ecosystem-services-assessment

name	ecosystem-services-assessment
description	Maps and quantifies ecosystem services including carbon stocks, water yield, soil erosion, and habitat quality with trade-off analysis. Use this skill when the user mentions ecosystem services, InVEST models, ES mapping, carbon sequestration, water yield estimation, RUSLE erosion modeling, habitat quality, pollination services, trade-off analysis, PES (payments for ecosystem services), natural capital, or ES valuation.
skill_version	1.0.0

Skill: ecosystem-services-assessment

Domain: ES indicators · Provisioning · Regulating · Cultural · Trade-offs
Phase: 3 — Specialist
Used by: assess-ecosystem-services

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Purpose

Guides the agent through the quantification and spatial representation of ecosystem services (ES): selecting appropriate indicators, computing biophysical ES estimates, mapping ES supply, and analysing trade-offs and synergies between services.

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When to Invoke

• Quantifying ecosystem services across a landscape or watershed
• Mapping ES supply and demand
• Analysing trade-offs between conservation and production services
• Supporting payments for ecosystem services (PES) design
• Environmental impact assessments requiring ES valuation

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Inputs

Input	Format	Required
Land cover / land use map	GeoTIFF, SHP	Yes
Biophysical data (rainfall, DEM, soil, biomass)	GeoTIFF, CSV	Yes
Socioeconomic data (population, demand)	GeoTIFF, CSV	Optional
Study area polygon	SHP, GPKG	Yes

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Outputs

Output	Description
es_indicator_maps/	One raster per ES indicator
es_summary_table.csv	ES value per land cover class
tradeoff_matrix.csv	Pairwise ES correlation matrix
tradeoff_plot.png	Scatter plots or heatmap of ES trade-offs
es_report.md	Full ES assessment narrative

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Steps

1. Define the ES Portfolio

Select relevant services for the context:

Category	Examples
Provisioning	Timber, water, food, fibre, genetic resources
Regulating	Carbon sequestration, water regulation, erosion control, pollination
Cultural	Recreation, aesthetic value, spiritual significance
Supporting	Habitat, nutrient cycling (underlying processes)

2. Select Indicators and Methods per Service

• Carbon: Above-ground biomass from REDD+ datasets or allometric models; soil carbon from SoilGrids
• Water regulation: Curve number (CN) approach; InVEST Seasonal Water Yield
• Erosion control: Revised Universal Soil Loss Equation (RUSLE); C-factor from land cover
• Pollination: Distance-weighted bee habitat index from land cover
• Recreation: Proximity index to natural areas weighted by accessibility

3. Compute Biophysical ES Values

• Apply selected method per service; store as raster layer
• Validate against field measurements or published benchmarks where possible
• Document all input parameters and data sources

4. Aggregate by Land Cover Class

• Zonal statistics: mean/total ES value per land cover polygon or raster class
• Build summary table: rows = land cover classes, columns = ES indicators
• Normalise to 0–1 for cross-service comparison

5. Trade-off and Synergy Analysis

• Compute pairwise Spearman correlations across pixels or land cover units
• Positive correlations = synergies; negative = trade-offs
• Visualise as correlation heatmap and scatter plots
• Identify land cover classes that maximise multiple services simultaneously

6. Beneficiary Mapping (optional)

• Map ES demand using population density, agricultural areas, or water intake points
• Overlay supply and demand to identify supply-demand gaps

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Decision Points

Condition	Diagnosis	Recommended Action
Land cover map accuracy < 85%	Classification error propagates into ES estimates	Perform uncertainty analysis; include classification error as confidence range in ES outputs
Land cover data > 5 years old	Land use change not captured — ES estimates may be outdated	Flag temporal gap; recommend updated land cover if available; note limitation in report
InVEST NoData fraction > 30% in study area	CRS or extent mismatch, or missing input raster	Check CRS alignment and extent; rerun after fixing spatial inputs
ES trade-off between two services is strongly negative (r < -0.7)	Managing for one service degrades the other	Report trade-off explicitly; do not recommend single-service optimisation without acknowledging cost

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Key Decisions to Document

• ES portfolio selection rationale
• Method and data source per service
• Normalisation method for cross-service comparison
• Trade-off analysis unit (pixel, land cover class, watershed)

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Tools and Libraries

R: terra, dplyr, ggplot2, corrplot
Python: rasterio, geopandas, seaborn
Dedicated: InVEST (Stanford Natural Capital Project), ARIES, Co$ting Nature

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Resources

• resources/es-indicator-reference.md — indicator definitions per service
• resources/invest-parameter-guide.md — InVEST model configuration
• resources/rusle-coefficients.md — RUSLE factor lookup tables
• examples/ — worked carbon + water regulation example

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Notes

• ES assessment does not require monetary valuation; biophysical indicators are often sufficient and more defensible
• InVEST is the most widely used open-source platform; use it unless a specific method is required
• Always report uncertainty in ES estimates, especially for carbon stocks