Towards a sustainable nature reserve management: Using Bayesian network to quantify the threat of disturbance to ecosystem services

Abstract

Ecosystem services (ESs) of mountain areas may be impacted by cumulative and interactive effects of multiple disturbances including insect infestations, wildfires, timber harvesting, and building construction. However, the impacts of natural and human disturbances on ESs and the trade-offs/synergies among ESs are poorly known, particularly in mountain ecosystems with diverse landscapes. Here, we used the Qilian Nature Reserve in northwestern China as a case study, for which we quantified mountain disturbances with a BEAST algorithm and three critical ESs (carbon sequestration, water yield, and habitat quality) with the CASA and InVEST models. We then simulated ESs using the BN model, and estimated the impacts of disturbances on ESs and their trade-offs in different environment conditions through multi-scenario analysis. Our results suggested that BEAST could effectively capture the patterns and dynamics of small-scale disturbances, which were previously difficult to predict with normal land use/cover products. The established BN model could simulate the spatio-temporal dynamics of carbon sequestration, water yield, and habitat quality with an average classification error of 17.8, 12.7 and 4.5% for each ES, respectively. Significant synergy existed between carbon sequestration and habitat quality at the regional scale, while trade-off existed between water yield and the other two ESs. Specifically, these trade-offs/synergies among ESs tended to be weak at medium value of ESs, but stronger at higher and lower states. Thus, significant differences existed in the “win-lose” solutions between water yield and the other two ESs, further resulting the limited space to simultaneously improve three ESs. Disturbances at medium frequency and low-medium intensity were beneficial for the maintenance and improvement of three ESs. The BN model is a promising decision support tool to integrate small-scale disturbances into ES evaluation and identify the most suitable management solutions for mountain ecosystems; this could provide critical information for decision-makers and guidance for sustainable development.