Aim: Effective vegetation conservation requires reasonable certainty regarding the distribution, extent and classification of plant communities and ecoregions for assessing rarity. In this paper we describe a multivariate clustering approach based on environmental data for objectively defining temperate treeless palustrine wetland communities.Location: New South Wales (NSW), Australia.Methods: In NSW no comprehensive state-wide map of wetland vegetation exists, with more than 200 vegetation maps produced by local and state governments at a range of spatial resolutions and extents. Using the available vegetation spatial data, we produced a composite map which identified 6323 wetlands >1 ha. We then used the partitioning around medoids cluster analysis method for grouping wetlands based on 12 climate, topography, geology and soils spatial data layers and the wetland locations. We tested a range of cluster numbers from three to 20, and assessed the stability of the clustering by calculating mean silhouette widths. The derived classes were then characterized in terms of number of individual wetlands and their area, and also the number and area of individual wetlands found within protected areas such as national parks. Results:We found a peak in the mean silhouette width at 11 clusters, indicating that this was the optimal number of clusters for classifying the wetland data. We produced maps of wetland density for each of the 11 clusters and described the mean and mode environmental characteristics of each cluster. Each cluster represented a unique combination of environmental variables. For example, wetlands in cluster 2 are typically in the south, in areas of low evaporation and low average temperatures. An assessment of rarity found that wetlands in the largest cluster class had an areal extent of 14 644 ha, compared to 1414 ha for the smallest cluster. All but one of the clusters had part of their range within protected areas.Conclusions: Clustering environmental variables is an important but underutilized method for characterizing vegetation communities/ecoregions such as wetlands spatially. This approach can be used to produce objective, repeatable and defensible wetland community maps for assessing rarity.
Summary This pilot study of the rare Pagoda Rock Daisy (Leucochrysum graminifolium) in the western Blue Mountains of New South Wales (Australia) proposes a simple survey method combining timed meander and grid‐cell survey design to improve the survey effort required for monitoring of species growing in remote and/or inaccessible field locations. Where Pagoda Rock Daisies were known to be present, detection time was both rapid and effective (mean of 4.9 min for each 1 ha grid). Notably, the total survey effort remained constant for all grids, even though Pagoda Rock Daisies were unevenly distributed in the landscape (approximately 17 min/ha). Ultimately, the time required to traverse the landscape was deemed to be the primary limiting factor affecting survey effort. The application of this method is not restricted to challenging locations such as cliff edges; this method could be scaled according to the landscape or organism under investigation, providing a rapid method for surveying and monitoring rare, introduced or other plants from a site‐based scale to a broader geographic area.
Summary Accurate, repeatable estimates of population densities are often desired for vegetation monitoring. However, conventional transect and quadrat field sampling methods are not always applicable to plants such as the rare shrub Epacris muelleri Sond., growing on largely inaccessible cliffs and rock faces. E. muelleri is an ericaceous shrub restricted to the Blue Mountains region in New South Wales, which is to be monitored to detect potential effects of underground coal mining. In this manuscript, we evaluated observer error associated with density estimates to assess suitability of applying the timed‐meander method to this species. The results indicate that a visual search method using binoculars can generate repeatable results among observers with very different experience levels. However, there is a large margin of error in estimating density when there are many plants growing in close association or overlapping on a cliff. Nonetheless, with a strict set of protocols and further evaluation, this method shows promise as a rapid yet robust method for carrying out repeatable surveys for quantifying changes in the population.
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