The project brought together a group of Australian researchers and managers with a broad range of expertise to identify current and emerging economies (‘drivers’) affecting regional agricultural landscapes and to suggest beneficial transformational changes for successful adaptation. A key challenge in these landscapes is altering how we use the land for ongoing, viable production while increasing native biodiversity. The group:<ul style="list-style-type: disc;"> <li>identified the major historical influences on Australian land use and the current social and economic drivers that are likely to increase in the future</li> <li>assessed the condition of five agro-climatic regions (adapted from Williams et al., 2002 and Hobbs and McIntyre, 2005) using a Delphi method. A small (4-person) expert panel scored the impact of historical and future scenarios on ten sustainability indicators (biodiversity, water, soil, social capital, built capital, food/fibre, carbon, energy, minerals and cultural). Five regions were chosen: Southern Mediterranean, Northern tropical, Central arid, North-east subtropical, and South-east temperate. This was an iterative process whereby scores were revisited until internal consistency between regions, scenarios, and indicators was achieved</li> <li>made projections of regional condition under the four global Representative Concentration Pathways (RCPs) based on van Vuuren et al. (2011)</li> <li>developed recommendations about land use and management, institutional and policy arrangements and social processes that will assist adaptation towards a values-rich vision of Australia in 2100.</li></ul>

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.

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 dataset contains global dryland literature abstracts from over the last 75 years (8218 articles) to identify areas in arid ecology that are well studied and topics that are emerging.

This record contains images that illustrates the topography of the area surrounding the tower and core monitoring plots of the Great Western Woodlands Supersite. It corresponds to an area approximately 43 x 43 km surrounding the tower (shown as a red star). The ZIP file contains the 3 second DEM from the USGS for the area in BIL format with associated header and other files.

Evaluation of the morphological variation within the genus <em>Polyosma</em> (<em>Escalloniaceae</em>) of Australia, New Caledonia and Papuasia based on herbarium specimens to clarify the taxonomy of the recognized species in this genus. These data also identified several previously unpublished species that are new to science.

The record contains images of elevation profile of the Tumbarumba Wet Eucalypt Site obtained from Airborne full waveform lidar and hyperspectral data in the VNIR bands using the a research aircraft of Flinders University – Airborne Research Australia (ARA).