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

Quantifying nocturnal bird migration at high resolution is essential for (1) understanding the phenology of migration and its drivers, (2) identifying critical spatio-temporal protection zones for migratory birds, and (3) assessing the risk of collision with man-made structures. We propose a tailored geostatistical model to interpolate migration intensity monitored by a network of weather radars. The model is applied to data collected in autumn 2016 from 69 European weather radars. To cross-validate the model, we compared our results with independent measurements of two bird radars. Our model estimated bird densities at high resolution (0.2°latitude-longitude, 15min) and assessed the associated uncertainty. Within the area covered by the radar network, we estimated that around 120 million birds were simultaneously in flight [10-90 quantiles: 107-134]. Local estimations can be easily visualized and retrieved from a dedicated interactive website: birdmigrationmap.vogelwarte.ch. This proof-of-concept study demonstrates that a network of weather radar is able to quantify bird migration at high resolution and accuracy. The model presented has the ability to monitor population of migratory birds at scales ranging from regional to continental in space and daily to yearly in time. Near-real-time estimation should soon be possible with an update of the infrastructure and processing software.

[1]  Hidde Leijnse,et al.  From Agricultural Benefits to Aviation Safety: Realizing the Potential of Continent-Wide Radar Networks , 2017, Bioscience.

[2]  Bruno Bruderer,et al.  Wind and rain govern the intensity of nocturnal bird migration in central Europe: A log-linear regression analysis , 2002 .

[3]  Elena Saltikoff,et al.  The Operational Weather Radar Network in Europe , 2014 .

[4]  Clayton V. Deutsch,et al.  GSLIB: Geostatistical Software Library and User's Guide , 1993 .

[5]  J. Jaime Gómez-Hernández,et al.  Theory and Practice of Sequential Simulation , 1994 .

[6]  Franz Bairlein,et al.  The decline of Afro-Palaearctic migrants and an assessment of potential causes , 2014 .

[7]  Ian J. Burfield,et al.  Long-term population declines in Afro-Palearctic migrant birds , 2006 .

[8]  Timothy C. Coburn,et al.  Geostatistics for Natural Resources Evaluation , 2000, Technometrics.

[9]  Bruno Bruderer,et al.  Vertical distribution of bird migration between the Baltic Sea and the Sahara , 2017, Journal of Ornithology.

[10]  Jean-Paul Chilbs,et al.  Geostatistics , 2000, Technometrics.

[11]  Robert H Diehl.  The airspace is habitat. , 2013, Trends in ecology & evolution.

[12]  Serge Zaugg,et al.  Size matters in quantitative radar monitoring of animal migration: estimating monitored volume from wingbeat frequency , 2019, Ecography.

[13]  T. Gneiting.  Nonseparable, Stationary Covariance Functions for Space–Time Data , 2002 .

[14]  Hugh P. Possingham,et al.  Conserving mobile species , 2014 .

[15]  Brian C. Weeks,et al.  Nocturnal flight-calling behaviour predicts vulnerability to artificial light in migratory birds , 2019, Proceedings of the Royal Society B.

[16]  Felix Liechti,et al.  Bird collisions at wind turbines in a mountainous area related to bird movement intensities measured by radar , 2018 .

[17]  Hidde Leijnse,et al.  Field validation of radar systems for monitoring bird migration , 2018, Journal of Applied Ecology.

[18]  C. Musgrave,et al.  Initial nitridation of the Ge(100)-2 x 1 surface by ammonia. , 2005, Langmuir : the ACS journal of surfaces and colloids.

[19]  Peter P. Marra,et al.  Direct Mortality of Birds from Anthropogenic Causes , 2015 .

[20]  Hidde Leijnse,et al.  Revealing patterns of nocturnal migration using the European weather radar network , 2018, Ecography.

[21]  B. Bruderer,et al.  VARIATION IN DENSITY AND HEIGHT DISTRIBUTION OF NOCTURNAL MIGRATION IN THE SOUTH OF ISRAEL , 2013 .

[22]  Sidney A. Gauthreaux,et al.  Using a Network of WSR-88D Weather Surveillance Radars to Define Patterns of Bird Migration at Large Spatial Scales , 2003 .

[23]  V. Alistair Drake,et al.  Aeroecological observation methods , 2017 .

[24]  K. Horton,et al.  High-intensity urban light installation dramatically alters nocturnal bird migration , 2017, Proceedings of the National Academy of Sciences.

[25]  Steve Kelling,et al.  Seasonal abundance and survival of North America’s migratory avifauna determined by weather radar , 2018, Nature Ecology & Evolution.

[26]  Hidde Leijnse,et al.  Continental-scale radar monitoring of the aerial movements of animals , 2014, Movement Ecology.

[27]  Rico Angell,et al.  Inferring Latent Velocities from Weather Radar Data using Gaussian Processes , 2018, NeurIPS.

[28]  S. Bauer,et al.  Migratory Animals Couple Biodiversity and Ecosystem Functioning Worldwide , 2014, Science.

[29]  Cecilia Nilsson,et al.  Projected changes in wind assistance under climate change for nocturnally migrating bird populations , 2018, Global change biology.

[30]  Carl E. Rasmussen,et al.  Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.

[31]  Hans van Gasteren,et al.  Aeroecology meets aviation safety: early warning systems in Europe and the Middle East prevent collisions between birds and aircraft , 2019, Ecography.

[32]  Silke Bauer,et al.  The natural link between Europe and Africa – 2.1 billion birds on migration , 2009 .

[33]  K. Horton,et al.  A continental system for forecasting bird migration , 2018, Science.

[34]  Willem Bouten,et al.  An operational model predicting autumn bird migration intensities for flight safety , 2007 .