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

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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… Click to show full abstract

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.

Keywords: including agricultural; suitable habitat; agricultural modification; remotely sensed; modification

Journal Title: International Journal of Geographical Information Science
Year Published: 2018

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