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Employing citizen-science avian data and environmental data for improved species distribution estimates and avian conservation in British Columbia

University of British Columbia

Previous work has shown that many populations of birds residing in British Columbia are declining in number. However, given the size and remoteness of many parts of the province, direct sampling of BC’s bird communities throughout the province is unlikely. Remote sensing has been shown to be an attractive option in these types of situations, providing environmental data in remote areas at spatial scales appropriate for a provincial level of analysis. Additionally, the spatial coverage of remotely sensed data allows for species occurrences to be estimated in un-sampled locations using models derived from areas where sampling has occurred. The overall objectives of this thesis are twofold, and were tested in two separate studies. First, the ability of remotely sensed environmental variables to predict the distribution of coastal bird species for the entire BC coast was investigated. Second, multiple environmental regionalization schemes were evaluated with regard to their ability to delineate avian Beta diversity across the province, and were compared to a regionalization built using species data directly. In Chapter 3, the distributions of 60 species of birds were estimated along the BC coast. Distribution models were built using species occupancy data linked to oceanic, terrestrial, and anthropogenic remotely sensed variables, as well as interpolated climate indices and spatial variables, in both single and ensemble models. The use of these four different types of environmental variables improved the distribution models’ ability to estimate species occurrence, as did the use of ensemble modeling and the inclusion of spatial variables. Significant changes in the amount of occupied habitat by year were detected in 16 species for the eight year study period. In Chapter 4, four environmental conservation regionalization schemes were compared using analysis of similarity (ANOSIM) tests to assess their ability to delineate Beta diversity. A new, species-based regionalization was then created to act as an ideal scenario, and was subsequently tested against each environmental regionalization scheme. These analyses demonstrated that all environmental regionalization schemes delineated significant patterns in Beta diversity, with the Bird Conservation Regions scoring highest in ANOSIM testing overall and being the most similar to the species-based regionalization.

Text

2013-09-03

Attribution-NonCommercial-NoDerivs 2.5 Canada

http://hdl.handle.net/2429/45005

Forestry

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/2.5/ca/