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976
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Diversity and Distributions. 2020;26:976–986.wileyonlinelibrary.com/journal/ddi
Received: 28 October 2019
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Revised: 21 Feb ruar y 2020
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Accepted: 21 Ma rch 2020
DOI : 10.1111/ddi .13068
BIODIVERSITY RESEARCH
Integrating citizen science data with expert surveys increases
accuracy and spatial extent of species distribution models
Orin J. Robinson1 | Viviana. Ruiz-Gutierrez1 | Mark D. Reynolds2 |
Gregory H. Golet3 | Matthew Strimas-Mackey1 | Daniel Fink1
This is an op en access arti cle under the ter ms of the Creative Commons Attribution L icense, which pe rmits use, dis tribu tion and reprod uction in any med ium,
provide d the original wor k is properly cited.
© 2020 The Authors. Diversity and D istrib ution Publisherd by John Wiley & Sons Ltd
1Cornell L abor atory of Ornit holog y, Cornell
University, Ithaca, NY, USA
2The Nature Conse rvan cy, San Fran cisco,
CA, USA
3The Nature Conse rvan cy, Chico, C A, USA
Correspondence
Orin J. Rob inson, Cornell Labo rator y
of Ornitholog y, Cornell University, 159
Sapsucker Woods Roa d, Ithaca, NY 1485 0
USA.
Email: ojr7@cornell.edu
Editor: Luigi Maiorano
Abstract
Aim: Information on species’ habitat associations and distributions, across a wide
range of spatial and temporal scales, is a fundamental source of ecological knowl-
edge. However, collecting information at relevant scales is often cost prohibitive, al-
though it is essential for framing the broader context of more focused research and
conservation efforts. Citizen science has been signalled as an increasingly important
source to fill in data gaps where information is needed to make comprehensive and
robust inferences on species distributions. However, there are perceived trade-offs
of combining highly structured, scientific survey data with largely un-structured, citi-
zen science data.
Methods: We explore these trade-offs by applying a simplified approach of filtering
citizen science data to resemble structured survey data and analyse both sources of
data under a common framework. To accomplish this, we integrated high-resolution
survey data on shorebirds in the northern Central Valley of California with observa-
tions in eBird for the entire region that were filtered to improve their quality.
Results: The integration of survey data with the filtered citizen science data resulted
in improved inference and increased the extent and accuracy of distribution models
on shorebirds for the Central Valley. The structured surveys improved the overall ac-
curacy of ecological inference over models using citizen science data only by increas-
ing the representation of data collected from high-quality habitats for shorebirds.
Main conclusions: The practical approach we have shown for data integration can
also be used to improve the efficiency of designing biological surveys in the context
of larger, citizen science monitoring efforts, ultimately reducing the financial and time
expenditures typically required of monitoring programs and focused research. The
simple method we present can be used to integrate other types of data with more
localized efforts, ultimately improving our ecological knowledge on the distribution
and habitat associations of species of conservation concern worldwide.
KEYWORDS
citizen science, data filtering, data integration, data quality, ecological niche model, species
distribution model, structured sur vey
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1 | INTRODUCTION
Information on species’ habitat associations and distributions is a
fundamental source of ecological knowledge (Sofaer et al., 2019).
This information is often of interest across a broad range of spa-
tial and temporal scales, from high-resolution information that is
more relevant for research on habitat selection (Matthiopoulos,
Hebblewhite, Aart s, & Fieberg, 2011) or needed to inform manage-
ment objectives (Zipkin, Andrew Royle, Dawson, & Bates, 2010) to
larger-scale inferences that are useful to address broader questions
(e.g. potential range shifts with changing climatic conditions; Lyon,
Debinski, & Rangwala, 2019). However, the process of collecting bi-
ological obser vations across large spatial scales is of ten cost prohib-
itive for most research and monitoring efforts. At best, researchers
and practitioners are able to monitor plant and animal communities
just within their study regions, and during specific times of year.
However, the need to make inferences beyond the sampled range of
environmental conditions and seasons often limits our understand-
ing of the broader context of our result s and can limit the use of
applied research to inform future monitoring efforts and effective
conservation actions.
Citizen science data have been signalled as a promising source
of information to fill in information gaps needed to model species
distributions (Bradter et al., 2018; Gouraguine et al., 2019). The
collection of citizen science data is growing rapidly, and for a num-
ber of taxa, large databases of observations exist. For example, the
National Moth Recording Scheme in Great Britain has collec ted
over 11 million observations (Fox et al., 2014). There are many more
taxa for which rich citizen science data sets exist, for example birds
(Sauer et al., 2017; Sullivan et al., 2014), invertebrates (Howard,
Aschen, & Davis, 2010), bats (Newson, Evans, & Gillings, 2015),
whales (Tonachella, Natasi, Kaufman, Maldini, & Rankin, 2012) and
frogs (Westgate et al., 2015) among others. However, there are few
examples of the potential trade-offs of combining observational
data collected at smaller spatial extents with citizen science data
collected across vast extent s. This might be due in part to inherent
differences that often exist between the two data types. On the one
hand, you may have data that is collected at a high spatial resolu-
tion using skilled observers, sampling effort is of ten standardized,
and sampling occurs across a habitat gradient that is representative
of the region of interest . On the other hand, is citizen science data,
which can be collected across a wide range of sampling conditions
by obser vers that vary widely in their level of expertise, collected
across a wide range of spatial resolutions, and sampling effort is not
standardized. This has created a perceived trade-off between data
quality and quantity among data collected from structured, scientific
surveys and data collected from larger-scale, volunteer-based mon-
itoring efforts (Figure 1). The assumption being made is that an in-
crease in quantity of citizen science data comes at a significant cost
to quality. Therefore, the logical framework to integrate these two
sources of information would be one that would treat them as inde-
pendent sources of information used to inform a common underlying
distribution for a given species (Pacifici et al., 2017).
The integration of different data sources is a growing area of
methodological development in ecological statistics, and recent
advances have been made to develop ways of integrating sur vey
data (e.g. structured) with citizen science data (e.g. un-structured
data) (Miller, Pacifici, Sanderlin, & Reich, 2019). For observational
data collected at discrete locations, these methods include spec-
ifying a joint likelihood for the two data sources to estimate the
underlying species distribution (Miller et al., 2019). In cases where
this is not possible, the data source that is deemed as of lower
quality (e.g. citizen science data, museum obser vations) can be
used in two ways: (a) modelled as a covariate of the underlying
distribution or (b) used to estimate a sepa rate sp ecies dis tribution,
where a correlation structure is specified to share information
across data sources. Pacifici et al. (2017) tested these different
approaches to integrate observational data from the citizen sci-
ence project eBird (Sullivan et al., 2014) with more structured
data from the North American Breeding Bird Survey (BBS; Sauer
et al., 2017). Their results showed that the joint-likelihood ap-
proach of combining eBird and BBS data outper formed all other
approaches, including using BBS data alone.
The approach used by Pacifici et al. (2017) and Miller el at
(2019) summarized observational data at a coarse, grid-level in
order to account for dif ferences in effort, sampling approach, and
other variables that are known to influence detectability (Guillera-
Arroita, 2017). In addition, Pacifici et al. (2017) wanted to reduce
potential bias related to the degree of uncertainty about the spatial
FIGURE 1 The perceived trade-off bet ween data qualit y and
quantity of data collected using structured surveys and citizen
science ef forts. You can bypass this trade-off by processing and
filtering citizen science data using criteria such as count type
(e.g. stationary versus. travelling), duration of counts (e.g. all
observations < 30mins) and other measures that align well with
the existing data set. This approach is recommended as the most
flexible data integration method for the application of a broad
range of species distribution models
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ROBINSON et al.
scale that observations were collected for each independent eBird
checklist. This mismatch in scales bet ween the two dat a sources is
what often makes data integration between high-resolution survey
data (e.g. point count observations) with lower-resolution citizen
science data non-trivial. However, many citizen science programs
collect high-scale resolution information (e.g. camera traps) in ways
that we can infer absences, and collect additional information on ef-
fort (e.g. distance travelled, number of hours sampled) that is highly
valuable for improving the accuracy of SDMs (Kelling et al., 2019).
Here, we explore a practical approach for data integration be-
tween high-quality, citizen science data with structured survey data
that builds upon existing methods for “data pooling” (e.g. Fithian,
Elith, Hastie, & Keith, 2015). We explore the trade-of fs in inference
of using citizen science data alone, more localized and structured
data alone, and pooling together both data sets combined. In addi-
tion, we examine specific trade-offs when combining structured and
un-str uct ured da ta sour ce s by explor in g the per form ance of incre as -
ing the quantity of citizen science data through simulations, versus.
the addition of data from more targeted survey effort s. To accom-
plish this, we use The Nature Conser vancy's (TNC) BirdReturns
project as a case study (Reynolds et al., 2017). We explore ways
of combining high-resolution bird survey data collected for shore-
birds on rice fields in the northern region of the Central Valley in
California, with observations in eBird for the entire Central Valley
that are filtered to improve their quality. The specific aim of the
case study is to provide a framework for leveraging survey dat a with
citizen science data to build more accurate distribution models for
shorebird species across the extent of the Central Valley.
2 | METHODS
2.1 | Data
We us ed poi nt cou nt s car ried ou t du ri ng spring sur veys (Fe bruar y 1–
May 31; n = 8,19 2) as par t of th e TNC Bir dRetu rns pro jec t con du c te d
in 2014–2017. This project used predicted shorebird occurrence
and abundance (Johnston et al., 2015) along with predicted surface
water in the Sacramento River Valley to identify times and locations
that were likely to be import ant for migrating shorebirds. TNC used
a reverse auction approach to select and incentivize rice farmers in
the identified locations to flood their fields during the spring and fall,
making temporar y wetlands available to the migrating shorebirds.
Observers made point counts at fields enrolled in the program and at
unenrolled control sites, surveying a semi-circle with a 200 m fixed
radius. Each site was sur veyed for at least two minutes and lasted as
long as necessary to count all birds present (for more detail on count
methods see Golet et al., 2018). Effort for each point count consisted
of date, time started and ended, and name of observer.
We combined the point counts with data from the citizen science
project eBird (Sullivan et al., 2014) collected during the same time
period as the point counts. The eBird data were restricted to the
Central Valley of California, USA, and to complete checklists so that
non-detection could be inferred (Johnston et al., 2019). We also re-
stricted the eBird data to stationary checklists and travelling check-
lists limited to 30 0 m. After filtering these data, we were left with
12,891 checklists. Effor t variables for the eBird data set were date,
time obser vations star ted, duration of observation in minutes, sur-
vey protocol (stationary or travelling), distance travelled in metres
and number of obser vers.
We calculated the effort variables from the point counts to
match those of the eBird data set . Obser ver name in the point count
data set was converted into number of observers (however, it was
always one for this study), time started and ended for the point
counts was used to calculate duration in minutes, and each point
count was treated as a stationary count (distance travelled = 0). By
doing this, the two data sets contained the same effor t information
and were identical in struc ture which allowed us to simply join them
into one combined data set. We added a variable to the combined
data set to note whether an observation was a TNC point count or
an eBird checklist. We also calculated a checklist calibration index
(CCI) for each checklist in the combined data set to account for vari-
ation in expertise among observers (e.g. expertise score; Johnston,
Fink, Hochachka, & Kelling, 2018). As environmental variables, we
attached the remotely sensed Cropland Data Layer (CDL; Boryan,
Yang, Mueller, & Craig, 2011; Han, Yang, Di, & Mueller, 2012) to the
combined data set by computing the per cent of each land cover or
crop type in the CDL that was present within a 300 m radius centred
on each point count or eBird checklist. We also similarly attached
cloud-filled data from Water Tracker (Reiter, Elliott, Barbaree, &
Moody, 2018), a high spatial and temporal resolution surface water
tracking system for the Central Valley of California.
A comprehensive data set was created using all of the above
eBird checklists (12,891) plus simulated eBird checklists equal to
the number of TNC point count s that were included in the combined
data set (8,192). This resulted in a data set that was the same magni-
tude (21,083) as the combined data set and allowed us to determine
whether the improvement in accuracy from combining the data sets
was simply a function of increasing the sample size. The simulated
eBird data were created by adding a small amount of noise (via the jit-
ter fun ct ion in base R; R Core Team, 2019) to the spatial covariates of
8,19 2 rando ml y chose n ch ecklist s , si mi la r to the ove rsa mp ling proc e-
dure in Fink et al. (2019); however, we used all checklists rather than
only positive observations here . Th is ens ur ed tha t we were mainta in -
ing roughly the same prevalence rate in the data set and also were
not simply making exact copies of the randomly chosen checklists.
2.2 | Spatial filtering and class imbalance
As spatial bias is always a concern when using citizen science data
(Geldmann et al., 2016), we spatially subsampled the combined data
set. The data were sparse for many species (proportion of detec-
tions for a species <0.05 for all checklists in the data set), so class
imbalance was also a concern (He & Garcia, 20 09). We spatially un-
dersampled the data (e.g. Robinson, Ruiz-Gutierrez, & Fink, 2017) by
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ROBINSO N et al.
first creating a hexagonal grid of 3.5 km (~10 times the radius of each
survey) cells over the region from which our observations came via
the dggridR package (Barnes et al., 2018) in R (R Core Team, 2019).
This was done to reduce the chance that we selected overlapping
observations at each run of each model. We then split the data for
a single species into checklists on which the species was detected
(positive observations) and those on which the species was not de-
tected (negative observations). We selected one checklist from the
negative observations from within each grid cell and recombined the
filtered negative observations with the positive observations. As al-
most all of the spatial bias was from the negative obser vations (i.e.
only a small percentage of the total number of observations were
positive observations), this procedure relieves much of the spatial
bias, and because only negative observations were filtered out of the
data set, class imbalance is also addressed here (King & Zeng, 2001;
Robinson et al., 2017).
To alleviate the effects of class imbalance, after spatially sam-
pling eBird checklists for training distribution and population trend
models, Fink et al. (2019) oversampled eBird checklists for species
that had a prevalence rate of less than 25%. After spatially under-
sampling our data for the current study, class imbalance was still a
concern, as our undersampling improved the imbalance, but it did
not improve class balance to 25% for species other than Yellowlegs.
Following the recommendation of Fink et al. (2019), we oversampled
the positive observations for each of our species and data sets if
prevalence was below 25% before training the distribution models.
We used the synthetic minority oversampling technique (SMOTE;
Chawla, Bowyer, Hall, & Kegelmeyer, 2002) to create one new ex-
ample of the positive class in the training data set for every positive
observation in the spatially undersampled data set. The SMOTE al-
gorithm does not create exact copies of the positive class as in tra-
ditional oversampling, but instead creates examples of the positive
class that occupy the parameter space between a randomly chosen
positive obser vation and a nearest neighbour. We did not randomly
undersample our data as is recommended when using SMOTE
(Chawla et al., 2002) because our data had already been spatially
undersampled as described above. The spatial undersampling and
SMOTE procedure were done to each of the data set s. As the spatial
undersampling randomly chooses from negative observations within
a grid cell, many negative observations may not be included in train-
ing the model. Therefore, we sampled each of the four data sets in
the study using this procedure 100 times, creating 100 unique data
sets unless it met the 25% prevalence criteria described above. In
that case, the data were only spatially undersampled to create the
100 unique data sets.
2.3 | Analysis
We selected and spatially subsampled (but did not oversample) 15%
of the combined data set to be the testing data for evaluation of
each model. This test set was selected because our goal is to make
accurate predictions across the entire Central Valley and give equal
importance to each location. Therefore, the test set must include
observations across the spatial extent of evaluation and be spatially
balanced. For the training data sets, we removed any checklist or
point count that was in the test set. We repeated this process 100
times creating 100 unique data sets against which our models were
tested.
We used the R package “ranger” (Wright, Wager, Probst, &
Maintainer, 2019) to train a random forest model for each of the
seven species (or combined species; Table 1) and for each of four
data sets: (a) TNC point counts alone, (b) eBird checklists alone, (c)
an oversampled eBird data set, and (d) the combined TNC and eBird
data set . For each species, 1,000 trees were grown in the ensem-
ble. The number of variables from which the model could select at
each split for each tree was initially set to the square root of the
number of variables included in the model (
n
=12 ≈
√142
); how-
ever, we allowed this to vary by half, double and triple this common
rule of thumb. We selected the output from the model that maxi-
mized accuracy (Data S1). We evaluated the accuracy of the models
using multiple predictive performance metrics (PPMs). We did not
Common Name Latin Name
Abbreviation in
Tables & Figures
American Avocet Recurvirostra americana AMAV
Dunlin Calidris alpina DUNL
Greater Yellowlegsa Tringa melanoleuca YLEGa
Least Sandpiper Calidris minutilla LESA
Lesser Yellowlegsa Tringa flavipes YLEGa
Long-billed Curlew Numenius americanus LBCU
Long-Billed Dowitcherb Limnodromus scolopaceus DOWIb
Short-billed Dowitcherb Limnodromus griseus DOWIb
Western Sandpiper Calidris mauri WESA
aCombined in analysis and referred to collectively as “Yellowleg s” in this study (e.g. Golet
et al., 2018)
bCombined in analysis and referred to collectively as “Dowitcher” in this study (e.g.Golet
et al., 2018)
TABLE 1 List of species for which we
modelled spring distribution in the Central
Valley of California with each of the four
data sets
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evaluate the models using the out of bag (OOB) metric calculated
by random forest internally as it favours total accuracy over cor-
rectly predicting the minority class and is highly sensitive to class
imbalance. We used the test data set to evaluate mean squared error
(MSE) between the model predictions of presence or absence and
the true presence or absence in the test set. We also evaluated error
using Brier score (Brier, 1950), the mean squared error of the prob-
abilistic model predic tions and the true presence or absence in the
test set. We evaluated each model's ability to rank positive observa-
tions higher than negative ones using the area under the curve (AUC;
Fielding & Bell, 1997). We evaluated each model's ability to predict
presence or absence using Cohen's Kappa (Kappa; Cohen, 1960) and
its components, sensitivity (true positive rate), and specificit y (true
negative rate). We produced distribution maps for each species and
data set, and we recorded the predictor importance metrics from
models trained on each data set. All variables included in the model
may be found in Data S2.
3 | RESULTS
The fields that are stored as part of the eBird project allowed us to
filter the data to be of a similar protocol as the TNC point count s.
This filtering reduced uncer taint y in the loc ation of eBird checklists
and eliminated the need for coarse level summaries of the data for
integration (Miller et al., 2019; Pacifici et al., 2017). For all species,
the combined data set had higher predictive accuracy than either the
TNC poi nt counts or the eBird checklis t data set s on their own; how-
ever, error (MSE, Brier score) was relatively low and AUC was rela-
tively high for all species and all data sets, particularly for the three
data sets where eBird checklists were included (Figure 2; Figure S3).
Improvement in accuracy varied among species, however; for the
Kappa statistic (predicting presence and absence against the test
set), the combined data set was an improvement over all three of
the other data sets evaluated (with exception of western sandpiper;
−3%–25.5% improvement; Figure 3). The improvement or loss in the
error statistics was usually negligible (with the exception of LBCU;
Figure 4; Figure S4) for the combined data set versus the next best
data set; however, error metrics were already very low for most
species, so great improvement here was not likely. Likewise, AUC
was relatively high for most species and for the three data sets con-
taining eBird checklists; therefore, the gain was negligible for many
species (improvement of up to 6%; Figure S4). For the few species/
metrics combinations where the combined data set was not the best
performing model, it was the second best, with the best being the
eBird checklists with simulated eBird checklists added. Neither TNC
FIGURE 2 Box plots for MSE, AUC , Brier score, Cohen's Kappa, Sensitivity and Specificit y for models run for Least Sandpiper (LESA) with
each data set; TNC point counts (TNC), eBird checklist s (eBird), eBird checklists with additional simulated eBird data (eBird + sim), and the
data set of TNC point counts and eBird checklists combined (Combined)
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ROBINSO N et al.
point counts, nor eBird checklists alone performed better for any
metric than the data set where the two were combined.
We produced distribution maps for each species (Figure 5;
Figure S5) to determine whether there was a difference in the over-
all pattern of distribution estimated by models trained on the dif-
ferent dat a sets. For most species, there was greater contrast
between the presence estimates and absence estimates for the
combined data set when compared to the other three. Visually,
this means more obvious differentiation between high predicted
probability of presence and absence (e.g. hotter “hotspots” and
darker regions where absence is predicted; Figure 5; Figure S5).
We collected the important variables identified by the model (via
Gini index) when run with each data set. For all species, the im-
portance of rice and water was apparent as it was among the most
important variables for each of the data sets; however, it was not
until the data sets were combined that each rice and the Water
Trac ker layer had a hi gh imp ort ance score (Figure 6; Figure S6). The
combination of the dat a allowed t he mo dels to home in on rice and
surface water as highly important, where they had only moderate
importance comparatively when using the other data sets. This is
likel y be cause onl y 1.5% of eBird checklist s in our st udy come fr om
locations where the per cent landcover is >50% rice. Conversely,
almost 60% of the TNC point counts come from locations where
the per cent landcover is >50% rice.
FIGURE 3 Average improvement in
Cohen's Kappa for distribution models of
each species when using the combined
data set versus the data set that provided
the next highest Kappa value
FIGURE 4 Average improvement or
loss in Brier score for distribution models
of each species when using the combined
data set versus the data set that provided
the next lowest value
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4 | DISCUSSION
Our results lend further support to effort s looking to combine data
from multiple sources to improve the inference and/or predictive abil-
ity of distribution models (Miller et al., 2019; Pacifici et al., 2017). We
have shown that citizen science data can be filtered to generate a high-
quality data set that can closely match the resolution and sampling
appr oa ch of str uct ur ed surveys, supp ort ing the call for cur re nt and fu-
ture citizen science project s to collect essential information related to
location and effort, as well as complete sur veys (Kelling et al., 2019).
The integration of survey data with the filtered citizen science data in
eBird resulted in improved inference, predictive ability, and ultimately
increased the extent of inference of the structured surveys. In turn,
the structured sur veys were able to improve the ecological inference
of the citizen science data, by improving the representation of sam-
pled habitats that are key for sh orebird species. Mos t impor tantly, the
practical approach we have shown for data integration is an improve-
ment on simpler “data pooling” approaches for data integration and
can be used to improve the efficienc y of designing biological sur veys
to collec t distribution information in the context of larger, citizen sci-
ence monitoring efforts, ultimately reducing the financial and time
expenditures typically required of monitoring programs and focused
research (Reich, Pacifici, & Stallings, 2018).
The combined data set resulted in improved accuracy across all
metrics relative to the TNC survey dat a or eBird data alone, for all
of the species considered in this study. Our combined approach also
predicted presence/absence via agreement with a test data set more
accurately than the different permutations of data sets considered
(e.g. TNC alone, eBi rd alone and eBird plus simulated eBird). The ob-
served improvement in accuracy is not likely a function of simply
increasing sample size, as augmenting eBird data with simulated data
did not show the same levels of improvement. The addition of the
TNC survey data to eBird data improved the coverage of the data
both spatially and in targeted habitats. Previous work has shown that
migrating shorebirds heavily use flooded rice fields in the Central
Valley, and will also use unflooded rice fields (Elphick & Oring, 1998;
Golet et al., 2018). Rice fields are the main focal habitat of the TNC
BirdReturns program, ~60% of the survey data from this project was
carried out at sites where at least 50% of the landcover is rice. In
contrast, the majority of the rice fields in the Central Valley are not
accessible to regular eBird participants, as they are often on private
lands. This is likely why we see so few (~1.5%) eBird checklists from
the Central Valley in locations where at least 50% of the landcover
is rice. The addition of more data from high-qualit y habitat is what
provided the improvement in accuracy of the combined eBird and
TNC data set. This highlights the importance and value of more tar-
geted research and sur vey efforts within the context of large-scale
citizen science monitoring efforts. Given that private lands make up
more than half of the land in the United States, supporting wildlife
monitoring efforts on privately held lands that are linked into large-
scale efforts such as eBird can greatly improve inferences on species
distributions and habitat associations across scales of interest for all
stakeholders (Hilty & Merenlender, 2003).
Interestingly, models based only on the TNC point counts strug-
gled to learn where each species was likely to be absent across the
entire Central Valley because the data came largely from “good”
shorebird habitat in the northern portion of the Central Valley. The
original intent of this project was not focused on species distribution
modelling, so this does not come as a surprise. However, given that
absence information allows for more accurate distribution models
(Brotons, Thuiller, Araújo, & Hirzel, 2004), the addition of eBird data
was able to provide information on where species are likely to be
absent, and improved inferences on what habitat types most benefit
shorebirds, but were not surveyed as part of the TNC monitoring
efforts. On the other hand, the models using the eBird data alone
were able to predict absences well and had relatively high accuracy
when predicting presence/absence, but overall ecological inference
was improved when combined with the point count data set. The
TNC point counts acted as targeted surveys in under-surveyed hab-
itat, which previous work has shown can improve the accuracy of
distribution models using eBird checklists (e.g. Xue, Davies, Fink,
Wood, & Gomes, 2016). The complementary nature of the data sets
is also shown when examining the impor tant predictors. For Dunlin
(Figure 6), the rice la nd cov er and the Water Tracker layer ar e im po rt-
ant predictors for each data set, however, their impor tance values
more than double when the combined data set is used to train the
model. While other species do not show such a drastic shift in the
importance value for these two habitat variables, the pattern is sim-
ilar for most in that these variables become more important to the
model's predictions when the data sets are combined.
The approach we present for combining data is applicable for
other conservation monitoring programs and ongoing research ef-
forts, given that a significant amount of research is conducted over
small spatial and temporal scales (Heidorn, 2008), and often dif fi-
cult to scale-up without similarly struc tured data (Poisot, Bruneau,
Gonzalez, Gravel, & Peres-Neto, 2019). The data fields that exist in
eBird data facilitated further processing and filtering to match the
structure of the individual data set of interest and allowed us to
leverage the strengths of both data sources. However, even citizen
science dat a sets that do collect effort information are often lacking
information on sampling locations, although incentivizing partici-
pants to collect data in these data-poor locations has been shown
to improve the accuracy of distribution models (e.g. avicaching; Xue
et al., 2016). Similarly, data from smaller-scale, individual research
projects can also help fill in these gaps in citizen science data. Given
that inferences from small-scale studies cannot be extrapolated to
larger spatial extents (Sandel & Smith, 2009), the approach we pres-
ent here for com binin g data from small-scale studie s with citizen sci-
ence data filtered to match the existing data structure will increase
the overall extent of inference, and improve our ability to concep-
tualize conservation actions within the larger context of the target
population(s) of interest.
Information on species distributions across large scales is one of
the most fundamental information needs for basic and applied re-
search fields in ecology. However, this level of information often re-
quires large-scale, coordinated surveys that can be time consuming
|
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ROBINSO N et al.
FIGURE 5 Predicted probability of an expert surveyor detecting a Dunlin in the spring of 2016 on a one-hour long checklist where the
distance travelled was < 30 0m., as estimated by the model using TNC point counts alone (A), eBird checklists alone (B), eBird checklists with
added simulated eBird checklists (C), and the combined data set of TNC point count s and eBird checklists
984
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ROBINSON et al.
and costly to manage. In addition, models used to estimate species
distributions are often data hungry, and are often unable to generate
information at the spatial and temporal scales that are most relevant
for rese ar ch and conser vatio n ef for t s. Citizen science data is a gr ow -
ing source of additional, minimal cost surveys for a number of taxa
including invertebrates, bats, marine mammals, lichens, amphibians
and more (Deutsch, Bilenca, & Agostini, 2017; Devictor, Whittaker,
& Beltrame, 2010; Howard et. al., 2010; Newson et al., 2015;
Tonachella et al., 2012; Westgate et al., 2015). We have shown the
utility of combining survey data with semi-struc tured citizen science
data (Kelling et al., 2019) for improving accuracy in species distribu-
tion models, which can result in more efficient and cost-effective
surveys (Miller et al., 2019; Pacifici et al., 2017; Reich et al., 2018).
The simple method we present for citizen science data allows for the
integration of a small-scale point count data set with eBird check-
lists, can be used to integrate similar types of data being collected
by citizen scientists (e.g. camera traps) with more localized efforts
(e.g. patrolling by park rangers), ultimately improving our ecological
knowledge on the distribution and habitat associations of species of
conservation concern worldwide.
ACKNOWLEDGEMENTS
We would like to thank Point Blue for supplying Central Valley water
data. We would also like to thank Tom Auer, Ali Johnston, Steve
Kelling and Matt Reiter for comments that improved this project and
the manuscript.
DATA AVA ILAB ILITY STATE MEN T
The Nature Conservancy point count data are available from the
Dryad Digital Repository: https://doi.org/10.5061/dryad.724vn
The eBird data used for analyses in this manuscript may be
downloaded from https://ebird.org/data/download
Cropland Data Layer may be downloaded from: https://www.
nass.usda.gov/Resea rch_and_Scien ce/Cropl and/Relea se/index.php
Wate r Trac ker Da t a may be down loa d ed fr om Po int Bl ue: ht tps ://
data.point blue.org/apps/autow ater/
ORCID
Orin J. Robinson https://orcid.org/0000-0001-8935-1242
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BIOSKETCH
Orin Robinson is an ecologist at the Cornell Lab of Ornithology
interested in using and developing quantitative tools to learn
about vertebrate population and community ecology, and using
lessons learned to inform conservation.
SUPPORTING INFORMATION
Additional supporting information may be found online in the
Supporting Information section.
How to cite this article: Robinson OJ, Ruiz-Gutierrez V,
Reynolds MD, Golet GH, Strimas-Mackey M, Fink D.
Integrating citizen science data with expert surveys increases
accuracy and spatial extent of species distribution models.
Divers Distrib. 2020;26:976–986. ht tp s://doi.o rg /10.1111/
ddi.13068
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