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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.
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 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>
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.
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.
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".
The record contains information on the Digital Elevation Model of the Alice Mulga site. Information an Airborne full waveform lidar and hyperspectral data in the VNIR bands was collected using the research aircraft of Flinders University – Airborne Research Australia (ARA).
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.
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.