DOI: 10.5061/dryad.5mkkwh7hq
title: Data and code from: Bioacoustic monitoring reveals patterns of landscape use by migrating birds at a Great Lakes barrier crossing

description: Understanding how highly mobile animals use landscapes at broad geographic
 scales remains a major challenge in ecology. Traditional monitoring
 approaches often lack the spatial and temporal resolution to monitor how
 migratory species use heterogeneous landscapes, contend with movement
 barriers, and interact with urban and developed landscapes. Here, we use a
 passive acoustic monitoring network to characterize landscape use of
 migrating songbirds in the Keweenaw Peninsula, a major barrier crossing
 point along the south shore of Lake Superior. Using nearly 3 million
 acoustic detections of migrants from 18 sites spanning 328 km2, we
 demonstrate that landscape use is shaped strongly by local geography and
 wind conditions during reoriented movements associated with barrier
 crossing. Generally, more songbirds used the peninsula during wind
 conditions favorable for migration. Following winds unfavorable for
 crossing in spring, birds concentrated in coastal and ridge landscapes
 oriented along an east-west axis. Geographic gradients and coastline
 orientation both played important additional roles in shaping migrants’
 landscape use. Together, our results illustrate the complex role of large
 water barriers in shaping landscape use in highly mobile animals. More
 broadly, our findings demonstrate the value of acoustic monitoring as a
 novel technique for studying migratory animals’ landscape use. This
 approach offers a powerful, scalable tool that can be deployed across
 complex landscapes and inform conservation priorities of hard-to-monitor
 species.
description: # Data and code from: Bioacoustic monitoring reveals patterns of landscape
 use by migrating birds at a Great Lakes barrier crossing # Dataset DOI:
 [10.5061/dryad.5mkkwh7hq] ## Description of the data and file structure
 Data sources for: Bioacoustic monitoring reveals patterns of landscape use
 by migrating birds at a Great Lakes barrier crossing Authors: Zach G. Gayk
 and Benjamin M. Van Doren Author contact: University of Illinois
 Urbana-Champaign, Department of Natural Resources and Environmental
 Sciences, Urbana, IL 61801, USA (1)
 [zachgayk@gmail.com](mailto:zachgayk@gmail.com) (2)
 [vandoren@illinois.edu](mailto:vandoren@illinois.edu) Data were collected
 by using acoustic monitoring units to detect migratory songbirds'
 flight calls between 2022-2024 at 12 sites in the Keweenaw Peninsula.
 Michigan, USA. This peninsula concentrates migratory birds attempting to
 cross Lake Superior in the Great Lakes of North America. The Nighthawk
 machine learning algorithm was used to detect and identify songbird flight
 calls in the dataset. Subsequent manual review of randomly sampled
 Nighthawk detections was used to filter Nighthawk detections to only
 include high-confidence data. ### Files and variables #### Files: (1)
 merged_three_year_dataframe_2_December_2024.txt (2)
 total_combined_seasons_PCs_spring_with_log_radar_3_December_2024.txt (3)
 total_combined_seasons_PCs_fall_with_log_radar_11_December_2024.txt Each
 file is found within the data folder of
 the Keweenaw_Reoriented_Migration.zip project folder.  **Description:** 
 ##### Variables * night_of: Night of migration * time_bin: period of
 Night/Day from which wind data were averaged * mean_u: mean **east-west
 component of wind (m/s) where negative is east and positive is west** *
 max_u: max **east-west component of wind (m/s) where negative is east and
 positive is west** * mean_v: mean **north-south component of wind (m/s)
 where negative is north and positive is south** * max_v: max **north-south
 component of wind (m/s) where negative is north and positive is south** *
 det_start_date: Calendar date of detection * predicted_category: Nighthawk
 taxonomic category * lat: Latitude * lon: Longitude * site_id: Site Id of
 recorder * observation_count: total daily count of each predicted category
 * season: Season * year: Year * night_of_numeric: *
 mean_u_6am_to_11am_next_day: mean U in this time period for each day *
 mean_u_9pm_to_midnight_prior: mean U in this time period for each day *
 mean_u_midnight_to_5am_next_day: mean U in this time period for each day *
 mean_u_noon_to_5pm_next_day: mean U in this time period for each day *
 mean_v_6am_to_11am_next_day: mean V in this time period for each day *
 mean_v_9pm_to_midnight_prior: mean V in this time period for each day *
 mean_v_midnight_to_5am_next_day: mean V in this time period for each day *
 mean_v_noon_to_5pm_next_day: mean V in this time period for each day *
 diff_mean_u_9pm_to_midnight_prior_6am_to_11am_next_day: the difference in
 mean U between each time period in delta m/s where negative is increasing
 west wind and positive is increasing east wind *
 diff_mean_v_9pm_to_midnight_prior_6am_to_11am_next_day: the difference in
 mean V between each time period in delta m/s where negative is increasing
 south wind and positive is increasing north wind *
 diff_mean_u_9pm_to_midnight_prior_midnight_to_5am_next_day: the difference
 in mean U between each time period in delta m/s where negative is
 increasing west wind and positive is increasing east wind *
 diff_mean_v_9pm_to_midnight_prior_midnight_to_5am_next_day: the difference
 in mean V between each time period in delta m/s where negative is
 increasing south wind and positive is increasing north wind * radar_KMQT:
 nightly migration from the KMQT radar * mt_KMQT: nightly migration from
 the KMQT radar ##### Variables only found in
 total_combined_seasons_PCs_spring_with_log_radar_3_December_2024.txt and
 total_combined_seasons_PCs_fall_with_log_radar_11_December_2024.txt * PC1
 (response variable summarizing overall daily intensity of avian migrant
 landscape use across the Keweenaw peninsula) * PC2 (response variable
 summarizing daily northwest-to-southeast (spring) and west-to-east (fall)
 gradients in the intensity of avian migrant landscape use across the
 Keweenaw peninsula) * PC3 (response variable summarizing daily
 north-to-south gradients in the intensity of avian migrant landscape use
 across the Keweenaw peninsula) ## Code/software Code was produced in R
 version 2023.12.1+402 All code consists of .R files. main dataset consists
 filtered Nighthawk detections of flight calling migrants with mean U and
 mean V wind, averaged across nightly time periods. This dataset is used to
 produced separate spring and fall datasets with daily PC Scores
 representing intensity of migration across 18 sites in the Keweenaw
 Peninsula, Michigan. Dataset name:
 merged_three_year_dataframe_2_December_2024.txt additional datasets
 consist of (1)
 total_combined_seasons_PCs_spring_with_log_radar_3_December_2024.txt and
 (2) total_combined_seasons_PCs_fall_with_log_radar_11_December_2024.txt
 these two datasets consist of mean U and mean V wind data across several
 time periods of the night, nightly radar traffic over Lake Superior, and
 daily PC scores summarizing bird migration for each day. Spring and fall
 data were analyzed separately with unique PCs for each season. These two
 datasets were used to run models on the influence of wind, nightly
 migration traffic (radar), and Date of season on daily PC scores
 representing diurnal migration and landscape use. ### Code:
 total_analyses_final_9_December_2025.R contains code to conduct PCA on
 migratory bird landscape use and to produce models of migratory landscape
 use versus wind data Final_paper_figures.R contains code to produce
 figures used in main manuscript based on partial residual plots from
 models of PC1, PC2, and PC3 each used separately as response variables of
 intensity of habitat use throughout the Keweenaw Peninsula all code is
 within the scripts folder of the Keweenaw_Reoriented_Migration.zip project
 folder.