Testing Predictions of the Prey of Lion Derived From Modeled Prey Preferences

Abstract

Abstract: Apex predators are often threatened with extinction, and reintroduction is one method conservation managers are using to secure their persistence. Yet the ability to predict what these predators will eat upon reintroduction is lacking. Here we test predictions of the diet of the lion (Panthera leo), derived from dietary electivity index and optimality theory, using independent data collected from reintroduced and resident populations. We solved the Jacobs’ index preference equation for each prey species of the lion using values calculated by Hayward and Kerley (2005) and prey abundance data from 4 reintroduction sites and one resident lion population over several years. We then compared these estimates with actual kill data gathered from each site and time period, using the log‐likelihood ratio and linear regression. The model precisely predicted the observed number of kills in 9 of the 13 tests. There was a highly significant linear relationship between the number of lion kills predicted to occur at a site and the number observed for all but one site (x̄r2 = 0.612; β = 1.03). Predicting predator diet will allow conservation managers to stop responding and start planning in advance for reintroductions and environmental variation. Furthermore, ensuring that sufficient food resources are available is likely to increase the success of reintroduction projects. In addition, managers responsible for threatened prey species will be able to predict the vulnerability of these species to predation in the event of predator reintroductions or changes in abundance. These methods are applicable to virtually all large predators that have been sufficiently studied.