A continental system for forecasting bird migration

Abstract

Billions of animals cross the globe each year during seasonal migrations, but efforts to monitor them are hampered by the unpredictability of their movements. We developed a bird migration forecast system at a continental scale by leveraging 23 years of spring observations to identify associations between atmospheric conditions and bird migration intensity. Our models explained up to 81% of variation in migration intensity across the United States at altitudes of 0 to 3000 meters, and performance remained high in forecasting events 1 to 7 days in advance (62 to 76% of variation was explained). Avian migratory movements across the United States likely exceed 500 million individuals per night during peak passage. Bird migration forecasts will reduce collisions with buildings, airplanes, and wind turbines; inform a variety of monitoring efforts; and engage the public.

Bright lights in the big cities: migratory birds’ exposure to artificial light

“…Migration periods may span more than 6 months in total, with hundreds of millions of individual migrants aloft on a given night; however, their passage occurs in sporadic waves, with a large majority of birds passing individual sites during just a few peak nights. We observed that half of the total number of migrants for each season passed each radar site in just 6.7 ± 2.6 (mean ± SD) nights, a notable finding when paired with the recent capacity to confidently forecast (12–72 hours in advance) these episodic events (Van Doren and Horton ). This advance has the potential to offer a detailed and tailored guide for mitigation actions to substantially lower the numbers of birds exposed to risks of ALAN while simultaneously minimizing adverse effects to stakeholders, including municipalities and industry.…”

“…These actions are typically carried out at the scale of individual buildings but occasionally at much larger scales (eg Lights Out Toronto). However, as the intensity and extent of bird migrations vary considerably in space and over time (Van Doren and Horton ), so too may exposure risks, requiring detailed and site‐ and time‐specific considerations when implementing mitigation actions and developing conservation plans. To this end, we used radar to quantify the passage of nocturnally migrating birds across the contiguous US, identified the areas where the greatest number of migrants are exposed to light pollution, and mapped this exposure across the US, focusing specifically on the 125 largest urban centers.…”

Holding steady: Little change in intensity or timing of bird migration over the Gulf of Mexico

“…To limit insect contamination, we eliminated data from height bins with airspeeds <5 m/s (Van Doren & Horton, 2018;Gauthreaux & Belser, 1998;Larkin, 1991). We calculated airspeeds through vector subtraction using measures of migrant groundspeed, wind direction, and wind speed.…”

“…Estimates of the numbers of individual birds involved in nocturnal migration-the primary diel period of movement for most terrestrial species-range in the millions (Van Doren & Horton, 2018;Gauthreaux, 1971;Horton, Van Doren, Stepanian, Hochachka, et al, 2016) and may approach the billions when accounting for full season movements across broad geographic regions (Dokter et al, 2018;Hahn, Bauer, & Liechti, 2009). However, objective estimates of the number of individuals that undertake nocturnal migration within North America are largely unavailable (Rich et al, 2004).…”

A geostatistical approach to estimate high resolution nocturnal bird migration densities from a weather radar network

“…For more local results, 262 interactive maps of the resulting bird density are available on a website with a 263 dedicated interface that facilitates the visualisation and the export of the estimated bird 264 densities and their associated uncertainty (birdmigrationmap.vogelwarte.ch, see 265 Supporting Information S5 for a user manual).This paper presents the first spatio-temporal interpolation of nocturnal bird densities at 268 the continental scale that accounts for sub-daily fluctuations and provides uncertainty 269 ranges. In contrast to the methods based on covariates that are deemed more reliable 270 for extrapolation in space and time(Erni et al, 2002; Van Belle, Shamoun-Baranes, Van 271 Loon, & Bouten, 2007;Van Doren & Horton, 2018), our interpolation approach does not 272 require any external covariate per se (e.g. temperature, rain, or wind).…”