Past, Present and Future: Combining habitat suitability and future landcover simulation for long-term conservation management of Indian rhino

Abstract

the indian rhino (Rhinoceros unicornis) is susceptible to habitat change and fragmentation due to illegal logging, rapid urbanization and non-forest use and therefore were confined in to isolated areas throughout its distribution. the present study was conducted in Gorumara landscape which is composed of two protected areas (pAs) viz., Gorumara national park (Gnp) and chapramari Wildlife Sanctuary. Both pAs were separated by a territorial forest range (Bridge Area), which is between both the pAs and under high anthropogenic disturbance. the study was designed to understand the impacts of landcover change on habitat suitability of R. unicornis in a multi-temporal scenario from 1998 to 2018 using ensemble approach and also to simulate the future habitat suitability for 2028. Our result suggests a significant increase in woodland cover inside the PAs, whereas the grassland cover has increased outside the pA in territorial range. We found a strong positive association of R. unicornis with grasslands. The Comparison of the future suitability model of 2028 with that of 2018 indicates a substantial increase in rhino suitable area by 13% in the territorial forest. Hence, bringing the territorial forest into the pA network, will be a crucial step to increase the fodder availability and better connectivity for the long term survival of the species.

Potential geographical distribution and environmental explanations of rare and endangered plant species through combined modeling: A case study of Northwest Yunnan, China

“…In order to avoid multicollinearity of environmental parameters that might result in model over‐fitting, Pearson correlation coefficient ( r ) was calculated between each variable in R 3.5.2 software ( https://www.r‐project.org/ ) (the Pearson correlation coefficient between each variable is in the supporting information ). After performed a multicollinearity test, 11 environmental variables (| r | < .8) were finally obtained to model the potential geographical distribution of each species (Mukherjee et al., 2020 ; Zhang et al., 2019 ) (Figure 2 ).…”

“…were finally obtained to model the potential geographical distribution of each species (Mukherjee et al, 2020;Zhang et al, 2019) (Figure 2).…”

Occupancy models reveal potential of conservation prioritization for Central American jaguars

“…Habitat suitability models can contribute to the prioritization of protected areas and support conservation planning for several species (Cabeza et al., 2004; Li et al., 2020; Mukherjee et al., 2020). These models relate species occurrence data to environmental conditions, using derived response curves that best reflect the set of ecological requirements of the species of concern (Guisan et al., 2017).…”

Potential geographical distribution and environmental explanations of rare and endangered plant species through combined modelling: A case study of Northwest Yunnan, China

“…In order to avoid multicollinearity of environmental parameters that might result in model over-fitting, we calculated Pearson correlation coefficient between each variable with the help of R 3.5.2 software (https://www.r-project.org/). After performed a multicollinearity test, we finally obtained 11 independent environmental variables (r < 0.8) to model the potential geographical distribution of each species (Zhang et al, 2019;Mukherjee et al, 2020) (Figure 2).…”