Determinants of tree assemblage composition at the mesoscale within a subtropical eucalypt forest.

A variety of environmental processes, including topography, edaphic and disturbance factors can influence vegetation composition. The relative influence of these patterns has been known to vary with scale, however, few studies have focused on environmental drivers of composition at the mesoscale. This study examined the relative importance of topography, catchment flow and soil in influencing tree assemblages in Karawatha Forest Park; a South-East Queensland subtropical eucalypt forest embedded in an urban matrix that is part of the Terrestrial Ecosystem Research Network South-East Queensland Peri-urban SuperSite. Thirty-three LTER plots were surveyed at the mesoscale (909 ha), where all woody stems ≥1.3 m high rooted within plots were sampled. Vegetation was divided into three cohorts: small (≥1-10 cm DBH), intermediate (≥10-30 cm DBH), and large (≥30 cm DBH). Plot slope, aspect, elevation, catchment area and location and soil chemistry and structure were also measured. Ordinations and smooth surface modelling were used to determine drivers of vegetation assemblage in each cohort. Vegetation composition was highly variable among plots at the mesoscale (plots systematically placed at 500 m intervals). Elevation was strongly related to woody vegetation composition across all cohorts (R2: 0.69-0.75). Other topographic variables that explained a substantial amount of variation in composition were catchment area (R2: 0.43-0.45) and slope (R2: 0.23-0.61). Soil chemistry (R2: 0.09-0.75) was also associated with woody vegetation composition. While species composition differed substantially between cohorts, the environmental variables explaining composition did not. These results demonstrate the overriding importance of elevation and other topographic features in discriminating tree assemblage patterns irrespective of tree size. The importance of soil characteristics to tree assemblages was also influenced by topography, where ridge top sites were typically drier and had lower soil nutrient levels than riparian areas

Relationship between environmental predictor variables.

<p>Pearson's correlation coefficients are shown.</p><p>Relationship between environmental predictor variables.</p

Proportion of trees in selected genera for each cohort at Karawatha.

<p>Proportion of trees in selected genera for each cohort at Karawatha.</p

Correlations of 14 soil variables with the three major axes of the soil PCA analysis.

<p>Correlations of 14 soil variables with the three major axes of the soil PCA analysis.</p

NMDS ordination for: woody vegetation with a DBH 1–10 cm (top-left); woody vegetation with a DBH 10–30 cm (top-right); and c.) woody vegetation with a DBH>30 cm (bottom-left).

<p>Although three NMDS axes were used, only the first two are shown for visual representation.</p

Plot layout within Karawatha Forest Park (KFP) within the TERN SEQ Peri-urban SuperSite.

<p>The midlines (thick black lines), and 10 m contour lines (thin grey lines with values in metres) demonstrate the positions for thirty three PPBio LTER plots surveyed in this study. The inset shows the location of KFP (star) within Queensland, Australia. Each midline starting point was placed systematically on a grid. These grid locations are also displayed as a combination of a letter and number.</p

The total species richness of each genus within each size cohort among the 33 plots at KFP.

<p>Plant abundance for each genus is also shown.</p><p>The total species richness of each genus within each size cohort among the 33 plots at KFP.</p

Surface modelling statistics for each cohort and significant environmental variable.

<p>R² value and P-value are shown. CA is catchment area.</p><p>*, <i>P</i><0.05;</p><p>**, <i>P</i><0.01;</p><p>***, <i>P</i><0.001; NS is <i>P</i>>0.05.</p><p>Surface modelling statistics for each cohort and significant environmental variable.</p

Relative abundance of woody vegetation species ordered by elevation.

<p>Relative abundance of woody vegetation species ordered by elevation.</p

Spatial modelling of bilby (Macrotis lagotis) and rabbit (Oryctolagus cuniculus) pellets within a predator-proof enclosure

A traditional design-reliant estimate of abundance is calculated by multiplying a density estimate obtained from transects to reflect the size of the study area. This type of estimate tells nothing about the nature of a species’ distribution between the samples. In contrast, model-based inference can better estimate abundance by interpolating transect estimates over the study area with the aid of covariates. This study used density surface modelling (DSM) to predict spatial distribution of greater bilby (Macrotis lagotis) and rabbit (Oryctolagus cuniculus) pellets within a predator-proof enclosure at Currawinya National Park, south-west Queensland. Pellets and latrines were counted using distance sampling and plot sampling on 30 PPBio plots during 2012 and 2014. Pellets and latrines were not strongly associated with habitat features, reflecting the generalist nature of both species. Bilby pellets were found on 23 plots in 2012 and 5 plots in 2014. Rabbit pellets were present on 29 plots in 2012 and 16 plots during 2014. These substantial declines in pellet abundances coincided with invasion of the feral cat (Felis catus) into the enclosure. While DSM modelling can allow managers to make informed decisions about applying survey effort or management practices, it is not suited to all species or situations