datasets. To estimate the median number of
hours individual participants spend volunteering for citizen scien-
tist projects, we used a subset of projects in the data base (n= 106).
Finally, we calculated an annual estimate for total in-kind value of
biodiversity citizen science volunteerism as the product of these
estimates (number of volunteers ⁄volunteer time estimates, in
hours) multiplied by the U.S. national volunteer hourly in-kind rate
(Independent Sector, 2011). We chose to use the U.S. national vol-
unteer hourly in-kind rate as the majority of the projects listed in
our database (89%) were housed in North America.
2.2.2. Publication rate
To understand the extent to which citizen science is integrated
into peer-reviewed biodiversity research, and what factors (if any)
constrain its integration, we used a logistic regression, ﬁt in a
Bayesian framework. For this analysis, we restricted our dependent
variable (publication yes/no) to our web-based dataset, as the pro-
ject manager survey was not comprehensive across our dataset
and indicated high variability in managers’ interpretation of the
term ‘‘peer-reviewed publication’’. In our analysis, we included
attributes that we thought were likely to affect publication, and
that did not covary. We included project characteristics: headquar-
ter type, project longevity, project spatial extent, and whether data
were publicly available (reduced to binary), and data quality char-
acteristics: method of training for specimen identiﬁcation and data
collection, degree that sampling was standardized, and whether
data were veriﬁable.
We ﬁt logistic regression models within a Bayesian framework
to account for missing data as well as correct for linear separation
in one variable (data availability). Specifying weakly informative
priors for the linearly separable variable, data availability, con-
strained the model likelihood and improved parameter conver-
gence (Gelman et al., 2008), but only affected the width of the
credible interval, not the qualitative results (Fig. A2). We used
non-informative priors for all other variables. For more details on
238 E.J. Theobald et al. / Biological Conservation 181 (2015) 236–244
variable levels and coding, treatment of missing data, and linear
separation, see the Supplemental Materials.
The model was ﬁt using OpenBugs called from the BRugs library
in R version 3.0.2 (Thomas et al., 2006; R Core Team, 2013). We ran
three chains each with a burn-in of 15 000 iterations, which was
sufﬁcient to ensure convergence, as judged by visual inspection
of the chain histories and the Gelman-Rubin statistic (Brooks and
Gelman, 1997). We then sampled the posterior distributions from
a further 10 000 iterations of each chain. The importance of explan-
atory variables was assessed using 95% Bayesian credible intervals
on these posterior distributions.
2.2.3. Project initiation rate
To assess the increase in interest in biodiversity citizen science
compared to biodiversity research by the professional scientiﬁc
community, we examined the rate of project initiation and com-
pared this rate to rates of professional scientiﬁc interest in biodi-
versity and conservation issues surrounding impacts to
biodiversity. As a proxy for professional scientiﬁc interest, we used
the number of peer-reviewed articles published per year on biodi-
versity, over exploitation, invasion, climate change, land-use
change, and pollution, as proportions of total peer-reviewed arti-
cles published in scientiﬁc journals each year, found in Web of Sci-
ence (for speciﬁc search terms used, see Table A3). We then ﬁt
logistic regressions, regressing the number of citizen science pro-
jects initiated annually (out of the total number of projects across
all years) or the number of scientiﬁc publications in a given cate-
gory (biodiversity and each of the global change drivers) annually
(out of the total number of publications each year). Explanatory
variables included year, category (i.e., citizen science, biodiversity,
over exploitation, etc.), and their interactions. We constrained our
analyses to the past 30 years (1982–2011), the period during
which trends in citizen science increased dramatically. All regres-
sions were performed in R version 3.0.2.
3.1. Current scope of biodiversity citizen science
3.1.1. Spatial and temporal extent
Citizen science projects are executed across a range of spatial and
temporal scales, with some attaining scales at or above mainstream
biodiversity science (Fig. 1). Of the 326 projects for which the spatial
extent of data collection could be assessed, 10% were local-scale pro-
jects (i.e., with 10 km or less separating the farthest two data collec-
tion sites), 22% operated at the 10–100 km scale, and 67% had a
regional or larger extent (100–10 000 km or larger, Fig. 1A). Accord-
ing to our scientist survey respondents, this is comparable to the
spatial scales at which professional scientists conduct biodiversity
research: the majority reported sampling at 100 km or greater
(55%; 354 out of 642 projects; Fig. 1B). Across the 328 projects for
which lifespan data were available, mean citizen science project lon-
gevity was 10.9 years (median = 7, min = 0, max = 132).
Projects with large spatial and temporal extents of sampling,
tend to enlist more volunteers (Table 1). We found that projects
with the largest spatial extent have 7.5–14 times the number of
participants compared with projects with small spatial extent
(depending on whether cumulative or current participation is con-
sidered; Table 1). We also calculated participant number per meter
for each project, in order to better understand if a disproportionate
number of volunteers participate in projects of a speciﬁc scale. We
found that small to medium projects (between 10 and 1000 linear
km) had the greatest number of current annual participants per
linear km. Furthermore, as project longevity increases, cumulative
and current participation also increase: cumulative participation
doubles every 6.4 years of project lifespan, and for every 41.2 years
that a project is active, current participation levels double. The dif-
ference in these ﬁgures (i.e., that cumulative participation doubles
faster than current participation) indicates that projects experience
attrition throughout their lifespan.
3.1.2. Taxonomic and system breadth
Collectively, projects spanned a wide range of vertebrates, as
well as major invertebrate phyla, plant families, bacteria, fungi,
and even protozoa (Fig. 2). Volunteers monitor everything from
thermophyllic bacteria in home water heaters and protozoan par-
asites (Ophryocystis elektroscirrha) of monarch butterﬂies through-
out North America, to house sparrows (Passer domesticus) in India,
and whale sharks (Rhincodon typus) around the world. Of the 388
projects in our database, 97% monitored taxonomic diversity, with
the vast majority (87%) collecting data on multiple species. Com-
pared to their presence on Earth, vertebrate groups and terrestrial
ecosystems were signiﬁcantly over-sampled, and invertebrates
and marine ecosystems were under-sampled by citizen science
= 6614, df = 10, p< 0.001; ecosystems:
= 5814, df =2, p< 0.001; Fig. 2). Within invertebrates, citizen
science sampling was biased relative to presence on Earth, with
butterﬂies and shellﬁsh oversampled and beetles and ﬂies under-
= 98.27, df = 11, p< 0.001). Focal taxa studied by citi-
zen science projects also differed signiﬁcantly from those studied
by professional biodiversity scientists (
= 232, df = 10,
p< 0.001), with a greater number of projects focused on reptiles,
amphibians, birds, and algae and fewer focused on plants (Fig. 2).
One-ﬁfth of the projects we surveyed monitored functional
diversity. Common traits measured included reproductive or migra-
tory phenology in birds and other organisms (e.g. MigrantWatch:
Tracking Bird Migration Across India), body length (e.g. North Caro-
lina Sea Turtle Project, Seabird Ecological Assessment Network), and
chlorophyll a concentration for algae (e.g. Coalition for Buzzard’s
Bay, Wisconsin Citizen Lake Monitoring). Only 2% of projects studied
genetic diversity, for example by collecting feather samples of rap-
tors to identify genetic lines (e.g. Seward Park Eagle and Raptor
Fig. 1. Scales of citizen science projects. (A) Spatial scale assessed as the linear
distance in kilometers between the farthest sampling locations, and binned into
order of magnitude categories versus project longevity in years. Each project is a
circle (N= 388) and the number of publications in the peer-reviewed scientiﬁc
literature is represented by shading. (B) Comparison of the proportion of projects
sampling at each spatial extent category for citizen science projects (from web-
based database) versus projects conducted by professional scientists (data from
survey of 423 biodiversity scientists).
E.J. Theobald et al. / Biological Conservation 181 (2015) 236–244 239
DNA Fingerprinting), or by examining genotypic differences across
plant clones (e.g. Cloned Plants Project).
3.1.3. Economic worth
We estimate that between 1.36 million and 2.28 million people
volunteer annually in the 388 projects we surveyed, though varia-
tion is great (website-derived data: average of 3505 people per
project per year, median = 50, standard error = 1914; project man-
ager-derived data: mean from minimum reported participa-
tion = 5037, median = 200). Across projects for which we
obtained time estimates (n= 106), volunteers spent an average of
21–24 h per person annually collecting biodiversity data (range:
0.5–107.1 h average per participant per year), which is comparable
to rates reported in the literature (e.g. 34 h (Corporation for
National and Community Service. 2011), and 51 h (Bureau of
Labor Statistics, United States Department of Labor, 2012). We esti-
mated the range of in-kind contribution of the volunteerism in our
388 citizen science projects as between $667 million to $2.5 billion
annually. Note that this represents a minimum estimate for biodi-
versity citizen science worldwide, as our project sampling was
restricted to only projects reporting in English and found in major
online citizen science clearinghouses.
3.2. Publication rate
Advancing scientiﬁc understanding was an explicit primary goal
for 97% of the citizen science projects we surveyed; however, only
12% of reviewed projects listed peer-reviewed scientiﬁc publica-
tions on their websites and/or returned results in Web of Science
searches with project names in the topic ﬁelds (446 publications
across 46 projects). Estimates of publication rates differed between
our web-derived data and survey data: in follow-up surveys to
project managers, 60% (n= 122) responded that data from their
projects had been used in ‘‘scientiﬁc publication in the peer-
reviewed science journal literature’’. Later in the survey, though,
45% responded that their projects had one or more ‘‘peer-reviewed
published journal articles’’. The inconsistent responses to survey
questions suggest that project manager respondents may be
confused about what constitutes peer-reviewed literature,
potentially misinterpreting ‘‘peer’’ or ‘‘journal’’. This interpretation
conforms to other ﬁndings that gray-literature and technical
reports are often included in citizen science self-reported
publication totals (Shirk et al., 2012).
We found that the likelihood that citizen science project data will
be published in a peer-reviewed scientiﬁc journal was related to
project extent (spatial and temporal), data availability, and one
aspect of data quality (Fig. 3). Speciﬁcally, citizen science project
data were more likely to be published in peer-reviewed scientiﬁc lit-
erature if projects sampled at a large spatial extent and had been
sampling for decades (Figs. 2 and 3). Data availability was also a
positive predictor of publication; in fact, all published projects made
data available in some form on their website. Of the 88% of projects
(n= 343) for which we could determine data availability, 37% made
all of their data available online, and 50% made some of it available;
only 13% did not provide data in any form. In our follow-up surveys
to citizen science project managers, only 3% of respondents said they
would not share data if contacted by a scientist. Finally, projects that
trained volunteers in species identiﬁcation methods, using in-per-
son or online training, were more likely to be published than projects
that provided no identiﬁcation training or trained with a combina-
Estimates for effects of spatial and temporal scales from four separate Poisson
generalized linear models with participation as the response variable.
Model Coefﬁcient Estimate
Cumulative participation–spatial scale (log10) Intercept 4.255
Spatial scale 0.939
Current participation–spatial scale (log10) Intercept 3.155
Spatial scale 1.547
Cumulative participation–project longevity Intercept 6.450
Current participation–project longevity Intercept 7.997
Indicates statistical signiﬁcance to p< 0.001.
Fig. 2. Taxonomic and ecosystem representation of citizen science projects relative to mainstream science. (A) Proportion of sampled citizen science projects sampling one or
more taxonomic category (middle stacked bar) compared to each groups’ relative abundance on Earth (measured as a proportion of total number of described species, left
bar) and professional scientists (data from survey of 423 biodiversity scientists, see Methods for details). (B) Taxonomic breakout of invertebrates, for relative abundance and
citizen science projects only. (C) Proportion of sampled citizen science projects collecting data in terrestrial, freshwater or marine environments relative to area coverage on
240 E.J. Theobald et al. / Biological Conservation 181 (2015) 236–244
tion of methods. By contrast, training in data collection methods did
not inﬂuence publication likelihood.
Other factors we tested had minor or no effect on publication
likelihood (Fig. 3). Headquarter type inﬂuenced publication, in that
projects housed in government agencies were slightly less likely to
publish compared to those housed at academic institutions. Pro-
jects housed in NGOs, in partnerships between institution types,
or in other institutions like zoos or museums, were just as likely
to publish as those housed in academic institutions. We found no
relationship between publication likelihood and data standardiza-
tion, or whether or not data were veriﬁable.
3.3. Project initiation rate
We found that the number of biodiversity-oriented citizen sci-
ence projects has increased dramatically in the last 75 years
(Fig. 4,Table 2). Project initiation rose particularly steeply during
the most recent 30 years, coincident with the rising scientiﬁc inter-
est in biodiversity in general (Table 2). In addition, although the
proportion of papers published on over-exploitation, invasion, cli-
mate change, land-use change, and pollution have all increased
(Fig. 4), none exceeds the rate of creation of citizen science projects
during this time period (Table 2). Within our citizen science sam-
ple set, over a quarter of projects purported to address factors
impacting biodiversity, speciﬁcally referencing land use change
(29%), invasive species (24%), and/or climate change (16%).
4.1. Citizen science as a resource
Biodiversity citizen science currently provides a valuable, albeit
underutilized, resource for global change research. Citizen scien-
tists act locally, so collectively they gather ﬁne-grain data that
achieve datasets reaching regional and even global scales, and long
temporal extents (Fig. 1). Indeed, our data suggest that citizen sci-
entist projects span spatial scales that are comparable to profes-
sional scientists (Fig. 1) and are, on average, 7 years longer than
the mean length of NSF grants (National Science Foundation,
2012). These types of datasets – that reach great spatial and tem-
poral extents without compromising ﬁne-grain resolution – are
particularly critical for tracking, understanding, and ameliorating
global change impacts to biodiversity (Magurran et al., 2010;
Andelman, 2011; Bellard et al., 2012; Jetz et al., 2012).
Additionally, despite the fact that many of the most well-known
citizen science projects focus on birds (e.g. Christmas Bird Count,
Breeding Bird Surveys, eBird), citizen science includes a broad
range of taxa (Fig. 2). Although there are biases in citizen science
sampling efforts relative to abundance on Earth, these biases are
consistent with biases found in professional science (Fig. 2, Erwin
and Johnson, 2000; Millennium Ecosystem Assessment, 2005;
Martin et al., 2012). Furthermore, the time donated by the large
number of engaged volunteers (approximately $0.7–2.5 billion
annually) is equivalent to 11–42% of the annual U.S. National
Science Foundation budget (National Science Foundation, 2013).
Finally, the growth in interest in citizen science (Fig. 4) represents
unprecedented opportunity and potential to contend with global
changes with local observers.
Citizen science datasets are also useful resources for global
change research because they are readily available. We found that
a majority of citizen science projects already make their data avail-
able online, and 72% of citizen science project manager survey
respondents (n= 118) said they would share data if contacted by
a scientist. In fact, biodiversity citizen science projects appear to
make their data publically available at higher rates than profes-
sional scientists in the same ﬁeld, as only 8% of Division of Environ-
Fig. 3. Probability of publication of citizen science project data. Points represent the posterior mean parameter estimate and bars represent 95% Bayesian credible intervals.
Comparison category within each variable with greater than two states is listed in parentheses. (A) General project characteristics. (B) Speciﬁc measures of quality control.
E.J. Theobald et al. / Biological Conservation 181 (2015) 236–244 241
mental Biology NSF-funded projects made any of their non-genetic
raw data public (Hampton et al., 2013).
4.2. Factors affecting publication
Although there is great potential for citizen science data to be
used to study global change impacts to biodiversity, citizen science
data are not currently fully incorporated into mainstream science.
The under-utilization of citizen science in published biodiversity
research represents a missed opportunity for science and society.
In the 21st century, professional scientists alone are not generally
capable of delivering the volume of data, analysis, and interpreta-
tion needed to match the speed at which policy decisions are made
(Erwin and Johnson, 2000; Millennium Ecosystem Assessment,
2005; Hochachka et al., 2012; Pimm et al., 2014). However, by
working collaboratively with millions of informed citizens,
scientists may be better able to quickly contend with emergent,
large-scale environmental crises – simply by having more
widespread access to information regarding biodiversity patterns
pre-perturbation, as well as shifts following global changes.
Our research identiﬁed measures of scale, both spatial scale and
longevity, as positive predictors of publication (Fig. 3A). There are
several examples of citizen science projects that have played
important roles in understanding global change impacts to
biodiversity – most of which are long-lived or broad-extent.
For example, the United Kingdom Butterﬂy Monitoring Scheme
(established: 1976, spatial extent: <10 000 km) contributed data
to one of the ﬁrst large-scale documentations of pole-ward range
shifts due to climate warming (Parmesan et al., 1999), Breeding
Bird Survey (established: 1994, spatial extent: <1000 km) data
were used to determine the spread of West Nile virus across North
America (LaDeau et al., 2007), and Reef Environmental Education
Foundation (established: 1990, spatial extent: >10 000 km) data
have been used to inform marine conservation efforts through
documentation of large-scale changes in world-wide shark species
abundance (Ward-Paige and Lotze, 2011; Ward-Paige et al., 2011).
Large-scale studies may be more likely to reach the scientiﬁc
literature because they are better able to measure change over
space and time (Tulloch et al., 2013; Bird et al., 2014), and there-
fore can quantify impacts of management and policy (e.g.
Danielsen et al., 2014). Another possible explanation could be that
scientists may be more aware of older, more widespread citizen
science projects, and thus more likely to use their data in publica-
tions. Regardless of the cause, at least one other study has also
found that broad-extent citizen science monitoring projects have
higher impact in scientiﬁc literature, in addition to being more
cost-effective, compared to more short-term, cross-sectional
studies (Tulloch et al., 2013).
We were surprised that probability of publication was largely
unaffected by the data quality assurance measures we assessed
(Fig. 3B), since concerns about quality, consistency, and reliability
of citizen science data are widespread (Silvertown, 2009;
Dickinson et al., 2010; Bonter and Cooper, 2012; Bird et al.,
2014). In our view, this result does not suggest that data quality
is unimportant. Rather, it suggests that perhaps most projects have
adequate data quality measures in place, or that non-professional
data can be comparable to data collected by professional scientists,
as others have suggested (Kremen et al., 2011; Holt et al., 2013;
Cooper et al., 2014). Alternatively, given the nature of complex eco-
logical data, individual observer variation may not contribute sub-
stantially to additional noise in the data (Bird et al., 2014). Taken
together with the positive effect of temporal and spatial scale on
publication (Fig. 3A), our ﬁndings suggest that even ‘‘messy’’ citi-
zen science datasets are valuable if sample sizes are large, as vari-
ation among observers can be reconciled statistically (Schmeller
et al., 2009; Dickinson et al., 2010; Bird et al., 2014).
Despite our ﬁndings that large-scale projects have a higher like-
lihood of reaching the peer-reviewed literature, we wish to empha-
size that smaller-scale projects can still provide utility to the
scientiﬁc community and to society. Speciﬁcally, projects that
monitor single species or that are constrained to narrow geo-
graphic regions could inform or improve conservation efforts. For
example, passage of the 1972 Clean Water Act in the U.S. required
states to assess the quality of their surface water, which spurred
many grassroots volunteer water monitoring efforts by lake and
stream conservation groups (Lee, 1994). The legacy of these efforts
is apparent in our dataset (Fig. 4), which includes many projects
monitoring biological indicators of water quality, including pres-
ence and/or abundance of invertebrates (Fig. 3). Indeed regional
projects may be likely to bypass the peer-reviewed literature and
affect management or policy directly, or reside in the gray litera-
ture, where they may inform natural resource management prac-
tices. We do not wish to discount the value of these regional
projects nor the utility and relevance of their data; nonetheless,
the data will be more useful to the scientiﬁc community at large
if they reach the peer-reviewed literature.
There is even greater potential for citizen science to address
global changes moving forward, given the rapid increase in citizen
Fig. 4. Annual trends of publication in peer-reviewed journals, as a proportion of
total publications (log scale); and of establishment of biodiversity-related citizen
science projects (1930–2011; log scale). Comparison statistics are reported in
Table 2. See Table A3 for Web of Science search terms.
Estimated annual increase in project initiation (for citizen science) or publication (for
global change factors) from 1982 to 2011, on a logit scale (i.e. coefﬁcients from
generalized linear model with binomial response variable; see section 2.2.3 for
Annual increase 95% CI
Citizen science projects 0.100 0.093, 0.107
Biodiversity 0.097 0.090, 0.105
Climate change 0.089 0.082, 0.096
Invasive species 0.079 0.071, 0.086
Land use change 0.065 0.057, 0.072
Overexploitation 0.036 0.028, 0.044
Pollution 0.015 0.008, 0.022
242 E.J. Theobald et al. / Biological Conservation 181 (2015) 236–244
science projects and people engaged in them (Fig. 4). However,
much of this potential in citizen science will not be fully realized
if citizen science data do not reach the peer-reviewed scientiﬁc
literature. The scientiﬁc impact of citizen science could be much
greater if it were embraced by, and better integrated into,
established modes of scientiﬁc research.
For citizen science data to become integrated into established
modes of inquiry, it must ﬁrst be explicitly acknowledged as a
source of information. While it is possible that more citizen science
data reach the peer-reviewed literature than our search identiﬁed,
our data suggest that professional science may not embrace citizen
science data as usable. For example, the higher publication rates
claimed by project manager survey respondents (45% or 60%) as
opposed to our web-based search results (12%) could indicate that
some project data are published without direct or prominent
advertisement of its origins (e.g. project name lacking from topic
ﬁelds), an interpretation also mentioned in Tulloch et al. (2013)
and Cooper et al. (2014).
Integration may be improved through increasing scientists’
awareness of and accessibility to citizen science data, and organi-
zations such as the National Phenology Network (http://www.
usanpn.org) and the recently created Citizen Science Association
(http://www.citizenscienceassociation.org) may be crucial vehi-
cles. These and other umbrella efforts will be most successful if
they facilitate and deepen connections between mainstream
science and citizen science. For example, if they serve as a match-
ing service, connecting professional scientists and citizen science
projects that are well-suited to one another, they may be able to
reduce taxonomic biases or increase genetic and functional
diversity sampling. Our results suggest that citizen science
projects should focus on data quantity, covering large spatial
and temporal scales, if they wish to be used in peer-reviewed
scientiﬁc publications. Thus, designed as a cooperative matching
service, umbrella efforts could further the utility of citizen
science datasets by targeting expansion efforts to broad spatial
scales. Scientists may be able to initiate their own citizen science
project or help ensure the continued existence of high quality
citizen science datasets by working more closely with citizen
science project managers to advocate for sustaining projects over
the long-term, to collaborate on grant applications and other
funding resources, and to publish their data in the peer-reviewed
For biodiversity science, the era of ivory tower science is over
(Devictor et al., 2010; Könneker and Lugger, 2013). We need a
paradigm shift, wherein scientists and nonscientists work collabo-
ratively to contend with emergent, large-scale environmental
issues. If biodiversity science does not engage nonscientists, as
biodiversity and ecosystem services continue to erode, it runs the
risk of becoming irrelevant in the eyes of a public that may offer
local solutions to global problems.
We gratefully acknowledge the Dimensions of Biodiversity Dis-
tributed Graduate Seminar for which funding was provided by a
National Science Foundation Grant to Sandy Andelman and Julia
Parrish (DEB1050680). Theobald, Ettinger, DeBey, and Froehlich
were NSF pre-doctoral fellows during this project (DGE-1256082,
DGE-0718124, DGE-0718124 and DGE-0718124/DGE-1256082,
respectively). We also thank T. Daniels, J. Hoekstra, T. Ricketts, S.
Reichard, E. Neeley, S. Andelman, and several anonymous review-
ers for providing feedback that greatly improved this manuscript.
Data reported here can be found housed in PANGEA.
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