Migration and stopover in a small pelagic seabird, the Manx shearwater Puffinus puffinus: insights from machine learning.
Proc Biol Sci
; 276(1660): 1215-23, 2009 Apr 07.
Article
en En
| MEDLINE
| ID: mdl-19141421
ABSTRACT
The migratory movements of seabirds (especially smaller species) remain poorly understood, despite their role as harvesters of marine ecosystems on a global scale and their potential as indicators of ocean health. Here we report a successful attempt, using miniature archival light loggers (geolocators), to elucidate the migratory behaviour of the Manx shearwater Puffinus puffinus, a small (400 g) Northern Hemisphere breeding procellariform that undertakes a trans-equatorial, trans-Atlantic migration. We provide details of over-wintering areas, of previously unobserved marine stopover behaviour, and the long-distance movements of females during their pre-laying exodus. Using salt-water immersion data from a subset of loggers, we introduce a method of behaviour classification based on Bayesian machine learning techniques. We used both supervised and unsupervised machine learning to classify each bird's daily activity based on simple properties of the immersion data. We show that robust activity states emerge, characteristic of summer feeding, winter feeding and active migration. These can be used to classify probable behaviour throughout the annual cycle, highlighting the likely functional significance of stopovers as refuelling stages.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Aves
/
Inteligencia Artificial
/
Migración Animal
Tipo de estudio:
Prognostic_studies
Límite:
Animals
Idioma:
En
Revista:
Proc Biol Sci
Asunto de la revista:
BIOLOGIA
Año:
2009
Tipo del documento:
Article
País de afiliación:
Reino Unido