Species richness and composition of avifaunal communities in a complex Amazonian landscape

Gilmore, B 2020, Species richness and composition of avifaunal communities in a complex Amazonian landscape , MSc by research thesis, University of Salford.

Download (524kB) | Preview


Little research has been undertaken to identify the effect that habitat heterogeneity and seasonality have on the diversity of birds in lowland Amazonia. This study aimed to identify how species richness and composition of avifaunal communities in the lower Rio Purús region of central Amazonia compare across terra firme, whitewater and blackwater seasonally flooded forests and their seasonal variation in water levels. Avifauna was sampled at nine sites (three terra firme, three whitewater and three blackwater) during low water, and repeated during high water. Using ten 15-minute point counts every 200m for each site a total of 284 species were recorded. Species richness in seasonally flooded forest was found to be similar but was significantly lower in terra firme. Almost 17 percent of species were significantly associated with a single habitat, with a further 4 percent associated with two of the three habitats. Species composition was significantly different between all three habitats. Seasonal variation in water levels had no effect on species richness or composition. The results suggest that habitat heterogeneity, caused by seasonal flood waters, contributes to creating unique communities of birds in Amazonian forests. Future conservation planning may need to include both terra firme and seasonally flooded forest in order to safeguard bird diversity.

Item Type: Thesis (MSc by research)
Contributors: Boubli, JP (Supervisor)
Schools: Schools > School of Environment and Life Sciences
Depositing User: Benjamin Gilmore
Date Deposited: 17 Dec 2020 13:38
Last Modified: 27 Aug 2021 21:47
URI: https://usir.salford.ac.uk/id/eprint/58957

Actions (login required)

Edit record (repository staff only) Edit record (repository staff only)


Downloads per month over past year