Biology subjects

Pagendam, D.

Publications and source records attributed to Pagendam, D..

4 recordsLinked to original sources

bioRxiv2025

Predicting animal movement with deepSSF: a deep learning step selection framework

O_LIPredictions of animal movement are vital for understanding and managing wild populations. However, the fine-scale, complex decision-making of animals can pose challenges for the accurate prediction of trajectories. Step selection functions (SSFs), a common tool for inferring relationships between animal movement and the environment, are also increasingly used to simulate animal trajectories for prediction. Although admitting a lot of flexibility, the SSF framework is limited to its reliance on pre-defined functional forms for fitting to data, and SSFs that involve complex functional forms to model detailed processes can be prohibitively difficult to fit and interpret. C_LIO_LIHere, we present deepSSF, an approach to fit and predict animal movement data using deep learning. Whilst not specific to any particular model, we denote the deepSSF approach as building and training a neural network architecture that receives multiple environmental layers and scalar values as inputs, and outputs a single layer representing the next-step probability. To demonstrate a deepSSF model, we build a model in PyTorch that has distinct but interacting habitat selection and movement subnetworks, which allows for explicit representation of both processes and interpretable intermediate outputs. We apply our model to GPS data of introduced water buffalo (Bubalus bubalis) in the tropical savannas of Northern Australia. C_LIO_LIOur deepSSF model was able to learn features that are present in the habitat covariate layers, such as linear features (rivers, forest edges), and the composition of certain habitat areas, without having to specify them pre-emptively within the SSF framework. It was also able to capture the complex interactions between the habitat covariates as well as temporal dynamics across time of day and year. C_LIO_LIWe expect that the deepSSF approach will generate accurate and informative animal movement trajectories, which can be used for deepening our understanding of animal-environment systems and for the practical management of species. Considering the wide range of deep learning tools, the deepSSF approach could be extended to represent memory and social dynamic processes, with the potential for integrating non-spatial data sources such as accelerometers and physiological sensors. C_LI

ecology↗

bioRxiv2024

Simulating animal movement trajectories from temporally dynamic step selection functions

Understanding and predicting animal movement is fundamental to ecology and conservation management. Models that estimate and then predict animal movement and habitat selection parameters underpin diverse conservation applications, from mitigating invasive species spread to enhancing landscape connectivity. However, many predictive models overlook fine-scale temporal dynamics within their predictions, despite animals often displaying fine-scale behavioural variability that might significantly alter their movement, habitat selection and distribution over time. Incorporating fine-scale temporal dynamics, such as circadian rhythms, within predictive models might reduce the averaging out of such behaviours, thereby enhancing our ability to make predictions in both the short and long term. We tested whether the inclusion of fine-scale temporal dynamics improved both fine-scale (hourly) and long-term (seasonal) spatial predictions for a significant invasive species of Northern Australia, the water buffalo (Bubalus bubalis). Water buffalo require intensive management actions over vast, remote areas and display distinct circadian rhythms linked to habitat use. To inform management operations we generated hourly and dry season prediction maps by simulating trajectories from static and temporally dynamic step selection functions (SSFs) that were fitted to the GPS data of 13 water buffalo. We found that simulations generated from temporally dynamic models replicated the buffalos crepuscular movement patterns and dynamic habitat selection, resulting in more informative and accurate hourly predictions. Additionally, when the simulations were aggregated into long-term predictions, the dynamic models were more accurate and better able to highlight areas of concentrated habitat use that might indicate high-risk areas for environmental damage. Our findings emphasise the importance of incorporating fine-scale temporal dynamics in predictive models for species with clear dynamic behavioural patterns. By integrating temporally dynamic processes into animal movement trajectories, we demonstrate an approach that can enhance conservation management strategies and deepen our understanding of ecological and behavioural patterns across multiple timescales.

ecology↗

bioRxiv2020

Mark-release-recapture of male Aedes aegypti (Diptera: Culicidae): use of rhodamine B to estimate movement, mating and population parameters in preparation for an incompatible male program.

Rapid advances in biological and digital technologies 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 mates. Currently, these interventions infer the success of mating interactions between male and female insects through area-wide population surveillance and observations of mating competitiveness are rare. Furthermore, little is known about male Ae. aegypti behaviours and biology in field settings. In preparation for a large, community scale IIT program, we undertook a series of mark-release-recapture experiments using rhodamine B to mark male Ae. aegypti sperm and measure mating interactions with females. We also developed the Spatial and Temporally Evolving Isotropic Kernel (STEIK) framework to assist researchers to estimate the movement of individuals through space and time. Results showed that ~40% of daily females captured were unmated, suggesting interventions will need to release males regularly to be effective at suppressing Ae. aegypti populations. Males moved rapidly through the landscape, particularly when released during the night. Although males moved further than what is typically observed in females of the species, survival was considerably lower. These unique insights will lead to a greater understanding of mating interactions in wild insect populations and lay the foundation for robust suppression strategies in the future. Author SummaryModern scientific techniques for controlling populations of the dengue vector, Aedes aegypti, utilize the mating biology of adult male mosquitoes to achieve suppression through a sterilization process. As the study of Ae. aegypti control has typically focused on adult female mosquitoes, knowledge on male movement, survival and mating interactions in the field is lacking. Here we undertook several mark-release-recapture experiments on adult male Ae. aegypti in Innisfail, Australia, and measured important biological parameters. For the first time in large field experiments, we employed rhodamine B as a marker that when fed to adult males, identified both marked males and the wild females they mated with. We observed males moving further through the landscape, but surviving for a shorter period, than previous measurements undertaken on females in a field setting. A high proportion (~40%) of unmated females suggests individuals are constantly available for mating. As such, sterile male strategies may need to release at regular intervals to achieve effective population suppression. The unique insights provided by this study will assist in designing future sterile male field interventions.

ecology↗

bioRxiv2020

Modelling the Wolbachia Incompatible Insect Technique: strategies for effective mosquito population elimination

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 separation of male from female insects in mass production systems for inundative release into landscapes. Processes for sex-separating mosquitoes are typically error prone, laborious and IIT programs run the risk of releasing Wolbachia infected females and replacing wild mosquito populations. We introduce a simple Markov Population Process (MPP) model for studying mosquito populations subjected to a Wolbachia-IIT program which exhibit an unstable equilibrium threshold. The model is used to study, in silico, scenarios that are likely to yield a successful elimination result. Our results suggest that elimination is best achieved by releasing males at rates that adapt to the ever-decreasing wild population, thus reducing the risk of releasing Wolbachia-infected females while reducing costs. While very high-fidelity sex-separation is required to avoid establishment, release programs tend to be robust to the release of a small number of Wolbachia-infected females. These findings will inform and enhance the next generation of Wolbachia-IIT control strategies that are already showing great promise in field trials.

zoology↗