ORCID Profile
0000-0003-1635-5554
Current Organisations
RMIT University
,
The University of Hong Kong
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In Research Link Australia (RLA), "Research Topics" refer to ANZSRC FOR and SEO codes. These topics are either sourced from ANZSRC FOR and SEO codes listed in researchers' related grants or generated by a large language model (LLM) based on their publications.
Environmental Science and Management | Conservation and Biodiversity | Biostatistics | Community Ecology | Stochastic Analysis and Modelling | Landscape Ecology
Expanding Knowledge in the Environmental Sciences | Expanding Knowledge in the Biological Sciences | Forest and Woodlands Flora, Fauna and Biodiversity | Ecosystem Assessment and Management at Regional or Larger Scales | Flora, Fauna and Biodiversity at Regional or Larger Scales | Expanding Knowledge in the Mathematical Sciences |
Publisher: Springer Science and Business Media LLC
Date: 19-03-2010
Publisher: Wiley
Date: 28-07-2010
Abstract: In epidemiology, capture-recapture models are commonly used to estimate the size of an unknown population based on several incomplete lists of in iduals. The method operates under two main assumptions: independence between the lists (local independence) and homogeneity of capture probabilities of in iduals. In practice, these assumptions are rarely satisfied. We introduce a multinomial latent class model that can account for both list dependence and heterogeneity. Parameter estimation is performed by maximizing the conditional likelihood function with the use of the EM algorithm. In addition, a new approach for evaluating the standard errors of the parameter estimates is discussed, which considerably reduces the computational burden associated with the evaluation of the variance of the population size estimate.
Publisher: Wiley
Date: 03-2004
DOI: 10.1111/J.0006-341X.2002.00192.X
Abstract: Conditional likelihood based on counting processes are combined with a Horvitz-Thompson estimator to yield a population size estimator that is more efficient than the existing ones. Random removals are allowed in the recapturing process. Simulation studies are shown to assess the performance of the proposed estimators. Ex les on a bird banding and a small mammal recapturing study are given.
Publisher: Cold Spring Harbor Laboratory
Date: 17-02-2021
DOI: 10.1101/2021.02.16.431325
Abstract: Joint species distribution models (JSDMs) are a recent development in biogeography and enable the spatial modelling of multiple species and their interactions and dependencies. However, most models do not consider imperfect detection, which can significantly bias estimates. This is one of the first papers to account for imperfect detection when fitting data with JSDMs and to explore the complications that may arise. A multivariate probit JSDM that explicitly accounts for imperfect detection is proposed, and implemented using a Bayesian hierarchical approach. We investigate the performance of the JSDM in the presence of imperfect detection for a range of factors, including varied levels of detection and species occupancy, and varied numbers of survey sites and replications. To understand how effective this JSDM is in practice, we also compare results to those from a JSDM that does not explicitly model detection but instead makes use of “collapsed data”. A case study of owls and gliders in Victoria Australia is also illustrated. Using simulations, we found that the JSDMs explicitly accounting for detection can accurately estimate intrinsic correlation between species with enough survey sites and replications. Reducing the number of survey sites decreases the precision of estimates, while reducing the number of survey replications can lead to biased estimates. For low probabilities of detection, the model may require a large number of survey replications to remove bias from estimates. However, JSDMs not explicitly accounting for detection may have a limited ability to dis-entangle detection from occupancy, which substantially reduces their ability to accurately infer the species distribution spatially. Our case study showed positive correlation between Sooty Owls and Greater Gliders, despite a low number of survey replications. To avoid biased estimates of inter-species correlations and species distributions, imperfect detection needs to be considered. However, for low probability of detection, the JSDMs explicitly accounting for detection is data hungry. Estimates from such models may still be subject to bias. To overcome the bias, researchers need to carefully design surveys and choose appropriate modelling approaches. The survey design should ensure sufficient survey replications for unbiased inferences on species inter-dependencies and occupancy.
Publisher: Wiley
Date: 2002
DOI: 10.1002/ENV.551
Publisher: Wiley
Date: 28-06-2013
DOI: 10.1111/NRM.12017
Publisher: Wiley
Date: 23-10-2003
Publisher: Copernicus GmbH
Date: 05-10-2011
Abstract: Abstract. GPS radio occultation (RO) has been recognised as an alternative atmospheric upper air observation technique due to its distinct features and technological merits. The CHAllenging Minisatellite Payload (CHAMP) RO satellite and FORMOSAT-3/COSMIC (Constellation Observing System for Meteorology, Ionosphere and Climate) RO constellation together have provided about ten years of high quality global coverage RO atmospheric profiles. This technique is best used for meteorological studies in the difficult-to-access areas such as deserts and oceans. To better understand and use RO data, effective quality assessment using independent radiosonde data and its associated collocation criteria used in tempo-spatial domain are important. This study compares GPS RO retrieved temperature profiles from both CHAMP (between May 2001 and October 2008) and FORMOSAT-3/COSMIC (between July 2006 and December 2009) with radiosonde data from 38 Australian radiosonde stations. The overall results show a good agreement between the two data sets. Different collocation criteria within 3 h and 300 km between the profile pairs have been applied and the impact of these different collocation criteria on the evaluation results is found statistically insignificantly. The CHAMP and FORMOSAT-3/COSMIC temperature profiles have been evaluated at 16 different pressure levels and the differences between GPS RO and radiosonde at different levels of the atmosphere have been studied. The result shows that the mean temperature difference between radiosonde and CHAMP is 0.39 °C (with a standard deviation of 1.20 °C) and the one between radiosonde and FORMOSAT-3/COSMIC is 0.37 °C (with a standard deviation of 1.24 °C). Different collocation criteria have been applied and insignificant differences were identified amongst the results.
Publisher: CSIRO Publishing
Date: 2013
DOI: 10.1071/AH12174
Abstract: Objective. Capture-recapture analysis was used to more accurately quantify the admission rate for acute pancreatitis in a regional hospital setting, in comparison to the usual method of case ascertainment. Reasons for differences in capture for the various methods were also sought. Methods. Admissions for acute pancreatitis were enumerated over a 40-month period using three data sources: hospital classification of admission diagnoses, prospective case identification, and receipt of diagnosis-specific pathology specimens. Capture-recapture analysis was applied with log-linear modelling to account for likely dependency between data sources. Covariates were noted to explain capture probability by the various data sources and for eventual stratification in the analysis process. Results. For the census period, there were 304 admissions after merging of data sources, giving a crude admission rate of 7.6 per month. Crude ascertainment rates for discharge records and prospective identification were 44% and 52% respectively. Following log-linear modelling, total admissions more than doubled to 644 (adjusted admission rate 16.1 per month). Of the covariates considered, admissions of less than three days’ duration and those occurring in December and January were significantly associated with increased capture by the hospital discharge records data source. Conclusions. In this clinical setting, admissions for acute pancreatitis are grossly underestimated by the standard case ascertainment method. The reasons for this are not clear. Hospital discharge records are nevertheless more effective than prospective case ascertainment for certain cases, such as brief admissions and those in holiday periods. What is known about the topic? Capture–recapture analysis was originally developed in animal ecology, but has since been used to estimate both prevalent and incident cases of human disease. What does this paper add? This study exposes possible deficiencies in the single-source case ascertainment methods used by most hospitals to enumerate incident cases. It is the first time that capture–recapture techniques have been used to estimate acute pancreatitis admissions. What are the implications for practitioners? To obtain accurate admissions estimates for diseases such as acute pancreatitis, capture–recapture analysis with multiple data sources is advisable. One possible solution may be to conduct intermittent prospective censuses to complement existing retrospective ascertainment methods. On a more general level, clinical staff should be better trained to provide more accurate and detailed information in case records.
Publisher: Wiley
Date: 29-12-0029
Publisher: Wiley
Date: 09-2005
Publisher: CSIRO Publishing
Date: 2007
DOI: 10.1071/PY07012
Abstract: An important problem for the homeless service sector is understanding the size of homeless populations, which has implications on planning services and social policy. The aim of this study is to apply capture-recapture methods to count the primary homeless population in the Adelaide city council area, to examine the use of an alternative method to the Australian Bureau of Statistics census. Capture-recapture techniques were used to analyse homeless registers from three different services to estimate the number of primary homeless people in the Adelaide city council area from 19 June to 19 September 2005. Log-linear model and the s le coverage method were employed to analyse the data. The log-linear model results gave a population estimate of 455 (95% confidence interval 299, 762), and the s le coverage method of 311 (95% confidence interval 229, 466), compared with 104 from the Australian Bureau of Statistics census. Multiple sources of information utilising different methodologies should be considered together when attempting to plan services for primary homeless people, as all available techniques have important limitations. Capture-recapture is an important method to supplement any attempt at enumeration of hidden, mobile or difficult-to-reach populations.
Publisher: Elsevier BV
Date: 11-2003
Publisher: Elsevier BV
Date: 03-2020
Publisher: Oxford University Press (OUP)
Date: 19-09-2022
DOI: 10.1111/RSSC.12596
Abstract: In an effort to effectively model observed patterns in the spatial configuration of in iduals of multiple species in nature, we introduce the saturated pairwise interaction Gibbs point process. Its main strength lies in its ability to model both attraction and repulsion within and between species, over different scales. As such, it is particularly well-suited to the study of associations in complex ecosystems. Based on the existing literature, we provide an easy to implement fitting procedure as well as a technique to make inference for the model parameters. We also prove that under certain hypotheses the point process is locally stable, which allows us to use the well-known ‘coupling from the past’ algorithm to draw s les from the model. Different numerical experiments show the robustness of the model. We study three different ecological data sets, demonstrating in each one that our model helps disentangle competing ecological effects on species' distribution.
Publisher: Wiley
Date: 06-2003
Publisher: Wiley
Date: 09-07-2021
Abstract: Joint species distribution models (JSDMs) are a recent development in biogeography and enable the spatial modelling of multiple species and their interactions and dependencies. However, most models do not consider imperfect detection, which can significantly bias estimates. This is one of the first papers to account for imperfect detection when fitting data with JSDMs and to explore the complications that may arise. A multivariate probit JSDM that explicitly accounts for imperfect detection is proposed, and implemented using a Bayesian hierarchical approach. We investigate the performance of the JSDM in the presence of imperfect detection for a range of factors, including varied levels of detection and species occupancy, and varied numbers of survey sites and replications. To understand how effective this JSDM is in practice, we also compare results to those from a JSDM that does not explicitly model detection but instead makes use of ‘collapsed data’. A case study of owls and gliders in Victoria, Australia, is also illustrated. Using simulations, we found that the JSDMs explicitly accounting for detection can accurately estimate intrinsic correlation between species with enough survey sites and replications. Reducing the number of survey sites decreases the precision of estimates, while reducing the number of survey replications can lead to biased estimates. For low probabilities of detection, the model may require a large number of survey replications to remove bias from estimates. However, JSDMs not explicitly accounting for detection may have a limited ability to disentangle detection from occupancy, which substantially reduces their ability to accurately infer the species distribution spatially. Our case study showed positive correlation between Sooty Owls and Greater Gliders, despite a low number of survey replications. To avoid biased estimates of inter‐species correlations and species distributions, imperfect detection needs to be considered. However, for low probability of detection, the JSDMs explicitly accounting for detection is data hungry. Estimates from such models may still be subject to bias. To overcome the bias, researchers need to carefully design surveys and choose appropriate modelling approaches. The survey design should ensure sufficient survey replications for unbiased inferences on species inter‐dependencies and occupancy.
Publisher: Wiley
Date: 03-2009
Publisher: Springer Science and Business Media LLC
Date: 12-2002
DOI: 10.1198/108571102771
Publisher: Cold Spring Harbor Laboratory
Date: 12-01-2023
DOI: 10.1101/2023.01.10.523499
Abstract: Poisson processes have become a prominent tool in species distribution modelling when analysing citizen science data based on presence records. This study examines four distinct statistical approaches, each of which utilises a different approximation to fit a Poisson point process. These include two Poisson regressions with either uniform weights or the more elaborate Berman-Turner device, as well as two logistic regressions, namely the infinitely weighted logistic regression method and Baddeley’s logistic regression developed in the context of spatial Gibbs processes. This last method has not been considered in depth in the context of Poisson point processes in the previous literature. A comprehensive comparison has been conducted on the performance of these four approaches using both simulated and actual presence data sets. When the number of dummy points is sufficiently large, all approaches converge, with the Berman-Turner device demonstrating the most consistent performance. A Poisson process model was developed to accurately predict the distribution of Arctotheca calendula, an invasive weed in Australia that does not appear to have been the subject of any species niche modelling analysis in the existing literature. Our findings are valuable for ecologists and other non-statistical experts who wish to implement the best practices for predicting species’ distribution using Poisson point processes.
Publisher: Springer Science and Business Media LLC
Date: 09-2019
Publisher: Walter de Gruyter GmbH
Date: 18-12-2012
Publisher: Cold Spring Harbor Laboratory
Date: 30-03-2022
DOI: 10.1101/2022.03.29.486220
Abstract: Estimating the prevalence or the absolute probability of presence of a species from presence-background data has become a controversial topic in species distribution modelling. In this paper we propose a new method by combining both statistics and machine learning algorithms that helps overcome some of the known existing problems. We have also revisited the popular but highly controversial Lele and Keim (LK) method by evaluating its performance and assessing the RSPF condition it relies on. Simulations show that the LK method with unfounded model assumptions would render fragile estimation rediction of the desired probabilities. Rather we propose the local knowledge condition, which relaxes the pre-determined population prevalence condition that has so often been used in much of the existing literature. Simulations demonstrate the performance of the CLK method utilising the local knowledge assumption to successfully estimate the probability of presence. The local knowledge extends the local certainty or the prototypical presence location assumption, and has significant implications for demonstrating the necessary condition for identifying absolute (rather than relative) probability of presence without absence data in species distribution modelling.
Publisher: Cambridge University Press (CUP)
Date: 20-04-2011
DOI: 10.1017/S1748499511000042
Abstract: This paper aims to evaluate the aggregate claims distribution under the collective risk model when the number of claims follows a so-called generalised ( a , b , 1) family distribution. The definition of the generalised ( a , b , 1) family of distributions is given first, then a simple matrix-form recursion for the compound generalised ( a , b , 1) distributions is derived to calculate the aggregate claims distribution with discrete non-negative in idual claims. Continuous in idual claims are discussed as well and an integral equation of the aggregate claims distribution is developed. Moreover, a recursive formula for calculating the moments of aggregate claims is also obtained in this paper. With the recursive calculation framework being established, members that belong to the generalised ( a , b , 1) family are discussed. As an illustration of potential applications of the proposed generalised ( a , b , 1) distribution family on modelling insurance claim numbers, two numerical ex les are given. The first ex le illustrates the calculation of the aggregate claims distribution using a matrix-form Poisson for claim frequency with logarithmic claim sizes. The second ex le is based on real data and illustrates maximum likelihood estimation for a set of distributions in the generalised ( a , b , 1) family.
Publisher: Springer Netherlands
Date: 2014
Publisher: Springer-Verlag
Publisher: Elsevier BV
Date: 05-2009
Publisher: Elsevier BV
Date: 05-2017
Publisher: Wiley
Date: 28-06-2020
Publisher: Elsevier BV
Date: 09-2013
Publisher: The University of Hong Kong Libraries
DOI: 10.5353/TH_B3124372
Publisher: Wiley
Date: 04-2017
Publisher: Scientific Research Publishing, Inc.
Date: 2012
Publisher: Wiley
Date: 06-2022
DOI: 10.1002/ECE3.8998
Abstract: Estimating the prevalence or the absolute probability of the presence of a species from presence‐background data has become a controversial topic in species distribution modelling. In this paper, we propose a new method by combining both statistics and machine learning algorithms that helps overcome some of the known existing problems. We have also revisited the popular but highly controversial Lele and Keim (LK) method by evaluating its performance and assessing the RSPF condition it relies on. Simulations show that the LK method with the RSPF assumptions would render fragile estimation rediction of the desired probabilities. Rather, we propose the local knowledge condition, which relaxes the predetermined population prevalence condition that has so often been used in much of the existing literature. Simulations demonstrate the performance of the new method utilizing the local knowledge assumption to successfully estimate the probability of presence. The local knowledge extends the local certainty or the prototypical presence location assumption, and has significant implications for demonstrating the necessary condition for identifying absolute (rather than relative) probability of presence from presence background without absence data in species distribution modelling.
Publisher: Hogrefe Publishing Group
Date: 07-2010
DOI: 10.1027/0027-5910/A000023
Abstract: Background: We study the impact of suicide-exclusion periods, common in life insurance policies in Australia, on suicide and accidental death rates for life-insured in iduals. If a life-insured in idual dies by suicide during the period of suicide exclusion, commonly 13 months, the sum insured is not paid. Aims: We examine whether a suicide-exclusion period affects the timing of suicides. We also analyze whether accidental deaths are more prevalent during the suicide-exclusion period as life-insured in iduals disguise their death by suicide. We assess the relationship between the insured sum and suicidal death rates. Methods: Crude and age-standardized rates of suicide, accidental death, and overall death, split by duration since the insured first bought their insurance policy, were computed. Results: There were significantly fewer suicides and no significant spike in the number of accidental deaths in the exclusion period for Australian life insurance data. More suicides, however, were detected for the first 2 years after the exclusion period. Higher insured sums are associated with higher rates of suicide. Conclusions: Adverse selection in Australian life insurance is exacerbated by including a suicide-exclusion period. Extension of the suicide-exclusion period to 3 years may prevent some “insurance-induced” suicides – a rationale for this conclusion is given.
Publisher: CSIRO Publishing
Date: 2020
DOI: 10.1071/WR19216
Abstract: Abstract Context. Feral cats (Felis catus) pose a significant threat to Australia’s native species and feral cat control is, therefore, an important component of threatened species management and policy. Australia’s Threatened Species Strategy articulates defined targets for feral cat control. Yet, currently, little is known about who is engaged in feral cat control in Australia, what motivates them, and at what rate they are removing feral cats from the environment. Aims. We aim to document who is engaging in feral cat control in Australia, how many cats they remove and to estimate the number of feral cats killed in a single year. Furthermore, we seek to better understand attitudes towards feral cat control in Australia. Methods. We used a mixed methods approach combining quantitative and qualitative techniques. Feral cat control data were obtained from existing data repositories and via surveys targeting relevant organisations and in iduals. A bounded national estimate of the number of feral cats killed was produced by combining estimates obtained from data repositories and surveys with modelled predictions for key audience segments. Attitudes towards feral cat control were assessed by exploring qualitative responses to relevant survey questions. Key results. We received information on feral cat control from three central repositories, 134 organisations and 2618 in iduals, together removing more than 35000 feral cats per year. When including projections to national populations of key groups, the estimated number of feral cats removed from the environment in the 2017–2018 financial year was 316030 (95% CI: 297742–334318). Conclusions. In iduals and organisations make a significant, and largely unrecorded, contribution to feral cat control. Among in iduals, there is a strong awareness of the impact of feral cats on Australia’s bio ersity. Opposition to feral cat control focussed largely on ethical concerns and doubts about its efficacy. Implications. There is significant interest in, and commitment to, feral cat control among some groups of Australian society, beyond the traditional conservation community. Yet more information is needed about control methods and their effectiveness to better understand how these efforts are linked to threatened species outcomes.
Start Date: 2022
End Date: 2024
Funder: Australian Research Council
View Funded ActivityStart Date: 2015
End Date: 2017
Funder: Australian Research Council
View Funded ActivityStart Date: 2019
End Date: 2021
Funder: Australian Research Council
View Funded ActivityStart Date: 05-2015
End Date: 10-2020
Amount: $295,900.00
Funder: Australian Research Council
View Funded ActivityStart Date: 07-2022
End Date: 06-2025
Amount: $469,107.00
Funder: Australian Research Council
View Funded ActivityStart Date: 05-2019
End Date: 12-2023
Amount: $437,000.00
Funder: Australian Research Council
View Funded Activity