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Updated: Sep 1, 2025

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Detecting target species: with how many samples?

Rosario Delgado1

  • 1Department of Mathematics, Universitat Autònoma de Barcelona, Campus de la UAB, Cerdanyola del Vallès 08193, Spain.

Royal Society Open Science
|August 12, 2022
PubMed
Summary
This summary is machine-generated.

Accurately determining species presence requires sufficient sampling. This study refines models to calculate the minimum samples needed, accounting for finite areas and detection errors, crucial for ecological management.

Keywords:
Negative BinomialPoissondetection errorsamplingtarget species

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Area of Science:

  • Ecology
  • Environmental Management
  • Conservation Biology

Background:

  • Accurate species detection is vital for ecological studies, environmental management, and conservation planning.
  • Current methods rely on sampling, but existing models by Green & Young (1993) have limitations.
  • These limitations include not accounting for finite area sizes and potential detection errors.

Purpose of the Study:

  • To generalize existing species detection models to account for finite area sizes and detectability errors.
  • To provide a more accurate estimation of the minimum number of samples required for species detection.
  • To address overestimation of sample sizes and biases caused by ignoring real-world sampling conditions.

Main Methods:

  • Generalization of Poisson and Negative Binomial distribution models for species detection.
  • Inclusion of two scenarios: finite area size (N) and detectability errors (probability δ).
  • Mathematical derivation to recover Green & Young's results under specific conditions (N → ∞, δ = 0).

Main Results:

  • Developed an approximation that corrects for overestimation of sample sizes in finite areas.
  • Developed an approximation that corrects for bias introduced by assuming no detectability errors.
  • The generalized models accurately reflect real-world ecological survey conditions.

Conclusions:

  • Ignoring finite area size leads to oversampling, increasing costs and effort.
  • Ignoring detection errors introduces bias, potentially leading to incorrect ecological assessments.
  • The proposed approximation effectively addresses these issues for both Poisson and Negative Binomial models, improving sampling strategies.