D.E. Pagendam

The Commonwealth Scientific and Industrial Research Organisation | CSIRO · Digital Productivity

A statistical framework we call CQUESST (Carbon Quantification and Uncertainty from Evolutionary Soil STochastics), which models carbon sequestration and cycling in soils, is applied to a long-running agricultural experiment that controls for crop type, tillage, and season. The experiment, known as the Millenium Tillage Trial (MTT), ran on 42 field...

The ever increasing popularity of machine learning methods in virtually all areas of science, engineering and beyond is poised to put established statistical modeling approaches into question. Environmental statistics is no exception, as popular constructs such as neural networks and decision trees are now routinely used to provide forecasts of phy...

There has been increasing interest in simulating stochastic movement paths from step selection models. Hitherto, models used to simulate trajectories have not included temporally dynamic coefficients on both the movement and external selection processes, despite animals having temporally dynamic behaviour over daily or seasonal timescales. Here, we...

Soil carbon accounting and prediction play a key role in building decision support systems for land managers selling carbon credits, in the spirit of the Paris and Kyoto protocol agreements. Land managers typically rely on computationally complex models fit using sparse datasets to make these accounts and predictions. The model complexity and spars...

Monitoring groundwater quality in economically important and other aquifers is carried out regularly as part of regulatory processes for water and other resource development. Many water quality parameters are measured as part of baseline monitoring around mining and onshore gas resource development regions to develop improved understanding of the h...

Traditional approaches for learning on categorical data underexploit the dependencies between columns (a.k.a. fields) in a dataset because they rely on the embedding of data points driven alone by the classification/regression loss. In contrast, we propose a novel method for learning on categorical data with the goal of exploiting dependencies betw...

Traditional approaches for learning on categorical data underexploit the dependencies between columns (\aka fields) in a dataset because they rely on the embedding of data points driven alone by the classification/regression loss. In contrast, we propose a novel method for learning on categorical data with the goal of exploiting dependencies betwee...

Aedes aegypti (L.) is an invasive mosquito responsible for vectoring diseases such as dengue, Zika and Chikungunya. Dengue affects a large proportion of the global population, with the World Health Organization estimating that half the global population is at risk, with 390 million infections occurring each year. Control of mosquito vector populati...

We apply the Physics Informed Neural Network (PINN) to the problem of wildfire fire-front modelling. We use the PINN to solve the level-set equation, which is a partial differential equation that models a fire-front through the zero-level-set of a level-set function. The result is a PINN that simulates a fire-front as it propagates through the spat...

We apply the Physics Informed Neural Network (PINN) to the problem of wildfire fire-front modelling. We use the PINN to solve the level-set equation, which is a partial differential equation that models a fire-front through the zero-level-set of a level-set function. The result is a PINN that simulates a fire-front as it propagates through the spat...

We used remote sensing data, field observations and numerical groundwater modelling to investigate long-term groundwater storage losses in the regional aquifer of the Ganga Basin in India. This comprised trend analysis for groundwater level observations from 2851 monitoring bores, groundwater storage anomaly estimation using GRACE and Global Land D...

Releases of Aedes aegypti carrying Wolbachia bacteria are known to suppress arbovirus transmission and reduce the incidence of vector-borne diseases. In planning for Wolbachia releases in the arid environment of Jeddah, Saudi Arabia, we collected entomological data with ovitraps across a 7-month period in four locations. Herein, we show that mosqui...

We propose a novel approach to perform approximate Bayesian inference in complex models such as Bayesian neural networks. The approach is more scalable to large data than Markov Chain Monte Carlo, it embraces more expressive models than Variational Inference, and it does not rely on adversarial training (or density ratio estimation). We adopt the r...

Time series data from environmental monitoring stations are often analysed with machine learning methods on an individual basis, however recent advances in the machine learning field point to the advantages of incorporating multiple related time series from the same monitoring network within a ‘global’ model. This approach provides the opportunity...

Microbial biomass carbon (MBC), a crucial soil labile carbon fraction, is the most active component of the soil organic carbon (SOC) that regulates bio-geochemical processes in terrestrial ecosystems. Some studies in the literature ignore the effect of microbial population growth on carbon decomposition rates. In reality, we might expect that the d...

We explore black-box model inversion using the Fourier Neural Operator (FNO) of Li et al [4]. The approach learns an emulator of a partial differential equation forward operator from simulated realisations and then infers unobserved system parameters by minimising emulator predictive loss with respect to the observations of the system outputs. Our...

A With the underlying aim of increasing efficiency of computational modelling pertinent for managing and protecting the Great Barrier Reef, we perform a preliminary investigation on the use of deep neural networks for opportunistic model emulation of APSIM models by repurposing an existing large dataset containing the outputs of APSIM model runs. T...

Aedes aegypti (Linnaeus) was once highly prevalent across eastern Australia, resulting in epidemics of dengue fever. Drought conditions have led to a rapid rise in semi-permanent, urban water storage containers called rainwater tanks known to be critical larval habitat for the species. The presence of these larval habitats has increased the risk of...

Significance With over 40% of humans at risk from mosquito-borne diseases such as dengue, yellow fever, chikungunya, and Zika, the development of environmentally friendly mosquito-control tools is critical. The release of reproductively incompatible male mosquitoes carrying a Wolbachia bacterium can drive mating events that kill the eggs. Through r...

With the underlying aim of increasing efficiency of computational modelling pertinent for managing & protecting the Great Barrier Reef, we perform a preliminary investigation on the use of deep neural networks for opportunistic model emulation of APSIM models by repurposing an existing large dataset containing outputs of APSIM model runs. The datas...

Gaussian processes (GPs) provide statistically optimal predictions in the sense of unbiasedness and maximal precision. Although the modern implementation of GPs as a machine learning technique is more capable and flexible than Kriging, their employment in environmental science is less routine. Their flexibility and capability as a spatial data inte...

Rapid advances in biological and digital support systems are revolutionizing the population control of invasive disease vectors such as Aedes aegypti. Methods such as the sterile and incompatible insect techniques (SIT/IIT) rely on modified males to seek out and successfully mate with females, and in doing so outcompete the wild male population for...

Soil carbon accounting and prediction play a key role in building decision support systems for land managers selling carbon credits, in the spirit of the Paris and Kyoto protocol agreements. Land managers typically rely on computationally complex models fit using sparse datasets to make these accountings and predictions. The model complexity and sp...

Aerial surveys are increasingly used for assessing the presence and abundance of many large faunal species. However, detection rates often suffer significant sightability errors. Using sensors that capture thermal infrared can sometimes improve the detectability of fauna by increasing the contrast of target animals against their surrounds. However,...

White sharks (Carcharodon carcharias) are attracted to and scavenge on floating whale carcasses. However, little is known about how stranded whale carcasses may affect their behaviour. With increasing whale populations and beach stranding events, sharks may be attracted to nearshore waters at carcass sites, increasing the potential conflict with hu...

This paper discusses several modern approaches to regression analysis involving time series data where some of the predictor variables are also indexed by time. We discuss classical statistical approaches as well as methods that have been proposed recently in the machine learning literature. The approaches are compared and contrasted, and it will b...

Unprovoked shark bites are one of the most recognised human-wildlife conflicts in the marine environment. Historically, management of this threat to public safety largely involved the implementation of lethal strategies. However, there is increasing environmental necessity and social pressure to adopt alternative strategies that minimise harm to sh...

The production of coalbed methane, or coal seam gas (CSG) in Australia increased 250-fold since the 1990s to around 1502 petajoules in 2019 and continues to expand. Groundwater flow in the aquifers intersected by gas wells could potentially facilitate a transport pathway for migration of contaminants or poorer quality water from deeper formations....

Sequestering carbon into the soil can mitigate the atmospheric concentration of greenhouse gases, improving crop productivity and yield financial gains for farmers through the sale of carbon credits. In this work, we develop and evaluate advanced Bayesian methods for modelling soil carbon sequestration and quantifying uncertainty around predictions...

Background: The Wolbachia incompatible insect technique (IIT) shows promise as a method for eliminating populations of invasive mosquitoes such as Aedes aegypti (Linnaeus) (Diptera: Culicidae) and reducing the incidence of vector-borne diseases such as dengue, chikungunya and Zika. Successful implementation of this biological control strategy relie...

Methods such as the sterile and incompatible insect techniques (SIT/IIT) rely on modified males to seek out and successfully mate with females, and in doing so outcompete the wild male population for mates. Currently, these interventions infer the success of mating interactions between male and female insects through area-wide population surveillan...

This paper discusses several modern approaches to regression analysis involving time series data where some of the predictor variables are also indexed by time. We discuss classical statistical approaches as well as methods that have been proposed recently in the machine learning literature. The approaches are compared and contrasted, and it will b...

The G-FLOWS Stage-3 project has developed and applied an integrated approach to the measurement, analysis, and modelling of geophysical, geochemical and hydrogeological techniques, which aim to help more efficiently and effectively target groundwater resources in a remote part of arid Australia. The project has focused on groundwater in the Anangu...

• Recent advances in aerial drones offer new insights into the biology, ecology and behaviour of marine wildlife found on or near the ocean’s surface. While opening up new opportunities for enhanced wildlife monitoring, the impacts of drone sampling and how it might influence interpretations of animal behaviour are only just beginning to be underst...

The Wolbachia Incompatible Insect Technique (IIT) shows promise as a method for eliminating invasive mosquitoes such as Aedes aegypti (Linnaeus)(Diptera: Culicidae) and reducing the incidence of vector-borne diseases such as dengue, chikungunya and Zika. Successful implementation of this biological control strategy relies on high-fidelity separatio...

Many large coastal sharks are vulnerable to population declines, however, conflict with human activities often results in unselective culls. Successfully and non-destructively, addressing human-wildlife conflicts requires understanding of animal behavior. However, knowledge about white shark (Carcharodon carcharias) behavior near surf zones, where...

The contribution of this study is a novel approach to introduce mean reversion in multi-step-ahead forecasts of state-space models. This approach is demonstrated in a prawn pond water quality forecasting application. The mean reversion constrains forecasts by gradually drawing them to an average of previously observed dynamics. This corrects deviat...

The application of river-system models to inform water-resource planning and management is a growing global phenomenon. This requires models to be applied so that they are useful to water decision makers charged with setting targets that provide adequate water flows to sustain landholders and communities. This article examines why and how the innov...

In this study, we developed a workflow that applies a complex groundwater model for purpose-driven groundwater monitoring network design and uses linear uncertainty analysis to explore the predictive dependencies and provide insights into the veracity of the monitoring design. A numerical groundwater flow model was used in a probabilistic modelling...

The contribution of this study is a novel approach to introduce mean reversion in multi-step-ahead forecasts of state-space models. This approach is demonstrated in a prawn pond water quality forecasting application. The mean reversion constrains forecasts by gradually drawing them to an average of previously observed dynamics. This corrects deviat...

Urban landscape features play an important role in the distribution and population spread of mosquito vectors. Furthermore, current insecticide and novel rear-and-release strategies for urban mosquito management rarely consider the spatial structure of the landscape when applying control practices. Here, we undertake a mark-recapture experiment to...

True setae borne on the abdominal tergites of Ochrogaster lunifer Herrich-Schӓffer caterpillars are the agents of an irritating contact dermatitis, osteomyelitis, ophthalmia, and severe allergic reactions in humans, and are the cause of Equine Amnionitis and Fetal Loss in Australia. The setae are detached and readily dislodge from the integument wh...

An increase in shark bites, declining shark populations, and changing social attitudes, has driven an urgent need for non-destructive shark monitoring. While drones may be a useful tool for marine aerial surveillance, their reliability in detecting fauna along coastal beaches has not been established. We developed a drone-based shark surveillance p...

As the incidence of arboviral diseases such as dengue, Zika, chikungunya, and yellow fever increases globally, controlling their primary vector, Aedes aegypti (L.) (Diptera: Culicidae), is of greater importance than ever before. Mosquito control programs rely heavily on effective adult surveillance to ensure methodological efficacy. The Biogents Se...

A crucial decision in defining the scope of an environmental impact assessment is to delineate the initial assessment area. We developed a probabilistic methodology to determine this area, which starts by identifying a key environmental variable, maximum acceptable change and acceptable probability of exceeding that threshold. The exceedance probab...

Sterile insect technique (SIT) and incompatible insect technique (IIT) are current methods for biological control of insect populations. Critical to the successful implementation of these biocontrol programs is quantifying the competitiveness of sterile/incompatible male insects for female mates relative to wildtype males. Traditionally, entomologi...

Bayesian inference provides a mathematically elegant and robust approach to constrain numerical model predictions with system knowledge and observations. Technical challenges, such as evaluating a large number of models with long runtimes, have restricted the application of Bayesian inference to groundwater modeling. To overcome such technical chal...

Modelling and monitoring pollutants entering into the Great Barrier Reef (GBR) lagoon remain important priorities for the Australian and Queensland governments. Uncertainty analysis of pollutant load delivery to the GBR would: (1) inform decision makers on their ability to meet environmental targets; (2) identify whether additional measurements are...

The aim of the history matching method is to locate non-implausible regions of the parameter space of complex deterministic or stochastic models by matching model outputs with data. It does this via a series of waves where at each wave an emulator is fitted to a small number of training samples. An implausibility measure is defined which takes into...

Complex, mechanistic hydrological models can be computationally expensive, have large numbers of input parameters, and generate multivariate output. Model emulators can be constructed to approximate these complex models with substantial computational savings, making activities such as sensitivity analysis, calibration and uncertainty analysis feasi...

Understanding the response of groundwater levels in alluvial and sedimentary basin aquifers to climatic variability and human water-resource developments is a key step in many hydrogeological investigations. This study presents an analysis of groundwater response to climate variability from 2000 to 2012 in the Queensland part of the sedimentary Cla...

A method for the stochastic design of groundwater quality observation well network is presented. The method uses calibration constrained Null-space Monte Carlo analysis for the stochastic simulation of the reduction ratio of peak concentration and the time corresponding to this in an injection well field. The numerical groundwater model simulations...

Changes in groundwater storage lead to a reduction in groundwater contribution to river flow and present as non-stationarity, especially during low-flow conditions. Conventional river models typically ignore this non-stationarity, and, hence, their predictions of declines in low flows during drought periods are likely to be compromised. The present...

This study identified the structural proteins of two badnavirus species, Banana streak MY virus (BSMYV) and Banana streak OL virus (BSOLV), and mapped the distribution of continuous B-cell epitopes. Two different CP isoforms of about 44 and 40 kDa (CP1 and CP2) and the virion-associated protein (VAP) were consistently associated with purified virio...

Soil erosion and sediment transport into waterways and the ocean can adversely affect water clarity, leading to the deterioration of marine ecosystems such as the iconic Great Barrier Reef (GBR) in Australia. Quantifying a sediment load and its associated uncertainty is an important task in delineating how changes in management practices can contri...

Strong statistical evidence was found for differences in tolerance to natural infections of Tobacco streak virus (TSV) in sunflower hybrids. Data from 470 plots involving 23 different sunflower hybrids tested in multiple trials over 5 years in Australia were analysed. Using a Bayesian Hierarchical Logistic Regression (BHLR) model for analysis provi...

De-tiding end-of-catchment flow data is an important step in determining the total volumes of freshwater (and associated pollutant loads) entering the ocean. We examine three approaches for separating freshwater and tidal flows from tidally-affected data: (i) a simple low-pass Butterworth filter (BWF); (ii) a robust, harmonic analysis with Kalman s...

One of the key challenges to assist in the understanding of the potential impacts of coal seam gas (CSG) extraction is the development of robust geological and numerical models. In the Clarence-Moreton Basin, this task is complicated by the need to integrate shallow alluvial aquifers (typically less than 30 m thick) and deep bedrock aquifers, which...

The application of river-system models to inform water-resource planning and management is a growing global phenomenon. This requires models to be applied so that they are useful to water decision makers charged with setting targets that provide adequate water flows to sustain landholders and communities. This article examines why and how the innov...

Global-scale studies of marine food webs are rare, despite their necessity for examining and understanding ecosystem level effects of climate variability. Here we review the progress of an international collaboration that compiled regional diet datasets of multiple top predator fishes from the Indian, Pacific and Atlantic Oceans and developed new s...

Quantifying riverine sediment loads, and the uncertainty around these estimates, is important for monitoring the impact of land use on ecologically sensitive receiving waters such as the Great Barrier Reef lagoon. We used a Bayesian Hierarchical Modelling approach that assimilates information from a process model for runoff, a mechanistically motiv...

The benefits of sequestering carbon are many, including improved crop productivity, reductions in greenhouse gases, and financial gains through the sale of carbon credits. Achieving better understanding of the sequestration process has motivated many deterministic models of soil carbon dynamics, but none of these models address uncertainty in a com...

Efficient and reliable diagnostic tools for the routine indexing and certification of clean propagating material are essential for the management of pospiviroid diseases in horticultural crops. This study describes the development of a true multiplexed diagnostic method for the detection and identification of all nine currently recognized pospiviro...