Ecology and behaviour of Palm-nut Vultures Gypohierax angolensis in the Bijagós Archipelago, Guinea-Bissau§. (31st August 2017)

• Record Type: Journal Article
• Authors: Carneiro, Camilo; Henriques, Mohamed; Barbosa, Castro; Tchantchalam, Quintino; Regalla, Aissa; Patrício, Ana R; Catry, Paulo
• Is Part Of: Ostrich. Volume 88:Number 2(2017)
• Page Start: 113
• Page End: 121
• Publication Date: 2017-08-31
• Subjects: Bayesian mixing models -- breeding -- diet -- moult -- simmr -- stable isotope analysis; Birds -- Periodicals; Birds -- South Africa -- Periodicals; 598.05
• DOI: 10.2989/00306525.2017.1291540
• Languages: English
• ISSNs: 0030-6525

Abstract

Abstract : Populations of many vulture species have undergone substantial declines. In Africa, 82% are threatened and although research on vultures has increased, the biology and ecology of several species is still poorly known. The Palm-nut Vulture Gypohierax angolensis has peculiar ecological characteristics, feeding on palm fruits, associated with the aquatic environment and taking a wide variety of prey. It has been over 40 years since the last thorough study on the ecology of the species was undertaken. This study investigated several ecological and behavioural aspects of Palm-nut Vultures, using a combination of direct observations, stable isotope analysis and Bayesian mixing models. The population of this species within the João Vieira and Poilão Marine National Park is estimated to be 21–30 birds km−2. Egg laying started in November (early dry season) and some chicks were still growing in early May. A minimum of 54% of nests produced a fledgling. Observations suggest that Palm-nut Vultures moult all flight feathers annually, between January and November. Their foraging behaviour is described and cafeteria trials showed their preference for fish over oil palm fruits. However, it is estimated that oil palm fruits account for almost 50% of the assimilated diet, followed by fish, crabs and Green Turtle hatchlings or eggs, as revealed by stable isotopes and Bayesian mixing models.