Andrew F. Le Brocque

AN ASSESSMENT OF SOME IMPROVED TECHNIQUES FOR ESTIMATING THE ABUNDANCE (FREQUENCY) OF SEDENTARY ORGANISMS

The traditional sampling method for estimating frequency (the number of sub-quadrats containing a basal part of the organisms) is compared, using both computer simulations and direct comparison in the field, to two new methods that use a compound series of variable-sized concentric sub-quadrats. Both the new frequency-score and the new importance-score methods are closer approximations of density than is the standard frequency method, and the estimates produced by both of the new methods are less affected by the choice of sub-quadrat size and the spatial distribution (dispersion) of the organisms (i.e. clumping and regularity). Thus, the two nested-quadrat methods appear to ameliorate the usual frequency limitations associated with sub-quadrat size and organism dispersion, by the use of a range of different sub-quadrat sizes. This is important in community studies, where the component species may show a wide range of densities and dispersions. Both of the new methods are easily employed in the field. The importance-score method involves no more sampling effort than does standard qualitative (presence-absence) sampling, and it can therefore be used to sample a larger quadrat area than would normally be used for frequency sampling. This makes the method much more cost-effective as a means of estimating abundance, and it allows a greater number of the rarer species to be included in the sampling. The frequency-score method is more time-consuming, but it is capable of detecting more subtle community patterns. This means that it is particularly useful for the study of species-poor communities or where small variations in composition need to be detected.

Spatial prioritization of revegetation sites for dryland salinity management: an analytical framework using GIS

To address the limited application of analytical and modelling techniques in prioritizing revegetation sites for dryland salinity (saline land) management, a case study of the Hodgson Creek catchment in Queensland, Australia, was conducted. An analytical framework was developed, incorporating the use of spatial datasets (Landsat 7 image, DEM, soil map, and salinity map), which were processed using digital image processing techniques and a geographic information system (GIS). Revegetation sites were mapped and their priority determined based on recharge area, land use/cover and sub‐catchment salinity. The analytical framework presented here enhances the systematic use of land information, widens the scope for scenario testing, and improves the testing of alternative revegetation options. The spatial patterns of revegetation sites could provide an additional set of information relevant in the design of revegetation strategies.

Spectral discrimination of bulloak (Allocasuarina luehmannii) and associated woodland for habitat mapping of the endangered bulloak jewel butterfly (Hypochrysops piceata) in southern Queensland

Abstract The bulloak jewel butterfly (Hypochrysops piceata) is an endangered species due to a highly restricted distribution and complex life history, yet little is known of the availability of suitable habitat for future conservation. The aim of this study was to examine the potential of hyperspectral reflectance data for the discrimination of woodland species in support of bulloak jewel butterfly’s habitat mapping. Sites from known butterfly sightings in Leyburn, Southern Queensland, Australia, were examined using hyperspectral scanning and vegetation species discrimination. Reflectance data of eight woodland vegetation species (Allocasuarina luehmannii, Eucalyptus crebra, Eucalyptus populnea, Callitris glauca, Corymbia maculata, Angophora leicarpa, Acacia sparsiflora, and Jacksonia scoparia) were collected at the leaf and canopy levels using a full-range (350 to 2500 nm) hand-held nonimaging spectroradiometer. Partial least square (PLS) regression was used to interpret the bulloak tree spectra against other vegetation species. The PLS results indicated high-prediction accuracies ranging from 78% to 95% and 52% to 5% for canopy and leaf levels, respectively. The highest spectral separability was observed at the near-infrared bands (approximately at 700 to 1355 nm), followed by selected ranges in the short-wave infrared band where separability peaked at 1670 and 2210 nm. The results confirmed the feasible use of hyperspectral sensing for discriminating vegetation species and its potential use for habitat mapping of the endangered bulloak jewel butterfly.