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Mário Ferreira1, Ana Filipa Filipe1, David C Bardos2

  • 1EDP Biodiversity Chair CIBIO/InBIO Centro de Investigação em Biodiversidade e Recursos Genéticos da Universidade do Porto Campus Agrário de Vairão, R. Padre Armando Quintas 4485-661 Vairão Portugal; CEABN/InBIO Centro de Ecologia Aplicada "Professor Baeta Neves" Instituto Superior de Agronomia Universidade de Lisboa Tapada da Ajuda 1349-017 Lisboa Portugal.

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Summary
This summary is machine-generated.

This study introduces an improved occupancy-detection model for species distribution modeling (SDM) that accounts for detection times recorded in intervals. The new method accurately estimates species distribution and detection rates in aquatic environments.

Keywords:
Distribution modelinghierarchical Bayesian modelsimperfect detectionoccupancy‐detection modelingstream fishsurvival analysistime to first detection

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Area of Science:

  • Ecology
  • Conservation Biology
  • Environmental Science

Background:

  • Accurate species distribution models (SDMs) require accounting for imperfect detection.
  • Time-to-detection models are useful but limited by precise time knowledge.

Purpose of the Study:

  • To extend time-to-detection models for interval-censored data.
  • To develop a robust framework for species distribution modeling with imperfect detection.

Main Methods:

  • Developed a Bayesian hierarchical framework incorporating interval-censored survival models.
  • Modeled occupancy and time-to-detection for six fish species in a Mediterranean watershed.
  • Used electrofishing data and environmental/spatial variables.

Main Results:

  • The modified time-to-detection model yielded unbiased parameter estimates.
  • Detection rates were influenced by water depth and stream width.
  • Species occupancy was affected by stream order, elevation, and precipitation.
  • Models demonstrated adequate fit and high discrimination ability.

Conclusions:

  • The interval-censored time-to-detection model offers a practical solution for occupancy-detection modeling with interval data.
  • This framework enhances SDMs by controlling for detection rate variation.
  • The approach utilizes readily collectible field data for ecological studies.