The role of the US Great Plains low-level jet in nocturnal migrant behavior
- 355 Downloads
The movements of aerial animals are under the constant influence of atmospheric flows spanning a range of spatiotemporal scales. The Great Plains nocturnal low-level jet is a large-scale atmospheric phenomenon that provides frequent strong southerly winds through a shallow layer of the airspace. The jet can provide substantial tailwind assistance to spring migrants moving northward, while hindering southward migration during autumn. This atmospheric feature has been suspected to play a prominent role in defining migratory routes, but the flight strategies used with respect to these winds are yet to be examined. Using collocated vertically pointing radar and lidar, we investigate the altitudinal selection behavior of migrants over Oklahoma during two spring and two autumn migration seasons. In general, migrants choose to fly within the jet in spring, often concentrating in the favorable wind speed maximum. Autumn migrants typically fly below the jet, although some will rapidly climb to reach altitudes above the inhibiting winds. The intensity of migration was relatively constant throughout the spring due to the predominantly favorable southerly jet winds. Conversely, autumn migrants were more apt to delay departure to wait for the relatively infrequent northerly winds.
KeywordsAeroecology Lidar Low-level jet Migration Radar Wind assistance
The lidar and Ka-band radar data were obtained from the Atmospheric Radiation Measurement (ARM) Climate Research Facility, a U.S. Department of Energy Office of Science user facility sponsored by the Office of Biological and Environmental Research. This work was partially supported by NSF Grant EF-1340921. The authors wish to thank Dr. Tim Bonin for providing the lidar processing algorithm. Discussions with Dr. Alan Shapiro improved the work contained herein.
- Able KP (1972) Fall migration in coastal Louisiana and the evolution of migration patterns in the Gulf region. Wilson Bull 84:231–242Google Scholar
- Akaike H (1973) Information theory as an extension of the maximum likelihood principle. In pp. 267–281. Akademiai Kiado, BudapestGoogle Scholar
- Bates D, Maechler M, Bolker B, Walker S (2014) lme4: linear mixed-effects models using eigen and S4. http://CRAN.R-project.org/package=lme4
- Burnham KP, Anderson DR (2002) Model selection and multimodel inference: a practical information-theoretic approach, 2nd edn. Springer, New YorkGoogle Scholar
- Chilson PB, Frick WF, Stepanian PM, Shipley JR, Kunz TH, Kelly JF (2012b) Estimating animal densities in the aerosphere using weather radar: to Z or not to Z? Ecosphere 3. Article 72Google Scholar
- Doviak RJ, Zrnić DS (2006) Doppler radar and weather observations, 2nd edn. Dover Publications, Mineola, New YorkGoogle Scholar
- Gauthreaux SA (1980) The influence of global climatological factors on the evolution of bird migratory pathways. Proc XVII Int Ornithol Congr 17: 517–525Google Scholar
- Mazerolle MJ (2015) AICcmodavg: model selection and multimodel inference based on (Q)AIC(c). R package version 2.0–3, url= http://CRAN.R-project.org/package=AICcmodavg.
- Widener K, Bharadwaj N, Johnson K (2012) Ka-band arm zenith radar handbook. ARM Technical report DOE/SC-ARM/TR-106, 19 pGoogle Scholar