3 Works

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

Marianna Chimienti, Thomas Cornulier, Ellie Owen, Mark Bolton, Ian M. Davies, Justin M. J. Travis, Beth E. Scott & Justin M.J. Travis
The recent increase in data accuracy from high resolution accelerometers offers substantial potential for improved understanding and prediction of animal movements. However, current approaches used for analysing these multivariable datasets typically require existing knowledge of the behaviors of the animals to inform the behavioral classification process. These methods are thus not well-suited for the many cases where limited knowledge of the different behaviors performed exist. Here, we introduce the use of an unsupervised learning algorithm....

Data from: The Pillars of Hercules as a bathymetric barrier to gene-flow promoting isolation in a global deep-sea shark (Centroscymnus coelolepis)

Diana Catarino, Halvor Knutsen, Ana Veríssimo, Esben Moland Olsen, Per Erik Jorde, Gui Menezes, Hanne Sannæs, David Stanković, Joan Batista Company, Francis Neat, Roberto Danovaro, Antonio Dell'Anno, Bastien Rochowski, Sergio Stefanni, Joan Baptista Company & Hanne Sannaes
Knowledge of the mechanisms limiting connectivity and gene-flow in deep-sea ecosystems is limited, especially for deep-sea sharks. The Portuguese dogfish (Centroscymnus coelolepis) is a globally distributed and Near Threatened deep-sea shark. C. coelolepis population structure was studied using 11 nuclear microsatellite markers and a 497 bp fragment from the mtDNA Control Region. High levels of genetic homogeneity across the Atlantic (ΦST=-0.0091, FST= 0.0024, P > 0.05) were found suggesting one large population unit at this...

Data from: Markov switching autoregressive models for interpreting vertical movement data with application to an endangered marine apex predator

Cecilia Pinto & Luigi Spezia
1.Time series of animal movement obtained from bio-loggers are becoming widely used across all taxa. These data are nowadays of high quality, combining high resolution with precision, as the tags are able to collect for longer times and store larger quantities of data. Due to their nature, high-frequency data sequences often pose non-trivial problems in time series analysis: non-linearity, non-Normality, non-stationarity, and long memory. These issues can be tackled by modelling the data sequence as...

Registration Year

  • 2015

Resource Types

  • Dataset


  • Marine Scotland
  • University of Aberdeen
  • Marche Polytechnic University
  • Royal Society for the Protection of Birds
  • University of Melbourne
  • University of Oslo
  • Institute of Marine Science
  • University of Trieste
  • Institute of Marine Research
  • University of Agder