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".

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 dataset consists of counts for multiple plant species obtained from the Ethabuka Station and Carlo Reserve in the Simpson Desert, Australia, from 2004-2013 by the Desert Ecology Research Group (DERG) in conjunction with LTERN. It also consists rainfall data obtained from 2004-2012. These datasets were used to perform a Dynamic Factor Analyses for the manuscript, "Life form explains consistent temporal trends across species: the application of dynamic factor analysis". For more information see: DERG; https://www.desertecology.edu.au.

Seedling surveys were conducted at the Tumbarumba Wet Eucalypt site in 2015. The identity and height of all seedlings were recorded along six 20 m x 1 m transects in the core 1 ha plot.

This dataset consists of counts of plants and seeds for the ephemeral desert herb <i>Trachymene glaucifolia</i> obtained from the Ethabuka and Carlo Reserves in the Simpson Desert, Australia, from 2004-2011 by the Desert Ecology Research Group (DERG) in conjunction with LTERN. It also consists of monthly rainfall data obtained from 1995-2012. Collectively, the dataset was used to construct Multivariate Auto-regressive State-Space (MARSS) models for the manuscript "Reducing common sources of uncertainty in time series population data using MARSS models". For more information see: DERG : https://www.desertecology.edu.au

Seedling surveys were conducted at the Robson Creek Rainforest site between 2010 - 2011. The identity and height of all seedlings were recorded along 98 x 20 m transects.

This dataset contains a series of spatial outputs describing probabilistic species predictive occupancy (Species Occupancy Models, or SOM) & habitat suitability (Maximum Entropy, or Maxent) surfaces, the underlying data used to calculate these models & model projections predicting the impact of climate change on flora Maxent surfaces. <br> Model outputs are combination outputs dependent on known species occurrence in the landscape, the species relationship with environmental variables (covariates) such as temperature, rainfall and topography; and its predicted occurrence based on covariate analysis. Maxent models do not predict actual occupancy, but rather habitat suitability, while SOMs predict actual occupancy. confounding factors such as inter-species competition, geographical barriers and disturbance events play a significant role in species occurrence, and are not considered in Maxent or SOM. Flora Maxent climate change projections used NSW and Australian Regional Climate Modelling (NARCliM) variables to predict habitat suitability for a baseline year 2000 and projections for 2030 and 2070. <br> Covariates, Fauna & Flora survey records used to create the models are included. <br> More detailed information regarding each model, its processes and outputs are included in the dataset. <br> A web mapping application on the NSW Spatial Collaboration Portal depicts Maxent & SOM of a selected group of vulnerable Flora & Fauna from this dataset. Access the webapp through the link below: <br> https://portal.spatial.nsw.gov.au/portal/home/item.html?id=78e6ae3d34aa45d2b8118fd0308d6459

Seedling surveys were conducted at the Cumberland Plain site in 2014. The identity and height of all seedlings were recorded along six 20 m x 1 m transects in the core 1 ha plot.

This dataset contains radio-tracking information on the long-haired rats (<i>Rattus villosissimus</i>). Data was collected between October 2011 and December 2012. It contains the data from enclosure in which a radio-collared rat was released and tracked (Enclosure = 1 or 2), the treatment (Cats = yes or no), the exact date (Date) for when a rat was released with a collar (collared_released), the last time it was recorded (last-time-rec), the time period in months over which the collar frequency was detected (time-collar-detected), the fate of the animal (Fate = unknown, dead or alive), the last location change detected (last_loc_change), based on the latter, the estimated time a rat was assumed alive (estimated_time_alive), the last time a signal was detected from the collar (last_signal detected), the date of the last time an animal was trapped (last_trapped), whether dead remains were found (dead_remains_found = na, yes, or no) and whether the collar was found (collar_found = na, yes, or no).