Improving landscape connectivity for the Yunnan snub-nosed monkey through cropland reforestation using graph theory

Abstract

, et al.. Improving landscape connectivity for the Yunnan snub-nosed monkey through cropland reforestation using graph theory. Journal for Nature Conservation, Elsevier, 2017Elsevier, , 38, pp.46 -55. 10.1016Elsevier, /j.jnc.2017 Journal for Nature Conservation 38 (2017) 46-55 http://dx.doi.org/10.1016/j.jnc.2017.06.002 Improving landscape connectivity for the Yunnan snub-nosed monkey through cropland reforestation using graph theory Abstract: Habitat fragmentation is a threat to biodiversity because it restricts the ability of animals to move. Maintaining landscape connectivity could promote connections between habitat patches, which is extremely important for the preservation of gene flow and population viability. This paper aims to evaluate the landscape connectivity of forest areas as it relates to the conservation of the Yunnan snub-nosed monkey (Rhinopithecus bieti), an emblematic and endemic endangered primate species. Specifically, this study seeks to model ways to improve connectivity via cropland reforestation scenarios which incorporate graph theory and genetic distances. The connectivity improvement assessment is performed at two nested scales.At the regional scale, the aim is to quantitatively assess the gain in connectivity from different reforestation scenarios, in which croplands are replaced by different kinds of forest habitats. At the local scale, the goal is to prioritize and to locate croplands based on the gain in connectivity that they would provide if they were reforested. The results indicate that the four reforestation scenarios have different impacts on connectivity; the fourth scenario, in which reforestation is accomplished with plant species that provide optimal monkey habitat, yields the greatest increase in connectivity (+24% versus less than +2% for the others). Prioritization of the 1482 cropland patches shows that the 10 best patches increase connectivity from 0.04% to 9.1% as the isolation threshold distance increases. This kind of graph theoretic approach appears to be a useful tool for connectivity assessment and the development of conservation measures for species impacted by human activities.

Identifying potential refugia and corridors under climate change: A case study of endangered Sichuan golden monkey (Rhinopithecus roxellana) in Qinling Mountains, China

“…Graph theory has become a popular tool for modeling the connectivity between suitable habitat patches (Foltête, Clauzel, & Vuidel, ; Foltête, Clauzel, Vuidel, & Tournant, ). We respectively used the inverse of the habitat suitability map as a resistance surface (Wang et al, ) and identified least‐cost paths (Li, Clauzel, et al, ) to identify the current and future potential habitat corridors for Sichuan golden monkey. All analyses were performed using Graphab 2.2 software (available at https://sourcesup.renater.fr/graphab/en/home.html) (Clauzel, Deng, Wu, Giraudoux, & Li, ).…”

China's Environment on a Metacoupled Planet

“…Animals and plants move among different regions of China through dispersal and migration, which support the structure and function of biodiversity and ecosystems. This is particularly important for long-term survival of endangered or rare animal (94) and plant (95) species. Such functional connections are also particularly crucial for the current and future success of protected areas in meeting their conservation goals (96).…”

Impact of cost distance and habitat fragmentation on the daily path length of Rhinopithecus bieti

“…With increased fragmentation, the spatial heterogeneity of the landscape becomes more complex and the resistance of different landscapes to the movement of species changes, which affects patterns of migration, dispersal, and gene flow (Laurance et al, 2002;Saunders, Hobbs & Margules, 1991;Zhang & Ma, 2014). Recent studies have tried to characterize the ability of species to move between different patches across the landscape by using cost distance modelling (Howard et al, 2015;Li et al, 2017). Cost distance modelling is a statistical tool designed to estimate the difficulty of a species moving across its distributional range by assigning resistance values for each pixel of a rasterized map and, using a species perspective approach, take into account landscape configuration and physical structure to evaluate landscape connectivity and its effects on the energetics of animal movements (Richard & Armstrong, 2010).…”