Wind as Driver of Bird and Bat Abundance, Flight Direction, Altitude, and Speed on the North Atlantic Shelf

TL;DR

The paper addresses offshore collision risk for birds and bats by linking high-resolution radar-tracked flight behavior with concurrent wind profiles from profiling lidars. It introduces a framework that combines co-located radar and lidar data, a reflectivity-based size clustering, and generalized additive models to quantify how wind speed, wind direction, turbulence, and sun position drive hourly abundance, flight direction, height, and speed. Key findings show wind strongly shapes presence and behavior, with small migratory animals aligned with wind and flying across a broader altitude range, while larger animals cluster near and below 100 m; ground speeds rise with wind assistance while air speeds remain relatively steady. The study provides a methodological pathway to improve offshore collision-risk forecasting and supports dynamic turbine-operational decision-making, highlighting the value of integrated radar-lidar analyses for predicting exposure at RSZ elevations.

Abstract

Quantifying the collision risk of birds and bats with offshore wind turbines requires an understanding of the drivers of flying animal behavior at offshore wind sites. An omnidirectional S-band radar system was deployed on a research barge on the Northeastern Shelf of the United States (40.9 deg N, 70.79 deg W) and collected data for a 5-week window during the 2024 autumn bird and bat migration. The barge also sup- ported two profiling lidar systems that measured the wind speed and direction. This study presents a first methodological approach for analyzing radar and lidar data together, providing a framework for future analyses of offshore bird and bat movements that can be used to improve collision risk models. Coupling the radar animal tracks with measured wind speed profiles revealed that wind is a driver of animal pres- ence, flight direction, flight height, and flight speed. Further, a hierarchical clustering methodology was developed to investigate behavior by approximate animal size. For example, smaller animals had con- centrated flight direction distributions aligned with the wind and flew at a variety of altitudes, whereas bigger animals flew in a wide variety of directions but were concentrated at low altitudes. Our results provide the first insights into animal behavior at offshore wind sites with paired radar and lidar data.

Figures (13)

• Figure 1: (a) Location of barge deployment south of Massachusetts, USA. Map was generated using the MATLAB satellite geobasemap, hosted by Esri. (b) Aerial photo of research barge, with locations of the DeTect avian radar and the two lidar systems used for wind measurements in this study.
• Figure 2: Species expected at the study location in autumn. Categorized by migration preference and seasonal versus year-round presence at the site. Migration preference and seasonal presence data are from Atlanticbirds and expert opinion from the Biodiversity Research Institute. Wing length data are from avonet2022.
• Figure 3: Data analysis flow chart
• Figure 4: (a) Flight speed and direction rose for all tracks. Note: flight direction is the direction the tracks are originating from. The inset highlights 100 randomly selected tracks from the radar dataset; the white dashed lines represent the range limits applied in post-processing. (b) Wind speed and direction rose at 100 m altitude for the 5-week study window.
• Figure 5: Bivariate histograms of reflectivity with (a) flight height, (b) flight direction, (c) flight speed, and (d) date. The cyan dashed line represents the reflectivity delineation between the big and small size clusters. Note: flight direction is the direction the tracks are originating from