Search

Among several hydrological processes, river flow is an essential parameter that is vital for different water resources engineering activities. Although several methodologies have been adopted over the literature for modeling river flow, the limitation still exists in modeling the river flow time series curve. In this research, a functional quantile autoregressive of order one model was developed to characterize the entire conditional distribution of the river flow time series curve. Based on the functional principal component analysis, the regression parameter function was estimated using a multivariate quantile regression framework. For this purpose, hourly scale river flow collected from three rivers in Australia (Mary River, Lockyer Valley, and Albert River) were used to evaluate the finite‐sample performance of the proposed methodology. A series of Monte‐Carlo experiments and historical data sets were examined at three stations. Further, uncertainty analysis was adopted for the methodology evaluation. Compared with the existing methods, the proposed model provides more robust forecasts for outlying observations, non‐Gaussian and heavy‐tailed error distribution, and heteroskedasticity. Also, the proposed model has the merit of predicting the intervals of future realizations of river flow time series at the central and non‐central locations. The results confirmed the potential for predicting the river flow time series curve with a high level of accuracy in comparison with the benchmark existing functional time series methods. Plain Language Summary: This paper proposes a functional quantile autoregressive model of order one, which is used to predict the entire distribution of the realizations of river flow time series curve. The proposed model allows modeling the conditional quantiles of the response variable as a function of its past values of it. The proposed method for historical river flow curves is an excellent alternative to existing mean regression methods at the 0.5 quantile level (median regression). Also, as an advantage over existing methods, it offers a more thorough explanation of the connection among previous and future realizations of river flow curves at various quantile levels, providing a more extensive understanding of the relationship. Moreover, this feature of the proposed method allows for the effortless generation of pointwise prediction intervals for future realizations of river flow curves. The numerical results obtained by Monte Carlo experiments and empirical data analyses exhibit that, compared with existing methods, the proposed method produces competitive or even better forecasting results. The results also indicate that the future realizations of the river flow measurements are well covered by the prediction intervals constructed by the proposed method. Key Points: Predicting the mean and extreme values of the river flow curve is important for various applications in water resources managementThe FQAR(1) allows predicting the entire distribution of future realizations of the river flow curve as a function of its past values of itNumerical results based on river flow measurements collected from the Australia Continent confirmed the potential of the FQAR(1) [ABSTRACT FROM AUTHOR]

With the development of the electric market, electric load forecasting has been increasingly pursued by many scholars. Because the electric load is affected by many factors, it is characterized by volatility and uncertainty, and it cannot be forecasted accurately only by a single model. In the research, a short‐term load forecasting integrated model is proposed to solve the problem of inaccurate forecasting of a single model. The key point of using the integrated model to forecast is to optimize the decomposed sequence to improve the accuracy of the forecast. empirical wavelet decomposition (EWT) is used to decompose the sequence into stationary sequences and avoid modal aliasing; the sparrow search algorithm (SSA) simulates the forecasting and anti‐forecasting behavior of the sparrow population, which is very similar to the electricity consumption behavior of various industries and has good optimization effect; generalized regression neural network (GRNN) is used for forecast and reconstruction; This is the EWT‐SSA‐GRNN model. This paper studies and analyzes the power load of a city in southern Australia. The results show that the integrated model reduces volatility through decomposition and optimization, and can improve forecast accuracy. [ABSTRACT FROM AUTHOR]

This paper evaluates the real‐time forecast performance of alternative Bayesian autoregressive (AR) and vector autoregressive (VAR) models for the Australian macroeconomy. To this end, we construct an updated vintage database and compare the predictive ability of a wide set of specifications that takes into account almost all possible combinations of nonstandard errors existing in the current literature. In general, we find that the models with flexible covariance structures can improve the forecast accuracy as compared with the standard variant. For forecasting GDP, both point and density forecasts consistently suggest small VARs tend to outperform their counterparts while AR models often predict inflation better. With the unemployment rate, large VAR models provide superior forecasts to the alternatives at almost all forecast horizons. The forecasting performance of these models slightly changes when we consider the first, second, and latest‐available vintage as actual values, highlighting the importance of using real‐time data vintages in forecasting. [ABSTRACT FROM AUTHOR]

The objective of the paper is to assess the resilience of the economy of Australia following the Covid‐19 pandemic that hit the global economy in Q4 2019, in years 2020, 2021 and 2022. Quarterly growth rates (annualised) of the Real GDP of Australia and Canada are forecasted between Q2 2022 and Q4 2050. Two sets of forecasts are generated: forecasts using historical data including the pandemic (from Q1 1961 to Q1 2022) and excluding the pandemic (from Q1 1961 to Q3 2019). The computation of the difference of their averages is an indicator of the resilience of the economies during the pandemic, the greater the difference the greater the resilience. Used as a benchmark, Canada's economy shows a slightly lower resilience to the Covid‐19 pandemic (+0.37%) than Australia's economy (+0.39%) based on Q2 2022–2050 forecasts. However, driven by stronger growth than Canada, the average estimate of the Q2 2022–Q4 2050 quarterly (annualised) growth rate forecasts of Australia is expected to be +2.09% with the Q1 1961–Q1 2022 historical data while it should be +1.61% for Canada. Supported by higher growth, Australia's Real GDP is expected to overtake Canada's in Q1 2040. [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]

Objective: This paper draws attention to the rapidly growing number of Australian nonagenarians, presents previously unpublished information on this sub‐group of older people and explores the implications for future patterns of service delivery, planning and policy. Methods: Statistical analyses of Census data and other Australian Bureau of Statistics Surveys using Table Builder Pro, combined with analysis of de‐identified Australian Institute of Health and Welfare (AIHW) unit record data on aged care and AIHW GRIM (mortality) data. Results: Male nonagenarians almost doubled from 2006 to 2016, while their female counterparts grew by 55%. This cohort is the first to reap cumulative advantage from the dramatic reduction in death rates from 1970. Their demographic circumstances reveal both changes and continuities compared to the previous cohort. Conclusion: Men and women aged 90 and over use a substantial proportion of aged care services, and their characteristics and circumstances are highly relevant to planning current and future aged care services. [ABSTRACT FROM AUTHOR]

Tweedie's compound Poisson regression models have been introduced in recent years to model and predict daily, monthly and seasonal precipitation data. Tweedie's compound Poisson regression analysis of precipitation data captures the relationship between the mean structure of precipitation and covariates while accounting for its right‐skewness and zero‐inflation appropriately, and thus extends trend analysis stage of classical time series techniques to handle right‐skewed time series data with possible zero‐inflation. A distinctive advantage of Tweedie's compound Poisson modelling of precipitation data is that the occurrence and quantity of precipitation can be simultaneously modelled using a single distribution; however, this approach ignores serial correlation between precipitation data observed over time. In this study, we propose to accommodate complex correlation structures of time series precipitation data using the Autoregressive Integrated Moving Average (ARIMA) models at the next stage as done in the classical time series analysis. Our analyses of monthly precipitation data in Australia demonstrated the usefulness of our two‐stage approach to prediction of precipitation. [ABSTRACT FROM AUTHOR]

Effective biodiversity conservation requires robust and transparent prioritization of management actions. However, this is often hampered by a lack of spatially‐explicit data on habitat variables and empirical data on the effect of management actions. Although approaches exist that integrate structured expert elicitation (SEE) with species distribution models (SDMs) to encode species responses across habitat gradients, difficulties remain in predicting management outcomes under different settings, at a region‐wide scale when key habitat covariates are not spatially explicit. Therefore, we developed an approach to integrate SDMs with SEE to capture expert understanding of likely outcomes of management actions for individual frog species, and use this to spatially predict the effect of management actions. We demonstrate our approach across approximately 4000 wetlands in greater Melbourne, Victoria, Australia. As a measure of management effectiveness, we used the change in predicted probability of occurrence of seven frog species at wetlands 10 years after conservation actions are implemented (or not implemented). Management effect was elicited from experts under six scenarios. Individual expert estimates were aggregated using generalized linear models that were then used to spatially predict expected management effects, and a measure of uncertainty in the prediction, at all wetlands. Predicted management effect was strongly influenced by species initial probability of occurrence, with enhancing aquatic and surrounding vegetation an effective action for most species. We discuss practical challenges and recommend solutions in the integration of SDMs and SEE for the spatial prediction of management effect. [ABSTRACT FROM AUTHOR]

Objective: The aim of the study was to improve the measurement of ageing in Oceania taking into account characteristics of populations and, in particular, changes in life expectancy. Method: Using past and projected life tables, we calculated prospective old age dependency ratios (POADRs) to 2060, placing the boundary to old age at a moving point with a fixed remaining life expectancy (RLE) for thirteen territories of Oceania. Results: In some territories, POADRs grow less rapidly than old age dependency ratios (OADRs). For example, in Australia and Guam, the OADR is forecast to increase from 0.20 and 0.07 in 1980, respectively, to 0.45 and 0.39 in 2050‐55, while the POADR is forecast to increase from 0.17 and 0.07 to 0.19 and 0.19, respectively, over the same period. Conclusion: Policymakers may consider this more rational approach to measurement when considering holistic policy responses to both current issues relating to ageing and mitigating against future challenges. [ABSTRACT FROM AUTHOR]

Aims: Climate change is expected to have profound effects on species' distributions into the future. Freshwater fishes, an important component of freshwater ecosystems, are no exception. Here, we project shifts in suitable conditions for Australian freshwater fishes under different climate change scenarios to identify species that may experience significant declines in habitat suitability. Location: Australia. Methods: We use MAXENT bioclimatic models to estimate the effect of climate change on the suitable conditions for 154 species of Australian freshwater fishes, of which 109 are endemic and 29 are threatened with extinction. Suitable conditions for freshwater fish species are modelled using three different Earth System climate models (ESMs) under two different emission scenarios to the year 2100. For each species, we examine potential geographic shifts in the distribution of suitable conditions from the present day to 2100 and quantify how habitat suitability may change at currently occupied sites by the end of this century. Results: Broadscale poleward shifts in suitable conditions are projected for Australian freshwater fishes by an average of up to 0.38° (~180 km) across all species, depending on the emission scenario. Considerable loss of suitable conditions is forecast to occur within currently recognized distributional extents by 2100, with a mean projected loss of up to 17.5% across species. Predicted geographic range shifts and declines are larger under a high‐emission scenario. Threatened species are projected to be more adversely affected than nonthreatened species. Main Conclusions: Our models identify species and geographic regions that may be vulnerable to climate change, enabling freshwater fish conservation into the future. [ABSTRACT FROM AUTHOR]

The article presents the discussion on emerging trends and phenomena projected to impact pharmacy practice and the health of Australian patients. Topics include providing advice and guidance on approaching issues showing to the times such as sustainably reducing the carbon footprint; and ensuring funding reforms prioritising safe and timely patient care.

Objectives: To determine in a sample of older community‐dwelling adults: (1) the proportion who have engaged in medical and financial planning; (2) the factors associated with advance care directive (ACD) completion and substitute decision maker (SDM) appointment; and (3) for those without an ACD, what might prompt completion. Methods: A cross‐sectional survey was undertaken with individuals receiving community aged care services in metropolitan and regional communities. Data collection was conducted by case managers during a home visit. Results: Overall, 158 individuals (M = 78 years, SD = 8.7) participated (53.5% consent rate). Financial planning participation (will = 85%; enduring power of attorney = 66%) was higher than medical planning participation (SDM: 54%; ACD: 30%). No health status factors were significantly associated with ACD completion or SDM appointment. Diagnosis of a life‐limiting illness was most likely to prompt ACD completion (77.6%). Conclusions: One third of older people residing in the community have completed an ACD. Strategies to improve uptake in this vulnerable population require further exploration. [ABSTRACT FROM AUTHOR]

Objectives: To examine if baseline stigma predicts psychological distress at 3 and 6 months follow‐up among patients newly diagnosed with lung cancer. Methods: This longitudinal study was nested within a larger randomised controlled trial. Eligible participants were recruited via respiratory and oncology out‐patient clinics in Australia (n = 194). Consenting participants were asked to complete surveys at baseline, 3 and 6 months post‐recruitment. Measures included lung cancer stigma (Cataldo Lung Cancer Stigma Scale) and psychological distress (General Health Questionnaire‐12 [GHQ‐12]). Results: One‐hundred and ninety‐four participants were included for analysis. Most were male (57.7%) with a mean age of 68 years (SD = 8.8). A significant relationship between baseline lung cancer stigma and psychological distress at 6 months was found, where a one unit increase in lung cancer stigma increases psychological distress by 0.044 when adjusting for age, gender, smoking status, baseline GHQ‐12 scores and intervention allocation (as part of the larger trial; p = 0.001; β = 0.044, 95% CI = 0.010, 0.079). Conclusion: Temporal links between lung cancer stigma and psychological distress was found at 6 months, suggesting stigma‐related experiences may have a delayed impact. Development of routine lung cancer stigma assessments is recommended to identify those at risk of psychological distress. [ABSTRACT FROM AUTHOR]

Summary: We forecast the old‐age dependency ratio for Australia under various pension age proposals, and estimate a pension age scheme that will provide a stable old‐age dependency ratio at a specified level. Our approach involves a stochastic population forecasting method based on coherent functional data models for mortality, fertility and net migration, which we use to simulate the future age‐structure of the population. Our results suggest that the Australian pension age should be increased to 68 by 2030, 69 by 2036 and 70 by 2050, in order to maintain the old‐age dependency ratio at 23%, just above the 2018 level. Our general approach can easily be extended to other target levels of the old‐age dependency ratio and to other countries. We forecast the old‐age dependency ratio for Australia under various pension schemes, and derive a scheme that will provide a stable old‐age dependency ratio. [ABSTRACT FROM AUTHOR]

This study examines the factors affecting the issuance, accuracy and usefulness of analysts' cash flow forecasts (CFFs) in Australia. Given the economic importance of the mining industry in Australia, we find that analysts are likely to provide CFFs for mining firms with poor financial health and high default risk. In contrast, analysts' provision of CFFs increases with the degree of financial health for non‐mining firms. The determinants of the issuance and accuracy of analysts' CFFs also differ in pre‐ and post‐IFRS adoption periods. Our results add new evidence on the effect of IFRS adoption on analysts' cash flow forecasting behaviours. [ABSTRACT FROM AUTHOR]

Accurate and reliable seasonal climate forecasts are frequently sought by climate‐sensitive sectors to support decision‐making under climate variability and change. Temperature trend is discernible globally over the past decades, but seasonal forecasts produced by a global climate model (GCM) generally underestimate such trend. Current statistical methods used for calibrating seasonal climate forecasts mostly do not explicitly account for climate trends. Consequently, the calibrated forecasts also fail to capture the observed trend. Solving this problem can enhance user confidence in seasonal climate forecasts. In this study, we extend the capability of the Bayesian joint probability (BJP) modelling approach for statistical calibration of seasonal climate forecasts. A trend component is introduced into the BJP algorithm for embedding the observed trend into calibrated ensemble forecasts. We apply the new model (named BJP‐t) to three test stations in Australia. Seasonal forecasts of daily maximum temperatures from the SEAS5 model, operated by the European Centre for Medium‐Range Weather Forecasts (ECMWF), are calibrated and evaluated. The BJP‐t calibrated ensemble forecasts can reproduce the observed trend, when the raw ensemble forecasts and the BJP calibrated ensemble forecasts both fail to do so. The BJP‐t calibration leads to more skilful, more reliable and sharper forecasts than the BJP calibration. [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]

Determining mean transit times in headwater catchments is critical for understanding catchment functioning and understanding their responses to changes in landuse or climate. Determining whether mean transit times (MTTs) correlate with drainage density, slope angle, area, or land cover permits a better understanding of the controls on water flow through catchments and allows first‐order predictions of MTTs in other catchments to be made. This study assesses whether there are identifiable controls on MTTs determined using 3H in headwater catchments of southeast Australia. Despite MTTs at baseflow varying from a few years to >100 years, it was difficult to predict MTTs using single or groups of readily‐measured catchment attributes. The lack of readily‐identifiable correlations hampers the prediction of MTTs in adjacent catchments even where these have similar geology, land use, and topography. The long MTTs of the Australian headwater catchments are probably in part due to the catchments having high storage volumes in deeply‐weathered regolith, combined with low recharge rates due to high evapotranspiration. However, the difficulty in estimating storage volumes at the catchment scale hampers the use of this parameter to estimate MTTs. The runoff coefficient (the fraction of rainfall exported via the stream) is probably also controlled by evapotranspiration and recharge rates. Correlations between the runoff coefficient and MTTs in individual catchments allow predictions of MTTs in nearby catchments to be made. MTTs are shorter in high rainfall periods as the catchments wet up and shallow water stores are mobilized. Despite the contribution of younger water, the major ion geochemistry in individual catchments commonly does not correlate with MTTs, probably reflecting heterogeneous reactions and varying degrees of evapotranspiration. Documenting MTTs in catchments with high storage volumes and/or low recharge rates elsewhere is important for understanding MTTs in diverse environments. [ABSTRACT FROM AUTHOR]

Aim: Knowledge of species responses to past environmental change provides a basis to predict and mitigate the outcomes of future environmental change. While paradigm studies of comparative phylogeography have surveyed dissimilar taxa as a means to identify generalities of species responses to past environmental change, the fact that such taxa are dissimilar also raises the chances that any shared patterns reflect coincident responses from different processes ('phylogeographic convergence'). Here we advocate for and demonstrate the value of examining closely related, ecologically similar co‐distributed species in comparative phylogeographic studies aimed at inferring the environmental processes driving distributional change. Closely related species with similar environmental requirements represent valid phylogeographic replicates, meaning that shared historical distributional responses can more confidently be attributed to the operation of the same process ('phylogeographic parallelism'). Location: Tasmania, temperate south‐east Australia. Taxa: We compared phylogeographic patterns observed in two closely related, co‐distributed skink species (Carinascincus) sharing similar ecological and physiological tolerances. Methods: Using mitochondrial and nuclear DNA, we reconstructed relationships between populations across the known range of C. metallicus and compared it with those of C. ocellatus. We also compared dated phylogenies inferred using Bayesian approaches, and analyses of population structure and genetic diversity. Results: We found remarkably similar geographic patterns of genetic diversity in these two species, suggesting they responded similarly to Plio‐Pleistocene climate cycling. We infer that glacial cold and aridity forced these species into similar lowland refugial regions throughout Tasmania and the Bass Strait Islands. Main conclusions: Examining phylogeographic patterns in closely related, ecologically similar co‐distributed species allows for confident inference of mechanisms driving historical distributional changes. This is an important step towards generating robust predictions of species responses to future environmental change. [ABSTRACT FROM AUTHOR]

Three statistical prediction models for the summer extreme precipitation frequency (EPF) in the middle and lower reaches of the Yangtze River valley (MLYRV) based on the winter sea surface temperature in the southern Indian Ocean (SIO‐SST; Scheme‐SST), the spring sea‐ice concentration in the Beaufort Sea (Scheme‐SIC), and both predictors (Scheme‐SS), are established by using the year‐to‐year increment method. The winter SIO‐SST may affect the SST anomaly in the east of Australia via a teleconnection pattern. The east of Australia SST signal in winter continues until the following summer and then affects the summer EPF in the MLYRV via modulation of the meridional Hadley circulation in the western North Pacific. The positive Beaufort SIC anomaly may result in a negative summer SST anomaly by the increased surface albedo. In response, the atmospheric circulation presents a dipole anomaly distribution in the Beaufort Sea and the Barents Sea. The Arctic dipole anomaly may bring about frequent extreme precipitation in the MLYRV by adjusting the position of the East Asian westerly jet and Eurasian teleconnection pattern. The prediction skill of the summer EPF for Scheme‐SS is higher than that of Scheme‐SST and Scheme‐SIC in the cross‐validation test during 1962–2017 and the independent hindcast during 1992–2017. Scheme‐SS shows a higher prediction skill of the summer EPF than that of Scheme‐SST and Scheme‐SIC, with a time correlation coefficient of 0.62, accounting for 39% of the total variance, of which 35% is the winter SIO‐SST and 4% the spring Beaufort SIC. Scheme‐SS not only shows a rather high predictive ability for the anomalous summer EPF but can also reproduce the increasing trend of extremely heavy precipitation. [ABSTRACT FROM AUTHOR]

A positive Indian Ocean Dipole (IOD) in 2019 that reached the level of the strongest events occurred in 1994 and 1997 and caused disasters in countries around the Indian Ocean. Using a quasi real‐time ensemble seasonal prediction system based on the Scale Interaction Experiment‐Frontier climate model, its occurrence was predicted a few seasons ahead and the possible impacts were warned by overcoming the so‐called winter predictability barrier. The successful prediction of such a super event at long lead time may contribute to reducing the risks of socioeconomic losses by introducing suitable measures for adaptation. Here, we have investigated possible sources of the successful prediction by analyzing covariability of intermember anomalies defined as deviations from the mean in the ensemble reforecasts. Interestingly, it is found that the potential predictability of the 2019 super positive event is linked with the preexisting El Niño Modoki in the tropical Pacific. Plain Language Summary: The Indian Ocean Dipole (IOD) is an intrinsic air‐sea coupled climate phenomenon in the tropical Indian Ocean, of which name was introduced because of the dipolar structure of both oceanic and atmospheric anomalies along the equator. Just 20 years after the introduction of IOD, another such extreme IOD event happened to emerge in 2019 and reached a level which is similar to the precedent events such as 1994 and 1997 and caused disasters in countries around the Indian Ocean. For example, many parts of Australia were seriously affected by a series of massive bushfires owing to the drier than normal conditions, while crop yield in East Africa was threatened by a plague of locusts owing to the wetter condition there. Its occurrence was predicted a few seasons ahead by overcoming the so‐called winter predictability barrier by a numerical prediction system. The successful prediction of such a super event at long lead time may contribute to reducing the risks of socioeconomic losses by introducing suitable measures for adaptation. Key Points: A positive Indian Ocean Dipole in 2019 that reached the level of the strongest events occurred in 1994 and 1997Its occurrence was predicted a few seasons ahead by overcoming the so‐called winter predictability barrierIts potential predictability is linked with the preexisting El Niño Modoki in the tropical Pacific [ABSTRACT FROM AUTHOR]

Native habitat‐forming species can facilitate invasion by reducing environmental stress or consumer pressure. However, the intensity of one stressor along a local gradient may differ when expanding the scale of observation to encompass major variations in background environmental conditions. In this study, we determined how facilitation of the invasive porcelain crab, Petrolisthes elongatus, by the native tube‐forming serpulid, Galeolaria caespitosa, varied with environmental gradients at local (tidal height) and larger (wave exposure) spatial scales. G. caespitosa constructs a complex calcareous matrix on the underside of intertidal boulders and we predicted that its positive effects on P. elongatus density would increase in intensity with shore height and be stronger at wave‐sheltered than wave‐exposed locations. To test these predictions, we conducted two experiments. First, we determined the effects of serpulid presence (boulders with live or dead serpulid matrix vs. bare boulders) at six shore heights that covered the intertidal distribution of P. elongatus. Second, we determined the effects of serpulid presence (present vs. absent), shore height (high vs. low) and wave exposure (sheltered vs. exposed) on crabs across six locations within the invaded range in northern Tasmania, Australia. In Experiment 1, the presence of serpulids (either dead or alive) enhanced P. elongatus densities at all shore heights, with facilitation intensity (as determined by a relative interaction index; RII) tending to increase with shore height. In Experiment 2, serpulids facilitated P. elongatus across shore heights and wave exposures, although crab densities were lower at high shore levels of wave‐sheltered locations. However, the intensity of crab facilitation by serpulids was greater on wave‐sheltered than on wave‐exposed shores, but only at the high shore level. This study demonstrates that local effects of native habitat‐formers on invasive species are dependent on prevailing environmental conditions at larger spatial scales and that, under more stressful conditions, invaders become increasingly reliant on positive interactions with native habitat‐formers. Increased strength of local‐scale facilitation by native species, dampening broader scale variations in environmental stressors, could enhance the ability of invasive species to establish self‐sustaining populations in the invaded range. [ABSTRACT FROM AUTHOR]

Deficits in muscles of the lumbo-pelvic region, such as a relatively small multifidus muscle, have been used to predict lower limb injuries in professional football players. Results have been less consistent for the size of the quadratus lumborum ( QL) muscle. Changes in size of the multifidus and QL muscles could be functionally related to each other, and modeling this relationship could improve prediction of lower limb injuries. Ultrasound imaging examinations were performed on male elite football players at the start of the Australian Football League ( AFL) pre-season and playing season. Injury data were obtained from records collected by each club. Results indicated that the cross-sectional area of the multifidus muscle was related to the occurrence of an injury in the pre-season (odds ratio [ OR] = 2.08/cm2 decrease below the mean; OR for dichotomized measure = 12.2) and in the season ( OR = 2.43/cm2). The size of the QL muscle was significantly related to an injury in the pre-season ( OR = 2.12/cm2 increase above the mean; OR for dichotomized measure = 7.26) but not in the season. A significant link was found between the ratio of the multifidus and QL muscles, and the incidence of pre-season ( OR = 14.71) and season injuries ( OR = 5.29). The sensitivity and specificity of the model in the pre-season were 75% and 85.7%, respectively; values for the playing season were 88.4% and 62.5%. A model was developed for prediction of lower limb injuries in football players. Combining size measurements of the multifidus and QL muscles improved predictive power. This information may have clinical implications for injury screening and prevention. [ABSTRACT FROM AUTHOR]