It is important to predict how many individuals of a predator species can survive in a given area on the basis of prey sufficiency and to compare predictive estimates with actual numbers to understand whether or not k...It is important to predict how many individuals of a predator species can survive in a given area on the basis of prey sufficiency and to compare predictive estimates with actual numbers to understand whether or not key threats are related to prey availability.Rugged terrain and low detection probabilities do not allow for the use of traditional prey count techniques in mountain areas.We used presence–absence occupancy modeling and camera-trapping to estimate the abundance and densities of prey species and regression analysis to predict leopard(Panthera pardus)densities from estimated prey biomass in the mountains of the Nuvadi area,Meghri Ridge,southern Armenia.The prey densities were 12.94±2.18 individuals km–2 for the bezoar goat(Capra aegagrus),6.88±1.56 for the wild boar(Sus scrofa)and 0.44±0.20 for the roe deer(Capreolus capreolus).The detection probability of the prey was a strong function of the activity patterns,and was highest in diurnal bezoar goats(0.59±0.09).Based on robust regression,the estimated total ungulate prey biomass(720.37±142.72 kg km–2)can support a leopard density of 7.18±3.06 individuals 100 km–2.The actual leopard density is only 0.34 individuals 100 km–2(i.e.one subadult male recorded over the 296.9 km2),estimated from tracking and camera-trapping.The most plausible explanation for this discrepancy between predicted and actual leopard density is that poaching and disturbance caused by livestock breeding,plant gathering,deforestation and human-induced wild fires are affecting the leopard population in Armenia.展开更多
文摘It is important to predict how many individuals of a predator species can survive in a given area on the basis of prey sufficiency and to compare predictive estimates with actual numbers to understand whether or not key threats are related to prey availability.Rugged terrain and low detection probabilities do not allow for the use of traditional prey count techniques in mountain areas.We used presence–absence occupancy modeling and camera-trapping to estimate the abundance and densities of prey species and regression analysis to predict leopard(Panthera pardus)densities from estimated prey biomass in the mountains of the Nuvadi area,Meghri Ridge,southern Armenia.The prey densities were 12.94±2.18 individuals km–2 for the bezoar goat(Capra aegagrus),6.88±1.56 for the wild boar(Sus scrofa)and 0.44±0.20 for the roe deer(Capreolus capreolus).The detection probability of the prey was a strong function of the activity patterns,and was highest in diurnal bezoar goats(0.59±0.09).Based on robust regression,the estimated total ungulate prey biomass(720.37±142.72 kg km–2)can support a leopard density of 7.18±3.06 individuals 100 km–2.The actual leopard density is only 0.34 individuals 100 km–2(i.e.one subadult male recorded over the 296.9 km2),estimated from tracking and camera-trapping.The most plausible explanation for this discrepancy between predicted and actual leopard density is that poaching and disturbance caused by livestock breeding,plant gathering,deforestation and human-induced wild fires are affecting the leopard population in Armenia.
