Mapping Ecosystem Services to Human Well‐being: a toolkit to support integrated landscape management for the SDGs
Bibliographic record
Abstract
The Sustainable Development Goals (SDGs) emphasize the global and multi-dimensional nature of sustainability and thus require improving our capacity to articulate and trace the impact of ecosystem change to measures of human well-being. Yet, the integrated nature of these goals is challenging to assess without similarly integrated assessment tools. We present a new modeling toolkit, "Mapping Ecosystem Services to Human well-being" (MESH), that integrates commonly used, stand-alone ecosystem services (ES) models from the InVEST suite of models to quantify and illustrate the trade-offs and synergies across five ecosystem services and up to 10 associated SDGs. Development of the software and its functionality were informed by a broad stakeholder consultation with ministries, non-governmental organizations and civil society groups in West Africa to identify common barriers to uptake and application of modeling tools in developing countries. In light of this process, key features included in MESH are (1) integration of multiple ecosystem service (ES) models into a common modeling framework supported by a curated base data set, (2) built-in scenario generation capacity to support policy analysis, (3) visualization of outcomes and trade-offs, and (4) mapping of ecosystem service change to SDG targets and goals. We illustrate the use of MESH in a case study in the Volta basin of West Africa comparing the effectiveness of three alternative conservation prioritization approaches: (1) land cover-based, (2) topographic-based, and (3) an ecosystem service-based approach to minimize the impact of agricultural expansion. We evaluate these approaches by linking changes in service supply to potential impacts on achievement of specific SDG goals and targets.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".