Applying surrogate species presences to correct sample bias in species distribution models: a case study using the Pilbara population of the Northern Quoll

Shaun W Molloy,Robert A Davis,Judy A Dunlop,Eddie J.B Van Etten

doi:10.3897/natureconservation.18.12235

Abstract

Applying surrogate species presences to correct sample bias in species distribution models: a case study using the Pilbara population of the Northern Quoll

Highlights

Species distribution models (SDMs) use environmental data from known locations of a species to predict places where that species could potentially occur within landscapes or regions (Booth et al 2014)
We reviewed the literature on the Northern Quoll in general and the Pilbara population in particular, to identify independent variables likely to be influential or linked to its distribution and suitable for producing a species distribution model (SDM)
By comparing the distributions identified through this exercise with proposed mining and infrastructure projects in the environmental impact assessment process, this SDM can be used to minimise impacts on this unique and important northern quoll population

Summary

Introduction

Species distribution models (SDMs) use environmental data from known locations of a species to predict places where that species could potentially occur within landscapes or regions (Booth et al 2014). Explicit probability of presence, or prediction of occurrence maps, generated using SDM algorithms, have been used to inform conservation planning and habitat management at both coarse and fine scales. They can guide or prioritise future survey efforts and aid in assessing the conservation status of target species. SDMs relate known occurrences of a species with various environmental variables and predict a probability that a species will occur in areas where no data on its occurrence is available They help to identify potentially suitable habitat (Elith and Leathwick 2009; Guisan and Thuiller 2005)

Objectives

Methods

Results

Discussion

Conclusion