DOI: 10.5061/dryad.ngf1vhhtp
title: Wildfire smoke affects detection of birds in Washington state

description: Wildfire smoke is likely to have direct health effects on birds, as well
 as influence movement, vocalization, and other avian behaviors. These
 behavioral changes may affect if and how birds are observed in the wild,
 although research on the effects of wildfire smoke on bird behavior is
 limited. To evaluate how wildfire smoke affects detection of birds, we
 combined data from eBird, an online community science program, with data
 from an extensive network of air quality monitors in the state of
 Washington over a 4-year period. We assessed how PM2.5, a marker of smoke
 pollution, affected the probability of observing 71 bird species during
 the wildfire seasons of 2015–2018 using bird observations from 62,908
 eBird checklists. After accounting for habitat, weather conditions,
 seasonality, and survey effort, we found that PM2.5 affected the
 probability of observing 37% of study species. The ambient concentration
 of PM2.5 was negatively associated with the probability of
 observing 16 species and positively associated with the probability of
 observing 10 species, indicating that birds exhibit species-specific
 behavioral changes during wildfire smoke events that influence how they
 are observed. Our results suggest that wildfire smoke impacts the
 presence, availability, and/or perceptibility of birds. Impacts
 of smoke pollution on human observers, such as impaired visibility, may
 also influence detection of birds. These results provide a
 foundation for developing mechanistic hypotheses to explain how birds, and
 our studies of them, are impacted by wildfire smoke. Given the projected
 increase in large-scale wildfire smoke events under future climate change
 scenarios, understanding how birds are affected by wildfire smoke –– and
 how air pollution may influence our ability to detect them –– are
 important next steps to inform wildlife research and avian conservation.
description: This dataset was used by Sanderfoot and Gardner (2021) to
 investigate how smoke pollution influenced the probability of detecting
 birds in Washington state during the wildfire seasons of 2015 – 2018. 
 This README file provides a brief overview of the dataset and a
 description of the variables included in our analysis. A complete
 description of the methods used to prepare the dataset is provided in
 Sanderfoot and Gardner 2021. <b>Bird
 Observations:</b> This dataset includes data on observations of 71
 species collected by the public and submitted to eBird (eBird Basic
 Dataset 2021). eBird supports opportunistic data collection by
 volunteers who submit online checklists of species they observe and other
 related information (e.g., date, time, duration and type of survey,
 distance traveled, number of observers, etc.) (Sullivan et al. 2009). We
 included bird observations from complete checklists documented in
 Washington state from July 1 – September 30, 2015 – 2018 from the eBird
 Basic Dataset (eBird Basic Dataset 2021). The observations in this dataset
 were filtered using the <i>auk</i> package (Strimas-Mackey et
 al. 2018) in R (R Core Team 2020) to include only those from stationary,
 traveling, and area counts. We also restricted the dataset to only include
 checklists within 32 kilometers of an active fine particulate matter
 (PM<sub>2.5) </sub>monitor in Washington state. For additional
 details on bird observations included in this dataset, please see the
 methods section of Sanderfoot and Gardner 2021.
 <b>Environmental Data:</b> 
 "particulates": daily mean concentration of
 PM<sub>2.5</sub> in μg/m<sup>3</sup> 
 We used data from the Environmental Protection Agency
 (EPA) Air Quality System (AQS) (US Environmental Protection Agency 2019).
 We downloaded daily PM<sub>2.5</sub> concentrations (24-hour
 averages) available from all ground-based
 PM<sub>2.5</sub> monitors in the state of Washington for the
 years 2015 through 2018. We then linked each of the checklists to the
 daily concentration of PM<sub>2.5</sub> at the monitoring
 station closest to the checklist location. 
 "air": daily mean air temperature in Kelvin
 "apcp": daily accumulated precipitation in
 mm We used data from the North American
 Regional Reanalysis (NARR) to determine daily mean air temperature and
 daily accumulated precipitation for each eBird checklist. NARR data were
 provided by the National Oceanic and Atmospheric Administration Physical
 Sciences Laboratory in Boulder, Colorado, USA from their website at https://psl.noaa.gov/.
 The spatial resolution of NARR data is approximately 32 kilometers. We
 used the <i>ncdf4</i> package (Pierce 2019) in R (R Core Team
 2020) to extract the weather data. 
 "land_cover": land cover class
 (categorical) We assigned a land cover
 type to each checklist using data from the 2016 National Land Cover
 Database (NLCD), developed by the Multi-Resolution Land Characteristics
 (MRLC) Consortium and made available as a layer in ArcMap by the
 Environmental Systems Research Institute (Esri) (Esri 2019, 2020). Land
 cover classifications were grouped into nine categories: open water,
 perennial ice/snow, developed, barren, forest, shrubland, herbaceous,
 planted/cultivated, and wetlands. 
 "year": year of observation date
 (categorical) <strong>Works
 Cited:</strong> eBird Basic Dataset. Version:
 EBD_relJan-2021. Cornell Lab of Ornithology, Ithaca, New York. January
 2021. Esri Inc.
 (2020). <i>ArcMap</i> (Version 10.8.1). Esri Inc. Redlands,
 California, USA. https://desktop.arcgis.com/en/arcmap/.
 Pierce, D. (2019). ncdf4: Interface to Unidata netCDF (Version 4
 or Earlier) Format Data Files. R package version 1.17.
 https://CRAN.R-project.org/package=ncdf4 R Core Team.
 2020. R: A language and environment for statistical computing. R
 Foundation for Statistical Computing, Vienna, Austria. URL
 https://www.R-project.org/. Strimas-Mackey, M., E.
 Miller, and W. Hochachka. 2018. auk: eBird Data Extraction and Processing
 with AWK. R package version 0.3.0.
 https://cornelllabofornithology.github.io/auk/
 Sullivan, B. L., C. L. Wood, M. J. Iliff, R. E. Bonney, D. Fink,
 and S. Kelling. 2009. eBird: a citizen-based bird observation network in
 the biological sciences. Biological Conservation 142:2282-2292.
 US Environmental Protection Agency. Air Quality System Data Mart
 [internet database] available via https://www.epa.gov/airdata. Accessed
 June 27, 2019.
description: The final dataset included detection/non-detection data for 71
 species from 62,908 eBird checklists, each of which represented a single
 survey. These checklists were submitted by 4,865 unique eBird observers
 and linked to air quality data from a total of 71
 PM<sub>2.5</sub> sensors. This dataset
 contains 4,466,610 rows and 40 columns. Each row
 includes detection/non-detection data for one of 71 study species and a
 complete set of covariates for a single eBird checklist (i.e., survey).
 For example, row 43 contains detection/non-detection data for American
 Robins (<i>Turdus migratorius</i>) in eBird
 checklist S24832105. There are 62,910 unique eBird checklists in the data
 set. Note that 62,910 checklists x 71 study species = 4,466,610
 rows. Most columns refer to information provided by
 eBird when downloading the Basic Dataset,
 including "checklist_id", "last_edited_date",
 "country", "country_code",
 "state", "state_code", "county",
 "county_code", "iba_code",  "bcr_code",
 "usfws_code", "atlas_block", "locality",
 "locality_id", "locality_type", "latitude",
 "longitude", "observation_date",
 "time_observations_started", "observer_id",
 "sampling_event_identifier", "protocol_type",
 "protocol_code", "project_code",
 "duration_minutes", "effort_distance_km",
 "effort_area_ha", "number_observers",
 "all_species_reported", "group_identifier",
 "trip_comments", "scientific_name",
 "observation_count" and "species_observed." Please see
 metadata provided with eBird data downloads for details. Download data
 at: https://ebird.org/data/download We added the
 following covariates (see above for details on each): 
  "particulates": daily mean concentration of
 PM<sub>2.5 </sub>(μg/m<sup>3</sup>)
  "air": daily mean air temperature (K)
  "apcp": daily accumulated precipitation
 (mm)  "land_cover": land cover class
 (categorical)  "year": year of observation
 date (categorical) Note that prior to our
 analysis, we converted the time observations started to the number of
 seconds past midnight, estimated the distance traveled for area checklists
 (see the methods section of Sanderfoot and Gardner 2021 for details), and
 scaled all numeric covariates. We also removed data from the only eBird
 checklist in this dataset with a recorded concentration of
 PM<sub>2.5</sub> above 300 and the only eBird checklist in
 this dataset for which no information on the duration of the survey was
 provided.