Data from: Wildfire smoke impacts the body condition and capture rates of birds in California
Data files
May 30, 2024 version files 1.89 MB
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AllPM25.csv
101.84 KB
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CaptureData.csv
1.55 MB
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Paired_input.csv
234.90 KB
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README.md
3.92 KB
Abstract
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.
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:
- “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 >35 ug/m3 PM2.5 in the calendar year - “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 >35 ug/m3 PM2.5 in the calendar year
Effort: Banding net-hours per banding day
Dayofyear: Ordinal day of year
Year - “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 >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.sfbbo.org/ccfs.html
- 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
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.