DOI: 10.5061/dryad.0vt4b8h5x
title: Supporting data from: When birding hotspots get too hot: A geographic evaluation of wildfire-related disturbance on spatiotemporal biases in citizen science data

description: Long-term monitoring is critical for ecology and conservation, especially
 as non-stationary climatic conditions increase. Citizen science projects
 offer long-term georeferenced data from thousands of observers across
 diverse geographic areas. Despite the attraction of these datasets for
 biogeographical research and conservation planning, data collection
 commonly lacks standardized probabilistic sampling, which can increase
 observer bias, decrease precision of parameter estimates, and increase
 risk of spurious results when using the associated species data.
 Additionally, environmental disturbance may affect observer behavior,
 confounding the observed patterns in species responses. We aimed to test
 the effects of wildfire disturbance on observer biases in locality
 selection and return rates by citizen scientists registered with eBird, a
 globally available bird observation database. Location. Western USA. We
 used a long-term (10-yr) dataset of 47,662 localities from 1,788 eBird
 observers to calculate resource selection functions and explain observer
 locality selection as a function of wildfire and non-fire-related
 environmental covariates. We calculated spatiotemporally explicit
 covariates from the Monitoring Trends in Burn Severity program, and also
 developed generalized linear mixed models to predict the probability of
 observers returning to localities in response to fire and non-fire
 variables. Our results show that fire characteristics predicted
 locality selection and the probability of returning to a locality. Closer,
 more recent, larger, and more severe fires showed the greatest effects on
 spatiotemporal patterns of observer sampling bias. Other non-fire-related
 variables related to locality attractiveness, land use, convenience, and
 accessibility were also important. Our results demonstrate that landscape
 disturbance introduces spatiotemporal biases in citizen scientist locality
 selection and revisitation. Researchers using citizen science data can
 follow our modeling approach to quantify disturbance-related observer
 sampling biases and estimate bias-corrected parameters necessary for
 ecological studies. Without this, observer biases inherent in these data
 can lead to increased bias, decreased precision in parameter estimates,
 and spurious results. We propose recommendations to enhance the value of
 citizen science data for biological monitoring and conservation.
description: <em>Study area</em> This study
 focuses on Western United States (USA), which has experienced increased
 wildfire frequency, size, and severity in recent decades (Figure 2, Weber
 and Yadav 2020), with these increases predicted to continue due to climate
 change (Abatzoglou et al. 2021, Wasserman and Mueller 2023, McGinnis et
 al. 2023). This region supports the widest elevation range in the
 continental USA (86m BSL to 4,418m ASL), containing large tracks of public
 land and a high diversity of biomes, including forested mountains, coastal
 environments, arid and semi-arid plateaus and plains, high-elevation
 subalpine and alpine areas, and rainforests. In addition to supporting
 high avian species diversity (&gt; 600 bird species), thousands of
 bird observers report bird sightings across this region each
 year. <em>Citizen-based wildlife observation
 data</em> We used data freely available from the
 eBird repository of citizen-based bird observations as a case study
 (Sullivan et al. 2009). This data source currently contains over 1.6
 billion bird records worldwide from more than 930,000 observers who have
 contributed to more than 900 scientific publications (ebird.org). To avoid
 potential confounding effects of the 2020-2021 COVID-19 pandemic-related
 lockdown, we used data reported by eBird observers from 2010 to 2019. We
 used only bird observations recorded during May each year to avoid
 potential effects of seasonal changes in survey effort and capture bird
 Spring migration, when birding activity peaks. We included checklists
 (species lists submitted) regardless of species because we were interested
 in explaining decisions made by observers rather than the birds reported.
 We focused on environments within the continental US and removed
 observations occurring in the Pacific Ocean because we did not expect fire
 to impact marine environments. The filtered eBird observation database
 contained observer ID, location, date, and time of each observation
 event. <em>Fire and environmental
 data</em> We calculated spatiotemporally explicit
 wildfire covariates, including burn status (burned/unburned), distance to
 closest fire (km), time since fire (years), fire severity index, and fire
 size (ha) across our study area using data from the Monitoring Trends in
 Burn Severity (MTBS) program (Eidenshink et al. 2007, Table 1). To account
 for other potential sources of variability in eBird data, we classified 8
 land cover types (Friedl and Sulla-Menashe 2019), human population density
 (inhabitants/km<sup>2</sup>, CIESIN - Columbia University
 2017), and distance from paved roads (km, U.S. Geological Survey 2014). We
 extracted these covariates for each eBird observation locality using the
 `sf ´, `terra´, and `exactextract´ packages in R (v.4.2.3; Pebesma 2018,
 Baston 2022, Hijmans 2022, R Core Team 2022).
 <em>Data analysis</em> To avoid
 problems with multicollinearity among independent covariates in our linear
 modeling framework, we calculated Pearson’s correlation coefficients among
 the various predictors (R Core Team 2022) and found no evidence of
 significant correlations (all r &lt; 0.5). Additionally, visual
 inspections of boxplots and scatterplots revealed no evidence of potential
 outliers in our predictors and response variables.
 <em>Probability of selecting a locality</em>.
 Locality selection by citizen science observers is a complex process
 involving multiple factors, including locality attractiveness (Romo et al.
 2006, Mancini et al. 2019), land cover type (Tiago et al. 2017, Mandeville
 et al. 2022), accessibility (Tiago et al. 2017, Zhang 2020), opportunity,
 and safety (Mancini et al. 2019, Zhang 2020), among other socio-economic
 factors. To evaluate post-fire effects on locality selection by observers,
 we calculated third-order resource selection functions (RSFs) following a
 use-availability design (Johnson et al. 2006). We used records located
 only in public lands to ensure that all modeled areas were available to
 observers. To avoid temporal and spatial autocorrelation of observations
 that can bias RSFs (Alston et al. 2023), we randomly selected a single
 locality (i.e., point coordinates where an observation was recorded in
 eBird) per day per observer. We then used eBird data from observers with ≥
 10 distinct observation localities within a given year to remove effects
 from incidental observers. We also removed localities containing only
 incidental protocols because those generally do not represent active
 locality selection by observers (e.g., locality not selected for wildlife
 viewing, observations recorded during other activities).
 For each year, we used the coordinates from localities reported
 by each observer to represent ‘used’ localities and generated random
 locations to represent available ‘unused’ localities. We generated the
 random unused localities on public land within the median distance
 traveled by an observer between consecutively-dated localities,
 representing the average distance an observer traveled between used
 localities and reducing the effects of long-distance travel unrelated to
 birdwatching. We generated ten random locations for each used locality to
 characterize available environmental conditions while maintaining an
 adequate class balance for calculating our binomial regression-based
 resource selection functions (Northrup et al. 2013, Salas-Eljatib et al.
 2018). At each used and random locality, we measured
 the distance from, size, and average severity of the nearest wildfire that
 occurred the previous fire season using data from the MTBS database. We
 only considered wildfires from the previous season because we expected
 fire effects to be stronger the following year after a fire event than in
 subsequent years. To account for other potential covariates that we
 postulated could influence locality selection, we measured human
 population density, landcover type (categorical variable with eight
 classes: forest = A, shrubland = B, grassland = C, wetland = D, cropland =
 E, urban = F, barren = G, and waterbody = H), distance from the nearest
 paved road, and bird species richness. We calculated expected bird species
 richness annually in each locality from a ~15x15 km raster of eBird
 observations in May of each year from 2010-2019. We found no evidence of a
 latitudinal pattern (R² = 0.003, p &lt; 0.01) or spatial
 autocorrelation (Moran’s I = 0.0225, p &lt; 0.01) in our species
 richness variable at this resolution. This supports our objective of
 representing the expected species richness that an observer anticipates
 when selecting a locality, which reflects a combination of the biological
 processes (true species richness) and sampling effort (number of
 checklists submitted to eBird for such locality). Localities with missing
 covariates were excluded from this analysis, resulting in 47,662 used and
 433,864 available localities from 1,788 observers distributed across the
 entire study area, covering a wide range of wildfire and environmental
 conditions. Following the information-theoretic
 approach (Burnham and Anderson 2002), we developed a series of
 ecologically grounded hypotheses in conjunction with our set of covariates
 to construct an a-priori set of candidate generalized linear mixed-effects
 models (<em>n</em> = 432) with a binomial error distribution
 and logit link function, where each model represented a competing
 hypothesis explaining locality selection by observers. Our candidate set
 of models represented a small proportion of the thousands possible. This
 and our large sample sizes minimized problems with model selection
 uncertainty and overparameterized models (Anderson and Burnham 2002). We
 defined observer ID as a random intercept to account for individual
 locality preferences (i.e., repeated visits by individuals Gillies et al.
 2006). Our saturated model included singular, additive, and interaction
 effects of distance to fire, fire size, and fire severity. Distance to
 fire was modeled as a linear, quadratic, or log function to test for
 different curvilinear observer responses to distance from fire. We
 reasoned that the probability of selecting a locality reached a threshold
 or asymptote at some distance to fire, whereas the quadratic function
 represented a possible peak in probability followed by a subsequent
 decline at increasing distance from fire due to a reduction in some
 desired condition (e.g., too remote). Other variables in the saturated
 model were bird species richness, land cover type, population density, and
 distance to road as a quadratic term (i.e., probability of selecting a
 locality was lowest at distances closest to and farthest from roads).
 Statistical analyses were conducted using the `lme4´ package in R (Bates
 et al. 2015, R Core Team 2022). We used the receiver
 operating characteristic curve (ROC) and its area under the curve (AUC) to
 evaluate the capability of our full model to distinguish between used and
 available locations using the package `pROC´ in R (Robin et al. 2011). We
 used the Bayesian Information Criterion (BIC) for model selection
 (Schwarz, 1978), which outperforms other information criteria when using
 large samples (Kass and Raftery 1995, Aho et al. 2014). We considered
 candidate models with ΔBIC ≤ 2 equally supported by the data and used BIC
 weight (<em>w<sub>i</sub></em>) to measure the
 weight of evidence or support for each model (Burnham and Anderson
 2002). <em>Short-term probability of returning to
 a locality.</em> To analyze short-term changes in the probability of
 observers returning to the same locality, given the occurrence of a
 wildfire the previous year, we applied a before-after fire study design
 using eBird data from observers with ≥ 20 observations per year from 2010
 to 2019. We queried for observers only in localities on public lands to
 ensure adequate sample sizes and reduce the influence of inconsistent
 property access and incidental observers. We selected localities within
 fire boundaries (burned localities, <em>n</em> = 8,095) that
 occurred one year before a fire event and randomly selected a similar
 number of localities outside fire boundaries (unburned localities,
 <em>n</em> = 10,309) as a control group. Using observer
 identities in each locality, we determined a
 '<em>return</em>’ event when a given observer revisited
 the same locality the following year and a
 ‘<em>no-return</em>’ event when an observer did not revisit
 the same locality the next year. This produced a sample of 18,403 records
 from 4,886 unique observers for this analysis. We
 measured the burn status (burned/unburned), fire size, and fire severity
 at each locality. We also measured additional covariates that we expected
 might influence the probability of a return; these were human population
 density, distance from roads, land cover type, and expected bird species
 richness. Given that little is known about how fire can affect observer
 behavior, we hypothesize that fire can have an absolute (burned/unburned)
 and/or gradual effect (fire size) on return probability. We fit a
 candidate set of generalized linear models (<em>n</em> = 127)
 with a binomial error structure and logit link function to the
 <em>return</em> (1) and <em>no</em>
 <em>return</em> (0) data, where each model predicted the
 probability of returning to a locality in response to a unique set of
 covariates. The saturated model included burn status, fire severity, fire
 size, bird species richness, land cover type, population density, and
 distance to road. We used BIC for model selection following the approach
 described above. <em>Long-term probability of
 returning to a locality. </em>Lastly, we examined the longer-term
 (multi-year) effects of time since wildfire on the probability of
 returning to a locality within a fire boundary over subsequent years, with
 our inference here focused on burned localities. To avoid small sample
 bias associated with incidental observers during this sampling period, we
 considered only observers with ≥ 10 species lists during each year from
 2010 to 2019. We identified locations where observers reported species
 lists within the fire boundary before and after a fire event, and selected
 only locations surveyed by the same observer one year before and ≤8 years
 after a fire. This generated a sample of 5,163 records from 916 observers
 and 732 burned localities for this analysis. Here, we recorded a
 <em>return</em> (1) or <em>no return</em> (0) each
 year after a fire event for each observer. Additionally, we calculated
 time since fire, fire size, and fire severity index for each
 <em>return</em> and <em>no return</em> event. We
 did not include other environmental variables that we assumed remained
 constant for observers who visited a locality before and after a fire
 (e.g., distance from road). We fit a candidate set of generalized linear
 models (<em>n </em>= 9) with a binomial error structure and
 log link function to explain revisitation probability over time in
 response to time since fire, fire severity, their interaction, and/or fire
 size, and used BIC for model selection. We included time since fire as a
 quadratic term to account for predicted initial disinterest of burned
 sites by birders soon after a fire and the eventual attraction to those
 sites after some time lapsed and vegetation/birding conditions
 improved.
description: # Supporting data from: When birding hotspots get too hot: A geographic
 evaluation of fire-related landscape disturbance on spatiotemporal biases
 in citizen science data
 [https://doi.org/10.5061/dryad.0vt4b8h5x](https://doi.org/10.5061/dryad.0vt4b8h5x) ## Description of the data and file structure Observer data obtained from the eBird database and fire metrics from the MTBS program to identify and quantify disturbance-related sources of bias in citizen science data. 'NA' values in CSV files will be removed when appropriate using the attached R code. ### Files and variables #### File: 3_Long_Term_Probability_of_Return_Data.csv **Description:**  ##### Variables * join_row: internal analysis * tsfire: time since fire (years) * Join_Count: internal analysis * TARGET_FID: internal analysis * LOCALIT: observation locality ID * OBSERVA: observation date * OBSERVE: observer ID * YEAR: Year of observation * DAY_OF_: julian calendar day * Evnt_ID: observation eBird ID * BrnBndA: burned acreage * Ig_Date: fire ignition date * fire_sz: fire size (sq km) * fr_svrt: fire severity index (see MTBS) * geometry: gis control field * yfire: year of fire * yobserv: year of first visit * return: binomial, 1=return, 0=no return * returny: year of return #### File: 2_Probability_of_Return_Data.csv **Description:**  ##### Variables * date: date of observation * fire_severity: fire severity index (see MTBS) * fire_size: fire size (sq km) * loc_ID: locality ID * obs_ID: observer ID * return: binomial, 1=return, 0=no return * state: political state boundaries * year: year of observation * pop_den: population density (habs/sq km) * distance_to_road: distance to road (m) * land_cov: land cover type, see tables in paper * longitude: longitude WGS84 * latitude: latitude WGS84 * spp: specis richness in locality #### File: 1_Locality_Selection_Data.csv **Description:**  ##### Variables * locality.id: locality id * observation.date: observation date * observer.id: observer ID * number.observers: number of observers * all.species.reported: logical, wheter all species were reported * year: year of observation * day_of_year: julian day of observation * use: binomial, 1=locality used, 0=locality available but not used * obs.id: Observer ID * lon: longitude WSG84 * lat: latitude WSG48 * Event_ID: fire event ID * BurnBndAc: burned acreage * Ig_Date: fire ignition date * fire_size: fire size (sq km) * fire_severity: fire severity index (see MTBS) * nearest: nearest fire ID * distance_to_fire: distance to fire (m) * pop_den: population density (hab/sq km) * id_road: id of closest road * distance_to_road: distance to road (m) * land_cover.ID: land cover type, see tables in paper * land_cover.land_cover: reclass of land cover type see rcode * sp_rich: species richness ## Code/software Three R scripts are provided with this submission: 1_Locality_Selection.R 2_Probability_of_Return.R 3_Long_Term_Probability_of_Return.R In conjunction with CSV files provided it ispossible to replicate analysis presented in the paper. A list of used packages is available in scripts provided. eBird raw data is not provided but can be accessed through: [https://ebird.org/download](https://ebird.org/download) ``` R version 4.2.3 (2023-03-15 ucrt) ``` ## Access information Data was derived from the following sources: * [https://ebird.org/download](https://ebird.org/download) * [https://www.mtbs.gov/direct-download](https://www.mtbs.gov/direct-download) * See paper citations