Novel procedures to determine where to use ecological restoration to improve and connect habitat for Rhinopithecus roxellana in Shennongjia, China

Abstract

Context: Global habitat fragmentation is a major threat to terrestrial wildlife biodiversity, and habitat restoration is an effective method to protect biodiversity. The Rhinopithecus roxellana (R. roxellana) in Hubei Province, is a first-class key protected wild animal in China, and now the fragmentation of its habitat has seriously affected species expansion and genetic diversity. Objectives: To identify the causes of habitat degradation and the location of recoverable habitat patches, improve habitat connectivity, better respond to species expansion and protect biodiversity effectively. Methods: Using two level sets of environmental variables to represent the past and current habitat conditions of R. roxellana in the Shennongjia region. MaxEnt model was firstly used to quantify the suitable habitat value to two levels, and then change detection method determined the habitat degradation areas between the two levels. Finally, the important values of recoverable areas were quantified and ranked with graph theory to clarify the priority of restored patches. Results: The patches affected by degraded or damaged vegetation were found, then the suitable restoration patches were mapped. At last 5 patches as the priority areas of habitat restoration were conserved. Conclusions: Based on MaxEnt, change detection and connectivity analysis, targeted intervention is carried out on the habitat environment of R. roxellana in Shennongjia. As an effective conservation way, restoration of degraded or damaged habitats provides a mechanism to improve habitat connectivity. Here we show a practical method for the increasingly fragmented landscape restoration.

Keywords

• Habitat restoration
• Connectivity analysis
• MaxEnt
• Change detection
• Remote sensing
• Vegetation restoration