The composition of many eastern Australian woodland and forest bird assemblages is controlled by a single, hyper-aggresive native bird, the noisy miner <em>Manorina melanocephala</em>. The "Avifaunal disarry from a single despotic species" working group harnessed diverse existing datasets and used them to develop and test models of noisy miner occupancy and impacts. Two datasets are published based on the analysis and synthesis.

The dataset catalogue information about research or management projects that have used remote devices to record behavioural, physiological or environmental data from free-ranging animals. The purpose of this dataset is to act as a conduit by which animal telemetry data, ideas, analysis and statistical tools may be shared between interested parties throughout Australasia. The animal telemetry projects collated in this dataset have been collated from peer-reviewed scientific papers published between 2000 and 2013. This represents the first-step in the creation of an Australasian focused database for animal telemetry research and management projects. If you have undertaken a telemetry project and it is not listed here, whether the study findings have been published or not, then please send details about the study to the dataset contact person. If applicable, the details will be incorporated. These data were compiled as part of the ACEAS working group project titled "Advancing the application of animal telemetry data in ecosystem management".

For some time, Remote Sensing Sciences, has produced Foliage Projective Cover (FPC) using a model applied to Landsat surface reflectance imagery, calibrated by field observations. An updated model was developed which relates field measurements of FPC to 2-year time series of Normalized Difference Vegetation Index (NDVI) computed from Landsat seasonal surface reflectance composites. The model is intended to be applied to Landsat and Sentinel-2 satellite imagery, given their similar spectral characteristics. However, due to insufficient field data coincident with the Sentinel-2 satellite program, the model was fitted on Landsat imagery using a significantly expanded, national set of field data than was used for the previous Landsat FPC model fitting. The FPC model relates the field measured green fraction of mid- and over-storey foliage cover to the minimum value of NDVI calculated from 2-years of Landsat seasonal surface reflectance composites. NDVI is a standard vegetation index used in remote sensing which is highly correlated with vegetation photosynthesis. The model is then applied to analogous Sentinel-2 seasonal surface reflectance composites to produce an FPC image at Sentinel-2 spatial resolution (i.e. 10&nbsp;m) using the radiometric relationships established between Sentinel-2 and Landsat in Flood (2017). This is intended to represent the FPC for that 2-year period rather than any single date, hence the date range in the dataset file name. The dataset is generally expected to provide a reasonable estimate of the range of FPC values for any given stand of woody vegetation, but it is expected there will be over- and under-estimation of absolute FPC values for any specific location (i.e. pixel) due to a range of factors. The FPC model is sensitive to fluctuations in vegetation greenness, leading to anomalies such as high FPC on irrigated pastures or locations with very green herbaceous or grass understoreys. A given pixel in the FPC image, represents the predicted FPC in the season with the least green/driest vegetation cover over the 2-year period assumed to be that with the least influence of seasonally variable herbaceous vegetation and grasses on the more seasonally stable woody FPC estimates. The two-year period was used partly because it represents a period relative to tree growth but was also constrained due to the limited availability of imagery in the early Sentinel-2 time series. The FPC dataset is constrained by the woody vegetation extent dataset for the FPC year.

We conducted a four-step Delphi expert elicitation procedure. This approach allowed us to address the gaps in knowledge regarding national koala populations with the robustness of collective judgement. The four steps of the Delphi method ask for an upper estimate, a lower estimate, a best guess and a percentage confidence interval. Prior to the commencement of the workshop, the participants were required to complete the first round of the questionnaire. These results were then re-evaluated during the workshop and a second round of elicitation was conducted. The outcome of the two workshops will be a synthesis of the distribution and abundance of koalas, population trends, and a region-specific summary of threats to koalas. Peer-reviewed journal publications will be produced. The information will be used to inform researchers, managers and decision-makers to ensure that viable koala populations persist across their natural range.

The seasonal dynamic reference cover method product compares the current ground cover level of each pixel to a reference pixel based on the historical timeseries and is available for Queensland from 1987 to present. It is created using a modified version of the dynamic reference cover method developed by <a href="https://doi.org/10.1016/j.rse.2012.02.021">Bastin et al (2012) </a>. This approach calculates a minimum ground cover image over all years to identify locations of most persistent ground cover in years with the lowest rainfall, then uses a moving window approach to calculate the difference between the window's central pixel and its surrounding reference pixels. The output is a difference image between the cover amount of a pixel's reference pixels and the actual cover at that pixel for the season being analysed. Negative values indicate pixels which have less cover than the reference pixels.<br> The main differences between this method and the original method are that this method uses seasonal fractional ground cover rather than the preceding ground cover index (GCI) and this method excludes cleared areas and certain landforms (undulating slopes), which are considered unsuitable for use as reference pixels.<br> This product is based upon the JRSRP Fractional Cover 3.0 algorithm.

The Sentinel-2 seasonal fractional ground cover product shows the proportion of bare ground, green and non-green ground cover and is derived directly from the Sentinel-2 seasonal fractional cover product, also produced by Queensland's Remote Sensing Centre. The seasonal fractional cover product is a spatially explicit raster product, which predicts vegetation cover at medium resolution (10&nbsp;m per-pixel) for each 3-month calendar season. However, the seasonal fractional cover product does not distinguish tree and mid-level woody foliage and branch cover from green and dry ground cover. As a result, in areas with even minimal tree cover (>15%), estimates of ground cover become uncertain. With the development of the fractional cover time-series, it has become possible to derive an estimate of ‘persistent green’ based on time-series analysis. The persistent green vegetation product provides an estimate of the vertically-projected green-vegetation fraction where vegetation is deemed to persist over time. These areas are nominally woody vegetation. This separation of the 'persistent green' from the fractional cover product, allows for the adjustment of the underlying spectral signature of the fractional cover image and the creation of a resulting 'true' ground cover estimate for each season. The estimates of cover are restricted to areas of <60% woody vegetation. Currently, the persistent green product has only been produced at 30&nbsp;m pixel resolution based on Landsat imagery, resulting in this Sentinel-2 seasonal ground cover product having a medium 30&nbsp;m pixel resolution also. This is an experimental product which has not been fully validated. This product is similar to the <a href="https://portal.tern.org.au/metadata/23884 ">Seasonal ground cover - Landsat, JRSRP algorithm Version 3.0, Australia Coverage</a> which is based on a different satellite sensor.

<p>The monthly blended ground cover product is a spatially explicit raster product that shows the proportion of bare ground, green and non-green ground cover at medium resolution (30&nbsp;m per-pixel) for each calendar month across Queensland from 2017 to present. It is derived directly from both the Landsat-based fractional cover product and the Sentinel-2-based fractional cover product by Queensland's Remote Sensing Centre.<br> A 3 band (byte) image is produced:<br> band 1 - bare ground fraction (in percent),<br> band 2 - green vegetation fraction (in percent),<br> band 3 - non-green vegetation fraction (in percent).<br> The no data value is 255.</p> <p>This product is derived from the <a href="https://portal.tern.org.au/metadata/TERN/8d3c8b36-b4f1-420f-a3f4-824ab70fb367 ">Monthly blended fractional cover - Landsat and Sentinel-2, JRSRP algorithm Version 3.0, Queensland coverage</a></p>

This data contains a once-off general structural description according to the National Vegetation Information System (NVIS) level 5 for the core 1 hectare plot in the Great Western Woodlands site in 2013. Dominant growth form, cover, height and species (up to 5 species in order of dominance) for up to 3 sub-stratum per traditional strata (Ground, Mid and Upper).

This data contains a once-off general structural description according to the National Vegetation Information System (NVIS) level 5 for the core 1 hectare plot in the Tumbarumba Wet Eucalypt site in 2015. Dominant growth form, cover, height and species (up to 5 species in order of dominance) for up to 3 sub-stratum per traditional strata (Ground, Mid and Upper).

This data contains a once-off general structural description according to the National Vegetation Information System (NVIS) level 5 for the core 1 hectare plot in the Mitchell Grass Rangeland site in 2018. Dominant growth form, cover, height and species (up to 5 species in order of dominance) for up to 3 sub-stratum per traditional strata (Ground, Mid and Upper).