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Updated: Apr 30, 2026

Modeling the Size Spectrum for Macroinvertebrates and Fishes in Stream Ecosystems
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Quantifying extinction probabilities from sighting records: inference and uncertainties.

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  • 1Commonwealth Scientific and Industrial Research Organisation Division of Computational Informatics, Canberra, Australia; Commonwealth Scientific and Industrial Research Organisation Biosecurity Flagship, Brisbane, Australia.

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Summary
This summary is machine-generated.

Estimating extinction probability is crucial for conservation and invasive species management. Complex models, while more realistic, may yield uncertain results with sparse data, potentially overestimating population persistence.

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Area of Science:

  • Ecology
  • Conservation Biology
  • Biostatistics

Background:

  • Estimating extinction probability is vital for managing endangered species and invasive populations.
  • Existing models often assume constant or declining sighting rates, which may not hold for invasive species with growing populations.

Purpose of the Study:

  • To develop and apply Bayesian methods for estimating extinction probability using sighting data.
  • To investigate the impact of incorporating density-dependent survival and detection rates into extinction models.
  • To assess the reliability of extinction probability estimates under different model complexities.

Main Methods:

  • Bayesian framework for estimating detection and survival probabilities.
  • Application of models to sparse carcass discovery data of European red fox (Vulpes vulpes) in Tasmania.
  • Comparison of a simple model with constant parameters versus a complex model with density-dependent parameters.

Main Results:

  • Simple models provided precise but potentially inaccurate estimates of extinction probability.
  • Complex models, accounting for density-dependent processes, revealed greater uncertainty due to sparse data.
  • Analysis suggested a higher probability of population persistence for the invasive red fox in Tasmania.

Conclusions:

  • Existing models assuming constant parameters are adequate only when these assumptions are valid.
  • Complex models are necessary when parameters vary, but require sufficient data to avoid overstating precision.
  • Inaccurate extinction probability estimates can lead to flawed conservation and management decisions.