Document Type

Journal Article

Publisher

Pensoft

School

School of Science

Comments

Originally published as:

Molloy, S. W., Davis, R. A., Dunlop, J. A., & van Etten, E. J. (2017). Applying surrogate species presences to correct sample bias in species distribution models: a case study using the Pilbara population of the Northern Quoll. Nature Conservation, 18, 25.

Original available here

Abstract

The management of populations of threatened species requires the capacity to identify areas of high habitat value. We developed a high resolution species distribution model (SDM) for the endangered Pilbara northern quoll Dasyurus hallucatus, population using MaxEnt software and a combined suite of bioclimatic and landscape variables. Once common throughout much of northern Australia, this marsupial carnivore has recently declined throughout much of its former range and is listed as endangered by the IUCN. Other than the potential threats presented by climate change, and the invasive cane toad Rhinella marina (which has not yet arrived in the Pilbara). The Pilbara population is also impacted by introduced predators, pastoral and mining activities. To account for sample bias resulting from targeted surveys unevenly spread through the region, a pseudo-absence bias layer was developed from presence records of other critical weight-range non-volant mammals. The resulting model was then tested using the biomod2 package which produces ensemble models from individual models created with different algorithms. This ensemble model supported the distribution determined by the bias compensated MaxEnt model with a covariance of of 86% between models with both models largely identifying the same areas as high priority habitat. The primary product of this exercise is a high resolution SDM which corroborates and elaborates on our understanding of the ecology and habitat preferences of the Pilbara Northern Quoll population thereby improving our capacity to manage this population in the face of future threats.

DOI

10.3897/natureconservation.18.12235

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

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