Moving at the speed of flight: dabbling duck-movement rates and the relationship with electronic tracking interval
Fiona McDuie
A B D , Michael L. Casazza A , David Keiter A , Cory T. Overton A , Mark P. Herzog A , Cliff L. Feldheim C and Joshua T. Ackerman A
+ Author Affiliations
- Author Affiliations
A US Geological Survey, Western Ecological Research Center, Dixon Field Station, 800 Business Park Drive, Suite D, Dixon, CA 95620, USA.
B San Jose State University Research Foundation, Moss Landing Marine Laboratories, 8272 Moss Landing Road, Moss Landing, CA 95039, USA.
C California Department of Water Resources, Suisun Marsh Program, West Sacramento, CA 95691, USA.
D Corresponding author. Email: fiona.mcduie@sjsu.edu
Wildlife Research 46(6) 533-543 https://doi.org/10.1071/WR19028
Submitted: 15 February 2019 Accepted: 22 June 2019 Published: 16 September 2019
Journal Compilation © CSIRO 2019 Open Access CC BY-NC-ND
Abstract
Context: Effective wildlife management requires information on habitat and resource needs, which can be estimated with movement information and modelling energetics. One necessary component of avian models is flight speeds at multiple temporal scales. Technology has limited the ability to accurately assess flight speeds, leading to estimates of questionable accuracy, many of which have not been updated in almost a century.
Aims: We aimed to update flight speeds of ducks, and differentiate between migratory and non-migratory flight speeds, a detail that was unclear in previous estimates. We also analysed the difference in speeds of migratory and non-migratory flights, and quantified how data collected at different temporal intervals affected estimates of flight speed.
Methods: We tracked six California dabbling duck species with high spatio-temporal resolution GPS–GSM transmitters, calculated speeds of different flight types, and modelled how estimates varied by flight and data interval (30 min to 6 h).
Key results: Median migratory speeds were faster (but non-significant) for the larger mallard (Anas platyrhynchos; 82.5 km h–1), northern pintail (Anas acuta; 79.0 km h–1) and gadwall (Mareca strepera; 70.6 km h–1), than the smaller-bodied northern shoveler (Spatula clypeata; 65.7 km h–1), cinnamon teal (Spatula cyanoptera; 63.5 km h–1) and American wigeon (Mareca Americana; 52 km h–1). Migratory flights were faster than non-migratory flights for all species and speeds were consistently slower with an increasing data interval.
Implications: The need to balance time and energy requirements may drive different speeds for migratory and non-migratory flights. Lower speeds at longer intervals are likely to be due to a greater proportion of ‘loafing’ time included in flighted segments, demonstrating that data acquired at different intervals provide a means to evaluate and estimate behaviours that influence speed estimation. Shorter-interval data should be the most accurate, but longer-interval data may be easier to collect over lengthier timeframes, so it may be expedient to trade-off a degree of accuracy in broad-scale studies for the larger dataset. Our updated flight speeds for dabbling duck species can be used to parameterise and validate energetics models, guide management decisions regarding optimal habitat distribution, and, ultimately, improve conservation management of wetlands for waterfowl.
Additional keywords: data frequency, energetics, flight speed, GPS tracking, interval bias, migration, habitat management.
References
Alerstam, T. (2011). Optimal bird migration revisited. Journal of Ornithology 152, 5–23.| Optimal bird migration revisited.Crossref | GoogleScholarGoogle Scholar |
Alerstam, T., and Lindström, Å. (1990). Optimal bird migration: the relative importance of time, energy, and safety. In ‘Bird Migration’. (Ed. E. Gwinner.) pp. 331–351. (Springer: Berlin, Heidelberg, Germany.)
Alerstam, T., Rosén, M., Bäckman, J., Ericson, P. G., and Hellgren, O. (2007). Flight speeds among bird species: allometric and phylogenetic effects. PLoS Biology 5, e197.
| Flight speeds among bird species: allometric and phylogenetic effects.Crossref | GoogleScholarGoogle Scholar | 17645390PubMed |
Alonso, J. C., Alonso, J. A., and Bautista, L. M. (1994). Carrying capacity of staging areas and facultative migration extension in common cranes. Journal of Applied Ecology 31, 212–222.
| Carrying capacity of staging areas and facultative migration extension in common cranes.Crossref | GoogleScholarGoogle Scholar |
Baldassarre, G. A. (2014) ‘Ducks, Geese, and Swans of North America.’ (Johns Hopkins University Press: Baltimore, MD, USA.)
Bartzen, B. A., Dickson, D. L., and Bowman, T. D. (2017). Migration characteristics of long-tailed ducks (Clangula hyemalis) from the western Canadian Arctic. Polar Biology 40, 1085–1099.
| Migration characteristics of long-tailed ducks (Clangula hyemalis) from the western Canadian Arctic.Crossref | GoogleScholarGoogle Scholar |
Bates, D., Mächler, M., Bolker, B., and Walker, S. (2015). Fitting linear mixed-effects models using lme4. Journal of Statistical Software 67, 1–48.
| Fitting linear mixed-effects models using lme4.Crossref | GoogleScholarGoogle Scholar |
Bellrose, F. C., and Crompton, R. C. (1981). Migration speeds of three waterfowl species. The Wilson Bulletin 93, 121–124.
Braithwaite, J. E., Meeuwig, J. J., and Hipsey, M. R. (2015). Optimal migration energetics of humpback whales and the implications of disturbance. Conservation Physiology 3, cov001.
| Optimal migration energetics of humpback whales and the implications of disturbance.Crossref | GoogleScholarGoogle Scholar | 27293686PubMed |
Bruderer, B., and Boldt, A. (2001). Flight characteristics of birds. The Ibis 143, 178–204.
| Flight characteristics of birds.Crossref | GoogleScholarGoogle Scholar |
Burnham, K. P., and Anderson, D. R. (2003) ‘Model Selection and Multimodel Inference: a Practical Information-theoretic Approach.’ (Springer Science & Business Media: New York, NY, USA.)
Calenge, C. (2006). The package ‘adehabitat’ for the R software: a tool for the analysis of space and habitat use by animals. Ecological Modelling 197, 516–519.
| The package ‘adehabitat’ for the R software: a tool for the analysis of space and habitat use by animals.Crossref | GoogleScholarGoogle Scholar |
Central Valley Joint Venture (2006). ‘Central Valley Joint Venture 2006 Implementation Plan: Conserving Bird Habitat.’ Central Valley Joint Venture [CVJV]. (US Fish and Wildlife Service: Sacramento, CA, USA.)
Cochran, W. W. (1980). Willdife telemetry. In ‘Willdife Management Techniques Manual’. (Ed. S. Schemnitz.) pp. 507–520. (The Wildlife Society: Washington, DC, USA.)
Cooke, M. T. (1933). Speed of bird flight. The Auk 50, 309–316.
| Speed of bird flight.Crossref | GoogleScholarGoogle Scholar |
Cox, R. R., and Afton, A. D. (1998). Effects of capture and handling on survival of female northern pintails (Efectos de la captura y la manipulación en la supervivencias de Anas acuta). Journal of Field Ornithology 69, 276–287.
Drewien, R. C., and Clegg, K. R. (1992). Capturing whooping cranes and sandhill cranes by night-lighting. In ‘Proceedings of the 6th North American Crane Workshop’, 1992, Grand Island, Nebraska, USA. (Ed. D. W. Stahlecker.) pp. 43–49. (North American Crane Working Group, University of Nebraska: Lincoln, NE, USA.)
Fair, J., Paul, E., and Jones, J. (2010). ‘Guidelines to the Use of Wild Birds in Research.’ (Ornithological Council: Washington, DC, USA.)
Finger, T. A., Afton, A. D., Schummer, M. L., Petrie, S. A., Badzinski, S. S., Johnson, M. A., Szymanski, M. L., Jacobs, K. J., Olsen, G. H., and Mitchell, M. A. (2016). Environmental factors influence lesser scaup migration chronology and population monitoring. The Journal of Wildlife Management 80, 1437–1449.
| Environmental factors influence lesser scaup migration chronology and population monitoring.Crossref | GoogleScholarGoogle Scholar |
Fox, A. D., and Abraham, K. F. (2017). Why geese benefit from the transition from natural vegetation to agriculture. Ambio 46, 188–197.
| Why geese benefit from the transition from natural vegetation to agriculture.Crossref | GoogleScholarGoogle Scholar | 28215009PubMed |
Fronczak, D. L., Andersen, D. E., Hanna, E. E., and Cooper, T. R. (2017). Distribution and migration chronology of eastern population sandhill cranes. The Journal of Wildlife Management 81, 1021–1032.
| Distribution and migration chronology of eastern population sandhill cranes.Crossref | GoogleScholarGoogle Scholar |
Furness, R. (1978). Energy requirements of seabird communities: a bioenergetics model. Journal of Animal Ecology 47, 39–53.
| Energy requirements of seabird communities: a bioenergetics model.Crossref | GoogleScholarGoogle Scholar |
Greenberg, R., and Marra, P. P. (2005) ‘Birds of Two Worlds: the Ecology and Evolution of Migration.’ (Johns Hopkins University Press: Baltimore, MD, USA.)
Gudmundsson, G. A., Alerstam, T., and Larsson, B. (1992). Radar observations of northbound migration of the Arctic tern, Sterna paradisaea, at the Antarctic Peninsula. Antarctic Science 4, 163–170.
| Radar observations of northbound migration of the Arctic tern, Sterna paradisaea, at the Antarctic Peninsula.Crossref | GoogleScholarGoogle Scholar |
Hays, G. C., Ferreira, L. C., Sequeira, A. M., Meekan, M. G., Duarte, C. M., Bailey, H., Bailleul, F., Bowen, W. D., Caley, M. J., and Costa, D. P. (2016). Key questions in marine megafauna movement ecology. Trends in Ecology & Evolution 31, 463–475.
| Key questions in marine megafauna movement ecology.Crossref | GoogleScholarGoogle Scholar |
Hays, G. C., Bailey, H., Bograd, S. J., Bowen, W. D., Campagna, C., Carmichael, R. H., Casale, P., Chiaradia, A., Costa, D. P., and Cuevas, E. (2019). Translating marine animal tracking data into conservation policy and management. Trends in Ecology & Evolution 34, 459–473.
| Translating marine animal tracking data into conservation policy and management.Crossref | GoogleScholarGoogle Scholar |
Hedenström, A. (1993). Migration by soaring or flapping flight in birds: the relative importance of energy cost and speed. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 342, 353–361.
| Migration by soaring or flapping flight in birds: the relative importance of energy cost and speed.Crossref | GoogleScholarGoogle Scholar |
Hedenström, A., and Alerstam, T. (1995). Optimal flight speed of birds. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 348, 471–487.
| Optimal flight speed of birds.Crossref | GoogleScholarGoogle Scholar |
Hein, A. M., Hou, C., and Gillooly, J. F. (2012). Energetic and biomechanical constraints on animal migration distance. Ecology Letters 15, 104–110.
| Energetic and biomechanical constraints on animal migration distance.Crossref | GoogleScholarGoogle Scholar | 22093885PubMed |
Kays, R., Crofoot, M. C., Jetz, W., and Wikelski, M. (2015). Terrestrial animal tracking as an eye on life and planet. Science 348, aaa2478.
| Terrestrial animal tracking as an eye on life and planet.Crossref | GoogleScholarGoogle Scholar | 26068858PubMed |
Kenward, R. E., Clarke, R. T., Hodder, K. H., and Walls, S. S. (2001). Density and linkage estimators of home range: nearest‐neighbor clustering defines multinuclear cores. Ecology 82, 1905–1920.
| Density and linkage estimators of home range: nearest‐neighbor clustering defines multinuclear cores.Crossref | GoogleScholarGoogle Scholar |
Kuznetsova, A., Brockhoff, P. B., and Christensen, R. H. B. (2015). Package ‘lmerTest’. Test fpr random and fixed effects for linear mixed effect models (lmer objects of lme4 package) R package version 2 software.
Lenth, R. V. (2016). Least-squares means: the R package lsmeans. Journal of Statistical Software 69, 1–33.
| Least-squares means: the R package lsmeans.Crossref | GoogleScholarGoogle Scholar |
Mazerolle, M. J., and Mazerolle, M. M. J. (2017). ‘Package ‘AICcmodavg’: Model Selection and Multimodel Inference Based on (Q)AIC(c).’ Available at https://cran.r-project.org/web/packages/AICcmodavg/index.html [verified 27 July 2018].
McDuie, F., Casazza, M. L., Overton, C. T., Herzog, M. P., Hartman, C. A., Peterson, S. H., Feldheim, C. L., and Ackerman, J. T. (2019). GPS tracking data reveals daily spatio-temporal movement patterns of waterfowl. Movement Ecology 7, 6.
| GPS tracking data reveals daily spatio-temporal movement patterns of waterfowl.Crossref | GoogleScholarGoogle Scholar | 30834128PubMed |
McGuire, L. P., Jonasson, K. A., and Guglielmo, C. G. (2014). Bats on a budget: torpor-assisted migration saves time and energy. PLoS One 9, e115724.
| Bats on a budget: torpor-assisted migration saves time and energy.Crossref | GoogleScholarGoogle Scholar | 25551615PubMed |
McNab, B. K. (1980). Food habits, energetics, and the population biology of mammals. American Naturalist 116, 106–124.
| Food habits, energetics, and the population biology of mammals.Crossref | GoogleScholarGoogle Scholar |
Meinertzhagen, R. (1955). The speed and altitude of bird flight (with notes on other animals). The Ibis 97, 81–117.
| The speed and altitude of bird flight (with notes on other animals).Crossref | GoogleScholarGoogle Scholar |
Miller, M. R., Takekawa, J. Y., Fleskes, J. P., Orthmeyer, D. L., Casazza, M. L., Haukos, D. A., and Perry, W. M. (2005). Flight speeds of northern pintails during migration determined using satellite telemetry. The Wilson Bulletin 117, 364–374.
| Flight speeds of northern pintails during migration determined using satellite telemetry.Crossref | GoogleScholarGoogle Scholar |
Miller, M. R., Takekawa, J. Y., Battaglia, D. S., Golightly, R. T., and Perry, W. M. (2010). Spring migration and summer destinations of northern pintails from the coast of southern California. The Southwestern Naturalist 55, 501–509.
| Spring migration and summer destinations of northern pintails from the coast of southern California.Crossref | GoogleScholarGoogle Scholar |
Miller, M. L., Ringelman, K. M., Schank, J. C., and Eadie, J. M. (2014). SWAMP: an agent-based model for wetland and waterfowl conservation management. Simulation 90, 52–68.
| SWAMP: an agent-based model for wetland and waterfowl conservation management.Crossref | GoogleScholarGoogle Scholar |
Nudds, R. L., and Bryant, D. M. (2000). The energetic cost of short flights in birds. The Journal of Experimental Biology 203, 1561–1572.
| 10769218PubMed |
Pennycuick, C. J. (1969). The mechanics of bird migration. The Ibis 111, 525–556.
| The mechanics of bird migration.Crossref | GoogleScholarGoogle Scholar |
Pennycuick, C. J. (1975). Mechanics of flight. Avian Biology 5, 1–73.
Pennycuick, C. J. (1978). Fifteen testable predictions about bird flight. Oikos 30, 165–176.
| Fifteen testable predictions about bird flight.Crossref | GoogleScholarGoogle Scholar |
Phillips, R. A., Jose, C. X., and Croxall, J. P. (2003). Effects of satellite transmitters on albatrosses and petrels. The Auk 120, 1082–1090.
| Effects of satellite transmitters on albatrosses and petrels.Crossref | GoogleScholarGoogle Scholar |
Pianka, E. R. (1981). Resource acquisition and allocation among animals. In ‘Physiological Ecology: an Evolutionary Approach to Resource Use’. (Eds C. R. Townsend, and P. Calow.) pp. 300–314. (Blackwell Scientific Publishers: Oxford, UK.)
Pietz, P. J., Krapu, G. L., Greenwood, R. J., and Lokemoen, J. T. (1993). Effects of harness transmitters on behavior and reproduction of wild mallards. The Journal of Wildlife Management 57, 696–703.
| Effects of harness transmitters on behavior and reproduction of wild mallards.Crossref | GoogleScholarGoogle Scholar |
R Core Team (2016). ‘R: a Language and Environment for Statistical Computing.’ (R: Foundation for Statistical Computing: Vienna, Austria.)
Raikow, R. J. (1973). Locomotor mechanisms in North American ducks. The Wilson Bulletin 85, 295–307.
Sapir, N., Butler, P. J., Hedenström, A., and Wikelski, M. (2011). Energy gain and use during animal migration. In: ‘Animal Migration A Synthesis’. (Eds E. J. Milner-Gulland, J. M. Fryxell, and A. R. E. Sinclair.) pp. 52–67. (Oxford University Press: Oxford, UK.)
Satterthwaite, F. E. (1946). An approximate distribution of estimates of variance components. Biometrics Bulletin 2, 110–114.
| An approximate distribution of estimates of variance components.Crossref | GoogleScholarGoogle Scholar | 20287815PubMed |
Savile, O. (1957). Adaptive evolution in the avian wing. Evolution 11, 212–224.
| Adaptive evolution in the avian wing.Crossref | GoogleScholarGoogle Scholar |
Stasinopoulos, D. M., and Rigby, R. A. (2007). Generalized additive models for location scale and shape (GAMLSS) in R. Journal of Statistical Software 23, 1–46.
| Generalized additive models for location scale and shape (GAMLSS) in R.Crossref | GoogleScholarGoogle Scholar |
Tucker, V. A. (1971). Flight energetics in birds. American Zoologist 11, 115–124.
| Flight energetics in birds.Crossref | GoogleScholarGoogle Scholar |
Tucker, V. A., and Schmidt-Koenig, K. (1971). Flight speeds of birds in relation to energetics and wind directions. The Auk 88, 97–107.
| Flight speeds of birds in relation to energetics and wind directions.Crossref | GoogleScholarGoogle Scholar |
Wilmers, C. C., Isbell, L. A., Suraci, J. P., and Williams, T. M. (2017). Energetics‐informed behavioral states reveal the drive to kill in African leopards. Ecosphere 8, e01850.
| Energetics‐informed behavioral states reveal the drive to kill in African leopards.Crossref | GoogleScholarGoogle Scholar |
Winship, A. J., Trites, A. W., and Rosen, D. A. (2002). A bioenergetic model for estimating the food requirements of Steller sea lions Eumetopias jubatus in Alaska, USA. Marine Ecology Progress Series 229, 291–312.
| A bioenergetic model for estimating the food requirements of Steller sea lions Eumetopias jubatus in Alaska, USA.Crossref | GoogleScholarGoogle Scholar |
