ORCID Profile
0000-0002-4628-3558
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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.
Economic Models and Forecasting | Ecological Impacts of Climate Change | Environmental Science and Management | Conservation and Biodiversity |
Economic Incentives for Environmental Protection | Flora, Fauna and Biodiversity at Regional or Larger Scales | Trade and Environment
Publisher: The University of Kansas
Date: 06-03-2022
Abstract: The field of distributional ecology has seen considerable recent attention, particularly surrounding the theory, protocols, and tools for Ecological Niche Modeling (ENM) or Species Distribution Modeling (SDM). Such analyses have grown steadily over the past two decades—including a maturation of relevant theory and key concepts—but methodological consensus has yet to be reached. In response, and following an online course taught in Spanish in 2018, we designed a comprehensive English-language course covering much of the underlying theory and methods currently applied in this broad field. Here, we summarize that course, ENM2020, and provide links by which resources produced for it can be accessed into the future. ENM2020 lasted 43 weeks, with presentations from 52 instructors, who engaged with participants globally through ,000 hours of viewing and ,000 views of instructional video and question-and-answer sessions. Each major topic was introduced by an “Overview” talk, followed by more detailed lectures on subtopics. The hierarchical and modular format of the course permits updates, corrections, or alternative viewpoints, and generally facilitates revision and reuse, including the use of only the Overview lectures for introductory courses. All course materials are free and openly accessible (CC-BY license) to ensure these resources remain available to all interested in distributional ecology.
Publisher: Wiley
Date: 11-02-2012
Publisher: MDPI AG
Date: 23-02-2022
Abstract: Gestational diabetes mellitus (GDM) has serious consequences for both maternal and neonatal health. The growing number of noncommunicable diseases and related risk factors as well as the introduction of new World Health Organization (WHO) diagnostic criteria for GDM are likely to impact the GDM prevalence in Bangladesh. Our study aimed to assess the national prevalence and identify the risk factors using the most recent WHO criteria. We used the secondary data of 272 pregnant women (weighted for s ling strategy) from the Bangladesh Demographic and Health Survey 2017–2018. Multivariate logistic regression was performed to determine the risk factors of GDM. The overall prevalence of GDM in Bangladesh was 35% (95/272). Increased odds of GDM were observed among women living in the urban areas (adjusted odds ratio (aOR) 2.74, 95% confidence interval (CI) 1.43–5.27) compared to rural areas and those aged ≥25 years (aOR 2.03, 95% CI 1.13–3.65). GDM rates were less prevalent in the later weeks of pregnancy compared to early weeks. Our study demonstrates that the national prevalence of GDM in Bangladesh is very high, which warrants immediate attention of policy makers, health practitioners, public health researchers, and the community. Context-specific and properly tailored interventions are needed for the prevention and early diagnosis of GDM.
Publisher: Elsevier BV
Date: 06-2019
Publisher: Wiley
Date: 18-05-2012
Publisher: Elsevier BV
Date: 2022
Publisher: Wiley
Date: 22-12-2023
DOI: 10.1002/EAP.2762
Abstract: Monitoring trends in animal populations in arid regions is challenging due to remoteness and low population densities. However, detecting species' tracks or signs is an effective survey technique for monitoring population trends across large spatial and temporal scales. In this study, we developed a simulation framework to evaluate the performance of alternative track‐based monitoring designs at detecting change in species distributions in arid Australia. We collated presence–absence records from 550 2‐ha track‐based plots for 11 vertebrates over 13 years and fitted ensemble species distribution models to predict occupancy in 2018. We simulated plausible changes in species' distributions over the next 15 years and, with estimates of detectability, simulated monitoring to evaluate the statistical power of three alternative monitoring scenarios: (1) where surveys were restricted to existing 2‐ha plots, (2) where surveys were optimized to target all species equally, and (3) where surveys were optimized to target two species of conservation concern. Across all monitoring designs and scenarios, we found that power was higher when detecting increasing occupancy trends compared to decreasing trends owing to the relatively low levels of initial occupancy. Our results suggest that surveying 200 of the existing plots annually (with a small subset resurveyed twice within a year) will have at least an 80% chance of detecting 30% declines in occupancy for four of the five invasive species modeled and one of the six native species. This increased to 10 of the 11 species assuming larger (50%) declines. When plots were positioned to target all species equally, power improved slightly for most compared to the existing survey network. When plots were positioned to target two species of conservation concern (crest‐tailed mulgara and dusky hopping mouse), power to detect 30% declines increased by 29% and 31% for these species, respectively, at the cost of reduced power for the remaining species. The effect of varying survey frequency depended on its trade‐off with the number of sites s led and requires further consideration. Nonetheless, our research suggests that track‐based surveying is an effective and logistically feasible approach to monitoring broad‐scale occupancy trends in desert species with both widespread and restricted distributions.
Publisher: The Royal Society
Date: 20-02-2019
Abstract: Biological invasions are on the rise globally. To reduce future invasions, it is imperative to determine the naturalization potential of species. Until now, screening approaches have relied largely on species-specific functional feature data. Such information is, however, time-consuming and expensive to collect, thwarting the screening of large numbers of potential invaders. We propose to resolve such data limitations by developing indicators of establishment success of alien species that can be readily derived from open-access databases. These indicators describe key features of successfully established aliens, including estimates of potential range size, niche overlap with human-disturbed environments, and proxies of species traits related to their palaeoinvasions and local dominance capacities. We demonstrate the utility of this new approach by applying it to two large and highly invasive plant groups: Australian acacias and eucalypts. Our results show that these indicators robustly predict establishment successes and failures in each clade independently, and that they can cross-predict establishment in these two clades. Interestingly, the indicator identified as most important was species potential range size on Earth, a variable too rarely considered as a predictor. By successfully identifying key features that predispose Australian plants to naturalize, we provide an objective and cost-effective protocol for flagging high-risk introductions.
Publisher: Elsevier BV
Date: 10-2018
DOI: 10.1016/J.TREE.2018.08.001
Abstract: Predictive models are central to many scientific disciplines and vital for informing management in a rapidly changing world. However, limited understanding of the accuracy and precision of models transferred to novel conditions (their 'transferability') undermines confidence in their predictions. Here, 50 experts identified priority knowledge gaps which, if filled, will most improve model transfers. These are summarized into six technical and six fundamental challenges, which underlie the combined need to intensify research on the determinants of ecological predictability, including species traits and data quality, and develop best practices for transferring models. Of high importance is the identification of a widely applicable set of transferability metrics, with appropriate tools to quantify the sources and impacts of prediction uncertainty under novel conditions.
Publisher: Wiley
Date: 2019
DOI: 10.1111/DDI.12885
Publisher: Wiley
Date: 19-06-2021
DOI: 10.1111/GCB.15723
Abstract: Predictions of species' current and future ranges are needed to effectively manage species under environmental change. Species ranges are typically estimated using correlative species distribution models (SDMs), which have been criticized for their static nature. In contrast, dynamic occupancy models (DOMs) explicitily describe temporal changes in species’ occupancy via colonization and local extinction probabilities, estimated from time series of occurrence data. Yet, tests of whether these models improve predictive accuracy under current or future conditions are rare. Using a long‐term data set on 69 Swiss birds, we tested whether DOMs improve the predictions of distribution changes over time compared to SDMs. We evaluated the accuracy of spatial predictions and their ability to detect population trends. We also explored how predictions differed when we accounted for imperfect detection and parameterized models using calibration data sets of different time series lengths. All model types had high spatial predictive performance when assessed across all sites (mean AUC 0.8), with flexible machine learning SDM algorithms outperforming parametric static and DOMs. However, none of the models performed well at identifying sites where range changes are likely to occur. In terms of estimating population trends, DOMs performed best, particularly for species with strong population changes and when fit with sufficient data, while static SDMs performed very poorly. Overall, our study highlights the importance of considering what aspects of performance matter most when selecting a modelling method for a particular application and the need for further research to improve model utility. While DOMs show promise for capturing range dynamics and inferring population trends when fitted with sufficient data, computational constraints on variable selection and model fitting can lead to reduced spatial accuracy of predictions, an area warranting more attention.
Publisher: Wiley
Date: 06-2020
DOI: 10.1111/ECOG.04960
Start Date: 11-2021
End Date: 10-2025
Amount: $770,971.00
Funder: Australian Research Council
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