This is a spatial dataset comprising predictions of vegetation condition for biodiversity for the Brigalow Belt bioregion. The dataset was created using a gradient boosting decision tree (GBDT) model based on 10 vegetation-specific remote sensing datasets and 7,938 training sites of known vegetation community and condition state across Southeast Queensland, Brigalow Belt and Central Queensland Coast bioregions. Condition score was modelled as a function of distance in the remote sensing (RS) space within homogeneous vegetation communities. The product is intended to represent predicted BioCondition for 2021 rather than any singe date.

This is a spatial dataset comprising predictions of vegetation condition for biodiversity for the Southeast Queensland bioregion. The dataset was created using a gradient boosting decision tree (GBDT) model based on 10 vegetation-specific remote sensing datasets and 7,938 training sites of known vegetation community and condition state across Southeast Queensland, Brigalow Belt and Central Queensland Coast bioregions. Condition score was modelled as a function of distance in the remote sensing (RS) space within homogeneous vegetation communities. The product is intended to represent predicted BioCondition for 2021 rather than any singe date.

This is a spatial dataset comprising predictions of vegetation condition for biodiversity for the Central Queensland Coast bioregion. The dataset was created using a gradient boosting decision tree (GBDT) model based on 10 vegetation-specific remote sensing datasets and 7,938 training sites of known vegetation community and condition state across Southeast Queensland, Brigalow Belt and Central Queensland Coast bioregions. Condition score was modelled as a function of distance in the remote sensing (RS) space within homogeneous vegetation communities. The product is intended to represent predicted BioCondition for 2021 rather than any singe date.

The ACEAS working group has developed a framework to evaluate the extent to which fire regimes are driven by climate and other environmental variables, and whether these fire and environment relationships concord with: (a) predictions of the group of conceptual models recently developed; and (b) predictions of process-based models. The dataset provides a distribution of major fire regimes niches throughout Australia ordered according to decreasing annual net primary productivity. The dataset published is the distribution of major fire regimes niches throughout Australia.

This is Version 1 of the Brigalow Belt Bioregion Spatial BioCondition dataset. It is superseded by the Version 2 dataset that can be found at: https://doi.org/10.25901/rnqz-cn10.<br><br> This is a spatial dataset comprising predictions of vegetation condition for biodiversity for the brigalow belt bioregion. The dataset was created using a gradient boosting decision tree (GBDT) model based on eight vegetation specific remote sensing (RS) datasets and 17,000 training sites of known vegetation community and condition state. Condition score was modelled as a function of the difference in the RS space within homogeneous vegetation communities. The product is intended to represent predicted BioCondition for year 2019 rather than any single date.

The dataset includes three csv files: [1] effects of pre-inhabitation and viruses on the feeding behavior of <i>Rhopalosiphum padi</i> and <i>R. maidis</i> (min). [2] effects of pre-inhabitation and viruses on the fecundity of<i> R. padi</i> and <i>R. maidis</i> (total offspring in laboratory and field). [3] effect of pre-inhabitation and viruses on the host plant nutrient content (amino acids, total sterols, and simple sugars-mg/g). These data might be used by researchers studying positive interactions, effects of viruses on host plants and vectors, phytochemistry of the wheat plant, and feeding behavior of phloem-feeders.

This data contains maximum vegetation height collected from airborne full waveform lidar and hyperspectral data in the VNIR bands in the Tumbarumba Wet Eucalypt site in 2012

Version 1 of the Southeast Queensland Bioregion Spatial BioCondition dataset is superseded by the Version 2 dataset that can be found at: https://doi.org/10.25901/r976-1v85.<br><br> Version 1 was an initial demonstration version. The version 1 data has been removed from publication to negate temporal comparisons between v1 (2019) and v2 (2021), as this is a future goal for the product but still in development phase. This was a spatial dataset comprising predictions of vegetation condition for biodiversity for the Southeast Queensland Bioregion. The dataset was created using a gradient boosting decision tree (GBDT) model based on eight vegetation specific remote sensing (RS) datasets and 17,000 training sites of known vegetation community and condition state. Condition score was modelled as a function of the difference in the RS space within homogeneous vegetation communities. The product was intended to represent predicted BioCondition for year 2019 rather than any single date.

This is the base geographical data for the Robson creek 25 ha plot. The data sets contain the 100 m grid, 20 m grid, 100 m points, 20 m points, major tracks and creeks. Data format is both Google earth KML and ESRI shapefile.

This dataset contains bird occurrence data collected at the Wombat Stringybark Eucalypt site in 2015.