Jonsen Ian

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Jonsen
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Ian
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  • Article
    A standardisation framework for bio-logging data to advance ecological research and conservation
    (Wiley, 2021-03-15) Sequeira, Ana M. M. ; O'Toole, Malcolm ; Keates, Theresa R. ; McDonnell, Laura H. ; Braun, Camrin D. ; Hoenner, Xavier ; Jaine, Fabrice R. A. ; Jonsen, Ian ; Newman, Peggy ; Pye, Jonathan ; Bograd, Steven ; Hays, Graeme ; Hazen, Elliott L. ; Holland, Melinda ; Tsontos, Vardis ; Blight, Clint ; Cagnacci, Francesca ; Davidson, Sarah C. ; Dettki, Holger ; Duarte, Carlos M. ; Dunn, Daniel C. ; Eguíluz, Víctor M. ; Fedak, Michael ; Gleiss, Adrian C. ; Hammerschlag, Neil ; Hindell, Mark ; Holland, Kim ; Janekovic, Ivica ; McKinzie, Megan K. ; Muelbert, Monica M. C. ; Pattiaratchi, Charitha ; Rutz, Christian ; Sims, David W. ; Simmons, Samantha E. ; Townsend, Brendal ; Whoriskey, Frederick G. ; Woodward, Bill ; Costa, Daniel P. ; Heupel, Michelle R. ; McMahon, Clive R. ; Harcourt, Robert ; Weise, Michael
    1. Bio-logging data obtained by tagging animals are key to addressing global conservation challenges. However, the many thousands of existing bio-logging datasets are not easily discoverable, universally comparable, nor readily accessible through existing repositories and across platforms, slowing down ecological research and effective management. A set of universal standards is needed to ensure discoverability, interoperability and effective translation of bio-logging data into research and management recommendations. 2. We propose a standardisation framework adhering to existing data principles (FAIR: Findable, Accessible, Interoperable and Reusable; and TRUST: Transparency, Responsibility, User focus, Sustainability and Technology) and involving the use of simple templates to create a data flow from manufacturers and researchers to compliant repositories, where automated procedures should be in place to prepare data availability into four standardised levels: (a) decoded raw data, (b) curated data, (c) interpolated data and (d) gridded data. Our framework allows for integration of simple tabular arrays (e.g. csv files) and creation of sharable and interoperable network Common Data Form (netCDF) files containing all the needed information for accuracy-of-use, rightful attribution (ensuring data providers keep ownership through the entire process) and data preservation security. 3. We show the standardisation benefits for all stakeholders involved, and illustrate the application of our framework by focusing on marine animals and by providing examples of the workflow across all data levels, including filled templates and code to process data between levels, as well as templates to prepare netCDF files ready for sharing. 4. Adoption of our framework will facilitate collection of Essential Ocean Variables (EOVs) in support of the Global Ocean Observing System (GOOS) and inter-governmental assessments (e.g. the World Ocean Assessment), and will provide a starting point for broader efforts to establish interoperable bio-logging data formats across all fields in animal ecology.
  • Article
    Dynamic fine-scale sea icescape shapes adult emperor penguin foraging habitat in east Antarctica
    (American Geophysical Union, 2019-09-16) Labrousse, Sara ; Fraser, Alexander D. ; Sumner, Michael ; Tamura, Takeshi ; Pinaud, David ; Wienecke, Barbara ; Kirkwood, Roger ; Ropert-Coudert, Yan ; Reisinger, Ryan ; Jonsen, Ian ; Porter‐Smith, Rick ; Barbraud, Christophe ; Bost, Charles-Andre ; Ji, Rubao ; Jenouvrier, Stephanie
    The emperor penguin, an iconic species threatened by projected sea ice loss in Antarctica, has long been considered to forage at the fast ice edge, presumably relying on large/yearly persistent polynyas as their main foraging habitat during the breeding season. Using newly developed fine‐scale sea icescape data and historical penguin tracking data, this study for the first time suggests the importance of less recognized small openings, including cracks, flaw leads and ephemeral short‐term polynyas, as foraging habitats for emperor penguins. The tracking data retrieved from 47 emperor penguins in two different colonies in East Antarctica suggest that those penguins spent 23% of their time in ephemeral polynyas and did not use the large/yearly persistent, well‐studied polynyas, even if they occur much more regularly with predictable locations. These findings challenge our previous understanding of emperor penguin breeding habitats, highlighting the need for incorporating fine‐scale seascape features when assessing the population persistence in a rapidly changing polar environment.
  • Article
    Regional variation in winter foraging strategies by Weddell Seals in Eastern Antarctica and the Ross Sea
    (Frontiers Media, 2021-09-22) Harcourt, Robert ; Hindell, Mark ; McMahon, Clive R. ; Goetz, Kimberly T. ; Charrassin, Jean-Benoit ; Heerah, Karine ; Holser, Rachel R. ; Jonsen, Ian ; Shero, Michelle R. ; Hoenner, Xavier ; Foster, Rose ; Lenting, Baukje ; Tarszisz, Esther ; Pinkerton, Matthew H.
    The relative importance of intrinsic and extrinsic determinants of animal foraging is often difficult to quantify. The most southerly breeding mammal, the Weddell seal, remains in the Antarctic pack-ice year-round. We compared Weddell seals tagged at three geographically and hydrographically distinct locations in East Antarctica (Prydz Bay, Terre Adélie, and the Ross Sea) to quantify the role of individual variability and habitat structure in winter foraging behaviour. Most Weddell seals remained in relatively small areas close to the coast throughout the winter, but some dispersed widely. Individual utilisation distributions (UDi, a measure of the total area used by an individual seal) ranged from 125 to 20,825 km2. This variability was not due to size or sex but may be due to other intrinsic states for example reproductive condition or personality. The type of foraging (benthic vs. pelagic) varied from 56.6 ± 14.9% benthic dives in Prydz Bay through 42.1 ± 9.4% Terre Adélie to only 25.1 ± 8.7% in the Ross Sea reflecting regional hydrographic structure. The probability of benthic diving was less likely the deeper the ocean. Ocean topography was also influential at the population level; seals from Terre Adélie, with its relatively narrow continental shelf, had a core (50%) UD of only 200 km2, considerably smaller than the Ross Sea (1650 km2) and Prydz Bay (1700 km2). Sea ice concentration had little influence on the time the seals spent in shallow coastal waters, but in deeper offshore water they used areas of higher ice concentration. Marine Protected Areas (MPAs) in the Ross Sea encompass all the observed Weddell seal habitat, and future MPAs that include the Antarctic continental shelf are likely to effectively protect key Weddell seal habitat.
  • Article
    Assessing performance of Bayesian state-space models fit to Argos satellite telemetry locations processed with Kalman filtering
    (Public Library of Science, 2014-03-20) Silva, Monica A. ; Jonsen, Ian ; Russell, Deborah J. F. ; Prieto, Rui ; Thompson, Dave ; Baumgartner, Mark F.
    Argos recently implemented a new algorithm to calculate locations of satellite-tracked animals that uses a Kalman filter (KF). The KF algorithm is reported to increase the number and accuracy of estimated positions over the traditional Least Squares (LS) algorithm, with potential advantages to the application of state-space methods to model animal movement data. We tested the performance of two Bayesian state-space models (SSMs) fitted to satellite tracking data processed with KF algorithm. Tracks from 7 harbour seals (Phoca vitulina) tagged with ARGOS satellite transmitters equipped with Fastloc GPS loggers were used to calculate the error of locations estimated from SSMs fitted to KF and LS data, by comparing those to “true” GPS locations. Data on 6 fin whales (Balaenoptera physalus) were used to investigate consistency in movement parameters, location and behavioural states estimated by switching state-space models (SSSM) fitted to data derived from KF and LS methods. The model fit to KF locations improved the accuracy of seal trips by 27% over the LS model. 82% of locations predicted from the KF model and 73% of locations from the LS model were <5 km from the corresponding interpolated GPS position. Uncertainty in KF model estimates (5.6±5.6 km) was nearly half that of LS estimates (11.6±8.4 km). Accuracy of KF and LS modelled locations was sensitive to precision but not to observation frequency or temporal resolution of raw Argos data. On average, 88% of whale locations estimated by KF models fell within the 95% probability ellipse of paired locations from LS models. Precision of KF locations for whales was generally higher. Whales’ behavioural mode inferred by KF models matched the classification from LS models in 94% of the cases. State-space models fit to KF data can improve spatial accuracy of location estimates over LS models and produce equally reliable behavioural estimates.