A note on investigating co-occurrence patterns and dynamics for many species, with imperfect detection and a...

Area of Science:

• Ecology
• Ecological modeling
• Wildlife biology

Background:

• Species co-occurrence patterns are crucial in ecology.
• Imperfect detection can bias ecological interaction inferences.
• Existing models for imperfect detection have limitations.

Purpose of the Study:

• To propose a novel parameterization for ecological models that accounts for imperfect detection.
• To enable robust estimation of covariate effects in species co-occurrence dynamics.
• To provide a flexible framework applicable to various species and estimation methods.

Main Methods:

• Developed a log-linear model parameterization.
• The parameterization uses current or previous species presence as predictors for current occurrence.
• Applied maximum likelihood or Bayesian estimation frameworks.

Main Results:

• The proposed parameterization avoids the need for a predefined species hierarchy.
• It allows for numerically robust estimation of covariate effects.
• Demonstrated application using camera-trapping data of mesocarnivores in South Texas.

Conclusions:

• The new parameterization offers symmetric interpretations of ecological interactions.
• It provides a robust and flexible approach to modeling species co-occurrence with imperfect detection.
• This method enhances ecological inference in complex community dynamics.