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Incorporating citizen science data in spatially explicit integrated population models.

Catherine C Sun1, J Andrew Royle2, Angela K Fuller3

  • 1New York Cooperative Fish and Wildlife Research Unit, Department of Natural Resources, Cornell University, 226 Mann Drive, Ithaca, New York, 14853, USA.

Ecology
|June 7, 2019
PubMed
Summary

Citizen science data, when integrated with spatial capture-recapture (SCR) data, significantly improves the accuracy and precision of wildlife population estimates. This approach enhances conservation and management strategies for species like black bears.

Keywords:
black bearcitizen scienceintegrated population modeloccupancyopportunisticpresence-absencerecruitmentspatial capture-recapturesurvival

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Area of Science:

  • Ecology
  • Conservation Biology
  • Wildlife Management

Background:

  • Accurate population data (abundance, distribution, demographics) are vital for species ecology, conservation, and management.
  • Citizen science offers an efficient method for collecting extensive data, especially for elusive or widespread species.
  • Integrated models effectively estimate population parameters by combining diverse data sources, including citizen science data.

Purpose of the Study:

  • To develop and evaluate a spatially explicit integrated model combining citizen science presence-absence (PA) data with spatial capture-recapture (SCR) data.
  • To assess the impact of varying amounts of opportunistic PA data on population parameter estimation for North American black bears (Ursus americanus).

Main Methods:

  • Developed a spatially explicit integrated model merging opportunistic PA data with systematic SCR data.
  • Conducted single and multi-season simulations using North American black bear population parameters.
  • Applied the integrated model to real-world SCR and citizen science PA data for black bears in New York.

Main Results:

  • Integrating opportunistic PA data enhanced the precision and accuracy of abundance, survival, and recruitment rate estimates.
  • Increased PA data improved abundance estimates more than enhanced PA detection probability in some simulations.
  • Integrated model estimates showed precision and unbiasedness comparable to using sparse, high-quality SCR data alone.

Conclusions:

  • Citizen science data, when incorporated into integrated models, provides a cost-effective means to improve population parameter estimates.
  • This approach expands the spatiotemporal scope of ecological inference.
  • Further development of integrated models and citizen science data utilization will advance understanding of population structure and demographics.