Toward accurate and precise estimates of lion density

Area of Science:

• Ecology
• Conservation Biology
• Wildlife Management

Background:

• Accurate animal density estimates are vital for ecological understanding and conservation decisions.
• Estimating density for wide-ranging, low-density carnivores like African lions (Panthera leo) is challenging.
• Current methods, such as abundance indices, may yield unreliable inferences for lion populations.

Purpose of the Study:

• To adapt and apply a Bayesian spatially explicit capture-recapture (SECR) model for estimating fine-scale lion density.
• To address the limitations of existing methods in estimating African carnivore populations.
• To provide statistically rigorous and precise population parameters for lion conservation in Kenya.

Main Methods:

• Utilized sighting data from a 3-month survey in Maasai Mara National Reserve and surrounding conservancies.
• Adapted a Bayesian spatially explicit capture-recapture (SECR) model incorporating search effort.
• Employed an unstructured spatial capture-recapture sampling design to estimate detection probability and density.

Main Results:

• Estimated an overall posterior mean lion density of 17.08 (posterior SD 1.310) lions >1 year old/100 km².
• Determined a lion sex ratio of approximately 2.2 females to 1 male.
• Demonstrated the statistical rigor and precision of the SECR model with narrow posterior standard deviations.

Conclusions:

• SECR methods offer statistically rigorous and precise estimates for population parameters, superior to abundance indices.
• The flexible modeling framework enables robust population estimates from diverse data types over short survey periods.
• Advocates for a unified framework using SECR for assessing lion numbers to enhance conservation management and policy.