Identifying ecosystem states with patterns of ecosystem service bundles

Abstract

Identifying the state of an ecosystem and any changes in its state can help in detecting critical ecosystem transitions and formulating adaptive measures to reduce losses of ecosystem services. Such efforts are important for regional sustainability. In this study, we propose a new perspective by identifying grassland ecosystem states as patterns of ecosystem service bundles. The total annual provisioning of five key grassland ecosystem services in Xilin Gol, including net primary productivity, soil conservation, soil erosion by wind, water yield, and water retention, was estimated from 2001 to 2014. The ecosystem service bundle in each pixel was represented by the ecosystem services, which accounted for 75% of the total ecosystem services after the values of the five ecosystem services for that pixel were ranked in descending order. Then, we selected the four main ecosystem service bundles that covered 99% of the area of Xilin Gol and analyzed their patterns, characterized by landscape indices, from 2001 to 2014. Finally, the influences of precipitation, temperature, solar radiation, and vegetation coverage on the patterns of ecosystem service bundles were explored through stepwise regression. The results showed that vegetation coverage greatly affected the patterns of ecosystem service bundles and promoted the shape complexity and diversity of the bundle patches. The main factors affecting bundle diversity were precipitation in meadow steppes, vegetation coverage and temperature in typical steppes, and temperature in desert steppes. Moreover, the ranges of the landscape indices mean patch size and area-weighted mean fractal dimension fluctuated greatly in ecosystems with lower stability. The results of this study confirmed the hypothesis that patterns in ecosystem service bundles can reflect ecosystem states and indicated that changes in ecosystem service bundle patterns can be used as early warnings of critical transitions in ecosystems.

Keywords

• State variables
• Landscape pattern
• Fluctuating range
• Critical transitions
• Early warning