Items included in the factor analysis and the percentage of respondents who agreed/strongly agreed with each statement (n = 123).

<p>Items included in the factor analysis and the percentage of respondents who agreed/strongly agreed with each statement (n = 123).</p

Map of study areas within the Amboseli-Tsavo ecosystem.

<p>Map of study areas within the Amboseli-Tsavo ecosystem.</p

From Attitudes to Actions: Predictors of Lion Killing by Maasai Warriors

<div><p>Despite legal protection, deliberate killing by local people is one of the major threats to the conservation of lions and other large carnivores in Africa. Addressing this problem poses particular challenges, mainly because it is difficult to uncover illicit behavior. This article examined two groups of Maasai warriors: individuals who have killed African lions (<i>Panthera leo</i>) and those who have not. We conducted interviews to explore the relationship between attitudes, intentions and known lion killing behavior. Factor analysis and logistic regression revealed that lion killing was mainly determined by: (a) general attitudes toward lions, (b) engagement in traditional customs, (c) lion killing intentions to defend property, and (d) socio-cultural killing intentions. Our results indicated that general attitudes toward lions were the strongest predictor of lion killing behavior. Influencing attitudes to encourage pro-conservation behavior may help reduce killing.</p></div

Pattern matrix of social predictors of lion killing: Four-factor model with promax rotation (n = 123).

<p>Pattern matrix of social predictors of lion killing: Four-factor model with promax rotation (n = 123).</p

Probability of lion occurrence in identified lion population patches.

<p>Occurrence was calculated on 25 habitat patches in Kenya and Tanzania based on the incidence function model for A) male dispersal using maximum observed distance, without human density, B) male dispersal using maximum observed distance with human density, C) female dispersal using maximum observed distance without human density, D) female dispersal using maximum observed distance with human density model, E) male dispersal using average of observed distances, without human density, F) male dispersal using average of observed distances with human density, G) female dispersal using average of observed distances without human density, H) female dispersal using average of observed distances with human density model. In plotting the results, we used color to show the predicted incidence with warmer colors (redder) showing higher probability of incidence and yellow to white showing lower probability of incidence.</p

Model outputs of eight African lion incidence function models.

<p>Male and female models were calculated separately as they used different alpha values (male and female maximum (Max) and average (Avg) observed dispersal distances). Additional models were calculated with (+) and without (−) human density (H) as a covariate, added to both male and female models using both average and maximum dispersal distances. To discern model fit, human density coefficient with standard error (SE), residual deviance, residual degree of freedom and Akaike’s Information Criteria (AIC) are reported for each model. Model parameters reported: a stochasticity parameter reported with standard error; <i>e,</i> the intrinsic extinction rate; <i>y’</i>, the colonizing ability; and a measure of connectivity , reported as an average for each model with standard deviation.</p

Map of study area in Africa.

<p>Darkened areas indicate patches of permanent lion populations (n = 25) across Kenya and Tanzania; black areas were considered occupied and striped areas were deemed unoccupied at time of survey.</p

Pairwise distances between 25 Kenya and Tanzania lion population patches.

<p>Distance was calculated in kilometers, using data obtained during 2008–2010.</p

A Metapopulation Approach to African Lion (<i>Panthera leo</i>) Conservation

<div><p>Due to anthropogenic pressures, African lion (<i>Panthera leo</i>) populations in Kenya and Tanzania are increasingly limited to fragmented populations. Lions living on isolated habitat patches exist in a matrix of less-preferred habitat. A framework of habitat patches within a less-suitable matrix describes a metapopulation. Metapopulation analysis can provide insight into the dynamics of each population patch in reference to the system as a whole, and these analyses often guide conservation planning. We present the first metapopulation analysis of African lions. We use a spatially-realistic model to investigate how sex-biased dispersal abilities of lions affect patch occupancy and also examine whether human densities surrounding the remaining lion populations affect the metapopulation as a whole. Our results indicate that male lion dispersal ability strongly contributes to population connectivity while the lesser dispersal ability of females could be a limiting factor. When populations go extinct, recolonization will not occur if distances between patches exceed female dispersal ability or if females are not able to survive moving across the matrix. This has profound implications for the overall metapopulation; the female models showed an intrinsic extinction rate from five-fold to a hundred-fold higher than the male models. Patch isolation is a consideration for even the largest lion populations. As lion populations continue to decline and with local extinctions occurring, female dispersal ability and the proximity to the nearest lion population are serious considerations for the recolonization of individual populations and for broader conservation efforts.</p></div