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Ian D. Jonsen

Researcher at Macquarie University

Publications -  96
Citations -  7189

Ian D. Jonsen is an academic researcher from Macquarie University. The author has contributed to research in topics: Foraging & Population. The author has an hindex of 37, co-authored 87 publications receiving 6093 citations. Previous affiliations of Ian D. Jonsen include Bedford Institute of Oceanography & Dalhousie University.

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Animal-borne telemetry: An integral component of the ocean observing toolkit

Robert Harcourt, +60 more
TL;DR: The use of animal telemetry is a powerful tool for observing marine animals and the physical environments that they inhabit, from coastal and continental shelf ecosystems to polar seas and open oceans.
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Tracking of marine predators to protect Southern Ocean ecosystems

Mark A. Hindell, +89 more
- 18 Mar 2020 - 
TL;DR: Tracking data from 17 marine predator species in the Southern Ocean is used to identify Areas of Ecological Significance, the protection of which could help to mitigate increasing pressures on Southern Ocean ecosystems.
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North Atlantic Blue and Fin Whales Suspend Their Spring Migration to Forage in Middle Latitudes: Building up Energy Reserves for the Journey?

TL;DR: Fin whales may alternate periods of active migration with periods of extended use of specific habitats along the migratory route, and a link between fin whales seen in the Azores and those summering in eastern Greenland-western Iceland along a migratory corridor located in central Atlantic waters is shown.
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Joint estimation over multiple individuals improves behavioural state inference from animal movement data.

TL;DR: It is shown how a joint estimation approach that assumes individuals share identical movement parameters can lead to improved inference of behavioural states associated with different movement processes, especially when location data are error-prone.
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State-space models' dirty little secrets: even simple linear Gaussian models can have estimation problems.

TL;DR: It is demonstrated that even simple linear Gaussian SSMs can suffer from parameter- and state-estimation problems, and it is urged to assess whether the parameters can be estimated accurately before drawing ecological conclusions from their results.