Publications

Autumn morning flights of migrant songbirds in the northeastern United States are linked to nocturnal migration and winds aloft

Authors:
Benjamin M. Van Doren, Daniel Sheldon, Jeffrey Geevarghese, Wesley M. Hochachka, and Andrew Farnsworth
Date:
2015
Publication:
The Auk
Abstract:
Many passerines that typically migrate at night also engage in migratory flights just after sunrise. These widely observed “morning flights” often involve birds flying in directions other than those aimed toward their ultimate destinations, especially in coastal areas. Morning flights have received little formal investigation, and their study may improve our understanding of how birds orient themselves during and after nocturnal movements and how they use stopover habitat. We studied autumn morning flights in the northeastern United States to identify associations between the number of birds undertaking morning flights and the magnitude of nocturnal migratory movements, nocturnal winds, and local topography. Our analyses included observations of more than 15,000 passerines at 7 locations. We found positive relationships between morning flight size and nocturnal migration density and winds aloft: Significantly more birds flew following larger nocturnal movements, quantified from weather surveillance radar and recordings of nocturnal flight calls, and after stronger nocturnal crosswinds. We also found consistent differences in morning flight size and direction among sites. These patterns are consistent with migrants engaging in morning flight as a corrective measure following displacement by nocturnal winds and to search for suitable stopover habitat.
Download:
auk-13-260%2E1.pdf
Online:
http://www.aoucospubs.org/doi/full/10.1642/AUK-13-260.1

Reconstructing Velocities of Migrating Birds from Weather Radar – A Case Study in Computational Sustainability

Authors:
Andrew Farnsworth, Daniel Sheldon, Jeffrey Geevarghese, Jed Irvine, Benjamin Van Doren, Kevin Webb, Thomas G Dietterich, and Steve Kelling
Date:
2014
Publication:
AI Magazine
Abstract:
Bird migration occurs at the largest of global scales, but monitoring such movements can be challenging. In the US there is an operational network of weather radars providing freely accessible data for monitoring meteorological phenomena in the atmosphere. Individual radars are sensitive enough to detect birds, and can provide insight into migratory behaviors of birds at scales that are not possible using other sensors. Archived data from the WSR-88D network of US weather radars hold valuable and detailed information about the continent-scale migratory movements of birds over the last 20 years. However, significant technical challenges must be overcome to understand this information and harness its potential for science and conservation. We describe recent work on an AI system to quantify bird migration using radar data, which is part of the larger BirdCast project to model and forecast bird migration at large scales using radar, weather, and citizen science data.
Download:
DietterichThomasElectricalEngineeringComputerScienceReconstructingVelocitiesMigrating.pdf
Online:
http://ir.library.oregonstate.edu/xmlui/bitstream/handle/1957/52353/DietterichThomasElectricalEngineeringComputerScienceReconstructingVelocitiesMigrating.pdf?sequence=1

Approximate Bayesian Inference for Reconstructing Velocities of Migrating Birds from Weather Radar

Authors:
SHELDON, D.; FARNSWORTH, A.; IRVINE, J.; VAN DOREN, B.; WEBB, K.; DIETTERICH, T.; and KELLING, S.
Date:
2013
Publication:
AAAI Conference on Artificial Intelligence, North America, jun. 2013.
Abstract:
Archived data from the WSR-88D network of weather radars in the US hold detailed information about the continent-scale migratory movements of birds over the last 20 years. However, significant technical challenges must be overcome to understand this information and harness its potential for science and conservation. We present an approximate Bayesian inference algorithm to reconstruct the velocity fields of birds migrating in the vicinity of a radar station. This is part of a larger project to quantify bird migration at large scales using weather radar data.
Download:
Approximate-Bayesian-Inference-for-Reconstructing-Velocities-of-Migrating-Birds-from-Weather-Radar.pdf
Online:
http://www.aaai.org/ocs/index.php/AAAI/AAAI13/paper/view/6468

Collective Graphical Models

Authors:
Sheldon, D., and T.G. Dietterich
Date:
2012
Publication:
Neural Information Processing Systems (NIPS), Grenada, Spain 12-15 December 2011
Abstract:
There are many settings in which we wish to fit a model of the behavior of individuals but where our data consist only of aggregate information (counts or low-dimensional contingency tables). This paper introduces Collective Graphical Models—a framework for modeling and probabilistic inference that operates directly on the sufficient statistics of the individual model. We derive a highly-efficient Gibbs sampling algorithm for sampling from the posterior distribution of the sufficient statistics conditioned on noisy aggregate observations, prove its correctness, and demonstrate its effectiveness experimentally.
Download:
Collective-Graphical-Models.pdf
Online:
http://people.cs.umass.edu/~sheldon/papers/cgm-single-file.pdf
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