Tigers on the edge: mortality and landscape change dominate individual-based spatially-explicit simulations of a small tiger population

Abstract

Abstract Context Reductions in the tiger’s (Panthera tigris) range in Southeast Asia have been concurrent with large infrastructure expansion and landscape change. Thailand’s Dong Phayayen-Khao Yai Forest Complex (DPKY), a landscape of tiger conservation priority, may be particularly vulnerable to such changes, necessitating investigations into effects on population dynamics. Objectives Evaluate relative effects of landscape change scenarios on the probability of tiger persistence in DPKY and sensitivity of predictions to spatially-explicit mortality risk, landscape resistance, and tiger population density. Methods We utilize individual-based, spatially-explicit population modelling to evaluate the trajectory of tiger population dynamics across 11 landscape change scenarios. Concurrently, we evaluate sensitivity of predictions to landscape resistance transformation, maximum population density, and spatially-explicit mortality across 20 generations. Results Spatially-explicit mortality risk dominated predictions of population persistence, frequently resulting in population declines/extinction. Adjustment of moderate mortality risk to slightly convex and concave forms shifted extinction rates from 46 to 12% and 85%, respectively. Holding mortality constant at moderate levels, strong negative effects were predicted in landscape change scenarios incorporating road expansion (46%-74% extinction) and construction of dams (52%). Strong negative effects of combined development persisted even when habitat restoration measures were applied (96% extinction). Adjusting resistance and maximum population density had marginal effects. Conclusions The high sensitivity and variability of predictions to spatial patterns of mortality risk suggest a population on a proverbial knife’s edge. Our results underscore the importance of incorporating spatial patterns of mortality risk in population modelling, highlighting their potentially dominating influence on population dynamics and extinction risk.