The occurrence of potential tree hollows in the dry eucalypt forests of south-eastern Tasmania, Australia

Abstract

The relationship between environmental variables and the occurrence of potential hollows and hollow-bearing trees in three dry forest types (dry Eucalyptus delegatensis forest, E. pulchella-E. globulus-E. viminalis grassy/shrubby forest and dry E. obliqua forest) in southeastern Tasmania, was examined using generalised linear modelling. The number of trees with potential hollows and the number of potential hollows was significantly higher in dry E. obliqua forest, compared with the other two forest types. The number of potential hollows per tree was best explained by tree species, tree form, degree of burn damage and the interaction between burn damage and tree species. There was no significant relationship between the number of trees with potential hollows per site and the environmental variables measured. However, the number of potential hollows per site was best explained by several environmental variables: vegetation type (highest in dry E. obliqua forest); topographic position; amount of dead trees on the ground; the age of the stand; the average total basal area of all trees; the height of the overstorey vegetation and various interactions between these variables and other variables, such as understorey cover. A model developed using a subset of the environmental variables measured was coupled with GIS data to develop a map of the predicted occurrence of trees with potential hollows throughout the study area. The use of such a predictive map for landscape level planning, in particular to assess the implications of land use scenarios on the hollow resource, is illustrated.

Does hollow occurrence vary with forest type? A case study in wet and dry Eucalyptus obliqua forest

“…This constant rate of hollow formation may occur for other tree species and in other forest types, although more research is required to confirm this. We consequently propose that differences in hollow occurrence previously found between different forest types may be a consequence of the dominant tree species examined, the disturbance history of the area and/or differences in geographic location as opposed to differences in the forest type itself (see Ambrose, 1982;Bennett et al, 1994;Wormington and Lamb, 1999;Munks et al, 2007).…”

“…Tree age was selected because it is a useful attribute to examine when considering the impact of harvest rotations on hollow availability. Tree diameter was selected because it is a standard inventory technique in production forests and so may be useful for both habitat tree selection and for constructing predictive maps (as per Munks et al, 2007). Pre-fall hollow counts was selected because it has been shown in other studies to be a strong and useful indicator of hollow abundance and would therefore be a practical method for selecting habitat trees (e.g.…”

“…A large number of attributes have been found to be important, many of which are related to the classification of forests into different communities or forest types (see Lindenmayer et al, 1993;Bennett et al, 1994;Moorman et al, 1999;Wormington and Lamb, 1999;Gibbons et al, 2000;Whitford, 2002;Whitford and Williams, 2002;Fan et al, 2003;Wormington et al, 2003;Harper et al, 2005;Remm et al, 2006;Munks et al, 2007). A difference in hollow availability among forest types may occur when the distribution of hollow excavators varies among forest types (see Remm et al, 2006).…”

Using aerial photographs to remotely assess tree hollow availability

“…We selected this region for study because it is covered by two GIS mapping layers that potentially reflect hollow availability. The first is a layer predicting hollow availability across much of the dry forest according to site-level parameters (Munks et al 2007). The second layer maps forest senescence, using the SenCode classification of forest senescence we have assessed in the current study (Table 1) (CoASoT 1996).…”

“…Information on hollow occurrence is available for some landscapes in the form of industry-based small-plot inventory data (Temesgen et al 2008;Fox et al 2009). Some work has been done using this type of ground-based data to model hollow availability according to landscape features such as stand age, basal area, forest type, slope and rainfall (Lindenmayer et al 1991a, b;Fan et al 2003a;Munks et al 2007;Temesgen et al 2008;Fox et al 2009). However, these models are relevant only for the areas from which the data have been collected.…”