Refining fine sediment source identification through integration of spatial modelling, concentration monitoring and source tracing: A case study in the Great Barrier Reef catchments.

Excess fine sediment delivery is a major contributor to the declining health of the Great Barrier Reef and identifying the dominant source areas of fine sediment has been critical to prioritising erosion remediation programs. The Bowen River catchment within the Burdekin Basin has been recognised as a major contributor and hence received considerable research investment over the last two decades. This study adopts a novel approach to integrate three independently derived sediment budgets produced from a catchment scale sediment budget model (Dynamic SedNet), targeted tributary water quality monitoring and geochemical sediment source tracing to refine and map the sediment source zones within the Bowen catchment. A four year study of water quality monitoring combined with modelled discharge estimates and geochemical source tracing both identified that the Little Bowen River and Rosella Creek were the largest sources of sediment in the Bowen River catchment. Both data sets contradicted initial synoptic sediment budget model predictions due to inadequate representation of hillslope and gully erosion. Recent improvements in model inputs have resulted in predictions that are consistent with the field data and are of finer resolution within the identified source areas. Priorities for further investigation of erosion processes are also revealed. Examining the benefits and limitations of each method indicates that these are complimentary methods which can effectively be used as multiple lines of evidence. An integrated dataset such as this provides a higher level of certainty in the prediction of fine sediment sources than a single line of evidence dataset or model. The use of high quality, integrated datasets to inform catchment management prioritisation will provide greater confidence for decision makers when investing in catchment management.

Rapid monitoring of cropland primary productivity and shipping activity in Ukraine.

Ukraine is an important global exporter of grain, especially to several countries with vulnerable food systems. The war in Ukraine may disrupt global food supply by limiting the planting, growth, and harvest of crops, or disrupting grain supply logistics. We apply a novel statistical modelling approach to satellite images of cropland in Ukraine for fast inference and exploration of cropping patterns and their influences in challenging environments. We also present satellite-derived cargo shipping activity as an accompaniment to these outputs to better explore the outcomes. Cropland Gross Primary Productivity in 2022 was 0.25gC/m2 lower than the 2010-2021 baseline period (p < 0.001). Similarly, cumulative annual cargo shipping activity ports in the Odesa and Mariupol regions were 45% and 62% lower in 2022 than in 2021, respectively. This suggests that cropland primary productivity has suffered during the conflict, and reliance on a few key port areas introduces vulnerability to the value chain.

Air ambulance retrievals of patients with suspected appendicitis and acute abdominal pain: The patients' journeys, referral pathways and appendectomy outcomes using linked data in Central Queensland, Australia.

INTRODUCTION: Acute appendicitis is the most common cause of acute abdominal pain presentations to the ED and common air ambulance transfer. AIMS: describe how linked data can be used to explore patients' journeys, referral pathways and request-to-activation responsiveness of patients' appendectomy outcomes (minor vs major complexity). METHODS: Data sources were linked: aeromedical, hospital and death. Request-to-activation intervals showed strong right-tailed skewness. Quantile regression examined whether the longest request-to-activation intervals were associated with appendicitis complexity in patients who underwent an appendectomy. RESULTS: There were 684 patients in three referral pathways based on hospital capability levels. In total, 5.6 % patients were discharged from ED. 83.3 % of all rural origins entered via the ED. 3.8 % of appendicitis patients were triaged to tertiary hospitals. Appendectomy patients with major complexity outcomes were less likely to have longer request-to-activation wait times & had longer lengths of stay than patients with minor complexity outcomes. CONCLUSIONS: Linked data highlighted four aspects of a functioning referral system: appendectomy outcomes of major complexity were less likely to have longer request-to-activation intervals compared to minor (sicker patients were identified); few were discharged from EDs (validated transfer); few were triaged to tertiary hospitals (appropriate level for need), and no deaths relating to appendectomy.

Using a Quality Framework to Explore Air Ambulance Patients' Journey Outcomes in Central Queensland, Australia.

INTRODUCTION: In Australia, aeromedical retrieval provides a vital link for rural communities with limited health services to definitive care in urban centers. Yet, there are few studies of aeromedical patient experiences and outcomes, or clear measures of the service quality provided to these patients. STUDY OBJECTIVE: This study explores whether a previously developed quality framework could usefully be applied to existing air ambulance patient journeys (ie, the sequences of care that span multiple settings; prehospital and hospital-based pre-flight, flight transport, after-flight hospital in-patient, and disposition). The study aimed to use linked data from aeromedical, emergency department (ED), and hospital sources, and from death registries, to document and analyze patient journeys. METHODS: A previously developed air ambulance quality framework was used to place patient, prehospital, and in-hospital service outcomes in relevant quality domains identified from the Institutes of Medicine (IOM) and Dr. Donabedian models. To understand the aeromedical patients' journeys, data from all relevant data sources were linked by unique patient identifiers and the outcomes of the resulting analyses were applied to the air ambulance quality framework. RESULTS: Overall, air ambulance referral pathways could be classified into three categories: Intraregional (those retrievals which stayed within the region), Out of Region, and Into Region. Patient journeys and service outcomes varied markedly between referral pathways. Prehospital and in-hospital service variables and patient outcomes showed that the framework could be used to explore air ambulance service quality. CONCLUSION: The air ambulance quality framework can usefully be applied to air ambulance patient experiences and outcomes using linked data analysis. The framework can help guide prehospital and in-hospital performance reporting. With variations between regional referral pathways, this knowledge will aid with planning within the local service. The study successfully linked data from aeromedical, ED, in-hospital, and death sources and explored the aeromedical patients' journeys.

Sampling re-design increases power to detect change in the Great Barrier Reef's inshore water quality.

Monitoring programs are fundamental to understanding the state and trend of aquatic ecosystems. Sampling designs are a crucial component of monitoring programs and ensure that measurements evaluate progress toward clearly stated management objectives, which provides a mechanism for adaptive management. Here, we use a well-established marine monitoring program for inshore water quality in the Great Barrier Reef (GBR), Australia to investigate whether a sampling re-design has increased the program's capacity to meet its primary objectives. Specifically, we use bootstrap resampling to assess the change in statistical power to detect temporal water quality trends in a 15-year inshore marine water quality data set that includes data from both before and after the sampling re-design. We perform a comprehensive power analysis for six water quality analytes at four separate study areas in the GBR Marine Park and find that the sampling re-design (i) increased power to detect trends in 23 of the 24 analyte-study area combinations, and (ii) resulted in an average increase in power of 34% to detect increasing or decreasing trends in water quality analytes. This increase in power is attributed more to the addition of sampling locations than increasing the sampling rate. Therefore, the sampling re-design has substantially increased the capacity of the program to detect temporal trends in inshore marine water quality. Further improvements in sampling design need to focus on the program's capability to reliably detect trends within realistic timeframes where inshore improvements to water quality can be expected to occur.

Statistical power of detecting trends in total suspended sediment loads to the Great Barrier Reef.

The export of pollutant loads from coastal catchments is of primary interest to natural resource management. For example, Reef Plan, a joint initiative by the Australian Government and the Queensland Government, has indicated that a 20% reduction in sediment is required by 2020. There is an obvious need to consider our ability to detect any trend if we are to set realistic targets or to reliably identify changes to catchment loads. We investigate the number of years of monitoring aquatic pollutant loads necessary to detect trends. Instead of modelling the trend in the annual loads directly, given their strong relationship to flow, we consider trends through the reduction in concentration for a given flow. Our simulations show very low power (<40%) of detecting changes of 20% over time periods of several decades, indicating that the chances of detecting trends of reasonable magnitudes over these time frames are very small.

Eliciting expert knowledge in conservation science.

Expert knowledge is used widely in the science and practice of conservation because of the complexity of problems, relative lack of data, and the imminent nature of many conservation decisions. Expert knowledge is substantive information on a particular topic that is not widely known by others. An expert is someone who holds this knowledge and who is often deferred to in its interpretation. We refer to predictions by experts of what may happen in a particular context as expert judgments. In general, an expert-elicitation approach consists of five steps: deciding how information will be used, determining what to elicit, designing the elicitation process, performing the elicitation, and translating the elicited information into quantitative statements that can be used in a model or directly to make decisions. This last step is known as encoding. Some of the considerations in eliciting expert knowledge include determining how to work with multiple experts and how to combine multiple judgments, minimizing bias in the elicited information, and verifying the accuracy of expert information. We highlight structured elicitation techniques that, if adopted, will improve the accuracy and information content of expert judgment and ensure uncertainty is captured accurately. We suggest four aspects of an expert elicitation exercise be examined to determine its comprehensiveness and effectiveness: study design and context, elicitation design, elicitation method, and elicitation output. Just as the reliability of empirical data depends on the rigor with which it was acquired so too does that of expert knowledge.

River loads of suspended solids, nitrogen, phosphorus and herbicides delivered to the Great Barrier Reef lagoon.

Degradation of coastal ecosystems in the Great Barrier Reef (GBR) lagoon, Australia, has been linked with increased land-based runoff of suspended solids, nutrients and pesticides since European settlement. This study estimated the increase in river loads for all 35 GBR basins, using the best available estimates of pre-European and current loads derived from catchment modelling and monitoring. The mean-annual load to the GBR lagoon for (i) total suspended solids has increased by 5.5 times to 17,000ktonnes/year, (ii) total nitrogen by 5.7 times to 80,000tonnes/year, (iii) total phosphorus by 8.9 times to 16,000tonnes/year, and (iv) PSII herbicides is 30,000kg/year. The increases in river loads differ across the 10 pollutants and 35 basins examined, reflecting differences in surface runoff, urbanisation, deforestation, agricultural practices, mining and retention by reservoirs. These estimates will facilitate target setting for water quality and desired ecosystem states, and enable prioritisation of critical sources for management.

A guide to eliciting and using expert knowledge in Bayesian ecological models.

Expert knowledge in ecology is gaining momentum as a tool for conservation decision-making where data are lacking. Yet, little information is available to help a researcher decide whether expert opinion is useful for their model, how an elicitation should be conducted, what the most relevant method for elicitation is and how this can be translated into prior distributions for analysis in a Bayesian model. In this study, we provide guidance in using expert knowledge in a transparent and credible manner to inform ecological models and ultimately natural resource and conservation decision-making. We illustrate the decisions faced when considering the use of expert knowledge in a model with the help of two real ecological case studies. These examples are explored further to examine the impact of expert knowledge through 'priors' in Bayesian modeling and specifically how to minimize potential bias. Finally, we make recommendations on the use of expert opinion in ecology. We believe if expert knowledge is elicited and incorporated into ecological models with the same level of rigour provided in the collection and use of empirical data, expert knowledge can increase the precision of models and facilitate informed decision-making in a cost-effective manner.

Zero tolerance ecology: improving ecological inference by modelling the source of zero observations.

A common feature of ecological data sets is their tendency to contain many zero values. Statistical inference based on such data are likely to be inefficient or wrong unless careful thought is given to how these zeros arose and how best to model them. In this paper, we propose a framework for understanding how zero-inflated data sets originate and deciding how best to model them. We define and classify the different kinds of zeros that occur in ecological data and describe how they arise: either from 'true zero' or 'false zero' observations. After reviewing recent developments in modelling zero-inflated data sets, we use practical examples to demonstrate how failing to account for the source of zero inflation can reduce our ability to detect relationships in ecological data and at worst lead to incorrect inference. The adoption of methods that explicitly model the sources of zero observations will sharpen insights and improve the robustness of ecological analyses.

Fitting genetic models to twin data with binary and ordered categorical responses: a comparison of structural equation modelling and Bayesian hierarchical models.

We compare Bayesian methodology utilizing free-ware BUGS (Bayesian Inference Using Gibbs Sampling) with the traditional structural equation modelling approach based on another free-ware package, Mx. Dichotomous and ordinal (three category) twin data were simulated according to different additive genetic and common environment models for phenotypic variation. Practical issues are discussed in using Gibbs sampling as implemented by BUGS to fit subject-specific Bayesian generalized linear models, where the components of variation may be estimated directly. The simulation study (based on 2000 twin pairs) indicated that there is a consistent advantage in using the Bayesian method to detect a "correct" model under certain specifications of additive genetics and common environmental effects. For binary data, both methods had difficulty in detecting the correct model when the additive genetic effect was low (between 10 and 20%) or of moderate range (between 20 and 40%). Furthermore, neither method could adequately detect a correct model that included a modest common environmental effect (20%) even when the additive genetic effect was large (50%). Power was significantly improved with ordinal data for most scenarios, except for the case of low heritability under a true ACE model. We illustrate and compare both methods using data from 1239 twin pairs over the age of 50 years, who were registered with the Australian National Health and Medical Research Council Twin Registry (ATR) and presented symptoms associated with osteoarthritis occurring in joints of the hand.

The validity and heritability of self-report osteoarthritis in an Australian older twin sample.

In order to investigate the genetic and environmental antecedents of osteoarthritis (OA), self-report measures of joint pain, stiffness and swelling were obtained from a population-based sample of 1242 twin pairs over 50 years of age. In order to provide validation for these self-report measures, a subsample of 118 twin pairs were examined according to the American College of Rheumatology clinical and radiographic criteria for the classification of osteoarthritis. A variety of statistical methods were employed to identify the model derived from self-report variables which would provide optimal prediction of these standardised assessments, and structural equation modelling was used to determine the relative influences of genetic and environmental influences on the development of osteoarthritis. Significant genetic effects were found to contribute to osteoarthritis of the hands, hips and knees in women, with heritability estimates ranging from 30-46% depending on the site. In addition, the additive genetic effects contributing to osteoarthritis in various parts of the body were confirmed to be the same. Statistically significant familial aggregation of osteoarthritis in men was also observed, but it was not possible to determine whether this was due to genetic or shared environmental effects.