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Updated: Oct 29, 2025

Spotting Cheetahs: Identifying Individuals by Their Footprints
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Evaluating and integrating spatial capture-recapture models with data of variable individual identifiability.

Joel S Ruprecht1, Charlotte E Eriksson1, Tavis D Forrester2

  • 1Department of Fisheries and Wildlife, Oregon State University, 104 Nash Hall, Corvallis, Oregon, 97331, USA.

Ecological Applications : a Publication of the Ecological Society of America
|July 10, 2021
PubMed
Summary

Comparing spatial capture-recapture (SCR) models for carnivore density estimation, this study found hybrid models integrating multiple data sources yielded the most precise results. Models requiring fewer individual identifications showed less consistency in real-world applications.

Keywords:
abundanceblack bearbobcatcamera trappingcarnivorecougarcountcoyotedensity estimationmark-resightnoninvasive genetic samplingspatial capture-recapture

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Area of Science:

  • Wildlife ecology
  • Population dynamics
  • Conservation biology

Background:

  • Spatial capture-recapture (SCR) models are standard for estimating carnivore densities.
  • Existing SCR models vary in data requirements, from full individual identification to no identification.
  • Real-world consistency and precision of these different SCR model types are not well understood.

Purpose of the Study:

  • To compare the consistency and precision of various spatial capture-recapture models for estimating carnivore population densities.
  • To evaluate a suite of models, from those using only unmarked individuals to hybrid models integrating multiple data types.
  • To assess the impact of individual identification requirements on density estimates for black bears, bobcats, cougars, and coyotes.

Main Methods:

  • Genotyped fecal DNA from scat samples collected by detection dogs for individual identification.
  • Deployed GPS collars on a subset of individuals and used remote cameras for resighting.
  • Applied a range of models, including genetic SCR, camera-based generalized spatial mark-resight (gSMR), unmarked models, and hybrid models integrating all data sources.

Main Results:

  • Camera-based gSMR and genetic SCR models produced comparable density estimates (<10% difference) for bears, cougars, and coyotes when controlling for covariates.
  • SCR estimates for bobcats were 33% higher than gSMR estimates, potentially due to species-specific challenges.
  • Unmarked models showed high variability, with estimates becoming more consistent as more individuals were identifiable; hybrid models offered the highest precision.

Conclusions:

  • In real-world scenarios, models lacking individual identification can produce highly variable density estimates.
  • Hybrid models integrating multiple data sources (genetic, GPS, camera) provide the most precise population density estimates.
  • Researchers should exercise caution with models requiring minimal individual identification and prioritize methods using marked individuals and multiple data streams for robust inference.