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Area of Science:

  • Ecology
  • Conservation Biology
  • Wildlife Management

Background:

  • Integrating diverse data sources is crucial for ecological inference.
  • Opportunistic data's value in spatial ecological processes remains unclear.
  • Formal methods to test data consistency for integration are lacking.

Purpose of the Study:

  • To develop and test an integrated spatial capture-recapture (SCR) model combining structured and opportunistic data.
  • To assess the consistency of parameter estimates across different data types.
  • To evaluate the utility of opportunistic data for improving inference on space use and population size.

Main Methods:

  • Developed a fully integrated spatial capture-recapture (SCR) model.
  • Incorporated a model-based test for data consistency.
  • Combined traditional spatial capture-recapture, telemetry, and opportunistic data from Italian Alps brown bears.

Main Results:

  • Opportunistic data integrate effectively within the SCR framework.
  • Opportunistic data significantly improve inference on space use and population size.
  • Demonstrated the importance of testing for and accounting for data inconsistencies.

Conclusions:

  • Integrated modeling frameworks can successfully combine diverse, spatially-referenced data.
  • Opportunistic data are valuable for studying rare or elusive species.
  • Accounting for data consistency is vital to avoid biased ecological estimates.