Pacific Conservation Biology Pacific Conservation Biology Society
A journal dedicated to conservation and wildlife management in the Pacific region.

Just Accepted

This article has been peer reviewed and accepted for publication. It is in production and has not been edited, so may differ from the final published form.

Predicting the occurrence of an endangered reptile based on habitat attributes

Sarsha Gorissen , Ian Baird , Matthew Greenlees , Ahamad Sherieff , Richard Shine

Abstract

The endangered Blue Mountains water skink Eulamprus leuraensis is the sole endemic vertebrate of the Blue Mountains region, Australia; a habitat specialist known from <60 threatened, highland peat swamps. We quantified the species’ habitat associations by surveying 10 such swamps annually for three years. We scored habitat features and trapped skinks, comparing habitat attributes of trap sites where skinks were and were not captured. The distribution of E. leuraensis was non-random: skinks were found at sites with high values for some variables (soil moisture, live vegetation, surface water, understorey density and numbers of burrows) and low values for others (dead vegetation, logs, rocks, bare ground, canopy cover, sunlight penetration and numbers of invertebrates); and were mostly found in sites that were close to surface water and far from trees and logs. Eulamprus leuraensis are widely distributed within swamps, with weak associations between microhabitat variation and skink presence. Skink abundance and mean body size were highest within swamp centres, decreasing towards the margins; larger skinks were found closer to water, gravid female skinks were found at wetter sites and juveniles occupied marginal habitat. Skinks were rarely recaptured >10 m from their original site, with adult males travelling further than adult females and juveniles. We developed a quick field detection method for managers to assess the likely presence of E. leuraensis using two habitat attributes (soil moisture and burrow abundance). We mapped the species’ known and predicted habitat using GIS spatial layers, including locality records, associated vegetation communities and digital elevation models.

PC17027  Accepted 06 November 2017

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