A simplified image of a small part of the ARIES knowledge base.

<p>The MEA ES categories on the left are broken down into the benefits in the middle, only some of which (in blue) are directly connected to beneficiaries. Dashed lines exemplify indirect relationships that, when taken as the description of legitimate ecosystem services, have the potential of causing “double counting” by identifying benefits that are “intermediate” and not “final”, i.e., not directly linked to beneficiaries. Beneficiaries are depicted on the right, with non-rival benefits in green and rival benefits in orange.</p

Relative values for open space proximity source, use, and flows source, under alternative urban growth scenarios Green-Duwamish watershed, WA, USA.

<p>Relative values for open space proximity source, use, and flows source, under alternative urban growth scenarios Green-Duwamish watershed, WA, USA.</p

Water supply sustainability (m³/year) for rice agriculture in the two areas considered.

<p>Water supply sustainability (m³/year) for rice agriculture in the two areas considered.</p

Open space proximity flows in the Green-Duwamish watershed under baseline conditions and constrained and open urban-growth scenarios.

<p>Theoretical values are in relative rankings, ranging from 0 to 100 for each cell. When multiple users have access to one source of proximity value, the value for this non-rival service is multiplied by the number of users, so total flow values can exceed 100.</p

Total estimated water budget (m³/year) for sample areas outside (1) and adjacent to (2) CAZ.

<p>Total estimated water budget (m³/year) for sample areas outside (1) and adjacent to (2) CAZ.</p

Water supply and quality in the CAZ area of Madagascar.

<p>From the left: total water demand across sectors, surface-water flow that is used by beneficiaries, and amount of sediment that is transported by hydrologic flows. Regions 1 and 2 (outlined in red) show the areas selected for comparison; the CAZ boundary is shown in black.</p

The ARIES conceptual model of ecosystem service flow dynamics.

<p>The ARIES conceptual model of ecosystem service flow dynamics.</p

Evaluative criteria to improve uptake and utility of ES quantification methods in decision-making.

<p>Evaluative criteria to improve uptake and utility of ES quantification methods in decision-making.</p

Flow characteristics for selected ecosystem services. Types are P (provisioning) or R (preventive). Rivalness is R (rival) or N (non-rival).

<p>Flow characteristics for selected ecosystem services. Types are P (provisioning) or R (preventive). Rivalness is R (rival) or N (non-rival).</p

On the Effects of Scale for Ecosystem Services Mapping

<div><p>Ecosystems provide life-sustaining services upon which human civilization depends, but their degradation largely continues unabated. Spatially explicit information on ecosystem services (ES) provision is required to better guide decision making, particularly for mountain systems, which are characterized by vertical gradients and isolation with high topographic complexity, making them particularly sensitive to global change. But while spatially explicit ES quantification and valuation allows the identification of areas of abundant or limited supply of and demand for ES, the accuracy and usefulness of the information varies considerably depending on the scale and methods used. Using four case studies from mountainous regions in Europe and the U.S., we quantify information gains and losses when mapping five ES - carbon sequestration, flood regulation, agricultural production, timber harvest, and scenic beauty - at coarse and fine resolution (250 m vs. 25 m in Europe and 300 m vs. 30 m in the U.S.). We analyze the effects of scale on ES estimates and their spatial pattern and show how these effects are related to different ES, terrain structure and model properties. ES estimates differ substantially between the fine and coarse resolution analyses in all case studies and across all services. This scale effect is not equally strong for all ES. We show that spatially explicit information about non-clustered, isolated ES tends to be lost at coarse resolution and against expectation, mainly in less rugged terrain, which calls for finer resolution assessments in such contexts. The effect of terrain ruggedness is also related to model properties such as dependency on land use-land cover data. We close with recommendations for mapping ES to make the resulting maps more comparable, and suggest a four-step approach to address the issue of scale when mapping ES that can deliver information to support ES-based decision making with greater accuracy and reliability.</p></div