| 1. |
- McMahon, C. R., et al.
(author)
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Animal Borne Ocean Sensors - AniBOS - An Essential Component of the Global Ocean Observing System
- 2021
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In: Frontiers in Marine Science. - : Frontiers Media SA. - 2296-7745. ; 8
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Research review (peer-reviewed)abstract
- Marine animals equipped with biological and physical electronic sensors have produced long-term data streams on key marine environmental variables, hydrography, animal behavior and ecology. These data are an essential component of the Global Ocean Observing System (GOOS). The Animal Borne Ocean Sensors (AniBOS) network aims to coordinate the long-term collection and delivery of marine data streams, providing a complementary capability to other GOOS networks that monitor Essential Ocean Variables (EOVs), essential climate variables (ECVs) and essential biodiversity variables (EBVs). AniBOS augments observations of temperature and salinity within the upper ocean, in areas that are under-sampled, providing information that is urgently needed for an improved understanding of climate and ocean variability and for forecasting. Additionally, measurements of chlorophyll fluorescence and dissolved oxygen concentrations are emerging. The observations AniBOS provides are used widely across the research, modeling and operational oceanographic communities. High latitude, shallow coastal shelves and tropical seas have historically been sampled poorly with traditional observing platforms for many reasons including sea ice presence, limited satellite coverage and logistical costs. Animal-borne sensors are helping to fill that gap by collecting and transmitting in near real time an average of 500 temperature-salinity-depth profiles per animal annually and, when instruments are recovered (similar to 30% of instruments deployed annually, n = 103 +/- 34), up to 1,000 profiles per month in these regions. Increased observations from under-sampled regions greatly improve the accuracy and confidence in estimates of ocean state and improve studies of climate variability by delivering data that refine climate prediction estimates at regional and global scales. The GOOS Observations Coordination Group (OCG) reviews, advises on and coordinates activities across the global ocean observing networks to strengthen the effective implementation of the system. AniBOS was formally recognized in 2020 as a GOOS network. This improves our ability to observe the ocean's structure and animals that live in them more comprehensively, concomitantly improving our understanding of global ocean and climate processes for societal benefit consistent with the UN Sustainability Goals 13 and 14: Climate and Life below Water. Working within the GOOS OCG framework ensures that AniBOS is an essential component of an integrated Global Ocean Observing System.
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| 2. |
- Wege, M., et al.
(author)
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Distribution and Habitat Suitability of Ross Seals in a Warming Ocean
- 2021
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In: Frontiers in Marine Science. - : Frontiers Media SA. - 2296-7745. ; 8
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Journal article (peer-reviewed)abstract
- Understanding the determinants of poorly studied species' spatial ecology is fundamental to understanding climate change impacts on those species and how to effectively prioritise their conservation. Ross seals (Ommatophoca rossii) are the least studied of the Antarctic pinnipeds with a limited knowledge of their spatial ecology. We present the largest tracking study for this species to date, create the first habitat models, and discuss the potential impacts of climate change on their preferred habitat and the implications for conservation. We combined newly collected satellite tracking data (2016-2019: n = 11) with previously published data (2001: n = 8) from the Weddell, King Haakon VII and Lazarev seas, Antarctica, and used 16 remotely sensed environmental variables to model Ross seal habitat suitability by means of boosted regression trees for summer and winter, respectively. Five of the top environmental predictors were relevant in both summer and winter (sea-surface temperature, distance to the ice edge, ice concentration standard deviation, mixed-layer depth, and sea-surface height anomalies). Ross seals preferred to forage in waters ranging between -1 and 2?C, where the mixed-layer depth was shallower in summer and deeper in winter, where current speeds were slower, and away from the ice edge in the open ocean. Receding ice edge and shoaling of the mixed layer induced by climate change may reduce swimming distances and diving depths, thereby reducing foraging costs. However, predicted increased current speeds and sea-surface temperatures may reduce habitat suitability in these regions. We suggest that the response of Ross seals to climate change will be regionally specific, their future success will ultimately depend on how their prey responds to regional climate effects and their own behavioural plasticity.
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| 3. |
- Ahlström, Christer, et al.
(author)
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Processing of Eye/Head-Tracking Data in Large-Scale Naturalistic Driving Data Sets
- 2012
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In: IEEE Transactions on Intelligent Transportation Systems. - 1524-9050 .- 1558-0016. ; vol.13:no.2, s. pp.553-564
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Journal article (peer-reviewed)abstract
- Driver distraction and driver inattention are frequently recognized as leading causes of crashes and incidents. Despite this fact, there are few methods available for the automatic detection of driver distraction. Eye tracking has come forward as the most promising detection technology, but the technique suffers from quality issues when used in the field over an extended period of time. Eye-tracking data acquired in the field clearly differs from what is acquired in a laboratory setting or a driving simulator, and algorithms that have been developed in these settings are often unable to operate on noisy field data. The aim of this paper is to develop algorithms for quality handling and signal enhancement of naturalistic eye- and head-tracking data within the setting of visual driver distraction. In particular, practical issues are highlighted. Developed algorithms are evaluated on large-scale field operational test data acquired in the Sweden-Michigan Field Operational Test (SeMiFOT) project, including data from 44 unique drivers and more than 10 000 trips from 13 eye-tracker-equipped vehicles. Results indicate that, by applying advanced data-processing methods, sensitivity and specificity of eyes-off-road glance detection can be increased by about 10%. In conclusion, postenhancement and quality handling is critical when analyzing large databases with naturalistic eye-tracking data. The presented algorithms provide the first holistic approach to accomplish this task.
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