Bundles and Hotspots of Multiple Ecosystem Services for Optimized Land Management in Kentucky, United States

Abstract

Ecosystem services are benefits that the natural environment provides to support human well-being. A thorough understanding and assessment of these services are critical to maintain ecosystem services flow through sustainable land management to optimize bundles of ecosystem services provision. Maximizing one particular ecosystem service may lead to reduction in another. Therefore, identifying ecosystem services tradeoffs and synergies is key in addressing this challenge. However, the identification of multiple ecosystem services tradeoffs and synergies is still limited. A previous study failed to effectively capture the spatial interaction among ecosystem services as it was limited by “space-to-time” substitution method used because of temporal data scarcity. The study was also limited by using land use types in creating ecosystem services, which could lead to some deviations. The broad objective of this study is therefore to examine the bundles and hotspots of multiple ecosystem services and their tradeoffs in Kentucky, U.S. The study combined geographic data and spatially-explicit models to identify multiple ecosystem services bundles and hotspots, and determined the spatial locations of ecosystem services hotspots. Results showed that the spatial interactions among ecosystem services were very high: of the 21 possible pairs of ecosystem services, 17 pairs were significantly correlated. The seven ecosystem services examined can be bundled into three groups, geographically clustered on the landscape. These results support the hypothesis that some groups of ecosystem services provision can present similar spatial patterns at a large mesoscale. Understanding the spatial interactions and bundles of the ecosystem services provides essential information for evidence-based sustainable land management.

Assessment and Management Zoning of Ecosystem Service Trade-Off/Synergy Based on the Social–Ecological Balance: A Case of the Chang-Zhu-Tan Metropolitan Area

“…Finally, based on the historical evolution patterns of management zones and current issues, the study promotes enhancing individual ecosystem service functions and overall ESV through structural regulation of land use and ecological policies. It provides a modifiable framework for future development [64][65][66]. This complex integrated study explores ecosystem services from both a "bottom-up" and a "top-down" perspective, addressing the shortcomings of previous methods that relied on a single ecological function as the basis for zoning.…”

High Spatial-Temporal Resolution Data across Large Scales Are Needed to Transform Our Understanding of Ecosystem Services

“…However, they also note that synergy was the dominant relationship among ecosystem services, with 53 pairs of ESs positively correlated (i.e., synergies), and 28 pairs negatively correlated (i.e., trade-offs) between 1990 and 2000. Bai et al [22] support this finding, using InVEST to show high levels of spatial interactions between ESs, with the majority (10 out of 17) showing synergies rather than trade-offs, grouping them into three bundles to highlight how multiple services can be delivered in combination.…”

The Temporal and Spatial Evolution Characteristics and Driving Factors of Ecosystem Service Bundles in Anhui Province, China

“…The utilization of ecosystem service bundles enables the identification of the dominant ecosystem services within a region, thus allowing for the analysis of the spatial interconnections among various ecosystem services [8]. Given their advantages for examining the relationships between multiple ecosystem services, numerous scholars have utilized ecosystem service bundles to conduct research on ecological function zoning [9][10][11], trade-offs and synergies [12][13][14], spatio-temporal evolution [15], and driving factors [16], among other topics. The identification methods for service bundles mainly include k-means clustering analysis [17], self-organizing network analysis [18], hierarchical clustering [19], principal component analysis (PCA) [20], multiple correspondence analysis (MCA) [21], and random forest methods [22].…”