DOI: 10.5061/dryad.n2z34tn92
title: Data from: Increased mortality rates caused by highly pathogenic avian influenza virus in a migratory raptor

description: Highly pathogenic avian influenza virus (HPAIV) has caused extensive
 mortalities in wild birds, with a disproportionate impact on raptors since
 2021. The population-level impact of HPAIV can be informed by telemetry
 studies that track large samples of initially healthy, wild birds. We
 leveraged movement data from 71 rough-legged hawks (Buteo lagopus) across
 all major North American migratory bird flyways concurrent with the
 2022–2023 HPAIV outbreak and identified a total of 29 mortalities, of
 which 11 were confirmed, and an additional ~9 were estimated to have been
 caused by HPAIV. We estimated a 28% HPAIV cause-specific mortality rate
 among rough-legged hawks during a single year concurrent with the HPAIV
 outbreak in North America. Additionally, the overall annual mortality rate
 during the HPAIV outbreak (47%) was significantly higher than baseline
 annual mortality rates (3–17%), suggesting that HPAIV-caused deaths were
 additive above baseline mortality levels. HPAIV mortalities were
 concentrated within the Central and Atlantic flyways during pre-breeding
 migration and peaked in April 2022 when large-scale HPAIV mortalities were
 reported in other wild birds throughout North America. HPAIV exposure was
 most likely caused by scavenging or preying on infected waterfowl, as
 rough-legged hawks are known to opportunistically scavenge during the
 nonbreeding season. We utilized movement data to identify a
 continental-scale HPAIV cause-specific mortality event in rough-legged
 hawks that has the potential to exacerbate ongoing population declines.
 Our study highlights the usefulness of monitoring movement data to
 pinpoint sources of mortality that can help better understand the drivers
 of population change, even if studies are focused on other research
 questions.
description: # Increased mortality rates caused by highly pathogenic avian influenza
 virus in a migratory raptor Dataset DOI:
 [10.5061/dryad.n2z34tn92](10.5061/dryad.n2z34tn92) ## Description of the
 data and file structure We leveraged movement data from GPS-tracked
 rough-legged hawks *Buteo lagopus* that coincided with the HPAIV panzootic
 in North America to determine its effect on annual mortality. All missing
 and unavailable data represented as NA  ### Files and variables #### File:
 AnnualMortality.csv **Description:** spreadsheet used to analyze the HPAIV
 effect on annual mortality. ##### Variables * index: index number * tagid:
 transmitter ID (unique to an individual hawk) * year: 12-month study
 period. 2020 = 1-Mar-2020 to 28-Feb-2021, etc... * date_begin: date within
 the 12-month study period that tracking began * date_end: date within the
 12-month study period that tracking ended * duration: tracking duration
 (number of days) * fate: individual fate during the 12-month study period.
 See the manuscript methods for details. * COD: cause of death. See the
 manuscript methods for details. * tagtype: transmitter type - PTT or GSM.
 * Sex: individual sex * day12mo: day within the 12-month study period that
 tracking began. 1-Mar = day 1 #### File: AnnualMortalityComparison.r
 **Description:** R script for comparison of annual mortality estimates.
 #### File: Figure_2_code.r **Description:**  R script for Figure 2 code.
 #### File: Figure_3_sex_analysis.r **Description:**  R script for Figure 3
 code and HPAIV sex/age analysis. #### File:
 Buteo_lagopus_Conway-Paprocki_NA.csv **Description:** Buteo lagopus
 movement data used in Figure 2.  ##### Variables * event-id: row
 identifier. * Timestamp: date and timestamp of bird GPS location. *
 Location-long: longitude associated with the GPS timestamp. *
 Location-lat: latitude associated with the GPS timestamp. * Sensor-type:
 location type - GPS or Argos. * individual-taxon-canonical-name: species
 name * individual-local-identifier: transmitter ID * study-name: movebank
 study name #### File: mortality-summary-RLHA.csv **Description:** data
 used for Figure 2 and Figure 3. ##### Variables * tagid: transmitter ID *
 fate: individual fate during 12-mo study period. See the manuscript
 methods for details. * COD: cause of death. See the manuscript methods for
 details. * Plotting: COD reformatted for graphing/plotting. *
 last_latitude: last known latitude. * last_longitude: last known
 longitude. * sex: F = female; M = male * date_end: end date of tracking
 period for tagid. #### File: sexing_Buteo_HPAI.csv **Description:** data
 used for age or sex effects on cause-specific mortality (HPAIV positive
 vs. all others) ##### Variables * Species: species 4-letter code * tagid:
 transmitter ID * mass: body mass (grams) at capture. * Wing: wing chord
 (mm) at capture. * ageCapture: age at capture. 1C = first cycle; 2C =
 second cycle; A = older than second cycle. * fate: individual fate during
 the 12-month study period. See the manuscript methods for details. * COD:
 cause of death. See the manuscript methods for details. * Age: age at end
 of study period. J = juvenile/first cycle; A = adult/older than first
 cycle * sex: F = female; M = male. #### File:
 Kidd_et_al._Rough-legged_Hawk_Movements_in_North_America.csv
 **Description:** Buteo lagopus movement data used in Figure 2.  #####
 Variables * event-id: row identifier. * Timestamp: date and timestamp of
 bird GPS location. * Location-long: longitude associated with the GPS
 timestamp. * Location-lat: latitude associated with the GPS timestamp. *
 Sensor-type: location type - GPS or Argos. *
 individual-taxon-canonical-name: species name *
 individual-local-identifier: transmitter ID * study-name: movebank study
 name ## Code/software We used logistic regression via the ‘glm’ function
 from the ‘stats’ R package (R Core Team, 2025) to predict HPAIV fate
 explained by sex and age class.  We used logistic regression via the ‘glm’
 function from the ‘stats’ R package (R Core Team, 2025) to predict the
 fate (1 = ‘likely dead’ or ‘confirmed dead’; 0 = ‘alive’ or ‘unknown’) of
 tracked birds across four, 12-mo periods. We used Tukey pairwise
 comparisons from the ‘emmeans’ package (Lenth, 2023) to determine pairwise
 differences in mortality rates among years. ## Access information Other
 publicly accessible locations of the data: * N/A Data was derived from the
 following sources: * N/A