Servicesheds connect people to the landscapes upon which they depend
Bibliographic record
Abstract
Abstract Ecosystem services (ES) are benefits people receive from nature. To sustain these benefits, we need to spatially connect communities benefitting from specific ES with landscape features that generate the ES. A variety of process‐based models support ES assessments by estimating the biophysical supply of ES that comes from landscapes. However, less attention has been given to how ES flow from landscapes to beneficiary groups. A ‘serviceshed’ is defined as the spatial area that provides an ecosystem service to beneficiaries at a specific location—thus connecting people to the landscapes and seascapes upon which they depend through ES flows. In this article, we propose a general framework to empirically define serviceshed boundaries. Using publicly available data, we apply this framework to two provisioning and two regulating services (1) agricultural and fishing livelihoods and (2) pollination and coastal flood control, respectively, in Canada. We find that agricultural fields of different types and sizes contribute livelihood value to 85% of the communities in the agricultural landscape, and fishing grounds of different sizes contribute to 24% of the communities in the fishing study area. On average, communities with a lower proportion of agricultural labour are associated with larger fields, whilst larger fishing grounds were associated with communities with a greater percentage of their population in fisheries, showing how different ES can have varied relationships with beneficiary communities. For regulating services, we find 66% of pollinator‐supplying areas are within the serviceshed of farming communities. Natural habitats and agricultural land account for 72% and 28% of this serviceshed area, respectively. Our models suggest also that 26% of saltmarshes are within the serviceshed of flood‐prone communities and most coastal communities in our study area benefit from saltmarshes, especially from those without dykes. We demonstrate how serviceshed mapping, when integrating social and ecological information, can be useful in multiple decision contexts. Servicesheds can help planners and managers better design zoning restrictions, restoration activities to benefit communities, or subsidy programmes to replace the value of ES lost due to climate change or land use development. Read the free Plain Language Summary for this article on the Journal blog.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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".