Volume 49, No. 2
Volumes > 38 (2010-->) Volumes 28-37 (2000-09) Volumes 18-27 (1990-99) Volumes 5-17 (1978-89)
Quick Search by author or article title:
Key words: Rissa tridactyla, Black-legged Kittiwake, productivity, phenology, nest site, seasonal weather, remote cameras
Monitoring seabirds and their responses to ecosystem change provides essential information for understanding the reasons behind any changes in productivity or populations. However, many species nest in remote locations, which poses logistical challenges for long-term studies. Remote cameras offer an opportunity to confront this issue. The Black-legged Kittiwake Rissa tridactyla (kittiwake) has been used as an indicator of changes in its environment and is a prime candidate for monitoring via remote cameras. To investigate the potential for camera application, we used a remote camera system to collect six years (2010-2015) of reproductive data from a sub-colony of kittiwakes in Resurrection Bay near Seward, Alaska, USA. Our objective was to identify factors influencing the reproductive success of kittiwakes at our study location by 1) establishing the reproductive phenology and estimates of productivity, 2) determining the effect of physical nest-site characteristics and locations on individual nest success, and 3) identifying the effect of seasonal weather patterns on nest, egg, and chick loss events. We found a significant positive correlation between nest success and both nest height from mean high tide level and nest location on island vs. mainland habitat. Nest loss was positively correlated with wind speed; egg loss was negatively correlated with wind speed; and chick loss was uncorrelated with measured weather conditions, including rainfall and air temperature. Remote camera technology proved to be a useful tool in monitoring and identifying factors influencing nesting parameters in this cliff-nesting seabird.
ABRÀMOFF, M.D., MAGALHAES, P.J. & RAM, S.J. 2004. Image Processing with ImageJ. Biophotonics International 11: 36-42.
BARTOŃ, K. 2015. MuMIn: Multi-Model Inference. R package version 1.15.1.
BATES D., MÄCHLER M., BOLKER B, & WALKER S. 2015. Fitting Linear Mixed-Effects Models Using lme4. Journal of Statistical Software 67: 1-48. doi:10.18637/jss.v067.i01
BLUHM, B.A., GEBRUK, A.V., GRADINGER, R. ET AL. 2011. Arctic marine biodiversity: An update of species richness and examples of biodiversity change. Oceanography 24: 232-248. doi:10.5670/oceanog.2011.75
BYRD, G.V., SYDEMAN, W.J., RENNER, H.M. & MINOBE, S. 2008. Responses of piscivorous seabirds at the Pribilof Islands to ocean climate. Deep-Sea Research Part II 55: 1856-1867. doi:10.1016/j.dsr2.2008.04.015
DRAGOO, D.E., RENNER, H.M. & KALER, R.S.A. 2019. Breeding status and population trends of seabirds in Alaska, 2018. US Fish and Wildlife Service Report AMNWR 2019/03. Homer, USA: Department of the Interior, US Fish and Wildlife Service.
GELMAN, A. 2008. Scaling regression inputs by dividing by two standard deviations. Statistics in Medicine 27: 2865-2873.
GELMAN, A. & SU, Y.-S. 2015. arm: Data Analysis Using Regression and Multilevel/Hierarchical Models. R package version 1.8-6.
GRUEBER, C.E., NAKAGAWA, S., LAWS, R.J. & JAMIESON, I.G. 2011. Multimodel inference in ecology and evolution: Challenges and solutions. Journal of Evolutionary Biology 24: 699-711. doi:10.1111/j.1420-9101.2010.02210.x
HARLEY, C.D.G., HUGHES, A.R., HULTGREN, K.M. ET AL. 2006. The impacts of climate change in coastal marine systems. Ecology Letters 9: 228-241. doi:10.1111/j.1461-0248.2005.00871.x
HATCH, S.A., ROBERTSON, G.J. & BAIRD, P.H. 2020. Black-legged Kittiwake (Rissa tridactyla), version 1.0. In: BILLERMAN, S.M. (Ed.) The Birds of North America. Ithaca, USA: Cornell Lab of Ornithology. doi:10.2173/bow.bklkit.01
KILDAW, S.D. 1999. Competitive displacement? An experimental assessment of nest site preferences of cliff-nesting gulls. Ecology 80: 576-586.
MANISCALCO, J.M., PARKER, P. & ATKINSON, S. 2006. Interseasonal and interannual measures of maternal care among individual Steller sea lions (Eumetopias jubatus). Journal of Mammalogy 87: 304-311. doi:10.1644/05-MAMM-A-163R2.1
MASSARO, M., CHARDINE, J.W. & JONES, I.L. 2001. Relationships between Black-legged Kittiwake nest-site characteristics and susceptibility to predation by large gulls. The Condor 103: 793-801.
OLSTHOORN, J.C.M. & NELSON, J.B. 1990. The availability of breeding sites for some British seabirds. Bird Study 37: 145-164. doi:10.1080/00063659009477052
PIATT, J.F., SYDEMAN, W.J. & WIESE, F. 2007. Introduction: Seabirds as indicators of marine ecosystems. Marine Ecology Progress Series 352: 199-204. doi:10.3354/meps07070
R CORE TEAM 2015. R: A language and environment for statistical computing. Vienna, Austria: The R Foundation for Statistical Computing.
REGEHR, H.M. & MONTEVECCHI, W.A. 1997. Interactive effects of food shortage and predation on breeding failure of Black-legged Kittiwakes: Indirect effects of fisheries activities and implications for indicator species. Marine Ecology Progress Series 155: 249-260. doi:10.3354/meps155249
REGEHR, H.M., RODWAY, M.S. & MONTEVECCHI, W.A. 1998. Antipredator benefits of nest-site selection in Black-legged Kittiwakes. Canadian Journal of Zoology 76: 910-915. doi:10.1139/cjz-76-5-910
ROBERTS, B.D. & HATCH, S.A. 1993. Behavioral ecology of Black-legged Kittiwakes during chick rearing in a failing colony. The Condor 95: 330-342.
SPRINGER, A.M., PIATT, J.F. & VAN VLIET, G.B. 1996. Sea birds as proxies of marine habitats and food webs in the western Aleutian Arc. Fisheries Oceanography 5: 45-55.
SYDEMAN, W.J., THOMPSON, S.A. & KITAYSKY, A. 2012. Seabirds and climate change: Roadmap for the future. Marine Ecology Progress Series 454: 107-117. doi:10.3354/meps09806
TANEDO, S.A. & HOLLMÉN, T.E. 2020. Refining remote observation techniques to estimate productivity of Black-legged Kittiwakes Rissa tridactyla in Resurrection Bay, Gulf of Alaska. Marine Ornithology 48: 61-69.
WARTON, D.I. 2005. Many zeros does not mean zero inflation: Comparing goodness-of-fit of parametric models to multivariate abundance data. Environmetrics 16: 275-289. doi:10.1002/env.702