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Summary
This summary is machine-generated.

Use-availability and presence-only analyses are synonyms, estimating parameters using a shared likelihood. This study generalizes this likelihood and clarifies that Resource Selection Functions (RSFs) should be estimated directly, not via logistic regression.

Keywords:
RSFRSPFhabitat studylikelihoodmodellingresource selection

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Area of Science:

  • Ecology
  • Statistical Modeling
  • Wildlife Management

Background:

  • Use-availability and presence-only analyses are statistically equivalent methods.
  • These methods compare locations where an organism was observed to potential locations.
  • Generalizing these designs allows estimation of relative event probabilities.

Purpose of the Study:

  • To generalize the use-availability likelihood for point locations.
  • To derive the same likelihood as Fithian & Hastie (2012) using a different method.
  • To establish Resource Selection Functions (RSFs) as the appropriate objective for estimation.

Main Methods:

  • Generalizing the use-availability likelihood using Lagrangian multipliers.
  • Defining Resource Selection Functions (RSFs) as ratios of density functions.
  • Demonstrating RSF estimation without logistic regression software.

Main Results:

  • The generalized likelihood matches Fithian & Hastie (2012) but allows more general link functions.
  • The exponential link function is identified as the most appropriate for RSFs.
  • Direct maximization of the use-availability likelihood is confirmed as the correct approach, not logistic regression.

Conclusions:

  • Resource Selection Functions (RSFs) are more general and useful than Resource Selection Probability Functions (RSPFs).
  • The logistic link function is inappropriate for RSFs, making 'logistic regression' a misleading term for this analysis.
  • Direct maximization of the use-availability likelihood is the accurate and recommended method for estimating RSFs.