DOI: 10.5061/dryad.2fqz6130t
title: Low migratory flight altitudes may explain increased collision risk for Scolopax minor (American Woodcock)

description: Understanding bird migration at low altitudes is critical to evaluating
 risk of collision with obstacles. Recent advances in satellite tracking
 technologies allow quantifying use of low altitudes by small migrating
 birds with a high level of precision, allowing species-level inference
 into potential collision risk based on flight altitude. The American
 Woodcock (Scolopax minor) is suspected to be a low altitude migrant due to
 its frequent collisions with buildings, and subsequent mortality during
 migration may be contributing to population declines. We investigated
 migratory flight altitudes using satellite transmitters deployed on
 woodcock in 2020–2024 and examined how flight altitudes compare to the
 heights of common airspace obstacles. Each transmitter recorded a
 nocturnal GPS location with an altitude reading every 1–3 days during fall
 and spring migrations. We implemented a Bayesian hierarchical mixture
 model to identify whether locations were recorded on the ground or during
 flight, isolate measurement error, and describe the distribution of flight
 altitudes. We found that migrating woodcock fly at mean altitudes of 379 m
 above ground level, flying higher during spring (mean: 444 m, 95% credible
 interval: 333–578 m) than fall (338 m, 95% CRI: 267–423 m). Woodcock
 flight altitudes were frequently lower than could be observed using
 weather radar (27% of observations), and 57% of observations fell within
 the altitude range of ≥1 airspace obstacle. Our results suggest that
 woodcock fly at altitudes lower than reported for most nocturnal migrants,
 which likely contributes to their vulnerability to obstacle collisions.
 Woodcock provide an example of how vulnerability to obstacle collisions
 during nocturnal migratory flights are often species-specific, and
 mitigation efforts should incorporate measures aimed at reducing
 collisions during both diurnal stopovers and nocturnal migratory flights
 to effectively reduce bird collision mortality.
description: Data Collection and Preprocessing We
 collected woodcock locations with altitude readings from 2020 to 2024
 using GPS transmitters as a part of a larger collaborative effort by the
 Eastern Woodcock Migration Research Cooperative (Blomberg et al. 2023,
 Clements et al. 2024, Fish et al. 2024). We captured woodcock at 100 sites
 across the eastern portion of their range, including Alabama, Florida,
 Georgia, Louisiana, Maine, Maryland, New Jersey, New York, North Carolina,
 Nova Scotia, Ontario, Pennsylvania, Québec, Rhode Island, South Carolina,
 Vermont, Virginia, West Virginia, and Wisconsin. We caught woodcock using
 a combination of spotlighting and mist netting (McAuley et al. 1993). We
 aged and sexed birds upon capture, where we classified birds undertaking
 their first fall and spring migrations as juveniles, and all other birds
 as adults. We then attached 4–7 g PinPoint transmitters (Lotek Wireless
 Inc., Newmarket, Ontario, CA) using a rump-mounted leg loop harness (Fish
 et al. 2024). We programmed transmitters to collect
 locations every 1–3 days during migration, with locations alternating
 between diurnal (1300–1500 hours Eastern Time) and nocturnal (0000–0100
 hours) times. Transmitters recorded time, latitude, longitude, and
 GPS-derived altitude above the WGS84 ellipsoid, and transmitted data back
 to the ARGOS satellite constellation after every third location. We subset
 these locations to include only those within the migratory classification
 dataset produced by Berigan (2024). This dataset classified individual
 locations as migratory or non-migratory based on the assumption that
 migration starts after the first ≥16.1 km movement and ends after the
 final ≥16.1 km movement of the season. We used ArcGIS Pro 3.2.1 (ESRI
 2024a) to calculate the difference between the altitude and orthometric
 elevation recorded for each location (ESRI composite elevation layer; ESRI
 2024b), providing a measurement of altitude above ground level for each
 point. We classified data for our models based on prior
 descriptions of woodcock activity patterns. Woodcock are ground-feeding
 birds that rarely fly outside of crepuscular hours (Rabe et al. 1983).
 When rare diurnal flights do occur, these are generally brief, comprising
 1–3% of diurnal time budgets, and close to the ground (McAuley et al.
 2020). We therefore made a modeling assumption that all diurnal locations
 could be treated as though they were known to be recorded on the ground
 (hereinafter “known ground locations”). As woodcock are nocturnal
 migrants, we define potential flight locations as all points that were
 nocturnal, occurred during migration based on the classification in
 Berigan (2024), and were preceded and followed by &gt;6.68 km steps
 (defined as lines connecting consecutive locations). The 6.68 km threshold
 was based on the 99<sup>th</sup> percentile of step lengths
 recorded within a stopover site (Berigan 2024). Ensuring that the
 preceding and following steps were &gt;6.68 km increased the
 likelihood that the bird had moved away from a stopover site before the
 point was recorded. References
 Berigan, L.A. (2024). <em>Full annual cycle analysis of
 American Woodcock (Scolopax minor) distribution, habitat use, and
 migration ecology</em>. PhD dissertation. University of Maine,
 Orono, Maine. Blomberg, E. J., A. C. Fish, L. A.
 Berigan, A. M. Roth, R. Rau, S. J. Clements, G. Balkcom, B. Carpenter, G.
 Costanzo, J. Duguay, C. L. Graham, et al. (2023). The American Woodcock
 Singing Ground Survey largely conforms to the phenology of male woodcock
 migration. <em>Journal of Wildlife Management</em>
 87:e22488. Clements, S. J., L. A. Berigan, A. C. Fish,
 R. L. Darling, A. M. Roth, G. Balkcom, B. Carpenter, G. Costanzo, J.
 Duguay, and K. Filkins (2024). Satellite tracking of American Woodcock
 reveals a gradient of migration strategies.
 <em>Ornithology</em>141:ukae008. ESRI
 (2024a). <em>ArcGIS Pro</em>. Redlands, CA, USA.
 ESRI (2024b). <em>Terrain</em>. [Online.] Available
 at
 https://www.arcgis.com/home/item.html?id=58a541efc59545e6b7137f961d7de883. Fish, A. C., A. M. Roth, G. Balkcom, L. Berigan, K. Brunette, S. Clements, G. Costanzo, C. L. Graham, W. F. Harvey, M. Hook, D. L. Howell, et al. (2024). American woodcock migration phenology in eastern North America: implications for hunting season timing. <em>Journal of Wildlife Management</em> 88:e22565. McAuley, D. G., D. M. Keppie, and R. M. Whiting Jr. (2020). American Woodcock (<em>Scolopax minor</em>), version 1.0. In <em>Birds of the World</em> (A. F. Poole, Editor). Cornell Lab of Ornithology, Ithaca, NY, USA. McAuley, D. G., J. R. Longcore, and G. F. Sepik (1993). Techniques for Research into Woodcocks: Experiences and Recommendations. <em>Proceedings of the eighth American woodcock symposium</em>. U.S. Fish and Wildlife Service, p. 5. Rabe, D. L., H. H. Prince, and E. D. Goodman (1983). The effect of weather on bioenergetics of breeding American woodcock. <em>Journal of Wildlife Management</em> 47:762–771.
description: # Flight altitudes of American Woodcock ### Description of the data and
 file structure We collected GPS locations and GPS-derived altitude data
 using PinPoint GPS transmitters attached to American Woodcock (*Scolopax
 minor*) throughout the eastern portion of their range from 2020 to 2024.
 This dataset includes both ground locations (i.e., locations recorded when
 the bird was not undergoing migratory flight) and flight locations; the
 procedure for differentiating between these locations is outlined in the
 accompanying paper in *Ornithological Applications*. #### Files and
 variables ##### File: amwo\_flight\_altitudes.csv **Description**: This
 csv file contains GPS locations and altitude data collected from American
 Woodcock, as well as additional covariates recorded on capture or imported
 from other sources. The data has 16020 rows of 13 variables. 1. ID: Unique
 ID for each woodcock. 2. time: Time at which the GPS location/altitude
 were recorded in Coordinated Universal Time. 3. lon: Longitude of the GPS
 location. 4. lat: Latitude of the GPS location. 5. sex: Sex of the
 woodcock. "m" is male, "f" is female, "u" is
 unknown. 6. age: Age of the woodcock when it was originally marked,
 following the Humphrey-Parkes system for categorizing bird ages. Age
 classes are "After Second Year", "Second Year",
 "After Hatch Year", "Hatch Year", and
 "Unknown". 7. height_above_wgs84: GPS-derived altitude of the
 location, recorded in meters above the WGS84 ellipsoid. 8.
 height_above_terrain: Processed GPS altitude, indicating the altitude
 (meters) above an orthometric elevation (i.e., terrain) layer (ESRI
 2024b). This can be interpreted as altitude above ground level. 9.
 migratory_state: Movement state classifications from Berigan (2024),
 showing whether a given GPS location was recorded during a migration or a
 long-distance movement. Options include: * "Point state:
 Stationary": Not recorded during a migration or long-distance
 movement. * "Point state: Migratory (spring)": Recorded during
 spring migration. * "Point state: Migratory (fall)": Recorded
 during fall migration. * "Point state: Migratory (summer)":
 Recorded during summer migration, described by Berigan (2024) as southerly
 migratory movements which occurred prior to August 1st. * "Point
 state: Foray loop": Foray loops are defined as circular or
 out-and-back movements with steps ≥16.1 km that result in &lt;16.1 km of
 net displacement between the first and last point. Foray loops begin and
 end in a non-migratory state. 10. moving: Binary variable indicating
 whether a given GPS location is ≥6.67 km from preceding and following
 points. Points with a FALSE designation are almost always ground
 locations. 11. day_night: Indicates whether a location was recorded during
 the day (after sunrise and before sunset) or during the night. Determined
 using the *suncalc* package in R (Version 0.5.1). 12.
 known_ground_location: Binary variable indicating whether a location was
 treated as a known ground location (i.e., recorded during the day). 13.
 possible_flight_location: Binary variable indicating whether a location
 was treated as a potential flight location (i.e., nocturnal, migratory,
 ≥6.67 km from preceding and following points). ### Code/software Any
 spreadsheet program (e.g., Microsoft Excel) can be used to access these
 data. The code required to run this analysis in its entirety is stored at
 [https://github.com/EWMRC/flight-altitude/](https://github.com/EWMRC/flight-altitude/). ### Access information There are no other publicly accessible locations of the data at the time of publication.