Estimating Lion Abundance using N-mixture Models for Social Species

Area of Science:

• Wildlife ecology
• Conservation biology
• Population dynamics

Background:

• Declining large carnivore populations necessitate accurate abundance estimates for effective conservation and human-wildlife conflict management.
• Traditional survey methods can be limited by factors like habituation and social behavior, impacting reliability.

Purpose of the Study:

• To estimate lion (Panthera leo) abundance in southeastern Serengeti National Park, Tanzania.
• To develop and apply a hierarchical N-mixture model accounting for social behavior and detection probabilities.

Main Methods:

• Utilized N-mixture models with a hierarchical observation process to analyze call-in and track survey data.
• Incorporated parameters such as landcover and luminosity to model lion detectability and abundance.
• Conditioned lion detectability on group response to call-ins and individual detection probabilities.

Main Results:

• Estimated lion abundance at 270 individuals (95% credible interval: 170-551) using call-in data; track data proved insufficient for abundance estimation.
• Identified a weak negative relationship between predicted track density and lion abundance.
• Found luminosity negatively correlated with individual detection probability during call-in surveys; landcover influenced abundance and track density, though effects were undetermined.

Conclusions:

• N-mixture models with hierarchical observation processes provide a robust framework for estimating abundance of social, grouping species like lions.
• This approach enhances accuracy by directly integrating environmental and behavioral factors influencing detection and abundance.
• Call-in surveys, when modeled appropriately, can yield reliable population estimates for large carnivores.