Keywords
uncertainty analysis; sensitivity analysis; spatial assessment
Start Date
5-7-2022 2:20 PM
End Date
5-7-2022 2:40 PM
Abstract
Ecosystem service assessments facilitate the valuation of nature and support decision-making for assessing change or the potential of change in the environment. To ensure robust decisions, analyses of uncertainty of ecosystem service values and the sensitivity of these values to the assessment inputs and parameters should be included. Currently, this is not common practice. Our global, land-based study assesses the uncertainty of the food service and the livestock subservice resulting from uncertainty estimates in six inputs: buffalo, cattle, goat, pig, poultry, and sheep. Uncertainty is estimated for the values and spatial distribution of the input data. Furthermore, it assesses the sensitivity of the food service and the livestock subservice to each of these six inputs using a global sensitivity analysis. Spatial modeling uses a cell length of 10,000 meters. In our preliminary results, the global total livestock subservice values have a mean of 1.34 * 10^12 Int$ and a standard deviation of 6.52 * 10^10 Int$ (4.86% of the mean), while the global total food service values have a mean of 1.52 * 10^12 Int$ and a standard deviation of 6.51 * 10^10 Int$ (4.29% of the mean). Sensitivity analysis shows that cattle contributes most to the total uncertainty with 80%, followed by pig (16%), sheep (2.7%), poultry and goat (both ~0.3%), and buffalo (0.2%). Spatially, there is expected variation; for example, sheep contribute up to 99% of the total uncertainty in areas like New Zealand and parts of Australia. Future work will expand to include all uncertain components in the food service and assess the sensitivity of each subservice to the food service.
Uncertainty and Sensitivity of Global Ecosystem Service Values
Ecosystem service assessments facilitate the valuation of nature and support decision-making for assessing change or the potential of change in the environment. To ensure robust decisions, analyses of uncertainty of ecosystem service values and the sensitivity of these values to the assessment inputs and parameters should be included. Currently, this is not common practice. Our global, land-based study assesses the uncertainty of the food service and the livestock subservice resulting from uncertainty estimates in six inputs: buffalo, cattle, goat, pig, poultry, and sheep. Uncertainty is estimated for the values and spatial distribution of the input data. Furthermore, it assesses the sensitivity of the food service and the livestock subservice to each of these six inputs using a global sensitivity analysis. Spatial modeling uses a cell length of 10,000 meters. In our preliminary results, the global total livestock subservice values have a mean of 1.34 * 10^12 Int$ and a standard deviation of 6.52 * 10^10 Int$ (4.86% of the mean), while the global total food service values have a mean of 1.52 * 10^12 Int$ and a standard deviation of 6.51 * 10^10 Int$ (4.29% of the mean). Sensitivity analysis shows that cattle contributes most to the total uncertainty with 80%, followed by pig (16%), sheep (2.7%), poultry and goat (both ~0.3%), and buffalo (0.2%). Spatially, there is expected variation; for example, sheep contribute up to 99% of the total uncertainty in areas like New Zealand and parts of Australia. Future work will expand to include all uncertain components in the food service and assess the sensitivity of each subservice to the food service.
Stream and Session
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