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Short-distance detectability in camera trap surveys: implications for population assessment.

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Wildlife population monitoring using camera traps can be inaccurate. This study found that detection probability near cameras is less than 1, leading to underestimated wild boar population density. Models must account for imperfect detection.

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Area of Science:

  • Wildlife ecology
  • Conservation biology
  • Statistical modeling

Background:

  • Camera trapping is crucial for monitoring unmarked wildlife populations.
  • Existing statistical methods often assume perfect detection near camera traps, potentially biasing estimates.
  • The Random Encounter Model (REM) is a common method for density estimation.

Purpose of the Study:

  • To test the assumption of perfect detectability near camera traps for wild boar (Sus scrofa).
  • To evaluate the impact of imperfect near-camera detection on population density estimates.
  • To compare the performance of different camera trap models.

Main Methods:

  • Conducted experiments to measure wild boar detection probability at varying distances (0-6m) from camera traps.
  • Compared population density estimates using a derived detection function versus standard REM functions.
  • Assessed three camera trap models (Bolyguard SG2060-K, Uovision UV595-HD, Browning Spec Ops HP5) for performance.

Main Results:

  • Detection probability was found to be less than 1 even at close proximity to camera traps.
  • The Random Encounter Model underestimated wild boar population density by 17% when not accounting for imperfect detection.
  • No significant performance differences were observed among the tested camera trap models for wild boar.

Conclusions:

  • The assumption of perfect detectability near camera traps is invalid for wild boar.
  • Camera trap population density estimates can be biased if imperfect detection is not incorporated.
  • Accurate wildlife population monitoring requires models that explicitly address detection probabilities below 1.