Consulta: creatorFacets:"Thiebault, Andrea"
Registros recuperados: 2
Data/hora: 13/06/2024 08:19:03

Seabird acoustic communication at sea: a new perspective using bio-logging devices
Provedor de dados: 5
Autores: Thiebault, Andrea; Pistorius, Pierre; Mullers, Ralf; Tremblay, Yann.
Most seabirds are very noisy at their breeding colonies, when aggregated in high densities. Calls are used for individual recognition and also emitted during agonistic interactions. When at sea, many seabirds aggregate over patchily distributed resources and may benefit from foraging in groups. Because these aggregations are so common, it raises the question of whether seabirds use acoustic communication when foraging at sea? We deployed video-cameras with built in microphones on 36 Cape gannets (Morus capensis) during the breeding season of 2010-2011 at Bird Island (Algoa Bay, South Africa) to study their foraging behaviour and vocal activity at sea. Group formation was derived from the camera footage. During similar to 42 h, calls were recorded on 72...
Tipo: Text
Ano: 2016
URL: https://archimer.ifremer.fr/doc/00625/73757/74429.pdf

"M2B" package in R: Deriving multiple variables from movement data to predict behavioural states with random forests
Provedor de dados: 5
Autores: Thiebault, Andrea; Dubroca, Laurent; Mullers, Ralf H. E.; Tremblay, Yann; Pistorius, Pierre A.
1. The behaviour of individuals affect their distributions and is therefore fundamental in determining ecological patterns. While, the direct observation of behaviour is often limited due to logistical constraints, collection of movement data has been greatly facilitated through the development of bio-logging. Movement data obtained through tracking instrumentation may potentially constitute a relevant proxy to infer behaviour. 2. To infer behaviour from movement data is a key focus within the "movement ecology" discipline. Statistical learning constitutes a number of methods that can be used to assess the link between given variables from a fully informed training dataset and then predict the values on a non-informed variable. We chose the random forest...
Tipo: Text
Palavras-chave: Cape gannet; Fisheries; GPS; Local enhancement; Machine learning; Onboard observers; Social interactions; Video cameras.
Ano: 2018
URL: https://archimer.ifremer.fr/doc/00445/55683/57354.pdf