Biology subjects

Vanderduys, E.

Publications and source records attributed to Vanderduys, E..

2 recordsLinked to original sources

bioRxiv2025

A typology of Australian terrestrial bird communities

AimIncreasing interest in holistic measurement of the response of fauna communities to interventions requires suitable community condition metrics. However, the development of such metrics is hindered by the absence of broad-scale typologies at suitable spatial and ecological resolutions. We aimed to derive a preliminary typology of terrestrial bird communities for Australia, based on bird co-occurrence data, and describe and map the likely distribution of each community type across the continent. LocationMainland Australia, continental islands Time period1973-2022 Major taxa studiedAves MethodsWe used fine-scale co-occurrence data from standard 2-ha surveys in BirdLife Australias citizen-science database. After filtering to reduce bias, we used hierarchical clustering followed by iterative consultation with experts to identify reliably distinct and recognisable terrestrial bird communities across Australia. We used Maxent to model the likely distributions of each community, and developed community descriptions based on each communitys composition and distribution. ResultsThe resultant typology included 29 reliably distinct and recognisable bird communities with major clusters corresponding with seven broad geographical regions. The distributions of bird communities did not correspond tightly to the boundaries of major vegetation groups, with most communities occurring across multiple vegetation types. Main ConclusionsOur preliminary typology of bird communities provides a standard classification at a continental scale. It newly defines distinct bird communities as entities for which condition benchmarks can be established to allow assessment of their conservation status and monitoring of change over time. Refinement will enable cryptic communities in areas with sparse data to be identified. The method could be translated to other regions where adequate coverage of data in the form of standardised surveys of fauna are available. Vast biodiversity datasets delivered through citizen science programs provide the opportunity to develop such typologies for fauna communities, as a precursor to developing targeted and informative community condition metrics.

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↗