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Using community observations to predict the occurrence of malleefowl (Leipoa ocellata) in the Western Australian wheatbelt
Authors:Blair Parsons  Jeff Short
Affiliation:a CSIRO, Sustainable Ecosystems, Private Bag 5, Wembley, WA 6913, Australia
b Wildlife Research and Management Pty Ltd., P.O. Box 1360, Kalamunda, WA 6926, Australia
c School of Animal Biology M092, University of Western Australia, 35 Stirling Highway, Crawley, WA 6009, Australia
Abstract:The Malleefowl is a ground-dwelling bird species that has declined in distribution and abundance in Australia since European settlement. These declines have been exacerbated in the Western Australian wheatbelt by the extensive clearing of native vegetation for agricultural development. A wealth of opportunistic, presence-only data exists for this species but absence data required for distribution modelling is lacking. This situation is typical of many species distribution datasets. We sought to establish the distribution of malleefowl within the Western Australian wheatbelt (210 000 km2) and their choice of habitat within this broad region. We supplemented a large presence-only dataset of malleefowl sightings with absence data derived from a bird atlas scheme and used these data to effectively predict the distribution of the species for the wheatbelt using a combined GAM/GLM approach. Both datasets were derived largely from community sightings. The distribution of malleefowl within the Western Australian wheatbelt was associated with landscapes that had lower rainfall, greater amounts of mallee and shrubland that occur as large remnants, and, lighter soil surface textures. This study illustrates how community knowledge, coupled with solid ecological understanding, can play a key role in developing the knowledge base to inform conservation and management of species in agricultural landscapes.
Keywords:Generalised additive model   Generalised linear model   Prediction   Presence-only   Species distribution   Bird habitat
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