This dataset contains measures of seasonal mean bird stopover densities and in seasonal mean bird density based on weather surveillance radar data from 20 radar locations in the Northeast U.S. across seven autumn migrations (15 August through 7 November of 2008-2014) [six autumn migrations for the terminal doppler weather radar (15 August through 7 November of 2009-2014)]. Data are present only in radar-sampled areas for each individual radar (see below for description on how these data are filtered). If you are interested in a continuous map of bird stopover densities for the entire region (and outside of these radar coverage areas), refer to layer “Predicted autumn migratory landbird density, 1km, Northeast U.S.”.
The dataset was originally developed as supplemental information for the cooperative agreement (#F13AC00402) between the U.S. Fish and Wildlife Service and the University of Delaware. The data contributed to a larger study to validate NEXRAD radar observations of emigrating birds and relate them to bird densities on the ground (for a list of NEXRAD radar stations see: https://www.weather.gov/media/tg/wsr88d-radar-list.pdf). Partners for this project include University of Delaware, Old Dominion University, and USGS.
Please see the accompanying report for details: Buler J. J., J. McLaren, T. Schreckengost, J. A. Smolinsky, E. Walters, J. A. Arnold, D. K. Dawson. 2017. Validation of NEXRAD data and models of bird migration stopover sites in the Northeast U.S. Final Report.
The dataset is intended to be used by land managers and conservationists looking to identify priority areas for bird conservation by providing estimates of autumn bird stopover use based on weather surveillance radar data.
Description and Derivation
The national network of weather surveillance radars (NEXRAD) detects birds in flight, and has proven to be a useful remote-sensing tool for ornithological study. These data were derived from data collected during Autumn (15 August through 7 November) 2008 to 2014 by 16 NEXRAD and four terminal Doppler weather radars (TDWR; 2009-2014) in the northeastern U.S. to map and study the spatial distribution of landbirds shortly after they leave daytime stopover sites to embark on nocturnal migratory flights.
For each suitable sampling night, we spatio-temporally interpolated radar reflectivity measures among volume scans at the time of “peak exodus”. Peak exodus is the time during the onset of nocturnal migratory bird flight at which the horizontal angle of the sun reaches the maximum rate of change in reflectivity. We then estimated the vertically-integrated reflectivity (VIR) within each sample volume following previously-established methods described in Buler and Dawson (2014) http://www.bioone.org/doi/full/10.1650/CONDOR-13-162.1 and Buler and Diehl (2009) http://dx.doi.org/10.1109/TGRS.2009.2014463. The VIR is an estimate of the total amount of reflected cross-sectional area of birds per hectare (i.e., bird density) from 0 to 1750 m above the ground in units of cm2 ha-1 for sampling volumes that have polar spatial dimensions of 250 m in range (length) from the radar and 0.5° in azimuth (width). These data are filtered to exclude areas of partial radar beam blockage, ground clutter (i.e., non-biological reflectivity from objects on the ground) and where radars were unable to detect birds for more than 25% of sampling nights.
For each sample volume, we calculated a seasonal mean VIR across days by year. We then tested for linear trends in seasonal mean VIR across years for each sample volume. These data are filtered where coeff ≠ 0, and is ≥ -5 and ≤ 5. The mean linear annual trend in VIR (i.e., bird stopover density) across the region indicated a 4% decline per year from the 2008 baseline density (29% decline over the seven years). Regionally, coastal Virginia and Maine had the steepest declines. The steepest increases in migrant densities across years occurred within the Delmarva Peninsula and in coastal Connecticut.
Known Issues and Uncertainties
Despite efforts of data quality control and reduction of radar measurement biases, there remain known issues for the use of weather radar data for analyzing bird stopover distributions. There is invariably some contamination of radar measures by non-bird targets (insects and bats), although bird targets are presumed to dominate because they are stronger reflectors than insects and mean air speeds of used data are above those of most insects. NEXRAD is unable to identify bird species. There is some spatial displacement of birds as they stream out of their true stopover locations dependent on their mean vertical ascent rate, horizontal flight speed, flight direction, and range from the radar. The amount of maximum displacement along shorelines can be visually estimated by comparing the distance between the land boundary and the outer boundary of birds aloft over water. Bird use cannot be known with certainty for spatially restricted or rare habitats that exist at a finer resolution than that of the radar measures. This is because birds emerging from narrow or small habitat patches may be spatially displaced from them by the time they are sampled by the radar, and birds emerging from adjacent dominant habitats contaminate the airspace over the small habitats.
Areas with high topographic relief also pose challenges in using radar for mapping bird distributions. There is increased uncertainty in the accuracy of altitudinal distributions of birds, modeled beam propagation and blockage, and, consequently, VIR in these areas. A close visual inspection of VIR measures often shows increased reflectivity within valleys which may be due in part to an artifact of the adjustment algorithm rather than to truly greater bird density in low-lying areas in mountainous terrain. Conversely, reflectivity measures can be inflated by radar energy reflected back from the beam striking the ground at higher elevations.
It’s important to keep in mind that our maps of discretely classified migrant stopover density (i.e., variable “stop_use”) can be powerfully effective at focusing conservation efforts, but can also oversimplify the dynamics of bird migration and the function of any particular area for stopover. Our classification scheme was coarse by having only a few categories to characterize seasonal patterns in bird use. Additionally, the function of particular stopover areas may not be closely tied to the density of bird use and likely varies among migrants at a site within and among days.
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