# Darwin Data

## Training Hub

- [Introduction to Darwin](https://docs.darwindata.ai/introduction.md): Darwin is a SaaS platform that helps businesses assess and manage their biodiversity footprint across operations and value chains.
- [Training Hub](https://docs.darwindata.ai/training-hub/overview.md): Short interactive demos to help you get up and running with Darwin — fast.
- [Setting Up Your Project](https://docs.darwindata.ai/training-hub/setup.md): Modules 1–2: Create your project and set up your data room in Darwin.
- [Adding Data](https://docs.darwindata.ai/training-hub/adding-data.md): Modules 3–7: Set up your organisation and import data into Darwin.
- [Calibrating Data](https://docs.darwindata.ai/training-hub/calibration.md): Modules 8–10: Map impact factors, create blueprints, and align GHG accounting rules.
- [Understanding Results](https://docs.darwindata.ai/training-hub/results.md): Modules 11–13: Navigate results, and create reduction and BAU scenarios.
- [Getting Support](https://docs.darwindata.ai/training-hub/support.md): Modules 14–15: Access Darwin support and meet Charles, the AI agent.

## Methodology

- [Overview](https://docs.darwindata.ai/methodology/how-we-assess.md): What Darwin is and how it assesses biodiversity impacts, dependencies and nature risks.
- [Input Data](https://docs.darwindata.ai/methodology/input-data.md): The data Darwin uses for biodiversity assessment: organisation structure and data points.
- [Organisation](https://docs.darwindata.ai/methodology/input-data/organisation.md): The entity model that structures a Darwin assessment: organisation units, sites, and the fields that describe them.
- [Data points](https://docs.darwindata.ai/methodology/input-data/data-points.md): The activity data Darwin handles, its value-chain position, and how heterogeneous data is reconciled.
- [Glossary](https://docs.darwindata.ai/methodology/glossary.md): Key terms, definitions and units used in Darwin
- [References](https://docs.darwindata.ai/methodology/references.md): Bibliography of scientific publications, databases and standards used in Darwin's methodology.
- [EXIOBASE](https://docs.darwindata.ai/methodology/exiobase.md): EMRIO model EXIOBASE 3.8.1 used in Darwin for monetary data transformation.
- [LCA Databases](https://docs.darwindata.ai/methodology/lca-databases.md): Life Cycle Assessment databases used in Darwin: ecoinvent 3.10 and Agribalyse 3.1.
- [LCIA Models](https://docs.darwindata.ai/methodology/lcia.md): Life Cycle Impact Assessment models used in Darwin: ReCiPe 2016, Impact World+ and GLOBIO/IBIF.
- [ENCORE](https://docs.darwindata.ai/methodology/encore.md): ENCORE database used in Darwin for dependency and impact assessment across economic activities.
- [Key Concepts](https://docs.darwindata.ai/methodology/impact-key-concepts.md): Overview of Darwin's biodiversity impact factors: input data types, factor types, and outputs.
- [Outputs](https://docs.darwindata.ai/methodology/impact-outputs.md): Darwin's output frameworks: pressure indicators, impact indicators, aggregated metrics, and commodity output framework.
- [Pressure Impact Factors](https://docs.darwindata.ai/methodology/impact-pressure.md): Pressure-impact factors from LCIA models (ReCiPe, IW+, GLOBIO) and Alien Invasive Species impact factors.
- [Product Impact Factors](https://docs.darwindata.ai/methodology/impact-product.md): How Darwin computes biodiversity impact factors for product and commodity data using Ecoinvent and Agribalyse.
- [Monetary Impact Factors](https://docs.darwindata.ai/methodology/impact-monetary.md): How Darwin computes biodiversity impact factors for financial data using EXIOBASE.
- [Data Quality](https://docs.darwindata.ai/methodology/data-quality-score.md): Darwin's data quality scoring system based on input data type.
- [Result Combination](https://docs.darwindata.ai/methodology/result-combination.md): How Darwin combines results across entities: avoiding double accounting and data propagation.
- [Carbon Footprint](https://docs.darwindata.ai/methodology/carbon-footprint.md): How Darwin computes a corporate carbon footprint: reporting frameworks, scope and category assignment, and the Carbon Footprint view.
- [Water Footprint](https://docs.darwindata.ai/methodology/water-footprint.md): Darwin's Water Focus view: total water footprint, blue-water traceability, and basin-level overexploitation exposure aligned with SBTN Freshwater.
- [Dependencies Assessment](https://docs.darwindata.ai/methodology/dependencies.md): Biodiversity dependency assessment methodology using ENCORE in Darwin.
- [Priority Sites Identification](https://docs.darwindata.ai/methodology/priority-sites.md): Methodology for identifying priority sites using the dual-flag approach aligned with TNFD LEAP.
- [Sourcing Risks](https://docs.darwindata.ai/methodology/sourcing-risks.md): Identifying nature-related risks in supply chains using HICL and EUDR flags.
- [Financial Risk Exposure](https://docs.darwindata.ai/methodology/financial-risk-exposure.md): Translating nature-related materiality into financial exposure across ENCORE risk indicators.
- [Nature Stress Test — Primer](https://docs.darwindata.ai/methodology/nature-var/primer.md): A jargon-free introduction to the Nature Stress Test for newcomers: what it measures, the key words you will meet (shock, ecosystem service, dependency, scenario), and how to read the results.
- [Nature Stress Test](https://docs.darwindata.ai/methodology/nature-var.md): How the Nature Stress Test turns forward-looking ecosystem-degradation scenarios into a site-, company- and portfolio-level estimate of the production and financial loss a business could face.
- [Nature Stress Test — Shock Layers](https://docs.darwindata.ai/methodology/nature-var/shock-layers.md): Sources and methodology for the horizon-resolved climate shock layers (ecosystem components and services) that drive the Nature Stress Test.

## Risk Layers

- [Risk Layers Overview](https://docs.darwindata.ai/risk-layers/overview.md): Plain-language fact sheets for every geospatial layer used in Darwin nature-risk screening, organised by the full risk taxonomy.
- [Drought risk (Aqueduct 4.0)](https://docs.darwindata.ai/risk-layers/drought-risk-aqueduct-4-0.md): This layer indicates where droughts are likely to occur and where their consequences are likely to be most severe.
- [Water Stress (Aqueduct 4.0)](https://docs.darwindata.ai/risk-layers/water-stress-aqueduct-4-0.md): This layer measures baseline water stress: how much of the renewable water available in an area is already being claimed by competing users.
- [Water Stress 2030 (Aqueduct 4.0)](https://docs.darwindata.ai/risk-layers/water-stress-2030-aqueduct-4-0.md): This layer projects baseline water stress — the share of available renewable water already claimed by competing users — forward to around 2030.
- [Water Stress 2050 (Aqueduct 4.0)](https://docs.darwindata.ai/risk-layers/water-stress-2050-aqueduct-4-0.md): This layer projects baseline water stress — the share of available renewable water already claimed by competing users — forward to around 2050.
- [Air quality risk (global, CAMS EAC4 2024)](https://docs.darwindata.ai/risk-layers/air-quality-risk-global-cams-eac4-2024.md): This layer estimates ambient air-quality risk based on the concentrations of key pollutants that affect health and ecosystems.
- [Soil condition (Global Soil Organic Carbon)](https://docs.darwindata.ai/risk-layers/soil-condition-global-soil-organic-carbon.md): This layer maps soil organic carbon, a core indicator of soil condition and fertility.
- [Coastal eutrophication potential (Aqueduct 3.0)](https://docs.darwindata.ai/risk-layers/coastal-eutrophication-potential-aqueduct-3-0.md): This layer measures the potential for river-borne nutrients to trigger harmful algal blooms in coastal waters.
- [Risk of pesticides pollution (Tang et al. 2023)](https://docs.darwindata.ai/risk-layers/risk-of-pesticides-pollution-tang-et-al-2023.md): This layer estimates the geography of environmental pollution risk from agricultural pesticides.
- [PM2.5 Air Pollution Risk (IPCC CMIP6)](https://docs.darwindata.ai/risk-layers/pm2-5-air-pollution-risk-ipcc-cmip6.md): This layer maps the concentration of fine airborne particulate matter (PM2.5 — particles 2.5 micrometres across or smaller).
- [PM2.5 Air Pollution Risk 2050 (IPCC CMIP6)](https://docs.darwindata.ai/risk-layers/pm2-5-air-pollution-risk-2050-ipcc-cmip6.md): This layer projects the concentration of fine particulate matter (PM2.5 — inhalable particles 2.5 micrometres across or smaller) for the year 2050.
- [Pollination Deficit (Chaplin-Kramer)](https://docs.darwindata.ai/risk-layers/pollination-deficit-chaplin-kramer.md): This layer maps the gap in crop production caused by insufficient pollination — the share of potential yield lost because wild pollinators are too scarce.
- [Coastal flood depth (Deltares + WRI Aqueduct, combined)](https://docs.darwindata.ai/risk-layers/coastal-flood-depth-deltares-wri-aqueduct-combined.md): This layer maps how deep coastal flooding could be at a given location for a severe storm-surge event, expressed as inundation depth in metres.
- [Coastal flood risk (Aqueduct 3.0)](https://docs.darwindata.ai/risk-layers/coastal-flood-risk-aqueduct-3-0.md): This layer estimates the share of the population that can be expected to be affected by coastal flooding in an average year.
- [Extreme Heat Risk (IPCC CMIP6)](https://docs.darwindata.ai/risk-layers/extreme-heat-risk-ipcc-cmip6.md): This layer measures exposure to extreme heat under a forward-looking climate scenario.
- [Extreme Heat Risk 2050 (France, DRIAS)](https://docs.darwindata.ai/risk-layers/extreme-heat-risk-2050-france-drias.md): This is a forward-looking scenario layer for France.
- [Extreme Precipitation Risk (IPCC CMIP6)](https://docs.darwindata.ai/risk-layers/extreme-precipitation-risk-ipcc-cmip6.md): This layer measures exposure to heavy rainfall under a forward-looking climate scenario.
- [Extreme Precipitation Risk 2050 (France, DRIAS)](https://docs.darwindata.ai/risk-layers/extreme-precipitation-risk-2050-france-drias.md): This is a forward-looking scenario layer for France.
- [Fire Weather Risk 2050 (France, DRIAS)](https://docs.darwindata.ai/risk-layers/fire-weather-risk-2050-france-drias.md): This is a forward-looking scenario layer for France.
- [Flood depth — 10-year return, present climate (GIRI)](https://docs.darwindata.ai/risk-layers/flood-depth-10-year-return-present-climate-giri.md): This layer estimates how deep floodwater would stand at a given location during a flood of the kind expected roughly once a decade, under present-day climate.
- [Hailstorm Climatology](https://docs.darwindata.ai/risk-layers/hailstorm-climatology.md): This layer maps the long-term frequency of hailstorms around the world.
- [Marine Flooding Risk (France, BRGM)](https://docs.darwindata.ai/risk-layers/marine-flooding-risk-france-brgm.md): This layer outlines French coastal areas potentially exposed to marine flooding during severe storm events combined with high tide.
- [Riverine Flood risk (Aqueduct 3.0)](https://docs.darwindata.ai/risk-layers/riverine-flood-risk-aqueduct-3-0.md): This layer estimates the share of population expected to be affected by river flooding in an average year, after accounting for existing flood-protection standards.
- [Surface Wind Risk (IPCC CMIP6)](https://docs.darwindata.ai/risk-layers/surface-wind-risk-ipcc-cmip6.md): This layer maps mean surface wind speed under a forward-looking climate scenario.
- [Tropical Cyclone Wind Hazard (c.2070)](https://docs.darwindata.ai/risk-layers/tropical-cyclone-wind-hazard-c-2070.md): This layer maps the wind hazard posed by tropical cyclones, expressed as the maximum sustained wind speed expected at a 50-year return period.
- [Wildfire Hazard (NASA FIRMS NOAA-21)](https://docs.darwindata.ai/risk-layers/wildfire-hazard-nasa-firms-noaa-21.md): This layer measures exposure to wildfire based on recent satellite observations of active fires.
- [Annual mean temperature — 1981-2010 (CHELSA)](https://docs.darwindata.ai/risk-layers/annual-mean-temperature-1981-2010-chelsa.md): This layer maps the average annual air temperature for the 1981–2010 reference period.
- [Annual precipitation — 1981-2010 (CHELSA)](https://docs.darwindata.ai/risk-layers/annual-precipitation-1981-2010-chelsa.md): This layer maps the average total annual precipitation for the 1981–2010 reference period.
- [Max temperature of warmest month — 1981-2010 (CHELSA)](https://docs.darwindata.ai/risk-layers/max-temperature-of-warmest-month-1981-2010-chelsa.md): This layer maps the maximum temperature of the warmest month for the recent reference period 1981–2010.
- [Min temperature of coldest month — 1981-2010 (CHELSA)](https://docs.darwindata.ai/risk-layers/min-temperature-of-coldest-month-1981-2010-chelsa.md): This layer maps the minimum temperature of the coldest month for the recent reference period 1981–2010.
- [Precipitation of driest month — 1981-2010 (CHELSA)](https://docs.darwindata.ai/risk-layers/precipitation-of-driest-month-1981-2010-chelsa.md): This layer maps how much rainfall falls in the driest month of a typical year, averaged over the 1981-2010 reference period.
- [Precipitation of wettest month — 1981-2010 (CHELSA)](https://docs.darwindata.ai/risk-layers/precipitation-of-wettest-month-1981-2010-chelsa.md): This layer maps how much rainfall falls in the wettest month of a typical year, averaged over the 1981-2010 reference period.
- [Precipitation seasonality (CV) — 1981-2010 (CHELSA)](https://docs.darwindata.ai/risk-layers/precipitation-seasonality-cv-1981-2010-chelsa.md): This layer maps how unevenly rainfall is spread across the year, averaged over the 1981-2010 reference period.
- [Annual mean temperature — SSP3-7.0 2071-2100 (CHELSA)](https://docs.darwindata.ai/risk-layers/annual-mean-temperature-ssp3-7-0-2071-2100-chelsa.md): This layer maps projected average annual air temperature for the late-century period 2071–2100 under a high-emissions future.
- [Annual precipitation — SSP3-7.0 2071-2100 (CHELSA)](https://docs.darwindata.ai/risk-layers/annual-precipitation-ssp3-7-0-2071-2100-chelsa.md): This layer maps projected total annual precipitation for the late-century period 2071–2100 under a high-emissions future.
- [Max temperature of warmest month — SSP3-7.0 2071-2100 (CHELSA)](https://docs.darwindata.ai/risk-layers/max-temperature-of-warmest-month-ssp3-7-0-2071-2100-chelsa.md): This layer projects the maximum temperature of the warmest month for the end of the century (2071–2100) under a high-emissions pathway (SSP3-7.0).
- [Min temperature of coldest month — SSP3-7.0 2071-2100 (CHELSA)](https://docs.darwindata.ai/risk-layers/min-temperature-of-coldest-month-ssp3-7-0-2071-2100-chelsa.md): This layer projects the minimum temperature of the coldest month for the end of the century (2071–2100) under a high-emissions pathway (SSP3-7.0).
- [Precipitation of driest month — SSP3-7.0 2071-2100 (CHELSA)](https://docs.darwindata.ai/risk-layers/precipitation-of-driest-month-ssp3-7-0-2071-2100-chelsa.md): This layer projects how much rainfall the driest month of a typical year will see late this century, under a high-emissions future.
- [Precipitation of wettest month — SSP3-7.0 2071-2100 (CHELSA)](https://docs.darwindata.ai/risk-layers/precipitation-of-wettest-month-ssp3-7-0-2071-2100-chelsa.md): This layer projects how much rainfall the wettest month of a typical year will see late this century, under a high-emissions future.
- [Precipitation seasonality (CV) — SSP3-7.0 2071-2100 (CHELSA)](https://docs.darwindata.ai/risk-layers/precipitation-seasonality-cv-ssp3-7-0-2071-2100-chelsa.md): This layer projects how unevenly rainfall will be spread across the year late this century, under a high-emissions future.
- [Clay shrink-swell risk (global)](https://docs.darwindata.ai/risk-layers/clay-shrink-swell-risk-global.md): This layer maps the global risk of clay shrink-swell, the behaviour of expansive soils that swell when wet and shrink when dry.
- [Clay Swelling and Shrinking Risk (France)](https://docs.darwindata.ai/risk-layers/clay-swelling-and-shrinking-risk-france.md): This layer maps susceptibility to clay swelling and shrinking across France.
- [Landslide susceptibility (global, NASA)](https://docs.darwindata.ai/risk-layers/landslide-susceptibility-global-nasa.md): This layer maps how susceptible the land surface is to landslides across the world.
- [Seismic Hazard (GSHAP PGA, 475-yr return)](https://docs.darwindata.ai/risk-layers/seismic-hazard-gshap-pga-475-yr-return.md): This layer maps the level of ground shaking a location can expect from earthquakes.
- [Subsidence susceptibility (global, Herrera GSS)](https://docs.darwindata.ai/risk-layers/subsidence-susceptibility-global-herrera-gss.md): This layer maps how prone the land is to sinking gradually as a result of groundwater depletion.
- [Herbicide resistance risk (global, Heap 2021)](https://docs.darwindata.ai/risk-layers/herbicide-resistance-risk-global-heap-2021.md): This layer maps the risk of herbicide and agrochemical resistance — that is, where weeds have evolved resistance to herbicides.
- [Zoonotic disease spillover risk (global, Allen et al. 2017)](https://docs.darwindata.ai/risk-layers/zoonotic-disease-spillover-risk-global-allen-et-al-2017.md): This layer measures the relative risk that new infectious diseases will emerge by jumping from animals to humans (zoonotic spillover).
- [Tourism intensity: estimated annual visitors number in 2019 (Adamiak et al.)](https://docs.darwindata.ai/risk-layers/tourism-intensity-estimated-annual-visitors-number-in-2019-adamiak-et-al.md): This layer maps the global distribution of tourism intensity, estimating how many tourists visit each area.
- [Bangladesh Forests and Parks](https://docs.darwindata.ai/risk-layers/bangladesh-forests-and-parks.md): This layer delineates the forests and natural parks of Bangladesh.
- [Europe Protected Areas](https://docs.darwindata.ai/risk-layers/europe-protected-areas.md): This layer maps protected and conservation areas across Europe — places designated for their recognised natural or cultural value, where human presence or the use of natu
- [India Protected Areas](https://docs.darwindata.ai/risk-layers/india-protected-areas.md): This layer maps India's officially designated protected areas, including national parks and wildlife sanctuaries.
- [Natura 2000](https://docs.darwindata.ai/risk-layers/natura-2000.md): This layer maps the Natura 2000 network — the European Union's coordinated network of protected sites for rare and threatened species and habitats.
- [Protected Areas (Brazil)](https://docs.darwindata.ai/risk-layers/protected-areas-brazil.md): This layer delineates Brazil's officially designated protected areas and indigenous territories.
- [Protected Areas (Colombia)](https://docs.darwindata.ai/risk-layers/protected-areas-colombia.md): This layer delineates Colombia's officially designated protected areas and indigenous lands.
- [Protected Areas (Costa Rica)](https://docs.darwindata.ai/risk-layers/protected-areas-costa-rica.md): This layer maps the network of legally protected areas across Costa Rica, including national parks, wildlife refuges, biological reserves, protected zones and nature rese
- [Protected Areas (Côte d'Ivoire)](https://docs.darwindata.ai/risk-layers/protected-areas-c-te-divoire.md): This layer maps the network of legally protected areas across Côte d'Ivoire.
- [Protected Areas (Kazakhstan)](https://docs.darwindata.ai/risk-layers/protected-areas-kazakhstan.md): This layer maps the network of legally protected territories across Kazakhstan.
- [Protected Areas (Madagascar)](https://docs.darwindata.ai/risk-layers/protected-areas-madagascar.md): This layer maps the national protected-area system of Madagascar, known as the Système des Aires Protégées de Madagascar (SAPM).
- [Protected Areas (New Caledonia)](https://docs.darwindata.ai/risk-layers/protected-areas-new-caledonia.md): This layer maps the network of protected areas across New Caledonia, a global biodiversity hotspot in the south-west Pacific.
- [Ramsar](https://docs.darwindata.ai/risk-layers/ramsar.md): This layer maps Ramsar sites — wetlands officially designated as Wetlands of International Importance under the Ramsar Convention.
- [UNESCO World Heritage Sites](https://docs.darwindata.ai/risk-layers/unesco-world-heritage-sites.md): This layer maps UNESCO World Heritage Sites — over 1,200 locations recognised worldwide for their outstanding cultural or natural significance, including ancient cities, 
- [UNESCO World Heritage Sites (with buffer)](https://docs.darwindata.ai/risk-layers/unesco-world-heritage-sites-with-buffer.md): This layer maps UNESCO World Heritage Sites together with a surrounding buffer zone.
- [US Protected Areas](https://docs.darwindata.ai/risk-layers/us-protected-areas.md): This layer maps the most strongly protected conservation lands in the United States — national parks, wilderness areas and nature reserves where natural ecosystems are pr
- [Biodiversity Intactness Index](https://docs.darwindata.ai/risk-layers/biodiversity-intactness-index.md): This layer estimates how much of an area's original biodiversity remains intact despite human impacts.
- [Ecosystem Integrity Index (EII)](https://docs.darwindata.ai/risk-layers/ecosystem-integrity-index-eii.md): The Ecosystem Integrity Index is a single, holistic measure of how healthy and intact terrestrial ecosystems are across the world.
- [Intact forest landscape 2021 (GFW)](https://docs.darwindata.ai/risk-layers/intact-forest-landscape-2021-gfw.md): This layer maps Intact Forest Landscapes (IFLs) — large, seamless mosaics of forest and naturally treeless ecosystems that show no remotely detected signs of human activi
- [Seed Biocomplexity Index](https://docs.darwindata.ai/risk-layers/seed-biocomplexity-index.md): This layer measures the complexity of biodiversity at each location, drawing together variation across genes, species and ecosystems into a single index.
- [Biodiversity Reservoir](https://docs.darwindata.ai/risk-layers/biodiversity-reservoir.md): This layer delineates biodiversity reservoirs identified within France's regional ecological-network planning.
- [Linear Corridor](https://docs.darwindata.ai/risk-layers/linear-corridor.md): This layer maps linear ecological corridors identified within France's Green and Blue Network (Trame Verte et Bleue).
- [Linear Water Stream](https://docs.darwindata.ai/risk-layers/linear-water-stream.md): This layer maps the linear watercourse continuities of France's Green and Blue Network (Trame Verte et Bleue) — the 'blue' element, formed by rivers and streams that conn
- [Surface Corridor](https://docs.darwindata.ai/risk-layers/surface-corridor.md): This layer maps surface ecological corridors identified under France's Green and Blue Network (Trame Verte et Bleue).
- [Surface Water Stream](https://docs.darwindata.ai/risk-layers/surface-water-stream.md): This layer maps the surface water component of France's Green and Blue Network (Trame Verte et Bleue) — the areal aquatic continuities, such as watercourses and associate
- [WWF G200 Freshwater](https://docs.darwindata.ai/risk-layers/wwf-g200-freshwater.md): This layer maps the freshwater priority ecoregions identified by WWF's Global 200 project — 53 freshwater areas singled out for their exceptional biodiversity.
- [WWF G200 Marine](https://docs.darwindata.ai/risk-layers/wwf-g200-marine.md): This layer delineates the marine ecoregions identified by WWF's Global 200 project as outstanding examples of the world's marine biodiversity.
- [WWF G200 Terrestrial](https://docs.darwindata.ai/risk-layers/wwf-g200-terrestrial.md): This layer delineates the terrestrial ecoregions identified by WWF's Global 200 project as outstanding examples of the world's land biodiversity.
- [ZNIEFF Type 1](https://docs.darwindata.ai/risk-layers/znieff-type-1.md): This layer delineates France's Type 1 Natural Zones of Ecological, Faunal and Floral Interest (ZNIEFF).
- [ZNIEFF Type 2](https://docs.darwindata.ai/risk-layers/znieff-type-2.md): This layer delineates France's Type 2 Natural Zones of Ecological, Faunal and Floral Interest (ZNIEFF).
- [Functional Ecological Network (FEN, Belgium, Antwerp)](https://docs.darwindata.ai/risk-layers/functional-ecological-network-fen-belgium-antwerp.md): This layer maps 'search zones' — areas of the Antwerp landscape with the greatest potential to act as connections between important natural cores.
- [Mangroves (Global Mangrove Watch)](https://docs.darwindata.ai/risk-layers/mangroves-global-mangrove-watch.md): This layer maps the global extent of mangrove forests as of 2020.
- [GBIF Threatened Species](https://docs.darwindata.ai/risk-layers/gbif-threatened-species.md): This layer maps the global presence of threatened species — those classed by the IUCN Red List as Critically Endangered or Endangered.
- [IUCN Red list plants (France)](https://docs.darwindata.ai/risk-layers/iucn-red-list-plants-france.md): This layer maps locations in France associated with plant species on the IUCN Red List — the global inventory of the conservation status and extinction risk of biological
- [LandMark Indigenous and Community Lands (IPLC)](https://docs.darwindata.ai/risk-layers/landmark-indigenous-and-community-lands-iplc.md): This layer maps the lands of Indigenous Peoples and Local Communities (IPLC) — formally recognised or documented territories held by Indigenous and community groups.
- [Global Forest Watch, canopy loss during 2020-2023](https://docs.darwindata.ai/risk-layers/global-forest-watch-canopy-loss-during-2020-2023.md): This layer maps where tree canopy was lost between 2020 and 2023.
- [Wetland Loss Variation Risk Map (2010-2020)](https://docs.darwindata.ai/risk-layers/wetland-loss-variation-risk-map-2010-2020.md): This layer measures how quickly wetlands have been lost in each country over the decade 2010–2020.
- [Freshwater Quantity Overshoot](https://docs.darwindata.ai/risk-layers/freshwater-quantity-overshoot.md): This layer flags sub-catchments where freshwater is being used beyond what the local water system can sustainably provide.
- [IUU Fishing Risk Index](https://docs.darwindata.ai/risk-layers/iuu-fishing-risk-index.md): This layer shows the risk of illegal, unreported and unregulated (IUU) fishing for each coastal country.
- [Water depletion (Aqueduct 4.0)](https://docs.darwindata.ai/risk-layers/water-depletion-aqueduct-4-0.md): This layer measures baseline water depletion: how much of an area's renewable water is permanently consumed — that is, withdrawn and not returned — relative to what is av
- [Night light](https://docs.darwindata.ai/risk-layers/night-light.md): This layer maps the brightness of artificial light at night across the globe.
- [Mismanaged Plastic Waste (Lebreton et al. 2019)](https://docs.darwindata.ai/risk-layers/mismanaged-plastic-waste-lebreton-et-al-2019.md): This layer maps where mismanaged plastic waste is generated — plastic that is littered, dumped or inadequately disposed of and so is liable to leak into the environment.
- [Air pollutant emissions risk (global, EDGARv8.1 2022)](https://docs.darwindata.ai/risk-layers/air-pollutant-emissions-risk-global-edgarv8-1-2022.md): This layer estimates how much air-pollutant emission pressure originates within each terrestrial area.
- [Nitrogen Fertilizer Use Risk Map (2023)](https://docs.darwindata.ai/risk-layers/nitrogen-fertilizer-use-risk-map-2023.md): This layer rates the biodiversity risk associated with nitrogen-fertiliser use in each country.
- [Pesticide Use Risk Map (2023)](https://docs.darwindata.ai/risk-layers/pesticide-use-risk-map-2023.md): This layer rates the biodiversity risk posed by pesticide use in each country, based on how much pesticide is applied per hectare of agricultural land.
- [Rarity-weighted Species Richness](https://docs.darwindata.ai/risk-layers/rarity-weighted-species-richness.md): This layer highlights places dominated by range-restricted and endemic species.
- [Rarity-weighted Species Richness for Threatened Species](https://docs.darwindata.ai/risk-layers/rarity-weighted-species-richness-for-threatened-species.md): This layer highlights places that concentrate threatened species with small global ranges.
- [Natural-habitat shock (LUH2) — Habitats component](https://docs.darwindata.ai/risk-layers/luh2-shock.md): Horizon-resolved projection of the change in ecosystem integrity, derived from LUH2 % natural-habitat extent. This layer family backs the Structural & Biotic Integrity (Habitats) component of the Nature Stress Test.
- [Water purification shock (NDR)](https://docs.darwindata.ai/risk-layers/ndr-shock.md): Horizon-resolved projection of the change in nitrogen-retention capacity, from the IMAGE-GNM river-N nutrient model. This layer family backs the Water purification ecosystem service of the Nature Stress Test.
- [Pest control shock (biological control)](https://docs.darwindata.ai/risk-layers/pest-control-shock.md): Horizon-resolved projection of the change in natural crop-pest-control capacity, from the GLOBIO-ES EBV cube. This layer family backs the Biological control ecosystem service of the Nature Stress Test.
- [Pollination shock](https://docs.darwindata.ai/risk-layers/pollination-shock.md): Horizon-resolved projection of the change in native-bee pollination supply, from the InVEST pollination model. This layer family backs the Pollination ecosystem service of the Nature Stress Test.
- [Soil protection shock (soil & sediment retention)](https://docs.darwindata.ai/risk-layers/soil-protection-shock.md): Horizon-resolved projection of the change in erosion-control and sediment-retention capacity, from the GLOBIO-ES EBV cube. This layer family backs the Soil and sediment retention ecosystem service of the Nature Stress Test.
- [Ecoregion shock (SSP1-2.6)](https://docs.darwindata.ai/risk-layers/ecoregion-shock-ssp1.md): This is a forward-looking scenario layer.
- [Ecoregion shock (SSP5-8.5)](https://docs.darwindata.ai/risk-layers/ecoregion-shock-ssp5.md): This is a forward-looking scenario layer.
- [MSA shock (SSP1-2.6)](https://docs.darwindata.ai/risk-layers/msa-shock-ssp1.md): This layer projects how much biodiversity could change under a sustainable-development future (the SSP1-2.6 pathway).
- [MSA shock (SSP5-8.5)](https://docs.darwindata.ai/risk-layers/msa-shock-ssp5.md): This layer projects how much biodiversity could change under a fossil-fuel-intensive future (the SSP5-8.5 pathway).
- [PM2.5 shock (SSP1-2.6)](https://docs.darwindata.ai/risk-layers/pm25-shock-ssp1.md): This layer measures the projected change in fine particulate matter (PM2.5) air pollution between today and mid-century under a sustainable-development future.
- [PM2.5 shock (SSP3-7.0)](https://docs.darwindata.ai/risk-layers/pm25-shock-ssp5.md): This layer measures the projected change in fine particulate matter (PM2.5) air pollution between today and mid-century under a high-emissions, regional-rivalry futu
- [Soil Organic Carbon shock (SSP1-2.6)](https://docs.darwindata.ai/risk-layers/soc-shock-ssp1.md): This layer is a forward-looking scenario variant showing the projected change in soil organic carbon under the SSP1-2.6 pathway — a sustainable-development scenario with 
- [Soil Organic Carbon shock (SSP3-7.0)](https://docs.darwindata.ai/risk-layers/soc-shock-ssp5.md): This layer is a forward-looking scenario variant showing the projected change in soil organic carbon under the SSP3-7.0 pathway — a high-emissions, regional-rivalry 
- [Water stress shock (SSP1-2.6)](https://docs.darwindata.ai/risk-layers/water-stress-shock-ssp1.md): This layer shows how much water stress is projected to change under a sustainable-development future (the SSP1-2.6 pathway, characterised by lower emissions and more coor
- [Water stress shock (SSP3-7.0)](https://docs.darwindata.ai/risk-layers/water-stress-shock-ssp5.md): This layer shows how much water stress is projected to change under a high-emissions 'regional rivalry' future (the SSP3-7.0 pathway).
- [WWF Ecoregions](https://docs.darwindata.ai/risk-layers/wwf-ecoregions.md): This layer maps the world's terrestrial ecoregions — relatively large areas of land that each contain a distinct assemblage of natural communities sharing most of their s