| Layer | Description | Dimension | References | Updates |
|---|---|---|---|---|
| Artisanal fisheries sustainability | Score based on the amount of sustainably and unsustainably caught artisanal fisheries, based on B/Bmsy values. (data prep 1, data prep 2) | AO | Pauly, Zeller, and Palomareas (2020); RAM Legacy Stock Assessment Database (2024) | same year of data (2019), but RAM data was updated (2023) |
| Artisanal fisheries opportunity | The opportunity for artisanal and recreational fishing based on the quality of management of the small-scale fishing sector (data prep) | AO | Food and Agriculture Organization of the United Nations (2023) | none |
| Economic need for artisanal fishing | Inverse of per capita purchasing power parity (PPP) adjusted gross domestic product (GDP): GDPpcPPP as a proxy for subsistence fishing need (data prep) | AO | World Bank (2023a) | additional year of GDP data (2023) |
| Habitat extent of coral | Area of coral habitat (data prep) | CP | UNEP-WCMC et al. (2018) | none |
| Habitat extent of kelp | Area of kelp habitat (data prep) | CP | Jayathilake and Costello (2020) | none |
| Habitat extent of seaice | Area of seaice (edge and shoreline) habitat (data prep) | CP | DiGirolamo et al. (2022) | additional year of data (2023) |
| Habitat condition of coral | Current condition of coral habitat relative to historical condition (data prep) | CP, HAB | Bruno and Selig (2007); Schutte, Selig, and Bruno (2010) | none |
| Habitat condition trend of coral | Estimated trend in coral condition (data prep) | CP, HAB | Bruno and Selig (2007); Schutte, Selig, and Bruno (2010) | none |
| Habitat condition of kelp | Current condition of kelp habitat relative to historical condition (data prep) | CP, HAB | Jayathilake and Costello (2020); Wernberg et al. (2019) | none |
| Habitat condition trend of kelp | Estimated trend in kelp condition (data prep) | CP, HAB | Spalding et al. (2007); Krumhansl et al. (2016) | none |
| Habitat condition of seaice | Current condition of seaice habitat relative to historical condition (data prep) | CP, HAB | DiGirolamo et al. (2022) | additional year of data (2023) |
| Habitat condition trend of seaice | Estimated trend in seaice condition (data prep) | CP, HAB | DiGirolamo et al. (2022) | additional year of data (2023) |
| Habitat extent of tidal flat | Area of tidal flat habitat (data prep) | CS | Murray et al. (2019) | none |
| Habitat extent of mangrove | Area of mangrove habitat (data prep) | CS, CP | Bunting et al. (2022) | none |
| Habitat extent of saltmarsh | Area of saltmarsh habitat (data prep) | CS, CP | Mcowen et al. (2017) | none |
| Habitat extent of seagrass | Area of seagrass habitat (data prep) | CS, CP | UNEP-WCMC and Short (2005) | none |
| Habitat condition of tidal flat | Current condition of tidal flat habitat relative to historical condition (data prep) | CS, HAB | Murray et al. (2019) | none |
| Habitat trend of tidal flat | Estimated trend in tidal flat condition (data prep) | CS, HAB | Murray et al. (2019) | none |
| Habitat condition of mangrove | Current condition of mangrove habitat relative to historical condition (data prep) | CS, HAB, CP | Nations (2007) | none |
| Habitat condition trend of mangrove | Estimated trend in mangrove condition (data prep) | CS, HAB, CP | Bunting et al. (2022) | none |
| Habitat condition of saltmarsh | Current condition of saltmarsh habitat relative to historical condition (data prep) | CS, HAB, CP | Mcowen et al. (2017) | none |
| Habitat condition trend of saltmarsh | Estimated trend in saltmarsh condition (data prep) | CS, HAB, CP | Campbell et al. (2022) | none |
| Habitat condition of seagrass | Current condition of seagrass habitat relative to historical condition (data prep) | CS, HAB, CP | IUCN (2021); Waycott et al. (2009) | none |
| Habitat condition trend of seagrass | Estimated trend in seagrass condition (data prep) | CS, HAB, CP | IUCN (2021); Waycott et al. (2009) | none |
| Chemical pollution trend | Trends in chemical pollution, based on commercial shipping traffic, ports and harbors, land-based pesticide use (organic pollution), and urban runoff (inorganic pollution) within EEZ (data prep) | CW | Halpern et al. (2008); Halpern, Frazier, et al. (2015); Homer et al. (2004); Nations (2016) | none |
| Nutrient pollution trend | Trends in nutrient pollution, using crop fertilizer and manure consumption as a proxy for nutrient pollution (data prep) | CW | Halpern et al. (2008); Halpern, Frazier, et al. (2015); Homer et al. (2004); United Nations (2022b); United Nations (2021); Tuholske et al. (2021); Halpern et al. (2022) | none |
| Pathogen pollution trend | Trends in percent of population without access to improved sanitation facilities as a proxy for pathogen pollution (data prep) | CW | WHO-UNICEF (2024) | corrected coastal population density |
| Plastic trash trends | Trends in trash estimated using improperly disposed of plastics (data prep) | CW | Jambeck et al. (2015) | none |
| Coastal chemical pollution | Modeled chemical pollution within 3nm of coastline from commercial shipping traffic, ports and harbors, land-based pesticide use (organic pollution), and urban runoff (inorganic pollution) (data prep) | CW, pressure | Halpern et al. (2008); Halpern, Longo, et al. (2015); Homer et al. (2004); Nations (2016) | none |
| Coastal nutrient pollution | Modeled nutrient pollution within 3nm based on crop fertilizer and manure consumption (data prep) | CW, pressure | Halpern et al. (2008); Halpern, Longo, et al. (2015); United Nations (2022b); United Nations (2021); Tuholske et al. (2021); Halpern et al. (2022) | none |
| Pathogen pollution | Percent of population without access to improved sanitation facilities as a proxy for pathogen pollution (data prep) | CW, pressure | WHO-UNICEF (2024) | corrected coastal population density |
| Marine plastics | Global marine plastic pollution (data prep) | CW, pressure | Eriksen et al. (2014) | none |
| Economic status scores | Calculated using corrected revenue data for several marine sectors (data not updated since 2013) (data prep) | ECO | Kaufmann, Kraay, and Mastruzzi (2010); O’Connor et al. (2009a); Nations (2013b); Nations (2013a); Nations (2012); Bank (2016) | none |
| Economic trend scores | Calculated using change in revenue for several marine sectors (data not updated since 2013) (data prep) | ECO | Kaufmann, Kraay, and Mastruzzi (2010); O’Connor et al. (2009a); Nations (2013b); Nations (2013a); Nations (2012); Bank (2014); Bank (2016) | none |
| Sectors in each region | Proportion of jobs within each marine sector (data prep) | ECO, LIV | France (2011); O’Connor et al. (2009a); Thorbourne (2011); communication (2011); WTTC (2013) | none |
| B/Bmsy estimates | The ratio of fish population abundance compared to the abundance required to deliver maximum sustainable yield (RAM and catch-MSY data) (data prep 1, data prep 2, data prep 3, data prep 4) | FIS | Anderson (2018); Free (2017); Martell and Froese (2013); RAM Legacy Stock Assessment Database (2024); Ricard et al. (2012); Rosenberg et al. (2014); Pauly, Zeller, and Palomareas (2020); FAO Fisheries and Aquaculture Department (2015) | updated RAM values (v4.65: 2023), but 2019 is still the year of data used like before since it is still what matches the fisheries catch data |
| Fishery catch data | Mean commercial catch for each OHI region (averaged across years) (data prep 1, data prep 2) | FIS | Froehlich (2018); Pauly, Zeller, and Palomareas (2020) | none |
| Food provision weights | Proportion of wild caught fisheries relative to total food production (e.g., fisheries and mariculture) (data prep) | FP | Pauly, Zeller, and Palomareas (2020); United Nations (2024) | additional year of data for mariculture (2022) but not fisheries (2019) |
| Habitat condition of softbottom | Current condition of softbottom habitat, based on demersal destructive fishing practices (e.g., trawling) (data prep) | HAB | Watson (2019); Halpern, Frazier, et al. (2015); “Global Fishing Watch Data Download Portal” (2022) | none |
| Habitat condition trend of softbottom | Estimated change in softbottom condition, based on trends in demersal destructive fishing practices (e.g., trawling) (data prep) | HAB | Watson (2019); Halpern, Frazier, et al. (2015); “Global Fishing Watch Data Download Portal” (2022) | none |
| IUCN extinction risk | IUCN extinction risk category for iconic species located within each region (data prep) | ICO | Halpern et al. (2012); IUCN (2024a); Reyes-García et al. (2023) | additional year of data (2023) |
| Livelihood status scores | Calculated using adjusted job and wage data in several marine sectors (data not updated since 2013) (data prep) | LIV | France (2011); Kaufmann, Kraay, and Mastruzzi (2010); O’Connor et al. (2009a); Oostendorp and Freeman (2012); Thorbourne (2011); communication (2011); Bank (2014); World Bank (2014a); Bank (2016); WTTC (2013) | none |
| Livelihood trend scores | Calculated using change in adjusted job and wage data in several marine sectors (data not updated since 2013) (data prep) | LIV | France (2011); Kaufmann, Kraay, and Mastruzzi (2010); O’Connor et al. (2009a); Oostendorp and Freeman (2012); Thorbourne (2011); communication (2011); World Bank (2014a); Bank (2016); World Bank (2014b); WTTC (2013) | none |
| Inland coastal protected areas | Protected areas located 1 km inland (data prep) | LSP | Lewis et al. (2017); UNEP-WCMC and IUCN (2024) | additional year of data (2023) |
| Offshore coastal protected areas | Protected areas located 3nm offshore (data prep) | LSP | Lewis et al. (2017); UNEP-WCMC and IUCN (2024) | additional year of data (2023) |
| Inland 1km area | Inland area of OHI regions within 1km of shoreline (data prep) | LSP | Claus et al. (2012); Esri (2010); Halpern et al. (2012); Halpern, Longo, et al. (2015) | none |
| Offshore 3nm area | Offshore area of OHI regions within 3nm of shoreline (data prep) | LSP | Claus et al. (2012); Esri (2010); Halpern et al. (2012); Halpern, Longo, et al. (2015) | none |
| Potential tonnes of mariculture harvest | Tonnes of mariculture harvest potential for each region based on biological variables and growth performance indices. Taken from Gentry et al. 2017 and adopted for the OHI. (data prep) | MAR | Gentry et al. (2019) | none |
| Mariculture harvest | Tonnes of mariculture harvest (data prep) | MAR | United Nations (2024) | additional year of data (2022) |
| Mariculture sustainability score | Mariculture sustainability based on the Monterey Bay Aquarium Seafood Watch Aquaculture Recommendations. (data prep) | MAR | “Monterey Bay Aquarium Seafood Watch” (2023) | none |
| Habitat extent of rocky reef | Area of rocky reef habitat (data prep) | NP | Halpern et al. (2008) | none |
| Exposure of ornamental fishing to coral and rocky reef habitats | The ln-transformed intensity of harvest calculated as tonnes of harvest per km2?of coral and/or rocky reef for ornamentals, relative to the global maxiumum. (data prep) | NP | UN-FAO (2024); Burke et al. (2011); Halpern et al. (2008) | additional year of data (2022) |
| Fish oil and fish meal score | Score based on the amount of sustainably and unsustainably caught forage fish used for fish oil and fish meal. Penalties are assigned for both under harvest and over harvested based on B/Bmsy estimates. (data prep) | NP | Pauly, Zeller, and Palomareas (2020); Froehlich (2018); RAM Legacy Stock Assessment Database (2024) | additional year of BBmsy values |
| Relative natural product harvest value | Relative importance of three marine commodities (fish oil, seaweeds ornamental fish) within each region determined by finding the relative contribution per a 5 year average of harvest (in USD) divided by the total 5 year average harvest value (in USD) per each commodity within each region. (data prep) | NP | UN-FAO (2024) | additional year of data (2022) |
| Relative ornamental natural product harvest tonnes | Tonnes of harvest of ornamentals relative to maximum harvest of the ornamentals within the region observed across years (data prep) | NP | UN-FAO (2024) | additional year of data (2022) |
| Risk of harvest practices for ornamental fish | Based on whether ornamental fishing has unsustainable harvest practices (i.e., the intensity of cyanide fishing for ornamental fish, and any harvest of corals since they are CITES protected species). Risk for ornamental fish was set based on assessments of cyanide or dynamite fishing by Reefs at Risk Revisited (www.wri.org/publication/reefs-at-risk-revisited) under the assumption that most ornamental fishes are harvested from coral reefs. (data prep) | NP | UN-FAO (2024); Burke et al. (2011) | additional year of data (2022) |
| Seaweed mariculture sustainability score | Seaweed mariculture sustainability based on the Mariculture Sustainability Index (MSI) (data prep) | NP | “Monterey Bay Aquarium Seafood Watch” (2023) | none |
| Seaweed natural product harvest | Yield in metric tonnes of seaweed (data prep) | NP | United Nations (2024) | additional year of data (2022) |
| Average species condition | Overall measure of species condition based on IUCN status of species within each region (data prep) | SPP | BirdLife International and Handbook of the Birds of the World (2020); IUCN (2024b); IUCN (2024a) | additional year of data (2023) |
| Average species condition trend | Overall measure of species condition trends based on change in IUCN status of species within each region (data prep) | SPP | BirdLife International and Handbook of the Birds of the World (2020); IUCN (2024b); IUCN (2024a) | additional year of data (2023) |
| Coastal tourism density | Proportion of coastal international and domestic arrivals relative to area of coastline (data prep) | TR | UNWTO (2022); World Bank (2023b); Data (2023); Statista (2023a); Statista (2023b); Lewis et al. (2017) | adding domestic data from the same data source & reworking of code to accommodate a new methodology: (tourism arrivals * (coastal pop per region/total pop per region))/coastal area |
| Tourism sustainability index | Travel and Tourism Development Index (TTDI) (data prep) | TR | CIA (2023); WEF (2024) | new data source/year of data (2023): World Economic Forum’s “Travel and Tourism Development Index” (https://www.weforum.org/publications/travel-tourism-development-index-2024/) |
| Ocean acidification | Pressure due to increasing ocean acidification, scaled using biological thresholds (data prep) | pressure | CMS (2022) | new data source (Copernicus Marine Service, 2022) |
| Sea level rise | Pressure due to rising mean sea level (data prep) | pressure | AVISO (2024) | additional year of data (2022) |
| Sea surface temperature | Presure due to increasing extreme sea surface temperature events (data prep) | pressure | NOAA (2022) | none |
| UV radiation | Pressure due to increasing frequency of UV anomolies (data prep) | pressure | Jari Hovila (2013) | additional year of data (2023) |
| High bycatch due to artisanal fishing | Pressure due to artisanal high bycatch fishing identified by discard tonnes and standardized by NPP (data prep 1, data prep 2) | pressure | Behrenfeld and Falkowski (1997); O’Malley (n.d.); Watson (2019) | none |
| Low bycatch due to artisanal fishing | Pressure due to artisanal low bycatch fishing identified by reported and IUU tonnes and standardized by NPP (data prep 1, data prep 2) | pressure | Behrenfeld and Falkowski (1997); O’Malley (n.d.); Watson (2019) | none |
| High bycatch due to commercial fishing | Pressure due to industrial high bycatch fishing identified by discard tonnes and standardized by NPP (data prep 1, data prep 2) | pressure | Behrenfeld and Falkowski (1997); O’Malley (n.d.); Watson (2019) | none |
| Low bycatch due to commercial fishing | Pressure due to industrial low bycatch fishing identified by reported and IUU tonnes and standardized by NPP (data prep 1, data prep 2) | pressure | Behrenfeld and Falkowski (1997); O’Malley (n.d.); Watson (2019) | none |
| Targeted harvest of cetaceans and marine turtles | Targeted harvest of cetaceans and marine turtles (data prep) | pressure | United Nations (2022a) | additional year of data (2022) |
| Coral harvest pressure | Pressure on coral due to harvesting as a natural product (data prep) | pressure | UNEP-WCMC et al. (2018); UN-FAO (2024) | additional year of data (2022) |
| Intertidal habitat destruction | Coastal population density (25 mi from shore) as a proxy for intertidal habitat destruction (data prep) | pressure | Center for International Earth Science Information Network (CIESIN) et al. (2000) | same year of data, corrected coastal population estimates |
| Subtidal hardbottom habitat destruction | Presence of blast fishing as an estimate of subtidal hard bottom habitat destruction (data prep) | pressure | Burke et al. (2011) | none |
| Subtidal soft bottom habitat destruction | Pressure on soft-bottom habitats due to demersal destructive commercial fishing practices (e.g., trawling) (data prep) | pressure | Halpern, Frazier, et al. (2015); Watson (2019); “Global Fishing Watch Data Download Portal” (2022) | none |
| Chemical pollution | Modeled chemical pollution within EEZ from commercial shipping traffic, ports and harbors, land-based pesticide use (organic pollution), and urban runoff (inorganic pollution) (data prep) | pressure | Halpern et al. (2008); Halpern, Longo, et al. (2015); Homer et al. (2004); Nations (2016) | none |
| Nutrient pollution | Modeled nutrient pollution within EEZ based on crop fertilizer and manure consumption (data prep) | pressure | Halpern et al. (2008); Halpern, Longo, et al. (2015); United Nations (2022b); United Nations (2021); Tuholske et al. (2021); Halpern et al. (2022) | none |
| Nonindigenous species | Measure of harmful invasive species (data prep) | pressure | Pagad et al. (2018) | none |
| Genetic escapes | Introduced mariculture species (Mariculture Sustainability Index) as a proxy for genetic escapes (data prep) | pressure | “Monterey Bay Aquarium Seafood Watch” (2023) | additional year of FAO mariculture yield data (2022); escapes data from Monterey Bay Aquarium Seafood Watch Aquaculture Recommendations (2023) was NOT updated |
| Weakness of social progress | Inverse of Social Progress Index scores (data prep) | pressure | Social Progress Index (2021); Stern et al. (2021) | none |
| Weakness of governance | Inverse of World Governance Indicators (WGI) six combined scores (data prep) | pressure | Kaufmann, Kraay, and Mastruzzi (2010); WorldBank (2024) | additional year of data (2022) |
| Artisanal fisheries management effectiveness | Quality of management of small-scale fishing for artisanal and recreational purposes (data prep) | resilience | Food and Agriculture Organization of the United Nations (2023) | none |
| Minderoo Global Fishing Index | Country scale fisheries governance capacity based on policy and objectives, management capacity, information availability and monitoring, level and control of access to fisheries resources, compliance management system, and stakeholder engagement and participation (data prep) | resilience | Travaille et al. (2022) | none |
| Management of habitat to protect fisheries biodiversity | Survey responses by country to the Convention on Biological Diversity (CBD) Third National Report: habitat related questions (data prep) | resilience | Secretariat of the Convention on Biological Diversity (2005) | none |
| Coastal protected marine areas (fishing preservation) | Protected marine areas within 3nm of coastline (lasting special places goal status score) (data prep) | resilience | Lewis et al. (2017); UNEP-WCMC and IUCN (2024) | additional year of data (2023) |
| EEZ protected marine areas (fishing preservation) | Protected marine areas within EEZ (lasting special places calculation applied to the entire EEZ) (data prep) | resilience | Lewis et al. (2017); UNEP-WCMC and IUCN (2024) | additional year of data (2023) |
| CITES signatories | Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) signatories (data prep) | resilience | CITES (2015) | none |
| Management of tourism to preserve biodiversity | Survey responses by country to the Convention on Biological Diversity (CBD) Third National Report: tourism related questions (data prep) | resilience | Secretariat of the Convention on Biological Diversity (2005) | none |
| Management of habitat to protect habitat biodiversity | Survey responses by country to the Convention on Biological Diversity (CBD) Third National Report: habitat related questions (data prep) | resilience | Secretariat of the Convention on Biological Diversity (2005) | none |
| Coastal protected marine areas (habitat preservation) | Protected marine areas within 3nm of coastline (lasting special places goal status score) (data prep) | resilience | Lewis et al. (2017); UNEP-WCMC and IUCN (2024) | none |
| EEZ protected marine areas (habitat preservation) | Protected marine areas within EEZ (lasting special places calculation applied to the entire EEZ) (data prep) | resilience | UNEP-WCMC and IUCN (2024) | none |
| Global Competitiveness Index (GCI) | Competitiveness in achieving sustained economic prosperity (data prep) | resilience | Schwab (2017) | none |
| Economic diversity | Sector evenness based on Shannon’s Diversity Index calculated on the proportion of jobs in each sector as a measure of economic diversity (data prep) | resilience | France (2011); O’Connor et al. (2009a); Thorbourne (2011); communication (2011); WTTC (2013) | none |
| Management of waters to preserve biodiversity | Survey responses by country to the Convention on Biological Diversity (CBD) Third National Report: clean water management related questions (data prep) | resilience | Secretariat of the Convention on Biological Diversity (2005) | none |
| Social Progress Index | Social Progress Index scores (data prep) | resilience | Social Progress Index (2021); Stern et al. (2021) | none |
| Measure of coastal ecological integrity | Marine species condition (same calculation and data as the species subgoal status score) calculated within 3 nm of shoreline as a proxy for ecological integrity (data prep) | resilience | BirdLife International and Handbook of the Birds of the World (2020); IUCN (2024b); IUCN (2024a) | additional year of data (2023) |
| Measure of ecological integrity | Marine species condition (species subgoal status score) as a proxy for ecological integrity (data prep) | resilience | BirdLife International and Handbook of the Birds of the World (2020); IUCN (2024b); IUCN (2024a) | additional year of data (2023) |
| Strength of governance | World Governance Indicators (WGI) six combined scores (data prep) | resilience | Kaufmann, Kraay, and Mastruzzi (2010); WorldBank (2024) | additional year of data (2022) |
| Region areas based on EEZ boundaries | Area of Ocean Health Index regions modified from exclusive economic zones, weights used to calculate global score (data prep) | spatial | Claus et al. (2012); Esri (2010); Halpern et al. (2012); Halpern, Longo, et al. (2015) | none |
| OHI region id | Subset of regions that are not deleted or disputed (data prep) | spatial | Claus et al. (2012); Esri (2010); Halpern et al. (2012); Halpern, Longo, et al. (2015) | none |
| Regions | Regions by type (eez, subocean, unclaimed) (data prep) | spatial | Claus et al. (2012); Esri (2010); Halpern et al. (2012); Halpern, Longo, et al. (2015) | none |
| Uninhabited regions | Regions with low and no number of inhabitants (also identifies Southern Islands) (data prep) | spatial | NA | none |
| Coastal protection weights | Habitat extent multiplied by habitat protection rank for: coral, mangrove (offshore and inland), saltmarsh, sea ice (shoreline), and seagrass (empty dataframe filled by functions.R in ohi-global) (data prep) | weighting | Tallis et al. (2011) | none |
| Carbon storage weights | Habitat extent multiplied by carbon storage capacity for: mangrove, saltmarsh, and seagrass (empty dataframe filled by functions.R in ohi-global) (data prep) | weighting | Chen and Lee (2022) | none |
| Habitat presence/absence | List of habitats in each region (empty dataframe filled by functions.R in ohi-global) (data prep) | weighting | NA | none |
Description of Data Layers
Tables describing data layers (Table 7.1) and sources (Table 7.2)
Table 7.1. Data layers of 2023 global OHI assessment A brief overview of all the data layers used to calculate the global OHI. The “Data layer” variable provides links to a full description of the data layer. The “Description” variable provides link/s to the data preparation scripts (when available). See Table 7.2 for a description of the data sources used to create these data layers.
Table 7.2. Data sources used to create data layers for 2024 global OHI assessment An in-depth description of data sources used for different data layers. References of interest from the previous table (7.1) can be found here under “References”. Information is provided on the last update of the data sources. An update of “NA” means that the data source does not update yearly, or that it is not currently being used to calculate scores.
Supplemental Methods by Layer
Artisanal fisheries management effectiveness
fp_artisanal
Resilience
Category: ecological/regulatory
Subcategory: fishing
This layer represents the opportunity for artisanal and recreational fishing in each country based on the quality of management of the small-scale fishing sector. Global data were extracted from United Nations Sustainable Development Goal (UN SDG) 14.b.1. UN SDG 14.b.1 (Food and Agriculture Organization of the United Nations 2023). FAO member country responses to the Code of Conduct for Responsible Fisheries (CCRF) (Table 6.1) survey questionnaire which is circulated by FAO every two years to members and IGOs and INGOs and are on a scale from 0 to 1.
Questions from UN SDG 14.b.1 that were used to evaluate access to artisanal scale fishing:
- Are there any laws, regulations, policies, plans or strategies that specifically target or address the small-scale fisheries sector?
| Yes | No | |
|---|---|---|
| Law | ||
| Regulation | ||
| Policy | ||
| Plan/strategy | ||
| Other (please specify) |
- The Voluntary Guidelines for Securing Sustainable Small-scale Fisheries in the Context of Food Security and Poverty Eradication (SSF Guidelines) were endorsed by COFI in June 2014. Does your country have a specific initiative to implement the SSF Guidelines?
Units
scaled 0-1
Artisanal fisheries opportunity
ao_access
This layer represents the opportunity for artisanal and recreational fishing in each country based on the quality of management of the small-scale fishing sector. Global data were extracted from United Nations Sustainable Development Goal (UN SDG) 14.b.1. UN SDG 14.b.1 (Food and Agriculture Organization of the United Nations 2023). FAO member country responses to the Code of Conduct for Responsible Fisheries (CCRF) (Table 6.1) survey questionnaire which is circulated by FAO every two years to members and IGOs and INGOs and are on a scale from 0 to 1.
Questions from UN SDG 14.b.1 that were used to evaluate access to artisanal scale fishing:
- Are there any laws, regulations, policies, plans or strategies that specifically target or address the small-scale fisheries sector?
| Yes | No | |
|---|---|---|
| Law | ||
| Regulation | ||
| Policy | ||
| Plan/strategy | ||
| Other (please specify) |
- The Voluntary Guidelines for Securing Sustainable Small-scale Fisheries in the Context of Food Security and Poverty Eradication (SSF Guidelines) were endorsed by COFI in June 2014. Does your country have a specific initiative to implement the SSF Guidelines?
Units
scaled 0-1
Artisanal fisheries sustainability
ao_sust
These data are used to estimate the sustainability of artisanal fishing practices. Artisanal and subsistence fishing scores were calculated from Sea Around Us catch data (Pauly, Zeller, and Palomareas 2020) and the RAM Legacy Database http://ramlegacy.org/ (RAM Legacy Stock Assessment Database 2024). For this layer, nearly identical methods were used as in the fisheries sub-goal calculations. Instead of producing scores for all global fish stocks, we only produced scores for catch that is denoted as artisanal and subsistence catch in the Sea Around Us catch data.
We used SeaLifeBase (Palomares and Pauly 2022) and FishBase (Froese and Pauly 2022) to align taxa names across datasets.
Units
scaled 0-1
Average species condition
spp_status
See Species goal for calculations.
These are the status data for the species subgoal, calculated using the species model. These data describe the average condition of species within each region based on risk status from the IUCN Red List of Threatened Species (http://www.iucnredlist.org/) (IUCN 2024a) and BirdLife International (http://datazone.birdlife.org) (IUCN 2024b; BirdLife International and Handbook of the Birds of the World 2020) data.
We include only species from comprehensively assessed groups (groups with >90% of species assessed) to help control for sampling bias. We found that some regions (e.g., Atlantic) had assessments for a much larger proportion of species than other regions, but this problem was less pronounced when we only included comprehensively assessed species.
These data incorporate regional IUCN assessment information when possible (i.e., when a species’ IUCN status varies geographically).
Units
status score
Average species condition trend
spp_trend
See Species goal for calculations.
These are the trend data for the species subgoal. These data describe changes in species condition within each region based on historical changes in risk status from IUCN Red List of Threatened Species http://www.iucnredlist.org/ (IUCN 2024a, 2024b; BirdLife International and Handbook of the Birds of the World 2020).
Units
trend score
B/Bmsy estimates
fis_b_bmsy
Status of global fish stocks based on B/Bmsy values (the ratio of population biomass compared to the biomass required to deliver maximum sustainable yield). We preferentially used B/Bmsy estimates from formal stock assessments from the
RAM Legacy Database http://ramlegacy.org/ (RAM Legacy Stock Assessment Database 2024). We assigned the stocks to OHI and FAO regions using Free (2017) spatial data describing the range of each stock. When a stock was missing the most recent years of data, carried over the most recent year after 2010 to estimate the missing years.
When RAM data were unavailable, we use the data-limited catch-MSY model (Martell and Froese 2013) to estimate B/Bmsy values using yearly fish catch reconstruction data. For the catch-MSY model, we defined a stock as a species caught within an FAO major fishing area (www.fao.org/fishery/area/search/en). This definition of a stock eliminated all taxa not identified to species level. This approach assumes that stocks are defined by FAO region, which we know is often not true because multiple stocks of the same species can exist within an FAO region and some stocks cover multiple FAO regions. However, this assumption is necessary without range maps of stocks. The catch data were summed for each species/FAO region/year, and the catch-MSY model was applied to each stock and FAO region to estimate B/Bmsy.
We used SeaLifeBase (Palomares and Pauly 2022) and FishBase (Froese and Pauly 2022) to align taxa names across datasets.
Units
B/Bmsy
CITES signatories
g_cites
Resilience
Category: ecological/regulatory
Subcategory: goal
Contracting parties to the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES, http://www.cites.org/eng/ disc/parties/alphabet.php) (CITES 2015). The Convention is an international agreement between governments that aims to ensure that any international trade in plants and animals “does not threaten their survival.” All countries party to the Convention are given full credit for membership (territories are given the same score as their administrative countries); those countries that are not contracting parties are given no credit (score = 0).
Units
0 or 1
Carbon storage weights
element_wts_cs_km2_x_storage
This layer describes the relative value of the habitats in each region to carbon storage, and is calculated by multiplying the habitat extent (km2) in each region by the amount of carbon the habitat sequesters (Chen and Lee 2022).
Data is generated in ohi-global/eez/conf/functions.R.
These data are called internally by ohicore functions (see: conf/config.R to see how these data are specified) to weight the data used to calculate pressure and resilience values.
Table 7.3. Carbon storage weights
| Habitat carbon storage | Sequestration (weight) |
|---|---|
| Mangrove | 230.9 |
| Saltmarsh | 244.7 |
| Seagrass | 138 |
| Tidal flat | 129.8 |
Units
extent*carbon_storage
Chemical pollution
po_chemicals
Pressure
Category: ecological
Subcategory: pollution
This pressure layer is calculated using modeled data for land-based organic pollution (pesticide data), land-based inorganic pollution (using impermeable surfaces as a proxy), and ocean pollution (shipping and ports). These global data are provided at ~1km resolution, with raster values scaled from 0-1 (Halpern et al. 2008, 2015). To obtain the final pressure values, the three raster layers were summed (with cell values capped at 1).
Land-based organic pollution
Data were calculated using modeled plumes of land-based pesticide pollution that provide intensity of pollution at 1km2 resolution (Halpern et al. 2008).
Organic pollution was estimated from FAO data on annual country-level pesticide use (http://faostat3.fao.org/faostat-gateway/go/to/browse/R/*/E) (Nations 2016), measured in metric tons of active ingredients. FAO uses survey methods to measure quantities of pesticides applied to crops and seeds in the agriculture sector, including insecticides, mineral oils, herbicides, fungicides, seed treatments insecticides, seed treatments fungicides, plant growth regulators and rodenticides. Missing values were estimated by regression between fertilizer and pesticides when possible, and when not possible with agricultural GDP as a proxy. Data were summed across all pesticide compounds and reported in metric tons. Upon inspection the data included multiple 0 values that are most likely data gaps in the time-series, so they were treated as such and replaced with NA. In addition, regions with only 1 data point and regions where the most recent data point was prior to 2005 were excluded. Uninhabited countries were assumed to have no pesticide use and thus excluded.
Region-level pollution values were then dasymetrically distributed over a region’s landscape using global landcover data from 2009, derived from the MODIS satellite data at ~500m resolution. These values were then aggregated by ~140,000 global basins, and diffusive plumes were modeled from each basin’s pourpoint. The final non-zero plumes (about ~76,000) were aggregated into ~1km Mollweide (wgs84) projection rasters to produce a single plume-aggregated pollution raster.
These raw values were then ln(X+1) transformed and normalized to 0-1 by dividing by the 99.99th quantile of raster values across all years. The zonal mean was then calculated for each region.
Land-based inorganic pollution
These data are from Halpern et al. (2008, 2015), and available from Knowledge Network for Biocomplexity (KNB, https://knb.ecoinformatics.org/#view/doi:10.5063/F19021PC, rescaled_2013_inorganic_mol). Non-point source inorganic pollution was modeled with global 1 km2 impervious surface area data http://www.ngdc.noaa.gov/dmsp/ under the assumption that most of this pollution comes from urban runoff. These data will not capture point-sources of pollution or nonpoint sources where paved roads do not exist (e.g., select places in developing countries). Values were aggregated to the watershed and distributed to the pour point (i.e., stream and river mouths) for the watershed with raster statistics (i.e., aggregation by watershed).
Ocean pollution (shipping lanes, ports)
These data are from Halpern et al. (2015), and available from the Knowledge Network for Biocomplexity (KNB, https://knb.ecoinformatics.org/#view/doi:10.5063/F1DR2SDD, rescaled_2013_one_ocean_pollution_mol). Ocean-based pollution combines commercial shipping traffic data and port data.
Shipping data was obtained from two sources: (1) Over the past 20 years, 10-20% of the vessel fleet has voluntarily participated in collecting meteorological data for the open ocean, which includes location at the time of measurement, as part of the Volunteer Observing System (VOS). (2) In order to improve maritime safety, in 2002 the International Maritime Organization SOLAS agreement required all vessels over 300 gross tonnage (GT) and vessels carrying passengers to equip Automatic Identification System (AIS) transceivers, which use the Global Positioning System (GPS) to precisely locate vessels.
Port data was based on the volume (measured in tonnes) of goods transported through commercial ports as a proxy measure of port traffic. Total cargo volume data by port was collected from regional and national statistical organizations, and from published port rankings.
Units
scaled 0-1
Chemical pollution trend
cw_chemical_trend
The inverse of the pressure data (1 - po_chemicals_3nm) was used to estimate chemical trends for the clean water goal. The proportional yearly change in chemical pressure values were estimated using a linear regression model of the most recent five years of data (i.e., slope divided by value from the earliest year included in the regression model). The slope was then multiplied by five to get the predicted change in 5 years.
The only layer with yearly data was land-based organic pollution (pesticide data). The land-based inorganic pollution (using impermeable surfaces as a proxy) and ocean pollution (shipping and ports) remained the same across years.
Units
trend
Coastal chemical pollution
po_chemicals_3nm
Pressure
Category: ecological
Subcategory: pollution
Methods follow those described in Table 7.3 in po_chemicals layer. However, the rescaled data were clipped to include only pixels within 3nm offshore, and the zonal mean for each region was calculated using this subset of data.
For the clean waters goal calculations, the inverse of the pressure values is used (1 minus chemical pressure).
Units
scaled 0-1
Coastal nutrient pollution
po_nutrients_3nm
Pressure
Category: ecological
Subcategory: pollution
Methods follow those described in Table 7.3 in po_nutrients layer. However, the rescaled data were clipped to include only pixels within 3nm offshore, and the zonal mean for each region was calculated using this subset of data.
For the clean waters goal calculations, the inverse of the pressure values is used (1 minus nutrient pressure).
Units
scaled 0-1
Coastal protected marine areas (fishing preservation)
fp_mpa_coast
Resilience
Category: ecological/regulatory
Subcategory: fishing
These data are calculated using the lasting special places status subgoal model using total marine protected area (km2) within 3 nm offshore (see Table 7.3 in lsp_prot_area_offshore3nm layer for information about the data). Following the lasting special places model, a reference point of 30% is used, such that any region with 30%, or more, protected area receives a score of 1.
Units
scaled 0-1
Coastal protected marine areas (habitat preservation)
hd_mpa_coast
Resilience
Category: ecological/regulatory
Subcategory: habitat
Units
scaled 0-1
Coastal protection weights
element_wts_cp_km2_x_protection
This layer describes the relative value of the habitats in each region to coastal protection, and is calculated by multiplying the habitat extent (km2) in each region by the habitat protection rank.
Data is generated in ohi-global/eez/conf/functions.R.
These data are called internally by ohicore functions (see: conf/config.R to see how these data are specified) to weight the data used to calculate pressure and resilience values.
Table 7.4. Coastal protection ranks
| Habitat | Protection rank |
|---|---|
| Coral | 4 |
| Mangrove | 4 |
| Seaice (shoreline) | 4 |
| Saltmarsh | 3 |
| Seagrass | 1 |
| Kelp | 1 |
Units
extent*rank_protection
Coral harvest pressure
hd_coral
Pressure
Category: ecological
Subcategory: habitat destruction
The total tonnes of coral harvest were determined for each region using export data from the FAO Global Commodities database (UN-FAO 2024). The tonnes of ornamental fishing was divided by the area of coral, taken from the hab_coral layer, to get the intensity of coral harvest per region. Following this, we set the reference value as the 95th quantile of coral harvest intensity. We then divided the intensity by the reference intensity. Anything that scored above 1 received a intensity pressure score of 1. To incorporate the health of the coral, we then multiplied the intensity pressure score by the health of the coral, to get the final pressure score.
Units
scaled 0-1
EEZ protected marine areas (fishing preservation)
fp_mpa_eez
Resilience
Category: ecological/regulatory
Subcategory: fishing
These data are calculated using the lasting special places status subgoal model (except this calculation is based on the entire eez region vs. 3 nm offshore), using the total marine protected area (km2) within the offshore eez region. Following to the lasting special places model, a reference point of 30% is used, such that any region with 30%, or more, protected area receives a score of 1.
Units
scaled 0-1
EEZ protected marine areas (habitat preservation)
hd_mpa_eez
Resilience
Category: ecological/regulatory
Subcategory: habitat
Units
scaled 0-1
Economic diversity
li_sector_evenness
Resilience
Category: social
Sector evenness was measured using Shannon’s Diversity Index, a common measure of ecological and economic diversity that has been applied previously to economic sectors (Attaran 1986). The Diversity Index is computed as \({ H }^{ ' }/{ H }_{ max }\) where:
\[ { H }^{ ' } = \sum _{ i }^{ z }{ { f }_{ i }\ast } \ln { { (f }_{ i }) }, (Eq. 7.1) \]
and Z is the total number of sectors, \(fi\) is the frequency of the ith sector (the probability that any given job belongs to the sector), and \(H_{max} = \ln { Z }\).
Units
scaled 0-1
Economic need for artisanal fishing
ao_need
These data are used to estimate the need for artisanal fishing opportunities given the purchasing power parity adjusted per capita gross domestic product (ppppcgdp) in “constant” USD (World Bank, http://data.worldbank.org/indicator/NY.GDP.PCAP.PP.KD) (World Bank 2023a). The World Bank defines gdp as the gross value of all resident producers in the economy plus product taxes and minus and subsidies not included in the value of the products. The gdp is adjusted by population size to get per capita output and by purchasing power parity (ppp) to account for the difference in exchange rates between countries. ppppcgdp data were rescaled to values between zero and one by taking the natural log of the values and dividing by the 99th quantile value across all years/regions (2005 to most recent year).
When a region is missing some years of data, a within region linear model is used to estimate the missing values.
This is actually a measure of prosperity, but it is converted to need in the artisanal opportunities goal model (1 minus ppppcgdp).
Units
scaled 0-1
Economic status scores
eco_status {-
This layer provides calculated status values for the economies subgoal. Economies is calculated using revenue data from marine sectors.
Note: These data are no longer supported. Consequently, this layer was last updated in 2013, and this goal will no longer be updated with these data.
Economies status is calculated as: (cur_base_value / ref_base_value) / (cur_adj_value / ref_adj_value)
Where, cur_base_value is the most recent revenue values for each sector/region, and ref_base_value is the earliest year of revenue data for each sector/region. These values are adjusted by dividing by the GDP of corresponding region/year to control for larger economic trends. National GDP data were obtained from the World Bank (http://data.worldbank.org/indicator/ NY.GDP.MKTP.CD). For the three EEZs that fall within the China region (China, Macau, and Hong Kong), we combined the values using a population-weighted average.
This layer includes yearly data for revenue in commercial fishing, aquarium trade fishing, mariculture, marine mammal watching, marine renewable energy, and, tourism. The data sources and methods for each sector are described below.
Aquarium trade fishing
To approximate revenue from aquarium fishing we used export data from the FAO Global Commodities database for ‘Ornamental fish’ for all available years. We used data from two of the four subcategories listed, excluding the subcategory ‘Fish for culture including ova, fingerlings, etc.’ because it is not specific to ornamental fish, and the subcategory ‘Ornamental freshwater fish’ because it is not from marine systems.
Commercial fishing
Revenue data for commercial fishing were obtained from FAO’s FishStat database, which provides yearly dollar values of commercial fisheries production for marine, brackish and freshwater species starting in 1950 and updated yearly. To isolate production values attributable to marine and brackish aquaculture, data pertaining to freshwater species were omitted. This species classification process was very time consuming as each species had to be queried individually per year. There was little year-to year variation, and thus data were extracted in 5 year increments, providing data for 1997, 2002 and 2007.
Mariculture
Data on revenues from marine aquaculture were derived from FAO’s FishStat database, which includes country-level data on total production values for marine, brackish, and freshwater species beginning in 1984 and updated yearly. To isolate production values attributable to marine and brackish aquaculture, data pertaining to freshwater species were omitted. This species classification process was very time consuming as each species had to be queried individually per year. There was little year-to year variation, and thus data were extracted in 5 year increments, providing data for 1997, 2002 and 2007.
Marine mammal watching
IFAW provides country-level data on total expenditures (including direct and indirect) attributable to the whale watching industry (O’Connor et al. 2009b). Here, total expenditures are used as a close proxy for total revenue. We used total expenditure data (direct and indirect expenditures) to avoid using a literature derived multiplier effect. When IFAW reported “minimal” revenue from whale watching, we converted this description to a 0 for lack of additional information. For countries with both marine and freshwater cetacean viewing, we adjusted by the proportion of marine revenue as described for the jobs dataset.
Marine renewable energy
The United Nations Energy Statistics Database provides production data, in kilowatt-hours (KWh), for tidal and wave electricity. However, only two countries, France and Canada, have high enough levels of production to be reported in this data source. For Canada, production data were replaced with production data (Gross Megawatt hours per year from 1995-2010) provided directly from the Annapolis tidal power plant because the plant provided a longer time series (Ruth Thorbourne, personal communication, Aug 9, 2011). To convert production data into revenue, production values were multiplied by average yearly prices of electricity per KWh specific to Canada and France, provided by the US Energy Information Administration (http://www.eia.gov/emeu/international/elecprii.html; updated June 2010) after conversion to 2010 USD. Some of the production data could not be used because there were no available electricity price data to convert production into revenue, truncating our time series.
Tourism
WTTC reports dollar values of visitor exports (spending by foreign visitors) and domestic travel and tourism spending; combining these two data sets creates a proxy for total travel and tourism revenues. WTTC was chosen as the source for tourism revenue data because of the near-complete country coverage, the yearly time series component starting in 1988 and updated yearly, and the inclusion of both foreign and domestic expenditures. This dataset lumps inland and coastal/marine revenues, and so was adjusted by the percent of a country’s population within a 25 mile inland coastal zone. We included no projected data. We used total contribution to GDP data (rather than direct contribution to GDP) to avoid the use of literature derived multiplier effects.
Units
status 0-100
## Economic trend scores #### eco_trend {-}
This layer provides calculated trend values for the economies subgoal. Economies is calculated using revenue data from marine sectors.
Note: These data are no longer supported. Consequently, this layer was last updated in 2013, and this goal will no longer be updated with these data.
Units
trend -1 to 1
Exposure of ornamental fishing to coral and rocky reef habitats
np_exposure_orn
Exposure is the log transformed intensity of harvest calculated as tonnes of harvest per km2 of coral and rocky reef of ornamental fishing, relative to the global maximum. We log transformed the harvest intensity scores because the distribution of values was highly skewed; because we do not know the true threshold of sustainable harvest, nearly all values would be considered highly sustainable without the log transformation. To estimate rocky reef extent area (km2) we used data from Halpern et al. (2008), which assumes rocky reef habitat exists in all cells within 1 km of shore. Coral extent area (km2) are from UNEP-WCMC et al. (2018).
Units
scaled 0-1
Fish oil and fish meal score
np_fofm_scores
Fish oil and fish meal scores from Sea Around Us catch data (Pauly, Zeller, and Palomareas 2020) and the RAM Legacy Database http://ramlegacy.org/. For this layer, nearly identical methods were used as in the fisheries sub-goal calculations. Instead of producing scores for all global fish stocks, we only produced scores for those that contribute to fish oil and fish meal (Froehlich 2018). The tonnes of harvest of these species were multiplied by 0.7 to reflect the proportion of this catch used for oil or feed.
We used SeaLifeBase (Palomares and Pauly 2022) and FishBase (Froese and Pauly 2022) to align taxa names across datasets.
Units
scaled 0-1
Fishery catch data
fis_meancatch
Fisheries catch data describe the average catch across years (from 1980 to present) for each fish stock and region. We have traditionally included all fisheries catch in the Food Provision goal. However, a substantial portion of the catch is not used for human consumption, but rather for fish oil and fish meal used primarily for animal feed. To account for this, we excluded the proportion of catch that produce fish oil and fish meal for animal feed (0.9) from the total catch. These values were used to weight stock status scores (derived from B/Bmsy values) in the fisheries model. The Sea Around Us fisheries catch data (Pauly, Zeller, and Palomareas 2020) is reported at EEZ and FAO Fishing Areas for taxonomic levels range from species to class to “Miscellaneous not identified” for each year. Tonnes of catch for each taxon and year were summed within each OHI region, and then the average catch across years (1980 to present) was determined for each taxon and region.
Units
tonnes
Food provision weights
fp_wildcaught_weight
To weight the relative contributions of fisheries and mariculture to the food provision goal, we calculate the tonnes of fisheries production relative to the total tonnes of food production from fisheries and mariculture.
Units
proportion
Genetic escapes
sp_genetic
Pressure
Category: ecological
Subcategory: nonindigenous species
This layer represents the potential for harmful genetic escapement based on whether the species being cultured is native or introduced. Data come from the Monterey Bay Aquarium Seafood Watch Aquaculture Recommendations (“Monterey Bay Aquarium Seafood Watch” 2023). Ten mariculture practice criteria from the Monterey Bay Aquarium Seafood Watch (SFW) Aquaculture Recommendations contributed to the sustainability of mariculture (data quality, effluent, habitat risk, chemical use, feed, escapes, disease, source of stock, predator and wildlife mortalities, and escape of secondary species). The escapes criteria was used for this layer, where a score of 1 is the lowest, and a score of 10 is the highest. The scores reflect the potential impact of genetic escapes on local biodiversity, if these species were to escape. Genetic ‘pollution’ can arise when larvae, spats or seeds escape from poorly managed hatcheries, making native species vulnerable to outbreeding depressions and/or genetic bottlenecks. All country average scores were then rescaled from 0 to 1 using the maximum possible raw SFW score of 10 and minimum of 1.
These scores are country and species-specific, however, many country/species combinations are not assessed by SeaFood Watch. Given that each mariculture record must have a corresponding escapes score we used a series of steps to estimate escapes scores for every country and species. If a country/species match was available we used that, otherwise, we gapfilled using the following sequence:
- Used the global species value provided by SeaFood Watch
- Within a country, used average of species within the same family
- Within a UN geo-political region, used average of species within the same family
- Global, use average of species within the same family
- Global, use average of species within a broad taxonomic grouping (e.g., crustaceans, algae, bivalves, etc.)
- Finally, if these scores were not available for the categories above, we used the global average of all species.
Seaweed or algae species were given the global seaweed escapes score provided by the Seafood Watch recommendations. We are aware that there is some bias associated with using scores derived as averages across countries because they were originally assigned to specific species-country pairs, nevertheless this is preferable to applying an escapes score solely based on a subset of the species harvested.
Units
scaled 0-1
Global Competitiveness Index (GCI)
li_gci
Resilience
Category: social
The World Economic Forum’s Global Competitiveness Index (GCI) provides a country level assessment of competitiveness in achieving sustained economic prosperity (Schwab (2017), http://gcr.weforum.org). The GCI is a weighted index based on 12 pillars of economic competitiveness: institutions, infrastructure, macroeconomic environment, health and primary education, higher education and training, goods market efficiency, labor market efficiency, financial market development, technological readiness, market size, business sophistication, and innovation. The GCI can in theory span from 1 to 7, based on this range, we rescaled the scores to range between 0 and 1. Uninhabited OHI regions are given an NA score.
Units
scaled 0-1
Habitat condition of coral
hab_coral_health
Coral condition was calculated using current condition data divided by reference condition. We used condition data from percent live coral cover from 12,634 surveys from 1975-2006 (Bruno and Selig 2007; Schutte, Selig, and Bruno 2010). When multiple data points were available for the same site and year, we averaged these data, and also averaged the site data to calculate a per country per year average. However, data were missing for several countries and some countries did not have data for the reference or current year time periods or had only 1-2 surveys. Because coral cover can be highly temporally and spatially dynamic, having only a few surveys that may have been motivated by different reasons (i.e., documenting a pristine or an impacted habitat) can bias results. To calculate condition we used fitted values from a linear trend of all data per country, which was more robust to data poor situations and allowed us to take advantage of periods of intense sampling that did not always include both current and reference years. Then, we created a fitted linear model of all these data points from 1975-2010, provided that 2 or more points are in 1980-1995 and 2 or more points are in 2000-2010. We defined the ‘current’ condition (health) as the mean of the predicted values for 2008-2010, and the reference condition as the mean of the predicted values for 1985-1987. Where country data were not available, we used an average from adjacent EEZs weighted by habitat area, or a georegional average weighted by habitat area, based on countries within the same ocean basin (Figure 7.1).
Figure 7.1. Georegions to gapfill coral reef data 
Units
proportion
Habitat condition of kelp
hab_kelp_health
An increasing or stable trend was assigned condition = 1.0, and a decreasing trend was assigned condition = 0.64, based on a 2% global yearly loss of kelp for 50 years. There is likely to be much spatial variation that isn’t accounted for by using a global value, but we are limited by the availability of data. Data was from Status and Trends for the World’s Kelp Forests (Wernberg et al. 2019).
Units
proportion
Habitat condition of mangrove
hab_mangrove_health
Mangrove condition was defined as the current cover divided by reference cover. 2020 extents from Global Mangrove Watch (2022) were used as current cover. Reference covers for most regions were 1980 estimates retrieved from FAO Forestry Paper 153 (Nations 2007). Regions for which 1980 estimates were not available instead used 1996 estimates from Global Mangrove Watch (2022) as reference covers.
Units
proportion
Habitat condition of saltmarsh
hab_saltmarsh_health
Due to a severe lack of historical data, a global conservative condition score of 0.75 was given to every region based on an estimated 25%-50% loss (Mcowen et al. 2017). There is likely to be much spatial variation that isn’t accounted for by using a global value, but we are limited by the availability of data.
Units
proportion
Habitat condition of seagrass
hab_seagrass_health
An increasing or stable trend was assigned condition = 1.0, and a decreasing trend was assigned condition = 0.71. 0.71 reflects global loss since 1879 (29% loss of known areal extent); however, most of the loss is expected to have occurred since 1990. There is likely to be much spatial variation that isn’t accounted for by using a global value, but we are limited by the availability of data. Data was from Waycott et al. (2009).
Units
proportion
Habitat condition of seaice
hab_seaice_health
Sea-ice condition was calculated using sea-ice concentrations from the USA National Snow and Ice Data Center (DiGirolamo et al. (2022); https://nsidc.org/cryosphere/quickfacts/seaice.html) as the current percent cover of sea-ice (average of 3 years of data) divided by the average historical percent cover, defined as the start of the data (1979) until the year 2000 as recommended by the National Snow & Ice Data Center for both sea-ice edge and sea-ice shoreline habitats.
Units
proportion
Habitat condition of softbottom
hab_softbottom_health
See hab_softbottom_extent layer for more information.
Soft bottom subtidal habitat condition was estimated using apparent fishing effort with demersal destructive gear types from Global Fishing Watch (“Global Fishing Watch Data Download Portal” 2022). This data includes the apparent hours of trawling and dredging on a global scale at a 0.01 degree resolution. Global Fishing Watch combines all trawling together, so we subset annual trawling raster to just reflect bottom trawling by multiplying the annual trawling rasters by the respective annual rasters for tonnes of fisheries harvest for marine species caught with the same gear types (Watson 2019). This fisheries harvest data serves as a proxy for the proportion of bottom trawling to mid-water trawling at each location. The annual fisheries catch rasters come with a 0.5 degree resolution and were partially composed of missing values, so it was interpolated and up-sampled to 0.01 degree resolution before it was used to subset the trawling data.
We spatially standardized the fishing effort by soft bottom habitat. We first summed rasters that represent several different types of soft bottom habitat, such as soft shelfs, sediment, and benthic mud. These rasters were sourced from Halpern et al. (2015). We accounted for error in the soft bottom habitat labels by aggregating the cells and deriving a proportion of the soft bottom habitat over that larger area. We multiplied this soft bottom habitat raster by the fishing effort to subset it to just the demersal fishing that occurred on soft bottom habitat, while also maintaining the soft bottom habitat that is undisturbed to assign high scores to regions that are not destructive. The annual destructive fishing effort was assigned to each OHI region by spatially extracting the effort that intersected the regional EEZ polygons.
Because the distribution of fishing effort density values for each region was extremely skewed, we took transformed them by taking the natural log using \(log(X+1)\) and then rescaled the values by dividing by the 95th quantile of density across all years and regions. Any rescaled density value greater than 1.0 was capped at 1.0. Condition was then calculated as one minus the rescaled fishing effort density for each year and region.
Units
proportion
Habitat condition of tidal flat
hab_tidal_flat_health
Tidal flat condition was defined as the current cover divided by reference cover. For current cover we use the average of the 2010 and 2013 extent. The reference cover is the average cover of 1989 and 1992.
Units
proportion
Habitat condition trend of coral
hab_coral_trend
Coral trend was calculated using condition data from 1975-2006 (Bruno and Selig 2007; Schutte, Selig, and Bruno 2010).
Units
trend
Habitat condition trend of kelp
hab_kelp_trend
Trend in kelp condition was determined using “Global patterns of kelp forest change over the past half-century” (Krumhansl et al. 2016). Krumhansl et al. (2016) calculated marine ecoregional kelp trend values using Bayesian hierarchical linear models. We extract these trends per the Marine Ecoregions of the World (MEOW) (Spalding et al. 2007), and a take weighted area mean to get final trends per region represented in the paper. Finally, where country data were not available, we gapfill by the georegional average.
Units
trend
Habitat condition trend of mangrove
hab_mangrove_trend
We used Global Mangrove Watch (2022) mangrove extent data to estimate the proportional yearly change in mangrove area using a linear regression model of years from 2007 up to the year in question. This length of data is longer than we usually do (typically 5 years), but we feel we get a better estimate using this period of time. Proportional yearly change is determined by first dividing yearly extents by the extent from the earliest year in the model. Year is then regressed on those proportions, and the resulting slope is multiplied by 5 to get the predicted change in five years. The original mangrove extent data are provided yearly (2007, 2008, 2009, 2010, 2015, 2016, 2017, 2018, 2019, 2020) in the form of polygons - these are subsequently rasterized to match our regions and extract the area.
Units
trend
Habitat condition trend of saltmarsh
hab_saltmarsh_trend
A general 0.28% per year decerasing trend in salt marsh area was applied to every region (Campbell et al. 2022). There is likely to be much spatial variation that isn’t accounted for by using a global value, but we are limited by the availability of data.
Units
trend
Habitat condition trend of seagrass
hab_seagrass_trend
Trend in seagrass condition was determined using three data sources [Waycott et al. (2009), Short et al. (2011), IUCN (2022)). Short et al. (2011) measured percent percent cover on a per sample, per site, per year basis, and determined species population trends (increasing, decreasing, or stable). The IUCN Red List of Threatened Species also provides species population trends (increasing, decreasing, or stable). Waycott et al. (2009) measured habitat area on a per site, per year basis. We used the population trends from Short et al. (2011) and the IUCN Red List of Threatened Species (IUCN 2024a) to assign population trends to the IUCN species distribution maps (IUCN 2022). Then we used median rate of change per year (%) from Waycott et al. (2009) (Table S2) to assign numerical trend values to each species population trend (-0.0767 for decreasing, 0 for stable, and 0.0845 for increasing). Following this we gapfill missing trends first by species family average, and then by the global seagrass average trend. We calculate the trend per OHI region using an average weighted by species distribution area. Finally, where country data were not available, we gapfill by the georegional average.
Units
trend
Habitat condition trend of seaice
hab_seaice_trend
Trends for sea-ice edge and sea-ice shoreline habitats were calculated using sea-ice concentrations from the USA National Snow and Ice Data Center (DiGirolamo et al. (2022); https://nsidc.org/cryosphere/quickfacts/seaice.html). The average yearly proportional change in extent was estimated using a linear regression model that included the most recent five years of data (e.g., slope estimate was divided by the extent for earliest year included in the regression model), and this value was multiplied by five to get the predicted change in 5 years. Each year of data represents a 3-year average, to smooth yearly variation.
Units
trend
Habitat condition trend of softbottom
hab_softbottom_trend
See hab_softbottom_extent and hab_softbottom_health layers for more information.
Trend in soft bottom subtidal condition was estimated using a linear regression model that included the most recent five years of condition data. The proportional change in condition was determined (e.g., slope estimate was divided by the condition value for earliest year included in the regression) and then multiplied by five to get the change predicted in five years.
Units
trend
Habitat extent of coral
hab_coral_extent
Coral data are used to calculate coastal protection goal, habitat subgoal, and exposure variable of the natural products goal.
Coral extent area (km2) are derived from the dataset Global Distribution of Coral Reefs (UNEP-WCMC et al. 2018).
Units
km2
Habitat extent of kelp
hab_kelp_extent
Kelp data are used to calculate the coastal protection goal, and the habitat subgoal.
Kelp extent area (km2) was calculated from vector-based data from A Modelled Global Distribution of the Kelp Biome (Jayathilake and Costello 2020).
Units
km2
Habitat extent of mangrove
hab_mangrove_extent
Mangrove data are used to calculate the coastal protection and carbon storage goals, and the habitat subgoal.
Mangrove extents (km2) are derived from shapefiles in the Global Mangrove Watch Version 3.0 Dataset (1996 - 2020) (Bunting et al. 2022). The shapefiles contain polygons denoting mangrove covered areas. Those polygons are rasterized and then area per OHI region is calculated based on the number of cells that fall within each OHI region, including land and EEZs.
Units
km2
Habitat extent of rocky reef
hab_rockyreef_extent
Rocky reef data is used to calculate the exposure variable in the natural product goal.
To estimate rocky reef extent area (km2) we used data from Halpern et al. (2008), which assumes rocky reef habitat exists in all cells within 1 km of shore.
Units
km2
Habitat extent of saltmarsh
hab_saltmarsh_extent
Saltmarsh data are used to calculate the coastal protection and carbon storage goals, and the habitat subgoal.
Saltmarsh extent area (km2) are derived from the dataset Global map of saltmarshes (Mcowen et al. 2017).
Units
km2
Habitat extent of seagrass
hab_seagrass_extent
Seagrass data are used to calculate the coastal protection and carbon storage goals, and the habitat subgoal.
Seagrass extent area (km2) was calculated from vector-based data from the Global Distribution of Seagrasses (version 7.1, UNEP-WCMC, Short FT 2021) (UNEP-WCMC and Short 2005).
Units
km2
Habitat extent of seaice
hab_seaice_extent
Sea-ice shoreline data are used to calculate the coastal protection goal and sea-ice edge data are used to calculate the habitat subgoal.
Sea-ice extent area (km2) was calculated using sea-ice concentrations from the USA National Snow and Ice Data Center (DiGirolamo et al. (2022); https://nsidc.org/cryosphere/quickfacts/seaice.html). These raster data are 25km in resolution (625km2 per pixel) in a Stereographic polar projection. Two sea-ice metrics are calculated using these data: sea-ice edge (pixels with 10-50% ice cover) and sea-ice shoreline (shoreline pixels with >15% ice cover). Calculations of area are based on 3-year averages (to smooth yearly variation, e.g., 2009 data is the average of 2007-2009) of the pixels meeting the habitat criteria.
Units
km2
Habitat extent of softbottom
hab_softbottom_extent
Softbottom data is one of the variables in the habitat goal and the inverse is a habitat destruction pressure.
Softbottom extent data is from Halpern et al. (2015), which provides 5 different spatial files that represent soft bottom habitat, such as soft sea shelfs, sediment, and benthic mud. These files’ native resolution is approximately 934 x 934 meters.
Units
km2
Habitat extent of tidal flat
hab_tidal_flat_extent
Tidal flat data are used to calculate the carbon storage goal and the habitat subgoal.
Tidal flat extents (km2) are derived from GeoTIFF files in the Murray Global Intertidal Change Dataset (Murray et al. 2019). The GeoTIFF files contain pixels denoting tidal flat covered areas. Area per OHI region is calculated based on the number of cells that fall within each OHI region, including land and EEZs.
Units
km2
Habitat presence/absence
element_wts_hab_pres_abs
This layer describes the habitats present in each region (based on the habitat extent data) and is called internally by ohicore functions to calculate pressure and resilience values based on the habitats present in each region.
Data is generated in ohi-global/eez/conf/functions.R.
Units
0 or 1
Habitat trend of tidal flat
hab_tidal_flat_trend
We used the Murray Global Intertidal Change Dataset (Murray et al. 2019) tidal flat extent data to estimate the proportional yearly change in tidal flat area using a linear regression model of years from 2001 up to 2013. This length of data is longer than we usually do (typically 5 years), but we feel we get a better estimate using this period of time. Proportional yearly change is determined by regressing extent on year, and the resulting slope is divided by the extent from the earliest year in the model and multiplied by 5 to get the predicted change in five years.
Units
trend
High bycatch due to artisanal fishing
fp_art_hb
Pressure
Category: ecological
Subcategory: fishing pressure
This layer describes the relative pressure of high bycatch artisanal fishing practices for each OHI region. The fishery data (Watson 2019) describe catch (tonnes) for each species at the 0.5 degree raster global scale for both non-industrial and industrial fishing. For each raster cell, we summed catch discards from the non-industrial global catch data.
The catch was then divided by the mean net primary productivity (mg C/m2/day) derived from monthly output from the Vertically Generalized Production Model (VGPM, http://www.science.oregonstate.edu/ocean.productivity/, 0.5 degree global raster data) (O’Malley n.d.). Standardizing catch by primary productivity controls for the fact that similar amounts of catch impart different pressures depending on the productivity in the region.
The layer was rescaled from 0 to 1 using the 99.99th quantile of the entire data layer across all years of data.
To summarize at the OHI region scale, the mean value of the raster cells within each OHI region was calculated.
Units
scaled 0-1
High bycatch due to commercial fishing
fp_com_hb
Pressure
Category: ecological
Subcategory: fishing pressure
This layer describes the relative pressure of high bycatch commercial fishing practices for each OHI region. The fishery data (Watson 2019) describe catch (tonnes) for each species and gear type at the 0.5 degree raster global scale for both non-industrial and industrial fishing. For each raster cell, we summed catch discards from the industrial global catch data.
The catch was then divided by the mean net primary productivity (mg C/m2/day) derived from monthly output from the Vertically Generalized Production Model (VGPM, http://www.science.oregonstate.edu/ocean.productivity/, 0.5 degree global raster data) (O’Malley n.d.). Standardizing catch by primary productivity controls for the fact that similar amounts of catch impart different pressures depending on the productivity in the region.
The layer was rescaled from 0 to 1 using the 99.99th quantile of the entire data layer across all years of data.
To summarize at the OHI region scale, the mean value of the raster cells within each OHI region was calculated.
Units
scaled 0-1
IUCN extinction risk
ico_spp_iucn_status
This data layer provides the risk category and the year the species was assessed from the IUCN Red List of Threatened Species (http://www.iucnredlist.org/) (IUCN 2024a) for the iconic species in each region. Regionally specific IUCN risk category data for subpopulations are included where available. Trend calculations are based on the change in each species’ IUCN risk category over time, based upon past and current IUCN assessments.
OHI defines iconic species as those relevant to local cultural identity through the species’ relationship to traditional activities such as fishing, hunting, commerce or involvement in local ethnic or religious practices; and species with locally-recognized aesthetic value (e.g., touristic attractions/common artistic subjects such as whales). Habitat forming species are excluded in this definition of iconic species. The OHI global iconic species list combines three species lists from WWF Global: global priorities, regional and local priorities, and flagship species. It also incorporates culturally important species (Reyes-García et al. 2023). The criteria for including species on these lists are consistent with the OHI’s definition of iconic species.
Once the species lists were obtained, each species was assigned to a region based on native range countries from the IUCN Red List.
Most of the iconic species are not region specific, and the global list is applied across all regions. However, some countries have developed national priority and flagship species lists in conjunction with WWF. These region-specific iconic species lists supplement the global list for those specific countries only. In addition, as countries and regions conduct OHI regional assessments (http://ohi-science.org/projects/), we will use the iconic species list developed by those countries/regions to supplement our global model. For example, iconic species identified for the Baltic Health Index regional assessment have been included for all countries bordering the Baltic Sea. Additional culturally important species were added to supplement the original iconic species list (Reyes-García et al. 2023). These culturally important species are listed as iconic at the continent level. Species are considered iconic for all regions where they are present within the continent they are listed.
Table 7.5. Iconic species resources
| Iconic List | Source |
|---|---|
| Priority Species | WWF |
| Flagship Species | WWF |
| Australia’s Flagship Species | WWF Australia |
| Pakistan’s Priority Species | WWF Priority Species |
| India’s Priority Species | WWF India |
| Madagascar’s Flagship Species | World Wildlife |
| Malaysia’s Flagship Species | WWF Malaysia |
| Culturally Important Species | Biocultural vulnerability exposes threats of culturally important species |
Units
IUCN risk category
Inland 1km area
rgn_area_inland1km
Area (km2) located from each region’s land-sea interface to 1 km inland.
For coastal land areas, we extracted hi-resolution country boundary data from Esri (2010), and rasterized it with a resolution to match our land-sea interface model. We grew this raster by 50 pixels to bridge gaps between the ESRI data and our land-sea model. Area values do not include inland lakes or EEZs.
Units
km2
Inland coastal protected areas
lsp_prot_area_inland1km
This includes protected areas 1km inland, but otherwise follows the methods described.
Units
km2
Intertidal habitat destruction
hd_intertidal
Pressure
Category: ecological
Subcategory: habitat destruction
Coastal population data was converted to average coastal density by dividing by the total 25 mile inland area. We then rescaled the data to have values between 0-1, by logging the density data and then dividing by the ln (maximum density) across all regions and years.
Units
scaled 0-1
Livelihood status scores
liv_status
This layer provides calculated status values for the livelihoods subgoal. Livelihoods is calculated using job and wage data from marine sectors.
Note: These data are no longer supported. Consequently, this layer was last updated in 2013, and this goal will no longer be updated with these data.
Livelihoods status is generally calculated as: (cur_base_value / ref_base_value) / (cur_adj_value / ref_adj_value)
Where, cur_base_value is the most recent value (i.e., jobs or wages) for each sector/region, and ref_base_value is the value for the earliest year of data for each sector/region. These values are adjusted to control for larger trends within the region. For example, jobs data for the livelihoods subgoal was adjusted by dividing by the percent employment of the corresponding year. For wage data, the adjustment was done a bit differently by multiplying wages by GDPpcPPP for each year/region to make wages comparable.
Jobs
Jobs includes yearly data for commercial fishing, mariculture, marine mammal watching, marine renewable energy, and, tourism. The data sources and methods for each sector are described below.
Percent employment during the current status year for each sector/region is calculated as (1 - percent unemployment)*total labor force (World Bank). Jobs data for the livelihoods subgoal were adjusted by dividing by the percent employment in the corresponding year.
Commercial fishing
Data are from the United Nations Food and Agriculture Organization (FAO) Fisheries and Aquaculture Department which provides a Global Number of Fishers dataset ( http://www.fao.org/fishery/statistics/global-fishers/en). The data include yearly total numbers of employees in commercial fishing, subsistence fishing, and aquaculture (land- and ocean-based combined) in more than 160 countries. The dataset includes the following occupational categories: aquatic-life cultivation, inland waters fishing, marine coastal waters fishing, marine deepsea waters fishing, subsistence and unspecified. We omitted jobs with an unspecified category to avoid overestimating employment for marine fishing or aquaculture. We omitted jobs in the subsistence category since subsistence opportunities are captured by the artisanal fishing opportunity goal and in the aquatic-life cultivation category since that represents a distinct sector (see mariculture below). For commercial fishing, we eliminated inland waters fishing and summed marine coastal waters and marine deep-sea waters fishing for each country in each year. Data are reported separately for men and women, but we summed these numbers. Employment is disaggregated into full-time, parttime, occasional, and unspecified statuses. These categories are defined as full time workers having > 90% of their time or livelihood from fishing/aquaculture, part time workers are between 30-90% time (or 30-90% of their livelihood) and occasional workers are < 30% time. Unspecified status workers could fall anywhere from 0-100% time. Taking the midpoints of those ranges, we assume that 1 part time worker = 0.6 full time workers, 1 occasional worker = 0.15 full time workers, and 1 unspecified worker = 0.5 full time workers, which we used as a weighting scheme for determining total numbers of jobs. The dataset has significant gaps, but it provides the most comprehensive source of global data on commercial fishing and aquaculture employment.
Mariculture
We used the FAO Global Number of Fishers dataset (see commercial fishing above for full description) to estimate jobs for mariculture. For this sector, we used data in the aquatic-life cultivation category. Again, employment is disaggregated into full-time, part-time, occasional, and unspecified statuses and we implement a weighting scheme where full time = 1 job, part-time = 0.6, occasional = 0.15, and unspecified = 0.5. Aquatic-life cultivation includes marine, brackish and freshwater aquaculture. In order to estimate the proportion of total aquaculture jobs that can be attributed to marine and brackish aquaculture, we used country-specific proportions of marine and brackish aquaculture revenues (compared to total revenues) calculated from FAO aquaculture production data, assuming that numbers of jobs approximately scale with production in terms of revenue. For country-years with no data for the proportion of marine/brackish production because of gaps in the FAO production data, we used the proportion from the most recent year for which data were available. For countries without proportion estimates from any years, we used the average proportion from the country’s geographic region (e.g., Caribbean, Polynesia, Eastern Asia), with the exception of American Samoa, for which we used the proportion value from Guam.
Marine mammal watching
The International Fund for Animal Welfare’s (IFAW) Whale Division provides time series data on whale watching in more than 115 coastal countries (O’Connor et al. 2009b). This dataset may be an imperfect representation of all marine mammal watching due to its focus on whales, although it does include data for other types of marine mammal watching (e.g., dolphins). However, to our knowledge, it is the most complete dataset pertaining to the global marine mammal watching industry. We obtained regional averages of the number of whale watchers per employee, as well as the number of whale watchers in each country. Using this information, we estimated the number of whale watching jobs in each country by dividing the country’s total number of whale watchers by the average number of whale watchers per employee for that country’s region (e.g., Africa & Middle East, Europe, North America). It is important to note that data are not annual, but there are at least four years of data for each country. When IFAW reported “minimal” numbers of whale watchers, we converted this description to a 0 for lack of additional information. Because some of the whale watching in O’Connor et al. focused on freshwater cetacean viewing, we categorized the target species listed for each country as freshwater or marine. For countries with both marine and freshwater species, we categorized the whale watching in those countries as either 50% or 90% marine, based on the number of marine versus freshwater target species and information provided in the report narrative. For Colombia and Indonesia, more detailed information in the report narrative allowed for a more precise determination of the percentage of marine-based whale watching. We applied these marine proportions to data on the number of whale watchers before converting these estimates into employment estimates.
Marine renewable energy
The number of marine renewable energy jobs was determined for the two countries, France and Canada, which produce significant enough amounts of tidal energy to register with the UN Energy Statistics Database http://data.un.org/Data.aspx?d=EDATA&f=cmID%3aEO. For the La Rance plant in France, employment information was obtained from a recent press statement (EDF 2011); we assumed employment stayed constant over the time period for which we had production data for this plant, given relatively consistent or even growing production. For the Annopolis Royal plant in Canada, we received yearly employment information from the plant (Ruth Thorbourne, personal communication, Aug 9 2011).
Marine renewable energy includes five major technologies: tidal barrages, marine currents, waves, ocean thermal converters and salinity gradients. However, we only include data for the largest tidal barrage plants, as these data are available.
Tourism
The World Travel & Tourism Council (WTTC) provides data on travel and tourism’s total contribution to employment for 180 countries (http://www.wttc.org/eng/Tourism_Research/Economic_Data_Search_Tool/). Although other global data sources on tourism are available (i.e., United Nations World Tourism Organization, UNTWO), the WTTC database was chosen because it offers yearly time series data that span through the current year, it includes nearly complete coverage of all nations, and it disaggregates direct and total (direct plus indirect) employment impacts of tourism. WTTC provides projected data, however, we do not use these values. We used total employment data to avoid the use of literature derived multiplier effects. The WTTC shares a significant drawback with UNTWO data, in that data on coastal/marine and inland tourism are lumped. Therefore, a country-specific coefficient must be applied to estimate the jobs provided by coastal/marine tourism alone. We adjusted national tourism data by the proportion of a country’s population that lives within a 25 mile inland coastal zone.
Wages
Wages were multiplied by GDPpcPPP for each country/year to make values comparable.
We used the Occupational Wages around the World (OWW) database produced by Remco H. Oostendorp and Richard B. Freeman in 2005 (http://www.nber.org/oww/). These data were drawn from the International Labour Organization and subjected to a standardization process (for more information, see http://www.nber.org/oww/Technical _document_1983-2003_standardizationv3.pdf). The database provides several different calibrations, and we use the “x3wl calibration”, described as a “country-specific and uniform calibration with lexicographic weighting,” and recommended as being the preferred calibration in most cases. Although significant gaps exist in this database, it contains country-specific information on average wages in many industries for more than 150 countries from 1983-2003. Data represent average monthly wages of a male worker. Wage data were divided by the inflation conversion factor for 2010 so that wage data across years would be comparable (http://oregonstate.edu/cla/polisci/sahr/sahr), and then multiplied by the purchasing power parity-adjusted per capita gdp (ppppcgdp, WorldBank). The adjusted wage data were then multiplied by 12 to get annual wages. We used the industry and occupation classifications reported in the OWW to estimate wages for marine-related sectors.
Table 7.6. Occupation classification for wage data sectors
| Sector | Occupation classifications |
|---|---|
| Commercial fishing | Industry: deep sea & coastal fishing; Occupations: deep sea fisher; inshore (coastal) maritime fisherman |
| Ports & harbors | Industry: supporting services to maritime transport; Occupation: dock worker |
| Ship & boat building | Industry: shipbuilding and repairing; Occupation: ship plater |
| Tourism | Industry: restaurants and hotels; Occupations: hotel receptionist; cook; waiter; room attendant or chambermaid. These data are not specific to coastal/marine tourism jobs, and thus we assumed that wages in these jobs are equal in coastal and non-coastal areas |
| Transportation & shipping | Industry: maritime transport; Occupations: ship’s chief engineer; ship’s passenger stewards; able seaman |
Units
status 0-100
Livelihood trend scores
liv_trend
This layer provides calculated trend values for the livelihoods subgoal. Livelihoods is calculated using job and wage data from marine sectors.
Note: These data are no longer supported. Consequently, this layer was last updated in 2013, and this goal will no longer be updated with these data.
Units
trend -1 to 1
Low bycatch due to artisanal fishing
fp_art_lb
Pressure
Category: ecological
Subcategory: fishing pressure
This layer describes the relative pressure of low bycatch artisanal fishing practices for each OHI region. The fishery data (Watson 2019) describe catch (tonnes) for each species at the 0.5 degree raster global scale for both non-industrial and industrial fishing. For each raster cell, we summed catch (which consisted of reported landings as well as illegal, unreported and regulated catch) from the non-industrial global catch data.
The catch was then divided by the mean net primary productivity (mg C/m2/day) derived from monthly output from the Vertically Generalized Production Model (VGPM, http://www.science.oregonstate.edu/ocean.productivity/, 0.5 degree global raster data) (O’Malley n.d.). Standardizing catch by primary productivity controls for the fact that similar amounts of catch impart different pressures depending on the productivity in the region.
The layer was rescaled from 0 to 1 using the 99.99th quantile of the entire data layer across all years of data.
To summarize at the OHI region scale, the mean value of the raster cells within each OHI region was calculated.
Units
scaled 0-1
Low bycatch due to commercial fishing
fp_com_lb
Pressure
Category: ecological
Subcategory: fishing pressure
This layer describes the relative pressure of low bycatch commercial fishing practices for each OHI region. The fishery data (Watson 2019) describe catch (tonnes) for each species and gear type at the 0.5 degree raster global scale for both non-industrial and industrial fishing. For each raster cell, we summed catch (which consisted of reported landings as well as illegal, unreported and regulated catch) from the industrial global catch data.
The catch was then divided by the mean net primary productivity (mg C/m2/day) derived from monthly output from the Vertically Generalized Production Model (VGPM, http://www.science.oregonstate.edu/ocean.productivity/, 0.5 degree global raster data) (O’Malley n.d.). Standardizing catch by primary productivity controls for the fact that similar amounts of catch impart different pressures depending on the productivity in the region.
The layer was rescaled from 0 to 1 using the 99.99th quantile of the entire data layer across all years of data.
To summarize at the OHI region scale, the mean value of the raster cells within each OHI region was calculated.
Units
scaled 0-1
Management of habitat to protect fisheries biodiversity
fp_habitat
Resilience
Category: ecological/regulatory
Subcategory: fishing
Country responses to the Convention on Biological Diversity (CBD) Third National Report (2010). Each question was weighted equally within each category and responses were averaged to give a score between 0 and 1 for all responding countries. The survey uses a 0 to 3 scale for questions 79 and 81, and a 0 to 2 scale for question 80, which we rescale linearly to 0 to 1.
All countries were given credit within each of the 4 resilience measures for simply being a member of the CBD (0.5), the other 0.5 of the resilience score came from each country’s response to the specific questions within each resilience measure. In cases where the “European Union” answered yes or was a signatory, all EU25 countries were given that answer if they did not provide one themselves.
The CBD has 193 members and 153 members responded to the Third National Survey (2005). We had data for 147 regions, and used geographical means, weighted by country area, for the remaining regions.
Questions: 153 (a,b,c,e,g) and 158 (a,b,c,f,g,h)
- Do your country’s strategies and action plans include the following?
- Developing new marine and coastal protected areas
- Improving the management of existing marine and coastal protected areas
- Building capacity within the country for management of marine and coastal resources, including through educational programmes and targeted research initiatives
- Protection of areas important for reproduction, such as spawning and nursery areas
- Controlling excessive fishing and destructive fishing practices
- Which of the following statements can best describe the current status of marine and coastal protected areas in your country?
- Marine and coastal protected areas have been declared and gazetted
- Management plans for these marine and coastal protected areas have been developed with involvement of all stakeholders
- Effective management with enforcement and monitoring has been put in place
- The national system of marine and coastal protected areas includes areas managed for purpose of sustainable use, which may allow extractive activities
- The national system of marine and coastal protected areas includes areas which exclude extractive uses
- The national system of marine and coastal protected areas is surrounded by sustainable management practices over the wider marine and coastal environment.
Units
scaled 0-1
Management of habitat to protect habitat biodiversity
hd_habitat
Resilience
Category: ecological/regulatory
Subcategory: habitat
Units
scaled 0-1
Management of nonindigenous species
sp_alien_species
Resilience
Category: ecological/regulatory
Subcategory: nonindigenous species
Country responses to the Convention on Biological Diversity (CBD) Third National Report (2010). Each question was weighted equally within each category and responses were averaged to give a score between 0 and 1 for all responding countries. The survey uses a 0 to 3 scale for questions 79 and 81, and a 0 to 2 scale for question 80, which we rescale linearly to 0 to 1.
All countries were given credit within each of the 4 resilience measures for simply being a member of the CBD (0.5), the other 0.5 of the resilience score came from each country’s response to the specific questions within each resilience measure. In cases where the “European Union” answered yes or was a signatory, all EU25 countries were given that answer if they did not provide one themselves.
The CBD has 193 members and 153 members responded to the Third National Survey (2005). We had data for 147 regions, and used geographical means, weighted by country area, for the remaining regions.
Questions: 160 (b-e)
- Has your country put in place mechanisms to control pathways of introduction of alien species in the marine and coastal environment? Please check all that apply and elaborate on types of measures in the space below.
- No
- Mechanisms to control potential invasions from ballast water have been put in place
- Mechanisms to control potential invasions from hull fouling have been put in place (please provide details below)
- Mechanisms to control potential invasions from aquaculture have been put in place (please provide details below)
- Mechanisms to control potential invasions from accidental releases, such as aquarium releases, have been put in place (please provide details below)
Units
scaled 0-1
Management of tourism to preserve biodiversity
g_tourism
Resilience
Category: ecological/regulatory
Subcategory: goal
Country responses to the Convention on Biological Diversity (CBD) Third National Report (2005) (United Nations Environment Program 2010). Each question was weighted equally within each category and responses were averaged to give a score between 0 and 1 for all responding countries. The survey uses a 0 to 3 scale for questions 79 and 81, and a 0 to 2 scale for question 80, which we rescale linearly to 0 to 1.
All countries were given credit within each of the 4 resilience measures for simply being a member of the CBD (0.5), the other 0.5 of the resilience score came from each country’s response to the specific questions within each resilience measure. In cases where the “European Union” answered yes or was a signatory, all EU25 countries were given that answer if they did not provide one themselves.
The CBD has 193 members and 153 members responded to the Third National Survey (2005). We had data for 147 regions, and used geographical means, weighted by country area, for the remaining regions.
Questions: 79, 80, 82
- Has your country established mechanisms to assess, monitor and measure the impact of tourism on biodiversity?
- No
- No, but mechanisms are under development
- Yes, mechanisms are in place (please specify below)
- Yes, existing mechanisms are under review
- Has your country provided educational and training programmes to the tourism operators so as to increase their awareness of the impacts of tourism on biodiversity and upgrade the technical capacity at the local level to minimize the impacts?
- No
- No, but programmes are under development
- Yes, programmes are in place (please describe below)
- Does your country provide indigenous and local communities with capacity-building and financial resources to support their participation in tourism policy-making, development planning, product development and management?
- No
- No, but relevant programmes are being considered
- Yes, some programmes are in place
- Yes, comprehensive programmes are in place
Units
scaled 0-1
Management of waters to preserve biodiversity
po_water
Resilience
Category: ecological/regulatory
Subcategory: water
Country responses to the Convention on Biological Diversity (CBD) Third National Report (2010). Each question was weighted equally within each category and responses were averaged to give a score between 0 and 1 for all responding countries. The survey uses a 0 to 3 scale for questions 79 and 81, and a 0 to 2 scale for question 80, which we rescale linearly to 0 to 1.
All countries were given credit within each of the 4 resilience measures for simply being a member of the CBD (0.5), the other 0.5 of the resilience score came from each country’s response to the specific questions within each resilience measure. In cases where the “European Union” answered yes or was a signatory, all EU25 countries were given that answer if they did not provide one themselves.
The CBD has 193 members and 153 members responded to the Third National Survey (2005). We had data for 147 regions, and used geographical means, weighted by country area, for the remaining regions.
Questions: 153 (d,f)
- Do your country’s strategies and action plans include the following?
- Instituting improved integrated marine and coastal area management (including catchments management) in order to reduce sediment and nutrient loads into the marine environment
- Improving sewage and other waste treatment
Units
scaled 0-1
Mariculture harvest
mar_harvest_tonnes
Mariculture production is calculated from the FAO Global Aquaculture Production Quantity dataset (United Nations 2024). Only production classified in the “Marine” and “Brackishwater” environments were included in the analysis (all “Freshwater” production was excluded). Marine species that are not consumed as food were excluded. Non-edible seaweeds were excluded because they are included in the natural products goal. Seaweeds that are partially consumed as food were weighted with a value between 0 and 1 that represents the proportion of the yield used as food. All species produced within a country were summed to give a single production value for each country in each year that production took place. For the three EEZs that fall within the China region (China, Macau, and Hong Kong), we combined the values by summing across these EEZs.
Units
tonnes
Mariculture sustainability score
mar_sustainability_score
Ten mariculture practice criteria from the Monterey Bay Aquarium Seafood Watch Aquaculture Recommendations (“Monterey Bay Aquarium Seafood Watch” 2023) contributed to the sustainability of mariculture (data quality, effluent, habitat risk, chemical use, feed, escapes, disease, source of stock, predator and wildlife mortalities, and escape of secondary species). These criteria represent the internal mariculture practices with the potential to affect the long term sustainability of the mariculture system. Scores for each assessment criterion were aggregated and averaged. All country average scores were then rescaled from 0 to 1 using the maximum raw Seafood Watch score and minimum of 0, under the assumption that the highest score is the best possible given current technologies.
These scores are country and species-specific, however, many country/species combinations are not assessed by Seafood Watch. Given that each mariculture record must have a corresponding sustainability score we used a series of steps to estimate sustainability scores for every country and species. If a country/species match was available we used that, otherwise, we gapfilled using the following sequence: 1. Used the global species value provided by SeaFood Watch 2. Within a country, used average of species within the same family 3. Within a UN geo-political region, used average of species within the same family 4. Global, use average of species within the same family 5. Global, use average of species within a broad taxonomic grouping (e.g., crustaceans, algae, bivalves, etc.) 6. Finally, if these scores were not available for the categories above, we used the global average of all species.
Seaweed or algae species were given the global seaweed sustainability score provided by the Seafood Watch recommendations. We are aware that there is some bias associated with using scores derived as averages across countries because they were originally assigned to specific species-country pairs, nevertheless this is preferable to applying a sustainability score solely based on a subset of the species harvested.
Table 7.8. Mariculture sustainability criteria
| Criteria | Description of practice and score scheme |
|---|---|
| Data | Poor data quality and availability limits the ability to assess and understand the impacts of aquaculture production. It also does not enable informed choices for seafood purchasers or enable businesses to be held accountable for their impacts. A score between 1 and 10 was given based on data availability, quality, and confidence. |
| Effluent | Aquaculture species, production systems and management methods vary in the amount of waste produced per unit of production. The combined discharge of farms, groups of farms or industries contribute to local and regional nutrient loads. A score of 1 to 10 is given based on the concern of the effluent produced from the practices. |
| Habitats | Aquaculture farms can be located in a wide variety of aquatic and terrestrial habitat types and have greatly varying levels of impact to both pristine and previously modified habitats as well as to the critical “ecosystem services” they provide. Scores are based on whether they are located at sites, scales and intensities that maintain the functionality of ecologically valuable habitats. |
| Chemical use | Improper use of chemical treatments impacts non-target organisms and leads to production losses and human health concerns due to the development of chemical-resistant organisms. Scores are based on if facilities limit the type, frequency of use, total use, or discharge of chemicals to levels representing a low risk of impact to non-target organisms. |
| Feed | Feed consumption, feed type, ingredients used and the net nutritional gains or losses vary dramatically between farmed species and production systems. Producing feeds and their ingredients has complex global ecological impacts, and the efficiency of conversion can result in net food gains or dramatic net losses of nutrients. Feed use is considered to be one of the defining factors of aquaculture sustainability. Scores are based on whether facilities source sustainable feed ingredients and convert them efficiently with net edible nutrition gains. |
| Escapes | Competition, altered genetic composition, predation, habitat damage, spawning disruption, and other impacts on wild fish and ecosystems resulting from the escape of native, non-native and/or genetically distinct fish or other unintended species from aquaculture operations. Scores are based on whether facilities prevent population-level impacts to wild species or other ecosystem-level impacts from farm escapes. |
| Disease, pathogen and parasite interaction | Amplification of local pathogens and parasites on fish farms and their transmission or retransmission to local wild species that share the same water body. Scores are based on whether facilities prevent population-level impacts to wild species through the amplification and retransmission, or increased virulence of pathogens or parasites. |
| Source of stock | The removal of fish from wild populations for growing to harvest size in farms. Scores are based on whether facilities use eggs, larvae, or juvenile fish produced from farm-raised broodstocks thereby avoiding the need for wild capture. |
| Predator and wildlife mortalities | Mortality of predators or other wildlife caused or contributed to by farming operations. Scores are based on whether facilities prevent population-level impacts to predators or other species of wildlife attracted to farm sites. |
| Escape of secondary species | Movement of live animals resulting in introduction of unintended species. Scores are based on whether facilities avoid the potential for the accidental introduction of secondary species or pathogens resulting from the shipment of animals. |
Units
scaled 0-1
Marine plastics
po_trash
Pressure
Category: ecological
Subcategory: pollution
Marine plastic pollution is modeled using data on the global distribution of floating marine plastics at 0.2 degree resolution (Eriksen et al. 2014). Specifically, weight of floating plastics (g/km2) across four different size classes were aggregated to represent total weight of plastic debris per km2. These data were log transformed and rescaled from 0 to 1 using the 99.99th quantile as the reference point.
Units
scaled 0-1
Measure of coastal ecological integrity
species_diversity_3nm
Resilience
Category: ecological/ecosystem
See Species goal for calculations.
This value reflects the average condition of species (based on risk status from the IUCN Red List of Threatened Species, http://www.iucnredlist.org/) (IUCN 2024a) located within 3 nm offshore of each region based on species range maps from IUCN (shapefiles, used preferentially) (IUCN 2022; BirdLife International and Handbook of the Birds of the World 2020) and Aquamaps (http://www.aquamaps.org/, half degree resolution rasters).
Units
scaled 0-1
Measure of ecological integrity
species_diversity_eez
Resilience
Category: ecological/ecosystem
See Species goal for calculations.
This value reflects the average condition of species (based on risk status from the IUCN Red List of Threatened Species, http://www.iucnredlist.org/) (IUCN 2024a) located within the eez of each region based on species range maps from IUCN (shapefiles, used preferentially) (IUCN 2024b; BirdLife International and Handbook of the Birds of the World 2020) and Aquamaps (http://www.aquamaps.org/, half degree resolution rasters).
Units
scaled 0-1
Minderoo Global Fishing Index
fp_fish_management
Resilience
Category: social
Fisheries management was determined using governance capacity data from the Minderoo Global Fishing Index (Travaille et al. 2022). This governance capacity data characterizes the development of a country’s fisheries governance system on a continuum from zero to 12, based on each country’s assessment score and balance across the Governance Conceptual Framework. The assessment score rages from 0-100 and is based on a region’s performance across 6 dimensions of fisheries governance and weights each of these dimensions unequally, based on survey responses from fisheries experts:
| Dimension | Definition | Weight |
|---|---|---|
| Policy and objectives | Assesses a country’s fisheries policy foundation and governance and management objectives | 22% |
| Management capacity | Assesses a country’s fisheries policy foundation and governance and management objectives | 14% |
| Information availability and monitoring | Assesses the range, quality and resolution of the fisheries information available to inform management decisions | 16% |
| Level and control of access to fisheries resources | Assesses the extent of fishing access granted to various fleets and the tools used to regulate access across these fleets | 15% |
| Compliance management system | Assesses the strength of a country’s fisheries compliance and enforcement program | 17% |
| Stakeholder engagement and participation | Assesses the capacity of stakeholders, including fishers and fish processors, governmental and non-governmental organizations, research institutions and local communities, to meaningfully participate in fisheries governance and management processes | 16% |
For more information about these variables and how they are calculated, see the following documentation by the Minderoo Global Fishing Index:
- Methodology
- Technical Documentation
- Indicator Codebook
To gapfill the governance capacity data, we used gross domestic product (GDP) converted to international dollars using purchasing power parity rates. An international dollar has the same purchasing power over GDP as the U.S. dollar has in the United States. GDP at purchaser’s prices is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. Data are in constant international dollars based on the 2011 ICP round. We used the predict() function from the stats package to gapfill governance capacity using the GDP and United Nations geopolitical regions as predictor variables. Uninhabited regions received no score.
Units
scaled 0-1
## Nonindigenous species #### sp_alien {-}
Pressure
Category: ecological
Subcategory: nonindigenous species
The Global Register of Introduced and Invasive Species (GRIIS) is a series of country-wise checklists of introduced (naturalized) and invasive (harmful) species (Pagad et al. 2018). These data report the number and type of introduced and invasive species for each country, with species habitat listed under marine, brackish, freshwater, terrestrial, or some combination thereof. We only select species listed as marine or brackish, including those which are listed in combination with other habitat types (typically birds, plants, and insects which intersect multiple habitats). For our purposes, a weighted average of invasive species (90%) and introduced species (10%) were used to calculate the score. This split was made to account for unreliable data on the harm caused by introduced species, especially in marine environments, and to capture data inaccuracies in the overall dataset from reporting on the distinction between introduced and invasive species from individual countries. OHI regions with no data reported are gapfilled with regional averages. All values are rescaled to between 0 and 1.
\[ Pressure = 0.1 * \frac{S_n - min(S_n)}{max(S_n) - min(S_n)} + 0.9 * \frac{S_h - min(S_h)}{max(S_h) - min(S_h)} \]
Where,
\(S_n\) = number of introduced (naturalized) species in the region.
\(min(S_n)\) = the minimum number of introduced (naturalized) species in all regions.
\(max(S_n)\) = the maximum number of introduced (naturalized) species in all regions.
\(S_h\) = number of invasive (harmful) species in the region.
\(min(S_h)\) = the minimum number of invasive (harmful) species in all regions.
\(max(S_h)\) = the maximum number of invasive (harmful) species in all regions.
Predicting the full potential impact of alien species depends in large part on having high-resolution spatial information on where they exist, how far they have spread and exactly which components of the food web they affect. The data from Pagad et al. (2018) approximate these impacts but at country scales. In addition, the impacts of introduced and invasive species will vary depending on the goal under consideration. This implies that harmful effects would need to be assessed separately for each goal. Such an endeavor may be possible when applying this framework to a smaller case-study where this type of information can be acquired.
Units
scaled 0-1
Nutrient pollution
po_nutrients
Pressure
Category: ecological
Subcategory: pollution
Data were calculated using modeled plumed of land-based nitrogen pollution that provide intensity of pollution at 0.5 degree gridded resolution.
Nitrogen pollution was estimated from FAO data on annual country level fertilizer application for agricultural use (https://www.fao.org/faostat/en/#data/RFN; United Nations (2022b)) and country level estimates of nitrogen (N) inputs to agricultural soils from livestock manure (https://www.fao.org/faostat/en/#data/EMN; United Nations (2021)).
Disaggregating fertilizer application at the crop-level
First, we extracted national fertilizer application for agricultural use from FAOSTAT (2022b) for each plant nutrient (N, P2O5, and K2O). To disaggregate national fertilizer application at the crop-level, we compiled fertilizer use by crop (FUBC) rates from the International Fertilizer Association’s (IFA) FUBC series (FAO/IFA/IFDC 2002; Heffer, Gruere, and Roberts 2017), FUBC rates provided by request. The FUBC series provides national application rates, measured in kilograms per hectare, of N, P2O5, and K2O for all crop categories. FUBC rates for 2014/15 were extracted from Heffer et al. (2017) (FUBC rates provided by request), and harmonised with general crop categories, with the exception of fodder, which is not included, and the residual category, which combines crop categories from our study that are unlikely to have similar fertilizer application rates. hence, we extracted FUBC rates for these remaining crop categories and fodder by extracting crop-specific FUBC rates from FAO (2002). While these rates are more specific at the national and crop level, they were reported between 1996-2001, which is relatively old compared to FUBC rates for 2014/15. To gapfill missing national rates for FUBC 2002, we used the mean fertilizer rate using the smallest regional mean or adopting the next largest regional mean if the smaller region had no data (“UNSD — Methodology” 2020). We also allocated national fertilizer use to grasslands using 2014/15 FUBC percentages.
To determine the proportion of fertilizer allocated by country, crop, production system, and fertilizer, we multiplied crop-specific rates by the total national harvested area of each crop and production system calculated from SPAM harvested area maps (production maps taken from Halpern et al. (2022), in review). Because the SPAM harvested area maps are only from data year 2017, the location of fertilizer use does not change across years, only relative intensity does. Fertilizer inputs to irrigated high input production systems were weighted two times higher than high-input rain-fed production systems for the same production (FAO 2005), while the two other production systems (low input and subsistence) were assigned a weight of zero as they do not require fertilizer use. The proportion of nutrient applied was calculated for each country, crop, production system, and fertilizer. We disaggregated national fertilizer application by multiplying national fertilizer with the proportions. We disaggregated national fertilizer input at the raster level by multiplying the cell-level proportion of harvested area in the country for each crop and production system with the national fertilizer use.
Calculating excess nitrogen from synthetic fertilizer applied to crops
Excess nutrients from crops were estimated using the proportion of N that is leached, and the proportion of N that is volatilised as NH3. We define excess nitrogen, \(N_{excess}\), as the tonnes of applied nitrogen that likely runsoffs/leaches, Nleach, or volatilizes as NH3, \(N_{{vol}_{NH3}}\), which is subsequently deposited on land and water:
\(N_{excess} = N_{{vol}_{NH3}} + N_{leach}\)
where,
\(N_{leach} = N_{applied} – N_{withdrawal} – N_{nitrification/denitrification} – N_{{vol}_{NH3}}\)
\(N_{withdrawal}\) = estimates of withdrawal of N by plants on both crops and grazed areas at supernational scale (NUE values from A. F. Bouwman, Van Drecht, and Van der Hoek (2005)).
\(N_{nitrification/denitrification}\) = estimate of N emissions through nitrification/denitrification processes (e.g., N20, NO, N2), with a global estimate of 10.2% used (A. F. Bouwman, Boumans, and Batjes 2002; Scheer et al. 2020).
\(N_{{vol}_{NH3}}\) = estimates of N volatilization as NH3 at supernational scale (A. Bouwman, Boumans, and Batjes (2002), Table 5 summed grasslands, crops, wetland rice).
Calculating excess nitrogen from livestock manure pathways
We estimated for four general manure pathways (managed then spread, spread on soil, spread on pasture, left on pasture) the proportion of excreted N (United Nations 2021) that is removed by plants, volatilizes as NH3, is converted to NOx products during nitrification/denitrification, or, is lost through leaching or runoff (Fig. 7.2).
Figure 7.2. Nitrogen leaching and volatilization from manure pathways. 
For manure applied to crop soils or pastures or left on pastures, we generally estimated the proportion of N that runs off or leaches, \(PN_{leach}\), as:
\(PN_{leach} = 1 – PN_{withdrawal} – PN_{nitrification/denitrification} – PN_{vol_{NH3}}\)
where the values vary according to the manure pathway (Table 7.9).
Table 7.9. Values used to estimate proportion of N loss due to withdrawal, nitrification/denitrification, and NH3 volatilization for different manure systems
| Definition | Applied to crops | Applied or left on pastures | |
|---|---|---|---|
| \(PN_{withdrawal}\) | Proportion of withdrawal of N by crops or grasses | Proportion of N recovery, Table 5 in (A. F. Bouwman, Van Drecht, and Van der Hoek 2005) | 60% of inputs post NH3 volatilization (A. F. Bouwman, Van Drecht, and Van der Hoek 2005) |
| \(PN_{nitrification/denitrification}\) | Proportion of N emissions through nitrification/denitrification processes (e.g., N20, NO, N2) | Global value of 0.102; calculated as N2O emission factor of 0.01 of total N excretion (A. F. Bouwman, Boumans, and Batjes 2002) multiplied by 10.2 based on the ratio of (N2O + N2)/N2O for “agricultural soils” (Scheer et al. 2020). | Global value of 0.09125 used; calculated as N2O emission factor of 0.01 of total N excretion (A. F. Bouwman, Boumans, and Batjes 2002) multiplied by 9.125 based on the ratio of (N2O + N2)/ N2O for “soils under natural vegetation” (Scheer et al. 2020) |
| \(PN_{vol_{NH3}}\) | Proportion of volatilization as NH3 | Proportion of volatilization (Table 5 in Bouwman et al. (2002) avg. of crops and rice values) | Proportion of grassland volatilization, Table 5 in Bouwman et al. (2002) |
In some cases, prior to being spread on soils, manure is collected and stored using a variety of management systems, s. The proportion of N leaching from manure management systems, \(PMMS_{leach}\), was calculated using a modified version of GLEAM equation 4.4.4 (FAO 2018):
\(PMMS_{leach} = ∑_s (MS_s × F_{leach,s} × (1 - ex_{vol}))\)
\(MMS_{leach}\) = proportion N leached for managed manure for animal system
\(MS_s\) = Fraction of manure treated in each system, s (FAO 2018)
\(F_{leach,s}\) = Fraction N leached in each manure management system (FAO 2018)
\(ex_{vol}\) = Fraction N volatilized after excretion, used global value of 0.0075 (FAO 2018)
To determine the proportion of N volatilizing as NH3 for manure treated in a manure management system, we determined the total proportion of all N lost during management (Table 10.23 in Gavrilova and et al. (2019)). Nitrogen loss includes runoff/leaching, volatilization of NH3, and NOx from denitrification/nitrification. We subtracted the proportion expected to leach in each manure management system (FAO 2018). The remaining loss included both NH3 volatilization and NOx from nitrification/denitrification, so we used an adjustment factor of 0.74 to account for only NH3 volatilization of N, based on the proportion of NH3/(NH3 + NOx) observed in directly applied manure.
Eventually, the managed manure is typically applied to either crop soils or pasture soils. Using the same assumption as A. F. Bouwman, Beusen, and Billen (2009), in industrialized countries we applied 50% of manure to pastures and 50% to crops, and in developing countries we applied 95% to crop land and 5% to pastures.
Next, for each livestock system and country, we estimated the total proportion of N that runsoffs/leaches, \(PN_{leach}\), or volatilizes as NH3, \(PN_{NH3}\) based on the proportion of manure, \(P_{manure}\), that enters each manure pathway for a given livestock system and country (FAO (2018), Figure 7.2):
\[\begin{align*} PN_{leach} = P_{manure} \times PMMS_{leach} + \\ P_{manure} \times PAppliedCrops_{leach} + \\ P_{manure} \times PAppliedPastures_{leach} + \\ P_{manure} \times PLeftPastures_{leach} \end{align*}\]
We estimated \(PN_{NH3}\) similarly.
For each livestock system, we then mapped the yearly tonnes of N that leaches or runsoff from manure, \(rastTN_{leach}\):
\(rastTN_{leach} = rastPN_{leach} \times rastN_{ex} \times rastN_{animals}\)
where,
\(rastPN_{leach}\) = rasterized data describing total proportion of N runoff/leaching for a specific animal system and country
\(rastN_{ex}\) = raster describing annual N excretion, tonnes N animal-1 year-1 (country and animal specific values from United Nations (2021))
\(rastN_{animals}\) = raster data describing number of animals for an animal system (FAO 2018)
We similarly mapped the total tonnes of N that volatilizes as NH3.
N leaching and N volatilization from synthetic fertilizers applied to grassland
We calculated national agricultural grassland fertilizer input by multiplying the national agricultural fertilizer input with the percentage of fertilizer allocated to grasslands nationally in 2014/15 (Heffer, Gruere, and Roberts 2017). We assumed all agricultural grassland fertilizer input was used on grass fed to livestock. To spatially disaggregate national fertilizer input, we multiplied national fertilizer input by the proportion of livestock units (LSU) for each country, animal and product (milk, meat, etc.) at the cell-level.
To calculate excess nutrients from synthetic fertilizers applied to grasslands, we estimated the proportion of N that is leached, and the proportion of N that is volatilized as NH3 (Figure 7.2). Methods are the same as manure pathways, with the exception of using grassland-specific proportions to calculate \(N_{{vol}_{NH3}}\) (A. Bouwman, Boumans, and Batjes 2002).
Pluming excess nitrogen to coastal areas
Final excess nitrogen layers from crop fertilizer and livestock manure were then combined together to create rasters describing excess nitrogen leaching at 0.5 degree gridded resolution and excess nitrogen volatilization at 0.5 degree gridded resolution. To estimate the amount of leached nitrogen which reaches coastal systems we assumed that any nitrogen which was more than 1km away from surface waters and coastlines would not contribute to effluent totals. To estimate the amount of volatilized nitrogen which reaches coastal systems, we multiplied yearly volatilized raster by a raster describing proportion of area within cells that are surface waters and cells that are 1km away from the coast. This was done under the assumption that all leaching and volatilization would not reach coastal areas due to uptake from soils and other processes. Surface water rasters were provided upon request from Tuholske (2021). Following this, we combined the coastal leaching and coastal volatilization rasters into single, yearly pollution rasters.
These yearly pollution rasters were then aggregated by ~140,000 global basins, and diffusive plumes were modeled from each basin’s pourpoint. The final non-zero plumes (about ~95,000 were aggregated into 0.5 degree gridded Mollweide (wgs84) projection rasters to produce a single plume-aggregated pollution raster for each year from 2005 to 2020.
These raw values were then normalized to 0-1 by dividing by the 99th quantile of raster values across all years. The zonal mean was then calculated for each region.
Units
scaled 0-1
Nutrient pollution trend
cw_nutrient_trend
The inverse of the pressure data (1 - po_nutrients_3nm) was used to estimate nutrient trends for the clean water goal. The proportional yearly change was estimated using a linear regression model of the most recent five years of data (i.e., slope divided by data from the earliest year included in the regression model). The slope was then multiplied by five to get the predicted change in 5 years.
Units
trend
OHI region id
rgn_global
OHI global region ID and name.
Units
label
Ocean acidification
cc_acid
Pressure
Category: ecological
Subcategory: climate change
This pressure layer models the difference in global distribution of the aragonite saturation state (\(\Omega_{arag}\)) of the ocean in the pre-industrial era and modern times. Global estimates through time (Chau, Marion Gehlen, and Frédéric Chevallier 2022) are modeled at 1-degree resolution. Changes in the saturation state can be attributed to changes in the concentration of CO2 and thus we use the difference between the pre-industrial and modern times as a proxy for ocean acidification due to human influences (Stephen Barker and Andy Ridgwell 2012; United States Environmental Protection Agency 2024). Values are rescaled from 0 to 1 using the threshold at which seawater becomes undersaturated, where \(\Omega_{arag} = 1\), and by relating the current aragonite saturation to a historical reference.
Units
scaled 0-1
Offshore 3nm area
rgn_area_offshore3nm
Area (km2) located from each region’s land-sea interface to 3nm offshore.
Units
km2
Offshore coastal protected areas
lsp_prot_area_offshore3nm
This includes marine protected areas within 3nm offshore of the coastline.
Data is from the United Nations Environment Programme - World Conservation Monitoring Centre’s World Database on Protected Areas (2024) (http://www.protectedplanet.net). Data includes all nationally designated (e.g., National Parks, Nature Reserves) and internationally recognized protected areas (e.g., UNESCO World Heritage Sites, Ramsar Wetlands of International Importance) as an ESRI shapefile. We used only WDPA polygons (not points) with a status of “designated” (not “proposed”). These polygons were converted to a 500 m Mollweide raster by the value of the year in which the park was decreed “designated”. For cases in which polygons overlapped, priority was given first to the parks with the earliest year. The total amount of protected area (km2) was calculated for each year for: the entire eez, 3 nm offshore, and 1km inland (depending on the dimension being calculated).
Units
km2
Pathogen pollution
po_pathogens
Pressure
Category: ecological
Subcategory: pollution
The percentage of the population with access to improved sanitation facilities (WHO-UNICEF 2024) was used in combination with measurements of coastal population as a proxy for pathogens in coastal waters. Access to improved sanitation facilities is defined as the percentage of the population in a country with at least adequate access to disposal facilities that can effectively prevent human, animal, and insect contact with excreta. These data are a country-wide average (not specific to the coastal region). Percentages (0-100) for each country were rescaled to 0-1 based on a maximum target of 100% of the population with access to improved sanitation, and a minimum value of 0. Reference point was defined as the 99th quantile across year/region with no buffer and constrained to the first 10 years of data (vs. all available years of data).
Units
scaled 0-1
Pathogen pollution trend
cw_pathogen_trend
The proportional yearly change in pathogen pressure values were estimated using a linear regression model of the most recent five years of data (i.e., slope divided by data from the earliest year included in the regression model). The slope was then multiplied by five to get the predicted change in 5 years.
Units
trend
Plastic trash trends
cw_trash_trend
Trend in trash was estimated using data on improperly disposed of plastics (Jambeck et al. 2016). Data are from the supplement, and describe mismanaged plastic waste in 2010 and projected mismanaged plastic waste in 2025. Using these data, we estimates proportional trash trends during a 5 year period as follows:
\(trend_{15year} = (value_{2025} - value_{2010})/value_{2010}\)
\(trend_{1year} = trend_{15year}/15\)
The trend_1year value was then multiplied by 5 to get the estimated proportional change in 5 years.
Missing data was gapfilled using a linear regression model with population as a predictor.
Units
trend
Potential tonnes of mariculture harvest
mar_capacity
Mariculture capacity/potential was determined for each region using Mapping the global potential for marine aquaculture (Gentry et al. 2019). Global data were provided at 0.0083 degree resolution. The total mariculture capacity count data was calculated for each region by extracting the relevant cell data using the OHI regions shapefile.
Units
tonnes
Proportion of total international arrivals to area of coastline to total population
tr_arrivals_props_tourism
The United Nations World Tourism Organization (UNWTO) (2022) includes total international arrivals data. This is provided in the form of thousands of people arriving, which we convert to total number of people arriving. We divide these arrivals by the total arrivals in the country.
To address missing values in arrivals, specifically referring to “Overnight visitors (tourists),” we employ a two-step process. First, we attempt to fill the gaps by subtracting “Same-day visitors (excursionists)” from “Total arrivals” if the latter is available. If this is not feasible, we resort to interpolating or extrapolating based on historical data spanning from 1995 to 2019, employing a linear model to estimate increases or decreases on a regional level.
In light of the Covid-19 pandemic, we have adopted a distinct approach for the years 2020 and 2021. We calculate the global average proportionate change from the preceding year, apply this percentage change to the previous year’s arrivals or total values, and then add the result to the corresponding previous year’s arrivals or total value.
Units
proportion scaled 0-1
Region areas based on EEZ boundaries
rgn_area
Area (km2) for each region’s EEZ-based boundary.
OHI offshore regions are based on exclusive economic zones (EEZ, VLIZ 2012). Unique country EEZs were typically used to define a region, except territorial regions were split from the administrative country. Many borders have been redrawn, such as the removal of UK claims around Cyprus. Gaps and extensions between this EEZ file and our land-sea mask were resolved through GIS operations (buffer, erase, and polygon neighbor analysis). Ocean area per region was calculated using geodesic area calculations on the region polygons in geographic coordinates. We exclude from regions the inland EEZs of the Caspian Sea and any disputed areas.
Units
km2
Regions
rgn_labels
OHI region ids for eez (1-250) and fao high seas regions (260-278).
Units
label
Relative natural product harvest value
np_harvest_product_weight
Within each region and year, the 5 year average value (USD) of harvest of each commodity (ornamental fish, seaweeds, and fish oil/fish meal) relative to 5 year average total harvest value of three marine commodities. This layer is used to weight contribution of each product to final natural product status score. The FAO Global Commodities database was used for this layer (UN-FAO 2024).
If a country was missing tonnes or dollar values (but had one of the values), the missing data were estimated. FAO provides yearly data for the tonnes and dollar value generated for each natural product, however, countries often provide only one of these variables (and the data provided varies across years). To estimate these missing data, we used country-specific linear models to predict tonnes based on the dollar value of a product (or, vice versa). For the countries that did not have enough data to develop an adequate model, our models included the data for all the countries within a UN geopolitical region. When there was not enough data at the geopolitical region scale, we used all the global data to predict missing values.
Table 7.10. FAO categories included in each natural product commodity
| commodity | subcategory |
|---|---|
| corals | Coral and the like |
| fish oil | Alaska pollack oil, nei, Anchoveta oil, Capelin oil, Clupeoid oils, nei, Cod liver oil, Fish body oils, nei, Fish liver oils, nei, Gadoid liver oils, nei, Hake liver oil, Halibuts, liver oils, Herring oil, Jack mackerel oil, Menhaden oil, Pilchard oil, Redfish oil, Sardine oil, Shark liver oil, Shark oil, Squid oil, Pelagic fish oils, nei, Gadiformes, oil, nei, Demersal fish oils, nei, Alaska pollock, oil, nei |
| ornamentals | Ornamental saltwater fish, Ornamental fish nei |
| seaweeds | Agar agar in powder, Agar agar in strips, Agar agar nei, Carrageen (Chondrus crispus), Green laver, Hizikia fusiforme (brown algae), Kelp, Kelp meal, Laver, dry, Laver, nei, Other brown algae (laminaria, eisenia/ecklonia), Other edible seaweeds, Other inedible seaweeds, Seaweeds and other algae, unfit for human consumption, nei, Seaweeds and other algae, fit for human consumption, nei, Other red algae, Other seaweeds and aquatic plants and products thereof, Undaria pinnafitida (brown algae) |
| shells | Abalone shells, Miscellaneous corals and shells, Mother of pearl shells, Oyster shells, Sea snail shells, Shells nei, Trochus shells |
| sponges | Natural sponges nei, Natural sponges other than raw, Natural sponges raw |
Units
proportion
Relative ornamental natural product harvest tonnes
np_orn_tonnes_relative
The total tonnes of ornamental fish were determined for each region using export data from the FAO Global Commodities database (UN-FAO 2024). For each group the sum of the subcategories was calculated. For ornamental fish we excluded the subcategory ‘Fish for culture including ova, fingerlings, etc.’ because it is not specific to ornamental fish, and the subcategory ‘Ornamental freshwater fish’ because it is not from marine systems. Following this, within each region, the harvest of ornamental fishing was scaled relative to its maximum value across all years. The tonnes of ornamental fishing is divided by the maximum value observed for the commodity in each region.
If a country was missing tonnes or dollar values (but had one of the values), the missing data were estimated. FAO provides yearly data for the tonnes and dollar value generated for each natural product, however, countries often provide only one of these variables (and the data provided vary across years). To estimate these missing data, we used country-specific linear models to predict tonnes based on the dollar value of ornamental fish (or, vice versa). For the countries that did not have enough data to develop an adequate model, our models included the data for all the countries within a UN geopolitical region. When there wasn’t enough data at the geopolitical region scale, we used all the global data to predict missing values.
Units
proportion
Risk of harvest practices for ornamental fish
np_risk_orn
The risk layer is based on whether ornamental fishing has unsustainable harvest practices. Specifically, the intensity of cyanide fishing was used as a proxy. Risk for ornamental fish was set based on assessments of cyanide or dynamite fishing by Reefs at Risk Revisited (Burke et al. 2011) under the assumption that most ornamental fishes are harvested from coral reefs.
Units
scaled 0-1
Sea level rise
cc_slr
Pressure
Category: ecological
Subcategory: climate change
The sea level rise pressure layer is derived from satellite altimetry data (http://www.aviso.altimetry.fr/en/data/products/sea-surface-height-products/global/msla-mean-climatology.html) (AVISO 2024). Monthly mean sea level anomalies since 1993 track changes in sea level (mm) compared to a reference period from 1993-2012. Raw monthly data are provided on a 0.25x0.25 degree grid. These data were clipped to within 3 nautical miles of the coast, and monthly data layers were aggregated and averaged across pixels to compute mean sea level anomalies. The 99.99th quantile of raster values from all years was used as the reference point to rescale the layer from 0 to 1. All negative values were set to zero (i.e., no negative pressure), such that only positive sea level rise values mattered. The mean value of the raster cells within each OHI region was calculated.
Units
scaled 0-1
Sea surface temperature
cc_sst
Pressure
Category: ecological
Subcategory: climate change
Sea surface temperature (SST) data were obtained from the Coral Reef Temperature Anomaly Database (CoRTAD) (NOAA 2022), which is produced by the NOAA National Center for Environmental Information (NCEI) using 4.6 km (nominally 21 km2 at the equator) Advanced Very High Resolution Radiometer (AVHRR) Pathfinder Version 6 SST data. Weekly SST data are used to compute the standard deviation (SD) of SST’s per pixel between 1982 to 2011. We define an anomaly as exceeding the standard deviation of SSTs from the climatology for that location (i.e., grid cell) and week of the year. The frequency of weekly anomalies was calculated for each year in the dataset. We then quantified the difference between the number of anomalies in the 5 most recent years and the 5 baseline years between 1985 and 1989. The 99.99th quantile of raster values from all years was used as the reference point to rescale the layer from 0 to 1, and the mean value of the raster cells within each OHI region was calculated.
Because SST measurements are less reliable where there is persistent ice, we created an ice mask to identify places near the poles that were almost always covered by significant sea ice. The ice mask was generated primarily from the OSI/SAF Global Daily Sea Ice Concentration Reprocessing Data Set, which was regridded and made available in the Pathfinder V5.2 dataset. In Pathfinder, when the OSI/SAF data are unavailable, the sea ice concentrations from the NCDC Daily OI SST data (Reynolds et al. 2007) are included. For each day of the climatological year (1 through 366), we read in the daily sea ice fraction for that day from all of the years and averaged them to create a daily, sea-ice fraction climatology. We then identified grid cells that always contained a sea ice fraction of greater than 0.15 and masked them out of the analysis.
Units
scaled 0-1
Seaweed mariculture sustainability score
np_seaweed_sust
The seaweed sustainability layer is derived from Table 7.3 in mar_sustainability_score. Here, we take the mariculture sustainability scores, and subset them for the seaweed species included in Table 7.3 in np_seaweed_tonnes.
Units
scaled 0-1
Seaweed natural product harvest
np_seaweed_tonnes
Seaweed mariculture production from the FAO Global Aquaculture Production Quantity dataset (United Nations 2024). Only production classified in the “Marine” and “Brackishwater” environments was included in the analysis (all “Freshwater” production was excluded). All non edible seaweed species produced within a country were summed to give a single production value for each country in each year that production took place. For the three EEZs that fall within the China region (China, Macau, and Hong Kong), we combined the values by summing across these EEZs.
Units
tonnes
Sectors in each region
le_sector_weight
Describes which livelihood and economy sectors are present in each region.
Units
value
Strength of governance
wgi_all
Resilience
Category: social
The Worldwide Governance Indicator (2024) is composed of six dimensions of governance: voice and accountability, political stability and absence of violence, government effectiveness, regulatory quality, rule of law, control of corruption. These 6 aggregate indicators combine data from a variety of survey institutes, think tanks, NGOs, and international organizations to report on the relative governance of 213 economies worldwide. The WGI combines individual indicators through an Unobserved Components Model to produce the 6 dimensions of governance that range in value from approximately -2.5 to 2.5, have a normal distribution, a mean of zero, and a standard deviation of 1. We take an average of the six dimension scores to produce a single governance score for each country. Social pressure is then calculated as one minus this average WGI score.
WGI scores are provided for China/Hong Kong/Macao and Puerto Rico/Virgin Island, which are combined OHI regions. These scores were averaged, weighted by population.
Units
scaled 0-1
Subtidal hardbottom habitat destruction
hd_subtidal_hb
Pressure
Category: ecological
Subcategory: habitat destruction
Reefs at Risk Revisited (Burke et al. 2011) recorded the global presence of destructive artisanal blast fishing based on survey observations and expert opinion. We reclassified the log-scale scoring system for the blast rasters, so 0 = 0, 100 = 1, 1000 = 2. The mean raster score was then determined for each OHI region. The blast values for each region were then summed to get the total.
Units
scaled 0-1
Subtidal soft bottom habitat destruction
hd_subtidal_sb
Pressure
Category: ecological
Subcategory: habitat destruction
The Pressure score was calculated as one minus soft-bottom habitat condition.
Units
scaled 0-1
Targeted harvest of cetaceans and marine turtles
fp_targetharvest
Pressure
Category: ecological
Subcategory: fishing pressure
This data layer describes the pressure on cetaceans and marine turtles for each country calculated using the FAO Global Capture Production Quantity dataset (United Nations 2022a). We extracted all catch records from the FAO data for cetaceans or marine turtles and aggregated to create a total reported catch count for each region. The summed catch was rescaled from 0-1, using the 95th quantile across all years (including and prior to the assessment year) and regions (values > 1 were capped at 1).
Units
scaled 0-1
Tourism sustainability index
tr_sustainability
The Travel and Tourism Development Index (TTDI) (WEF n.d.) — successor to the Travel and Tourism Competitiveness Index — is produced by the World Economic Forum and measures “the set of factors and policies that enable the sustainable and resilient development of the Travel and Tourism (T&T) sector, which in turn contributes to the development of a country.” The index is comprised of five subindexes 17 pillars and 112 individual indicators, distributed among the different pillars. The 2021 report included scores ranging from 1-6 for 117 countries.
To calculate this layer we used scores from the Travel and Tourism Sustainability Subindex which encompasses three pillars: Environmental Sustainability, Socioeconomic Resilience and Conditions, and Travel and Tourism Demand Pressure and Impact.
For countries not assessed, values were estimated using a linear regression model specific to each UN geopolitical region using per-capita GDP as a predictor variable.
Units
scaled 0-1
UV radiation
cc_uv
Pressure
Category: ecological
Subcategory: climate change
The ultraviolet radiation (UV) pressure layer is derived from daily Local Noon Erythemal UV Irradiance (mW/m2) data. The Aura/OMI satellite provides data at 1x1 degree resolution from September 2004 through present, spanning 180 degrees latitude and 360 degrees longitude. Raster data are provided in HDF5 format by the NASA Goddard Earth Sciences Data and Information Services Center (GESDISC, (http://disc.sci.gsfc.nasa.gov/Aura/data-holdings/OMI/omuvbd_v003.shtml) (Jari Hovila 2013). Raw data was downloaded, translated to GeoTIFFs using R and aggregated to weekly means.
This pressure measures the number of times the weekly average of each 1 degree cell exceeds the climatological mean + 1 standard deviation, defined as an anomalous value. The frequency of weekly anomalies was calculated for each year in the dataset. We then quantified the difference between the number of anomalies in the 5 most recent years and the 5 oldest years in the dataset. The 99.99th quantile of raster values from all years was used as the reference point to rescale the layer from 0 to 1, and the mean value of the raster cells within each OHI region was calculated.
Units
scaled 0-1
Uninhabited regions
uninhabited
This layer is a list of low and zero population regions based on Wikipedia.
Units
population
Weakness of governance
ss_wgi
Pressure
Category: social
When used as a social pressure, 1 minus the Worldwide Governance Indicator (2024) is used.
Units
scaled 0-1
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Social Progress Index
res_spi
Resilience
Category: social
The Social Progress Index (http://www.socialprogressimperative.org/global-index/) (Social Progress Index 2021) includes several quality of life measures. The SPI score is the average of 3 dimensions, and each dimension is the average of 4 components. Each component includes several indicators that are scaled from 0 to 100. When a region was missing 1 or more component, but not all of them, we used the aregImpute function from the Hmisc package to estimate the dimension value based on the available component values. Regions with no dimension/component data were estimated using a linear regression model with UN geopolitical region and WGI data as predictor variables. Uninhabited regions received no score.
Units
scaled 0-1