Results Parameter estimation Introduction Animal movement patterns can yield information about foraging behavior and prey abundance and distribution. These are important components of understanding breeding behavior, individual survival, and ultimately population dynamics. Over time, sensor technology for measuring movement patterns have changed and improved. These changes render old technologies obsolete, but the older data are still valuable, especially if new and old data can be compared to check if behaviors have changed over time. However, technologies that have aggregated measurements at different temporal scales make comparisons challenging, and it is unclear to which extent this impacts conclusions that can be drawn from data. Changing measurements of changing movements? Detecting movement patterns across multiple generations of tracking technology Leah R Johnson1, Philipp H Boersch-Supan1,2, Sadie J Ryan2 & Richard A Phillips3 1Integrative Biology, University of South Florida, Tampa, FL, 2Geography & Emerging Pathogens Institute, University of Florida, Gainesville, FL, 3British Antarctic Survey, Cambridge, UK Data sources Devices: immersion loggers were attached to albatrosses, creating series of trip data, which represent dry periods (flights) between wet periods (water landings). Data were aggregated by the devices using a fixed time interval, which was dictated by logger design and deployment duration. We use data collected a decade apart[3,4]. A selection of sampling characteristics are given below. Study species Our models focus on two species of albatross that breed on Bird Island, South Georgia, in the South Atlantic. With a wingspan of up to 3.7m, the wandering albatross is the largest of all seabirds. Wanderers feed on squid, fish and carrion, and some birds follow fishing vessels to feed on bait and offal. The black-browed albatross is an annual breeder. It has a wing span of c. 2.3m and nests in dense hillside colonies. Chicks are fed a diet dominated by krill. Both species have a circumpolar distribution breeding on a number of subantarctic islands. The tracking data for This study come from colonies on South Georgia in the South Atlantic,which have been studied since the 1950’s. Some of the first bio-logging devices were deployed here[2,3]. References [1] Edwards AM, Phillips RA, Watkins NW, et al. 2007 Nature 449, 1044-1048 [2] Prince PA & Francis MD 1984, The Condor 86, 297-300 [3] Afanasyev V & Prince PA 1993 Ornis Scandinavica 24, 243-246 [4] Phalan B, Phillips RA et al. 2007 Marine Ecology Progress Series 340, 271-286 Follow our project: http://leah.johnson-gramacy.com/albatross Contact lrjohnson0@gmail.com pboesu@gmail.com @pboesu Acknowledgements This project is funded by an NSF grant (PLR-1341649) to LRJ and SJR. The British Antarctic Survey developed and deployed the immersion loggers. We thank Andrew Edwards for sharing his MLE code. Interested in Bayesian inference for differential equation models? Come to our talk about the R package deBInfer Friday 1.30pm HUB250. More info at https://github.com/pboesu/debinfer Approach Wandering albatross Diomedea exulans ©Jamie Coleman Black-browed albatross Thalassarche melanophris ©British Antarctic Survey Foraging tracks of wandering albatrosses breeding on South Georgia. A 1990’s-type immersion sensor is deployed on an albatross. ©British Antarctic Survey Conclusions Our approach allows us to: 1) examine the impact of the nature of the observational method on scientific conclusions 2) make suggestions for comparing and combining disparate data sets, to maximize their value. We were not able to identify the process model underlying the earliest observations. Thus, they can not be used as baseline data to address whether this particular aspect of the birds’ foraging strategy has changed over the past two decades. We combine a simulation study with an analysis of two suites of data on water landings collected a decade apart in 1992-1993 and 2002-2004, resp