4.3.2.1 Ecological pressure

We assessed five broad, globally-relevant categories of ecological stressors: fishing pressure, habitat destruction, climate change (including ocean acidification), water pollution, and species introductions (invasive species and genetic escapes). The five categories are intended to capture known pressures to the social-ecological system associated with each goal. Each pressure category may include several stressors. The intensity of each stressor within each OHI region is scaled from 0 to 1, with 1 indicating the highest stress (e.g., example of one of these data layers is sea surface temperature).

We determined the rank sensitivity of each goal/subgoal to each stressor (or, when possible, an element of the goal, such as a specific habitat). We ranked ecological pressures as having ‘high’ (score = 3), ‘medium’ (score = 2), ‘low’ (score = 1), or ‘no’ (score = NA) impact (Table 4.2). Wherever possible we relied on peer-reviewed literature to establish these rankings, and relied on our collective expert judgment in cases with no available literature (Table S28 in Halpern et al. 2012). The pressure ranks are based on a rough estimate of the global average intensity and frequency of the stressor. We recognize that this will create over- and under-estimates for different places around the planet, but to address such variance in a meaningful way would require a separate weighting matrix for every single region on the planet, which is not feasible at this time.

Table 4.2. Pressure matrix Rank sensitivity of each goal (or, goal element) to each stressor.

To estimate the cumulative effect of the ecological pressures, \(P_E\), we first determined the cumulative pressure, \(p\), within each ecological category, \(i\) (e.g., pollution, fishing, etc.):

\[ { p }_{ i }\quad =\quad \frac { \displaystyle\sum _{ i=1 }^{ N }{ { w }_{ i }{ s }_{ i } } }{ 3 }, (Eq. 4.7) \]

Where \(w_i\) is the sensitivity ranks (Table 4.2) describing the relative sensitivity of each goal to each stressor, and \(s_i\) is intensity of the stressor in each region on a scale of 0-1. We divided by the maximum weighted intensity that could be achieved by the worst stressor (max = 3.0).

If \(p_i\) > 1.0, we set the value equal to 1.0. This formulation assumes that any cumulative pressure load greater than the maximum intensity of the worst stressor is equivalent to maximum stressor intensity.

For the goals for which sensitivity ranks were assigned for specific habitats or livelihood sectors (i.e., goal elements), we calculated the weighted sum of the pressures for only those habitats or sectors that were present in the country.

The overall ecological pressure, \(p_E\), acting on each goal and region was calculated as the weighted-average of the pressure scores, \(p\), for each category, \(i\), with weights set as the maximum rank in each pressure category (\(w_{i\_max}\)) for each goal, such that:

\[ { p }_{ E }\quad =\quad \frac { \displaystyle\sum _{ i=1 }^{ N }{ { (w }_{ i\_ max }*{ p }_{ i }) } }{ \displaystyle\sum_{ i=1 }^{ N } { { w }_{ i\_ max } } }, (Eq. 4.8) \]

Stressors that have no impact drop out rather than being assigned a rank of zero, which would affect the average score.

4.3.2.2 Social pressures

Social pressures describe the lack of effectiveness of government and social institutions. Social stressors are described for each region on a scale of 0 to 1 (with one indicating the highest pressure). Social pressure is then calculated as the average of the social stressors:

\[ { p }_{ S }\quad =\quad \frac { \displaystyle\sum _{ i=1 }^{ N }{ z_{ i } } }{ N }, (Eq. 4.9) \]

where \(z_{i}\) are the social pressure measures specific to the goal. Unequal weighting may be appropriate in some cases but is difficult to assess currently, particularly at the global scale.

4.3.2.3 Caveats

There were a number of ecological pressures not included in our assessment, including altered sediment regimes, noise and light pollution, toxic chemicals from point sources, nutrient pollution from atmospheric deposition and land-based sources other than fertilizer application to agricultural land. In all cases, global data do not exist in a format that would allow for adequate comparisons within and among countries. Future global or regional iterations of the Index could include these data as they become available.

The calculation of ecological pressures is sensitive to the number of stressors within each category (but not to the number of categories). Inclusion of additional stressors within categories would require careful calibration of ranks so that the cumulative effect of a larger number of stressors does not overestimate pressure.

A key assumption in our assessment of ecological pressures is that each goal has a linear and additive response to increases in intensity of the stressors. Clearly many ecosystems respond non-linearly to increased stressor intensity, exhibiting threshold responses, and there are likely nonlinear interactions among stressors. Unfortunately little is known about the nature of these types of nonlinearities and interactions so we could not include them in any meaningful way.

4.3.3.1 Ecological resilience

4.3.3.2 Social integrity resilience

Social integrity is intended to describe those processes internal to a community that affect its resilience. It is a function of a wide range of aspects of social structure. Social Integrity per goal for each region, \(Y_{S}\), is therefore:

\[ Y_{ S}\quad =\quad \frac { \displaystyle\sum _{ i=1 }^{ N }{ { y }_{ S,i } } }{ N }, (Eq. 4.13) \]

where \(y_{S,i}\) are the social integrity measures specific to the goal.

Ideally, assessments of social resilience would include state and federal level rules and other relevant institutional mechanisms as well. However, such information is extremely difficult to access for every single country, and so we relied on global datasets that focus on international treaties and assessments. Another key gap is information on social norms and community (and other local-scale) institutions (such as tenure or use rights) that influence resource use and management in many settings. Information on these institutions is also extremely difficult to find at a global scale, although the World Governance Indicator (Kaufmann et al. 2010) partly measures their effectiveness through its inclusion of corruption indices.

5.2.1.1 Current status

The status of the habitat sub-goal, \(x_{hab}\), was assessed as the average of the condition estimates, \(C\), for each habitat, \(k\), present in a region; measured as the loss of habitat and/or % degradation of remaining habitat, such that:

\[ x_{hab} = \frac { \displaystyle\sum _{ k=1 }^{ N }{ { C }_{ k } } }{ N}, \quad \quad (Eq. 5.3) \]

where, \(C_{k} = C_{c}/C_{r}\) and \(N\) is the number of habitats in a region. \(C_{c}\) is the current condition and \(C_{r}\) is the reference condition specific to each \(k\) habitat present in the region (Table 5.2). This formulation ensures that each country is assessed only for those habitats that can exist (e.g., Canada is not assessed on the status of its nonexistent coral reefs). We generally considered the reference years to be between 1980-1995 and the current years to be between 2001-2010, although these varied by habitat due to data availability.

Table 5.2. Habitat data Description of condition, extent, and trend calculations for habitat data (Note: extent is not used to calculate the habitat subgoal, but is used for the coastal protection and carbon storage goals). More information about the sources used to generate these values is located in Section 6 and Table 6.1.

A significant amount of pre-processing of the habitat data was needed to fill data gaps and resolve data quality issues (Section 6). Because consistent habitat monitoring data was unavailable for many countries, anomalous values can occur. This is particularly true for highly variable habitats like seagrasses or coral reefs which can have significant site-to-site and year-to-year differences in extent and condition (Orth et al. 2006; Bruno & Selig 2007). Biases may also have been introduced from spatial (e.g., protected or impacted sites) and temporal (e.g., directly after a disturbance event) selections in sampling. In regions where we had a limited number of surveys in a particular country, overall status can be under- or overestimated because of these fluctuations.

5.2.1.2 Trend

Trend in habitat data were calculated as the linear trend in extent or condition with slight variations depending on habitat type. Coral reef habitat trends were calculated on a per country basis, using all available data. For seagrasses we calculated trends on a per site basis. For mangroves we used the rate of change in areal extent over the most recent 5 years of available data. For sea ice we calculated the slope across the most recent 5 years of data, where each year of data is based on a three-year moving averages to smooth out potential climate variation. For soft bottom habitat we simply calculated the slope of the recent change in condition over the past five years, i.e., the change in proportion of catch from trawl fishing per unit area of habitat within a region.

5.2.1.3 Data

Status and trend

Habitat condition (hab_health): Current condition of habitat relative to historical condition

Habitat condition trend (hab_trend): Estimated change in habitat condition

Habitat extent (hab_extent): Area of habitats: mangrove, saltmarsh, seagrass, soft bottom, seaice, coral

Pressure

Coastal chemical pollution (po_chemicals_3nm): 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)

Coastal nutrient pollution (po_nutrients_3nm): Modeled nutrient pollution within EEZ based on fertilizer consumption

High bycatch due to artisanal fishing (fp_art_hb): The presence of destructive artisanal blast and poison (cyanide) fishing.

High bycatch due to commercial fishing (fp_com_hb): Modeled destructive commercial fishing practices by 5 gear types and scaled by Net Primary Productivity

Intertidal habitat destruction (hd_intertidal): Coastal population density (25 mi from shore) as a proxy for intertidal habitat destruction

Low bycatch due to artisanal fishing (fp_art_lb): Extent of artisanal fishing (including: artisanal, subsistence, and recreational catch)

Low bycatch due to commercial fishing (fp_com_lb): Modeled destructive commercial fishing practices by 2 gear types and scaled by Net Primary Productivity

Nonindigenous species (sp_alien): Measure of harmful invasive species

Ocean acidification (cc_acid): Ocean acidification pressure scaled using biological thresholds

Sea level rise (cc_slr): Sea level rise pressure

Sea surface temperature (cc_sst): Sea surface temperature anomalies

Subtidal hardbottom habitat destruction (hd_subtidal_hb): High bycatch artisanal fishing practices (blast fishing) as a proxy for subtidal hard bottom habitat destruction

Subtidal soft bottom habitat destruction (hd_subtidal_sb): Demersal destructive commercial fishing practices (i.e., trawling) in soft bottom habitat as a proxy for soft bottom habitat destruction

UV radiation (cc_uv): Modeled UV radiation

Weakness of governance (ss_wgi): Inverse of World Governance Indicators (WGI) six combined scores

Weakness of social progress (ss_spi): Inverse of Social Progress Index scores

Resilience

Artisanal fisheries management effectiveness (fp_mora_artisanal): Quality of management of small-scale fishing for artisanal and recreational purposes

Coastal protected marine areas (fishing preservation) (fp_mpa_coast): Protected marine areas within 3nm of coastline (lasting special places goal status score)

Coastal protected marine areas (habitat preservation) (hd_mpa_coast): Protected marine areas within 3nm of coastline (lasting special places goal status score)

Commercial fishing management (fp_mora): Regulations and management of commerical fishing

EEZ protected marine areas (fishing preservation) (fp_mpa_eez): Protected marine areas within EEZ (lasting special places calculation applied to the entire EEZ)

EEZ protected marine areas (habitat preservation) (hd_mpa_eez): Protected marine areas within EEZ (lasting special places calculation applied to the entire EEZ)

Management of habitat to protect fisheries biodiversity (fp_habitat): Survey responses by country to the Convention on Biological Diversity (CBD) Third National Report: habitat related questions

Management of habitat to protect habitat biodiversity (hd_habitat): Survey responses by country to the Convention on Biological Diversity (CBD) Third National Report: habitat related questions

Management of mariculture to preserve biodiversity (g_mariculture): Survey responses by country to the Convention on Biological Diversity (CBD) Third National Report: mariculture related questions

Management of tourism to preserve biodiversity (g_tourism): Survey responses by country to the Convention on Biological Diversity (CBD) Third National Report: tourism related questions

Management of waters to preserve biodiversity (po_water): Survey responses by country to the Convention on Biological Diversity (CBD) Third National Report: tourism related questions

Measure of coastal ecological integrity (species_diversity_3nm): 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

Measure of ecological integrity (species_diversity_eez): Marine species condition (species subgoal status score) as a proxy for ecological integrity

Social Progress Index (res_spi): Social Progress Index scores

Strength of governance (wgi_all): World Governance Indicators (WGI) six combined scores

5.2.2.1 Current status

Species status was calculated as the area and status-weighted average of assessed species within each region. Marine species distribution and threat category data mostly came from IUCN Red List of Threatened Species, and we limited data to all species having IUCN habitat system of “marine” (IUCN 2016a). Seabird distributions data came from Birdlife International (‘Bird species distribution maps of the world’ 2015). When neither IUCN nor BirdLife International distributions were available, we supplemented with AquaMaps (Kaschner et al. 2015) using any non-zero probability of occurrence to indicate species presence to create a binary range map. Range maps were analyzed at the 0.5 degree scale.

We scaled the lower end of the biodiversity goal to be 0 when 75% species are extinct, a level comparable to the five documented mass extinctions (Barnosky et al. 2011) and would constitute a catastrophic loss of biodiversity.

Threat weights, \(w_{i}\), were assigned based on the IUCN threat categories status of each \(i\) species, following the weighting schemes developed by Butchart et al. (2007) (Table 5.3). For the purposes of this analysis, we included only data for extant species for which sufficient data were available to conduct an assessment. We did not include the Data Deficient classification as assessed species following previously published guidelines for a mid-point approach (Schipper et al. 2008; Hoffmann et al. 2010).

We first calculated each the region’s area-weighted average species risk status, \(\bar R_{spp}\). For each 0.5 degree grid cell within a region, \(c\), the risk status, \(w\), for each species, \(i\), present is summed and multiplied by cell area \(A_c\), to get an area- and count-weighted species risk for each cell. This value is then divided by the sum of count-weighted area \(A_c \times N_c\) across all cells within the region. The result is the area-weighted mean species risk across the entire region.

\[ \bar R_{spp} = \frac { \displaystyle\sum_{ c=1 }^{ M } \left( \displaystyle\sum _{ i=1 }^{N_c} w_i \right) \times A_c } { \displaystyle\sum_{ c=1 }^{ M } A_c \times N_c }, (Eq. 5.4) \] To convert \(\bar R_{spp}\) into a score, we set a floor at 25% (representing a catastrophic loss of biodiversity, as noted above) and then rescaled to produce a \(x_{spp}\) value between zero and one.

\[ x_{spp} = max \left( \frac { \bar R_{SPP} - .25 }{ .75 }, 0 \right), (Eq. 5.5) \]

Table 5.3. Weights for assessment of species status based on IUCN risk categories

5.2.2.2 Trend

We calculated trend as the average of the population trend assessments for all species within a region, with species’ trends assigned a value of 0.5 for increasing, 0 for stable, and -0.5 for decreasing using the population trend data associated with the species assessment conducted by IUCN.

5.2.2.3 Data

Status and trend

Average species condition (spp_status): Species condition based on average of IUCN threat categories

Average species condition trend (spp_trend): Species trends based on average of IUCN population trend data

Pressure

Chemical pollution (po_chemicals): Modeled chemical pollution within EEZ from commercial shipping traffic, ports and harbors, land-based pesticide use (organic pollution), and urban runoff (inorganic pollution)

Genetic escapes (sp_genetic): Introduced mariculture species (Mariculture Sustainability Index) as a proxy for genetic escapes

High bycatch due to artisanal fishing (fp_art_hb): The presence of destructive artisanal blast and poison (cyanide) fishing.

High bycatch due to commercial fishing (fp_com_hb): Modeled destructive commercial fishing practices by 5 gear types and scaled by Net Primary Productivity

Intertidal habitat destruction (hd_intertidal): Coastal population density (25 mi from shore) as a proxy for intertidal habitat destruction

Low bycatch due to artisanal fishing (fp_art_lb): Extent of artisanal fishing (including: artisanal, subsistence, and recreational catch)

Low bycatch due to commercial fishing (fp_com_lb): Modeled destructive commercial fishing practices by 2 gear types and scaled by Net Primary Productivity

Marine plastics (po_trash): Global marine plastic pollution

Nonindigenous species (sp_alien): Measure of harmful invasive species

Nutrient pollution (po_nutrients): Modeled nutrient pollution within 3nm of coastline based on fertilizer consumption

Ocean acidification (cc_acid): Ocean acidification pressure scaled using biological thresholds

Sea surface temperature (cc_sst): Sea surface temperature anomalies

Subtidal hardbottom habitat destruction (hd_subtidal_hb): High bycatch artisanal fishing practices (blast fishing) as a proxy for subtidal hard bottom habitat destruction

Subtidal soft bottom habitat destruction (hd_subtidal_sb): Demersal destructive commercial fishing practices (i.e., trawling) in soft bottom habitat as a proxy for soft bottom habitat destruction

Targeted harvest of cetaceans and marine turtles (fp_targetharvest): Targeted harvest of cetaceans and marine turtles

UV radiation (cc_uv): Modeled UV radiation

Weakness of governance (ss_wgi): Inverse of World Governance Indicators (WGI) six combined scores

Weakness of social progress (ss_spi): Inverse of Social Progress Index scores

Resilience

Artisanal fisheries management effectiveness (fp_mora_artisanal): Quality of management of small-scale fishing for artisanal and recreational purposes

CITES signatories (g_cites): Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) signatories

Commercial fishing management (fp_mora): Regulations and management of commerical fishing

EEZ protected marine areas (fishing preservation) (fp_mpa_eez): Protected marine areas within EEZ (lasting special places calculation applied to the entire EEZ)

EEZ protected marine areas (habitat preservation) (hd_mpa_eez): Protected marine areas within EEZ (lasting special places calculation applied to the entire EEZ)

Management of habitat to protect fisheries biodiversity (fp_habitat): Survey responses by country to the Convention on Biological Diversity (CBD) Third National Report: habitat related questions

Management of habitat to protect habitat biodiversity (hd_habitat): Survey responses by country to the Convention on Biological Diversity (CBD) Third National Report: habitat related questions

Management of mariculture to preserve biodiversity (g_mariculture): Survey responses by country to the Convention on Biological Diversity (CBD) Third National Report: mariculture related questions

Management of tourism to preserve biodiversity (g_tourism): Survey responses by country to the Convention on Biological Diversity (CBD) Third National Report: tourism related questions

Management of waters to preserve biodiversity (po_water): Survey responses by country to the Convention on Biological Diversity (CBD) Third National Report: tourism related questions

Social Progress Index (res_spi): Social Progress Index scores

Strength of governance (wgi_all): World Governance Indicators (WGI) six combined scores

5.4.1.1 Data

Status and trend

Habitat condition (hab_health): Current condition of habitat relative to historical condition

Habitat condition trend (hab_trend): Estimated change in habitat condition

Habitat extent (hab_extent): Area of habitats: mangrove, saltmarsh, seagrass, soft bottom, seaice, coral

Pressure

Coastal chemical pollution (po_chemicals_3nm): 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)

Coastal nutrient pollution (po_nutrients_3nm): Modeled nutrient pollution within EEZ based on fertilizer consumption

Intertidal habitat destruction (hd_intertidal): Coastal population density (25 mi from shore) as a proxy for intertidal habitat destruction

Nonindigenous species (sp_alien): Measure of harmful invasive species

Ocean acidification (cc_acid): Ocean acidification pressure scaled using biological thresholds

Sea level rise (cc_slr): Sea level rise pressure

Sea surface temperature (cc_sst): Sea surface temperature anomalies

Weakness of governance (ss_wgi): Inverse of World Governance Indicators (WGI) six combined scores

Weakness of social progress (ss_spi): Inverse of Social Progress Index scores

Resilience

Coastal protected marine areas (habitat preservation) (hd_mpa_coast): Protected marine areas within 3nm of coastline (lasting special places goal status score)

Management of habitat to protect habitat biodiversity (hd_habitat): Survey responses by country to the Convention on Biological Diversity (CBD) Third National Report: habitat related questions

Management of waters to preserve biodiversity (po_water): Survey responses by country to the Convention on Biological Diversity (CBD) Third National Report: tourism related questions

Social Progress Index (res_spi): Social Progress Index scores

Strength of governance (wgi_all): World Governance Indicators (WGI) six combined scores

5.6.1.1 Current status

Spatial allocation of catch to regions

The data we use to calculate \(B/B_{MSY}\) and the weights used in the geometric mean are from Sea Around Us Project (SAUP) catch reconstruction data (Pauly & Zeller 2015). SAUP uses a spatial allocation method to distribute FAO catches (which are reported at the country scale) to a global grid of one half-degree cell resolution based on the spatial distribution of commercial taxa and the allocation of catch to fleets based on fishing access agreements (Zeller et al. 2016).

We aggregate the SAUP catch data in two ways. To get the data needed to weight the stock status scores, we sum the total catch for each taxa within each region’s EEZ to get the total catch in tonnes for each year. To get the data needed to calculate \(B/B_{MSY}\) values, we sum the total catch for each taxa within each major fishing region (FAO Fisheries and Aquaculture Department 2015) for each year.

Estimating \(B/B_{MSY}\)

When we were unable to obtain \(B/B_{MSY}\) values from the RAM database for a stock, we calculated them using a data-poor, or catch only model, developed by Martell & Froese (2013), and hereafter referred to as the “catch-MSY” method. The latter was chosen, among other data-limited methods because it was as good, or better, at predicting RAM \(B/B_{MSY}\) values than other methods based on our initial testing. We compared \(B/B_{MSY}\) scores from three catch models (catch-MSY, SSCOM, and COMSIR) as well as a variety of ensemble methods (Anderson et al. 2017). The catch_MSY model performed as well or better than the other models at predicting RAM \(B/B_{MSY}\) values. Furthermore, this method performed well (although not as well as the Random Forest ensemble approach, based on a rank correlation analysis) in analyses using simulated stocks with a broad range of life history traits and different known sources of uncertainty (Anderson et al. 2017).

We defined a stock as taxa occurring within a major fishing area, and consequently, we ran the catch-MSY model using yearly catch data aggregated to FAO region from 1950 to the most current year. We chose this definition because many fish populations straddle the boundaries of EEZs. Any aggregation method will be biased in some way, but populations with the largest catches are most often straddling stocks, so a bias in assessments due to erroneous aggregation of catch could occur more often with cosmopolitan species that include small, sedentary (i.e., patchily distributed) populations that are less likely to play a dominant role in a country’s fisheries. The catch-MSY model was applied only to stocks identified to the species level.

The catch-MSY method is based on the same assumptions used in many stock assessment models (Schaefer 1954), namely that the change in a population’s biomass depends on its biomass in the previous year and two population-specific parameters: the carrying capacity (\({K}\)) and rate of population increase (\({r}\)). The method estimates the status of a given population using landings time-series as proxies for biomass removals from the population, and using empirically derived relationships of relative peak to current catch values to estimate depletion at the end of the time series (Martell & Froese 2013). Then, a sampling procedure is used to estimate the distribution of values of \({r}\) and \({K}\) that are compatible with the estimated current depletion levels, and are constrained within the range that maintains viable population abundance and at the same time does not exceed the population’s carrying capacity. In the original formulation of Martell & Froese (2013) the geometric mean \({r}\) and \({K}\) were used to derive an estimate of MSY. Rosenberg et al. (2014) modified this method by producing a biomass time series for each of the viable \({r-K}\) pairs using the surplus production model. The arithmetic mean biomass time series was selected and the current year stock abundance (\({B}\)) relative to the abundance that achieves \({MSY}\) (\(B_{MSY}\)) produced a measure, \(B/B_{MSY}\).

A potential issue of the catch-MSY method (when using the default “constrained” prior) is that declining catch is assumed to indicate declining population biomass (resulting in lower \(B/B_{MSY}\) values) rather than reduced effort or improved management. When declining catch is known to be due to reduced effort and/or improved management this results in artificially low \(B/B_{MSY}\) values; however, the catch-MSY model can be modified by using a “uniform” prior distribution for the final biomass. However, this adjustment should be considered carefully because the model will assume that all stocks with declining catch are rebuilding (resulting in higher \(B/B_{MSY}\) values), which is unrealistic. Previously, for the 2015 assessment, we attempted to use the constrained vs. uniform prior for each stock based on the catch weighted fishery management scores of the regions catching the stock. However, recent analyses suggest this method did not improve the ability of the catch-MSY derived \(B/B_{MSY}\) values to predict RAM values, suggesting we were adding additional complication that did not improve our model. Consequently, all analyses are done using the “constrained” prior.

Weights for stock status scores

To get the data needed to weight the stock status scores, we sum the total catch for each taxa within each region’s EEZ to get the total catch in tonnes for each year. We then average each taxa’s catch within each region across all years from 1980 to the most current year’s data. Consequently, for a taxa within a region, the average catch value is the same across all years (only the \(B/B_{MSY}\) value will vary across years.). This provides an estimate of the mean potential contribution of each species to total food provision, independent of yearly stochastic fluctuations of the population and possible recent declines.

Goal model calculations

The status of wild caught fisheries, \(x_{fis}\), for each reporting region in each year was calculated as the geometric mean of the stock status scores, \(SS\) (derived from \(B/B_{MSY}\) score for each stock, described below) and weighted by the stock’s relative contribution to overall catch, \(C\), such that:

\[ x_{fis} = \prod _{ i=1 }^{ n }{ { SS }_{ i }^{ (\frac { { C }_{ i } }{ \sum { { C }_{ i } } } ) } }, (Eq. 5.9) \]

where \({i}\) is an individual taxon and \(n\) is the total number of taxa in the reported catch for each region throughout the time-series, and \({C}\) is the average catch, since the first non null record, for each taxon within each region.

We used a geometric weighted mean to account for the portfolio effect of exploiting a diverse suite of resources, such that small stocks that are doing poorly will have a stronger influence on the overall score than they would using an arithmetic weighted mean, even though their \({C}\) contributes relatively little to the overall tonnage of harvested seafood within a given region. The behavior of the geometric mean is such that improving a well-performing stock is not rewarded as much as improving one that is doing poorly. We believe this behavior is desirable because the recovery of stocks in poor condition requires more effort and can have more important effects on the system than making a species that is already abundant even more abundant. In this way, the score is not solely driven by absolute tonnes of fish produced and accounts for preserving the health of a diversity of species.

\(B/B_{MSY}\) values were used to derive stock status scores, \({SS}\), such that the best score is achieved for stocks at \(B/B_{MSY} = 1\%\), with a 5% error buffer, and it decreases as the distance of \(B\) from \(B_{MSY}\) increases, due to under- or over-exploitation (Figure 5.2). For each species reported, within each major fishing area, the stock status score was calculated as:

where, for \(B/B_{MSY} < 1\) (with a 5% buffer), status declines with direct proportionality to the decline of \(\beta\) with respect to \(B_{MSY}\), while for \(B/B_{MSY} > 1\) (with a 5% buffer), status declines at rate \(\alpha\), where \(\alpha = 0.5\), so that as the distance of \(\beta\) from \(B_{MSY}\) increases, status is penalized by half of that distance. For \(B/B_{MSY} > 1.05\), \(\beta\) is the minimum score a stock can get, and was set at \(\beta = 0.25\). The \(\alpha\) value ensures that the penalty for under-harvested stocks is half of that for over-harvested stocks (\(\alpha = 1.0\) would assign equal penalty). The \(\beta\) value ensures stocks with \(B/B_{MSY} > 1.4\) due to, for example, an exceptionally productive year, are not unduly penalized, and also recognizes that it is much easier to improve the goal score when stocks are under-harvested (i.e., increase fishing pressure) than it is when populations are over-harvested and need to be rebuilt. Both parameters \(\alpha\) and \(\beta\) were chosen arbitrarily because there is no established convention for this particular approach. Thus, consistent with previous work (Halpern et al. 2012), countries are rewarded for having wild stocks at the biomass that can sustainably deliver the maximum sustainable yield, +/-5% to allow for measurement error, and are penalized for both over- or under-harvesting.

For the 2016 assessment, we did not apply underharvest penalties to the following stocks: Katsuwonus pelamis (FAO region 71), Clupea harengus (FAO region 27), Trachurus capensis (FAO region 47), Sardinella aurita (FAO region 34), Scomberomorus cavalla (FAO region 31). These stocks are fished in multiple regions and the current model formulation ends up penalizing the regions that have the highest proportion of catch of these stocks, which is opposite of what we would like to do. This suggests that our approach to penalizing underharvested stocks could be improved at the model level.

Figure 5.2: Conversion of \(B/B_{MSY}\) to stock status, \(SS\) score.

We needed to gapfill missing status, \(SS\), scores for a large proportion of the catch. Gapfilling was necessary because we could only estimate \(B/B_{MSY}\) values for taxa identified to the species level. Furthermore, we were unable to estimate \(B/B_{MSY}\) values for some species due to model non-convergence or too few years of catch data. Missing status scores were gapfilled using the median status scores of the stocks sharing a region and year, the median value was then adjusted using a taxonomic reporting penalty (Table 5.7). For catch not reported to the species level, a penalty was applied for increasingly coarser taxonomic reporting, as this is considered a sign of minimal monitoring and management. We based the penalty on the ISSCAAP convention for taxon codes (http://www.fao.org/fishery/collection/asfis/en), which defines 6 levels of taxonomic aggregation, from 6 (species) to 1 (order or higher). When \({g}<6\), a penalized gapfilled value for status was estimated for the taxa in each region:

Table 5.7: Penalty applied to gapfilled stock status scores The penalty is multiplied by the gapfilled stock status score to obtain the final stock status score.

Model limitations

This model is based on single-species assessments of stock status and thus cannot predict the effect of multi-species interactions. This model adopts \(B=B_{MSY}\) as a single-species reference point, which by various assessment frameworks is considered very conservative (e.g., Froese et al. (2011)), and the fact that the single-species values are aggregated using a geometric mean ensures that some multi-species effects may influence the scores. Nonetheless, a better understanding of the emerging effects of fishing various species at their reference levels would be desirable and will hopefully be possible in the future.

Despite the fact that invertebrates represent a large proportion of global caught biomass, and represent the dominant stocks in many regions, stock assessment approaches for these taxa are poorly developed. The catch-MSY approach was applied to invertebrates even though the model developers only tested it on fish (Martell & Froese 2013). Part of the challenge in broadly testing this approach on organisms other than fish is the lack of a large enough collection of invertebrate assessments to use for validation testing.

This approach captures whether stocks have been historically well managed, but it is worth noting that it does not directly measure current food production.

5.6.1.2 Trend

Trend was calculated as described in section 4.3.1.

5.6.1.3 Data

Status and trend

B/Bmsy estimates (fis_b_bmsy): The ratio of fish population abundance compared to the abundance required to deliver maximum sustainable yield (RAM data and catch-MSY)

Fishery catch data (fis_meancatch): Mean commercial catch for each OHI region (averaged across years)

Pressure

Chemical pollution (po_chemicals): Modeled chemical pollution within EEZ from commercial shipping traffic, ports and harbors, land-based pesticide use (organic pollution), and urban runoff (inorganic pollution)

Genetic escapes (sp_genetic): Introduced mariculture species (Mariculture Sustainability Index) as a proxy for genetic escapes

High bycatch due to artisanal fishing (fp_art_hb): The presence of destructive artisanal blast and poison (cyanide) fishing.

High bycatch due to commercial fishing (fp_com_hb): Modeled destructive commercial fishing practices by 5 gear types and scaled by Net Primary Productivity

Intertidal habitat destruction (hd_intertidal): Coastal population density (25 mi from shore) as a proxy for intertidal habitat destruction

Low bycatch due to artisanal fishing (fp_art_lb): Extent of artisanal fishing (including: artisanal, subsistence, and recreational catch)

Low bycatch due to commercial fishing (fp_com_lb): Modeled destructive commercial fishing practices by 2 gear types and scaled by Net Primary Productivity

Nonindigenous species (sp_alien): Measure of harmful invasive species

Nutrient pollution (po_nutrients): Modeled nutrient pollution within 3nm of coastline based on fertilizer consumption

Subtidal hardbottom habitat destruction (hd_subtidal_hb): High bycatch artisanal fishing practices (blast fishing) as a proxy for subtidal hard bottom habitat destruction

Subtidal soft bottom habitat destruction (hd_subtidal_sb): Demersal destructive commercial fishing practices (i.e., trawling) in soft bottom habitat as a proxy for soft bottom habitat destruction

Weakness of governance (ss_wgi): Inverse of World Governance Indicators (WGI) six combined scores

Weakness of social progress (ss_spi): Inverse of Social Progress Index scores

Resilience

Artisanal fisheries management effectiveness (fp_mora_artisanal): Quality of management of small-scale fishing for artisanal and recreational purposes

Commercial fishing management (fp_mora): Regulations and management of commerical fishing

EEZ protected marine areas (fishing preservation) (fp_mpa_eez): Protected marine areas within EEZ (lasting special places calculation applied to the entire EEZ)

EEZ protected marine areas (habitat preservation) (hd_mpa_eez): Protected marine areas within EEZ (lasting special places calculation applied to the entire EEZ)

Management of habitat to protect fisheries biodiversity (fp_habitat): Survey responses by country to the Convention on Biological Diversity (CBD) Third National Report: habitat related questions

Management of habitat to protect habitat biodiversity (hd_habitat): Survey responses by country to the Convention on Biological Diversity (CBD) Third National Report: habitat related questions

Measure of ecological integrity (species_diversity_eez): Marine species condition (species subgoal status score) as a proxy for ecological integrity

Social Progress Index (res_spi): Social Progress Index scores

Strength of governance (wgi_all): World Governance Indicators (WGI) six combined scores

5.6.2.1 Current status

The status of the mariculture subgoal, \(x_{mar}\), was defined as production of strictly marine taxa from both the marine and brackish water FAO categories, excluding aquatic plants such as kelps and seaweeds, which were assumed to contribute predominantly to medicinal and cosmetic uses rather than as a source of food. The data reported by FAO does not always clearly describe whether harvest is derived through mariculture or from land-based facilities. Wherever possible, we excluded species that could not have been harvested from coastal waters, such as freshwater cyclids. Mariculture status was therefore assessed as the current sustainably-harvested yield, \(Y_{c}\), within each country, such that:

\[ x_{mar}= \frac {Y_c}{Y_{c, ref}}, (Eq. 5.10) \] where, \(Y_{c,ref}\) is the \(Y_c\) value that corresponds to the 95th quantile across all regions and years (including and prior to the year of the assessment year data), and \(Y_c\) is:

\[ Y_{c} = \frac { \displaystyle\sum _{ k=1 }^{ N }{ { Y }_{ k }{ S }_{ k,r } } }{ { P }_{ r } }, (Eq. 5.11) \]

where, \(Y_{k}\) is the 4-year moving window average of tonnes of production (United Nations 2016b) for each \({k}\) mariculture species that is currently or at one time cultured within a country, \(S_{k,r}\) is the sustainability score for each \(k\) mariculture species and region, and \(P_{r}\) is the population within 25 miles of the region’s coast.

Sustainable harvest data is adjusted for coastal population within a country given the assumption that production depends on the presence of coastal communities that can provide the labor force, infrastructures, and economic demand to support the development and economic viability of mariculture facilities. Thus, two regions with an equal number of coastal inhabitants harvesting an equal tonnage of cultured seafood will score the same, as productivity is commensurate to each region’s socio-economic potential to develop mariculture. Stated another way, mariculture development is assumed to scale proportionally with coastal population, which is a proxy for potential logistic limitations to farm development, e.g., presence of infrastructures, coastal access, and locally available workforce.

The reference point was the \(Y_c\) value of the region scoring in 95th percentile across all years of data, with all regions scoring above that value given a status score of 1.0.

The sustainability score, \(S_{k,r}\), for each species in each region is based on the Mariculture Sustainability Index (MSI, (1990)). We used the three sub-indices that directly measured long-term renewability of a given mariculture practice: the wastewater treatment index, the origin of feed index (i.e., fishmeal or other) and the origin of seed (i.e., hatchery or wild caught). These scores are country and species-specific, and we require each species’ yield, \(Y_{k}\), to have a corresponding sustainability score, \(S_{k,r}\). However, if a country farms a species that was not assessed by the MSI for that country, but it was assessed in other countries, a global average score is used for that species and country. If a country farms a species that was not assessed at all by the MSI but a species within the same genus was assessed, a global average for the genus was used. Finally, if these scores were not available for the categories above, we used the global average for broad taxonomic grouping (e.g., crustaceans, algae, bivalves, etc.). 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.

5.6.2.2 Trend

Trend was calculated as described in section 4.3.1.

5.6.2.3 Data

Status and trend

Inland coastal population (mar_coastalpopn_inland25mi): Total coastal population within 25 miles of coast.

Mariculture harvest (mar_harvest_tonnes): Tonnes of mariculture harvest

Mariculture sustainability score (mar_sustainability_score): Mariculture sustainability based on the Mariculture Sustainability Index (MSI)

Pressure

Chemical pollution (po_chemicals): Modeled chemical pollution within EEZ from commercial shipping traffic, ports and harbors, land-based pesticide use (organic pollution), and urban runoff (inorganic pollution)

Coastal nutrient pollution (po_nutrients_3nm): Modeled nutrient pollution within EEZ based on fertilizer consumption

Sea level rise (cc_slr): Sea level rise pressure

Weakness of governance (ss_wgi): Inverse of World Governance Indicators (WGI) six combined scores

Weakness of social progress (ss_spi): Inverse of Social Progress Index scores

Resilience

Management of mariculture to preserve biodiversity (g_mariculture): Survey responses by country to the Convention on Biological Diversity (CBD) Third National Report: mariculture related questions

Management of waters to preserve biodiversity (po_water): Survey responses by country to the Convention on Biological Diversity (CBD) Third National Report: tourism related questions

Mariculture Sustainability Index (g_msi_gov): Mariculture practice assessment criteria from the Mariculture Sustainability Index (MSI)

Social Progress Index (res_spi): Social Progress Index scores

Strength of governance (wgi_all): World Governance Indicators (WGI) six combined scores

5.7.1.1 Current status

The model to estimate the status of the economies sub-goal, \(x_{eco}\), is:

\[ x_{eco} = \frac { \displaystyle\sum _{ k=1 }^{ N }{ { e }_{ c,k } } }{ \displaystyle\sum _{ k=1 }^{ N }{ { e }_{ r,k } } }, (Eq. 5.12) \]

where, \(e\) is the total adjusted revenue generated directly and indirectly from sector \(k\), at current, \(c\), and reference, \(r\), time points.

Because there is no absolute global reference point for revenue (i.e., a target number would be completely arbitrary), the economies subgoal uses a moving baseline as the reference point. Reference revenue is calculated as the value in the current year (or most recent year), relative to the value in a recent moving reference period, defined as 5 years prior to the current year. This reflects an implicit goal of maintaining coastal revenue on short time scales, allowing for decadal or generational shifts in what people want and expect. We allowed for a longer or shorter gap between the current and recent years if a 5 year span was not available from the data, but the gap could not be greater than 10 years. Our preferred gap between years was as follows (in order of preference): 5, 6, 4, 7, 3, 8, 2, 9, 1, and 10 years.

Absolute values for \(e\) in the current and reference periods were lumped across all sectors before calculating reference values (even though the current and reference years will not be exactly the same for all sectors), allowing a decrease in one sector to be balanced by an increase in another sector. As such, we do not track the status of individual sectors and instead always focus on the status of all sectors together.

To control for inflation/deflation, we used a standard dollar year. To account for broader economic forces that may affect revenue independent of changes in ocean health (e.g., a global recession), we adjusted revenue based on a country’s GDP (i.e., must keep pace with growth in GDP). The current and reference years used for GDP data were based on the average current year and average reference year across the sector data sources used for revenue.

5.7.1.2 Trend

Trend was calculated as the slope in the individual sector values (not summed sectors) for revenue over the most recent five years (as opposed to the status, which examines changes between two points in time, current versus five years prior to current), corrected by GDP. We calculated the average for revenue by averaging slopes across sectors weighted by the revenue in each sector.

5.7.1.3 Data

Status and trend

Adjustment factor for revenue (current) (le_rev_cur_adj_value): GDP during the current status year of each sector/region (World Bank)

Adjustment factor for revenue (reference) (le_rev_ref_adj_value): GDP during the reference year (i.e., earliest year of data) of each sector/region (World Bank)

GDP (le_gdp): Gross Domestic Product (GDP)

Revenue adjustment (GDP) (le_revenue_adj): GDP data used to adjust revenue values

Revenue for each sector and year (le_rev_sector_year): Revenue for each sector, region, and year

Revenue, most current value (le_rev_cur_base_value): Revenue data for the most recent year of data (relative to the assessment year) for each sector/region

Revenue, reference value (le_rev_ref_base_value): Revenue data for the earliest year of each sector for each sector/region

Sectors in each region (le_sector_weight): Proportion of jobs within each sector

Pressure

Chemical pollution (po_chemicals): Modeled chemical pollution within EEZ from commercial shipping traffic, ports and harbors, land-based pesticide use (organic pollution), and urban runoff (inorganic pollution)

Coastal nutrient pollution (po_nutrients_3nm): Modeled nutrient pollution within EEZ based on fertilizer consumption

Genetic escapes (sp_genetic): Introduced mariculture species (Mariculture Sustainability Index) as a proxy for genetic escapes

High bycatch due to artisanal fishing (fp_art_hb): The presence of destructive artisanal blast and poison (cyanide) fishing.

High bycatch due to commercial fishing (fp_com_hb): Modeled destructive commercial fishing practices by 5 gear types and scaled by Net Primary Productivity

Intertidal habitat destruction (hd_intertidal): Coastal population density (25 mi from shore) as a proxy for intertidal habitat destruction

Low bycatch due to artisanal fishing (fp_art_lb): Extent of artisanal fishing (including: artisanal, subsistence, and recreational catch)

Low bycatch due to commercial fishing (fp_com_lb): Modeled destructive commercial fishing practices by 2 gear types and scaled by Net Primary Productivity

Marine plastics (po_trash): Global marine plastic pollution

Nonindigenous species (sp_alien): Measure of harmful invasive species

Nutrient pollution (po_nutrients): Modeled nutrient pollution within 3nm of coastline based on fertilizer consumption

Ocean acidification (cc_acid): Ocean acidification pressure scaled using biological thresholds

Pathogen pollution (po_pathogens): Percent of population without access to improved sanitation facilities as a proxy for pathogen pollution

Sea level rise (cc_slr): Sea level rise pressure

Sea surface temperature (cc_sst): Sea surface temperature anomalies

Subtidal hardbottom habitat destruction (hd_subtidal_hb): High bycatch artisanal fishing practices (blast fishing) as a proxy for subtidal hard bottom habitat destruction

Subtidal soft bottom habitat destruction (hd_subtidal_sb): Demersal destructive commercial fishing practices (i.e., trawling) in soft bottom habitat as a proxy for soft bottom habitat destruction

Weakness of governance (ss_wgi): Inverse of World Governance Indicators (WGI) six combined scores

Weakness of social progress (ss_spi): Inverse of Social Progress Index scores

Resilience

Global Competitiveness Index (GCI) scores (li_gci): Competitiveness in achieving sustained economic prosperity

Social Progress Index (res_spi): Social Progress Index scores

Strength of governance (wgi_all): World Governance Indicators (WGI) six combined scores

5.7.2.1 Current status

The status of the livelihoods sub-goal, \(x_{liv}\), is calculated as:

\[ x_{liv} = \frac { \frac { \sum _{ 1 }^{ k }{ { j }_{ c,k } } }{ \sum _{ 1 }^{ k }{ { j }_{ r,k } } } \quad +\quad \frac { \sum _{ 1 }^{ k }{ { w }_{ m,k } } }{ \sum _{ 1 }^{ k }{ { w }_{ r,k } } } }{ 2 }, (Eq. 5.13) \]

where \(j\) is the adjusted number of direct and indirect jobs within sector \(k\) within a region and \(w\) is the average PPP-adjusted wages per job within the sector. Jobs are summed across sectors and measured at current, \(c\), and reference, \(r\), time points. Adjusted wage data are averaged across sectors within each region, \(m\), and the reference country, \(r\), with the highest average wages across all sectors.

Because there is no absolute global reference point for jobs (i.e., a target number would be completely arbitrary), this component of the livelihoods subgoal uses a moving baseline as the reference point. Jobs, \(j\), are calculated as a relative value: the value in the current year (or most recent year), \(c\), relative to the value in a recent moving reference period, \(r\), defined as 5 years prior to \(c\). This reflects an implicit goal of maintaining coastal jobs on short time scales, allowing for decadal or generational shifts in what people want and expect. We allowed for a longer or shorter gap between the current and recent years if a 5 year span was not available from the data, but the gap could not be greater than 10 years. Our preferred gap between years was as follows (in order of preference): 5, 6, 4, 7, 3, 8, 2, 9, 1, and 10 years. For wages, \(w\), we assumed the reference value was the highest value observed across all regions.

Absolute values for \(j\) and \(w\) in the current and reference period (jobs) or region (wages) were lumped across all sectors before calculating relative values (even though the current and reference years will not be exactly the same for all sectors), allowing a decrease in one sector to be balanced by an increase in another sector. As such, we do not track the status of individual sectors and instead always focus on the status of all sectors together. For wages, we use the most current data available for each country and each sector, but only use data from 1990 on, assuming that wages are relatively slow to change over time (apart from inflation adjustments, which we control for by using real dollars) and thus can be compared across sectors and countries without controlling for year.

Wages data were divided by the inflation conversion factor so that wage data across years would be comparable in 2010 US dollars (inflation conversion factors were downloaded from http://oregonstate.edu/cla/polisci/sahr/sahr). These data were also multiplied by the purchasing power parity-adjusted per capita GDP (World Bank 2016). To account for broader economic forces that may affect jobs independent of changes in ocean health (e.g., a global recession), we adjusted jobs data by dividing by percent employment for the corresponding year: (1 – percent unemployment) * total labor force (World Bank 2014a,b). For example, if unemployment increased from the reference to the current period, we would expect the number of marine-related jobs to decrease by a comparable proportion, without causing a lower score for the goal. Therefore, the objective of the goal is actually no loss of jobs and jobs must keep pace with growth in employment rates or sustain losses no greater than national increases in unemployment rates. The current and reference years used for unemployment data were based on the average current year and average reference year across the sector data sources used for number of jobs.

5.7.2.2 Trend

Trend was calculated as the slope in the individual sector values (not summed sectors) for jobs and wages over the most recent five years (as opposed to the status, which examines changes between two points in time, current versus five years prior to current), corrected by national trends in employment rates and average wages. We then calculated the average trend for jobs across all sectors, with the average weighted by the number of jobs in each sector. We calculated the average trend for wages across all sectors. We then averaged the wages and jobs average slopes to get the livelihoods trend.

5.7.2.3 Data

Status and trend

Adjusted workforce size (le_workforcesize_adj): Total labor force (number of people) from the World Bank

Adjustment factor for jobs (current) (le_jobs_cur_adj_value): Percent employment during the current status year for each sector/region

Adjustment factor for jobs (reference) (le_jobs_ref_adj_value): Percent employment during the reference year (i.e., earliest year of data) for each sector/region

Adjustment factor for wages (current) (le_wage_cur_adj_value): Values are 1, no subsequent adjustments were made to the wage data

Adjustment factor for wages (reference) (le_wage_ref_adj_value): Values are 1, no subsequent adjustments were made to the wage data

GDP per capita PPP (le_gdp_pc_ppp): Gross domestic product per person at purchasing power parity

Jobs for each sector and year (le_jobs_sector_year): Number of jobs in each sector, region, and year.

Jobs, most current value (le_jobs_cur_base_value): Number of jobs in each sector and region for the most recent year of data

Jobs, reference value (le_jobs_ref_base_value): Number of jobs in each sector and region for the reference year (earliest year of data for sector/region) of data

Sectors in each region (le_sector_weight): Proportion of jobs within each sector

Total human population (le_popn): Human population of OHI regions

Unemployment (le_unemployment): Unemployment data from the World Bank

Wages for each sector and year (le_wage_sector_year): Wages for each sector, region, and year

Wages, current value (le_wage_cur_base_value): Wage data for the most recent year of data (relative to the assessment year) for each sector/region

Wages, reference value (le_wage_ref_base_value): Wage data for the earliest year of data for each sector/region

Pressure

Chemical pollution (po_chemicals): Modeled chemical pollution within EEZ from commercial shipping traffic, ports and harbors, land-based pesticide use (organic pollution), and urban runoff (inorganic pollution)

Coastal nutrient pollution (po_nutrients_3nm): Modeled nutrient pollution within EEZ based on fertilizer consumption

Genetic escapes (sp_genetic): Introduced mariculture species (Mariculture Sustainability Index) as a proxy for genetic escapes

High bycatch due to artisanal fishing (fp_art_hb): The presence of destructive artisanal blast and poison (cyanide) fishing.

High bycatch due to commercial fishing (fp_com_hb): Modeled destructive commercial fishing practices by 5 gear types and scaled by Net Primary Productivity

Intertidal habitat destruction (hd_intertidal): Coastal population density (25 mi from shore) as a proxy for intertidal habitat destruction

Low bycatch due to artisanal fishing (fp_art_lb): Extent of artisanal fishing (including: artisanal, subsistence, and recreational catch)

Low bycatch due to commercial fishing (fp_com_lb): Modeled destructive commercial fishing practices by 2 gear types and scaled by Net Primary Productivity

Marine plastics (po_trash): Global marine plastic pollution

Nonindigenous species (sp_alien): Measure of harmful invasive species

Nutrient pollution (po_nutrients): Modeled nutrient pollution within 3nm of coastline based on fertilizer consumption

Pathogen pollution (po_pathogens): Percent of population without access to improved sanitation facilities as a proxy for pathogen pollution

Sea level rise (cc_slr): Sea level rise pressure

Subtidal hardbottom habitat destruction (hd_subtidal_hb): High bycatch artisanal fishing practices (blast fishing) as a proxy for subtidal hard bottom habitat destruction

Subtidal soft bottom habitat destruction (hd_subtidal_sb): Demersal destructive commercial fishing practices (i.e., trawling) in soft bottom habitat as a proxy for soft bottom habitat destruction

Weakness of governance (ss_wgi): Inverse of World Governance Indicators (WGI) six combined scores

Weakness of social progress (ss_spi): Inverse of Social Progress Index scores

Resilience

Economic diversity (li_sector_evenness): Sector evenness based on Shannon’s Diversity Index calculated on the proportion of jobs in each sector as a measure of economic diversity

Global Competitiveness Index (GCI) scores (li_gci): Competitiveness in achieving sustained economic prosperity

Social Progress Index (res_spi): Social Progress Index scores

Strength of governance (wgi_all): World Governance Indicators (WGI) six combined scores

5.9.1.1 Current status

The status of this sub-goal, \(x_{ico}\), is the average of status scores of the iconic species in each region based on their IUCN Red List threat categories (IUCN 2016a):

\[ x_{ico} = \frac { \displaystyle\sum_{ i=EX }^{ LC }{ S_{i}\times w_{i} } }{ \displaystyle\sum_{ i=EX }^{ LC }{ S_{i} } }, (Eq. 5.17) \]

where for each IUCN threat category \(i\), \(S_{i}\) is the number of assessed species and \(w_{i}\) is the status (Table 5.3) following the methods described by Butchart et al. (2007). This formulation gives partial credit to species that still exist but are in one of the other threat categories. The reference point is to have the risk status of all assessed species as Least Concern (i.e., a goal score = 1.0). Species that have not been assessed or labeled as data deficient are not included in the calculation.

The list of iconic species was drawn from several (data sources, Section 6, IUCN extinction risk), but primarily from the World Wildlife Fund’s global and regional lists for Priority Species (especially important to people for their health, livelihoods, and/or culture) and Flagship Species (‘charismatic’ and/or well-known). Many lists exist for globally important, threatened, endemic, etc. species, but in all cases it is not clear if or to what extent these species represent culturally iconic species. The World Wildlife Fund is the only data source that included cultural reasons for listing iconic species. Although, iconic species vary largely among regions, we include little regional information in our list (i.e., the same list is applied to nearly all regions). Our ultimate goal is to develop a more region-specific iconic species list.

5.9.1.2 Trend

We calculate trend using data the IUCN provides for current and past assessments of species, which we use to create a time series of risk status for each species. Because IUCN assessments are generally infrequent for any given species, we derive the trend as the annual change in risk status for each species across the previous ten years, rather than a five-year window typical of other goals.

5.9.1.3 Data

Status and trend

IUCN extinction risk (ico_spp_iucn_status): IUCN extinction risk category for iconic species located within each region

Pressure

Coastal chemical pollution (po_chemicals_3nm): 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)

Coastal nutrient pollution (po_nutrients_3nm): Modeled nutrient pollution within EEZ based on fertilizer consumption

High bycatch due to artisanal fishing (fp_art_hb): The presence of destructive artisanal blast and poison (cyanide) fishing.

High bycatch due to commercial fishing (fp_com_hb): Modeled destructive commercial fishing practices by 5 gear types and scaled by Net Primary Productivity

Intertidal habitat destruction (hd_intertidal): Coastal population density (25 mi from shore) as a proxy for intertidal habitat destruction

Marine plastics (po_trash): Global marine plastic pollution

Nonindigenous species (sp_alien): Measure of harmful invasive species

Ocean acidification (cc_acid): Ocean acidification pressure scaled using biological thresholds

Sea surface temperature (cc_sst): Sea surface temperature anomalies

Subtidal hardbottom habitat destruction (hd_subtidal_hb): High bycatch artisanal fishing practices (blast fishing) as a proxy for subtidal hard bottom habitat destruction

Targeted harvest of cetaceans and marine turtles (fp_targetharvest): Targeted harvest of cetaceans and marine turtles

Weakness of governance (ss_wgi): Inverse of World Governance Indicators (WGI) six combined scores

Weakness of social progress (ss_spi): Inverse of Social Progress Index scores

Resilience

Artisanal fisheries management effectiveness (fp_mora_artisanal): Quality of management of small-scale fishing for artisanal and recreational purposes

CITES signatories (g_cites): Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) signatories

Commercial fishing management (fp_mora): Regulations and management of commerical fishing

EEZ protected marine areas (fishing preservation) (fp_mpa_eez): Protected marine areas within EEZ (lasting special places calculation applied to the entire EEZ)

EEZ protected marine areas (habitat preservation) (hd_mpa_eez): Protected marine areas within EEZ (lasting special places calculation applied to the entire EEZ)

Management of habitat to protect fisheries biodiversity (fp_habitat): Survey responses by country to the Convention on Biological Diversity (CBD) Third National Report: habitat related questions

Management of habitat to protect habitat biodiversity (hd_habitat): Survey responses by country to the Convention on Biological Diversity (CBD) Third National Report: habitat related questions

Management of waters to preserve biodiversity (po_water): Survey responses by country to the Convention on Biological Diversity (CBD) Third National Report: tourism related questions

Measure of ecological integrity (species_diversity_eez): Marine species condition (species subgoal status score) as a proxy for ecological integrity

Social Progress Index (res_spi): Social Progress Index scores

Strength of governance (wgi_all): World Governance Indicators (WGI) six combined scores

5.9.2.1 Current status

We calculate the status of this goal as:

\[ x_{lsp} = \frac { \left( \frac{\%_{CMPA}}{\%_{Ref_{CMPA}}} + \frac {\%_{CP}}{\%_{Ref_{CP}}} \right) }{ 2 }, (Eq. 5.18) \]

where, \(\%_{CMPA}\) is the proportion of coastal marine protected area, \(\%_{CP}\) is the proportion of coastline protected, and \(\%_{Ref} = 30%\) for both measures.

We focus only on coastal waters (within 3 nautical miles of shore) for marine special places because we assume lasting special places are primarily in coastal areas. For coastlines, we focus only on the first 1-km-wide strip of land as a way to increase the likelihood that the area being protected by terrestrial parks is connected to the marine system in some way.

We use the United Nation’s World Database on Protected Areas (WDPA) to identify protected areas (UNEP-WCMC 2015). The WDPA aggregates several key databases: IUCN’s World Commission on Protected Areas, Global Marine Protected Areas, UNESCO World Heritage Marine sites, National Parks and Nature Reserves, and the United Nations List of Protected Places. In most cases the year of designation is listed for each protected area.

5.9.2.2 Trend

Trend was calculated as described in section 4.3.1.

5.9.2.3 Data

Status and trend

Inland area (rgn_area_inland1km): Inland area of OHI regions within 1km of shoreline

Inland coastal protected areas (lsp_prot_area_inland1km): Protected areas located 1 km inland

Offshore area (rgn_area_offshore3nm): Offshore area of OHI regions within 3nm of shoreline

Offshore coastal protected areas (lsp_prot_area_offshore3nm): Protected areas located 3nm offshore

Pressure

Coastal chemical pollution (po_chemicals_3nm): 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)

Coastal nutrient pollution (po_nutrients_3nm): Modeled nutrient pollution within EEZ based on fertilizer consumption

Intertidal habitat destruction (hd_intertidal): Coastal population density (25 mi from shore) as a proxy for intertidal habitat destruction

Marine plastics (po_trash): Global marine plastic pollution

Nonindigenous species (sp_alien): Measure of harmful invasive species

Sea level rise (cc_slr): Sea level rise pressure

Subtidal hardbottom habitat destruction (hd_subtidal_hb): High bycatch artisanal fishing practices (blast fishing) as a proxy for subtidal hard bottom habitat destruction

Weakness of governance (ss_wgi): Inverse of World Governance Indicators (WGI) six combined scores

Weakness of social progress (ss_spi): Inverse of Social Progress Index scores

Resilience

Management of habitat to protect habitat biodiversity (hd_habitat): Survey responses by country to the Convention on Biological Diversity (CBD) Third National Report: habitat related questions

Management of waters to preserve biodiversity (po_water): Survey responses by country to the Convention on Biological Diversity (CBD) Third National Report: tourism related questions

Social Progress Index (res_spi): Social Progress Index scores

Strength of governance (wgi_all): World Governance Indicators (WGI) six combined scores