Who needs closure? Estimating abundance with a Markovian availability model for geographically open removal sampling

Area of Science:

• Ecology
• Population Dynamics
• Statistical Modeling

Background:

• Removal sampling is crucial for estimating population abundance.
• Traditional removal models assume population closure during sampling, which is often unrealistic.
• Geographic openness and migration can violate closure assumptions, biasing abundance estimates.

Purpose of the Study:

• To develop and validate a novel removal model that incorporates a Markovian availability process to address open populations.
• To relate local abundance to a superpopulation through recruitment dynamics.
• To provide a method for formally testing closure assumptions and estimating abundance without bias when closure is violated.

Main Methods:

• Incorporated a Markovian availability process into an N-mixture model framework.
• Conducted parameter identifiability analysis.
• Fit the model to simulated removal data from a random walk movement model and analyzed empirical data.

Main Results:

• Parameter identifiability improved with capture probability >0.25 and 3-6 removal samples.
• Abundance estimates were unbiased when parameters were identifiable, except with behavioral responses to sampling.
• The model indicated closure for benthic fishes but detected openness for mobile juvenile Chinook salmon.

Conclusions:

• The developed removal model effectively handles open populations, allowing for formal closure testing and unbiased abundance estimation.
• The model's performance is dependent on parameter identifiability, which is influenced by capture probability and sample size.
• Empirical application demonstrated the model's ability to differentiate between closed and open populations for different species.