Foraging Cape gannets

This dataset provides the positions in space and time of Cape gannets ( Morus capensis , Lichtenstein 1823) with their associated activities (flying, sitting on the water, diving) and associations with conspecifics. It supports the analyses of a paper published in Methods in Ecology and Evolution en...

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Bibliographic Details
Main Authors: Thiebault, Andréa, Mullers, Ralf, Pistorius, Pierre, Tremblay, Yann
Format: Other/Unknown Material
Language:unknown
Published: Zenodo 2018
Subjects:
Online Access:https://doi.org/10.5281/zenodo.1168918
Description
Summary:This dataset provides the positions in space and time of Cape gannets ( Morus capensis , Lichtenstein 1823) with their associated activities (flying, sitting on the water, diving) and associations with conspecifics. It supports the analyses of a paper published in Methods in Ecology and Evolution entitled ""m2b" package in R: deriving multiple variables from movement data to predict behavioural states with random forests." by Andréa Thiebault, Laurent Dubroca, Ralf Mullers, Yann Tremblay and Pierre Pistorius, together with the methods of the R package "m2b" ( https://cran.r-project.org/package=m2b ). The data_gannets.csv file is csv (comma separated value) files with a header. The variables are x : longitude y : latitude t : time in year-month-day hour:minutes:second format b: behaviour (1, 2 ,3 for "diving", "sitting on the water", "flying" respectively) g : presence (1) or absence (0) of conspecific in the vicinity of the individual id: unique id for each individual. More information about data collection and processing: The birds were equipped on Bird Island (Algoa Bay, South Africa, under a permit from SANParks) with GPS devices (i-GotU GT-600, Mobile Action Technology Inc., Taipei, Taiwan) to record the movement path and video cameras (Camsports nano, CamsportsTM, Estrablin, France) to observe the behaviour and surroundings of the animal while at sea. The GPS loggers were set to record a geographical position every five seconds when the animal moved faster than 10km.h -1 and every 10 or 30 seconds otherwise. The raw tracking data were interpolated using a Bézier curve to obtain a track with regular step durations of 5 s (Tremblay et al. 2006). The video footage were observed frame by frame using a video reader in Matlab software and the events of interest were visually flagged using a purpose-built video event recorder (Tremblay, unpublished software). Because GPS and video data were not sampled at similar rates, the video observations were related to GPS locations as followed. Each of the GPS positions was ...