DOI: 10.5061/dryad.k6djh9wfw
title: Data from: Wildfire smoke impacts the body condition and capture rates of birds in California

description: Despite the increased frequency with which wildfire smoke now blankets
 portions of world, the effects of smoke on wildlife, and birds in
 particular, are largely unknown. We used two decades of banding data from
 the San Francisco Bay Bird Observatory to investigate how fine particulate
 matter (PM2.5) – a major component and indicator of wildfire smoke –
 influenced capture rates and body condition of 21 passerine or
 near-passerine bird species. Across all study species, we found a negative
 effect of acute PM2.5 exposure and a positive effect of chronic PM2.5
 exposure on avian capture rates. Together, these findings are indicative
 of decreased bird activity or local site removal during acute periods of
 wildfire smoke, but increased activity or site colonization under chronic
 smoke conditions. Importantly, we also observed a negative relationship
 between chronic PM2.5 exposure and body mass change in individuals with
 multiple captures per season. Our results indicate that wildfire smoke
 likely influences the health and behavior of birds, ultimately
 contributing to a shift in activity and body condition, with differential
 short-term versus long-term impacts. Although more research is needed on
 the mechanisms driving these observed changes in bird health and behavior,
 as well as validation of these relationships in more areas, our results
 suggest that wildfire smoke is a potentially frequent large-scale
 environmental stressor to birds that deserves increasing attention and
 recognition.
description: We used bird banding data spanning 2000–2021 from CCFS, a bird
 banding station located in north San Jose, a city in the southern region
 of the California Bay Area. Banding data and metadata were used to create
 multiple variables for our analysis. For the primary analysis of capture
 trends, we calculated the number of unique captures of each species for
 each day of banding (i.e., excluding daily recaptures). For the analysis
 of changes in body condition, we restricted the analysis to individually
 marked birds that were captured at least twice between the months of
 July–November in at least one year. We restricted our analysis to bird
 species common to California and excluded migratory species that are known
 to generally be absent from the banding station vicinity during the months
 of October and November (generally, ‘peak’ fire season along the
 California coast). For the second analysis of changes in body mass, we
 began by reducing the data down to individuals that were captured at least
 twice during the fire season in single year. To estimate exposure to
 smoke, we used data from ground-based air quality monitors included in the
 EPA Air Quality System (AQS) near the CCFS banding station. See Methods in
 published manuscript for full explanation and processing steps.
description: # Wildfire smoke impacts the body condition and capture rates of birds in
 California
 [https://doi.org/10.5061/dryad.k6djh9wfw](https://doi.org/10.5061/dryad.k6djh9wfw) We used bird banding data spanning 2000–2021 from CCFS, a bird banding station located in north San Jose, a city in the southern region of the California Bay Area. Banding data and metadata were used to create multiple variables for our analysis. For the primary analysis of capture trends, we calculated the number of unique captures of each species for each day of banding (i.e., excluding daily recaptures). For the analysis of changes in body condition, we restricted the analysis to individually marked birds that were captured at least twice between the months of July–November in at least one year. We restricted our analysis to bird species common to California and excluded migratory species that are known to generally be absent from the banding station vicinity during the months of October and November (generally, ‘peak’ fire season along the California coast). For the second analysis of changes in body mass, we began by reducing the data down to individuals that were captured at least twice during the fire season in single year. To estimate exposure to smoke, we used data from ground-based air quality monitors included in the EPA Air Quality System (AQS) near the CCFS banding station. See Methods in published manuscript for full explanation and processing steps.Description of the data and file structure. We provide three data files for replication of the full analysis: 1. "AllPM25.csv" provides smoke data for every day during our study. Columns are as follows:Date: in m/d/y format Daily Mean PM2.5 Concentration: in micrograms per cubic m dayofyear: ordinal day of year (1 to 366) year 3_day_avg: rolling 3-day average of PM2.5 mean daily concentration count35: Count of the cumulative number of days with &gt;35 ug/m3 PM2.5 in the calendar year 2. "CaptureData.csv" provides data on the number of birds captured per day. Columns are as follows: Count: Count of unique individuals Species: Species ID as 4-letter US Banding Code X3day_PM2.5: 3-day rolling average of PM2.5 mean daily concentration Exposure35_PM2.5: Count of the cumulative number of days with &gt;35 ug/m3 PM2.5 in the calendar year Effort: Banding net-hours per banding day Dayofyear: Ordinal day of year Year 3. "Paired_input.csv" provides data on the change in body mass for recaptured birds in each year band_number: the USGS Bird Banding Lab band number species_code_no_subsp: Species ID as 4-letter US Banding Code Year mass_alpha: Mass of first capture (g) mass_omega: Mass at last capture (g) mass_delta: Change in mass (g) day_delta: Number of days between first and last capture exp35_delta: Cumulative number of days  with &gt;35 ug/m3 PM2.5 between first and last capture relative.mass_delta: % Change in mass relative to the average body mass of captured birds of that species ## Sharing/Access information Data was derived from the following sources: * [https://www.epa.gov/outdoor-air-quality-data](https://www.epa.gov/outdoor-air-quality-data) * [https://www.sfbbo.org/ccfs.html](https://www.sfbbo.org/ccfs.html) * [https://www.usgs.gov/labs/bird-banding-laboratory](https://www.usgs.gov/labs/bird-banding-laboratory) ## Code/Software The three data files are associated with 5 code files (R language) which replicate all major analyses and data figures in the manuscript. The R scripts are meant to be run in order (based on file name), as some files create data objects (.Rdata) that are passed on to other scripts. Each script includes header information which briefly provides the purpose of the script. Script file names are as follows: * 1_MSZIP_model.R * 2_MSZIP_plot.R * 3_Paired_Model.R * 4_Paired_Plot.R * 5_PM25_Plot.R