Landscape connectivity and population density of snow leopards across a multi‐use landscape in Western Himalaya

2021 ◽  
Author(s):  
R. Pal ◽  
C. Sutherland ◽  
Q. Qureshi ◽  
S. Sathyakumar
Keyword(s):  
Population Density ◽  
Landscape Connectivity ◽  
Western Himalaya ◽  
Snow Leopards

scholarly journals Spatial capture–recapture models for jointly estimating population density and landscape connectivity

Ecology ◽  
2013 ◽  
Vol 94 (2) ◽  
pp. 287-294 ◽  
Author(s):  
J. Andrew Royle ◽  
Richard B. Chandler ◽  
Kimberly D. Gazenski ◽  
Tabitha A. Graves
Keyword(s):  
Population Density ◽  
Landscape Connectivity ◽  
Capture Recapture

scholarly journals How Important Are Resistance, Dispersal Ability, Population Density and Mortality in Temporally Dynamic Simulations of Population Connectivity? A Case Study of Tigers in Southeast Asia

Land ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 415
Author(s):  
Eric Ash ◽  
Samuel A. Cushman ◽  
David W. Macdonald ◽  
Tim Redford ◽  
Żaneta Kaszta
Keyword(s):  
Population Density ◽  
Southeast Asia ◽  
Population Distribution ◽  
Landscape Connectivity ◽  
Relative Effect ◽  
Relative Strength ◽  
Population Connectivity ◽  
Dispersal Ability ◽  
Landscape Resistance ◽  
Kernel Approach

Development of landscape connectivity and spatial population models is challenging, given the uncertainty of parameters and the sensitivity of models to factors and their interactions over time. Using spatially and temporally explicit simulations, we evaluate the sensitivity of population distribution, abundance and connectivity of tigers in Southeast Asia to variations of resistance surface, dispersal ability, population density and mortality. Utilizing a temporally dynamic cumulative resistant kernel approach, we tested (1) effects and interactions of parameters on predicted population size, distribution and connectivity, and (2) displacement and divergence in scenarios across timesteps. We evaluated the effect of varying levels of factors on simulated population, cumulative resistance kernel extent, and kernel sum across nine timesteps, producing 24,300 simulations. We demonstrate that predicted population, range shifts, and landscape connectivity are highly sensitive to parameter values with significant interactions and relative strength of effects varying by timestep. Dispersal ability, mortality risk and their interaction dominated predictions. Further, population density had intermediate effects, landscape resistance had relatively low impacts, and mitigation of linear barriers (highways) via lowered resistance had little relative effect. Results are relevant to regional, long-term tiger population management, providing insight into potential population growth and range expansion across a landscape of global conservation priority.

Focus on Thermokarst Lakes in Indian Himalaya: Inception and Implication under Warming Climate

2020 ◽  
Vol 6 (2) ◽  
pp. 59-69
Author(s):  
Pratima Pandey ◽  
S. Nawaz Ali ◽  
Vikram Sharma ◽  
Prashant K. Champati Ray
Keyword(s):  
Western Himalaya ◽  
High Mountain ◽  
Thermokarst Lakes ◽  
Glacial Environments ◽  
Global Warming Scenario ◽  
Scientific Attention ◽  
Topographic Depressions ◽  
Permafrost Thawing ◽  
The Impact ◽  
Thaw Lakes

Thermokarst (Thaw) lakes are landforms found in topographic depressions created by thawing ground ice in permafrost zones. They play an important role in the regulation of climatic functions. These lakes are a manifestation of warming surface temperatures that accelerates the ice-rich permafrost to degrade by creating marshy hollows/ponds. In the current global warming scenario, the thermokarst lakes in the high mountain regions (Himalaya) are expected to grow further. This accelerate permafrost thawing which will affect the carbon cycle, hydrology and local ecosystems. This phenomenon has attracted huge scientific attention because it has led to a rapid mass change of glaciers in the region, including extensive changes occurring on peri-glacial environments. The most striking fact is the release of an enormous amount of greenhouse gases, including methane, carbon dioxide and nitrous oxide that is locked in these lakes. The present study delves into the thermokarst lakes in the upper reaches of Chandra Valley and Western Himalaya. The study also aims at designating the impact of their changes on the ecosystem, particularly their influence on the atmospheric greenhouse gas concentrations.

Convergence of regional population density in Russia over 120 years

2020 ◽  
pp. 133-158
Author(s):  
K. A. Kholodilin ◽  
Y. I. Yanzhimaeva
Keyword(s):  
Population Density ◽  
Spatial Dependence ◽  
Population Distribution ◽  
Regional Level ◽  
Capital City ◽  
County Level ◽  
Spatial Effects ◽  
Russian Regions ◽  
Regional Population ◽  
Regional Population Density

A relative uniformity of population distribution on the territory of the country is of importance from socio-economic and strategic perspectives. It is especially important in the case of Russia with its densely populated West and underpopulated East. This paper considers changes in population density in Russian regions, which occurred between 1897 and 2017. It explores whether there was convergence in population density and what factors influenced it. For this purpose, it uses the data both at county and regional levels, which are brought to common borders for comparability purposes. Further, the models of unconditional and conditional β-convergence are estimated, taking into account the spatial dependence. The paper concludes that the population density equalization took place in 1897-2017 at the county level and in 1926—1970 at the regional level. In addition, the population density increase is shown to be influenced not only by spatial effects, but also by political and geographical factors such as climate, number of GULAG camps, and the distance from the capital city.

Policy Foundations for a Path Forward in Large Landscape Connectivity Conservation

2012 ◽  
Vol 30 (4) ◽  
pp. 308-311 ◽  
Author(s):  
K. Fields ◽  
R. Ament ◽  
D. Johns ◽  
J. Davis ◽  
K. Bowers
Keyword(s):  
Landscape Connectivity
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