Journal article
Dealing with sampling bias and inferring absence data to improve distribution models of a widely distributed vulnerable marsupial
D Brizuela-Torres, J Elith, G Guillera-Arroita, NJ Briscoe
Austral Ecology | WILEY | Published : 2024
DOI: 10.1111/aec.13474
Abstract
Species distribution models are widely used to identify potential and high-quality habitat of endangered species to inform conservation decisions. However, their usefulness is constrained by the amount and quality of biodiversity data and the approaches for dealing with data deficiencies. Presence-only data, used in presence/background modelling methods, are widely available but are often affected by sampling bias. Presence/absence modelling methods are less affected by biases, but data are less common. We modelled the distribution of a widely distributed, endangered species from Australia – the greater glider – and tested how predictions were influenced by data treatment and modelling frame..
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Awarded by Australian Research Council
Funding Acknowledgements
The authors thank Brendan Wintle (University of Melbourne), Teresa Eyre (Queensland Herbarium) and Lindy Lumsden (Arthur Rylah Institute for Environmental Research) for guidance and for reviewing models' predictions. DB was supported by a Melbourne Research Scholarship and a CONACYT-Regional Occidente 2019 scholarship (739843) granted by the Mexican National Council for Science and Technology (Consejo Nacional de Ciencia y Tecnologia). JE, GGA and NB acknowledge funding from the Australian Research Council that employed NB: DP180101852. GGA is currently supported by a 'Ramon y Cajal' grant (RYC2020-028826-I) funded by the Spanish Ministry of Science and Innovation, the Agencia Estatal de Investigacion (10.13039/501100011033) and 'ESF Investing in your future'. Open access publishing facilitated by The University of Melbourne, as part of the Wiley - The University of Melbourne agreement via the Council of Australian University Librarians.