The use of an unsupervised learning approach for characterizing latent behaviors in accelerometer data-Reference-Cited by-同舟云学术

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The use of an unsupervised learning approach for characterizing latent behaviors in accelerometer data

Published:2016-01-11 Issue:3 Volume:6 Page:727-741
ISSN:2045-7758
Container-title:Ecology and Evolution
language:en
Short-container-title:Ecol Evol

Author:

Chimienti Marianna¹²,Cornulier Thomas¹,Owen Ellie³,Bolton Mark⁴,Davies Ian M.²,Travis Justin M.J.¹,Scott Beth E.¹

Affiliation:

1. School of Biological Sciences University of Aberdeen Tillydrone Avenue Aberdeen AB24 2TZ UK

2. Marine Scotland Science Scottish Government Marine Laboratory PO Box 101 375 Victoria Road Aberdeen AB11 9DB UK

3. RSPB Centre for Conservation Science North Scotland Office Etive House, Beechwood Park Inverness IV2 6AL UK

4. RSPB Centre for Conservation Science The Lodge Sandy Bedfordshire SG19 2DL UK

Funder

Natural Environment Research Council

Publisher

Wiley

Subject

Nature and Landscape Conservation,Ecology,Ecology, Evolution, Behavior and Systematics

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/ece3.1914

Reference66 articles.

1. The diet of Common GuillemotUria aalgechicks provides evidence of changing prey communities in the North Sea

2. Modelling harbour seal habitat by combining data from multiple tracking systems

3. Prey density, value, and spatial distribution affect the efficiency of area-concentrated search

4. Integrative modelling of animal movement: incorporating in situ habitat and behavioural information for a migratory marine predator

5. Taking animal tracking to new depths: synthesizing horizontal–vertical movement relationships for four marine predators

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