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Global declines in ecosystem extent and condition mean there is an increasing demand for recovery and conservation plans. Conservation plans for ecological communities require a management framework with measurable, time‐bound objectives. Efficient and structured processes that facilitate timely and comparable conservation plans are essential, especially where resources are constrained. We describe a process to streamline the development of conservation plans by combining functionally similar community sub‐types into a multi‐community State and Transition Model that can be used to guide conservation planning. We demonstrate this approach in a case study using eucalypt dominated woodlands of southern Australia—an ecosystem which occupies a vast geographical range across temperate Australia and includes many distinct vegetation communities, a growing number of which are endangered or threatened. Australian woodland ecologists (grouped according to their knowledge of three broad woodland sub‐types) were asked to develop causal chains to describe all factors associated with transitions among woodland condition states, and estimate the likelihoods associated with each transition at two time‐scales. The resultant State and Transition Model includes a set of eight general condition states that are common to eucalypt dominated woodlands, and 364 unique causal chains describing the drivers of all plausible transitions. We include an example of how the same information can be presented as a series of decision trees aimed at supporting on‐ground management decisions. The case study demonstrates that it is possible to construct a detailed State and Transition Model that synthesizes knowledge across multiple similar vegetation communities. To date, State and Transition Models focused on single communities or a smaller spatial scale, and this is the first attempt to construct a nationally relevant multi‐community State and Transition Model via a structured and participatory process. Synthesis and applications. This approach can be applied at multiple spatial scales to improve and streamline the development of robust conservation plans to improve how we plan for, implement and measure global biodiversity outcomes. [ABSTRACT FROM AUTHOR]

Ex situ ('off‐site') management refers to keeping species in artificial conditions away from their natural habitat and includes captive breeding facilities, botanical gardens and seed banks. There is scope for ex situ programmes to be more commonly used for supplementing or establishing wild populations. However, undertaking ex situ management comes with risks, costs and uncertainties, which must be assessed in the context of available in situ ('on‐site') management options. The PACES (Planning and Assessment for Conservation through Ex situ management) tool tailors the principles of structured decision‐making to the specific problem of assessing and comparing ex situ and in situ management options. We applied the PACES tool to the mahogany glider (Petaurus gracilis), a threatened arboreal marsupial endemic to north Queensland, Australia. Through an expert elicitation process, we predicted the likely benefits of an ex situ and two in situ management options, as compared to a baseline 'do‐nothing' scenario. The 'in situ plus' alternative (where extra resources are dedicated to in situ management) was predicted to result in the largest population increase according to the participants' best estimates. However, this benefit came at a much larger cost than the ex situ alternative, and without the benefit of an ex situ insurance population. The PACES tool assessment allowed the Mahogany Glider Recovery Team to document and plan the financial costs, risks and benefits of potential future management options for the mahogany glider, laying a transparent basis for future assessment and decision‐making. [ABSTRACT FROM AUTHOR]

Objective: To evaluate the Canadian Syncope Risk Score (CSRS) in syncope patients presenting to the ED from an economic perspective, using very‐low and low‐risk patients (CSRS −3 to 0) as a threshold for avoiding hospital admissions. Methods: A decision‐analytic model, specifically a decision‐tree, was developed to evaluate application of the CSRS. A hypothetical cohort of 1000 patients was modelled based on characteristics and outcome of patients enrolled in a clinical validation study performed alongside this evaluation. Several analytic based approaches were used to handle model outputs and uncertainties. Results: For a cohort of 1000 patients, applying the CSRS was associated with 169 less inpatient admissions from the ED, when compared to usual care. There was also a cost‐saving of $8255 per admitted patient, when the CSRS was applied, compared to usual care. Adopting the CSRS was the optimal approach in all scenario analyses and was robust to changes in model parameters. More than three‐quarters (78.6%) of all model simulations showed that applying the CSRS is a cost‐saving approach to managing syncope. There was high confidence in all results, with the approach using the CSRS reducing the costs and number of syncope‐related hospital admissions. Conclusions: Compared to usual care, applying the CSRS appeared as a cost‐effective strategy. This new evidence will help decision‐makers choose cost‐effective approaches for the management of patients presenting to the ED with syncope, as they search for efficient ways to maximise health gain from a finite budget. [ABSTRACT FROM AUTHOR]

Introduction: The objective was to compare the diagnostic accuracy of the decision tree analysis prediction model P1, which incorporates the human chorionic gonadotropin (hCG) ratio (hCG 48 hours/hCG 0 hour), and risk prediction model M4 in the management of women with pregnancy of unknown location (PUL).Material and Methods: A retrospective diagnostic accuracy study was performed on PUL data collected between August 2011 and September 2018. Women with a PUL were prospectively managed according to the P1 prediction model, which utilizes the hCG ratio and, if necessary, a day (D) 7 hCG. We compared the performance of P1 with the M4 model, a logistic regression mathematical model using initial hCG and hCG ratio, to classify PULs as low risk (failed PUL [failed] or intrauterine pregnancy) or high risk (ectopic pregnancy or persistent PUL). The reference standard was defined as the final PUL outcome.Results: Transvaginal ultrasound was done in 3847 consecutive women for early pregnancy complications, 437 (11.3%) of whom were classified as PUL. Final analysis comprised 413 cases with complete data. Final PUL clinical outcomes were: 247 (59.8%) failed PUL, 94 (22.7%) intrauterine pregnancy, 49 (11.8%) ectopic pregnancy and 23 (5.5%) persistent PUL. The sensitivity of P1 and M4 in predicting high-risk PUL were 81.9% (95% confidence interval [CI] 71.1-90.0) and 80.6% (95% CI 69.5-88.9), respectively. The specificities were 74.5% (95% CI 69.5-79.1) and 75.6% (95% CI 70.7-80.1), respectively.Conclusions: P1 and M4 performed similarly with respect to diagnostic accuracy in predicting PUL outcome. P1 needs to be externally validated. [ABSTRACT FROM AUTHOR]

This study evaluates the ability of machine learning algorithms to classify tropical depressions (TDs) and tropical storms (TSs) in the western region of the southwest Pacific Ocean (SWPO). Decision rules are generated to predict the environment required for a depression to fully develop into a mature storm, and the most influential predictors in the classification decision are ranked. TD and TS are discriminated based on a maximum sustained wind speed threshold (≥17 ms−1). Various aerosol, thermodynamic, and dynamic parameters are extracted closest to the initiation point of each non‐developing and developing sample. The covariates associated with each labelled sample are used to train a decision tree and random forest model. Results using a testing dataset suggest the random forest approach more accurately distinguishes between non‐developing and developing samples. The classification accuracy of the decision tree and random forest are 72% and 91%, respectively. Random forest outperformed the decision tree by providing higher accuracy in test data. The most important variables for binary classification are sea salt aerosol optical depth (AOD), 1,000 mb relative humidity, and sea surface temperature. AOD is a quantitative estimate of the aerosols presents in the air through the extinction of a ray of light as it passes through the atmosphere. Mean composite maps constructed in an unsupervised manner have been created for the most important variables identified by the random forest classifier during TD and TS events to highlight the difference in geophysical and aerosol variables' climatology during the two different classifications. This work will advance the risk management strategies for northeastern Australia and other SWPO basin islands to control their tropical cyclone related losses through prioritizing forecasting variables that are the strongest predictors of the strengthening of tropical depressions into tropical cyclones. [ABSTRACT FROM AUTHOR]

Objective: Certification by treating physicians of fitness to drive in people with epilepsy creates a conflict of interest that may result in unsafe decisions, damage the doctor‐patient relationship, expose the physician to legal liability and prevent optimal treatment. Ideally, the treating physician should provide objective clinical information to the driver licensing authority (DLA), which then determines fitness or otherwise. However, DLAs in Australia do not employ medical staff and the national standards are complex. Fitness is determined by the treating physician, according to published national standards. The purpose of this study was to determine the feasibility of using a decision tree to determine fitness, according to the Australian standards. Methods: A decision tree was constructed to use clinical data to determine whether a patient met the national standard to drive a private motorcar, failed to meet it or required further assessment. A form was designed to collect the necessary clinical data from the treating physician. A computerized version of the decision tree was then used in a pilot in two Australian states in parallel with the existing certification system. Four hundred thirty‐nine drivers with declared epilepsy and their treating physicians were invited to participate when their annual driver licence review was due. Results: Two hundred fifty‐three (58%) forms were returned. All patients were considered fit to drive by their physician. Seventy‐six percent had not had a seizure for over two years. In 88.1%, there was agreement between the decision tree and treating physician, with 3.6% identified by the decision tree as requiring review. Although considered fit by their physician, 6.3% did not meet the national standard to drive. Significance: The decision tree model is a practical alternative to fitness certification by treating physicians. This Australian pilot can serve as a model for applying objective standards to driving assessments in other jurisdictions, using local driving standards. It has the potential to improve road safety by avoiding the negative effects of certification by treating physicians and can cope with complex standards. It is now in use in two states of Australia. [ABSTRACT FROM AUTHOR]

Lightning-caused wildfires are a significant contributor to burned areas, with lightning ignitions remaining one of the most unpredictable aspects of the fire environment. There is a clear connection between fuel moisture and the probability of ignition; however, the mechanisms are poorly understood and predictive methods are underdeveloped. Establishing a lightning-ignition relationship would be useful in developing a model that would complement early warning systems designed for fire control and prevention. A machine learning (ML) approach was used to define a predictive model for wildfire ignition based on lightning forecasts and environmental conditions. Three different binary classifiers were adopted: a decision tree, an AdaBoost and a Random Forest, showing promising results, with both ensemble methods (Random Forest and AdaBoost) exhibiting an out-ofsample accuracy of 78%. Data provided by a Western Australia wildfire database allowed a comprehensive verification on over 145 lightning-ignited wildfires in regions of Australia during 2016. This highlighted that in a minimum of 71% of the cases the ML models correctly predicted the occurrence of an ignition when a fire was actually initiated. The super-learner developed is planned to be used in an operational context to the enhance information connected to fire management. [ABSTRACT FROM AUTHOR]

Aim: To estimate the change in average cost and length of stay (LOS) for the neonatal birth admission resulting from use of the neonatal early‐onset sepsis (EOS) calculator compared to guideline‐based management, in an Australian perinatal health‐care setting. Methods: A decision‐analytic model (decision tree) was constructed to assess admission cost and LOS with EOS calculator use compared to guideline‐based management. Probabilities of clinical sepsis‐related outcomes were obtained via review of published literature. Costs and average LOS were obtained from Australia's Independent Hospital Pricing Authority. Results: EOS calculator use was associated with a reduction in costs of AUD$25806 and in average LOS of 25.4 days per 1000 babies born. Sensitivity analyses demonstrated greater net benefits could be expected for services where there is a higher baseline rate of antibiotic use. Conclusion: This model demonstrates a significant cost reduction for the neonatal birth admission, associated with use of the EOS calculator as compared to existing guidelines. The net benefit may be greater in Australia, where rates of empiric antibiotic use are reportedly high, compared to some European countries and the United States. Future research opportunities include prospective collection of economic data alongside the introduction of the EOS calculator. [ABSTRACT FROM AUTHOR]

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Rationale Little scrutiny has been applied to how 'cost per fall' values have been calculated and applied. This paper presents discourse discussing how the cost of fall statistic could potentially be misleading when applied to inpatient health or residential care settings and provides estimates of the cost of falls and cost of falls per person. Method Burden of disease was estimated using a decision tree approach. Data informing the decision tree were drawn from a retrospective audit of 545 falls in a residential care facility, a visual observation study of 46 residents from the same facility and a cohort study of 186 residents from nine different facilities in Australia. Acute care and transportation costs were extracted from the retrospective audit using incident reports and care note review. The distribution of falls per person and associations between falls, fractures and change in resident mobility were extracted from the cohort study. The association between resident mobility and the amount of time required to perform toileting, transfer and dressing activities was extracted from the visual observational study. Results The minimum 'cost per fall' was estimated to be $AUD 841 and the maximum was $AUD 1024. The 'cost of falls per person' estimate was $AUD 1887 (2008 base year). Conclusions This cost per fall estimate was substantially lower than three previous internationally derived estimates possibly as a consequence of how fall data were collected and modelled in these studies. Cost of falls per person may be a preferable statistic for future use. [ABSTRACT FROM AUTHOR]

Objective: To distinguish psychotic, melancholic and a residual non-melancholic class on the basis of clinical features alone. Previous studies at our Mood Disorders Unit (MDU) favour a hierarchical model, with the classes able to be distinguished by two specific clinical features, but any such intramural study risks rater bias and requires external replication., Method: This replication study involved 27 Australasian psychiatrist raters, thus extending the sample and raters beyond the MDU facility. They collected clinical feature data using a standardized assessment with precoded rating options. A psychotic depression (PD) class was derived by respecting DSM-IV decision rules while a cluster analysis distinguished melancholic (MEL) and non-melancholic classes., Results: The MELs were distinguished virtually entirely by the presence of significant psychomotor disturbance (PMD), as rated by the observationally based CORE measure, with over-representation on only three of an extensive set of 'endogeneity symptoms'., Conclusion: In comparison to PMD, endogeneity symptoms appear to be poor indicators of 'melancholic' type, confounding typology with severity. Results again support the hierarchical model.

The authors reflect on pharmacist medication review services. They suggest that pharmacists use clinical pharmacy services to minimize the risks associated with the use of medicines, to increase consumer safety and to optimize patient health outcomes. They argue that medication review services should be structured, funded and reviewed as a suite of interlinked programs.

Purpose: Pain management within emergency departments (ED) remains challenging. Given that unrelieved pain in children is linked to a number of negative physiological and psychological consequences, optimal management of children's pain is paramount. Many studies exploring children's pain have adopted quantitative methods or sought the perspectives of adults. Compared to adults, studies examining children's views on pain and pain management are limited. This study aimed to explore children's pain experiences, their perception of pain management and expectations of the role of the nurse. Design: This was a qualitative descriptive study using an inductive approach. Methods: Fifteen children, aged 4–8 years who presented to the ED of an Australian tertiary pediatric hospital in acute pain participated. Data were collected using draw, write, and tell (DWT) technique and analyzed using thematic analysis. Results: Three themes emerged (1) "Security," (2) "My pain" with subthemes: "The pain feelings" and "My sad/happy feelings," (3) "Comfort and relief" with subthemes: "Taking my mind off it," "Resting" and "Hospital things." When in pain children needed to feel secure. Parents and nurses were important in fostering a secure environment for children. Children were capable of describing their pain and identified nonpharmacological strategies to help their pain. Practice implications: Children as young as 4 years old can provide detailed accounts of their pain, which extends beyond physical dimensions to include visual, auditory, and sensory features. Nurses need to listen, be honest, and develop trust with children to be helpful. Nonpharmacological pain‐relieving strategies can be implemented by parents and nurses in collaboration with the child. Fostering a secure environment is essential. [ABSTRACT FROM AUTHOR]

Objective: To further develop and refine an Emergency Department (ED) in‐patient admission prediction model using machine learning techniques. Methods: This was a retrospective analysis of state‐wide ED data from New South Wales, Australia. Six classification algorithms (Bayesian networks, decision trees, logistic regression, naïve Bayes, neural networks and nearest neighbour) and five feature selection techniques (none, manual, correlation‐based, information gain and wrapper) were examined. Presenting problem was categorised using broad (n = 20) and specific (n = 100) representations. Models were evaluated based on Area Under the Curve (AUC) and accuracy. The results were compared with the Sydney Triage to Admission Risk Tool (START), which uses logistic regression and six manually selected features. Results: Sixty admission prediction models were trained and validated using data from 1 721 294 patients. Under the broad representation of presenting problem, the nearest neighbour algorithm with manual feature selection had the best AUC of 0.8206 (95% CI ±0.0006), while the decision tree with no feature selection had the best accuracy of 74.83% (95% CI ±0.065). Under the specific representation, almost all models improved; the nearest neighbour with information gain feature selection had the best AUC of 0.8267 (95% CI ±0.0006), while the decision tree with wrapper or no feature selection had the best accuracy of 75.24% (95% CI ±0.064). Eleven of the machine learning models had slightly better AUC than the START model. Conclusion: Machine learning methods demonstrate similar performance to logistic regression for ED disposition prediction models using basic triage information. This should be investigated further, especially for larger data sets with more complex clinical information. [ABSTRACT FROM AUTHOR]

1. The importance of hydrologic variability for shaping the biophysical attributes and functioning of riverine ecosystems is well recognised by ecologists and water resource managers. In addition to the ecological dependences of flow for aquatic organisms, human societies modify natural flow regimes to provide dependable ecological services, including water supply, hydropower generation, flood control, recreation and navigation. Management of scarce water resources needs to be based on sound science that supports the development of environmental flow standards at the regional scale. 2. Hydrological classification has long played an essential role in the ecological sciences for understanding geographic patterns of riverine flow variability and exploring its influence on biological communities, and more recently, has been identified as a critical process in environmental flow assessments. 3. We present the first continental-scale classification of hydrologic regimes for Australia based on 120 metrics describing ecologically relevant characteristics of the natural hydrologic regime derived from discharge data for 830 stream gauges. Metrics were calculated from continuous time series (15–30 years of record constrained within a 36-year period) of mean daily discharge data, and classification was undertaken using a fuzzy partitional method – Bayesian mixture modelling. 4. The analysis resulted in the most likely classification having 12 classes of distinctive flow-regime types differing in the seasonal pattern of discharge, degree of flow permanence (i.e. perennial versus varying degrees of intermittency), variations in flood magnitude and frequency and other aspects of flow predictability and variability. Geographic, climatic and some catchment topographic factors were generally strong discriminators of flow-regime classes. The geographical distribution of flow-regime classes showed varying degrees of spatial cohesion, with stream gauges from certain flow-regime classes often being non-contiguously distributed across the continent. These results support the view that spatial variation in hydrology is determined by interactions among climate, geology, topography and vegetation at multiple spatial and temporal scales. Decision trees were also developed to provide the ability to determine the natural flow-regime class membership of new stream gauges based on their key environmental and/or hydrological characteristics. 5. The need to recognise hydrologic variation at multiple spatial scales is an important first step to setting regional-scale environmental flow management strategies. We expect that the classification produced here can underpin the development of a greater understanding of flow-ecology relationships in Australia, and management efforts aimed at prescribing environmental flows for riverine restoration and conservation. [ABSTRACT FROM AUTHOR]

Introduction: Current Australian guidelines differ in their recommendations on optimal iron deficiency anaemia screening and management during pregnancy. A more active approach to screening and treatment of iron deficiency in pregnancy has been beneficial within a tertiary population. However, this approach has not been evaluated within a regional healthcare setting. Objective: To evaluate the clinical impact of standardised screening and management for iron deficiency in pregnancy within a regional Australian centre. Design: Single centre, retrospective cohort observational study that audited medical records pre and post implementation of standardised screening and management for antenatal iron deficiency. We compared rates of anaemia at birth, rates of peripartum blood transfusions and rates of peripartum iron infusions. Findings: There were 2773 participants with 1372 participants in the pre‐implementation group and 1401 in the post‐implementation group. Participant demographics were similar. Anaemia at admission for birth decreased from 35% to 30% (RR 0.87, 95% CI (0.75, 1.00), p 0.043) Fewer blood transfusions were required (16 (1.2%) pre‐implementation, 6 (0.4%) post‐implementation, RR 0.40, 95% CI(0.16, 0.99), p 0.048). Antenatal iron infusions increased from 12% to 18% of participants post implementation (RR 1.47, 95% CI(1.22, 1.76), p 〈0.001).We audited compliance with the guidelines and found improvements post implementation. Discussion: This is the first study to show clinically useful and statistically significant reduction in rates of anaemia and blood transfusions, post implementation of routine ferritin screening and management within a regional Australian population. Conclusion: The results of this study suggest there is benefit to implementation of standardised ferritin screening and management packages in Australian antenatal care. It also encourages RANZCOG to review current recommendations on screening for iron deficiency anaemia during pregnancy. [ABSTRACT FROM AUTHOR]

Recent forest fires potentially intensify flood hazards. However, forest fire amplification of floods is not well understood at a large scale due to the complex compound impacts of forest fires and climate variability, while available small‐scale cases may not represent regional changes. Here, we show that the 2019–2020 mega forest fires in southeast Australia, with unprecedented burned areas, significantly (p < 0.05) increased the peak discharges of floods during the 2 years after the fires. Spatially, fire impacts on these floods are much stronger in regions with winter‐dominated and uniform rainfall but insignificant in regions with summer‐dominated rainfall. The regional divergence reveals that burned areas can aggravate floods by exacerbating infiltration‐excess runoff processes but may not exert significant effects where saturation‐excess processes dominate. People may be increasingly exposed to such flood hazards, especially in regions where forest fires have become more frequent under climate change. Plain Language Summary: Wildfires and floods are both natural hazards that cause damage to lives and properties. Understanding how these two are linked is critical not only for catchment hydrological processes but also for hazard control and water resources management. However, it is unclear whether mega forest fires can influence the subsequent floods on a large scale. This study demonstrates that the recent mega forest fires in southeast Australia have significantly amplified floods for most fire‐impacted catchments. Apart from raising awareness of global fire activities, this study indicates that people living in the fire‐prone area may be also susceptible to more flood hazards after fires in the future. Key Points: The unprecedented 2019–2020 forest fires intensified subsequent river flood magnitudes in southeast AustraliaForest fire amplification of floods is divergent in different regionsRegional divergence in fire impacts is attributed to different burned areas and dominant flood generating mechanisms [ABSTRACT FROM AUTHOR]

Energy forecasting plays a vital role in mitigating challenges in data rich smart grid (SG) systems involving various applications such as demand‐side management, load shedding, and optimum dispatch. Managing efficient forecasting while ensuring the least possible prediction error is one of the main challenges posed in the grid today, considering the uncertainty in SG data. This paper presents a comprehensive and application‐oriented review of state‐of‐the‐art forecasting methods for SG systems along with recent developments in probabilistic deep learning (PDL). Traditional point forecasting methods including statistical, machine learning (ML), and deep learning (DL) are extensively investigated in terms of their applicability to energy forecasting. In addition, the significance of hybrid and data pre‐processing techniques to support forecasting performance is also studied. A comparative case study using the Victorian electricity consumption in Australia and American electric power (AEP) datasets is conducted to analyze the performance of deterministic and probabilistic forecasting methods. The analysis demonstrates higher efficacy of DL methods with appropriate hyper‐parameter tuning when sample sizes are larger and involve nonlinear patterns. Furthermore, PDL methods are found to achieve at least 60% lower prediction errors in comparison to other benchmark DL methods. However, the execution time increases significantly for PDL methods due to large sample space and a tradeoff between computational performance and forecasting accuracy needs to be maintained. [ABSTRACT FROM AUTHOR]

Aim: Here we investigate the strength of the relationships between meteorological factors and calling behaviour of 100 Australian frog species using continent‐wide citizen science data. First, we use this dataset to quantify the meteorological factors that best predict frog calling. Second, we investigate the strength of interactions among predictor variables. Third, we assess whether frog species cluster into distinct groups based on shared drivers of calling. Location: Australia. Method: To assess the relationship between calling and meteorological traits, we used spatio‐temporal subsampling (daily data fitted to 10 km2 grid cells) of call and meteorological data as inputs to a boosted regression tree. We scaled the model outputs, which created a descriptive ranking of predictor importance. For strongly day‐driven species, we conducted further analyses to examine the influences of meteorological factors within the breeding season. Results: We found a strong seasonal signal, with day of year the strongest relationship to calling in 67 out of our 100 species, moderate relationships between temperature and calling, and weak relationships between rainfall and calling. Despite the common narratives, we found that frogs did not group into distinct categories based upon the influence of meteorological factors. For strongly day‐driven species, we found similar patterns within the breeding season. Main conclusions: We demonstrate the importance of day of year and temperature thresholds in predicting frog calling behaviour in Australia. Understanding how meteorological conditions influence phenological events, such as breeding, will be increasingly important considering the rapid changes in environmental conditions and stability throughout most of the world, and how important breeding is to species survival. [ABSTRACT FROM AUTHOR]

Aim: The goal of this study was to investigate the invasion history of the weed Sonchus oleraceus in Australia by comparing the population genetic structure of individuals at different locations in Australia, and in the most likely areas of origin in the native range. Location: Samples were collected in Europe and Morocco, North Africa (27 locations), and Australia (17 locations). Methods: We performed population genetic analyses using a large dataset comprising 2883 single nucleotide polymorphism markers from 547 plant samples and investigated the invasion history of S. oleraceus with Approximate Bayesian Computation and Random Forest classification algorithms. We compared single and multiple invasion scenarios considering admixture having occurred before and after introduction. Results: Our results revealed high levels of inbreeding within sampling locations in the two ranges. Analyses also showed that S. oleraceus was possibly introduced to Australia at least twice: a first introduction around 1000 years ago before British settlement and a more recent introduction (~65 years ago) from Europe and North Africa. We also found evidence of post‐introduction admixture and a potential reintroduction of S. oleraceus from Australia back to its native range. Main conclusions: We conclude that the invasion history of S. oleraceus into Australia is probably historic (i.e. prior to British settlements) and complex showing recent evidence of post‐introduction admixture. The complex invasion history of S. oleraceus in Australia poses challenges for the search of potential biological control agents. [ABSTRACT FROM AUTHOR]

Background: Exercise physiologists and physiotherapists can provide exercise interventions for cancer survivors; however, many do not access this support. Our primary aim explored referral habits to exercise professionals and attitudes of oncology professionals. Secondary aims explored fitness levels of oncology professionals and the relationship between fitness and exercise referral habits. Methods: Oncology professionals (n = 67) attending a national cancer conference in Australia participated in this cross‐sectional study (23% doctors, 11% nurses, 25% allied health, and 41% other). A subgroup (n = 49) completed a fitness assessment measuring aerobic fitness (3‐minute step test), muscle strength (handgrip dynamometry), and body composition (waist‐to‐hip ratio). Oncology professionals in a position to refer patients to exercise professionals were assessed on their history of patient referrals. Results: Sixty‐seven of 750 eligible conference delegates participated (9% recruitment rate), of which 73% completed fitness assessments. Participants displayed above average physical activity and fitness levels with 60% meeting exercise guidelines. The majority (92%) agreed that exercise is important among cancer survivors to attenuate treatment‐associated symptoms. Most understand the role of exercise physiologists (61%) and physiotherapists (64%) in cancer care. Seventy‐three percent reported that referral to exercise professionals is optimal to increase exercise participation. Most (82%) oncology professionals who are able to refer patients to exercise professionals have self‐reported doing so, while 91% recommended exercise themselves. Sixty‐two percent were aware of evidence‐based exercise guidelines. Fitness levels (p = 0.25), strength (p = 0.88), and physical activity (p = 0.33) did not impact referrals made to exercise professionals. Conclusion: There was high awareness exercise‐oncology benefits and evidence‐based guidelines existed among sampled participants. Oncology professionals understand the role of exercise professionals, reflected by high self‐reported referral rates. Implementing referral pathways to exercise professionals in cancer care may facilitate improved patient outcomes. [ABSTRACT FROM AUTHOR]

Identification and pairing of hydrologic events form the basis of various analyses, from identifying events for the calibration of hydrologic models, to calculation of event runoff coefficients for catchment characterization. Despite this, there is no unified approach for identifying hydrologic events. Here, using the R package, hydroEvents (https://CRAN.R-project.org/package=hydroEvents), we compare multiple methods of extracting and pairing hydrologic events focussing on the relationship between rainfall and runoff. We find the four common analytical approaches used to identify runoff events—based on either event threshold, local maxima/minima, or proportion of baseflow contribution, give similar results. However, when rainfall events are paired to runoff, the type of algorithm and the direction of pairing (either from rainfall to runoff, or runoff to rainfall) make a considerable difference to the final event pairs identified and resulting analyses. Here, we demonstrate the value of automated event extraction and pairing algorithms for large‐sample hydrology analysis by calculating event runoff coefficients across Australia. Our results show that climatology is a key driver of catchment rainfall‐runoff response with much of Australia dominated by excess rainfall runoff generation. However, our results also show that the variability due to pairing method can introduce a variability equal to that of the climatology due to biasing the runoff mechanism within the sample. With this analysis we demonstrate the importance of systematic and consistent approaches to hydrologic characterization when identifying and pairing hydrological events. [ABSTRACT FROM AUTHOR]

Understanding broad trends in the distribution and composition of wetlands is essential for making evidence‐based management decisions. Determining temporal change in the extent of inundation in wetlands using remote sensing remains challenging and requires on‐ground verification to determine accuracy and precision. Therefore, optimization and validation of remote sensing methods in threatened wetlands is a high priority for their conservation. Despite their ecological importance in the landscape, we have little knowledge of the variation in the spatial extent of inundation in upland lagoons, a threatened ecological community in New South Wales, Australia. Our project developed locally trained algorithms to predict the extent of water and emergent vegetation using imagery from the Landsat‐5, ‐7, and ‐8 satellites. The best model for upland lagoons used shortwave infrared reflectance (performing better than normalized difference spectral indices), with model accuracy against validation transects greater than 95%. We applied the model to images from 1988 to 2020 across 58 lagoons to generate a dataset that demonstrates the variable water regime and vegetation change in response to local rainfall over 32 years such as in the lagoons. Our results reduce threats to a dynamic threatened ecological community by filling an important knowledge gap and demonstrate a valuable method to understand historical and current changes in the hydrology of dynamic wetland systems more broadly. [ABSTRACT FROM AUTHOR]

Herbivore foraging decisions are closely related to plant nutritional quality. For arboreal folivores with specialized diets, such as the vulnerable greater glider (Petauroides volans), the abundance of suitable forage trees can influence habitat suitability and species occurrence. The ability to model and map foliar nitrogen would therefore enhance our understanding of folivore habitat use at finer scales. We tested whether high‐resolution multispectral imagery, collected by a lightweight and low‐cost commercial unoccupied aerial vehicle (UAV), could be used to predict total and digestible foliar nitrogen (N and digN) at the tree canopy level and forest stand‐scale from leaf‐scale chemistry measurements across a gradient of mixed‐species Eucalyptus forests in southeastern Australia. We surveyed temperate Eucalyptus forests across an elevational and topographic gradient from sea level to high elevation (50–1200 m a.s.l.) for forest structure, leaf chemistry, and greater glider occurrence. Using measures of multispectral leaf reflectance and spectral indices, we estimated N and digN and mapped N and favorable feeding habitat using machine learning algorithms. Our surveys covered 17 Eucalyptus species ranging in foliar N from 0.63% to 1.92% dry matter (DM) and digN from 0.45% to 1.73% DM. Both multispectral leaf reflectance and spectral indices were strong predictors for N and digN in model cross‐validation. At the tree level, 79% of variability between observed and predicted measures of nitrogen was explained. A spatial supervised classification model correctly identified 80% of canopy pixels associated with high N concentrations (≥1% DM). We developed a successful method for estimating foliar nitrogen of a range of temperate Eucalyptus species using UAV multispectral imagery at the tree canopy level and stand scale. The ability to spatially quantify feeding habitat using UAV imagery allows remote assessments of greater glider habitat at a scale relevant to support ground surveys, management, and conservation for the vulnerable greater glider across southeastern Australia. [ABSTRACT FROM AUTHOR]

To understand how ecological communities will respond to global change we need new tools and datasets on species across large spatial and temporal scales. Hyperspectral reflectance 'spectra' capture a promising set of traits that show potential to be scaled up in time and space via remote sensing. Thus far, spectra have been shown to distinguish the taxa and trait responses of a substantial number of species within a plethora of vascular plant communities, but not yet for biological soil crust communities (biocrusts).Here, we assess if spectra can be applied to identify biocrust species and their trait variation. We collected biocrust specimens across an aridity gradient spanning 650 km within drylands of Eastern Australia and acquired their spectra, over 12,700 spectral readings, with a high‐resolution radiospectrometer. A machine learning method (random forests) was used to assess how well the spectra of biocrust specimens could distinguish their species and broader structural and chemical traits.Spectra were able to differentiate a substantial number of biocrust species (35) with considerable accuracy (~78.5%). Furthermore, spectral features related to chemical traits were found to primarily drive species spectral differences.Synthesis. Our findings establish that biocrust species hold unique and detectable spectral responses, providing an important basis for remote sensing applications on biocrust species and their trait responses across dryland systems. [ABSTRACT FROM AUTHOR]

Understanding vegetation recovery after drought is critical for projecting vegetation dynamics in future climates. From 1997 to 2009, Australia experienced a long‐lasting drought known as the Millennium Drought (MD), which led to widespread reductions in vegetation productivity. However, vegetation recovery post‐drought and its determinants remain unclear. This study leverages remote sensing products from different sources—fraction of absorbed photosynthetically active radiation (FPAR), based on optical data, and canopy density, derived from microwave data—and random forest algorithms to assess drought recovery over Australian natural vegetation during a 20‐year period centered on the MD. Post‐drought recovery was prevalent across the continent, with 6 out of 10 drought events seeing full recovery within about 6 months. Canopy density was slower to recover than leaf area seen in FPAR. The probability of full recovery was most strongly controlled by drought return interval, post‐drought hydrological condition, and drought length. Full recovery was seldom observed when drought events occurred at intervals of 3 months or less, and moderately dry (standardized water balance anomaly [SWBA] within [−1, −0.76]) post‐drought conditions resulted in less complete recovery than wet (SWBA > 0.3) post‐drought conditions. Press droughts, which are long term but not extreme, delayed recovery more than pulse droughts (short term but extreme) and led to a higher frequency of persistent decline. Following press droughts, the frequency of persistent decline differed little among biome types but peaked in semi‐arid regions across aridity levels. Forests and savanna required the longest recovery times for press drought, while grasslands were the slowest to recover for pulse drought. This study provides quantitative thresholds that could be used to improve the modeling of ecosystem dynamics post‐drought. [ABSTRACT FROM AUTHOR]

Introduction: There is a concern in many countries about violence from late‐night alcohol sales and appropriate regulatory responses. However, economic losses arising from this activity rarely feature in public debate. Credible estimates are lacking because economic evaluations have not taken a 'societal perspective', costing health, policing and criminal justice outcomes. Our aims were to: (i) develop an analytic model capable of informing cost–benefit analysis of policy changes; and (ii) estimate costs of alcohol‐related assault (ARA) in a major city. Methods: We employed decision‐analytic cohort models of health and judicial consequences of ARA in Sydney, Australia. We constructed two 6‐branch decision‐analytic models of patient and offender pathways through the health and judicial systems. We produced overall estimates and analysed their sensitivity to key assumptions. Results: Combining results from a Health model comprising 40 pathways and 137 cost events, and a Justice model comprising 20 pathways and 48 cost events, yielded an overall cost estimate of $85 093 per ARA, of which 64% was health‐related, while 36% was justice‐related. Estimates were sensitive to values assigned for 'pain, suffering, morbidity and wellbeing', and to costs of incarceration. Health service‐related costs accounted for 1.3% of the total. Discussion and Conclusion: The costs of ARA are significant and dominated by incidents in which a victim does not necessarily receive acute medical care but suffers loss in quality‐of‐life, and where a perpetrator is processed through the criminal justice system. Being derived transparently, within a theoretical framework, the estimates are adaptable to inform cost–benefit analyses of policy options in Australia and other countries. [ABSTRACT FROM AUTHOR]

Genotype × environment interaction (GEI) is one of the key factors affecting breeding value estimation accuracy for agronomic traits in plant breeding. Measures of GEI include fitting prediction models with various kernels to capture the variance resulting from GEI, and characterizing trials into megaenvironment (ME) clusters within which breeding values can be estimated to remove the main GEI effects. However, many of the current approaches require observations of common genotypes across all trials, which is unavailable in most breeding programs. Our study introduces two methods that can be implemented on unbalanced data to categorize trials into clusters, where both need a correlation matrix between trials: one estimated via a factor analytic (FA) model and another estimated via weather variables. The methods were tested using empirical barley (Hordeum vulgare L.) yield data in a commercial breeding program from 102 trials over 5 yr spread across multiple locations in Australia. Leave‐one‐year‐out cross‐validation achieved comparable predictive accuracies using either trials or clusters as the observed variable in GEI FA models (max. 0.45), which was higher than the accuracy achieved using the non‐GEI model (0.37). In the random cross‐validations, accuracies achieved within clusters (0.42–0.64) were mostly comparable with those achieved in the full population (0.62). In the within‐cluster validations, higher predictive accuracies were achieved when the training population was from the same cluster (mean 0.22) than outside of the cluster (mean 0.16). Our proposed methods of characterizing multienvironment trials into clusters provides a novel way to define training populations by reducing the variance resulting from GEI and could be implemented in any plant breeding program. Core Ideas: Genomic prediction implemented in breeding practiceMinimize variance caused by genotype × environment interactionclustering on multienvironment trials [ABSTRACT FROM AUTHOR]

The "United Nations 2030 Agenda for Sustainable Development" adopted in 2015 covers 17 Sustainable Development Goals (SDGs). Fire is a common form of disaster and has a wide range of impacts on climate, biogeochemical cycle and human health, highly related to the SDGs. Satellite remote sensing technology provides an effective way for the dynamic monitoring of global burned area (BA). However, the existing global BA products are mainly at low and medium spatial resolution, which is difficult for detecting small fires, and also has large errors in calculating area of burned land. Based on Landsat 8 surface reflectance data and Google Earth Engine platform, a novel multi‐year (from the starting year of SDGs [2015] to the nearest year [2019]) 30 m resolution global BA products were generated. Based on these products, spatial distribution, influencing factors and change characteristics of BA were analyzed. The results show that from 2015 to 2019 the total area of BA in the world was 365.27, 368.56, 374.56, 345.55, and 363.09 × 106 ha. From 2015 to 2019, the total area of global BA was relatively stable, however there were significant differences among various continents and regions. During the study period, BA in the Amazon and Australia changed dramatically. The huge Amazon fire in 2019 caused a reduction of 4.67 × 106 ha of vegetated area in the Amazon basin, an increase in BA of 119.25% compared with 2018. In 2019, BA on the eastern and southeastern coasts of Australia increased abnormally. The possible reasons for these changes were comprehensively discussed and analyzed. Plain Language Summary: Fire is a common form of disaster, and has a wide range of impacts on climate, surface vegetation and human health. The "United Nations 2030 Agenda for Sustainable Development" has been implemented for 5 years, and fire is highly related to the Sustainable Development Goals, for which global information on fire effects is a key variable. In order to carry out high‐precision and fine monitoring of the spatial distribution and changes of fire within the past 5 years, in this study 30 m global burned area (BA) products were generated and analyzed from 2015 to 2019. During the study period, we found that the total area of global BA was relatively stable, however there were significant differences among various continents and regions. For example, the huge Amazon fire in 2019 caused a reduction of 4.67 × 106 ha of vegetated area in the Amazon basin, an increase in BA of 119.25% compared with 2018. In 2019, BA on the eastern and southeastern coasts of Australia increased abnormally. The possible reasons for these changes were comprehensively discussed and analyzed. Key Points: Novel multi‐year (from 2015 to 2019) 30 m resolution global burned area products were generated and acted as an Sustainable Development Goals indicatorSpatial distribution, influencing factors and change characteristics of burned areas were discussed from different perspectivesBurned areas in the Amazon and Australia changed dramatically. The possible reasons for these changes were comprehensively discussed [ABSTRACT FROM AUTHOR]

Aims: To identify potential hull fouling marine invasive species that could survive in East Antarctica presently and in the future. Location: Australia's Antarctic continental stations: Davis, Mawson and Casey, East Antarctica; and subantarctic islands: Macquarie Island and Heard and McDonald Islands. Methods: Our study uses a novel machine‐learning algorithm to predict which currently known hull fouling MIS could survive in shallow benthic ecosystems adjacent to Australian Antarctic research stations and subantarctic islands, where ship traffic is present. We used gradient boosted machine learning (XGBoost) with four important environmental variables (sea surface temperature, salinity, nitrate and pH) to develop models of suitable environments for each potentially invasive species. We then used these models to determine if any of Australia's three Antarctic research stations and two subantarctic islands could be environmentally suitable for MIS now and under two future climate scenarios. Results: Most of the species were predicted to be unable to survive at any location between now and the end of this century; however, four species were identified as potential current threats and five as threats under future climate change. Asterias amurensis was identified as a potential threat to all locations. Main conclusions: This study suggests that the risks are very low, but plausible, that known hull fouling species could survive in the shallow benthic habitats near Australia's East Antarctica locations and suggest a precautionary approach is needed by way of surveillance and monitoring in this region, particularly if propagule pressure increases. While some species could survive as adults in the region, their ability to reach these locations and undergo successful reproduction is considered unlikely based on current knowledge. [ABSTRACT FROM AUTHOR]

Automated information extraction might be able to assist with the collection of stroke key performance indicators (KPI). The feasibility of using natural language processing for classification‐based KPI and datetime field extraction was assessed. Using free‐text discharge summaries, random forest models achieved high levels of performance in classification tasks (area under the receiver operator curve 0.95–1.00). The datetime field extraction method was successful in 29 of 43 (67.4%) cases. Further studies are indicated. [ABSTRACT FROM AUTHOR]

We employ 47 different algorithms to forecast Australian log real house prices and growth rates, and compare their ability to produce accurate out‐of‐sample predictions. The algorithms, which are specified in both single‐ and multi‐equation frameworks, consist of traditional time series models, machine learning (ML) procedures, and deep learning neural networks. A method is adopted to compute iterated multistep forecasts from nonlinear ML specifications. While the rankings of forecast accuracy depend on the length of the forecast horizon, as well as on the choice of the dependent variable (log price or growth rate), a few generalizations can be made. For one‐ and two‐quarter‐ahead forecasts we find a large number of algorithms that outperform the random walk with drift benchmark. We also report several such outperformances at longer horizons of four and eight quarters, although these are not statistically significant at any conventional level. Six of the eight top forecasts (4 horizons × 2 dependent variables) are generated by the same algorithm, namely a linear support vector regressor (SVR). The other two highest ranked forecasts are produced as simple mean forecast combinations. Linear autoregressive moving average and vector autoregression models produce accurate olne‐quarter‐ahead predictions, while forecasts generated by deep learning nets rank well across medium and long forecast horizons. [ABSTRACT FROM AUTHOR]

Summary: Australia is an excellent setting to explore relationships between climate change and diversification dynamics. Aridification since the Eocene has resulted in spectacular radiations within one or more Australian biomes. Acacia is the largest plant genus on the Australian continent, with around 1000 species, and is present in all biomes. We investigated the macroevolutionary dynamics of Acacia within climate space.We analysed phylogenetic and climatic data for 503 Acacia species to estimate a time‐calibrated phylogeny and central climatic tendencies for BioClim layers from 132 000 herbarium specimens. Diversification rate heterogeneity and rates of climate space exploration were tested.We inferred two diversification rate increases, both associated with significantly higher rates of climate space exploration. Observed spikes in climate disparity within the Pleistocene correspond with onset of Pleistocene glacial–interglacial cycling.Positive time dependency in environmental disparity applies in the basal grade of Acacia, though climate space exploration rates were lower. Incongruence between rates of climate space exploration and disparity suggests different Acacia lineages have experienced different macroevolutionary processes. The second diversification rate increase is associated with a south‐east Australian mesic lineage, suggesting adaptations to progressively aridifying environments and ability to transition into mesic environments contributed to Acacia's dominance across Australia. [ABSTRACT FROM AUTHOR]

Critical care admission may be necessary for surgical patients requiring organ support or invasive monitoring in the peri-operative period. Unplanned critical care admission poses a potential risk to patients and pressure on services. Existing guidelines base admission criteria on predicted risk of 30-day mortality; however, this may not provide the best predictor of which patients would benefit from this service, and how unplanned admission might be avoided. A systematic review of MEDLINE, Embase, CINAHL, Web of Science, the Cochrane database and the grey literature identified 44 studies assessing risk factors for unplanned critical care admission in adult populations undergoing non-cardiac, non-thoracic and non-neurological surgery. Comparative, quantitative analysis of the admission criteria was not feasible due to heterogeneity in study design. Age, anaemia, ASA physical status, body mass index, comorbidity burden, emergency surgery, high-risk surgery, male sex, obstructive sleep apnoea, increased blood loss and operative duration were all independent risk factors for unplanned critical care admission. Age, body mass index, comorbidity extent and emergency surgery were the most common independent risk factors identified in the USA, UK, Asia and Australia. These risk factors could be used in the development of a risk tool or decision tree for determining which patients might benefit from planned critical care admission. Future work should involve testing the sensitivity and specificity of these measures, either alone or in combination, to guide planned critical care admission, reduce patient deterioration and unplanned admissions. [ABSTRACT FROM AUTHOR]

Ecological specialization enables the partitioning of resources and thus can facilitate the coexistence of species and promote higher species richness. Specialization and niche partitioning are expected to exert a decisive influence on local spatial scales, while species richness at regional scales should be shaped mostly by historical factors and abiotic conditions. Moreover, specialization is expected to be particularly important in communities that are exceptionally species rich for their environmental conditions. Concurrently, niche overlap in these communities should be minimized to enable species coexistence.We tested these hypotheses by studying specialization–richness relationship and niche overlap in assemblages of 298 species of songbirds (Passeriformes) across Australia. We used local (2–6 ha) to regional (bioregions) spatial scales and detailed data on habitat, diet and foraging behaviour (method, substrate and stratum).We expected the richness–specialization relationship to be particularly strong (a) on local spatial scales and (b) in communities exceptionally species rich for given environmental conditions (approximated by moisture and vegetation complexity). We also expected (c) low niche overlap in assemblages with specialized species.Only the third prediction was partly supported. First, while the specialization and species richness were often positively related, the strength and the direction of the relationship changed between traits and across spatial scales. The strength of the specialization–richness relationship was consistently positive only in foraging stratum, and it increased towards smaller spatial scales only in case of habitat and diet. Simultaneously, species in local communities demonstrated high overlap in habitat and diet. Second, we did not find particularly strong specialization–richness relationships in exceptionally species‐rich communities. Third, we found the expected negative relationship between specialization and overlap in foraging stratum and substrate (in local communities), suggesting that species partition ecological space locally in terms of where they find food.Our expectations were only weakly supported. Specialization on foraging stratum was probably important in facilitating species coexistence. Conversely, although species were often specialized on habitat and diet, high overlap in these traits did not preclude their local coexistence. Overall, specialization and overlap in foraging traits were more important for species coexistence than habitat or diet. [ABSTRACT FROM AUTHOR]

In the face of increasing cumulative effects from human and natural disturbances, sustaining coral reefs will require a deeper understanding of the drivers of coral resilience in space and time. Here we develop a high‐resolution, spatially explicit model of coral dynamics on Australia's Great Barrier Reef (GBR). Our model accounts for biological, ecological and environmental processes, as well as spatial variation in water quality and the cumulative effects of coral diseases, bleaching, outbreaks of crown‐of‐thorns starfish (Acanthaster cf. solaris), and tropical cyclones. Our projections reconstruct coral cover trajectories between 1996 and 2017 over a total reef area of 14,780 km2, predicting a mean annual coral loss of −0.67%/year mostly due to the impact of cyclones, followed by starfish outbreaks and coral bleaching. Coral growth rate was the highest for outer shelf coral communities characterized by digitate and tabulate Acropora spp. and exposed to low seasonal variations in salinity and sea surface temperature, and the lowest for inner‐shelf communities exposed to reduced water quality. We show that coral resilience (defined as the net effect of resistance and recovery following disturbance) was negatively related to the frequency of river plume conditions, and to reef accessibility to a lesser extent. Surprisingly, reef resilience was substantially lower within no‐take marine protected areas, however this difference was mostly driven by the effect of water quality. Our model provides a new validated, spatially explicit platform for identifying the reefs that face the greatest risk of biodiversity loss, and those that have the highest chances to persist under increasing disturbance regimes. [ABSTRACT FROM AUTHOR]

Background: In the 2009 National Health and Medical Research Council (NHMRC) Australian Guidelines to Reduce Health Risks from Drinking, a guideline specific to lactating women was included. For the first time the effect of alcohol at this important developmental stage was nationally and internationally acknowledged. Aims: To determine the degree to which maternal health practitioners adopted an Australian national policy guideline (Guideline 4B) on alcohol and breastfeeding into their everyday health practice and the factors associated with adoption. Materials and Methods: A convenience sample of participants completed an online survey based on the Nursing Practice Questionnaire (NPQ) to elicit measures of knowledge, awareness and practice implementation. Results: In total, 240 maternal health practitioners completed the online survey. All statistical analyses clearly indicated a higher adoption of Guideline 4B by the child health nurses, midwives and general practitioners (GPs) than was the case for paediatricians and obstetricians. A mean adoption score (range 0–4) indicative of 'some' practice implementation of 3.13, 3.04 and 2.73 was obtained by child health nurses, midwives and GPs, respectively. Obstetricians and paediatricians scored a mean of 2.0, indicating they remain to be 'persuaded about the practice' of incorporating discussions around alcohol and breastfeeding into their practice. Conclusion: Despite the existence of a national alcohol guideline for breastfeeding women, maternal health practitioners are not incorporating this advice into their everyday practice with lactating women. Opportunities exist for all maternal practitioners along the reproductive continuum to protect long‐term breastfeeding duration using an evidence‐based harm minimisation approach to alcohol consumption. [ABSTRACT FROM AUTHOR]

Global climate models (GCMs) are useful tools for assessing climate change impacts on temperature and rainfall. Although climate data from various GCMs have been increasingly used in climate change impact studies, GCMs configurations and module characteristics vary from one to another. Therefore, it is crucial to assess different GCMs to confirm the extent to which they can reproduce the observed temperature and rainfall. Rather than assessing the interdependence of each GCM, the purpose of this study is to compare the capacity of four different multi‐model ensemble (MME) methods (random forest [RF], support vector machine [SVM], Bayesian model averaging [BMA] and the arithmetic ensemble mean [EM]) in reproducing observed monthly rainfall and temperature. Of these four methods, the RF and SVM demonstrated a significant improvement over EM and BMA in terms of performance criteria. The relative importance of each GCM based on the RF ensemble in reproducing rainfall and temperature could also be ranked. We compared the GCMs importance and Taylor skill score and found that their correlation was 0.95 for temperature and 0.54 for rainfall. Our results also demonstrated that the number of GCMs ensemble simulations could be reduced from 33 to 25 in RF model while maintaining predictive error less than 2%. Having such a representative subset of simulations could reduce computational costs for climate impact modelling and maintain the quality of ensemble at the same time. We conclude that machine learning MME could be efficient and useful with improved accuracy in reproducing historical climate variables. The relative change of RMSE for different size of the CMIP5 subset compared with full number of GCMs using the random forest (RF) ensemble simulations. Our results demonstrated that the number of GCMs ensemble simulations could be reduced from 33 to 25 in RF model while maintaining predictive error less than 2%. Having such a representative subset of simulations could reduce computational costs for climate impact modelling and maintain the quality of ensemble at the same time. [ABSTRACT FROM AUTHOR]

Summary: Mature forests have structural habitat features that can take hundreds of years to develop, and large reserves alone are unlikely to ensure conservation of the species that rely on these features. This paper outlines a proposed new approach to managing mature forest features, the ‘mature habitat management approach’, in areas outside of reserves. The objective was to maintain a network of current and future mature forest habitat distributed across the landscape. The approach is designed to complement the existing reserve network and management actions and is tenure‐blind. Spatial information on the availability of mature forest habitat at the local (1‐km radius) and landscape (5‐km radius) scales is used for decisions on retention within a 1‐km radius of a harvest area, to reach the minimum target of 20% and 30% retention of mature forest at the local and landscape spatial scales, respectively. We believe this approach could contribute to meeting the conservation needs of many species that require mature forest features for refuge and breeding in Tasmania and elsewhere. [ABSTRACT FROM AUTHOR]

To investigate the comparative abilities of six different bioclimatic models in an independent area, utilizing the distribution of eight different species available at a global scale and in Australia. Global scale and Australia. We tested a variety of bioclimatic models for eight different plant species employing five discriminatory correlative species distribution models ( SDMs) including Generalized Linear Model ( GLM), MaxEnt, Random Forest ( RF), Boosted Regression Tree ( BRT), Bioclim, together with CLIMEX ( CL) as a mechanistic niche model. These models were fitted using a training dataset of available global data, but with the exclusion of Australian locations. The capabilities of these techniques in projecting suitable climate, based on independent records for these species in Australia, were compared. Thus, Australia is not used to calibrate the models and therefore it is as an independent area regarding geographic locations. To assess and compare performance, we utilized the area under the receiver operating characteristic ( ROC) curves ( AUC), true skill statistic ( TSS), and fractional predicted areas for all SDMs. In addition, we assessed satisfactory agreements between the outputs of the six different bioclimatic models, for all eight species in Australia. The modeling method impacted on potential distribution predictions under current climate. However, the utilization of sensitivity and the fractional predicted areas showed that GLM, MaxEnt, Bioclim, and CL had the highest sensitivity for Australian climate conditions. Bioclim calculated the highest fractional predicted area of an independent area, while RF and BRT were poor. For many applications, it is difficult to decide which bioclimatic model to use. This research shows that variable results are obtained using different SDMs in an independent area. This research also shows that the SDMs produce different results for different species; for example, Bioclim may not be good for one species but works better for other species. Also, when projecting a 'large' number of species into novel environments or in an independent area, the selection of the 'best' model/technique is often less reliable than an ensemble modeling approach. In addition, it is vital to understand the accuracy of SDMs' predictions. Further, while TSS, together with fractional predicted areas, are appropriate tools for the measurement of accuracy between model results, particularly when undertaking projections on an independent area, AUC has been proved not to be. Our study highlights that each one of these models ( CL, Bioclim, GLM, MaxEnt, BRT, and RF) provides slightly different results on projections and that it may be safer to use an ensemble of models. [ABSTRACT FROM AUTHOR]

Understanding the potential of soil to store soil organic carbon (SOC) is important for potential climate change mitigation strategies and assessing soil health issues. We examined the factors controlling SOC storage in eastern Australian soils and how these vary with depth. Models were developed using a set of readily interpreted covariates to represent key soil forming factors together with multiple linear regression (MLR) and Cubist piecewise decision tree techniques. Independent validation demonstrated concordance correlation coefficients up to 0.68 for SOC density in near surface layers but progressively decreasing with depth. The results demonstrate the key role of climate (rainfall and maximum temperatures) in controlling SOC stocks, with parent material (lithology) and vegetation cover also being key drivers, while topography and aspect are of lesser influence, at least at this sub-continental scale. The relative influence of temperature and land use/vegetation cover decreases with depth, while that of parent material increases. The necessity of considering a combination of factors when deriving meaningful estimates of current or projected SOC storage is demonstrated with quantitative estimates of SOC stocks in 45 different climate-parent material-vegetation cover regimes. Average SOC stocks in the 0- to 30-cm depth interval range from 16.3 Mg ha-1 in dry, highly siliceous parent material environments with low vegetation cover, up to over 145.0 Mg ha-1 in wet, mafic parent material environments with high vegetation cover. Results suggest that the proportion of SOC stock in the 30- to 100-cm interval as a proportion of the top 100 cm varies from a low of 41% in wet climates up to a high of 59% in dry climates. Climate appears to be the dominant controller of subsoil SOC storage proportion, with parent material and vegetation cover also having restricted influence. [ABSTRACT FROM AUTHOR]

The Australian home loan market has seen a significant and persistent boom over more than two decades. The extant literature exploring the underlying factors explaining this boom has predominantly looked at the demand side rather than the supply side. In this paper, we look at a major supply-side issue, the introduction of mortgage-backed securities and its likely impact on the home loan market. In doing so, we have developed a mathematical model that theorises this likely relationship. Our mathematical model predicts possible existence of an unstable equilibrium in the home loan market in the presence of mortgage-backed securities. We have subsequently backed up our theoretical exercise with sound empirical evidence acquired and analysed as a natural experiment in the Australian scenario using quarterly market data on home loans and mortgage-backed securities data for a 36-year period from 1976 to 2012. Using unknown structural break tests, we have identified significant breaks around late 1992 to mid-1995, clearly indicating that there were significant changes in the housing market due to the introduction of mortgage-backed securities in early 1993. We have also performed a stability test confirming that under certain conditions this market can become unstable. [ABSTRACT FROM AUTHOR]

Assisted colonization, the intentional movement of species beyond their native range, has been proposed as a climate change adaptation tool for biodiversity conservation. The risks and benefits of its implementation are still being debated but already the climate is changing, species are moving and the pressure on at-risk species must therefore be increasing. However, instances where moving species beyond their natural range purely for conservation purposes due to climate change are few, and the opportunity for science to inform practice is limited. Here we survey active participants in flora translocations and/or flora conservation in Australia in order to investigate the gap between theoretical and conceptual ideas about assisted colonization and to gauge preparedness for its implementation. We found that actions that mitigate proximal threats are preferred over those that move species beyond their current range. A lack of knowledge of species biology and ecology is an impediment to the acceptance of assisted colonization. In addition, prohibitive costs and the potential increased risk of the spread of diseases, pests and/or pathogens are viewed as more important obstacles of successful assisted colonization than potential for invasion at the recipient site. Full approval from all stakeholders at the source and recipient sites was found to be the most important factor for the successful assisted colonization of flora. [ABSTRACT FROM AUTHOR]

To support coastal planning through improved understanding of patterns of biotic and abiotic surrogacy at broad scales, we used gradient forest modeling (GFM) to analyze and predict spatial patterns of compositional turnover of demersal fishes, macroinvertebrates, and macroalgae on shallow, temperate Australian reefs. Predictive models were first developed using environmental surrogates with estimates of prediction uncertainty, and then the efficacy of the three assemblages as biosurrogates for each other was assessed. Data from underwater visual surveys of subtidal rocky reefs were collected from the southeastern coastline of continental Australia (including South Australia and Victoria) and the northern coastline of Tasmania. These data were combined with 0.018-resolution gridded environmental variables to develop statistical models of compositional turnover (beta diversity) using GFM. GFM extends the machine learning, ensemble tree-based method of random forests (RF), to allow the simultaneous modeling of multiple taxa. The models were used to generate predictions of compositional turnover for each of the three assemblages within unsurveyed areas across the 6600 km of coastline in the region of interest. The most important predictor for all three assemblages was variability in sea surface temperature (measured as standard deviation from measures taken interannually). Spatial predictions of compositional turnover within unsurveyed areas across the region of interest were remarkably congruent across the three taxa. However, the greatest uncertainty in these predictions varied in location among the different assemblages. Pairwise congruency comparisons of observed and predicted turnover among the three assemblages showed that invertebrate and macroalgal biodiversity were most similar, followed by fishes and macroalgae, and lastly fishes and invertebrate biodiversity, suggesting that of the three assemblages, macroalgae would make the best biosurrogate for both invertebrate and fish compositional turnover. [ABSTRACT FROM AUTHOR]

This article examines the importance of schools relative to individual characteristics in explaining Tertiary Entrance Rank scores, using Longitudinal Surveys of Australian Youth data. The school characteristics explain around 20 per cent of the variance in Tertiary Entrance Rank scores, with the measured school characteristics explaining around 7 percentage points, indicating that idiosyncratic factors-which we interpret as reflecting schools' ethos-dominate. School sector is an important explanator; whilst the government, independent and Catholic systems all contain 'good' schools, schools that have low Tertiary Entrance Rank scores, after controlling for individual characteristics, are dominated by the government sector. [ABSTRACT FROM AUTHOR]

Aim A third of all modern (after 1500) mammal extinctions (24/77) are Australian species. These extinctions have been restricted to southern Australia, predominantly in species of 'critical weight range' (35-5500 g) in drier climate zones. Introduced red foxes ( Vulpes vulpes) that prey on species in this range are often blamed. A new wave of declines is now affecting a globally significant proportion of marsupial species (19 species) in the fox-free northern tropics. We aim to test plausible causes of recent declines in range and determine if mechanisms differ between current tropical declines and past declines, which were in southern (non-tropical) regions. Location Australian continent Methods We used multiple regression and random forest models to analyse traits that were associated with declines in species range, and compare variables associated with past extinctions in the southern zones with current tropical (northern) declines. Results The same two key variables, body mass and habitat structure, were associated with proportion-of-decline in range throughout the continent, but the form of relationships differs with latitude. In the south, medium-sized species in open habitats of lower rainfall were most likely to decline. In the tropics, small species that occupy open vegetation with moderate rainfall (savanna) are now experiencing the most severe declines. Throughout the continent, large-bodied species and those in structurally complex habitats (rainforest) are secure. Main conclusions Our results indicate that there is no mid-sized 'critical weight range' in the north. Because foxes are absent from the tropics, we suggest that northern Australian marsupial declines are associated with predation by feral cats ( Felis catus) exacerbated by reduced ground level vegetation in non-rainforest habitats. To test this, we recommend experiments to remove cats from some locations where tropical mammals are threatened. Our results show that comparative analysis can help to diagnose potential causes of multi-species decline. [ABSTRACT FROM AUTHOR]

The intensity and position of the sub-tropical ridge ( STR) have strong relationships with rainfall variability in southern Australia. The combined effect of intensity and position in March-April-May ( MAM) and June-July-August ( JJA) is the focus of this research. Linear statistics were used first: area-averaged and Australia-wide spatial correlations of STR intensity and position with precipitation in south-west eastern Australia reveal that STR intensity has a much stronger and more widespread relationship with precipitation in both seasons. Over time, these relationships vary in magnitude and spatial extent with the sign of the correlation changing between two 50-year epochs. These nonlinearities were investigated further using classification trees. Area-averaged precipitation data (terciles) for south-west eastern Australia was classified on the basis of STR intensity and position. In both seasons the classification trees identify STR intensity as the primary partition defining the dry group, supporting the linear analysis. In the transition season of MAM, the time of year when the mean position of the STR is more southerly, STR position is important in distinguishing between a 'winter-like' and a 'summer-like' wet groups, providing STR intensity is low. Vector wind analyses were computed to explain the composite seasonal precipitation anomaly results in terms of different circulation patterns associated with these two wet groups. The frequency of wet and dry cases in each group was examined with changes evident over the recent years. The research confirms that STR intensity is more important than STR position in explaining inter-annual rainfall variability across southern Australia but also demonstrates the additional role of STR position in MAM. These results explain the low correlation between rainfall and STR position and why this relationship has evolved during the 20th century as the mean location of the STR has shifted south in MAM. [ABSTRACT FROM AUTHOR]

The article presents abstracts on oncology research in Australia topics including rising cost of anti-cancer drugs in, use and cost of chemotherapy in and changes in cancer mortality and incidence.

ABSTRACT Floodplain wetlands in the wet-dry tropics are under increasing pressure from water resource development, and there is a need for methods to assess the biophysical dynamics of these extensive and often remote ecosystems. This study assessed the capacity of optical remote sensing methods to monitor the seasonal dynamics of inundation, turbidity, and aquatic vegetation cover for a northern Australian savanna catchment. MODIS data were used to map seasonal flood inundation patterns, and Landsat 5 TM data were used to map dry-season waterbody dynamics. A network of water-depth loggers and temperature sensors provided ground observations of surface inundation dynamics, and was used to validate the inundation mapping. Post-flood waterbody surface area declined by 89% over the dry season, with 70% of the decline occurring for Palustrine (floodplain) waterbodies. All aquatic systems became increasingly disconnected as the dry season progressed. Statistical relationships were developed between seasonal measurements of turbidity, aquatic vegetation cover, and Landsat spectral data. Catchment wide predictions showed that turbidity increased and macrophyte cover decreased for the Palustrine and Lacustrine (lake) systems, while the Riverine systems became less turbid over the dry season. These results show that, for open savanna landscapes where cloud cover does not limit waterbody detection, optical remote sensing methods can be effectively applied to assess seasonal patterns of inundation and accompanying biophysical dynamics. This provides an effective tool to evaluate the impact of river flow regime changes from water resource use or climate change in these regions. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The article presents abstracts on Australasian Faculty of Occupational & Environmental Medicine which include the future of occupational medicine practice for the next 20 years, use of RailCorp Lantern (RL) test for rail workers to determine colour vision deficit, and management for occupational violence encountered in health care through balancing the needs of public and health providers.