1. Effective management of charismatic large carnivores requires robust monitoring of their population at local, regional and global scales. While enormous progress has been made to estimate carnivore populations at local scales, estimates at regional and global scales remain elusive. In the first systematic effort at a large regional scale, we estimated the population of the elusive snow leopard Panthera uncia over an area of 26,112 km 2 in the Indian state of Himachal Pradesh.2. We stratified the entire snow leopard habitat in Himachal Pradesh based on an occupancy survey. Subsequently, we conducted camera trapping surveys at 10 sites distributed proportionately, that is with similar coverage probability across the three strata. We conducted simulations to understand how unidentified captures could affect our model estimate. We also assessed populations of the primary wild ungulate prey of snow leopards -blue sheep Pseudois nayaur and Siberian ibex Capra sibirica.3. Our results yielded a mean estimated density of 0.19 (95% confidence interval [CI]: 0.12-0.31) snow leopards per 100 km 2 and population size of 51 (95% CI: 34-73) snow leopards in Himachal Pradesh. The density estimates for individual sites ranged from 0.08 to 0.37 snow leopards per 100 km 2 . Simulations showed that unidentified snow leopard captures did not seem to affect the accuracy of our model estimate but could have affected the precision. Wild ungulate prey density ranged from 0.11 to 1.09 per km 2 . Snow leopard density showed a positive linear relationship with prey density (slope = 0.25, SE = 0.08, P = 0.01, R 2 = 0.51).4. Our study shows the earlier opinion-based estimate for Himachal Pradesh to have been significantly positively biased. Using occupancy surveys to stratify large areas in order to design camera trap surveys addresses one of the common spatial sampling biases, that is limited sampling of only prime snow leopardThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Iteroparous species may reproduce at many different ages, resulting in a reproductive dispersion that affects the damping of population perturbations, and varies among life histories. Since generation time (Tc) is known to capture aspects of life‐history variation, such as life‐history speed, does Tc also determine reproductive dispersion (S) or damping time (τ)? Using phylogenetically corrected analyses on 633 species of animals and plants, we find, firstly, that reproductive dispersion S scales isometrically with Tc. Secondly, and unexpectedly, we find that the damping time (τ) does not scale isometrically with generation time, but instead changes only as Tcb with b<1 (also, there is a similar scaling with S). This non‐isometric scaling implies a novel demographic contrast: increasing generation times correspond to a proportional increase in reproductive dispersion, but only to a slower increase in the damping time. Thus, damping times are partly decoupled from the slow‐fast continuum, and are determined by factors other than allometric constraints.

Understanding the effect of fluctuations on populations is crucial in the context of increasing habitat fragmentation, climate change, and biological invasions, among others. Migration in response to environmental disturbances enables populations to escape unfavorable conditions, benefit from new environments and thereby ride out fluctuations in variable environments. Would populations disperse if there were no uncertainty? Karlin showed in 1982 that when sub-populations experience distinct but fixed growth rates at different sites, greater mixing of populations will lower the overall growth rate relative to the most favorable site. Here we ask, when might environmental variability favor migration over no-migration? Specifically, in random environments, would a small amount of migration increase the overall long-run growth rate relative to the zero migration case? We use mathematical analysis and simulations to show how long-run growth rate is affected by migration rate. Our results show that when fitness (dis)advantages fluctuate over time across sites, migration may allow populations to benefit from variability. When there is one best site with highest expected growth rate, the effect of migration on long-run growth rate depends on the difference in expected growth between sites, scaled by the variance of the difference (for two sites). When variance is large, there is a substantial probability of an inferior site experiencing occasional bursts of higher growth than its average. Thus, a high variance can compensate for a large difference in average growth rates between sites. With multiple sites and large fluctuations, we explore the interplay between the length of the shortest cycle linking the best site with the second best, the average differences in growth rates between sites, and the size of fluctuations. Our findings have implications for conservation biology: even when there are superior sites in a sea of poor habitats, variability and habitat quality across space may be key to determining the importance of migration.

Environmental stochasticity is a key determinant of population viability. Decades of work exploring how environmental stochasticity influences population dynamics have highlighted the ability of some natural populations to limit the negative effects of environmental stochasticity, one of these strategies being demographic buffering. Whilst various methods exist to quantify demographic buffering, we still do not know which environment factors and demographic characteristics are most responsible for the demographic buffering observed in natural populations. Here, we introduce a framework to quantify the relative effects of three key drivers of demographic buffering: environment components (e.g., temporal autocorrelation and variance), population structure, and demographic rates (e.g., progression and fertility). Using Integral Projection Models, we explore how these drivers impact the demographic buffering abilities of three plant species with different life histories and demonstrate how our approach successfully characterises a population's capacity to demographically buffer against environmental stochasticity in a changing world.

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