D. Spear Senior Thesis

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Citizen Science: A Valuable Tool for Urban Biodiversity Research
Experimental Senior Thesis
Dakota Spear
May 1, 2015
Advisor: Dr. Kristine Kaiser

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Abstract
Careful study of urban biodiversity is necessary as urbanization changes ecosystem dynamics
throughout the world. Yet urban regions are large and often difficult subjects of research, due in
part to private property inaccessible to scientists. Citizen science is a promising tool for
producing large-scale data sets about biodiversity in urban regions. In this study, I evaluate the
online citizen science platform iNaturalist to determine factors that influence success as
measured by participation and extent of data collection. I then examine one iNaturalist project,
Reptiles and Amphibians of Southern California (RASCals), by comparing data collected by
RASCals participants to data present in the VertNet database (www.vertnet.org) to evaluate the
ability of an iNaturalist project to record species distributions. I use RASCals data to investigate
species distribution of Phrynosoma blainvillii and Elgaria multicarinata, two native species, and
Trachemys scripta and Lithobates catesbeianus, two invasive species, in the context of
urbanization in Southern California. iNaturalist is a promising tool for large-scale biodiversity
and distributional data collection, but its success changes according to location and taxon of
focus, as well as other demographic factors such as population density. RASCals participants
provide observations of invasive species and species in urban areas that are sparsely recorded in
the VertNet database. RASCals data demonstrate that E. multicarinata is able to adapt to urban
regions, while P. blainvillii is largely extirpated. The project increased known VertNet records of
T. scripta, but more sampling is needed to determine the full range of L. catesbeianus.
Introduction
Urbanization
Urban development is one of the greatest threats to biodiversity in the world (McKinney 2002,
Alvey 2006, Czech et al. 2000). Over 50% of the world’s population lives in cities, and that
number is expected to reach 80% in the next 50 years (Grimm et al. 2008). In most industrialized
nations, including the United States, over 5% of the total surface area is urban (USCB 2001) and
urban regions are expanding rapidly, faster than protected parks or conservation regions
(Fragkias et al. 2013, McKinney 2002). Such population and development growth will produce
increasing demands on surrounding ecosystems for food production and other services.
Yet urban development already threatens more endangered species than any other human activity
(Czech et al. 2000). As the gradient of urban development changes from less developed on city
outskirts, to more developed in city interiors, the level of air and soil pollution, road density,
population density, average ambient temperature, amount of impervious surface, and other
metrics of human disturbance also increase (McKinney 2002). These urban-associated metrics
are known to be stressors for many species, and combined with habitat loss produced by urban
development, can cause myriad effects in local ecosystems, including changing species
assemblages and lower species diversity (Mackin-Rogalska et al. 1988, Kowarik 1995, Denys
and Schmidt 1998, McIntyre 2000, Blair 2001, Ditchkoff et al. 2006).

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Though many species are completely extirpated from urban areas, others are able to adapt to
varying levels of urbanization (Gilbert 1989, Adams 1994, Ditchkoff et al. 2006). Invasive
species in particular, due to the very traits that allow them to become successful invaders into
new habitats, are often more resilient, and can displace native species in urban habitats
(McKinney 2002, Whitney 1985, Kowarik 1995, Alvey 2006, Tait et al. 2005). Fragments of
green space in urban areas frequently become some of the only regions where local species are
able to persist (Alvey 2006). The number of species of many animal taxa, including insects
(Majer 1997) and birds (Goldstein et al. 1986) is often correlated with the number of plants in
urban regions, indicating that any remaining habitat fragments, such as backyards and local
parks, are particularly important for species persistence.
Citizen Science
As urbanization increases in scope, it will become increasingly important to understand the
effects it has on biodiversity and ecosystem dynamics, because biodiversity is critical to long-
term ecosystem functioning (Groombridge and Jenkins 2002). Despite the extensive level of
human manipulation of urban environments, these areas are widely understudied in an ecological
context, and we still do not have adequate understanding of how human activity impacts
ecosystem functioning and biodiversity (Collins et al. 2000, Grimm et al. 2008). One reason for
this lack may be the logistical difficulties associated with studying biodiversity in urban regions.
First, a large extent of urban green space is privately owned or otherwise inaccessible to
researchers, requiring scientists to gain access to private property, or use other means to
approximate information from these areas. In addition, a complete understanding of the effects of
urbanization requires comprehensive data collected over extremely large geographic areas: most
cities and their surrounding suburbs are many dozens or even hundreds of square miles in area.
Citizen science is one way that such intensive monitoring can be carried out. Citizen science is
the use of non-scientist volunteers to collect data. Volunteer-based data collection is one solution
to the lack of funding or personnel that makes intensive monitoring of large areas difficult, and it
can allow scientists indirect access to private residential areas (Bonney et al. 2009, Delaney et al.
2008). Bonney (1991) found that for one citizen science ornithological study, participants
provided nearly 200,000 hours of data collection for an estimated value of $1 million, based on
minimum wage. The citizen science platform eBird collects over 1 million observations from
participants each month, and now comprises over 200 million observations (Bonney et al. 2009).
Citizen science has been used to collect terrestrial and aquatic data of all types, including coral
reef and ornithological studies, and water quality monitoring (Darwall and Dulvy 1996, Ohrel et
al. 2000, Bray and Schramm 2001). One of the most well known citizen science initiatives in the
United States is the National Audubon Society’s Christmas Bird Count, started in 1900. The
Cornell lab of ornithology carries out dozens of successful citizen science projects, and has used
data collected by participants to publish papers on bird distribution changes (Hochachka et al.
1999, Cooper et al. 2007, Bonter and Harvey 2008, Bonter et al. 2009), breeding success (Hames
et al. 2002a, Cooper et al. 2005a, 2005b, 2006), and infectious disease spread through bird
populations (Hartup et al. 2001, Altizer et al. 2004, Hochachka et al. 2004).

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Citizen science has been a valuable tool for detecting range shifts of both native and invasive
species, as well as first detection of invasive species (Delaney et al. 2008). Early detection is
important because it significantly increases the likelihood of successful eradication of highly
invasive species (US Congress OTA 1993, Myers et al. 2000, Lodge et al. 2006). Delaney et al.
(2008) used citizen science to characterize the changing distribution of two invasive and several
native crabs in seven eastern states of the United States, and their citizen scientist participants
detected the first Asian shore crab (Hemigrapsus sanguineus) in Massachusetts.
Yet the use of citizen science for data collection has been limited. One reason may be the
necessity of publicity and volunteer recruitment for successful implementation, the training
required for many studies, and the cost associated with training and implementation. Another
reason may be the lack of assessment of the validity or accuracy of data collected and their
perceived worth in academic research or management (Delaney et al. 2008). Because participants
are typically not educated as researchers, citizen science is most effectively used for the
collection of data that requires minimal training, such as species presence/absence data for
population structure or distribution information (Delaney et al. 2008, Bonney et al. 2009).
Careful consideration of the limitations of citizen science data sets is necessary when analyzing
the results of volunteer-based studies. For example, Delaney et al. (2008) found that education
level was a highly reliable predictor of the accuracy of volunteers’ identifications of crab age and
sex. In addition, data collection was found to be less complete the more complicated the
collection process was (Delaney et al. 2008). Increasing the number of studies that assess the
quality of citizen science data sets, the number of secondary data sets that can be used to validate
citizen science collected data, and the use of citizen science data for publication or management
decisions, is necessary to increase more widespread use of citizen science in research and
management (Boudreau and Yan 2004, Delaney et al. 2008). Moreover, greater use of citizen
science will allow greater understanding of the ways and extent to which volunteer-collected
data sets can be used. If the limitations of the data sets are assessed and considered appropriately,
citizen science can be an invaluable asset to research initiatives.
iNaturalist
iNaturalist (inaturalist.org), owned by the California Academy of Sciences, is one internet
citizen-science platform that eliminates some of the primary problems associated with citizen
science research. It is free or low cost for researchers and participants, requires no participant
training, and the data is easily accessible. The staff of iNaturalist describe it as a “crowd-sourced
species identification system and an organism occurrence recording tool” (inaturalist.org). The
goals of iNaturalist are both to generate appreciation for the natural world, and to create large-
scale biodiversity data sets that are useful to both researchers and land managers (inaturalist.org).
Anyone can become a member of iNaturalist or start a “project” on the platform at no cost.
Members take photos of the taxa of interest in the region of focus and contribute them to a
project by uploading the photo and proposing an identification of the species. Other members
can assist with identifications of species in the photograph. iNaturalist encourages scientists and
other experts to contribute to species identifications, and observations can be qualified as

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“research grade” if the species observation has a photograph, a date, coordinates (i.e., latitude
and longitude), and a community-supported identification (i.e., the species identification has
been corroborated). Coordinates are automatically included if the photograph is taken with a
camera that georeferences photos, such as a cell phone camera. All data are freely available and
can be downloaded as a CSV file or mapped using Google Earth™. Data about the project,
including number of participants and number of observations contributed by each participant, are
also accessible.
iNaturalist is used by non-professional naturalists, but also often by parks services for research
bioblitzes, by teachers and schools as an educational tool, and by professional research
organizations such as the California Academy of Sciences, National Geographic, several state
wildlife agencies and Natural History Museums. There is no cost associated with training for
participants, as there is very little training involved. Data collection is uniform and simple.
iNaturalist projects require little maintenance and data mining is easy, as all data are collated
automatically and are accessible in spreadsheet format. iNaturalist projects have also been
widely successful for collecting data on many species over large areas. Some projects have
garnered over 50,000 observations, such as the National Geographic Great Nature Project.
However, there are certain caveats associated with the platform that are important to consider.
There are, for example, trade-offs between population density and project size: sampling may be
more complete in some areas compared to others, and a large region with many participants is
less likely to have thorough sampling coverage than a smaller region with many participants.
Larger regions, however, are also more likely to attract more participants. In addition, because
participation is voluntary and depends on knowledge of the project and of iNaturalist, the level of
participation may depend on factors external to the project such as the education level or
socioeconomic status of a region. Obtaining sufficient participation to gain a complete data set
can require intensive engagement from the sponsoring organization, through advertisement,
outreach, and education. Finally, in order to obtain an accurate sense of species distributions,
many thousands of observations may be necessary. It may be impossible to gain accurate
distributional data for rare or cryptic species that non-experts may not see or know how to look
for. Such drawbacks must be considered before using project data for a professional purpose.
My goal in this study was to evaluate iNaturalist as a citizen science platform and its use for
collecting distributional data in an urban region. First, I appraised factors that influence the use
of iNaturalist to conduct distributional research and assess biodiversity in specified regions. I
hypothesized that location and taxon of interest influence participation in a project. Higher
population density in a location, and greater availability of outdoor recreation area, may correlate
with opportunities for more people to participate in outdoor-based pursuits such as participation
in iNaturalist. In addition, there may be greater public interest in certain taxa, such as birds or
mammals.
Second, I analyzed one iNaturalist project, RASCals, to assess the ability of this platform to
accurately record reptile and amphibian species distributions across Southern California. I
compared RASCals observations to observations found in VertNet (www.vertnet.org), an NSF-
funded database that contains millions of georeferenced records from museums and universities
across the country, from as early as the 1800s. I used amphibian and reptile records from VertNet

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as a professionally collected depiction of species distributions in Southern California to which I
compared RASCals observations. I hypothesized that RASCals participants fail to record certain
groups of cryptic or rare species, but that urban areas are better sampled by participants due
convenience of location and the ability to sample within private property.
Finally, I used RASCals observations, in comparison with historical records from VertNet, to
evaluate the distributional shifts over time of four reptile and amphibian species in the context of
urbanization in Southern California. I evaluated RASCals data of two native species (the
Southern alligator lizard, Elgaria multicarinata, and the coast horned lizard, Phrynosoma
blainvillii), and two invasive species (the red-eared slider turtle, Trachemys scripta, and the
American bullfrog, Lithobates catesbeianus). I examined trends in where participants were
collecting data on particular species, and investigated the effect of urban development on native
and invasive species that are differentially affected by urbanization (Brattstrom 2013, Thomson
et al. 2010, D’Amore et al. 2010). I hypothesized that the invasive species, which are often able
to invade disturbed habitats because they are better at adapting to disturbance, are more prevalent
in urban areas than the native species.
Methods
Comparing iNaturalist projects
The first objective of this study was to determine which characteristics of an iNaturalist project
are most relevant to its success, as defined by number of observations and number of
participants. In order to identify these characteristics, I used the classification and regression
algorithm called random forest (Breiman 2001). Random forest is particularly useful for large
numbers of variables with many classifications and a mixture of continuous and categorical
variables (Díaz-Uriarte and Alvarez de Andrés 2006). The importance of each explanatory
variable to the classification or regression process is assessed using four measures of importance:
the mean decrease in accuracy and the decrease in the Gini impurity index when classifying
according to a categorical variable, and the percent increase in the mean squared error (MSE)
and the increase in node purity when classifying according to a continuous variable (i.e., using
regression instead of classification). A higher score for each measure of importance indicates the
variable has better predictive power.
I used the randomForest R package first to rank the variables most important for predicting
whether an iNaturalist project was one of the top 50 projects in terms of number of observations,
or one of the bottom 50 projects with more than 10 observations (observations as of December
2014) (Liaw and Wiener 2014). I excluded all projects that were intentionally temporary, such as
bioblitzes and school projects, and thus included only projects that were supposed to be ongoing.
I assessed only variables that are readily available from the iNaturalist website (Table 1).
Table 1. Variables used in random forest algorithm to predict whether a project was one of the
50 projects with the most observations or the 50 projects with the least observations, and the
average number of observations per day as well as the number of participants of the 100 projects
with the most observations.

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Variable Name Description
days.active The number of days the project has been active, from project start to the most
recent observation recorded
days.existed The number of days the project has existed, from project start to the arbitrarily
chosen date March 3, 2015
journal The number of “journal” pieces posted by the project creator. Used as proxy
for creator’s involvement in project.
participants The number of participants
starter.category The category of the creator of the project, defined as either a “scientific
organization”, such as the California Academy of Sciences, Los Angeles
County Natural History Museum, or National Geographic, or an “iNaturalist
member” if not a reputed organization
purpose The purpose of the project, defined as either “scientific data collection” or
“non-science” for all purposes reported as educational or for general curiosity
geographic.size The general size of the area of the project, divided into nine broad categories:
city; continent; country; county; park; region; state; world; and backyard
property
location The specific location of the project
scope The scope of the project, i.e., whether it attempted to record all species, a
particular taxon, or a category of species
general.target The general target of the project, divided into 10 broad categories: wildlife;
birds; all species; fungi; reptiles and amphibians; insects and other arthropods;
invertebrates; mammals; “category” such as animal tracks,invasive or
threatened species; and plants
target.taxon The more specific target taxon of the project
I then used random forest to rank the importance of the variables used to predict the number of
observations per day (defined as total number of observations over the number of days the
project existed) and the total number of participants of the top 100 projects. I included the same
variables listed above in analyses for observations per day, and all variables excluding number of
participants in analyses for number of participants.
Evaluation of RASCals
I compared total species observed by participants of the RASCals projects to total reptile and
amphibian species recorded in the ten counties of Southern California according to VertNet
records to evaluate the ability of the RASCals project to completely record reptile and amphibian
species diversity of the region. Though subspecies were often included in both VertNet and
RASCals records, I grouped all subspecies together, using only species names in all analyses. I
used a species accumulation curve to assess the progress of RASCals toward recording all reptile
and amphibian species present in Southern California, particularly whether it can be expected
that more species will be observed, and to determine whether it will be possible to record total
expected species with the current level of sampling effort. Species accumulation curves were
created in R version 3.1.0 by creating 1000 independent permutations of the list of species
observed by RASCals participants, sampling each without replacement and plotting the average
length of the vector of species each time a unique species was sampled.

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In order to determine whether sampling effort differs by geographic region, I also compared the
number of observations in each county of Southern California. I evaluated demographic and
landscape factors to determine which influence the number of observations recorded by RASCals
participants within each county. Factors evaluated included: the percent of the county that is
government protected area; population density; the percent of the population that has a
Bachelor’s degree or higher; the percent of the population that is white; and median household
income. I created a generalized linear mixed model to determine which variables best predict the
number of observations made by RASCals participants in each county. I log transformed the
number of observations to better fit a normal distribution. I then used stepwise model selection,
using both the Akaike information criterion (AIC) and the Bayesian information criterion (BIC),
to select the model parameters. I used both the AIC and BIC to avoid overfitting the model and
take advantage of the strengths of both types of information criterion for model selection (for
discussion of the strengths of AIC vs. BIC, see Yang 2005 or Burnham and Anderson 2004). I
reported coefficients, T-values and P-values for the final selected variables included within the
model. The final variables are not all significant according to an α-value of 0.05, because I used
solely AIC and BIC values (compared to the null model with no variables included) to select the
final model. In addition, because there were so few data points (n=10, i.e., the ten counties of
Southern California), a p-value of less than 0.05 is difficult to achieve, and so I determined p-
values should not be the sole criteria for variable selection.
Finally, to determine where RASCals participants were taking observations, I determined the
number of observations that occurred within protected areas (national forest, national park, state
park, city park or managed land). I did this by mapping all RASCals observations on a basemap
of protected areas (“Protected Areas of the Pacific States (USA)” 2008) using ArcGIS (version
10.2.2, Esri) and determining how many RASCals observations intersected any protected area.
Urbanization and species distributions
Study Species
Phrynosoma blainvillii, the coast horned lizard, historically occurs from Sacramento Valley to
Baja California, Mexico (Brattstrom 2013). However, populations have been in rapid decline in
urban areas of Southern California due to habitat destruction and other human activities
(Jennings 1987, Fisher and Case 2000, Fisher et al. 2002, Lemm 2006, Brattstrom 2013) and it is
now a California species of special concern (Jennings and Hayes 1994). Brattstrom (2013)
published a comprehensive study of past and present coast horned lizard distribution, and found
that though the lizard has been extirpated from highly urbanized city centers, it persists across
much of its historical range and is able to persist in habitat fragments surrounded by urban
development, such as parks. It is also able to breed near cities, suggesting that as urban areas
expand, populations may be able to persist near these regions (Brattstrom 2013).
Sullivan et al. (2014) found that other Phrynosoma species persist in some urban habitat
fragments but not others, depending on the density of preferred prey (seed-harvesting ant
species). This may be a particular problem in Southern California, as many urban habitat
fragments are now invaded by non-native Argentine ants, Linepithine humile (Holway 1999,

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Bolger 2002, Foster et al. 2007, Menke et al. 2009). Coast horned lizards do not eat Argentine
ants, and the Argentine ants reduce populations of native seed-harvesting ants (Suarez et al.
1998, Holway 1999, Bolger 2002). This is of concern in city habitat fragments or the interface
between urban areas and preserved habitat, as Argentine ants require moist soil, and land is more
likely to be watered in urban regions (Suarez et al. 1998, Holway 1999).
The coast horned lizard is also known to be a cryptic species, and has long periods of inactivity
throughout the year (Brattstrom 1996, 2001, 2013, Hager and Brattstrom 1997). This may make
it an unlikely species for RASCals participants to find and record. Therefore, apparent species
absence from particular regions may be an indication of the inability of citizen scientists to
accurately record cryptic species more than species extirpations, and a comparison of RASCals
records to both historical and current records from VertNet and to Brattstrom’s study can be used
as an important assessment of the accuracy and utility of RASCals data.
Elgaria multicarinata, the Southern alligator lizard, is native to the Pacific coast region of the
United States and is common throughout Southern California (Stebbins and McGinnis 2012). It
is found in most habitat types of the region, including grassland, chaparral, sage scrub and urban
areas (Stebbins and McGinnis 2012). It is well camouflaged, however, and therefore difficult to
see (Stebbins and McGinnis 2012). There have been few if any studies conducted regarding the
response of E. multicarinata to urbanization. However, the range of E. multicarinata includes
heavily developed areas, and it has expanded into urban regions that less adaptable species
cannot use (Greg Pauly pers. comm.).
Trachemys scripta, the red-eared slider, and Lithobates catesbeianus, the American bullfrog, are
both invasive species that are widely distributed and well established throughout California. The
red-eared slider is known as the most widespread invasive reptile species in the world (Kraus
2009). It occurs in several breeding populations throughout California, and is known to
negatively impact populations of the native Western pond turtle, Emys marmorata (Spinks et al.
2003, Patterson 2006, Fidenci 2006, Thomson 2010). Red-eared sliders are particularly common
in places with high human density and moderately or highly modified habitats (Spinks et al.
2003, Conner et al. 2005, Eskew et al. 2010, Thomson et al. 2010), which may indicate
continuous introduction of pets into the population, but also demonstrates the ability to live
successfully in developed regions. However, there has been no systematic review of current
California distribution (Thomson et al. 2010).
Native to Eastern North America, the American bullfrog, Lithobates catesbeianus, is now
common throughout the Western United States (Hayes and Jennings 1986). It is thought to be
one of the primary causes of native frog decline in the region, because bullfrogs may both
outcompete and depredate native anurans (Bury et al. 1980, Applegarth 1983, Hayes and
Jennings 1986, Blaustein and Kiesecker 2002, Kats and Ferrer 2003). Bullfrogs are also tolerant
hosts of the fungal infection Batrachochytrium dendrobatidis, or chytrid fungus, and frequently
cause its spread to other susceptible species (Gervais et al. 2013). In addition, several studies
have shown that urban development and habitat modification do not significantly impact bullfrog
populations or reproduction, as long as permanent bodies of water are available (D’Amore et al.
2010, Gagne and Fahrig 2010, Ficetola et al. 2010). Bullfrogs are able to persist in highly
modified landscapes where native frogs do not (D’Amore et al. 2010, Gagne and Fahrig 2010).

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Species Distribution Mapping
To assess distribution of P. blainvillii, E. multicarinata, T. scripta, and L. catesbeianus, I used
ArcGIS (ArcMap 10.2.2, Esri) to map all observation points of these four species from the
RASCals data set (observations as of December 2014) onto a standard basemap of the counties
and interstate highways of Southern California (“County Boundaries of California, USA” 2010,
“USA Freeway System” 2014). I compared these maps to maps of all georeferenced observations
of these species from VertNet (for a full list of the collections from which these VertNet records
came, see references). For E. multicarinata, P. blainvillii and L. catesbeianus, I divided
observations by a span of decades of collection and mapped according to these divisions, in order
to provide a better picture of how distribution has changed over the past century. I made
divisions so that each span of time included at least 100 observations, and the last span of time
always included observations recorded after 1990, to provide information about present and
recent distribution. I was only able to create one map for T. scripta, for which there were few
georeferenced observations in VertNet, and all were collected in recent decades.
I then mapped RASCals observations for these species, and VertNet observations recorded after
1990, on map layers depicting impervious surface cover and protected areas of Southern
California, in order to assess potential patterns of urban avoidance or exploitation in recent
decades (“National Land Cover Database – percent imperviousness, superzone 2” 2011,
“Protected Areas of the Pacific States (USA)” 2008). I used these maps to determine how many
observations of each species fell within the protected areas, both to determine whether RASCals
participants were sampling protected areas more often, and whether each species is more often
found in protected habitat. I conducted a two-tailed Z-test of differences in population
proportions to determine whether these four species were sampled more or less frequently in
protected areas than were all RASCals species combined.
Results
Comparing iNaturalist Projects
To assess the success or failure of a project, I used random forest measures of variable
importance to evaluate the importance of project characteristics for predicting whether the
project is one of the 50 projects with the greatest number of observations, or the 50 with the least
observations (Fig. 1), and for predicting number of observations per day (Fig. 2) and number of
participants (Fig. 3) of the 100 projects with the most observations. In all cases, the two
measures of variable importance differed in their rankings of the variables from most important
to least important (Fig. 1 – 3). Mean decrease in accuracy and percent increase in MSE are the
most reliable measures of variable importance (Breiman 2001, Díaz-Uriarte and Alvarez de
Andrés 2006); therefore I assessed the order of variable importance solely according to mean
decrease in accuracy and percent increase in MSE.

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The random forest algorithm produced a model to predict whether a project is one of the top 50
or bottom 50 projects with an Out Of Bag (OOB) error rate of 2.13%. Therefore the model
misclassified only two out of 94 total observations. The number of variables used at each split
was three, and the number of trees produced 500. The most important variable was the number of
participants, followed by the number of days a project was active (from start date to date of the
last observation) (Fig. 1, Table 2). There is a clear relation between the amount of time a project
is able to remain active, and the number of observations it accumulates (Table 1).
Figure 1. Rank of variable importance (mean decrease in accuracy and the mean decrease in the
Gini coefficient) produced by random forest for predicting whether an iNaturalist project is one
of the 50 projects with the most observations, or the 50 projects with the least observations.
Table 2. Mean value, minimum value, and maximum value of characteristics of top and bottom
projects (top = 50 projects with most observations; bottom = 50 projects with least observations).
Top Projects Bottom Projects
Characteristic Mean Value (SD) Min.
Value
Max.
Value
Mean Value (SD) Min.
Value
Max.
Value
# Observations 9,733.8(11,409.4) 2998 54,665 10.3(0.5) 10 11
# Participants 258.9(310.8) 4 1984 4.1(3.2) 1 14
Days Active 759.9(294.8) 78 1453 138.4(200.6) 1 802
Days Existed 775.6(294.4) 224 1457 474.2(312.9) 74 1265
Species Recorded 1162.0(1344.1) 1 8558 4.5(3.3) 0 11
# Journal Posts 3.6(11.9) 0 79 0 0 0
Creator category, geographic size, and the number of journal posts the creator has posted also
influenced project success (Fig. 1). Top projects were much more likely to be started by a
scientific organization than by a member, and were more likely to survey larger regions, such as
states, national parks, or entire countries, as opposed to local parks or cities. Top projects were
also more likely to have journal posts (Table 2). “Journals” are posts made by project creators on
project pages, and often discuss milestones reached (such as 1000, 2000 or a greater number of
observations) or specific instructions for participants. Purpose of the project was minimally

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important, even though purpose and creator category were often highly related, i.e., the purpose
of the project was only ever reported to be for data collection if it was started by a scientific
organization.
Location of a project, scope of a project (whether it was to survey a specific taxon or all forms of
biodiversity), and target taxon were minimally important for predicting top or bottom projects
(Fig. 1). However, some trends in these characteristics are present. Of top projects based in the
United States, the majority was in Texas or California. Projects were less likely to be successful
if they focused on plants as opposed to animal or insect taxa. Top projects were also more likely
to record a greater number of species than bottom projects (Table 2).
The random forest model to predict the number of observations per day of the top 100 projects
explained 12.27% of the variation in observations per day. The number of variables used at each
split was also three, and the number of trees produced 500. The number of participants, number
of days the project was active, and the creator category were also the three most important
variables for predicting the average number of observations per day a project receives, similar to
the model for top and bottom projects (Fig. 2). The 100 projects with the most observations
received a maximum of 83.3 average observations per day, and a minimum of 1.4 average
observations per day. Geographic size was no longer as important to predict number of
observations per day of the top 100 projects as it was to predict top or bottom projects, though
the number of journal entries is similarly important (Fig. 1, 2). Other variables are similarly less
important, including the target taxon, scope, and purpose of the project (Fig. 1, 2).
Figure 2. Rank of variable importance (percent increase in the mean squared error (MSE) and
the increase in node purity) produced by random forest for predicting the number of observations
per day of the 100 iNaturalist projects with the most observations.
The random forest model explained 22.12% of the variation in number of participants of the top
100 projects. The number of variables used at each split was also three, and the number of trees
produced 500. Location was the most important variable to predict the number of participants of

13
the top 100 iNaturalist projects (Fig. 3). Days active, creator category and journal entries were
important for predicting number of participants, similar to the model for top and bottom projects
and the model for average observations per day (Fig. 3). Top projects had a maximum of 1984
participants, and a minimum of 4 participants (Table 1).
Figure 3. Rank of variable importance (percent increase in the mean squared error (MSE) and
the increase in node purity) produced by random forest for predicting the number of participants
of the 100 iNaturalist projects with the most observations.
There have been a total of 118 reptile and amphibian species observed by RASCals participants
between the project start on June 7, 2013 and February 2015, with a total of 4,903 observations.
Of the 4,903 observations, 1,935 (39.5%) were recorded from within government-protected areas
of Southern California. The species accumulation curve has not yet but almost reached
asymptote (Fig. 4). According to VertNet, there are 318 reptile and amphibian species recorded
in the ten counties of Southern California out of a total of 142,623 records. This number may be
inflated by synonymous species.

14
Figure 4. Species accumulation curve of total species observed by RASCals participants as of
February 2015. One sampling event consists of a participant uploading one photo (n = 4903).
RASCals participants sampled some counties of Southern California more thoroughly, in terms
of number of observations, than other counties (Table 3). More species are recorded in VertNet
than are recorded by RASCals participants for all ten counties of Southern California (Table 3).
Of the species recorded in the VertNet database, 215 species were not observed by RASCals
participants. The most common genera of the species unique to VertNet are listed in Table 4.
These are genera for which four or more species were unrecorded by RASCals (though RASCals
participants may have recorded other species in these genera). Of the species recorded by
RASCals participants, 14 were not listed in the VertNet database (Table 4).

15
Table 3. Number of species and number of observations or samples recorded by RASCals participants and in the VertNet database by county,
compared to county area, population density, percent protected land, and other population demographics.
RASCals VertNet
County
%
Bachelors
or higher1
%
White1
Median household
income1
%
Protected
Area2
County
Area
(km2
)1
Population
Density
(persons/km2
)1
Observations Species Samples Species
Santa Barbara 31.3 46.5 62,779 45.89 7083.3 59.9 84 20 9330 132
Kern 15 36.9 48,552 25.54 21053.5 39.9 138 37 10798 135
Ventura 31.4 47.3 76,544 54.32 4771.8 172.5 168 34 3334 87
San Luis
Obispo 31.5 69.9 58,697 25.26 8540.1 31.6 178 25 4602 81
Imperial 13.3 12.8 41,807 58.85 10,813.2 16.2 208 36 7833 127
Orange 36.8 42.6 75,422 27.14 2046.9 1470.7 258 30 4461 96
San Bernardino 18.7 31.4 54,090 67.12 51927.3 39.2 536 55 19935 172
Riverside 20.5 38 56,592 61.85 18657.6 117.3 712 62 21452 214
San Diego 34.6 47.2 62,962 49.79 10890.9 284.2 1159 81 32834 245
Los Angeles 29.7 27.2 55,909 34.13 10503.6 934.6 1460 71 27956 193
1. US Census Bureau State and County Quick Facts 2010
2. California Protected Areas Database Statistics (Orman & Dreger 2014)

16
Table 4. Genera for which four or more species listed in the VertNet database were not listed in
RASCals records, and species unique to RASCals, with their common names.
Common genera of
species unique to
VertNet
Common name Species unique to
RASCals
Common name
Ambystoma Salamander
Anniella stebbensi
Southern California
legless lizard
Batrachoseps Salamander
Coluber fuliginosus
Baja California
coachwhip snake
Bufo Toad Graptemys ouachitensis Ouachita map turtle
Cnemidophorus Whiptail lizard Graptemys
pseudogeographica
False map turtle
Crotalus Pit viper
Hemidactylus platyurus
Flat-tailed house
gecko
Crotaphytus Collared lizard
Hypsiglena chlorophaea
Northern desert
nightsnake
Hyla Tree frog Lampropeltis
multifasciata
Coast mountain
kingsnake
Hypsiglena Night snake
Lithobates berlandieri
Rio Grande leopard
frog
Lampropeltis King snake Pantherophis guttatus Corn snake
Masticophis Whip snake
Phyllodactylus nocticolus
Peninsular leaf-toed
gecko
Phrynosoma Horned lizard Pseudacris
hypochondriaca
Baja California tree
frog
Rana Frog Pseudacris sierra Sierran tree frog
Sceloporus Spiny/Fence
lizard Sceloporus uniformis
Yellow-backed spiny
lizard
Thamnophis Garter snake Takydromus sexlineatus Asian grass lizard
Uta Side-blotched
lizard
Of the 14 species that were recorded by RASCals participants but are not listed in VertNet, six
are non-native to Southern California (Graptemys ouachitensis, Graptemys pseudogeographica,
Hemidactylus platyurus, Lithobates berlandieri, Pantheris guttatus, and Takydromus
sexlineatus). Four species have older synonyms by which they might be listed in the VertNet
database. Sceloporus uniformis used to be called Sceloporus magister (Schulte et al. 2006);
Pseudacris sierra and Pseudacris hypochondriaca used to be one species, called Pseudacris
regilla (Recuero et al. 2006); and Lampropeltis multifasciata was synonymous with
Lampropeltis zonata (Myers et al. 2013). Of the four remaining species, one was recently
described in 2013 (Aniella stebbinsi) (Papenfuss and Parham 2013).
I used a generalized mixed linear model to evaluate which demographic or geographical factors
influence the number of observations made by RASCals participants in each of the ten counties
of Southern California. The models created by stepwise selection based on both BIC and AIC
were the same, and so one model is reported (Table 5). Parameters that remain in the model

17
include percent protected area, population density, percent of the population that is white, and
median household income (Table 5).
Table 5. Coefficients, T-values and p-values of variables that remain in the final generalized
linear mixed model used to predict the number of observations of the 10 counties of Southern
California.
Variable Coefficient T-value P-value
Percent Protected Area 0.064 1.989 0.103
Population Density 0.001 2.202 0.079
Percent White 0.056 1.366 0.230
Median Household Income 9.418e-05 -1.683 0.153
None of the variables were significant according to an α-value of 0.05. Population density was
significant according to an α-value of 0.1. All variables only had a small effect on number of
observations recorded by RASCals participants in each county, according to coefficient values
(Table 5). There is no immediately obvious trend in the number of observations by county
according to any of the demographic variables (Table 3).
The species Elgaria multicarinata and Phrynosoma blainvillii were both well represented across
many decades in the VertNet database, and well sampled by RASCals participants (Fig. 5, 6).
For both of these species, sampling after 1990 as recorded in the VertNet database dropped off
considerably, with lower sample sizes for recent years (Fig. 5, 6). For E. multicarinata in
particular, RASCals observations demonstrate a clear presence of the species in urban regions of
Los Angeles that VertNet does not record (Fig. 5). The distribution of E. multicarinata does not
appear to have changed much throughout the past century (Fig. 5). In contrast, RASCals records
and VertNet records from after 1990 of P. blainvillii demonstrate a similar distribution (Fig. 6).
P. blainvillii is not recorded in urban Los Angeles, where it was found in the decades before
1970 according to VertNet records (Fig. 6).
Lithobates catesbeianus and Trachemys scripta both had considerably fewer records in the
VertNet database, and records from before the mid-twentieth century were scarce (Fig. 7, 8). T.
scripta was barely represented in VertNet, with only 16 records, and none before 1970.
However, RASCals participants have demonstrated that this species is much more abundant and
widely distributed throughout Southern California than is indicated in the VertNet database (Fig.
8). More records of L. catesbeianus exist in the VertNet database, particularly after 1990, than
have been recorded by RASCals participants (Fig. 7). However, there also seems to be an
indication of greater abundance in Los Angeles before 1990 than in recent decades (Fig. 7).
Maps depicting protected areas and impervious surface cover of Southern California demonstrate
that E. multicarinata is found within highly urban Los Angeles, and that these urban regions are
the areas best sampled by RASCals participants for this species (Fig. 9). Only 42 of the 363
observations (11.6%) of E. multicarinata made by RASCals participants were recorded from
within protected areas. This is significantly fewer than the total proportion of RASCals

18
observations taken within protected areas (Z = 10.5906, p-value = <0.0001). P. blainvillii, in
contrast, is not recorded by VertNet or by RASCals participants in areas with high impervious
surface cover (Fig. 10). It is, however, present throughout protected regions of Southern
California (Fig. 10). 34 of the 66 RASCals observations of P. blainvillii (51.5%) were recorded
from within protected areas, significantly more than the total proportion of RASCals
observations taken within protected areas (Z = –1 .988, p-value = 0.0466). L. catesbeianus,
similarly, is found less frequently in regions with high impervious surface cover (Fig. 11). 23 of
the 53 RASCals observations of L. catesbeianus (43.4%) were recorded from within protected
areas. This proportion is not significantly different from the total proportion of RASCals
observations recorded within protected areas (Z = -0.5822, p-value = 0.5619). RASCals
participants demonstrate that T. scripta is found within areas of high impervious surface cover
(Fig. 12). Only 22 of the 144 RASCals observations of T. scripta (15.3%) were recorded from
within protected areas, significantly fewer than the total proportion of observations recorded
within protected areas (Z = 5.8715, p-value = <0.0001).

19
Figure 5. Distribution of Elgaria multicarinata in Southern California according to all VertNet database
records that include GPS coordinates from before 1920 (n=190), 1920 to 1949 (n=509), 1950 to 1970 (n=875),
1971 to 1990 (n=563), and after 1990 (n=175), as well as the distribution of research-grade observations by
RASCals participants as of December 2015 (n=363).

20
Figure 6. Distribution of Phrynosoma blainvillii in Southern California according to all VertNet database
records that include GPS coordinates from before 1915 (n=406), 1915 to 1950 (n=346), 1951 to 1970 (n=315),
1971 to 1990 (n=175), and after 1990 (n=77), as well as the distribution of research-grade observations by
RASCals participants as of December 2015 (n=66).

21
Figure 7. Distribution of Lithobates catesbeianus in Southern California according to all VertNet database
records that include GPS coordinates from before 1960 (n=155), 1960 to 1989 (n=232), and after 1990
(n=206), as well as the distribution of research-grade observations by RASCals participants as of December
2015 (n=53).

22
Figure 8. Distribution of Trachemys scripta in Southern California according to all VertNet database records
that include GPS coordinates (n=16), as well as the distribution of research-grade observations by RASCals
participants as of December 2015 (n=144).

23
Figure 9. Distribution of Elgaria multicarinata from VertNet records after 1990 (n=175), and as recorded by RASCals participants (n=363). Maps
depict current California protected areas according to the Commission for Environmental Cooperation (“Protected Areas of the Pacific States
(USA)” 2008) and percent impervious surface cover according to the National Land Cover Database ((“National Land Cover Database – percent
imperviousness, superzone 2” 2011).
Protected Areas
Ia - Strict Nature Reserve
Ib - Wilderness Area
II - National Park
III - Natural Monument
IV - Habitat/Species Management Area
V - Protected Landscape/Seascape
VI - Managed Resource Protected Area
Unknown
Percent Impervious Surface Cover
0%
1 - 25%
26 - 50%
51 - 75%
76 - 100%

24
Figure 10. Distribution of Phrynosoma blainvillii from VertNet records after 1990 (n=77), and as recorded by RASCals participants (n=66). Maps
Protected Areas
II - National Park
Unknown
0%
1 - 25%
26 - 50%
51 - 75%
76 - 100%

25
Figure 11. Distribution of Lithobates catesbeianus from VertNet records after 1990 (n=206), and as recorded by RASCals participants (n=53).
Maps depict current California protected areas according to the Commission for Environmental Cooperation (“Protected Areas of the Pacific States
Protected Areas
II - National Park
Unknown
0%
1 - 25%
26 - 50%
51 - 75%
76 - 100%

26
Figure 12. Distribution of Trachemys scripta from VertNet records after 1990 (n=206), and as recorded by RASCals participants (n=144). Maps
Protected Areas
II - National Park
Unknown
0%
1 - 25%
26 - 50%
51 - 75%
76 - 100%

27
Discussion
Comparing iNaturalist Projects
I used random forest to evaluate what factors influence the success (in terms of number of
observations and number of participants) of an iNaturalist project. I used number of observations
and number of participants as a measure of success because larger data sets are generally more
reliable for scientific analyses, especially in terms of distribution and diversity studies. On
iNaturalist, for example, projects with more observations also tended to record a greater number
of species. The number of participants was clearly the most important variable for predicting
both whether a project was in the top or bottom category, and the number of observations per day
of the top 100 projects.
Very few studies have investigated what factors influence participation in ecological citizen
science projects, despite its importance for success. Participant recruitment and motivation is
critical to iNaturalist because participants are not often actively recruited (exceptions include
projects publicized by their creators, like RASCals), but independently find and choose to
participate in a project. In addition, participants may stop participating at any time with little
consequence, an effect that may be compounded by the inherent anonymity of the internet (Nov
et al. 2011). Therefore finding factors that do influence participation in individual projects is
critical to maintaining the success of projects on such platforms. A few studies have investigated
factors that influence participant motivation, and thus contribute more to continued participation
than initial recruitment. Nov et al. (2011) found that rewards such as reputation and social
interaction, a desire to contribute to a project’s specific objectives, or the influence of friends,
family or colleagues, influence motivation and contribution to online citizen science research,
but focused on two location-independent and purely computer-based projects. Rotman et al.
(2012) also included eBird in their analysis, and found that a desire for social collaboration,
assistance to scientists, and desire to make scientific knowledge more accessible also influence
participation. eBird is similar to iNaturalist, and different from other online citizen science
projects, in that many of its projects require outdoor access and location-specific participation,
making recruitment of a specific set of volunteers necessary.
However, by analyzing factors that are important for predicting the number of participants a
project has, evaluation of continued participant contribution is limited, because a participant only
needs to contribute once to be included in the number of participants. Considering the variables
that influence number of participants limits the scope of the implications to participant
recruitment rather than participant contribution. Therefore it is critical to consider variables that
influence both number of participants and number of observations. The results are complicated
by the fact that a somewhat different set of variables is important for predicting number of
participants than number of observations (or average observations per day). The category of the
creator (i.e. member or larger organization) was important for predicting average observations
per day, indicating that a project had greater success if it was started by a scientific organization.
The number of days a project was active was important to both participation and number of
observations, though this may be primarily due to correlation between the age of the project, the
fact that it had achieved recent contributions, and the number of participants. Older projects

28
might be more likely to accumulate more participants, while more participants might also
increase the likelihood of recently posted observations, thus increasing the number of days a
project was active. However, creator engagement may also impact whether or not a project
remains active after initial creation. The active recruitment of new volunteers or encouragement
of maintaining participation would ensure the continued contribution of new observations.
More journal entries posted by the creator could be an indication of creator engagement with the
project, and therefore might indicate a greater willingness to engage in efforts to recruit
volunteers or maintain participant interest. Greg Pauly, the creator of RASCals, which is one of
iNaturalist’s top projects, reaches out to target participants and encourages continued
contributions from key locations or participation by top participants. Such engagement helps
ensure the continued success of a project. In addition, content of the journal entries may have
some influence on participant success. Journal entries were frequently congratulatory posts about
reaching a specific number-of-observations milestone, such as 1000, 2000, or more observations.
Such content highlights the success of a project as well as the contributions that participants have
made in advancing the goals of a project, providing a reward (i.e. specific congratulations or
praise) for progress, all of which are important motivations for participants of online citizen
science projects (Nov et al. 2011, Rotman et al. 2012, Newman et al. 2012).
Texas and California had the greatest representation among top projects. The importance of
location highlights the difference between the semi-computer-based iNaturalist and other purely
computer-based citizen science projects, such as Foldit, an online game in which participants
fold proteins to determine chemically stable arrangements. Participants of iNaturalist must be
present at specific locations to contribute to the location-focused projects. In order to understand
what about a location makes it particularly suitable for gaining a large number of participants in
these citizen science projects requires further investigation. California and Texas may have the
greatest representation among top projects simply because they are so large. Projects focused on
large regions were more likely to have a greater number of participants. However, other factors
in a state or region may influence the level of participation, such as the availability of open space
suitable for participation (i.e. suitable for finding species of interest and taking photos), the
education level of the population, the prevalence of outdoor education that might increase
interest in outdoor or biological activities, or the ability of the project creators to achieve
publicity for their projects.
Finally, target taxon (or general target, in the case of number of observations) was also important
for predicting the number of participants. Birds, for example, have traditionally been the focus of
many large-scale and popular citizen science projects, and were also popular on iNaturalist
(Bonney et al. 2009). The projects with the most participants included diverse taxonomic
categories. However, the more specific the taxon of interest, the less likely it was to have a large
number of participants. Projects with fewer participants were more frequently focused on
individual species or plant groups, and also the category I labeled “everything”, i.e., projects
whose goal was to document all life forms. Therefore projects that were too broad or too specific
had the fewest participants.
Other factors that I did not assess might be of even greater importance in predicting the success
of a project. The random forest model was only able to explain 22.12% of the variation in

29
number of participants, so other factors must influence participation and observations. For
example, how well known the creator of the project is might influence how many participants it
has. The project created by National Geographic, as might be expected, is the most popular on
iNaturalist. As stated, the amount of effort the creator puts into publicizing and recruiting for the
project may have tremendous influence on participation. Finally, on iNaturalist and similar
platforms, the first or most successful project of a certain type is more likely to keep gaining
observations and participants. More popular projects are often featured on the “recent activity”
feature of the home page, and so may be more likely to receive contributions from new members.
Thus using iNaturalist as an example, we gain insight into factors that increase the success of a
citizen science project. Focusing on a specific taxonomic group, especially one that is generally
considered charismatic and is easy to document, such as birds, reptiles, or butterflies, increases
the likelihood of participation. A project is more likely to be successful if it focuses on a large
region such as a national park or state as opposed to a local park or town, reinforcing the value of
citizen science projects for gathering data about large regions that would be impossible for a
single scientific team to survey. However, it is critical to remember that the larger the region, the
more difficult it is to sample the area thoroughly and evenly. Active creator engagement, such as
making efforts to publicize and recruit volunteers, helps maintain participation, and can also
ensure even sampling of larger areas. Even efforts as simple as informing participants when
certain milestones are reached, such as 500, 1000, or more observations, may ensure continued
interest in the project’s success.
Though VertNet records suggest there may be as many as 200 species that RASCals participants
have not recorded, the species accumulation curve indicates that participants will record some
portion of these species with future observations (Fig. 4). Moreover, RASCals participants
recorded six invasive species not present in the VertNet database, reinforcing the value of urban
citizen science projects for detecting range changes and introductions of invasive species
(Delaney et al. 2008, Bernstein and Bernstein 2013). In addition, RASCals participants
contributed considerably to existing distributional data of the species Aniella stebbinsi, described
only recently in 2013 (Papenfuss and Parham 2013). The continuing collection of distributional
and biodiversity data such as that collected by iNaturalist members remains important for
expanding our knowledge of new or little-studied species.
However, the 200 species not recorded by RASCals participants also emphasizes the limits of
citizen science. Of the most common genera, several could be considered taxa that are cryptic,
rare, or difficult to find, such as the salamanders, vipers, or nocturnal snakes. Participants of
ecological citizen science studies may be less adept at identifying or recording cryptic or rare
species (Delaney et al. 2008). A more ominous interpretation of these data, however, is that some
species may be becoming rare within California. Amphibian decline is prevalent in California,
and has been attributed to chytrid fungus, pesticide use, habitat destruction and other factors
(Davidson et al. 2002). Gibbons et al. (2000) also found that reptiles are declining across the
world, due to habitat loss, pollution, introduced species and climate change, factors that may also
affect reptile populations in Southern California.

30
The results also highlight the difficulty posed by changing taxonomic classification, both for
citizen science projects, and for museum collections. As taxa are renamed, historic collections
are rarely updated, and only specialists with historical knowledge of these synonymies can easily
make accurate comparisons. For example, several species observed in the RASCals database are
likely recorded in VertNet under a different name, including Pseudacris hypochondriaca and P.
sierra, which were recently split from Pseudacris regilla (Recuero et al. 2006). This instance
highlights the importance of maintaining up to date records in museums and databases like
VertNet, and of continuing to collect new distributional data to maintain an accurate records of
species distributions. Distributional data such as that collected on iNaturalist can be valuable for
such a need, in particular as biologists abandon traditional widespread specimen collection as a
method for maintaining records. Museum collections can be critical to contemporary biological
research, and yet collections of new specimens have declined along with decreases in funding
and in the popularity of scientific collecting (Suarez and Tsutsui 2004). Inexpensive and simple
citizen science such as iNaturalist may be key to maintaining aspects of museum collections in
the future. The RASCals project, for example, has already contributed its several thousand
photographic records to the herpetology collection at the Natural History Museum of Los
Angeles County, ensuring maintenance of museum records without extensive specimen
collection (Greg Pauly pers. comm.).
Sampling coverage of RASCals data differs by region, suggesting that data of less well-sampled
regions should be used with caution (Table 2). The model created to predict number of
observations by county indicates that counties with a greater number of observations have a
greater population density, a greater percentage of the population that is white, a higher median
income and a larger amount of protected land. Education level (as represented by percent of the
population with a Bachelor’s degree or higher) was excluded from the final model. Though none
of the variables were significant at the 0.05 level, p-values are often unreliable indicators of
variable effect size, as sample size and standard error of the data often disproportionately
influence the statistical significance of a result (Gelman and Stern 2006). Because of the small
sample size of the data set, AIC and BIC values are more reliable indicators of the variables that
have importance in predicting observations within a county.
Though the effect size of percent protected area was small, RASCals participants did record a
large proportion (39.5%) of species observations from within protected regions. Protected areas
were therefore an important source of observations. However, the majority of observations
(60.5%) were consequently recorded in non-protected urban or suburban areas. Though
evaluation of the specific sites of RASCals observations is necessary to determine whether
observations are being recorded from backyards, city centers, or other urban or private areas, it
can be concluded that participants are recording species outside of parks or other protected land.
The strong mix of observations from both undeveloped parks and more urban areas indicates that
projects such as RASCals are promising for urbanization research. Data collected from both
protected and developed regions is necessary to gain complete knowledge of biodiversity
dynamics in a region with increasing urbanization such as Southern California, in particular
which species are extirpated from urban areas, and which species are able to persist.

31
The data also suggest that iNaturalist, like many online citizen science projects, attracts a
majority white, middle and upper class participant population (Newman et al. 2012). iNaturalist
and similar projects require access to both technology and outdoor spaces as well as leisure time
for collecting and uploading observations, factors that may be associated with income (Newman
et al. 2012, Ess and Sudweeks 2001). New technologies can increase access to citizen science for
a more diverse population, but consideration of social and cultural factors may be critical to the
future success of citizen science, particularly if success is defined not only in terms of a project’s
‘scientific value’, but also in terms of its educational value, i.e. its ability to teach as many
participants as possible about the scientific process (Ballard et al. 2008, Newman et al. 2012).
The need to attract diverse participants may be particularly relevant in urban citizen science
studies such as RASCals, because urban areas more often have highly diverse populations
(Nyden et al. 1998). Moreover, the educational value of citizen science may be greatest in urban
areas, because over half of Americans live in urbanized regions (USCB 2001).
In summary, the RASCals project has demonstrated the clear value of citizen science for
sampling within urban areas. Citizen science therefore should be considered as a valuable
alternative to more traditional methods of maintaining records of species distributions and
diversity in these regions. In addition, both the educational and scientific value of citizen science
could increase, particularly in diverse urban regions, if care is taken to reach out to a more
diverse participant population.
A detailed evaluation of the data collected by RASCals participants on two native and two
invasive species demonstrates that each of the four species does seem to display a different
response to urbanization (Fig. 5-12). E. multicarinata is still clearly present in the urban center of
Los Angeles, indicating its ability to adapt to urban environments. Interestingly, RASCals
participants sampled the distribution of E. multicarinata much more thoroughly from within the
urban center of Los Angeles than outside of it, with only 11.6% of observations coming from
protected land, although VertNet data demonstrate that E. multicarinata is present outside of
urban Los Angeles (Fig. 5, 9). E. multicarinata was observed much less frequently inside
protected areas than RASCals observations in general, suggesting that the species may be found
less frequently in these areas. The combined VertNet and RASCals data confirm that E.
multicarinata is able to flourish even in heavily urbanized Los Angeles (Greg Pauly pers.
comm., Stebbins and McGinnis 2012). The density of RASCals observations from within the
urban areas of Southern California, and the lack of recent VertNet records from these same areas,
again highlights the utility of citizen science for sampling within urban/suburban regions, as well
as for supplementing declining museum collections such as those found on VertNet.
In contrast, both RASCals and VertNet data indicate that the second native species, P, blainvillii,
is absent from urban Los Angeles, confirming studies that show that P. blainvillii is extirpated
from heavily urbanized regions (Fisher and Case 2000, Fisher et al. 2002, Lemm 2006,
Brattstrom 2013). Many observations were recorded from within protected areas, more than the
total frequency of RASCals observations recorded from protected areas, suggesting the particular
persistence of P. blainvillii in these regions. Thus the species is still present across much of its

32
historical range, as VertNet and RASCals records demonstrate, confirming Brattstrom’s 2013
findings (Fig. 6). Moreover, it is important to note the particular value of photographic data
collection methods for species of special concern such as P. blainvillii. As species of special
concern cannot be collected for museum collections, projects such as RASCals could become
critical to maintaining knowledge of distributions.
L. catesbeianus has many fewer observations in the RASCals database than are present in
VertNet in the last decade. Records from both VertNet and RASCals indicate the species is
present in some urbanized or suburban regions (Fig. 11). However, further sampling is necessary
to gain a complete understanding of the distribution of this species. RASCals observations
suggest that L. catesbeianus may not be widespread, especially when comparing observations of
L. catesbeianus to those of T. scripta. As both species require standing water and similar habitat
types, the presence of one species with the other might be expected (Conner et al. 2005, Ficetola
et al. 2010). Yet T. scripta is much more extensively observed by RASCals participants than L.
catesbeianus. L. catesbeianus may be more cryptic than T. scripta, and therefore simply not
observed. Lack of records may also be due to a bias in what participants record. As sampling by
participants is not systematic, we cannot assume the absence of a species from a lack of data.
In contrast to the paucity of RASCals observations of L. catesbeianus, there are many more
observations of T. scripta recorded by RASCals participants than are present in the VertNet
database, for all decades combined. In this case, RASCals far outstrips the completeness of data
found in the VertNet database. RASCals data demonstrates that T. scripta is able to persist in the
heavily urban environment of the city of Los Angeles, but participants have not recorded very
many occurrences outside of urban regions. The presence of L. catesbeianus outside of urban
regions suggests that there is sufficient water for T. scripta to also survive in these regions.
Therefore the lack of T. scripta in regions outside of the city suggest that the prevalence of the
species is due to the release of pet turtles into local water sources by urban residents, a known
source population of T. scripta (Cadi and Poly 2004).
In summary, RASCals data confirm that E. multicarinata and T. scripta are able to adapt to
urban environments, but demonstrate that P. blainvillii may not be urban-adaptable. More data is
needed to understand current distributions of L. catesbeianus.
Conclusions
iNaturalist has been able to attract thousands of participants to upload hundreds of thousands of
photographic observations across the world, and thus has demonstrated its potential as a
scientific tool. RASCals highlights the particular value of citizen science for both urban
biodiversity and introduced species research, as long as consideration is taken of the factors that
influence participation and any bias that may be inherent to the data collection process. Greater
value still can be gained from looking at specific species occurrences, such as invasive or
endangered species, which are the focus of many other iNaturalist projects. Since the date and
location of each observation is recorded, knowledge can be gained about changing migration
patterns, new invasions, or shifting distribution in the face of climate change or habitat
modification.

33
However this study also underlines the necessity for and benefits of a more seamless interchange
of citizen science data with existing scientific records and databases. Citizen science could be a
critical tool for maintaining records of species distributions as biologists collect specimens for
museums less frequently. Moreover, if greater consideration is taken of the factors that influence
participation, citizen science could become an even more effective educational tool for urban
populations.
Acknowledgements
I would like to thank Kristine Kaiser for being a wonderful advisor, and Greg Pauly, manager of
the Los Angeles County Natural History Museum herpetology collections and creator of the
RASCals project, for all his assistance. Thank you to the creators of iNaturalist, and to Jessica
Hernandez, Brenna Gormally, Jon Feingold and Maria Caruso for their help and support
throughout the research process. Thank you to Gabriel Chandler and Johanna Hardin for
assistance with statistics and analyses, and to the Pomona College Department of Biology for
providing the resources necessary to complete this study.
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