Remotely sensed agricultural modification improves prediction of suitable habitat for a threatened lizard

David T.Y. Wong, William S. Osborne, Stephen D. Sarre, Bernd Gruber

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

The geographical distribution of a species is limited by factors such as climate, resources, disturbances and species interactions. Environmental niche models attempt to encapsulate these limits and represent them spatially but do not always incorporate disturbance factors. We constructed MaxEnt models derived from a remotely sensed vegetation classification with, and without, an agricultural modification variable. Including agricultural modification improved model performance and led to more sites with native vegetation and fewer sites with exotic or degraded native vegetation being predicted suitable for A. parapulchella. Analysis of a relatively well-surveyed sub-area indicated that including agricultural modification led to slightly higher omission rates but markedly fewer likely false positives. Expert assessment of the model based on mapped habitat also suggested that including agricultural modification improved predictions. We estimate that agricultural modification has led to the destruction or decline of approximately 30–35% of the most suitable habitat in the sub-area studied and approximately 20–25% of suitable habitat across the entire study area, located in the Australian Capital Territory, Australia. Environmental niche models for a range of species, particularly habitat specialists, are likely to benefit from incorporating agricultural modification. Our findings are therefore relevant to threatened species planning and management, particularly at finer spatial scales.

Original languageEnglish
Pages (from-to)1006-1025
Number of pages20
JournalInternational Journal of Geographical Information Science
Volume32
Issue number5
DOIs
Publication statusPublished - 2018

Fingerprint

Dive into the research topics of 'Remotely sensed agricultural modification improves prediction of suitable habitat for a threatened lizard'. Together they form a unique fingerprint.

Cite this