Mayfield, H. J. | Search bioRxiv

bioRxiv2025

Evaluating a structured expert elicitation approach for adaptive conservation: Lessons from five years in practice

Threatened species management relies on Ex ante estimates of species responses to different interventions to generate meaningful predictions. Structured expert elicitation is often used to generate these estimates, but comparisons of these expert-predicted outcomes with observed results are rare. This study aims to evaluate the utility of expert elicitation for adaptive management in the New South Wales Saving our Species (SoS) program in Australia by revisiting six species management plans that were generated from bespoke structured elicitation guidelines five years prior. Each species management plan included a defined scope, conceptual model, monitoring indicators and estimated response to management curves under different scenarios. Experts reviewed the conceptual models after five years of management and monitoring and compared the predicted response to management with observed monitoring data. In three of the six case studies, observed outcomes closely matched predictions. Where predictions diverged, factors such as unanticipated new threats and unexpected responses to interventions contributed to discrepancies. However, in all cases, the structured approach provided a clear logic for planning, enabling managers to systematically refine their understanding. The conceptual models and response curves proved valuable for collaboration, communication, and generating hypotheses for unexpected results. This work demonstrates the value of the bespoke guidelines in supporting adaptive management processes, strengthening the knowledge base for threatened species conservation while improving alignment between predictions and real-world outcomes.

ecology↗

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↗