Estimating population extinction thresholds with categorical classification trees for Louisiana black bears

Area of Science:

• Ecology
• Conservation Biology
• Wildlife Management

Background:

• Effective species conservation relies on monitoring vulnerable populations and identifying extinction thresholds.
• Quantitative methods for determining these thresholds are underdeveloped.
• The Louisiana black bear (Ursus americanus luteolus) was delisted in 2016, necessitating new monitoring protocols.

Purpose of the Study:

• To determine optimal parameters and thresholds for monitoring the Louisiana black bear population.
• To identify demographic rates indicative of heightened extinction risk.
• To establish reliable predictors of population viability for management intervention.

Main Methods:

• Utilized capture-mark-recapture (CMR) data from 2006-2012 to estimate population parameters.
• Employed stochastic population simulations to model population dynamics.
• Applied conditional classification trees to identify key demographic predictors and thresholds.

Main Results:

• Annual apparent survival rates for adult females, averaged over 5 years, were identified as the most sensitive predictor of population persistence.
• A threshold of [Formula: see text] for this survival rate predicted ≥95% population persistence over 100 years.
• Developed cost-effective monitoring protocols that reliably predict population persistence.

Conclusions:

• Population projections and conditional classification trees are valuable tools for establishing extinction thresholds.
• The identified survival rate threshold provides a clear metric for triggering conservation management actions for the Louisiana black bear.
• This approach enhances the ability to proactively manage vulnerable species populations.