ArticlePDF Available

Many cameras make light work: opportunistic photographs of rare species in iNaturalist complement structured surveys of reef fish to better understand species richness


Abstract and Figures

Citizen science is on the rise, with growing numbers of initiatives, participants and increasing interest from the broader scientific community. iNaturalist is an example of a successful citizen science platform that enables users to opportunistically capture and share biodiversity observations. Understanding how data from such opportunistic citizen science platforms compare with and complement data from structured surveys will improve their use in future biodiversity research. We compared the opportunistic fish photographs from iNaturalist to those obtained from structured surveys at eight study reefs in Sydney, Australia over twelve years. iNaturalist recorded 1.2 to 5.5 times more fish species than structured surveys resulting in significantly greater annual species richness at half of the reefs, with the remainder showing no significant difference. iNaturalist likely recorded more species due to having simple methods, which allowed for broad participation with substantially more iNaturalist observation events (e.g., dives) than structured surveys over the same period. These results demonstrate the value of opportunistic citizen science platforms for documenting fish species richness, particularly where access and use of the marine environment is common and communities have the time and resources for expensive recreational activities (i.e., underwater photography). The datasets also recorded different species composition with iNaturalist recording many rare, less abundant, or cryptic species while the structured surveys captured many common and abundant species. These results suggest that integrating data from both opportunistic and structured data sources is likely to have the best outcome for future biodiversity monitoring and conservation activities.
This content is subject to copyright. Terms and conditions apply.
1 3
Received: 25 August 2021 / Revised: 8 February 2022 / Accepted: 21 February 2022 /
Published online: 23 March 2022
© The Author(s) 2022
Communicated by James Tony Lee.
This article belongs to the Topical Collection: Coastal and marine biodiversity
Extended author information available on the last page of the article
Many cameras make light work: opportunistic photographs
of rare species in iNaturalist complement structured surveys
of reef fish to better understand species richness
Christopher J.Roberts1,2 · AdrianaVergés1,2 · Corey T.Callaghan2,3 ·
Alistair G. B.Poore1,2
Biodiversity and Conservation (2022) 31:1407–1425
Citizen science is on the rise, with growing numbers of initiatives, participants and in-
creasing interest from the broader scientic community. iNaturalist is an example of a suc-
cessful citizen science platform that enables users to opportunistically capture and share
biodiversity observations. Understanding how data from such opportunistic citizen sci-
ence platforms compare with and complement data from structured surveys will improve
their use in future biodiversity research. We compared the opportunistic sh photographs
from iNaturalist to those obtained from structured surveys at eight study reefs in Sydney,
Australia over twelve years. iNaturalist recorded 1.2 to 5.5 times more sh species than
structured surveys resulting in signicantly greater annual species richness at half of the
reefs, with the remainder showing no signicant dierence. iNaturalist likely recorded
more species due to having simple methods, which allowed for broad participation with
substantially more iNaturalist observation events (e.g., dives) than structured surveys over
the same period. These results demonstrate the value of opportunistic citizen science plat-
forms for documenting sh species richness, particularly where access and use of the ma-
rine environment is common and communities have the time and resources for expensive
recreational activities (i.e., underwater photography). The datasets also recorded dierent
species composition with iNaturalist recording many rare, less abundant, or cryptic species
while the structured surveys captured many common and abundant species. These results
suggest that integrating data from both opportunistic and structured data sources is likely
to have the best outcome for future biodiversity monitoring and conservation activities.
Keywords Reef life survey · Unstructured biodiversity data · Presence-only data ·
Species occurrence data · Citizen Science · Community Science
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Biodiversity and Conservation (2022) 31:1407–14251408
1 3
Global biodiversity patterns are being fundamentally altered in response to climate change
and other human impacts (Blowes et al. 2019). A key component of managing and conserv-
ing biodiversity is the ability to monitor species occurrences at both local and global scales
in a timely and cost-eective manner (Dickman and Wardle 2012; Sullivan et al. 2017).
Species richness, that is the number of species at a given location, is a key measure used in
conservation actions such as protecting biodiversity hotspots or identifying habitats of rare
and endangered species (Gotelli and Chao 2013; Chao and Chiu 2016). Given that gathering
biodiversity data takes a considerable amount of time, eort and resources, citizen science
(also termed community science), is increasingly being used to eciently gather and pro-
cess large volumes of species occurrence data (Thiel et al. 2014; Follett and Strezov 2015;
Theobald et al. 2015; Pocock et al. 2017). In the last decade, new citizen science initiatives
have tended towards having simpler methods that encourage mass participation (Pocock et
al., 2017) such as gathering observations of living organisms opportunistically (i.e., during
normal daily activities) through photographs or recordings. These observations are gener-
ally collected in an unstructured format without formal survey methods or guidance from
professional scientists.
iNaturalist, one of the most popular citizen science platforms, has over 1.3 million users
contributing millions of observations globally each month (Seltzer et al. 2020). The increas-
ing popularity of platforms such as iNaturalist is likely due, at least in part, to participants
having freedom to choose both where and when to make observations (i.e., during recre-
ational activities such as bush walking and scuba diving) as well as how (i.e., no restrictive
survey protocols). As participation in platforms such as iNaturalist continues to grow and
observations rise rapidly (Mesaglio and Callaghan 2021), it becomes increasingly important
to explore the potential of opportunistic datasets for biodiversity monitoring.
The reliability of data gathered through citizen science is often regarded with some
degree of scepticism among scientists (Riesch and Potter 2014; Burgess et al. 2017), despite
numerous studies indicating that citizen science can provide data comparable in quality to
data gathered by trained scientists (see review by Aceves-Bueno et al. 2017). Data derived
from citizen science projects that use highly structured survey methods such as Reef Life
Survey (Edgar and Stuart-Smith 2009) or even semi-structured checklists such as eBird
(Sullivan et al. 2014) are increasingly being used in peer-reviewed ecological research (Fol-
lett and Strezov 2015). In contrast, the vast amount of valuable biodiversity information
contained in databases of opportunistic observations is underutilised due to concerns about
data quality and potential biases (Dickinson et al. 2010; Isaac et al. 2014; Rapacciuolo
et al. 2021) and uncertainty regarding the use of presence-only data (Giraud et al. 2016;
Bradter et al. 2018). Where opportunistic observations have been used, it has predominately
been to map species distribution (van Strien et al. 2013; Fourcade 2016; Wang et al. 2018)
rather than addressing questions such as quantifying spatial patterns in abundance or species
In the absence of standardised and structured sampling methods, potential biases in
opportunistic observation databases include the over-representation of colourful, interesting
or rare species (Isaac and Pocock 2015; Prudic et al. 2018; Caley et al. 2020) or the over-
sampling of accessible locations such as those closer to roads and/or urban centres (Reddy
and Dávalos 2003; Szabo et al. 2007; Tiago et al. 2017). Consequently, the number of obser-
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Biodiversity and Conservation (2022) 31:1407–1425 1409
1 3
vations may indicate the amount of interest in a species rather than its abundance, and
the location of observations may reect the distribution of observers more than that of the
target species (Williams et al. 2002; Snäll et al. 2011; Giraud et al. 2016). Use of data from
opportunistic observations will be improved by a greater understanding of how it diers
from or complements structured surveys, particularly in terms of potential biases toward, or
away from, certain taxa. For example, if opportunistic observers record more rare species,
but tend to overlook or undersample common species, then the most eective means of
documenting biodiversity is likely to involve a combination of structured and unstructured
sampling (Giraud et al. 2016; Soroye et al. 2018; Rapacciuolo et al. 2021).
To date, there have been numerous assessments of the data generated by structured
surveys conducted by citizen scientists compared to professionals (Aceves-Bueno et al.
2017). In contrast, studies comparing presence-only data from unstructured opportunistic
observations to data generated from structured surveys are limited but examples include
comparisons of species richness of birds, ladybeetles and butteries (Losey et al. 2012;
Klemann-Junior et al. 2017; Prudic et al. 2018) and temporal and spatial trends in bird
abundance (Snäll et al. 2011; Giraud et al. 2016; Kamp et al. 2016). A recent study of
marine intertidal communities demonstrated the value of combining opportunistic observa-
tions with structured surveys observations to monitor temporal trends in intertidal species
(Rapacciuolo et al. 2021).
Monitoring marine biodiversity is particularly challenging, time-consuming and expen-
sive due to the need for calm ocean conditions and good water clarity, specialised scuba
training and equipment, and often a dive vessel. To address this, scientists are increasingly
turning to citizen science to gather the data needed for marine life monitoring and biodiver-
sity conservation. In Australia, several marine citizen science projects have been running
for many years including Reef Life Survey, Reef Check, Redmap, Eye on the Reef, and the
Australasian Fishes project in iNaturalist. These programs have dierent aims and objec-
tives, use dierent approaches, vary in sampling eort and generate dierent data. Indi-
vidually, these projects have generated much valuable information, however, limited work
has been done comparing these data sources. Consequently, the data generated from each
program are generally considered and used in isolation (Peterson et al. 2020). The ability to
combine data from programs that use dierent approaches, such as opportunistic observa-
tions with structured surveys, could considerably improve the biodiversity data available for
marine conservation and ecological research (Ballard et al. 2017; Kelly et al. 2020; Peter-
son et al. 2020). To facilitate this, it is important to understand how these structured and
unstructured approaches dier in terms of the biodiversity data they generate and quantify
the dierences in sampling eort in given localities through time. Here, we compare the sh
species photographed and contributed to the opportunistic observation database iNaturalist
to structured data gathered by Reef Life Survey (RLS) (Edgar and Stuart-Smith 2014) at
eight dive sites in Sydney, Australia. Specically, we quantied how opportunistic observa-
tions and structured surveys diered in: (1) species richness, (2) species composition, and
(3) to what extent sampling eort explained the dierences observed.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Biodiversity and Conservation (2022) 31:1407–14251410
1 3
Unstructured citizen science data: iNaturalist
iNaturalist ( is an online platform for users to share their nature observations
(e.g., photographs) which has been operating since 2008. The platform was designed with
the primary intention of engaging people with the natural world, with the potential second-
ary use of the observations for scientic purposes. It has not been designed to follow any
structured scientic sampling methods or techniques and there are few constraints around
providing observations. The main constraint is the requirement to provide evidence of an
observation, generally a photograph, along with the location and time of the sighting. This
does potentially place some limitations on the iNaturalist dataset as the ability to capture an
identiable photograph of some sh species is often challenging or not possible for many
The iNaturalist platform allows projects to be created that target specic taxa, places,
and/or times. The Australasian Fishes project was started by the Australian Museum in late
2016 and targets observations of marine and freshwater sh from Australia, New Zealand
and their respective territorial waters (shes). Contribu-
tions to this project can include any sh photograph within the region including from divers,
snorkellers and shers. It is important to note, however, that the contribution of shers to
the current dataset is likely negligible with only eight shing-based photographs (i.e., a sh
removed from the water) of the approximately 7600 photographs used in this study. Data
for this study were downloaded from the Australasian Fishes project on 13 February 2020.
iNaturalist observations are identied to various taxonomic levels based on combination
of computer vision suggestions and identications provided by the iNaturalist community
(i.e., citizen scientists). Observations become ‘research grade’ when at least two iNaturalist
users provide a consistent species level identication, or if more than two thirds of sugges-
tions are for the same species. The Australasian Fishes project is curated by the Australian
Museum and many observations, particularly unusual sightings or dicult identications,
are referred to trained sh taxonomists. The referral process is primarily driven by iNatu-
ralist users who, if necessary, can refer observations to Australian Museum sta or to a
taxon specialist (i.e., by mentioning them in an observation by using @UserName), many of
whom are active members of the Australasian Fishes project. It is worth highlighting that the
data quality assurance is greater for the Australasian Fishes project than it may be for iNatu-
ralist more broadly, due to the association with the Australian Museum and consequently the
large number of sh taxonomic experts involved in identifying and checking observations.
Data used in this project were restricted to research grade identications. Research grade
iNaturalist observations have previously been found to be between 65% accurate for insects
to 91% accurate for birds (Ueda 2019), although sh were not included in this assessment.
iNaturalist observations were also excluded if their positional accuracy was reported as
> 500 m or if the true co-ordinates of an observation were obscured by the contributor for
privacy reasons.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Biodiversity and Conservation (2022) 31:1407–1425 1411
1 3
Structured citizen science data: Reef Life Survey
Reef Life Survey (RLS; ree is a citizen science biodiversity monitoring
program which started in 2007. The program uses standardised underwater surveys, which
are done by a mixture of specialist scientists and experienced recreational scuba divers
who undergo a rigorous training and testing program in species identication and underwa-
ter surveying techniques (Reef Life Survey Foundation 2019). An assessment of RLS data
quality found that volunteers generated sh and invertebrate data indistinguishable from
experienced scientists associated with the program (Edgar and Stuart-Smith 2009). RLS
database administrators check uploaded data for potential errors such as species outside of
their normal region of occurrence.
The use of standardised survey techniques creates a structured data source, although
there are generally no constraints on timing of surveys, resulting in a temporally variable
dataset. RLS uses two methods to survey sh species along a 50 m transect line. The main
method includes all sh species observed 5 m to either side and above the transect line.
The counts are done separately on each side of the transect either by two separate divers
simultaneously, or on a return swim by the same diver. In addition, a second count is done
for cryptic sh, covering an area of 1 m to either side of the transect line. Since only spe-
cies presence was required for this study, data from the two methods (i.e., the 5 m and 1 m
survey) were combined to generate the species list for each survey.
The RLS data were extracted from the data portal (Edgar and Stuart-Smith 2020a, b) on
14 February 2020. The data extracted from RLS were cleaned to exclude individuals not
identied to species level as well as non-sh observations (e.g., cephalopods).
Fig. 1 The location of the eight
study sites in Sydney, Australia.
Pie charts show the proportion
of species at each site recorded
by iNaturalist only, RLS only or
both between 2017 and 2019.
Chart size indicates the relative
dierence in total number of
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Biodiversity and Conservation (2022) 31:1407–14251412
1 3
Study site selection
Eight popular dive sites in Sydney, Australia, were selected for inclusion in this study:
Shelly Beach, Camp Cove, Clifton Gardens, Gordons Bay, Bare Island, Kurnell, Shiprock
and Oak Park (Fig. 1). The sites were chosen as they had the greatest number of contribu-
tions to iNaturalist in the Sydney region and have been repeatedly sampled by RLS. The
selected sites in the Sydney region encompass a wide range of conditions including vari-
able exposure, seabed composition, depth, and marine protected area status. The study was
constrained to between 2008 and 2019 (inclusive) as limited RLS surveys or iNaturalist
observations were available prior to 2008. Although the Australasian Fishes project only
commenced in 2016, observations can be retrospectively added from earlier years. As such,
the iNaturalist dataset includes observations from before 2016, but at a much lower rate of
contribution than after 2016.
iNaturalist photographs were assigned to sites based on their geographic co-ordinates
falling within an approximately 500 × 500 m bounding box centred on each site. The exact
size was varied slightly to encompass the entire “dive site” at each location based on the
natural coastline of each site. RLS surveys are repeated at a consistent GPS co-ordinate
through time at each site. In some cases, multiple surveys are conducted for dierent areas
within a site and these were included in the analysis.
Contrasting fish communities between datasets
The two datasets were transformed into lists of species recorded at each site during each
sampling year (i.e., presence/absence) to allow direct comparison. There is potential for
duplication of observations between the two datasets, however, less than 1% of iNaturalist
photographs came from the same day and site of an RLS survey. Further, many of these
observations were likely not taken by divers involved in the RLS surveys, so on this basis
we consider the two datasets to be largely independent of each other.
All data manipulation, statistical analyses and graphing was done in R version 3.6.3 (R
Core Team 2020). Species lists generated from both data sources were cross checked against
species names in FishBase using the R package ‘rshbase’ (Boettiger et al. 2012). Species
that did not match a record in the FishBase species list were manually inspected and names
were changed to be consistent with FishBase for both datasets. Mismatches were generally
either due to a change in the accepted name, which had not been adopted by one of the
datasets or a spelling discrepancy.
The dierence in the average annual species richness between iNaturalist and RLS was
tested for the 2017 to 2019 period using a two-factor analysis of variance with dataset and
site as xed factors. This constrained time-period was used as both programs were run-
ning, resulting in few sites or years with very low numbers of iNaturalist contributions or
no RLS surveys. Plots of annual and cumulative species richness from 2008 to 2019 were
also used to compare between the two datasets. Cumulative species richness was calculated
using the ‘accumcomp’ function of the R package BiodiversityR (Kindt and Coe 2005). The
‘collector’ method was used to add species in order of the sampling year to visualize the
actual increase in species richness over the sampling period. As a measure of similarity, the
number of species common to both the methods at each site was calculated for all years
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Biodiversity and Conservation (2022) 31:1407–1425 1413
1 3
The variation in community composition between the two datasets and among sites for
the 2017 to 2019 period was visualized with an ordination plot based on a Generalised Lin-
ear Latent Variable Model (GLLVM). The GLLVM was t using two latent variables based
on a binomial complementary log log link transformation with random row eects included
in the model. The GLLVM model t was checked using a ‘Residuals vs Linear Predictors’
plot and a ‘Normal Q-Q’ plot. A Multivariate Generalised Linear Model (MGLM) based
on 1000 permutations was used to test for statistically signicant dierences between the
datasets (iNaturalist and RLS), sites (eight levels) and for an interaction between dataset
and site. Pairwise comparisons for dierences between datasets for each site was done by
running the MGLM analysis on the data for each site separately. Univariate comparisons,
adjusted for multiple comparisons, were done to test which species showed a signicant
dierence between the datasets. The analyses were done using the ‘gllvm’ function in the
gllvm package (Niku et al. 2020) and the manyglm function of the mvabund package (Wang
et al. 2020).
Variation in sampling effort between datasets
The relative eort was compared between iNaturalist and RLS based on the number of
sampling events. An iNaturalist ‘observation event’ was considered as all records submitted
by a single observer from one site on the same day, while an RLS observation event was
one survey transect. Plots of the number of observation events, and the number of iNatural-
ist photograph submissions were used to assess trends through time. Analysis of individual
submissions was limited to iNaturalist as no meaningful equivalent measure is available for
the RLS dataset. The relative sampling eciency was also compared between iNaturalist
and RLS by visually comparing the number of species recorded per observation event. The
mean species observed per event at each site was also calculated for the two datasets.
Variation in species richness between datasets
Overall, iNaturalist recorded 363 opportunistic species observations between 2017 and 2019
while structured surveys by RLS recorded 150 species for the eight study sites combined. At
a site level, iNaturalist recorded between 1.2 (Camp Cove) and 5.5 times (Clifton Gardens)
more species than RLS for the 2017–2019 period (Supplementary Material 1).
Prior to 2017, iNaturalist generally had lower numbers of species recorded per year than
RLS at most sites (Fig. 2). The main exception was Shelly Beach where iNaturalist recorded
more species than RLS in all surveys except between 2010 and 2012. Since 2017, iNatu-
ralist has recorded more species per year for most sites. The exception was Camp Cove,
where RLS recorded more species in all years, and Gordons Bay where RLS had more
species in 2017. For the time period 2017 to 2019, when both the iNaturalist Australasian
Fishes project and RLS were active, annual species richness was, on average, signicantly
greater for iNaturalist at Shelly Beach (F = 93.40, p < 0.0001), Shiprock (F = 5.84, p = 0.022),
Clifton Gardens (F = 18.68, p = 0.0002), Oak Park (F = 4.616, p = 0.0399) and Bare Island
(F = 4.22, p = 0.049) (Supplementary Material 2). No dierence in annual species richness
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Biodiversity and Conservation (2022) 31:1407–14251414
1 3
was detected at Kurnell (F = 2.59, p = 0.12), Gordons Bay (F = 1.56, p = 0.22) and Camp
Cove (F = 0.41, p = 0.53).
Cumulative species richness increased relatively quickly for RLS at most sites and gener-
ally began to atten after 1–3 years of surveys (Fig. 2). In contrast, species richness for iNat-
uralist increased gradually until 2016 at most sites, before rapidly increasing between 2017
and 2019. The exception was Shelly Beach, which started with a relatively high number of
species observations in 2008 gradually increasing through to 2012 before rapidly growing
between 2013 and 2019. This dierence in the species accumulation trends between the
iNaturalist and RLS programs meant that cumulative species richness was greater for RLS
than iNaturalist through to 2017 or 2018 at most sites at which point the cumulative number
of species recorded by iNaturalist surpassed that recorded by RLS at most sites. At Shelly
Beach, however, iNaturalist consistently recorded a greater cumulative species richness
throughout the whole monitoring period. At Camp Cove, the cumulative species richness
remained greater for RLS than iNaturalist for the whole 2008–2019 study period.
Total species richness between 2017 and 2019 varied considerably between datasets and
sites (Fig. 1, Supplementary Material 1). Shelly Beach reported the greatest species richness
Fig. 2 Species richness recorded
per year (bars) and cumulative
species richness (lines) for iNatu-
ralist and RLS
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Biodiversity and Conservation (2022) 31:1407–1425 1415
1 3
for both datasets, with 261 and 97 species for iNaturalist and RLS, respectively. However,
discrepancies occurred at other sites such as Camp Cove, which had the second most spe-
cies recorded by RLS (79 species) but the second least recorded by iNaturalist (93 species).
Conversely, Clifton Gardens had the second highest richness recorded by iNaturalist (117
species) while RLS recorded the lowest species richness (24 species) of all the sites.
Variation in species composition between datasets
Overall, between 2017 and 2019 there were 142 species, which were recorded by both RLS
and iNaturalist across all sites. A further 221 species were recorded exclusively by iNatural-
ist while RLS recorded only 8 species not submitted to iNaturalist between 2017 and 2019
at any study site. At a site level, the proportion of species shared by the two datasets ranged
between 15% at Clifton Gardens (20 of 137 species) and 47% at Shiprock (55 of 117)
(Fig. 1, Supplementary Material 1). The proportion of species unique to iNaturalist at each
site range between 35% at Camp Cove (43 of 122) to 82% at Clifton Gardens (113 of 137).
In contrast, the proportion of species only recorded by RLS ranged from 3% at Shelly Beach
and Clifton Gardens (8 of 269 and 4 of 137 respectively) to 24% at Camp Cove (29 of 122).
The species recorded by iNaturalist diered signicantly to those recorded by RLS
but only at some sites (Supplementary Material 3, signicant dataset x site interaction:
Dev = 650.6, p 0.001). Pairwise comparisons showed that datasets were signicantly dif-
ferent at Shelly Beach (Dev = 758.0, p = 0.04), Bare Island (Dev = 285.2, p = 0.048) and Kur-
nell (Dev = 290.2, p = 0.03). There was no evidence for a dierence in species composition
between datasets at Clifton Gardens (Dev = 286.6, p = 0.12), Gordons Bay (Dev = 0.237.3,
p = 0.17) and Oak Park (Dev = 215.3, p = 0.13), Camp Cove (Dev = 308.5, p = 0.06) and
Shiprock (Dev = 212.7, p = 0.07).
Overall, 311 species were more frequently recorded by iNaturalist, while only 44 spe-
cies were recorded more frequently by RLS. Twelve species were recorded the same num-
ber of times by both datasets. Univariate analyses contrasting datasets showed 16 species
were recorded signicantly more often by iNaturalist than RLS while only 2 species were
recorded signicantly more by RLS (Fig. 3).
Fig. 3 Number of recorded
occurrences for species with a
signicant dierence between
the iNaturalist and RLS datasets.
Most of the signicant dier-
ences were for species that were
only recorded in the iNatural-
ist dataset. Species sorted by
the dierence between RLS
and iNaturalist. The family of
each species is represented by a
silhouette to aid visual interpreta-
tion of the graph
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Biodiversity and Conservation (2022) 31:1407–14251416
1 3
Comparison of sampling effort between datasets
Almost 7600 unique photographic species records (i.e., unique species observed by a single
user from the same day and site) were submitted to iNaturalist for the eight monitoring sites
between 2008 and 2019 (Fig. 4). A large proportion of the iNaturalist observations and sam-
pling eort occurred between 2017 and 2019 with nearly 5600 photographic records across
all sites from over 1200 observation events (i.e., all photos from a distinct user, site and day)
(Fig. 4, Supplementary Material 4, Supplementary Material 5). There were ve or fewer
iNaturalist sampling events (e.g., dives) occurring in most years until 2016 after which the
number of events increased to between 5 and 27 events from 2017 to 2019 (Supplementary
Material 4). In contrast, only 71 RLS observation events (i.e., transects) occurred from 2008
to 2019 and there were generally 6 or fewer RLS transects at each site with only a few years
with greater numbers of surveys (Supplementary Materials 4 & 5).
iNaturalist was highly skewed towards low numbers of observations per event, with three
or fewer species photographed during nearly 65% of events, whereas RLS recorded a mini-
mum of 11 species per event (Fig. 5). Similarly, the average number of species submitted
to iNaturalist per observation event ranged from 2 (± 0.2 SE) at Kurnell to 7 (± 0.9 SE)
at Shiprock between 2017 and 2019 (Supplementary Material 5). In contrast, the average
Fig. 5 The number of species
recorded per observation event
(e.g., iNaturalist dive or RLS sur-
vey) as a proportion of the total
number of observation events
(y-axis square root transformed)
Fig. 4 Number of photographic
observations (log10 scale)
submitted to iNaturalist between
2008 and 2019 at each of the
monitoring sites
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Biodiversity and Conservation (2022) 31:1407–1425 1417
1 3
number of species observed per RLS event ranged between 17 (± 0 SE) at Clifton Gardens
and 43 (± 1.5 SE) at Shiprock.
We found that opportunistic observations by iNaturalist users recorded more species than
structured surveys cumulatively at most sites and, on average, more species per year at half
the monitoring sites. In addition, iNaturalist recorded a dierent subset of species, with
fewer than half the species observed opportunistically by iNaturalist users being recorded by
structured surveys. iNaturalist likely recorded more species in this study, at least in part, due
to the substantially greater sampling eort, with the iNaturalist observations being acquired
from more than 1200 observation events between 2017 and 2019 (i.e., dives where at least
one species was recorded and submitted to iNaturalist) compared to only 71 structured sur-
veys done over the same period. The high number of species recorded by iNaturalist clearly
demonstrates the considerable potential of opportunistic observations as an eective tool
for documenting species richness. Tiralongo et al. (2021) similarly noted the eciency of
using opportunistic observations to record sh biodiversity, with considerably more species
recorded during underwater photography competitions in the Mediterranean than various
standardised survey techniques.
Sydney has a large community of predominantly local divers and underwater camera
ownership is prevalent, and this likely helped the region accumulate such a substantial
number of observations in the relatively short 3-year period since the Australasian Fishes
project commenced. The fact that the study region is also dominated by local divers who
often revisit the same sites frequently may mean that many contributors have a high degree
of familiarity with local species and actively seek out rare or cryptic species. The high
number of submissions in Sydney may have resulted in more species being recorded than
in less populated areas of Australia or those with less active diving, snorkelling, or shing
communities. This bias towards areas of high population density in opportunistic databases
and other citizen science initiatives has been shown previously and discussed extensively
(Szabo et al. 2007; Tiago et al. 2017; Callaghan et al. 2019). Despite this, we consider the
success of Australasian Fishes in Sydney within a relatively short time period to indicate the
potential of iNaturalist in regions with less diving, snorkelling or shing, given sucient
time and promotional eort to grow the project.
Losey et al. (2012) found that the species richness derived from opportunistic observa-
tion of ladybugs was similarly greater than the combined richness of several structured
professional taxonomic surveys. However, in that case the dierence was attributed to not
only the greater number of opportunistic samples but also to a greater geographic spread.
A greater spread of sampling eort is likely to have also inuenced species richness in
this study, but at a localised site scale. That is, the structured surveys were constrained to
standardised transects at a consistent depth, with only one 50 m stretch of reef generally
sampled at each dive site. In addition, a similar area is sampled on repeat surveys with tran-
sects commencing from a consistent GPS coordinate. In contrast, a recreational diver could
easily cover several hundred meters of reef on a single dive, and the depths and area covered
would vary among dierent divers and visits. Further, iNaturalist observations come from
a range of dierent types of contributors, including snorkelers and shers, and these groups
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Biodiversity and Conservation (2022) 31:1407–14251418
1 3
may observe species that are less frequently encountered by scuba divers. Snorkelers, for
example, will likely encounter more species that inhabit shallower waters, which may be
under-represented in the structured surveys which were done by Scuba diving only. Conse-
quently, most of a site is likely to be covered by the combined eorts of many iNaturalist
contributors, which in this dataset included hundreds of visits to some sites. Although sh-
ers have the potential to contribute unique observations of species, which are attracted to
bait but may avoid divers or snorkelers, this is unlikely to have occurred in this study as only
a very small proportion of observations (8 of 7600 photos) were contributed by shers, all
of which were of species also observed in-situ by divers or snorkelers.
It is important to note that the structured surveys used by Reef Life Survey are not spe-
cically designed to measure species richness, rather, it is a global scale survey with eort
primarily directed at sampling many sites with a consistent methodology, instead of sam-
pling individual sites intensively (Edgar and Stuart-Smith 2014). It is also worth highlight-
ing that the structured surveys were considerably more ecient at recording species with
approximately ve times as many species recorded per dive. This is likely due to the struc-
tured surveys recording all species observed within the sampling parameters while iNatural-
ist users are highly selective about what they photograph and contribute. Importantly, the
use of a consistent methodology by RLS and similar structured survey approaches allows
for robust comparison of trends through time and across sites, on a global scale. In addi-
tion, RLS gathers a suite of information, which is not readily obtainable from iNaturalist
photographs such as the relative abundance of species, the size of species, as well as docu-
menting the habitat composition using photo-quadrats. Comparison of iNaturalist, or similar
opportunistic observations, to a more intensive structured survey program that is designed
to specically capture biodiversity would be a valuable future research direction. Such a
comparison would help better understand how much sampling eort is required to capture
similar amounts of biodiversity using structured and unstructured approaches.
The fact that fewer than half of the species recorded at all sites between 2017 and 2019
were present in both datasets demonstrates a considerable dierence in the species recorded
by the opportunistic observers and structured surveys. In part, this is likely to result from the
greater overall species richness recorded by iNaturalist at most sites, which is also reected
by the large proportion of species that were unique to iNaturalist at each site. The large
number of species unique to iNaturalist suggests that users are photographing and contribut-
ing species that are not readily captured by conventional structured surveys. Several cryptic
species such as Weedy Seadragon (Phyllopteryx taeniolatus), White’s Seahorse (Hippo-
campus whitei), Sydney Pygmy Pipehorse (Idiotropiscis lumnitzeri), and Dwarf Lionsh
(Dendrochirus brachypterus) were recorded frequently by iNaturalist but rarely present in
the RLS dataset. In addition, some rare or low abundance species were also recorded more
by iNaturalist including Port Jackson Sharks (Heterodontus portusjacksoni), Smooth Sting-
ray (Bathytoshia brevicaudata), Three Bar Porcupinesh (Dicotylichthys punctulatus), and
Comb Wrasse (Coris picta). In contrast, the two species more frequently detected by RLS,
the Girdled Parma (Parma unifasciata) and Clark’s Threen (Trinorfolkia clarkei), are com-
monly encountered on Sydney’s rocky reefs. A similar result was reported by Tiralongo et
al. (2020) who found that underwater photographers were eective at nding rare, small
and cryptic sh species while Snäll et al. (2011) found that rare and uncommon bird species
were essentially missed by structured surveys but captured by opportunistic citizen records.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Biodiversity and Conservation (2022) 31:1407–1425 1419
1 3
Many iNaturalist contributors are likely to spend a substantial part of their dive searching
for rare or cryptic species, simply for the challenge and reward of photographing species
that are dicult to nd. They may also be more likely to contribute photographs of these
species to iNaturalist as their perceived value as a biodiversity observation may be greater
due to their rarity. In contrast, rare or less abundant species are likely to be missing from
the RLS dataset simply due to the reduced sampling eort and consequently a decreased
probability of encounter during surveys. Further, although RLS includes a specic method
for cryptic species, including looking in caves and overhangs along the transect, a conse-
quence of using standardised transects means that observers are not free to ‘roam’ the dive
site searching for certain species. The tendency of opportunistic observers to seek out rare
species can be considered as a bias, however as noted by others, the fact that species are
recorded that are often missed by structured surveys can equally be viewed as one of the key
benets of such methods (Snäll et al. 2011; Kamp et al. 2016).
In addition to rare species being favoured over common ones, there is potential for bias
towards interesting species and away from less remarkable ones (Isaac and Pocock 2015;
Prudic et al. 2018; Caley et al. 2020). Indeed, many of the species recorded more frequently
by iNaturalist in this study, are also arguably very ‘photogenic’ such as seahorses and other
syngnathids or ‘charismatic’ such as sharks and rays. There is also the potential for iNatural-
ist observations to be skewed towards species, which are more readily photographed, with
many of the species more commonly recorded by iNaturalist in this study being benthic or
slow-moving species. A recent traits analysis for birds found evidence that large-bodied
species and those that occur in large ocks are over-represented in iNaturalist compared to
the semi-structured eBird checklists, potentially as they are easier to nd and photograph
(Callaghan et al. 2021). A similar quantitative assessment of which sh traits aect the
likelihood of a species being represented in opportunistic databases such as iNaturalist,
although beyond the scope of this study, deserves further exploration as it inuences how
opportunistic photographs can be utilised for future research and biodiversity monitoring.
Action to conserve biodiversity, such as determining locations for protection, often relies
on species occurrence data to identify biodiversity hotspots or areas that contain rare or
endangered species. This is particularly important for rare or cryptic species, which can
require substantial time and eort to nd using conventional structured surveys. The high
species richness and rare species recorded by iNaturalist in this study clearly demonstrates
the enormous potential of platforms such as iNaturalist as a tool for documenting biodiver-
sity and species conservation. Importantly, a large proportion of the observations were sub-
mitted over a relatively short 3-year period, following the launch and active promotion of
the Australasian Fishes project, demonstrating the potential to gather large numbers of bio-
diversity observations through opportunistic observation platforms such as iNaturalist. This
is largely the result of the relative ease of gathering and contributing iNaturalist observa-
tions, where essentially the only requirement is a photograph, compared to the high level of
training and dedication required to gain the knowledge and skills required to do structured
surveys. This means that large numbers of people can easily contribute to platforms such as
iNaturalist, since the barriers to participation are low, resulting in substantial sampling eort
due to greater ‘people power’.
In addition to having a large recreational diving community, the rapid growth of the
Australasian Fishes iNaturalist project may be attributable, at least in part, to the various
marine citizen science projects that preceded it in Australia (e.g., RLS, Redmap). These
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Biodiversity and Conservation (2022) 31:1407–14251420
1 3
have potentially helped establish a highly engaged diving community, which is willing to
contribute to citizen science initiatives. The ability to replicate the success of Australasian
Fishes or similar citizen science initiatives may also be limited in lower socioeconomic
countries where there is less time and money for expensive activities (Haklay 2013; Walker
et al. 2021) like scuba diving and underwater photography. However, iNaturalist is a global
platform with high levels of engagement world-wide, and substantial numbers of sh pho-
tographs have been contributed for many geographic areas including lower socioeconomic
areas such as South-east Asia, Central America and The Caribbean. Importantly, for many
of these regions there is often limited monitoring of marine environments by scientists due
to a lack of funding, however, they are popular destinations for scuba diving tourists. As
such, there is considerable potential to supplement structured survey data in undersampled
regions by recruiting tourists (Schaer and Tham 2020; Callaghan et al. 2021). The relative
ease of contributing observations means that platforms such as iNaturalist may be particu-
larly well suited to documenting biodiversity in areas dominated by tourism diving where
potential participants are unlikely to have the time or local species knowledge to do more
complex surveys (Hermoso et al. 2021). However, given the considerable dierences in
the experience and motivations between tourist and local divers (Hermoso et al. 2021), it
is dicult to know for certain how the results of our study, in a region with a highly active
community of local divers would translate to areas dominated by tourist divers. In areas
where recreational diving or snorkelling is minimal, including by tourists, it may be possible
to gather opportunistic observations by engaging with other users of the marine environ-
ment such as commercial or subsistence shers (Fulton et al. 2019). Expanding the current
study to regions dominated by tourist divers, or those used by recreational, commercial or
subsistence shers would be an important future research direction and further exploration
of the dierences in experience, knowledge and motivation to participate in citizen science
would be a valuable addition.
The lack of standardised methods for gathering observations, and the subsequent vari-
ability in eort and numbers of observations, is clearly one of the main limitations of oppor-
tunistic observation databases. For example, almost two-thirds the iNaturalist observation
events (e.g., dives) in this study had three or less sh species yet it is considered likely that
in many of these cases more sh were photographed but not submitted. Further, there is
likely to also be many additional observation events where users didn’t submit any pho-
tographs to iNaturalist as they didn’t record any species or photographs which they con-
sidered worth submitting. If some users are only submitting ‘interesting’ observations or
‘good’ photographs, then simply encouraging existing users to share all their observations
may improve the representation of more common species. Alternatively, more data could
be gathered by capitalising on incidental data (Callaghan et al. 2021) as common species
may often be captured in the background of photographs, and this is an area that deserves
further exploration.
Ultimately, the greater number of species recorded by iNaturalist than structured surveys
does not mean that opportunistic observations are a better way of measuring species rich-
ness or monitoring biodiversity. Indeed, relying on opportunistic observations alone for bio-
diversity conservation decision making could be highly problematic due to the biases of this
method. For example, the increase in species recorded with greater observation eort could
potentially result in more popular sites being protected, such as those with greater acces-
sibility, instead of more biodiverse ones (Nelson et al. 1990; Reddy and Dávalos 2003). Our
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Biodiversity and Conservation (2022) 31:1407–1425 1421
1 3
results from Camp Cove illustrate this point, as iNaturalist recorded the second lowest num-
ber of species at this site, hypothetically making it a low priority for protection, however it
had the second most species based on the structured surveys. The low iNaturalist species
count in this case was likely due to Camp Cove being a less popular dive site with both the
lowest number of iNaturalist sampling events and the least photographs submitted. As has
been suggested and demonstrated by others (Fithian et al. 2015; Giraud et al. 2016; Soroye
et al. 2018; Rapacciuolo et al. 2021), integrating opportunistic citizen science observations
with structured survey data from more traditional sources (e.g., government monitoring and
university research) will help ensure that both common and rarer species are well repre-
sented in biodiversity monitoring. It is worth noting however, that combining data sources
may not always be the best approach, and where there are sucient structured surveys it
may be more ecient and reliable to use these data alone, especially if there is considerable
and unknown bias in the opportunistic observations (Simmonds et al. 2020).
Although the value of a single opportunistic observation may be small, collectively, the
vast quantities of opportunistic observations now being shared through platforms such as
iNaturalist makes such data sources hard to ignore for biodiversity monitoring. Here we
demonstrated the potential of platforms such as iNaturalist to document species, including
many not recorded by structured surveys, due largely to the high number of participants
who spent considerable time making observations. Although iNaturalist may currently
have the greatest potential in regions like Sydney, where many individuals have the time
and resources for expensive recreational activities, we expect this success will likely be
reected more broadly as the popularity of iNaturalist continues to grow and spread across
the globe. Indeed, the relative simplicity of making opportunistic observations, including
during everyday activities, means platforms like iNaturalist are well suited to expand the
reach of citizen science into regions and communities where few individuals have the time
and resources to dedicate to more complex biodiversity surveys.
The fact that iNaturalist users are unconstrained by survey methods in terms of how (e.g.,
diving, snorkelling, shing), where (e.g., dierent habitats, in caves), and when (e.g., all
seasons, nighttime) to look, also greatly enhances their ability to nd a much broader suite
of species, including rare and cryptic individuals potentially missed by conventional struc-
tured surveys. However, opportunistic observers are also less likely to document common
and abundant sh than structured surveys as these species may be considered less interest-
ing to photograph or share. The eects of observer bias and selectivity has important impli-
cations for the analytical approaches and potential inferences that can be drawn from the
data. There is a need for more research, across a range of taxa, into how factors like rarity or
colour drive the contribution of opportunistic observations to platforms such as iNaturalist.
Ultimately, to account for the dierent species recorded by opportunistic observations and
structured surveys, integrating data from citizen science, research institutions and govern-
ment initiatives, is likely to have the best outcome for future biodiversity monitoring and
conservation activities.
Supplementary Information The online version contains supplementary material available at https://doi.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Biodiversity and Conservation (2022) 31:1407–14251422
1 3
Acknowledgements We thank the Australasian Fishes community for their ongoing contributions to iNatu-
ralist and Mark McGrouther and Amanda Hay for their work managing the project and for providing the
unobscured dataset used for this research. We also thank the Reef Life Survey team and their volunteer divers
for collecting, and making publicly available, the survey data used in this research. We thank M. Thiel and
the anonymous reviewers for comments that improved this manuscript.
Authors’ Contributions CRediT Statement: Christopher J. Roberts: Conceptualization, Methodology,
Formal analysis, Visualization, Writing - Original Draft. Adriana Vergés: Conceptualization, Methodol-
ogy, Writing - Review & Editing Supervision, Funding acquisition. Corey T. Callaghan: Conceptualization,
Methodology, Writing - Review & Editing, Funding acquisition. Alistair G. B. Poore: Conceptualization,
Methodology, Writing - Review & Editing, Supervision, Funding acquisition.
Funding This research was supported by an Australian Government Research Training Program (RTP)
Scholarship to C.J.R. and by grant SWR/10/2020 provided by Sea World Research & Rescue Foundation
Inc (SWRRFI) and the Winifred Violet Scott Charitable Trust to A.V., C.T.C., A.G.B.P. and C.J.R. CTC was
supported by a Marie Skłodowska-Curie Individual Fellowship (no. 891052).
Open Access funding enabled and organized by CAUL and its Member Institutions
Availability of data and code. Data used in this study are open access from iNaturalist (https://www.inatural-shes) and RLS (https://ree The cleaned data used
and the code to reproduce our analyses are available on Zenodo (
Conflict of interest The authors have no conicts of interest to declare that are relevant to the content of this
Ethics approval Not applicable.
Consent to participate Not applicable.
Consent for publication Not applicable.
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which
permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give
appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence,
and indicate if changes were made. The images or other third party material in this article are included in the
article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is
not included in the article’s Creative Commons licence and your intended use is not permitted by statutory
regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright
holder. To view a copy of this licence, visit
Aceves-Bueno E, Adeleye AS, Feraud M et al (2017) The Accuracy of Citizen Science Data: A Quantitative
Review. Bull Ecol Soc Am 98:278–290.
Ballard HL, Robinson LD, Young AN et al (2017) Contributions to conservation outcomes by natural history
museum-led citizen science: Examining evidence and next steps. Biol Conserv 208:87–97. https://doi.
Blowes SA, Supp SR, Antão LH et al (2019) The geography of biodiversity change in marine and terrestrial
assemblages. Science 366:339–345.
Boettiger C, Lang DT, Wainwright PC (2012) rshbase: exploring, manipulating and visualizing FishBase
data from R. J Fish Biol 81:2030–2039.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Biodiversity and Conservation (2022) 31:1407–1425 1423
1 3
Bradter U, Mair L, Jönsson M et al (2018) Can opportunistically collected Citizen Science data ll a data gap
for habitat suitability models of less common species? Methods Ecol Evol 9:1667–1678. https://doi.
Burgess HK, DeBey LB, Froehlich HE et al (2017) The science of citizen science: Exploring barriers to use
as a primary research tool. Biol Conserv 208:113–120.
Caley P, Welvaert M, Barry SC (2020) Crowd surveillance: estimating citizen science reporting prob-
abilities for insects of biosecurity concern. J Pest Sci (2004) 93:543–550.
Callaghan CT, Poore AGB, Hofmann M et al (2021) Large-bodied birds are over-represented in unstructured
citizen science data. Sci Rep 11:1–11.
Callaghan CT, Poore AGB, Mesaglio T et al (2021) Three Frontiers for the Future of Biodiversity Research
Using Citizen Science Data. Bioscience 71:55–63.
Callaghan CT, Rowley JJL, Cornwell WK et al (2019) Improving big citizen science data: Moving beyond
haphazard sampling. PLOS Biol 17:e3000357.
Chao A, Chiu C-H (2016) Species Richness: Estimation and Comparison. In: Wiley StatsRef: Statistics Ref-
erence Online. pp 1–26
Dickinson JL, Zuckerberg B, Bonter DN (2010) Citizen Science as an Ecological Research Tool:
Challenges and Benets. Annu Rev Ecol Evol Syst 41:149–172.
Dickman CR, Wardle GM (2012) Monitoring for Improved Biodiversity Conservation in Arid Australia. In:
Lindenmayer DB, Gibbons P (eds) Biodiversity Monitoring in Australia. CSIRO Publishing, Colling-
wood, VIC, pp 157–164
Edgar GJ, Stuart-Smith RD (2009) Ecological eects of marine protected areas on rocky reef communi-
ties — a continental-scale analysis. Mar Ecol Prog Ser 388:51–62.
Edgar GJ, Stuart-Smith RD (2014) Systematic global assessment of reef sh communities by the Reef Life
Survey program. Sci Data. 1:140007
Edgar GJ, Stuart-Smith RD (2020a) Reef Life Survey (RLS): Global reef sh dataset. Institute for Marine
and Antarctic Studies (IMAS). https://ree Accessed 14 Feb 2020
Edgar GJ, Stuart-Smith RD (2020b) Reef Life Survey (RLS): Cryptic Fish. Institute for Marine and Antarctic
Studies (IMAS). https://ree Accessed 14 Feb 2020
Fithian W, Elith J, Hastie T, Keith DA (2015) Bias correction in species distribution models: Pool-
ing survey and collection data for multiple species. Methods Ecol Evol 6:424–438. https://doi.
Follett R, Strezov V (2015) An Analysis of Citizen Science Based Research: Usage and Publication Patterns.
PLoS ONE 10:e0143687.
Fourcade Y (2016) Comparing species distributions modelled from occurrence data and from expert-based
range maps. Implication for predicting range shifts with climate change. Ecol Inf 36:8–14. https://doi.
Fulton S, López-Sagástegui C, Weaver AH et al (2019) Untapped Potential of Citizen Science in Mexican
Small-Scale Fisheries. Front Mar Sci 6:517.
Giraud C, Calenge C, Coron C, Julliard R (2016) Capitalizing on opportunistic data for monitoring relative
abundances of species. Biometrics 72:649–658.
Gotelli NJ, Chao A (2013) Measuring and Estimating Species Richness, Species Diversity, and Biotic Simi-
larity from Sampling Data. In: Levin S (ed) Encyclopedia of Biodiversity (Second Edition), 2nd edn.
Academic Press, Waltham, MA, pp 195–211
Haklay M (2013) Citizen Science and Volunteered Geographic Information: Overview and Typology of Par-
ticipation. In: Sui D, Elwood S, Goodchild M (eds) Crowdsourcing Geographic Knowledge: Volun-
teered Geographic Information (VGI) in Theory and Practice. Springer, Dordrecht, pp 105–121
Hermoso M, Narváez S, Thiel M (2021) Engaging recreational scuba divers in marine citizen science: Dif-
ferences according to popularity of the diving area. Aquat Conserv Mar Freshw Ecosyst 31:441–455.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Biodiversity and Conservation (2022) 31:1407–14251424
1 3
Isaac NJB, Pocock MJO (2015) Bias and information in biological records. Biol J Linn Soc 115:522–531.
Isaac NJB, van Strien AJ, August TA et al (2014) Statistics for citizen science: Extracting signals of change
from noisy ecological data. Methods Ecol Evol 5:1052–1060.
Kamp J, Oppel S, Heldbjerg H et al (2016) Unstructured citizen science data fail to detect long-term popu-
lation declines of common birds in Denmark. Divers Distrib 22:1024–1035.
Kelly R, Fleming A, Pecl GT et al (2020) Citizen science and marine conservation: a global review. Philos
Trans R Soc B 375:20190461.
Kindt R, Coe R (2005) Tree diversity analysis: A manual and software for common statistical methods for
ecological and biodiversity studies. World Agroforestry Centre, Nairobi, Kenya
Klemann-Junior L, Villegas Vallejos MA, Scherer-Neto P, Vitule JRS (2017) Traditional scientic data vs.
uncoordinated citizen science eort: A review of the current status and comparison of data on avifauna
in Southern Brazil. PLoS ONE 12:e0188819.
Losey J, Allee L, Smyth R (2012) The Lost Ladybug Project: Citizen Spotting Surpasses Scientist’s Surveys.
Am Entomol 58:22–24.
Mesaglio T, Callaghan CT (2021) An overview of the history, current contributions and future outlook of
iNaturalist in Australia. Wildl Res 48:289–303.
Nelson BW, Ferreira CAC, da Silva MF, Kawasaki ML (1990) Endemism centres, refugia and botanical col-
lection density in Brazilian Amazonia. Nature 345:714–716.
Niku J, Brooks W, Herliansyah R et al (2020) gllvm: Generalized Linear Latent Variable Models. R package
version 1.2.2
Peterson EE, Santos-Fernández E, Chen C et al (2020) Monitoring through many eyes: Integrating dispa-
rate datasets to improve monitoring of the Great Barrier Reef. Environ Model Softw 124. https://doi.
Pocock MJO, Tweddle JC, Savage J et al (2017) The diversity and evolution of ecological and environmental
citizen science. PLoS ONE 12:e0172579.
Prudic KL, Oliver JC, Brown BV, Long EC (2018) Comparisons of citizen science data-gathering approaches
to evaluate urban buttery diversity. Insects 9:186.
R Core Team (2020) R: A language and environment for statistical computing. R Foundation for Statistical
Computing, Vienna.
Rapacciuolo G, Young A, Johnson R (2021) Deriving indicators of biodiversity change from unstructured
community-contributed data. Oikos 130:1225–1239.
Reddy S, Dávalos LM (2003) Geographical sampling bias and its implications for conservation priorities in
Africa. J Biogeogr 30:1719–1727.
Reef Life Survey Foundation (2019) Standardised Survey Procedures for Monitoring Rocky & Coral Reef
Ecological Communities. https://ree
Riesch H, Potter C (2014) Citizen science as seen by scientists: Methodological, epistemological and ethical
dimensions. Public Underst Sci 23:107–120.
Seltzer C, Iwane T, Misraraj A, Loarie S (2020) 50 million observations on iNaturalist! https://www.inatural- Accessed 21 Jan 2021
Schaer V, Tham A (2020) Engaging tourists as citizen scientists in marine tourism. Tour Rev 75:333–346.
Simmonds EG, Jarvis SG, Henrys PA et al (2020) Is more data always better? A simulation study of ben-
ets and limitations of integrated distribution models. Ecography (Cop) 43:1413–1422. https://doi.
Snäll T, Kindvall O, Nilsson J, Pärt T (2011) Evaluating citizen-based presence data for bird monitoring. Biol
Conserv 144:804–810.
Soroye P, Ahmed N, Kerr JT (2018) Opportunistic citizen science data transform understanding of species
distributions, phenology, and diversity gradients for global change research. Glob Chang Biol 24:5281–
Sullivan BL, Aycrigg JL, Barry JH et al (2014) The eBird enterprise: An integrated approach to devel-
opment and application of citizen science. Biol Conserv 169:31–40.
Sullivan BL, Phillips T, Dayer AA et al (2017) Using open access observational data for conservation action:
A case study for birds. Biol Conserv 208:5–14.
Szabo JK, Davy PJ, Hooper MJ, Astheimer LB (2007) Predicting spatio-temporal distribution for eastern
Australian birds using Birds Australia’s Atlas data: survey method, habitat and seasonal eects. Emu -
Austral Ornithol 107:89–99.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Biodiversity and Conservation (2022) 31:1407–1425 1425
1 3
Theobald EJ, Ettinger AK, Burgess HK et al (2015) Global change and local solutions: Tapping the unre-
alized potential of citizen science for biodiversity research. Biol Conserv 181:236–244. https://doi.
Thiel M, Penna-Díaz MA, Luna-Jorquera G et al (2014) Citizen Scientists and Marine Research: Volunteer
Participants, Their Contributions, and Projection for the Future. In: Hughes RN, Hughes DJ, Smith IP
(eds) Oceanography and Marine Biology:An Annual Review. Taylor & Francis, pp 257–314
Tiago P, Ceia-Hasse A, Marques TA et al (2017) Spatial distribution of citizen science casuistic observations
for dierent taxonomic groups. Sci Rep 7:12832.
Tiralongo F, Crocetta F, Riginella E et al (2020) Snapshot of rare, exotic and overlooked sh species in the Ital-
ian seas: A citizen science survey. J Sea Res 164:101930.
Tiralongo F, La Mesa G, De Paladini F et al (2021) Underwater photo contests to complement coastal sh
inventories: results from two Marine Protected Areas in the Mediterranean. Mediterr Mar Sci 22:436–
Ueda K (2019) Identication Quality On iNaturalist. In: iNatForum.ca-
tion-quality-on-inaturalist/7507. Accessed 13 Aug 2021
van Strien AJ, van Swaay CAM, Termaat T (2013) Opportunistic citizen science data of animal species pro-
duce reliable estimates of distribution trends if analysed with occupancy models. J Appl Ecol 50:1450–
Walker DW, Smigaj M, Tani M (2021) The benets and negative impacts of citizen science applications to
water as experienced by participants and communities. WIREs Water 8:1–32.
Wang Y, Casajus N, Buddle C et al (2018) Predicting the distribution of poorly-documented species, North-
ern black widow (Latrodectus variolus) and Black purse-web spider (Sphodros Niger), using museum
specimens and citizen science data. PLoS ONE 13:1–14.
Wang Y, Naumann U, Eddelbuettel D et al (2020) mvabund: Statistical Methods for Analysing Multivariate
Abundance Data. R package version 4.1.3
Williams PH, Margules CR, Hilbert DW (2002) Data requirements and data sources for biodiversity priority
area selection. J Biosci 27:327–338.
Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and
institutional aliations.
Authors and Aliations
Christopher J.Roberts1,2· AdrianaVergés1,2· Corey T.Callaghan2,3·
Alistair G. B.Poore1,2
Christopher J. Roberts
1 Centre for Marine Science and Innovation, School of Biological, Earth and Environmental
Sciences, UNSW Sydney, Sydney, NSW, Australia
2 Ecology & Evolution Research Centre, School of Biological, Earth and Environmental
Sciences, UNSW Sydney, Sydney, NSW, Australia
3 German Centre for Integrative Biodiversity Research (iDiv) – Halle-Jena-Leipzig, Leipzig,
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Terms and Conditions
Springer Nature journal content, brought to you courtesy of Springer Nature Customer Service Center
GmbH (“Springer Nature”).
Springer Nature supports a reasonable amount of sharing of research papers by authors, subscribers
and authorised users (“Users”), for small-scale personal, non-commercial use provided that all
copyright, trade and service marks and other proprietary notices are maintained. By accessing,
sharing, receiving or otherwise using the Springer Nature journal content you agree to these terms of
use (“Terms”). For these purposes, Springer Nature considers academic use (by researchers and
students) to be non-commercial.
These Terms are supplementary and will apply in addition to any applicable website terms and
conditions, a relevant site licence or a personal subscription. These Terms will prevail over any
conflict or ambiguity with regards to the relevant terms, a site licence or a personal subscription (to
the extent of the conflict or ambiguity only). For Creative Commons-licensed articles, the terms of
the Creative Commons license used will apply.
We collect and use personal data to provide access to the Springer Nature journal content. We may
also use these personal data internally within ResearchGate and Springer Nature and as agreed share
it, in an anonymised way, for purposes of tracking, analysis and reporting. We will not otherwise
disclose your personal data outside the ResearchGate or the Springer Nature group of companies
unless we have your permission as detailed in the Privacy Policy.
While Users may use the Springer Nature journal content for small scale, personal non-commercial
use, it is important to note that Users may not:
use such content for the purpose of providing other users with access on a regular or large scale
basis or as a means to circumvent access control;
use such content where to do so would be considered a criminal or statutory offence in any
jurisdiction, or gives rise to civil liability, or is otherwise unlawful;
falsely or misleadingly imply or suggest endorsement, approval , sponsorship, or association
unless explicitly agreed to by Springer Nature in writing;
use bots or other automated methods to access the content or redirect messages
override any security feature or exclusionary protocol; or
share the content in order to create substitute for Springer Nature products or services or a
systematic database of Springer Nature journal content.
In line with the restriction against commercial use, Springer Nature does not permit the creation of a
product or service that creates revenue, royalties, rent or income from our content or its inclusion as
part of a paid for service or for other commercial gain. Springer Nature journal content cannot be
used for inter-library loans and librarians may not upload Springer Nature journal content on a large
scale into their, or any other, institutional repository.
These terms of use are reviewed regularly and may be amended at any time. Springer Nature is not
obligated to publish any information or content on this website and may remove it or features or
functionality at our sole discretion, at any time with or without notice. Springer Nature may revoke
this licence to you at any time and remove access to any copies of the Springer Nature journal content
which have been saved.
To the fullest extent permitted by law, Springer Nature makes no warranties, representations or
guarantees to Users, either express or implied with respect to the Springer nature journal content and
all parties disclaim and waive any implied warranties or warranties imposed by law, including
merchantability or fitness for any particular purpose.
Please note that these rights do not automatically extend to content, data or other material published
by Springer Nature that may be licensed from third parties.
If you would like to use or distribute our Springer Nature journal content to a wider audience or on a
regular basis or in any other manner not expressly permitted by these Terms, please contact Springer
Nature at
... Citizen science programs can take on a variety of formats, which include those that train citizens to conduct standardized in situ fish surveys (Edgar and Stuart-Smith, 2014), or those based on opportunistic photographic submissions verified by research scientists and the broader community (Nugent, 2018;Pecl et al., 2014;Pecl et al., 2019). Each approach has inherent biases, with recent research highlighting differences in species richness between opportunistic observations versus structured surveys (Roberts et al., 2022). Hybrid approaches that include natural history collections may therefore be the best option to achieve an exhaustive survey of a region. ...
Fishes represent an important natural resource and yet their diversity and function in dynamic estuaries with relatively high levels of human pressure such as Sydney Harbour have rarely been quantified. Further, Eastern Australia supports the survival and persistence of an increasing number of tropical species found within temperate estuaries owing to increasing average ocean temperatures. A re-valuation of the number of fish species known from Sydney Harbour is therefore needed. In this study, we generated an up-to-date and annotated checklist of fishes recorded from Sydney Harbour based on verified natural history records as well as newly available citizen science records based on opportunistic observations and structured surveys. We explored the spatial and temporal distribution of these records. In addition, we quantified the function, conservation status, and commercial importance of the identified fishes. The number of fish species recorded from Sydney Harbour now stands at 675, an increase of 89 species (15 %) when compared to the most recent evaluation in 2013. We attribute this increase in fish diversity over a relatively short time to the contribution of newer citizen science programs as well as the influx and survival of fishes in the Harbour with preferences for warmer waters. Some fish families were also overrepresented in the more urbanized and polluted sections of the Harbour. In forecasting further environmental impacts on the fishes of Sydney Harbour, we recommend increased integration of collaborative citizen science programs and natural history collections as a means to track these changes.
... Observational data mostly include the common species with fewer observations of the rare or range restricted species. It was recently demonstrated, however, that the use of underwater camera equipment can be very useful in documenting rare and opportunistic species of reef fishes to complement iNaturalist data (Roberts et al., 2022). Initial concerns focused on data quality, but this no longer appears to be a pressing issue, with several studies highlighting the accuracy of identifications, and providing methods to correct for biases and improve quality (Wiggins et al., 2011;Kelling et al., 2015;Kosmala et al., 2016;Hochmair et al., 2020;Arazy & Malkinson, 2021;Barbato et al., 2021). ...
Full-text available
The value of the citizen science platform iNaturalist was explored using photographic observations of southern African freshwater crabs (Potamonautidae Bott, 1970, among two genera Potamonautes sensu stricto MacLeay, 1838 and MartimonautesCumberlidge & Daniels, 2022) in combination with specimen data deposited in the South African Museum (Iziko Museums of Cape Town, South Africa). Species identification of photographic observations were assessed, and the identification of taxa corrected where necessary. From these records, the habitat type and distribution of the species were noted. The area of occupancy (AOO) and the extent of occurrence (EOO) were calculated where range extensions for species were observed. The results demonstrate that riverine/mountain stream freshwater crabs are over-represented in their frequency but species occurring in swamps/forests were under-represented. Using iNaturalist spatial data for South Africa we can identify several unsampled gaps in the country which included the Free State and North West, Gauteng and the Northern Cape, followed by Limpopo, Mpumalanga, and the Eastern Cape provinces. The Western Cape and KwaZulu-Natal had the highest number of freshwater crab records on iNaturalist. The remaining southern African countries, Angola, Botswana, Eswatini, Lesotho, Malawi, Mozambique, Namibia, and Zimbabwe, have few iNaturalist records. Range extensions were observed in four South African species based on iNaturalist records (P. clarusGouws, Stewart & Coke, 2000, P. flavusjoDaniels, Phiri, & Bayliss, 2014, P. isimangaliso Peer & Gouws, 2015, and P. mariepskoppieDaniels, Barnes, Marais & Gouws, 2021) with increases in AOO and EOO. We also observed a new undescribed freshwater crab on iNaturalist and corroborated this observation by sequencing these specimens for the cytochrome oxidase one subunit (COI) locus. Photographic quality is critical for taxonomic inference. Citizen science ­platforms such as iNaturalist can be invaluable for the discovery and documentation of biodiversity and provide new spatial data for species distributions that in turn can aid conservation tools.
Full-text available
Citizen science platforms are quickly accumulating hundreds of millions of biodiversity observations around the world annually. Quantifying and correcting for the biases in citizen science datasets remains an important first step before these data are used to address ecological questions and monitor biodiversity. One source of potential bias among datasets is the difference between those citizen science programs that have unstructured protocols and those that have semi-structured or structured protocols for submitting observations. To quantify biases in an unstructured citizen science platform, we contrasted bird observations from the unstructured iNaturalist platform with that from a semi-structured citizen science platform—eBird—for the continental United States. We tested whether four traits of species (body size, commonness, flock size, and color) predicted if a species was under- or over-represented in the unstructured dataset compared with the semi-structured dataset. We found strong evidence that large-bodied birds were over-represented in the unstructured citizen science dataset; moderate evidence that common species were over-represented in the unstructured dataset; strong evidence that species in large groups were over-represented; and no evidence that colorful species were over-represented in unstructured citizen science data. Our results suggest that biases exist in unstructured citizen science data when compared with semi-structured data, likely as a result of the detectability of a species and the inherent recording process. Importantly, in programs like iNaturalist the detectability process is two-fold—first, an individual organism needs to be detected, and second, it needs to be photographed, which is likely easier for many large-bodied species. Our results indicate that caution is warranted when using unstructured citizen science data in ecological modelling, and highlight body size as a fundamental trait that can be used as a covariate for modelling opportunistic species occurrence records, representing the detectability or identifiability in unstructured citizen science datasets. Future research in this space should continue to focus on quantifying and documenting biases in citizen science data, and expand our research by including structured citizen science data to understand how biases differ among unstructured, semi-structured, and structured citizen science platforms.
Full-text available
Marine Protected Areas (MPAs) are particularly useful to assess fish assemblages and to obtain reliable fish inventories. In this study we demonstrate the value of underwater photo contests as complementary tools to achieve these goals. We examined 3513 underwater pictures taken by free divers and scuba divers participating in two photo contests organized by the Italian Federation of Sport Fishing and Underwater Activities (FIPSAS). The competitions were held in the Italian MPAs of Punta Campanella (Tyrrhenian Sea; 2017) and Capo Rizzuto (Ionian Sea; 2018). Altogether, 97 fish species, 89 at Punta Campanella and 75 at Capo Rizzuto, were identified in different coastal habitats (depth range, 0-19 m). Their number was considerably higher than the one obtained with other census techniques and was close to the maximum number of species described at the two locations, as shown by accumulation curves. Significant differences in species richness were demonstrated at the level of both location and habitat type. The reasons for such differences are discussed along with the advantages and limitations of underwater photo contests as a participatory tool to obtain regular updates on coastal fish inventories in MPAs and in wider areas.
Full-text available
Citizen science initiatives and the data they produce are increasingly common in ecology, conservation and biodiversity monitoring. Although the quality of citizen science data has historically been questioned, biases can be detected and corrected for, allowing these data to become comparable in quality to professionally collected data. Consequently, citizen science is increasingly being integrated with professional science, allowing the collection of data at unprecedented spatial and temporal scales. iNaturalist is one of the most popular biodiversity citizen science platforms globally, with more than 1.4 million users having contributed over 54 million observations. Australia is the top contributing nation in the southern hemisphere, and in the top four contributing nations globally, with over 1.6 million observations of over 36 000 identified species contributed by almost 27 000 users. Despite the platform’s success, there are few holistic syntheses of contributions to iNaturalist, especially for Australia. Here, we outline the history of iNaturalist from an Australian perspective, and summarise, taxonomically, temporally and spatially, Australian biodiversity data contributed to the platform. We conclude by discussing important future directions to maximise the usefulness of these data for ecological research, conservation and policy.
Full-text available
Climate change, overfishing, marine pollution and other anthropogenic drivers threaten our global oceans. More effective efforts are urgently required to improve the capacity of marine conservation action worldwide, as highlighted by the United Nations Decade of Ocean Science for Sustainable Development 2021–2030. Marine citizen science presents a promising avenue to enhance engagement in marine conservation around the globe. Building on an expanding field of citizen science research and practice, we present a global overview of the current extent and potential of marine citizen science and its contribution to marine conservation. Employing an online global survey, we explore the geographical distribution, type and format of 74 marine citizen science projects. By assessing how the projects adhere to the Ten Principles of Citizen Science (as defined by the European Citizen Science Association), we investigate project development, identify challenges and outline future opportunities to contribute to marine science and conservation. Synthesizing the survey results and drawing on evidence from case studies of diverse projects, we assess whether and how citizen science can lead to new scientific knowledge and enhanced environmental stewardship. Overall, we explore how marine citizen science can inform current understanding of marine biodiversity and support the development and implementation of marine conservation initiatives worldwide. This article is part of the theme issue ‘Integrative research perspectives on marine conservation’.
Full-text available
Citizen science is proliferating in the water sciences with increasing public involvement in monitoring water resources, climate variables, water quality, and in mapping and modeling exercises. In addition to the well‐reported scientific benefits of such projects, in particular solving data scarcity issues, it is common to extol the benefits for participants, for example, increased knowledge and empowerment. We reviewed 549 publications concerning citizen science applications in the water sciences to examine personal benefits and motivations, and wider community benefits. The potential benefits of involvement were often simply listed without explanation or investigation. Studies that investigated whether or not participants and communities actually benefitted from involvement, or experienced negative impacts, were uncommon, especially in the Global South. Assuming certain benefits will be experienced can be fallacious as in some cases the intended benefits were either not achieved or in fact had negative impacts. Identified benefits are described and we reveal that more consideration should be given to how these benefits interrelate and how they build community capitals to foster their realization in citizen science water projects. Additionally, we describe identified negative impacts showing they were seldom considered though they may not be uncommon and should be borne in mind when implementing citizen science. Given the time and effort commitment made by citizen scientists for the benefit of research, there is a need for further study of participants and communities involved in citizen science applications to water, particularly in low‐income regions, to ensure both researchers and communities are benefitting. This article is categorized under: • Human Water > Human Water Abstract A substantial review of citizen science applications to water showed that benefits, and especially negative impacts, experienced by participants and communities are seldom researched. We describe the benefits and negative impacts that can occur to encourage their greater consideration when designing citizen science projects.
Full-text available
Italy, at the center of the Mediterranean Sea, hosts a high diversity of fishes, but to a certain extent, this richness remains hidden or poorly known because of rare, cryptic or recently introduced species, that are hardly to detect with the traditional sampling approaches. In this study, we gained complementary knowledge, engaging Italian sea users, especially fishers and underwater photographers, to share their observations. Results obtained during 2019 provided 124 new records distributed in 40 species. Most of these records are related to native-rare and native-thermophilic fishes (~95%), while few observations (~5%) concern non-indigenous taxa. Records of thermophilic species were reported from the Tyrrhenian Sea and the Ionian Sea, while alien species were mostly reported from the southernmost coasts of Italy. Our findings highlight the potential of participatory actions and emphasize the value of a close collaboration between researchers and sea-lovers for monitoring marine biodiversity on large spatial scales.
Full-text available
Species distribution models are popular and widely applied ecological tools. Recent increases in data availability have led to opportunities and challenges for species distribution modelling. Each data source has different qualities, determined by how it was collected. As several data sources can inform on a single species, ecologists have often analysed just one of the data sources, but this loses information, as some data sources are discarded. Integrated distribution models (IDMs) were developed to enable inclusion of multiple datasets in a single model, whilst accounting for different data collection protocols. This is advantageous because it allows efficient use of all data available, can improve estimation and account for biases in data collection. What is not yet known is when integrating different data sources does not bring advantages. Here, for the first time, we explore the potential limits of IDMs using a simulation study integrating a spatially biased, opportunistic, presence‐only dataset with a structured, presence–absence dataset. We explore four scenarios based on real ecological problems; small sample sizes, low levels of detection probability, correlations between covariates and a lack of knowledge of the drivers of bias in data collection. For each scenario we ask; do we see improvements in parameter estimation or the accuracy of spatial pattern prediction in the IDM versus modelling either data source alone? We found integration alone was unable to correct for spatial bias in presence‐only data. Including a covariate to explain bias or adding a flexible spatial term improved IDM performance beyond single dataset models, with the models including a flexible spatial term producing the most accurate and robust estimates. Increasing the sample size of presence–absence data and having no correlated covariates also improved estimation. These results demonstrate under which conditions integrated models provide benefits over modelling single data sources.
Opportunistic and unstructured observations of biodiversity crowdsourced from volunteers, community, and citizen scientists make up an increasingly large proportion of our global biodiversity knowledge. This incredible wealth of information exists in real time at both high resolutions and large extents of space, time, and taxonomy, thus holding huge potential to fill gaps in global biodiversity monitoring coverage in a cost‐effective way. Yet, the full potential of these data to provide essential indicators of biodiversity change for both research and management remains mostly unrealized, in large part due to the prevailing perception that the lack of standardization presents an unsurmountable barrier. In this paper, we provide an overview of the main challenges of working with unstructured community‐contributed data and synthesize the four fundamental approaches to overcome these challenges and extract useful inferences of biodiversity change, namely: 1) reverse‐engineering survey structure; 2) borrowing strength across taxa; 3) modeling the observation process, and; 4) integrating standardized data sources. To illustrate each of these approaches, we provide examples comparing community‐contributed observations crowdsourced via iNaturalist with long‐term standardized monitoring surveys for a subset of rocky intertidal organisms on the California coast from 2010 to 2019. We conclude by highlighting ways forward for the successful integration of unstructured community‐contributed observations within the global ecosystem of biodiversity change monitoring tools. Our ultimate goal is to update the prevailing perception among researchers and practitioners that unstructured community‐contributed observations of biodiversity are too noisy to use, and help establish this data stream as a key tool for research and management.
• Characterizing the composition of divers visiting different diving areas could help to design marine citizen science (MCS) projects that support biodiversity monitoring and marine conservation. • Recreational scuba divers mostly prefer warm and clear waters with coral reefs, and based on the Duffus and Dearden’s wildlife tourism framework we hypothesized that a more popular diving area is visited mostly by generalist divers, whereas in a less popular diving area a higher proportion of specialist divers would be found. • Recreational scuba divers were surveyed in diving centres at two diving areas, Rapa Nui (more popular, with warm and clear coral‐reef waters) and the Chilean mainland (less popular, with productive and temperate–cold waters), to determine their diving profile, visiting profile, marine species knowledge, and interest and participation in MCS. • Support for our hypothesis (generalist divers on Rapa Nui and specialist divers on the mainland) was weak, but recreational divers on Rapa Nui were mostly foreign visitors who come for single visits, whereas divers from the mainland were predominantly Chileans who return repeatedly to the diving area. In both diving areas the divers expressed a strong interest to be trained and to participate in MCS, but divers from Rapa Nui were interested in brief pre‐dive inductions, whereas divers from the Chilean mainland preferred intensive training courses. • Based on these findings we recommend specific MCS strategies for divers in both types of areas, e.g. simple protocols in more popular diving areas, with short pre‐dive briefings for divers, and medium or long‐term programmes in areas where most divers are local with high return rates. In these latter conditions more extensive training will be useful, which allows divers to gain more experience and assume higher responsibilities within an MCS project.
Citizen science is fundamentally shifting the future of biodiversity research. But although citizen science observations are contributing an increasingly large proportion of biodiversity data, they only feature in a relatively small percentage of research papers on biodiversity. We provide our perspective on three frontiers of citizen science research, areas that we feel to date have had minimal scientific exploration but that we believe deserve greater attention as they present substantial opportunities for the future of biodiversity research: sampling the undersampled, capitalizing on citizen science's unique ability to sample poorly sampled taxa and regions of the world, reducing taxonomic and spatial biases in global biodiversity data sets; estimating abundance and density in space and time, develop techniques to derive taxon-specific densities from presence or absence and presence-only data; and capitalizing on secondary data collection, moving beyond data on the occurrence of single species and gain further understanding of ecological interactions among species or habitats. The contribution of citizen science to understanding the important biodiversity questions of our time should be more fully realized.