Land management to increase food production while conserving the environment and associated ecosystem services (ESs) is one of the major development and research challenges of the 21st Century. Any land-use practice or change to obtain a particular ecosystem service affects the other ES positively or negatively. The dynamics of these changes is more marked in biodiversity hotspot areas like UNESCO registered Yayo coffee forest biosphere reserve in southwestern Ethiopia. We used a time series InVEST modeling framework to estimate six ESs and analyze their spatial and temporal dynamics due to land-use/cover change over the last 31 years. Pearson correlation coefficients and k-mean clustering were employed to analyze tradeoffs/synergies and to cluster ESs supply spatially. The analysis also considers land-use change impact in the three management zones (core, transition and buffer) of the Yayo biosphere area. The production efficient frontier is used to identify the optimal combination of ESs and to suggest where an increase of one ES is possible without decreasing the others. Mostly, the highest change is observed in the transition zone followed by buffer zones. Positive correlation (synergies) are observed between regulating ecosystem services. Negative correlations (tradeoffs) are observed between provision ecosystem services. The clustering analysis shows that the spatial ESs can be divided in two clusters (bundle): cluster 1 with “High regulating ESs” that can be characterized by core zone and some forest patches in the central part of the biosphere reserve, and cluster 2 with “High provisioning ESs areas'' that can be characterized by cultivated lands at transition and buffer zones. The result shows that the existing ES pairs are far from the Pareto efficient combination(s), confirming that landscape optimization for ES bundles are rarely possible on the ground due to many reasons and indicating the need for well thought land restoration strategies and land management practices that are forest type and context specific.
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