DOI: 10.5061/dryad.7sqv9s530
title: Examining inter-regional and intra-seasonal differences in wintering waterfowl landscape associations among Pacific and Atlantic flyways

description: The Central Valley of California (CVC) and Mid-Atlantic (MA) in the U.S.
 are both critical sites for nationwide food security (California Poultry
 Federation 2016, Prosser et al. 2017), and many waterfowl species
 annually, especially during the winter, providing feeding and roosting
 locations for a variety of species. Mapping waterfowl distributions, using
 NEXRAD, may aid in the adaptive management of important waterfowl habitat
 and allow various government agencies to better understand the interface
 between wild and domestic birds and commercial agricultural practices. We
 used 9 years (2014–2023) of data from the US NEXRAD network to model
 winter waterfowl relative abundance in the CVC and MA as a function of
 weather, temporal period, environmental conditions, and landcover
 characteristics using Boosted Regression Tree modelling. We were able to
 quantify the variability in effect size of 28 different covariates across
 space and time within two geographic regions which are critical to
 nationwide waterfowl management and host a high density of nationally
 important commercial agriculture. In general, weather, geographic
 (distance to features), and landcover condition (wetness index) predictors
 had the strongest relative effect on predicting wintering waterfowl
 relative abundance in both regions, while effects of land cover
 composition were more regionally and temporally specific. Increased daily
 mean temperature was a major predictor of increasing relative waterfowl
 abundance in both regions throughout the winter. Increasing precipitation
 had differing effects within regions, increasing relative waterfowl
 abundance in the MA, while decreasing in general within the CVC.
 Increasing relative waterfowl abundance in the CVC is strongly tied to the
 flooding of the landscape and rice availability, whereas waterfowl in the
 MA, where water is less limiting, are generally governed by waste grain
 availability and emergent wetland on the landscape. Waterfowl relative
 abundance in the MA was generally higher nearer to the Atlantic coast and
 lakes, while in the CVC they were higher nearer to lakes. Our findings
 promote a better understanding of spatial associations of waterfowl to
 landscape features and may aid in conservation and biosecurity management
 protocols.
description: We analyzed NEXRAD data from November 1 to March 15 (2014–2023)
 to estimate waterfowl densities in the Central Valley of California and
 the Mid-Atlantic. Radar reflectivity, measured in units of Z (mm⁶ m⁻³),
 was used to quantify waterfowl biomass by assessing the density and size
 of targets. To capture waterfowl departing feeding
 grounds, we analyzed radar scans during evening civil twilight when
 detection was highest. Data were collected from the lowest elevation
 sweeps (0.5°) with volume dimensions of 250 m x 0.5°. Scans were manually
 screened to exclude precipitation and other non-bird sources, using NOAA’s
 Weather and Climate Toolkit and Amazon Web Services for radar
 data. Reflectivity in dBZ was converted to total bird
 reflectivity (η) [cm² km⁻³] and grouped into 10 m altitude bins to create
 a vertical profile of reflectivity (VPR). The VPR, derived from the five
 lowest sweeps (0.5°–5.5° tilt) within 5–20 km of the radar, was adjusted
 for range bias to calculate vertically integrated reflectivity (VIR) [cm²
 ha⁻¹], representing the average waterfowl biomass across the air
 column.
description: # Examining inter-regional and intra-seasonal differences in wintering
 waterfowl landscape associations among Pacific and Atlantic flyways ##
 List of Associated Documents and Descriptions of Files ### (1) Screening
 Spreadsheets .csv files of days of the winter November 1st to March 15th
 2014-2022 for each of the 6 radars (KDAX, KBBX, KHNX, KDOX, KMHX, and
 KLTX). File and variables included: * RADAR = 4 Digit Radar Site Code *
 DATE = Day of the Year in Month/Day/Year format * SEASON =
 'Winter' to distinguish from other season in projects dealing
 with songbirds. * DOWNLOAD = Purposely left blank, it's filled in
 with a Yes or No by the data handler depending on the STATUS column *
 STATUS = Classed as biological (B), contaminated (C), or No Data/missing
 (ND) when visually screened using NOAA's Weather Climate Toolkit.
 Biological days were used for modelling * CONTAMINATION_TYPE = If STATUS =
 B, then 'blank', if STATUS = C OR ND, CONTAMINATION_TYPE is
 listed. P = Precipitation, CL = Clutter, and AP = Anomalous Propagation of
 the radar beam. ND STATUS is also left 'blank'. * TARGET_ID =
 Intentionally left blank * SCREENER = Initials of researcher who visually
 screened raw radar data. * SURFACE_WIND, WIND_DIRECTION,
 APPROXIMATE_SAMPLING_TIME, TARGET_SPEED, GROUND_HEADING, and COMMENTS
 intentionally left blank and can be used for data annotation by the person
 screening the weather radar data. We use wind direction and target speeds
 to distinguish between songbirds and insects, but given this was a
 waterfowl study, and in winter, we do not have to distinguish between
 insects. This is our standard template and is why numerous columns are
 blank. --- ### (2) Modelling Grids and Summary-Winter I provide modelling
 grids and all their properties used for the 6 radar domains. These can be
 read in GIS or other software such as R. The modelling grids are a 250m
 resolution raster of the unique identifier values for each grid cell that
 match up with the “id” variable in summary_winter files of data tables.
 Use this raster for visualizing data as a spatial map. File includes: * id
 = Cell ID in radar modelling grids * range = The range the radar beam is
 scanning from the centroid of the radar (meters) * azimuth = The angle the
 radar beam is scanning at, ranging from 0.25 to * groundht = Refers to the
 ground height (meters) or the elevation of the ground surface at a
 specific location relative to sea level. This value is used to calculate
 the actual height of radar targets * pblock = Quantifies how much of the
 radar beam's energy is reduced or blocked due to obstructions.
 Expressed as a percentage. * vprfilter = Refers to adjustments or filters
 applied to account for the Vertical Profile of Reflectivity (VPR). The VPR
 describes how radar reflectivity changes with altitude and is used to
 better interpret and process radar data. * clutter = Was a manual clutter
 filter applied (binary, 1 = yes, 0 = no) * pwater = Quantifies how much of
 the reflectivity is over water. Expressed as a percentage. * waterfilt =
 Mask of reflectivity values over water. Was a manual water filter applied
 (binary, 1 = yes, 0 = no) * utm_e = Easting * utm_n = Northing * elev =
 elevation above sea level (meters) of the radar itself * hectares =
 Measure of area (hectares) of land that can be used for analyzing or
 interpreting radar data. * awater = Measure of area (hectares) of water
 that can't be used for analyzing or interpreting radar data. * lcv11
 = How many 30 x 30m grid cells of Open Water * lcv12 = How many 30 x 30m
 grid cells of Ice/Snow * lcv20 = How many 30 x 30m grid cells of Grain *
 lcv21 = How many 30 x 30m grid cells of Developed-Open Space * lcv22 = How
 many 30 x 30m grid cells of Developed- Medium * lcv23 = How many 30 x 30m
 grid cells of Developed- High * lcv24 = How many 30 x 30m grid cells of
 Developed- Very High * lcv25 = How many 30 x 30m grid cells of
 Developed-Other * lcv30 = How many 30 x 30m grid cells of Barren * lcv31 =
 How many 30 x 30m grid cells of Barren * lcv41 = How many 30 x 30m grid
 cells of Forest-Deciduous * lcv42 = How many 30 x 30m grid cells of
 Forest-Coniferous * lcv43 = How many 30 x 30m grid cells of Forest-Mixed *
 lcv44 = How many 30 x 30m grid cells of Forest-Other * lcv52 = How many 30
 x 30m grid cells of Scrub/Shrub * lcv71 = How many 30 x 30m grid cells of
 Grassland * lcv81 = How many 30 x 30m grid cells of Pasture/Hay * lcv82 =
 How many 30 x 30m grid cells of Cultivated Crops * lcv84 = How many 30 x
 30m grid cells of Sod * lcv90 = How many 30 x 30m grid cells of
 Woody/Forested Wetland * lcv91 = How many 30 x 30m grid cells of
 Unidentified * lcv92 = How many 30 x 30m grid cells of Unidentified *
 lcv93 = How many 30 x 30m grid cells of Unidentified * lcv94 = How many 30
 x 30m grid cells of Unidentified * lcv95 = How many 30 x 30m grid cells of
 Emergent Herbaceous Wetland * lcvsum = sum of 30x30 m landcover gridcells
 for that specific id NOTES: Values of lcv01v95 and similar denote
 proportions of that landcover type that makes up that 250m x 250m grid,
 values of z### are the rraw reflectivity values of aerial targets measured
 in dbz. .csv file continued...... * long = Longitude of grid cell * lat =
 Latitude of grid cell * dcoast = Distance to coast (kilometers) from radar
 centroid * mbeam = Height of the middle of the radar beam (meters) * bbeam
 = Height of the bottom of the radar beam (meters) * tbbeam = Height of the
 top of the radar beam (meters) * corrz = How well radar signals correlate
 across the radar volume scan. This variable is important because it helps
 identify areas where radar returns may be unreliable due to issues such as
 beam blocking, ground clutter, or other distortions. The higher the
 correlation, the more reliable the data is likely to be. * propdist =
 refers to the propagation distance or distance to the radar beam at a
 particular point in time. It is essentially the distance from the radar
 site to the location where the radar signal is being reflected or
 detected. Expressed as a percentage. The decreasing amount of area covered
 as a function of increasing beam height Missing Data notes Unless
 otherwise stated above, 0 values are true zeroes. Missing values indicate
 data were not available/corrupt/missing at that grid or interval. --- ###
 (3) Predictions Description: The Mean Vertically Integrated Reflectivity
 (VIR) of waterfowl refers to a radar-derived measurement of the total
 reflectivity of waterfowl targets (like flocks of birds) along the
 radar's vertical scan. It is an estimate of the radar reflectivity
 accumulated over the entire column of air from the ground up to the
 maximum height scanned by the radar. Items: .rds files of predicted mean
 vertically integrated reflectivity (VIR) for each radar domain. Content
 Description: There are two prediction folders Predictions_CVC.zip = Mean
 VIR predictions for the Central Valley of California region (Radars KDAX,
 KBBX, and KHNX) Predictions_MA.zip = Mean VIR predictions for the
 Mid-Atlantic region (Radars KDOX, KLTX, and KMHX) Zip File Contents We
 created mean VIR for every half-month period through 2020-2022 beginning
 with Early and late November (eNov, and lNov) and ending in March (eMar)
 for each year and each domain (MA or CVC). eNov, lNov, eDec, lDec, eJan,
 lJan, eFeb, lFeb, and eMar --- ## (4) Accessing the Archive Data ︎or
 Getting Data from Other Radars
 [https://aws.amazon.com/public-datasets/nexrad/](https://aws.amazon.com/public-datasets/nexrad/) The full historical archive from NOAA from June 1991 to present is available. The NEXRAD Level II archive data is hosted in the noaa-nexrad-level2 Amazon S3 bucket in the us-east-1 AWS region. The address for the public bucket is: [https://noaa-nexrad-level2.s3.amazonaws.com](https://noaa-nexrad-level2.s3.amazonaws.com). Each volume scan file of archival data is available as an object in Amazon S3. The basic data format is: ///// Where: is the year the data was collected is the month of the year the data was collected is the day of the month the data was collected is the NEXRAD ground station (map of ground stations) is the name of the file containing the data. These are compressed files (compressed with gzip). The file name has more precise timestamp information. All files in the archive use the same compressed format (.gz). The data file names are, for example, KDAX20010101_080138.gz. The file naming convention is: GGGGYYYYMMDD_TTTTTT Where: GGGG = Ground station ID (map of ground stations) YYYY = year MM = month DD = day TTTTTT = time when data started to be collected (GMT) Note that the 2015 files have an additional field on the file name. It adds “_V06” to the end of the file name. An example is KDAX20150303_001050_V06.gz.