Dan Pagendam

Commonwealth Scientific and Industrial Research Organisation — Australia

Environmental Science · Physical Sciences

21h-index1.5kcitations106works2.22yr avg
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Research Topics

Hydrology and Watershed Management Studies(26), Soil Geostatistics and Mapping(12), Insect symbiosis and bacterial influences(12), Mosquito-borne diseases and control(11), Flood Risk Assessment and Management(11)

Publications106 total

Estimating the dispersal of the malaria vector <i>Anopheles farauti</i> through a natural ecosystem in north Queensland, Australia using mark release and recapture experiments
Journal of Medical Entomology·2025Open Access
CQUESST: A dynamical stochastic framework for predicting soil-carbon sequestration
The Annals of Applied Statistics·2025· 1 cited
Predicting animal movement with <scp>deepSSF</scp> : A deep learning step selection framework
Methods in Ecology and Evolution·2025Open Access
Author response for "Predicting animal movement with deepSSF: A deep learning step selection framework"
2025
Effective <i>Aedes</i> mosquito reduction through In2Care interventions in an extreme environment
Journal of Medical Entomology·2025
Author response for "Predicting animal movement with deepSSF: A deep learning step selection framework"
2025
Modelling \textit{Aedes albopictus} management, incorporating immigration and bi-directional \textit{Wolbachia} interactions
arXiv (Cornell University)·2025Open Access
Predicting animal movement with deepSSF: a deep learning step selection framework
2025Open Access
Author response for "Predicting animal movement with deepSSF: A deep learning step selection framework"
2025
Predicting fine‐scale distributions and emergent spatiotemporal patterns from temporally dynamic step selection simulations
Ecography·2024· 10 citedOpen Access
CQUESST: A dynamical stochastic framework for predicting soil-carbon sequestration
arXiv (Cornell University)·2024Open Access
Author response for "Predicting fine‐scale distributions and emergent spatiotemporal patterns from temporally dynamic step selection simulations"
2024
Assessing predictability of environmental time series with statistical and machine learning models
Environmetrics·2024· 13 citedOpen Access
Author response for "Predicting fine‐scale distributions and emergent spatiotemporal patterns from temporally dynamic step selection simulations"
2024
Predicting fine-scale distributions and emergent spatiotemporal patterns from temporally dynamic step selection simulations
2024· 3 citedOpen Access
Gaussian Ensemble Belief Propagation for Efficient Inference in High-Dimensional Systems
arXiv (Cornell University)·2024Open Access
Innovative approaches in soil carbon sequestration modelling for better prediction with limited data
Scientific Reports·2024· 11 citedOpen Access
Modelling and predicting soil carbon sequestration: is current model structure fit for purpose?
Research Square (Research Square)·2023Open Access
Geostatistical based optimization of groundwater monitoring well network design
Frontiers in Earth Science·2023· 10 citedOpen Access
The effect of biologically mediated decay rates on modelling soil carbon sequestration in agricultural settings
Environmental Modelling & Software·2023· 6 citedOpen Access
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Frequent Co-authors

Brendan Trewin(7), Andrew P. Colefax(6), Paul A. Butcher(6), Brendan P. Kelaher(6), Petra Kuhnert(6), Nigel W. Beebe(5), Nigel Snoad(5), Scott A. Ritchie(5), David Rassam(5), Sreekanth Janardhanan(5), Daniel W. Gladish(5), Kyran M. Staunton(4), Gregor J. Devine(4), Tao Cui(4), P. K. Pollett(4), Christopher J. Paton(3), Mat Gilfedder(3), Joel Janek Dabrowski(3), Andrew D. W. Geering(3), Luk Peeters(3)