From 9eca4f77be90ee4a5ebc54967695271fe3a2ec3d Mon Sep 17 00:00:00 2001 From: annaramji Date: Tue, 25 Jun 2024 15:51:18 -0700 Subject: [PATCH] big updates: added reference sections (and to TOC for each page), updated banner for Home page, worked through other issues with Sophia. Big favicon changes. --- OHI-score-anatomy.qmd | 19 +- _quarto.yml | 2 +- data-inclusion-gaps.qmd | 1 + data-layers/data-layer-descriptions.qmd | 17 +- docs/OHI-score-anatomy.html | 22 +- docs/OHI-theory.html | 2 +- docs/about.html | 2 +- docs/data-inclusion-gaps.html | 39 +- docs/data-layers/data-layer-descriptions.html | 18 +- docs/goals/goal-models-data.html | 356 ++++++++++++++---- docs/index.html | 52 ++- docs/media/favicon-ohi-test.png | Bin 0 -> 5993 bytes docs/media/favicon-option.png | Bin 55602 -> 0 bytes docs/media/sample-banner.jpg | Bin 0 -> 792311 bytes docs/models/models.html | 48 ++- .../figure-html/unnamed-chunk-1-1.png | Bin 650844 -> 654266 bytes .../figure-html/unnamed-chunk-2-1.png | Bin 488673 -> 490755 bytes docs/regions.html | 40 +- docs/search.json | 45 ++- docs/site_libs/bootstrap/bootstrap.min.css | 2 +- goals/goal-models-data.qmd | 4 + index.qmd | 29 +- media/favicon-ohi copy.png | Bin 0 -> 131660 bytes media/favicon-ohi-test copy.png | Bin 0 -> 40599 bytes media/favicon-ohi-test.png | Bin 0 -> 175779 bytes media/favicon-ohi-wider.png | Bin 0 -> 140522 bytes media/favicon-ohi.png | Bin 0 -> 131660 bytes media/favicon-option-test.png | Bin 0 -> 49458 bytes media/favicon-option.png | Bin 55602 -> 0 bytes media/sample-banner.jpg | Bin 0 -> 792311 bytes models/models.qmd | 4 + ohi-methods.Rproj | 2 + references.bib | 12 + regions.qmd | 1 + 34 files changed, 552 insertions(+), 165 deletions(-) create mode 100644 docs/media/favicon-ohi-test.png delete mode 100644 docs/media/favicon-option.png create mode 100644 docs/media/sample-banner.jpg create mode 100644 media/favicon-ohi copy.png create mode 100644 media/favicon-ohi-test copy.png create mode 100644 media/favicon-ohi-test.png create mode 100644 media/favicon-ohi-wider.png create mode 100644 media/favicon-ohi.png create mode 100644 media/favicon-option-test.png delete mode 100644 media/favicon-option.png create mode 100644 media/sample-banner.jpg diff --git a/OHI-score-anatomy.qmd b/OHI-score-anatomy.qmd index 00b678c..2dea276 100644 --- a/OHI-score-anatomy.qmd +++ b/OHI-score-anatomy.qmd @@ -1,11 +1,14 @@ --- title: "Brief Anatomy of OHI Scores" +bibliography: references.bib editor_options: chunk_output_type: inline --- We define ocean health as the sustainable delivery of ten widely-held public goals for ocean ecosystems (Table 2.1). These goals represent the full suite of benefits that people want and need from the ocean, including the traditional ‘goods and services’ people often consider (e.g., fish to eat, coastal protection from nearshore habitats) as well as benefits less commonly accounted for, such as cultural values and biodiversity. Within each region, scores, ranging from 0 to 100, are calculated for the 10 goals (section 5.2). Four of the goals are calculated from 2 subgoals. The subgoals are calculated independently (i.e., they are treated as if they are goals) and then combined into the goal status score (Table 2.2). +
+ ## 10 Goals of OHI **Table 2.1. The 10 goals of the Ocean Health Index** @@ -50,26 +53,14 @@ Goal (and subgoal scores) are calculated using several variables (referred to as **Table 2.3. Dimension used to calculate an OHI goal score** Goal scores are the average of current and likely future status. Likely future status adjusts current status scores based on pressures and resilience variables acting on the goal as well as recent trends in status. | Dimension | Subdimension | Description | More information | Calculating | -|--------------|--------------|----------------|--------------|--------------| +|---------------|---------------|---------------|---------------|---------------| | Current status | \- | Current state of the goal relative to the desired "reference point". Values range from 0-100. | *Section 6. Goal models and data* | Calculated using functions in ohi-global repo: https://github.com/OHI-Science/ohi-global/blob/draft/eez/conf/functions.R and the *scenario_data_years.csv* file (in same folder) | | Predicted future status | Resilience | Variables such as good governance and ecological factors that provide resilience to pressures, and thus, are likely to improve future status. Values range from 0-100 | *Section 5.3 Likely future status dimensions* | Calculated using functions in ohicore package.And, files: *resilience_categories.csv* and *resilience_matrix.csv* located here: https://github.com/OHI-Science/ohi-global/tree/draft/eez/conf | | Predicted future status | Pressure | Pressures stress the system and threaten future delivery of benefits, and thus, are likely to reduce future status. Values range from 0-100 | *Section 5.3 Likely future status dimensions* | Calculated using function in ohicore package. And, files: *pressure_categories.csv* and *pressures_matrix.csv*, located here: https://github.com/OHI-Science/ohi-global/tree/draft/eez/conf | | Predicted future status | Trend | Average yearly change in status (typically estimated using most recent 5 years of data) multiplied by 5 to esimate five years into the future. Units are proportional change (absolute change/year is divided by the value of the earliest year) and range from -1 to 1 | *Section 5.3 Likely future status dimensions* | Calculated using functions from ohi-global repo: https://github.com/OHI-Science/ohi-global/blob/draft/eez/conf/functions.R and the scenario_data_years.csv file (in same folder) | -![**Figure 2.1. Relationship between OHI dimensions and scores**. This figure describes how the dimensions come together to calculate a goal score, and represents equations 4.3 and 4.5.](media/figs/OHI dimensions.png){fig-align="center"} +![**Figure 2.1. Relationship between OHI dimensions and scores**. This figure describes how the dimensions come together to calculate a goal score, and represents equations 4.3 and 4.5.](media/figs/OHI%20dimensions.png){fig-align="center"} Finally, an overall Index score for each region is calculated by averaging the goal scores (Figure 2.2). ![**Figure 2.2. Example flowerplot of goal scores for a region.** Goal and subgoal scores for Canada. The middle value is the regional Index score, and is calculated by averaging the goal scores.](media/figs/flower_Canada_alt.png) - -# Data inclusion and data gaps - -Ideally, regional and local assessments should use the best available data, but this decision limits the ability to compare across scales. For direct comparisons among locations to be valid, they must use consistent data. For this reason, we focused on using global datasets so differences in Index scores across regions are driven by differences in ocean health rather than variation in the data. Although, in reality, many global datasets are compilations of local or regional datasets and their quality varies spatially. In some cases, data for a particular component or dimension of a goal were available for most, but not all, countries. Gaps in these data were known to not be true zero values. Rather than exclude these data layers, we employed several different methods to fill these data gaps [@frazier2016mapping]. - -These guidelines both motivated and constrained our methods. The development of the model frameworks for each goal (including reference points) was heavily dictated by the availability of global datasets. And, ultimately, several key elements related to ocean health could not be included due to lack of existing or appropriate global datasets. As new and better data become available in the future, details of how goals or dimensions are modeled will likely change, although the framework we have developed can accommodate these changes. - -For Index scores to be comparable, every region must have a value for each data layer included in the analysis, unless it is known to not be relevant to a region. In other words, missing data are not acceptable [@burgass2017navigating]. Adhering to this criterion is critical to avoid influencing the Index score simply because of inclusion (or absence) of a particular data layer for any reporting region. - -Gaps in data are common; many developing countries lack the resources to gather detailed datasets, and even developed, data-rich countries have inevitable data gaps. We use a variety of methods to estimate missing data, including: averages of closely related groups (e.g., regions sharing ecological, spatial, political attributes; taxonomic groups; etc.), spatial or temporal interpolation (e.g., raster or time-series data), and predictive models (e.g., regression analysis, machine learning, etc.). Gapfilling is a major source of uncertainty, especially for certain goals and regions. Given how common gaps in data are, clear documentation of gapfilling is a critical step of index development because it provides a measure of the reliability of index scores. - -One of the ongoing goals of the Ocean Health Index (OHI) has been to improve our approach to dealing with missing data, by quantifying the potential influence of gapfilled data on index scores, and developing effective methods of tracking, quantifying, and communicating this information [@frazier2016mapping]. diff --git a/_quarto.yml b/_quarto.yml index 1a4dbe7..12a3114 100644 --- a/_quarto.yml +++ b/_quarto.yml @@ -9,7 +9,7 @@ project: website: - favicon: media/favicon-option.png + favicon: media/favicon-ohi-test.png sidebar: title: "OHI Methods 2024" logo: media/OHI_Logo_Blue.png diff --git a/data-inclusion-gaps.qmd b/data-inclusion-gaps.qmd index bad7942..6111dbe 100644 --- a/data-inclusion-gaps.qmd +++ b/data-inclusion-gaps.qmd @@ -1,5 +1,6 @@ --- title: "Data Inclusion & Gaps" +bibliography: references.bib --- Ideally, regional and local assessments should use the best available data, but this decision limits the ability to compare across scales. For direct comparisons among locations to be valid, they must use consistent data. For this reason, we focused on using global datasets so differences in Index scores across regions are driven by differences in ocean health rather than variation in the data. Although, in reality, many global datasets are compilations of local or regional datasets and their quality varies spatially. In some cases, data for a particular component or dimension of a goal were available for most, but not all, countries. Gaps in these data were known to not be true zero values. Rather than exclude these data layers, we employed several different methods to fill these data gaps [@frazier2016mapping]. diff --git a/data-layers/data-layer-descriptions.qmd b/data-layers/data-layer-descriptions.qmd index 3c9cdc4..0883352 100644 --- a/data-layers/data-layer-descriptions.qmd +++ b/data-layers/data-layer-descriptions.qmd @@ -1,8 +1,9 @@ --- title: "Description of Data Layers" +bibliography: ../references.bib toc: true callout-icon: false -callout-appearance: simple +callout-appearance: minimal --- ### Tables describing data layers (Table 7.1) and sources (Table 7.2) @@ -118,3 +119,17 @@ kable(ds_source) %>% + +Notes for continuing this work: + +- consider making this into 2 sections with the first 2 tables in the first section and the veeeeery long github user content (all layers Rmd) as the second section (it's extremely long... like super long and could be frustrating for users). + +- look into formatting of long tables in Quarto websites (currently annoying to scroll to the side to read the full width of the long table -- see Table 7.1) -- floating/hover (not anchored to the bottom) scroll bar (horizontal scroll bar) + +- make a section of which data layers apply to which goals -- like Table 7.1, maybe just edit it so Dimension (goal/subgoal) is before description? + +- consider renaming tables (current names not awesome) + +- try reading in the githubuser content (even though it's super long, could be good to just see what it looks like...) + +## References diff --git a/docs/OHI-score-anatomy.html b/docs/OHI-score-anatomy.html index 5ff358d..4f2c661 100644 --- a/docs/OHI-score-anatomy.html +++ b/docs/OHI-score-anatomy.html @@ -30,7 +30,7 @@ - + @@ -176,7 +176,6 @@

On this page

  • 10 Goals of OHI
  • Subgoals
  • Dimensions
    • -
  • Data inclusion and data gaps
    • @@ -201,6 +200,7 @@

    Brief Anatomy of OHI Scores

    We define ocean health as the sustainable delivery of ten widely-held public goals for ocean ecosystems (Table 2.1). These goals represent the full suite of benefits that people want and need from the ocean, including the traditional ‘goods and services’ people often consider (e.g., fish to eat, coastal protection from nearshore habitats) as well as benefits less commonly accounted for, such as cultural values and biodiversity. Within each region, scores, ranging from 0 to 100, are calculated for the 10 goals (section 5.2). Four of the goals are calculated from 2 subgoals. The subgoals are calculated independently (i.e., they are treated as if they are goals) and then combined into the goal status score (Table 2.2).

    +


    10 Goals of OHI

    Table 2.1. The 10 goals of the Ocean Health Index

    @@ -349,11 +349,11 @@

    Dimensions

    Table 2.3. Dimension used to calculate an OHI goal score Goal scores are the average of current and likely future status. Likely future status adjusts current status scores based on pressures and resilience variables acting on the goal as well as recent trends in status.

    -----+++++ @@ -408,14 +408,6 @@

    Dimensions

    Figure 2.2. Example flowerplot of goal scores for a region. Goal and subgoal scores for Canada. The middle value is the regional Index score, and is calculated by averaging the goal scores.
    - -
    -

    Data inclusion and data gaps

    -

    Ideally, regional and local assessments should use the best available data, but this decision limits the ability to compare across scales. For direct comparisons among locations to be valid, they must use consistent data. For this reason, we focused on using global datasets so differences in Index scores across regions are driven by differences in ocean health rather than variation in the data. Although, in reality, many global datasets are compilations of local or regional datasets and their quality varies spatially. In some cases, data for a particular component or dimension of a goal were available for most, but not all, countries. Gaps in these data were known to not be true zero values. Rather than exclude these data layers, we employed several different methods to fill these data gaps [@frazier2016mapping].

    -

    These guidelines both motivated and constrained our methods. The development of the model frameworks for each goal (including reference points) was heavily dictated by the availability of global datasets. And, ultimately, several key elements related to ocean health could not be included due to lack of existing or appropriate global datasets. As new and better data become available in the future, details of how goals or dimensions are modeled will likely change, although the framework we have developed can accommodate these changes.

    -

    For Index scores to be comparable, every region must have a value for each data layer included in the analysis, unless it is known to not be relevant to a region. In other words, missing data are not acceptable [@burgass2017navigating]. Adhering to this criterion is critical to avoid influencing the Index score simply because of inclusion (or absence) of a particular data layer for any reporting region.

    -

    Gaps in data are common; many developing countries lack the resources to gather detailed datasets, and even developed, data-rich countries have inevitable data gaps. We use a variety of methods to estimate missing data, including: averages of closely related groups (e.g., regions sharing ecological, spatial, political attributes; taxonomic groups; etc.), spatial or temporal interpolation (e.g., raster or time-series data), and predictive models (e.g., regression analysis, machine learning, etc.). Gapfilling is a major source of uncertainty, especially for certain goals and regions. Given how common gaps in data are, clear documentation of gapfilling is a critical step of index development because it provides a measure of the reliability of index scores.

    -

    One of the ongoing goals of the Ocean Health Index (OHI) has been to improve our approach to dealing with missing data, by quantifying the potential influence of gapfilled data on index scores, and developing effective methods of tracking, quantifying, and communicating this information [@frazier2016mapping].

    diff --git a/docs/OHI-theory.html b/docs/OHI-theory.html index 924f7b4..048cc7b 100644 --- a/docs/OHI-theory.html +++ b/docs/OHI-theory.html @@ -30,7 +30,7 @@ - + diff --git a/docs/about.html b/docs/about.html index 897a310..01cc867 100644 --- a/docs/about.html +++ b/docs/about.html @@ -30,7 +30,7 @@ - + diff --git a/docs/data-inclusion-gaps.html b/docs/data-inclusion-gaps.html index e2e7117..e9a7151 100644 --- a/docs/data-inclusion-gaps.html +++ b/docs/data-inclusion-gaps.html @@ -20,7 +20,26 @@ margin: 0 0.8em 0.2em -1em; /* quarto-specific, see https://github.com/quarto-dev/quarto-cli/issues/4556 */ vertical-align: middle; } - +/* CSS for citations */ +div.csl-bib-body { } +div.csl-entry { + clear: both; +} +.hanging-indent div.csl-entry { + margin-left:2em; + text-indent:-2em; +} +div.csl-left-margin { + min-width:2em; + float:left; +} +div.csl-right-inline { + margin-left:2em; + padding-left:1em; +} +div.csl-indent { + margin-left: 2em; +} @@ -30,7 +49,7 @@ - + @@ -188,15 +207,23 @@

    Data Inclusion & Gaps

    -

    Ideally, regional and local assessments should use the best available data, but this decision limits the ability to compare across scales. For direct comparisons among locations to be valid, they must use consistent data. For this reason, we focused on using global datasets so differences in Index scores across regions are driven by differences in ocean health rather than variation in the data. Although, in reality, many global datasets are compilations of local or regional datasets and their quality varies spatially. In some cases, data for a particular component or dimension of a goal were available for most, but not all, countries. Gaps in these data were known to not be true zero values. Rather than exclude these data layers, we employed several different methods to fill these data gaps [@frazier2016mapping].

    +

    Ideally, regional and local assessments should use the best available data, but this decision limits the ability to compare across scales. For direct comparisons among locations to be valid, they must use consistent data. For this reason, we focused on using global datasets so differences in Index scores across regions are driven by differences in ocean health rather than variation in the data. Although, in reality, many global datasets are compilations of local or regional datasets and their quality varies spatially. In some cases, data for a particular component or dimension of a goal were available for most, but not all, countries. Gaps in these data were known to not be true zero values. Rather than exclude these data layers, we employed several different methods to fill these data gaps (Frazier, Longo, and Halpern 2016).

    These guidelines both motivated and constrained our methods. The development of the model frameworks for each goal (including reference points) was heavily dictated by the availability of global datasets. And, ultimately, several key elements related to ocean health could not be included due to lack of existing or appropriate global datasets. As new and better data become available in the future, details of how goals or dimensions are modeled will likely change, although the framework we have developed can accommodate these changes.

    -

    For Index scores to be comparable, every region must have a value for each data layer included in the analysis, unless it is known to not be relevant to a region. In other words, missing data are not acceptable [@burgass2017navigating]. Adhering to this criterion is critical to avoid influencing the Index score simply because of inclusion (or absence) of a particular data layer for any reporting region.

    +

    For Index scores to be comparable, every region must have a value for each data layer included in the analysis, unless it is known to not be relevant to a region. In other words, missing data are not acceptable (Burgass et al. 2017). Adhering to this criterion is critical to avoid influencing the Index score simply because of inclusion (or absence) of a particular data layer for any reporting region.

    Gaps in data are common; many developing countries lack the resources to gather detailed datasets, and even developed, data-rich countries have inevitable data gaps. We use a variety of methods to estimate missing data, including: averages of closely related groups (e.g., regions sharing ecological, spatial, political attributes; taxonomic groups; etc.), spatial or temporal interpolation (e.g., raster or time-series data), and predictive models (e.g., regression analysis, machine learning, etc.). Gapfilling is a major source of uncertainty, especially for certain goals and regions. Given how common gaps in data are, clear documentation of gapfilling is a critical step of index development because it provides a measure of the reliability of index scores.

    -

    One of the ongoing goals of the Ocean Health Index (OHI) has been to improve our approach to dealing with missing data, by quantifying the potential influence of gapfilled data on index scores, and developing effective methods of tracking, quantifying, and communicating this information [@frazier2016mapping].

    +

    One of the ongoing goals of the Ocean Health Index (OHI) has been to improve our approach to dealing with missing data, by quantifying the potential influence of gapfilled data on index scores, and developing effective methods of tracking, quantifying, and communicating this information (Frazier, Longo, and Halpern 2016).

    - + +

    References

    +
    +Burgass, Michael J., Benjamin S. Halpern, Emily Nicholson, and E. J. Milner-Gulland. 2017. “Navigating Uncertainty in Environmental Composite Indicators.” Ecological Indicators 75 (April): 268–78. https://doi.org/10.1016/j.ecolind.2016.12.034. +
    +
    +Frazier, Melanie, Catherine Longo, and Benjamin S. Halpern. 2016. “Mapping Uncertainty Due to Missing Data in the Global Ocean Health Index.” PLOS ONE 11 (8): e0160377. https://doi.org/10.1371/journal.pone.0160377. +
    +
    - + @@ -174,7 +174,10 @@

    On this page

    @@ -1802,8 +1805,19 @@

    RETURN TO THIS!

    +

    Notes for continuing this work:

    + +
    +

    References

    +
    diff --git a/docs/goals/goal-models-data.html b/docs/goals/goal-models-data.html index 2e963c7..3356ae2 100644 --- a/docs/goals/goal-models-data.html +++ b/docs/goals/goal-models-data.html @@ -20,7 +20,26 @@ margin: 0 0.8em 0.2em -1em; /* quarto-specific, see https://github.com/quarto-dev/quarto-cli/issues/4556 */ vertical-align: middle; } - +/* CSS for citations */ +div.csl-bib-body { } +div.csl-entry { + clear: both; +} +.hanging-indent div.csl-entry { + margin-left:2em; + text-indent:-2em; +} +div.csl-left-margin { + min-width:2em; + float:left; +} +div.csl-right-inline { + margin-left:2em; + padding-left:1em; +} +div.csl-indent { + margin-left: 2em; +} @@ -30,7 +49,7 @@ - + @@ -204,6 +223,7 @@

    On this page

  • Lasting special places (subgoal of sense of place)
  • Tourism and recreation
    • +
  • References
    • @@ -237,25 +257,25 @@

    5. Goal Models and Data

    previously {r child = ‘../../metadata_documentation/ohi_model/goal_descriptions/AO_long.Rmd’}

    Artisanal opportunities

    -

    Artisanal fishing, often also called small-scale fishing, provides a critical source of food, nutrition, poverty alleviation and livelihood opportunities for many people around the world, in particular in developing nations [@allison2001livelihoods]. Artisanal fishing refers to fisheries involving households, cooperatives or small firms (as opposed to large, commercial companies) that use relatively small amounts of capital and energy and small fishing vessels (if any), make relatively short fishing trips, and use fish mainly for local consumption or trade. These traits differ from commercial scale fisheries that serve the global fish trade, and commercial and artisanal scale fisheries also differ in how they are valued by many communities around the world.

    -

    Artisanal fisheries contribute over half of the world’s marine and inland fish catch, nearly all of which is used for direct human consumption [@unitednations2010faostat]. They employ over 90 percent of the world’s more than 35 million capture fishers and support another approximate 90 million people employed in jobs associated with fish processing, distribution and marketing [@unitednations2010faostat]. Artisanal fisheries also are distinguished by the role they play in shaping and sustaining human cultures around the world; this role contributes to their distinct value [@mcgoodwin2001understanding]. For this reason, we designate artisanal fishing opportunities as a distinct public goal. In some countries like the U.S.A., artisanal fishing may happen under a commercial license (e.g., a family run lobster boat or individual shellfish harvesting permit), or under a recreational fishing permit (e.g., families fishing with rods for fish to eat); the food provided by these activities should ideally be captured under the food provision goal, whereas the opportunity to pursue artisanal fishing is captured here. The goal is not about recreational fishing for sport, which is captured in food provision (if it provides food) and tourism and recreation.

    -

    The livelihood and household economy provided by fishing are considered part of the coastal livelihoods and economies goal, although similar to food provision from artisanal fishing it is currently impossible to measure on a global scale. Our focus is on the opportunity to conduct this kind of fishing. What is intended by the idea of ‘opportunity’ is the ability to conduct sustainable artisanal-scale fishing when the need is present, rather than the actual amount of catch or household revenue that is generated. Although this may seem nuanced on the value and intent of artisanal fishing, the opportunity to conduct this fishing is clearly of great importance to many people [@mcgoodwin2001understanding]. Status for this goal is a function of need for artisanal fishing opportunities and whether or not the opportunity is permitted and/or encouraged institutionally and done sustainability. This need could potentially be driven by any number of socio-economic factors, but perhaps the simplest and most directly tied to this need is the percent of the population that is below the poverty level. Data on how many people live below the poverty level are not available for many countries. Therefore, we used an analogous proxy that is more complete globally: per capita gross domestic product (pcGDP) adjusted by the purchasing power parity (PPP). This metric translates the average annual income (pcGDP) into its local value (PPP). These data correlate with UN data on the percent of a population living below the $2/day international poverty standard (linear: R2 = 0.61, p <0.001; logarithmic regression: R2 = 0.76, p < 0.001). Because the relationship is a better fit with the ln-linear regression, we ln-transform the PPPpcGDP scores.

    +

    Artisanal fishing, often also called small-scale fishing, provides a critical source of food, nutrition, poverty alleviation and livelihood opportunities for many people around the world, in particular in developing nations (Allison and Ellis 2001). Artisanal fishing refers to fisheries involving households, cooperatives or small firms (as opposed to large, commercial companies) that use relatively small amounts of capital and energy and small fishing vessels (if any), make relatively short fishing trips, and use fish mainly for local consumption or trade. These traits differ from commercial scale fisheries that serve the global fish trade, and commercial and artisanal scale fisheries also differ in how they are valued by many communities around the world.

    +

    Artisanal fisheries contribute over half of the world’s marine and inland fish catch, nearly all of which is used for direct human consumption (Nations 2010). They employ over 90 percent of the world’s more than 35 million capture fishers and support another approximate 90 million people employed in jobs associated with fish processing, distribution and marketing (Nations 2010). Artisanal fisheries also are distinguished by the role they play in shaping and sustaining human cultures around the world; this role contributes to their distinct value (McGoodwin 2001). For this reason, we designate artisanal fishing opportunities as a distinct public goal. In some countries like the U.S.A., artisanal fishing may happen under a commercial license (e.g., a family run lobster boat or individual shellfish harvesting permit), or under a recreational fishing permit (e.g., families fishing with rods for fish to eat); the food provided by these activities should ideally be captured under the food provision goal, whereas the opportunity to pursue artisanal fishing is captured here. The goal is not about recreational fishing for sport, which is captured in food provision (if it provides food) and tourism and recreation.

    +

    The livelihood and household economy provided by fishing are considered part of the coastal livelihoods and economies goal, although similar to food provision from artisanal fishing it is currently impossible to measure on a global scale. Our focus is on the opportunity to conduct this kind of fishing. What is intended by the idea of ‘opportunity’ is the ability to conduct sustainable artisanal-scale fishing when the need is present, rather than the actual amount of catch or household revenue that is generated. Although this may seem nuanced on the value and intent of artisanal fishing, the opportunity to conduct this fishing is clearly of great importance to many people (McGoodwin 2001). Status for this goal is a function of need for artisanal fishing opportunities and whether or not the opportunity is permitted and/or encouraged institutionally and done sustainability. This need could potentially be driven by any number of socio-economic factors, but perhaps the simplest and most directly tied to this need is the percent of the population that is below the poverty level. Data on how many people live below the poverty level are not available for many countries. Therefore, we used an analogous proxy that is more complete globally: per capita gross domestic product (pcGDP) adjusted by the purchasing power parity (PPP). This metric translates the average annual income (pcGDP) into its local value (PPP). These data correlate with UN data on the percent of a population living below the $2/day international poverty standard (linear: R2 = 0.61, p <0.001; logarithmic regression: R2 = 0.76, p < 0.001). Because the relationship is a better fit with the ln-linear regression, we ln-transform the PPPpcGDP scores.

    Current status

    Status for this goal (\(x_{ao}\)) is therefore measured by unmet demand (\(D_u\)), which includes measures of opportunity for artisanal fishing (\(O_{ao}\), defined below) and the sustainability of the methods used (\(S_{ao}\)):

    \[ x_{ao} = (1 – D_u) * S_{ao}, (Eq. 6.1) \]

    -

    where \(S_{ao}\) indicates whether artisanal fishing is done in a sustainable manner. This was approximated using Sea Around Us Project (SAUP) [@pauly2020] global marine fisheries catch data [@pauly2016catch] and B/Bmsy data [@ricard2012examining; @costello_status_2016; @martell2016simple; @thorson_new_2016; @rosenberg2014developing; @costello2016global; @ramlegacystockassessmentdatabase2023] calculated from our fisheries sub-goal (methods can be found in section 6.6.1), and subsetted for artisanal and subsistence stocks notated in the SAUP data. And, \(D_u\) is calculated as:

    +

    where \(S_{ao}\) indicates whether artisanal fishing is done in a sustainable manner. This was approximated using Sea Around Us Project (SAUP) (D. Pauly, Zeller, and Palomareas 2020) global marine fisheries catch data (Daniel Pauly and Zeller 2016) and B/Bmsy data (Ricard et al. 2012; C. Costello et al. 2012; Martell and Froese 2013; Thorson et al. 2013; Rosenberg et al. 2014; Christopher Costello et al. 2016; ramlegacystockassessmentdatabase2023?) calculated from our fisheries sub-goal (methods can be found in section 6.6.1), and subsetted for artisanal and subsistence stocks notated in the SAUP data. And, \(D_u\) is calculated as:

    \[ D_{u} = (1 – PPPpcGDP) * (1 – O_{ao}), (Eq. 6.2) \]

    -

    where, \(PPPpcGDP\) is the ln-transformed, rescaled purchasing power parity adjusted per capita GDP, and \(O_{ao}\) is the access to artisanal-scale fishing determined by the United Nations Sustainable Development Goal (UN SDG) 14.b.1 [@sdg_14_b_1].

    +

    where, \(PPPpcGDP\) is the ln-transformed, rescaled purchasing power parity adjusted per capita GDP, and \(O_{ao}\) is the access to artisanal-scale fishing determined by the United Nations Sustainable Development Goal (UN SDG) 14.b.1 (Food and Agriculture Organization of the United Nations 2023).

    We rescaled the ln-transformed \(PPPpcGDP\) values from 0-1 by dividing by the value corresponding to the 99th quantile across all regions and years from 2005 to 2015 (values > 1 were capped at 1). Developed countries with lower demand for artisanal scale fishing (i.e., low poverty indicated by high PPPpcGDP) score high, regardless of the opportunity made available (since it would not matter to many), and developing countries with high demand and opportunity would also score high.

    -

    To assess the opportunity or ability to meet this demand, \(O_{ao}\), we used data from UN SDG 14.b.1 [@sdg_14_b_1], which scores countries on the institutional measures that support or protect access to artisanal and small-scale fishing. The data come are 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.

    +

    To assess the opportunity or ability to meet this demand, \(O_{ao}\), we used data from UN SDG 14.b.1 (Food and Agriculture Organization of the United Nations 2023), which scores countries on the institutional measures that support or protect access to artisanal and small-scale fishing. The data come are 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.

    The sustainability of artisanal fishing practices was estimated by subsetting for artisanal and subsistence stock B/Bmsy values which were calculated in our fisheries sub-goal (section 6.6.1).

    -

    Several issues and datasets relevant to artisanal fishing opportunities were not included in our calculations for a number of reasons. High unemployment can lead to a greater demand for artisanal fishing opportunities [@cinner2009socioeconomic], but unemployment is not a good measure of potential ‘demand’ for most developing countries since many people not working do not get recorded in unemployment statistics, even though it may be relevant for developed countries. Regardless, it is very difficult to set an arbitrary cut-off for developing versus developed countries, and so there is no clear way to use unemployment data for this goal.

    -

    Table 6.1. Artisanal access. Questions from UN SDG 14.b.1 [@sdg_14_b_1] that were used to evaluate access to artisanal scale fishing.

    +

    Several issues and datasets relevant to artisanal fishing opportunities were not included in our calculations for a number of reasons. High unemployment can lead to a greater demand for artisanal fishing opportunities (Cinner, Daw, and McCLANAHAN 2009), but unemployment is not a good measure of potential ‘demand’ for most developing countries since many people not working do not get recorded in unemployment statistics, even though it may be relevant for developed countries. Regardless, it is very difficult to set an arbitrary cut-off for developing versus developed countries, and so there is no clear way to use unemployment data for this goal.

    +

    Table 6.1. Artisanal access. Questions from UN SDG 14.b.1 (Food and Agriculture Organization of the United Nations 2023) 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?
    @@ -334,12 +354,12 @@

    Data

    Biodiversity

    -

    People value biodiversity in particular for its existence value. The risk of species extinction generates great emotional and moral concern for many people. As such, this goal assesses the conservation status of species based on the best available global data through two sub-goals: species and habitats. Species were assessed because they are what one typically thinks of in relation to biodiversity. Because only a small proportion of marine species worldwide have been mapped and assessed, we also assessed habitats as part of this goal, and considered them a proxy for condition of the broad suite of species that depend on them. For the species sub-goal, we used species risk assessments from the International Union for Conservation of Nature [@iucn2022] for a wide range of taxa to provide a geographic snapshot of how total marine biodiversity is faring, even though it is a very small sub-sample of overall species diversity [@mora2011how]. We calculate each of these subgoals separately and weight them equally when calculating the overall goal score.

    +

    People value biodiversity in particular for its existence value. The risk of species extinction generates great emotional and moral concern for many people. As such, this goal assesses the conservation status of species based on the best available global data through two sub-goals: species and habitats. Species were assessed because they are what one typically thinks of in relation to biodiversity. Because only a small proportion of marine species worldwide have been mapped and assessed, we also assessed habitats as part of this goal, and considered them a proxy for condition of the broad suite of species that depend on them. For the species sub-goal, we used species risk assessments from the International Union for Conservation of Nature (IUCN 2022a) for a wide range of taxa to provide a geographic snapshot of how total marine biodiversity is faring, even though it is a very small sub-sample of overall species diversity (Mora et al. 2011). We calculate each of these subgoals separately and weight them equally when calculating the overall goal score.

    previously {r child = ‘../../metadata_documentation/ohi_model/goal_descriptions/HAB_long.Rmd’}

    Habitat (subgoal of biodiversity)

    The habitat subgoal measures the average condition of marine habitats present in each region that provide critical habitat for a broad range of species (mangroves, coral reefs, seagrass beds, kelp forests, salt marshes, sea ice edge, tidal flats, and subtidal soft bottom). This subgoal is considered a proxy for the condition of the broad suite of marine species.

    -

    Data availability remains a major challenge for species and habitat assessments. We compiled and analyzed the best available data in both cases, but key gaps remain. Although several efforts have been made in recent years to create or compile the data necessary to look at the status and trends of marine habitats, most efforts are still hampered by limited geographical and temporal sampling [@joppa2016filling], although sea ice @digirolamo2022 data is an exception. In addition, most marine habitats have only been monitored since the late 1970s at the earliest, many sites were only sampled over a short period of time, and very few sites were monitored before the late 1990s so establishing reference points was difficult. Salt marshes, kelp forests, and seagrasses were the most data-limited of the habitats included in the analysis.

    +

    Data availability remains a major challenge for species and habitat assessments. We compiled and analyzed the best available data in both cases, but key gaps remain. Although several efforts have been made in recent years to create or compile the data necessary to look at the status and trends of marine habitats, most efforts are still hampered by limited geographical and temporal sampling (Joppa et al. 2016), although sea ice DiGirolamo et al. (2022) data is an exception. In addition, most marine habitats have only been monitored since the late 1970s at the earliest, many sites were only sampled over a short period of time, and very few sites were monitored before the late 1990s so establishing reference points was difficult. Salt marshes, kelp forests, and seagrasses were the most data-limited of the habitats included in the analysis.

    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:

    @@ -403,7 +423,7 @@

    Current status

    - + @@ -414,7 +434,7 @@

    Current status

    Soft bottom Soft-bottom destructive fishing practices relative to area of soft-bottom habitat and rescaled to 0-1 based on relative global valuesHalpern et al. [-@halpern2008global]Halpern et al. (2008) Calculated using 5 most recent years of condition data
    -

    A significant amount of pre-processing of the habitat data was needed to fill data gaps and resolve data quality issues (Section 7). 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 [@bruno_regional_2016]. 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.

    +

    A significant amount of pre-processing of the habitat data was needed to fill data gaps and resolve data quality issues (Section 7). 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 (Bruno and 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.

    Trend

    @@ -480,9 +500,9 @@

    This goal aims to assess the average condition of the marine species within each region based on IUCN status. The target for the species subgoal is to have all species at a risk status of Least Concern.

    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” http://www.iucnredlist.org [@iucn2022; @iucn_spatial_2022]. Seabird distributions data came from Birdlife International http://datazone.birdlife.org [@birdlifeinternationalandhandbookofthebirdsoftheworld2020].

    -

    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_has_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. [-@butchart2007improvements] (Table 6.3). 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 [@schipper2008status; @hoffmann_impact_2010].

    +

    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” http://www.iucnredlist.org (IUCN 2022a, 2022b). Seabird distributions data came from Birdlife International http://datazone.birdlife.org (BirdLife International and Handbook of the Birds of the World 2020).

    +

    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 6.3). 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 } @@ -592,7 +612,7 @@

    Current status

    h = \frac { C_{ c } }{ { C }_{ r } } \]

    where, \(C_c\) is current condition and \(C_r\) is reference condition.

    -

    Table 6.4. Coastal protectiveness ranks Scores range from 1-4, with 4 being the most protective [@tallis2011invest].

    +

    Table 6.4. Coastal protectiveness ranks Scores range from 1-4, with 4 being the most protective (Tallis et al. 2011).

    @@ -627,7 +647,7 @@

    Current status

    -

    The reference area for each habitat is treated as a fixed value; in cases where current area might exceed this reference value (e.g., through restoration), we cap the score at the maximum value (1.0). Although this does not give credit for restoration, data tend to be of poor quality making it difficult to determine true increases, and in general habitat restoration beyond reference values is extremely unlikely. Rank weights for the protective ability of each habitat (\(w_{k}\)) come from previous work [@tallis2011invest].

    +

    The reference area for each habitat is treated as a fixed value; in cases where current area might exceed this reference value (e.g., through restoration), we cap the score at the maximum value (1.0). Although this does not give credit for restoration, data tend to be of poor quality making it difficult to determine true increases, and in general habitat restoration beyond reference values is extremely unlikely. Rank weights for the protective ability of each habitat (\(w_{k}\)) come from previous work (Tallis et al. 2011).

    Trend

    @@ -678,11 +698,11 @@

    Data

    Carbon storage

    -

    The present-day pelagic ocean sink for anthropogenic carbon dioxide, estimated at approximately 2000 TgC yr-1, accounts for about a quarter of total anthropogenic CO2 emissions to the atmosphere and helps mitigate a key driver of global climate change [@lequere2009trends; @sabine2010estimation]. The physical-chemical mechanisms driving the ocean sink are well understood but are not directly amenable to human management. Highly productive coastal wetland ecosystems (e.g., mangroves, salt marshes, seagrass beds) have substantially larger areal carbon burial rates than terrestrial forests, and “Blue Carbon” has been suggested as an alternate, more manageable carbon sequestration approach. The rapid destruction of these coastal habitats may release large amounts of buried carbon back into the ocean-atmosphere system. Donato and colleagues [-@donato_mangroves_2011], for example, estimate that mangrove deforestation generates emissions of 20-120 TgC yr-1. Our focus here, therefore, is on coastal habitats because they are threatened, have large amounts of stored carbon that would rapidly be released with further habitat destruction, have the highest per-area sequestration rates of any habitat on the planet, and are amenable to management, conservation, and restoration efforts. We refer to this goal as carbon storage but intend its meaning to include sequestration.

    -

    We focused on four coastal habitats known to provide meaningful amounts of carbon storage (Table 6.2): mangroves, seagrasses, salt marshes, and tidal flats [@chen_tidal_2022]. For mangroves, we used coastal mangroves that are on land or in river deltas.

    +

    The present-day pelagic ocean sink for anthropogenic carbon dioxide, estimated at approximately 2000 TgC yr-1, accounts for about a quarter of total anthropogenic CO2 emissions to the atmosphere and helps mitigate a key driver of global climate change (Le Quéré et al. 2009; Sabine and Tanhua 2010). The physical-chemical mechanisms driving the ocean sink are well understood but are not directly amenable to human management. Highly productive coastal wetland ecosystems (e.g., mangroves, salt marshes, seagrass beds) have substantially larger areal carbon burial rates than terrestrial forests, and “Blue Carbon” has been suggested as an alternate, more manageable carbon sequestration approach. The rapid destruction of these coastal habitats may release large amounts of buried carbon back into the ocean-atmosphere system. Donato and colleagues (2011), for example, estimate that mangrove deforestation generates emissions of 20-120 TgC yr-1. Our focus here, therefore, is on coastal habitats because they are threatened, have large amounts of stored carbon that would rapidly be released with further habitat destruction, have the highest per-area sequestration rates of any habitat on the planet, and are amenable to management, conservation, and restoration efforts. We refer to this goal as carbon storage but intend its meaning to include sequestration.

    +

    We focused on four coastal habitats known to provide meaningful amounts of carbon storage (Table 6.2): mangroves, seagrasses, salt marshes, and tidal flats (Chen and Lee 2022). For mangroves, we used coastal mangroves that are on land or in river deltas.

    Current status

    -

    We measured the status of carbon storage, \(x_{cs}\), as a function of the carbon storing habitats’ current condition, \(C_{c}\), relative to their reference condition, \(C_{r}\). The habitat condition values were averaged, weighted by the area of each habitat, \(A_{k}\), and a coefficient, \(w_k\), to account for the relative contribution of each habitat type, \(k\), to total carbon storage [@laffoley2009management] (Table 6.5):

    +

    We measured the status of carbon storage, \(x_{cs}\), as a function of the carbon storing habitats’ current condition, \(C_{c}\), relative to their reference condition, \(C_{r}\). The habitat condition values were averaged, weighted by the area of each habitat, \(A_{k}\), and a coefficient, \(w_k\), to account for the relative contribution of each habitat type, \(k\), to total carbon storage (Laffoley and Grimsditch 2009) (Table 6.5):

    \[ x_{cs} = \frac { \displaystyle\sum _{ k=1 }^{ N }{ { (h }_{ k } } \times { w }_{ k }\times { A }_{ k }) }{ \displaystyle\sum _{ k=1 }^{ N }{ { (w }_{ k }\times { A }_{ k }) } }, (Eq. 6.7) \]

    @@ -691,7 +711,7 @@

    Current status

    h = \frac { C_{ c } }{ { C }_{ r } } \]

    We employed several different methods for calculating habitat condition scores depending on the habitat of interest and available data (Table 6.2).

    -

    Table 6.5. Carbon sequestration data Weighting factors based on carbon sequestration rates for habitats used in the carbon storage goal [@chen_tidal_2022].

    +

    Table 6.5. Carbon sequestration data Weighting factors based on carbon sequestration rates for habitats used in the carbon storage goal (Chen and Lee 2022).

    @@ -762,7 +782,7 @@

    Data

    Clean waters

    People value marine waters that are free of pollution and debris for aesthetic and health reasons. Contamination of waters comes from oil spills, chemicals, eutrophication, algal blooms, disease pathogens (e.g., fecal coliform, viruses, and parasites from sewage outflow), floating trash, and mass kills of organisms due to pollution. People are sensitive to these phenomena occurring in areas they access for recreation or other purposes as well as for simply knowing that clean waters exist. This goal scores highest when the contamination level is zero.

    -

    We include four measures of pollution in the clean waters goal: eutrophication (nutrients), chemicals, pathogens and marine debris. This decision was meant to represent a comprehensive list of the contamination categories that are commonly considered in assessments of coastal clean waters [@borja2008overview] and for which we could obtain datasets (Table 6.6). The status of these components is the inverse of their intensity (i.e., high input results in low status score).

    +

    We include four measures of pollution in the clean waters goal: eutrophication (nutrients), chemicals, pathogens and marine debris. This decision was meant to represent a comprehensive list of the contamination categories that are commonly considered in assessments of coastal clean waters (Borja et al. 2008) and for which we could obtain datasets (Table 6.6). The status of these components is the inverse of their intensity (i.e., high input results in low status score).

    Table 6.6. Clean waters goal components

    @@ -780,30 +800,30 @@

    Clean waters

    - + - + - + - - + +
    Eutrophication (nutrient)FAO fertilizer and manure data [@unitednations2022faostata; @unitednations2021faostat; @halpern2022cumulative; @tuholske2021]FAO fertilizer and manure data (United Nations 2022, 2021; Halpern et al. 2022; Tuholske et al. 2021) Standard method (section 5.3.1)
    ChemicalLand-based organic pollution (FAO pesticide data), Land-based inorganic pollution (based on run-off from impermeable surfaces), ocean-based pollution based on commercial shipping and port traffic [@unitednations2022faostata; @unitednations2021faostat; @halpern2008global]Land-based organic pollution (FAO pesticide data), Land-based inorganic pollution (based on run-off from impermeable surfaces), ocean-based pollution based on commercial shipping and port traffic (United Nations 2022, 2021; Halpern et al. 2008) Trend based only on changes in organic pollution, other variables remained the same
    PathogensProportion of population without access to improved sanitation facilities [@who-unicef2023]Proportion of population without access to improved sanitation facilities (who-unicef2023?) Standard method
    Marine debrisPlastic pollution [@eriksen2014plastic]Data on improperly disposed of plastics [@jambeck2015plastic]Plastic pollution (Eriksen et al. 2014)Data on improperly disposed of plastics (Jambeck et al. 2015)
    -

    We used the modeled input of land-based nitrogen input from livestock manure and crop fertilizer application as a proxy for nutrient input following similar methods to @halpern2022cumulative and @tuholske2021. The modeled proxy approach does not allow the distinction between toxic and non-toxic bloom events that can arise from excess nutrient input (often both referred to in the literature as harmful algal blooms, or HABs) or at what nutrient concentration an ecosystem is pushed into a HAB condition (i.e., the threshold value). Local studies may be able to obtain information on such non-linear responses and include it as part of this status measure.

    -

    For the chemical pollution component [@halpern2008global], we used a combination of modeled input of fertilizer input as a proxy for land-based organic pollution, and impermeable surfaces as a proxy for land-based organic pollution, and shipping and port traffic for ocean based pollution. We were not able to assess specific toxic chemicals at the global scale; however regional case studies often will have data available for the quantities and toxicity of a range of chemicals put into watersheds and coastal waters. We also did not have global data for oil spills and so could not include oil pollution, but in future assessments where such data exist it would be included in chemical pollution as well.

    -

    Human-derived pathogens are found in coastal waters primarily from sewage discharge or direct human defecation. Since we did not have access to a global database of in situ measurements of pathogen levels, we used a proxy measure for the status of pathogen pollution, namely the number of people in coastal areas without access to improved sanitation facilities [@who-unicef2023]. The underlying assumption is that locations with a low number of people with access to improved facilities will likely have higher levels of coastal water contamination from human pathogens. To estimate this pathogen intensity, we multiplied the human population within 25 miles of the coast by the percentage of population without access to improved sanitation. This allows countries with low coastal population densities and low access to improved sanitation to score better than high population countries with better access if the absolute number of people without access is lower in the small country.

    -

    The status of trash pollution was estimated using globally-available plastic pollution data [@eriksen2014plastic].

    +

    We used the modeled input of land-based nitrogen input from livestock manure and crop fertilizer application as a proxy for nutrient input following similar methods to Halpern et al. (2022) and Tuholske et al. (2021). The modeled proxy approach does not allow the distinction between toxic and non-toxic bloom events that can arise from excess nutrient input (often both referred to in the literature as harmful algal blooms, or HABs) or at what nutrient concentration an ecosystem is pushed into a HAB condition (i.e., the threshold value). Local studies may be able to obtain information on such non-linear responses and include it as part of this status measure.

    +

    For the chemical pollution component (Halpern et al. 2008), we used a combination of modeled input of fertilizer input as a proxy for land-based organic pollution, and impermeable surfaces as a proxy for land-based organic pollution, and shipping and port traffic for ocean based pollution. We were not able to assess specific toxic chemicals at the global scale; however regional case studies often will have data available for the quantities and toxicity of a range of chemicals put into watersheds and coastal waters. We also did not have global data for oil spills and so could not include oil pollution, but in future assessments where such data exist it would be included in chemical pollution as well.

    +

    Human-derived pathogens are found in coastal waters primarily from sewage discharge or direct human defecation. Since we did not have access to a global database of in situ measurements of pathogen levels, we used a proxy measure for the status of pathogen pollution, namely the number of people in coastal areas without access to improved sanitation facilities (who-unicef2023?). The underlying assumption is that locations with a low number of people with access to improved facilities will likely have higher levels of coastal water contamination from human pathogens. To estimate this pathogen intensity, we multiplied the human population within 25 miles of the coast by the percentage of population without access to improved sanitation. This allows countries with low coastal population densities and low access to improved sanitation to score better than high population countries with better access if the absolute number of people without access is lower in the small country.

    +

    The status of trash pollution was estimated using globally-available plastic pollution data (Eriksen et al. 2014).

    Current status

    The status of this goal, \(x_{cw}\), was calculated as the geometric mean of the four components, such that:

    @@ -811,13 +831,13 @@

    Current status

    x_{cw} = \sqrt [ 4 ]{ a\ast u\ast l\ast d }, (Eq. 6.8) \]

    where \(a\) = 1 - the number of people without access to sanitation, rescaled to the global maximum; \(u\) = 1 – (nutrient input), rescaled at the raster level by the 99th quantile value; \(l\) = 1 – (chemical input), rescaled at the raster level by the 99.99th quantile value; and \(d\) = 1 – (marine debris), rescaled at the raster level by the 99.99th quantile value.

    -

    We used a geometric mean, as is commonly done for water quality indices [@liou2004generalized], because a very bad score for any one subcomponent would pollute the waters sufficiently to make people feel the waters were ‘too dirty’ to enjoy for recreational or aesthetic purposes (e.g., a large oil spill trumps any other measure of pollution). However, in cases where a subcomponent was zero, we added a value of 0.01 (on a scale of 0 to 1) to prevent the overall score from going to zero. Given that there is uncertainty around our pollution estimates, a zero score resulting from one subcomponent seemed too extreme.

    +

    We used a geometric mean, as is commonly done for water quality indices (Liou, Lo, and Wang 2004), because a very bad score for any one subcomponent would pollute the waters sufficiently to make people feel the waters were ‘too dirty’ to enjoy for recreational or aesthetic purposes (e.g., a large oil spill trumps any other measure of pollution). However, in cases where a subcomponent was zero, we added a value of 0.01 (on a scale of 0 to 1) to prevent the overall score from going to zero. Given that there is uncertainty around our pollution estimates, a zero score resulting from one subcomponent seemed too extreme.

    Although clean waters are relevant and important anywhere in the ocean, coastal waters drive this goal both because the problems of pollution are concentrated there and because people predominantly access and care about clean waters in coastal areas. The nearshore area is what people can see and where beach-going, shoreline fishing, and other activities occur. Furthermore, the data for high seas areas is limited and there is little meaningful regulation or governance over the input of pollution into these areas. We therefore calculate this goal only for the first 3 nm of ocean for each country’s EEZ. We chose 3 nm for several reasons, but found the status results to be relatively insensitive to different distances.

    A number of potential components of clean water were not included due to lack of global datasets, including toxic algal blooms, oil spills, turbidity (sediment input), and floating trash. In future applications of the Index where such data are available, they would be included in their appropriate component of clean waters (nutrients, chemicals, and trash, respectively).

    Trend

    -

    Trends in eutrophication, pathogens, and chemical pollution are estimated as described in section 5.3.1. Because only one of the inputs (organic pollution) of the chemical pollution layer includes data over time, the trend only reflects this input. Marine debris trends are estimated using a secondary dataset describing the amount of improperly disposed of plastics within each country [@jambeck2015plastic].

    +

    Trends in eutrophication, pathogens, and chemical pollution are estimated as described in section 5.3.1. Because only one of the inputs (organic pollution) of the chemical pollution layer includes data over time, the trend only reflects this input. Marine debris trends are estimated using a secondary dataset describing the amount of improperly disposed of plastics within each country (Jambeck et al. 2015).

    Data

    @@ -851,7 +871,7 @@

    Food Provision

    previously {r child = ‘../../metadata_documentation/ohi_model/goal_descriptions/FIS_long.Rmd’}

    Fisheries (subgoal of food provision)

    -

    This model aims to assess the amount of wild-caught seafood that can be sustainably harvested with penalties assigned for over-harvesting. As such, one must establish a reference point at which harvest is both maximal and sustainable. We assess food provision from wild caught fisheries by estimating population biomass relative to the biomass that can deliver maximum sustainable yield (\(B/B_{MSY}\)) for each stock. When available, we obtained \(B/B_{MSY}\) values from the RAM Legacy Stock Assessment Database [@ricard2012examining; @ramlegacystockassessmentdatabase2023], which contains stock assessment information for a portion of global fish stocks. When RAM data were not available, we used data-limited approaches that have been developed to estimate \(B/B_{MSY}\) values using globally available catch data [@costello_status_2016; @martell2016simple; @thorson_new_2016; @rosenberg2014developing; @costello2016global]. To calculate the status for each region and year, \(B/B_{MSY}\) values were converted to a stock status score between 0-1 that penalizes over-harvesting. To obtain the overall status for each region, the stock status scores for all the stocks within a region were averaged using a geometric mean weighted by the average catch (tonnes) of each stock using Sea Around Us catch data [@pauly2020].

    +

    This model aims to assess the amount of wild-caught seafood that can be sustainably harvested with penalties assigned for over-harvesting. As such, one must establish a reference point at which harvest is both maximal and sustainable. We assess food provision from wild caught fisheries by estimating population biomass relative to the biomass that can deliver maximum sustainable yield (\(B/B_{MSY}\)) for each stock. When available, we obtained \(B/B_{MSY}\) values from the RAM Legacy Stock Assessment Database (Ricard et al. 2012; ramlegacystockassessmentdatabase2023?), which contains stock assessment information for a portion of global fish stocks. When RAM data were not available, we used data-limited approaches that have been developed to estimate \(B/B_{MSY}\) values using globally available catch data (C. Costello et al. 2012; Martell and Froese 2013; Thorson et al. 2013; Rosenberg et al. 2014; Christopher Costello et al. 2016). To calculate the status for each region and year, \(B/B_{MSY}\) values were converted to a stock status score between 0-1 that penalizes over-harvesting. To obtain the overall status for each region, the stock status scores for all the stocks within a region were averaged using a geometric mean weighted by the average catch (tonnes) of each stock using Sea Around Us catch data (D. Pauly, Zeller, and Palomareas 2020).

    Figure 6.1: Overview of fisheries status calculations.

    @@ -861,13 +881,13 @@

    Fisher

    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 (2020) global marine fisheries catch data [@pauly2020]. Sea Around Us (2020) uses a spatial allocation method to distribute FAO catch data (reported at the country scale) to a global grid of one half-degree cell resolution based on the spatial distribution of fished taxa from FishBase [@froese2022fishbase] and SeaLifeBase [@palomares2022search].

    -

    We use the Sea Around Us (2020) catch data in two ways. To get the data needed to weight the stock status scores, we download 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 download the total catch for each taxa within each major fishing region [@faofisheriesandaquaculturedepartment2015cwp] for each year. We have traditionally included all fisheries catch in the Fisheries subgoal. 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. Currently, it is estimated about 10% of forage fish enter the human diet directly [@froehlich2020avoiding]. To account for this, we excluded the proportion of catch that produce fish oil and fish meal for animal feed from the total catch.

    -

    To combine the \(B/B_{MSY}\) values from the RAM database with the Sea Around Us (2020) global fish catch data, we used the spatial boundaries that Christopher Free created for the RAM stocks up to 2017 and assigned FAO and OHI regions to each stock [@free2017mapping]. Newly added stocks to the RAM database had some spatially explicit information, but were mostly manually assigned regions based on best available information on stock distribution.

    +

    The data we use to calculate \(B/B_{MSY}\) and the weights used in the geometric mean are from Sea Around Us (2020) global marine fisheries catch data (D. Pauly, Zeller, and Palomareas 2020). Sea Around Us (2020) uses a spatial allocation method to distribute FAO catch data (reported at the country scale) to a global grid of one half-degree cell resolution based on the spatial distribution of fished taxa from FishBase (R. Froese and Pauly 2022) and SeaLifeBase (Palomares and Pauly 2022).

    +

    We use the Sea Around Us (2020) catch data in two ways. To get the data needed to weight the stock status scores, we download 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 download the total catch for each taxa within each major fishing region (FAO Fisheries and Aquaculture Department 2015) for each year. We have traditionally included all fisheries catch in the Fisheries subgoal. 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. Currently, it is estimated about 10% of forage fish enter the human diet directly (Froehlich 2018). To account for this, we excluded the proportion of catch that produce fish oil and fish meal for animal feed from the total catch.

    +

    To combine the \(B/B_{MSY}\) values from the RAM database with the Sea Around Us (2020) global fish catch data, we used the spatial boundaries that Christopher Free created for the RAM stocks up to 2017 and assigned FAO and OHI regions to each stock (Free 2017). Newly added stocks to the RAM database had some spatially explicit information, but were mostly manually assigned regions based on best available information on stock distribution.

    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 [-@martell2016simple], 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 [@anderson2017improving]. 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 [@anderson2017improving].

    +

    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 species 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 [@schaefer2017aspects], 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 [@martell2016simple]. 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 [-@martell2016simple] the geometric mean \({r}\) and \({K}\) were used to derive an estimate of MSY. Rosenberg et al. [-@rosenberg2014developing] 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, \(B/B_{MSY}\).

    +

    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 and 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.

    @@ -884,7 +904,7 @@

    Current status

    -

    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 \(B/B_{MSY} > 1\), status is given a perfect score. Thus, consistent with previous work [@halpern2012index], 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 over-harvesting.

    +

    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 \(B/B_{MSY} > 1\), status is given a perfect score. 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 over-harvesting.

    For the 2021 assessment, we decided to exclude all underharvest penalties because by applying an underharvest penalty, we ended up unduly penalizing regions that have high proportions of underharvested stocks, which may be intentional in many cases. This suggests that an improvement to our fisheries approach by including a more just underharvest penalty could be needed.

    Figure 6.2: Conversion of \(B/B_{MSY}\) to stock status, \(SS\) score.

    @@ -941,8 +961,8 @@

    Current status

    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_generic_2016), 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 [@martell2016simple]. 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 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., Rainer 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 and 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.

    @@ -983,7 +1003,7 @@

    Data

    Mariculture (subgoal of food provision)

    -

    The mariculture subgoal attempts to measure each region’s food production from mariculture relative to its capacity [@unitednations2023a] and sustainability [@2023]. A basic problem facing previous assessments of mariculture was the lack of an ecologically- and socially-based reference point for the potential yield of every suitable mariculture species for every type of geographic habitat and location. Determining such reference points for every country at the global scale is difficult, not only because of key missing data and information, but also because species, genotypes and habitats suitable for production in any given location are likely to change from year to year. However, recent research [@gentry2019exploring] estimated the global biological potential for marine aquaculture at a high resolution spatial scale, addressing one of these key gaps: ecological reference points. To account for the social and economic realities and constraints to these ecological potentials, we constrained the per-country potential to 1% of this tonnage estimate and used these country values as reference points. Furthermore, the paper does not exclude high biodiversity or environmentally sensitive areas, meaning 100% of potential aquaculture tonnage estimate is a large overestimation of what is actually possible. Additionally, we include a sustainability score for each species in each region which is based on the Monterey Bay Aquarium Seafood Watch aquaculture recommendations [@2023].

    +

    The mariculture subgoal attempts to measure each region’s food production from mariculture relative to its capacity (United Nations 2023) and sustainability (“Monterey Bay Aquarium Seafood Watch” 2023). A basic problem facing previous assessments of mariculture was the lack of an ecologically- and socially-based reference point for the potential yield of every suitable mariculture species for every type of geographic habitat and location. Determining such reference points for every country at the global scale is difficult, not only because of key missing data and information, but also because species, genotypes and habitats suitable for production in any given location are likely to change from year to year. However, recent research (Gentry et al. 2017) estimated the global biological potential for marine aquaculture at a high resolution spatial scale, addressing one of these key gaps: ecological reference points. To account for the social and economic realities and constraints to these ecological potentials, we constrained the per-country potential to 1% of this tonnage estimate and used these country values as reference points. Furthermore, the paper does not exclude high biodiversity or environmentally sensitive areas, meaning 100% of potential aquaculture tonnage estimate is a large overestimation of what is actually possible. Additionally, we include a sustainability score for each species in each region which is based on the Monterey Bay Aquarium Seafood Watch aquaculture recommendations (“Monterey Bay Aquarium Seafood Watch” 2023).

    Current status

    The status of the mariculture subgoal, \(x_{mar}\), was defined as production of strictly marine taxa from FAO categories for both marine and brackish waters, excluding species that were not used as a source of food for human consumption. In particular this was only an issue for seaweeds, as many seaweed species are not used for human consumption (or only partially used for human consumption). Table 6.8 shows the proportions, with a justification column, explaining the proportions of each seaweed species used for human consumption.

    @@ -1298,13 +1318,13 @@

    Current status

    \[ Y_{c}= \frac {\displaystyle\sum _{ k=1 }^{ N }{ { Y }_{ k } }}{Y_{r, ref}}, \ (Eq. 6.11) \]

    -

    where, \(Y_{r,ref}\) is the value that corresponds to 1% of the potential aquaculture harvest in each region, and \(Y_{k}\) is the 4-year moving window average of tonnes of production [@unitednations2023a] for each \({k}\) mariculture species that is currently or at one time cultured within a country. \(Y_{c}\) is then capped at 1, so that no country can receive a better-than-perfect score. \(S_{c}\) is the production weighted average of sustainability of mariculture in each country, such that:

    +

    where, \(Y_{r,ref}\) is the value that corresponds to 1% of the potential aquaculture harvest in each region, and \(Y_{k}\) is the 4-year moving window average of tonnes of production (United Nations 2023) for each \({k}\) mariculture species that is currently or at one time cultured within a country. \(Y_{c}\) is then capped at 1, so that no country can receive a better-than-perfect score. \(S_{c}\) is the production weighted average of sustainability of mariculture in each country, such that:

    \[ S_{c} = \frac { \displaystyle\sum _{ k=1 }^{ N }{ { Y }_{ k }{ S }_{ k,r } } } {{\displaystyle\sum _{ k=1 }^{ N }{ { Y }_{ k } }}}, (Eq. 6.12) \]

    -

    where, \(Y_{k}\) is the 4-year moving window average of tonnes of production [@unitednations2023a] for each \({k}\) mariculture species that is currently or at one time cultured within a country, and \(S_{k,r}\) is the sustainability score for each \(k\) mariculture species and region.

    -

    All regions scoring above 1.0 are given a score of 1.0. A score of one could occur when the current aquaculture harvest is greater than 1% of the biological production potential (taken from [@gentry2019exploring]) and mariculture harvest is perfectly sustainable within a region. However, when a region has a current harvested yield that is less than 100 tonnes, and when 1% of potential mariculture harfvest is less than 100 tonnes, a score of NA is assigned. This assumption is made to avoid giving a perfect score to regions that have essentially zero harvested yield, and essentially zero biological production potential.

    -

    The sustainability score, \(S_{k,r}\), for each species in each region is based on the Monterey Bay Aquarium Seafood Watch aquaculture recommendations [@2023]. Ten mariculture practice criteria from the Monterey Bay Aquarium Seafood Watch 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). 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 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 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:

    +

    where, \(Y_{k}\) is the 4-year moving window average of tonnes of production (United Nations 2023) for each \({k}\) mariculture species that is currently or at one time cultured within a country, and \(S_{k,r}\) is the sustainability score for each \(k\) mariculture species and region.

    +

    All regions scoring above 1.0 are given a score of 1.0. A score of one could occur when the current aquaculture harvest is greater than 1% of the biological production potential (taken from (Gentry et al. 2017)) and mariculture harvest is perfectly sustainable within a region. However, when a region has a current harvested yield that is less than 100 tonnes, and when 1% of potential mariculture harfvest is less than 100 tonnes, a score of NA is assigned. This assumption is made to avoid giving a perfect score to regions that have essentially zero harvested yield, and essentially zero biological production potential.

    +

    The sustainability score, \(S_{k,r}\), for each species in each region is based on 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 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). 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 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 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 the average of species within the same family.
      3. @@ -1333,7 +1353,7 @@

      Livelihoods and

      Due to discontinued and non-updated source datasets, we have not updated the status of this goal since 2013 (changes across scenario years after 2013 are due to changes in pressures/resilience).

      The jobs and revenue produced from marine-related industries are clearly of huge value to many people, even those who do not directly participate in the industries but value community identity, tax revenue, and indirect economic and social impacts of a stable coastal economy.

      This goal is composed of two equally important sub-goals, livelihoods and economies, which are assessed across as many marine-related sectors as possible (Table 6.8). Livelihoods includes two equally important sub-components, the number of jobs, which is a proxy for livelihood quantity, and the per capita average annual wages, which is a proxy for job quality. Economies is composed of a single component, revenue. We track the two halves of this goal separately because the number and quality of jobs and the amount of revenue produced are both of considerable interest to stakeholders and governments, and could show very different patterns in some cases (e.g., high revenue sectors do not necessarily provide large employment opportunities). The status of the livelihoods and economies goal is the average of the livelihoods and economies subgoals.

      -

      The total value of economic industries cannot be captured fully by measuring only the jobs and revenue generated directly by those industries, since activity in the direct industry stimulates additional jobs and revenue in related industries. For example, the fishing industry provides direct jobs to fishers, indirect jobs to fishing gear manufacturing companies, and induced jobs to the restaurants and movie theaters where those manufacturing employees spend their income. In the case of tourism, data describing total jobs and revenue (direct plus indirect and induced) were available from the primary data source, and so we used that information as the best estimate of total employment and total revenue for that sector. For all other sectors we used sector- and development status-specific multipliers derived from the literature to estimate total job or revenue impacts. We did not apply multiplier values to wages since the cascading effects of earned income are more contentious. We assumed that sector-specific job and revenue multipliers are static and globally consistent, but distinct for developed versus developing countries (when such information was available), because we do not have data to resolve temporal or regional differences (Table 6.9). Countries were classified as developed or developing using the Human Development Index (HDI, @undp_human_2010), with all countries identified as “very high human development” classified as developed and all others as developing. We classified regions not assessed by the HDI by compiling information used to calculate the HDI score (schooling, life expectancy and per capita Gross National Income statistics).

      +

      The total value of economic industries cannot be captured fully by measuring only the jobs and revenue generated directly by those industries, since activity in the direct industry stimulates additional jobs and revenue in related industries. For example, the fishing industry provides direct jobs to fishers, indirect jobs to fishing gear manufacturing companies, and induced jobs to the restaurants and movie theaters where those manufacturing employees spend their income. In the case of tourism, data describing total jobs and revenue (direct plus indirect and induced) were available from the primary data source, and so we used that information as the best estimate of total employment and total revenue for that sector. For all other sectors we used sector- and development status-specific multipliers derived from the literature to estimate total job or revenue impacts. We did not apply multiplier values to wages since the cascading effects of earned income are more contentious. We assumed that sector-specific job and revenue multipliers are static and globally consistent, but distinct for developed versus developing countries (when such information was available), because we do not have data to resolve temporal or regional differences (Table 6.9). Countries were classified as developed or developing using the Human Development Index (HDI, UNDP (2010)), with all countries identified as “very high human development” classified as developed and all others as developing. We classified regions not assessed by the HDI by compiling information used to calculate the HDI score (schooling, life expectancy and per capita Gross National Income statistics).

      For a job or wage sector to be included in our assessment it needed to report at least two time points and have data for all or most coastal regions (reported separately, not as a single global number). However, a sector did not need to have data for all three measures – jobs, wages, and revenue – as this would have eliminated almost every sector. Consequently, the sectors that comprise each of the three measures differ (Table 6.8) and there is variation across regions in which sectors and measures comprise the status score (because of gaps in datasets and the fact that not all sectors exist in all countries). If a region only had one data layer (a single sector for only one measure), a status score was not calculated for that region and instead, a regional average was applied. We used a weighted average of the region’s UN geopolitical region; revenue values were weighted by each region’s GDP, jobs were weighted by each region’s workforce size, and wages were unweighted.

      A number of sectors were not included primarily because sufficient data do not exist. In the future, particularly in finer scale applications, it would be desirable to include these sectors, including (but not limited to) ecotourism (beyond just cetacean watching), sailing/kayaking/boating, surfing/kiteboarding, etc., offshore wind and wave energy, navigation assistance, safety and security, coastal development, scientific research, and restoration and conservation.

      Table 6.9. Livelihoods and economies sectors. Sectors for which data were available for each component of the livelihoods and economies goal.

      @@ -1474,11 +1494,11 @@

      Livelihoods and

      This goal aims to maintain coastal livelihoods and economies (i.e., avoid the loss of, coastal and ocean-dependent jobs and revenues), while also maximizing livelihood quality (relative wages). It does not attempt to capture any aspects of job identity (i.e., the reputation, desirability or other social or cultural perspectives associated with different jobs), although one can examine the component parts that make up this goal to evaluate individual sectors and infer implications for job identity. We make the assumption that all marine-related jobs are equivalent, such that, for example, a fisherman could transition to a job in mariculture or ship-building without affecting the score of this goal. While job identity has social and cultural value, there are not adequate data to track individual workers and assess their job satisfaction on a global scale. We also do not include any measure of petroleum extraction, as we do not consider these practices to be related to the biophysical state of the system and, because they rely on a non-renewable resource, they are inherently unsustainable. Furthermore, because of data constraints, this goal does not provide more credit for sectors or economic activities that are more ecologically sustainable. Future, finer scale applications of the Index may incorporate these key considerations.

      -

      Gaps were filled in the adjustment datasets (national GDP and national employment) by first determining the average metric value (e.g., average employment rate) in UN geopolitical regions (@unitednations2013statistics) for each year based on all countries in that region for which there were data. Using these regional average time series, we fit nonlinear models to the adjustment data. Using the model fit, we determined the slope between each year. To fill in missing data points in country time series, we applied the slope (percent change in the metric) between the missing year and the following year (or previous year, if necessary). We prioritized filling in backwards (e.g., if a country has data from 2006 and 2008, to fill in 2007, one would use the regional delta between 2008 and 2007), but filled forwards when there were no data for a subsequent year.

      +

      Gaps were filled in the adjustment datasets (national GDP and national employment) by first determining the average metric value (e.g., average employment rate) in UN geopolitical regions (Nations (2013b)) for each year based on all countries in that region for which there were data. Using these regional average time series, we fit nonlinear models to the adjustment data. Using the model fit, we determined the slope between each year. To fill in missing data points in country time series, we applied the slope (percent change in the metric) between the missing year and the following year (or previous year, if necessary). We prioritized filling in backwards (e.g., if a country has data from 2006 and 2008, to fill in 2007, one would use the regional delta between 2008 and 2007), but filled forwards when there were no data for a subsequent year.

      previously {r child = ‘../../metadata_documentation/ohi_model/goal_descriptions/ECO_long.Rmd’}

      Economies (subgoal of livelihoods and economies)

      -

      Due to discontinued and static source datasets, we have not updated the status of this subgoal since 2013 (changes across scenario years after 2013 are due to changes in pressures/resilience) [@unitednations2013faob].

      +

      Due to discontinued and static source datasets, we have not updated the status of this subgoal since 2013 (changes across scenario years after 2013 are due to changes in pressures/resilience) (Nations 2013a).

      This subgoal measures the revenue produced from marine-related industries.

      Current status

      @@ -1541,7 +1561,7 @@

      Current status

      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 https://data.worldbank.org/indicator/NY.GDP.PCAP.CD. 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 [@worldbank2014labor; @worldbank2014unemployment]. 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.

      +

      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 https://data.worldbank.org/indicator/NY.GDP.PCAP.CD. 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 (Bank 2014a, 2014b). 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.

      Trend

      @@ -1627,7 +1647,7 @@

      Natural products

      As such, we focus on three natural product categories: ornamental fish, fish oil and fish meal, and inedible seaweeds and marine plants (Table 6.12).

      Current status

      -

      To determine the total production in tonnes for seaweed we summed seaweed production provided in the FAO global aquaculture production data [@unitednations2023a]. To determine total production in tonnes of ornamental fish we summed the products provided in the FAO commodities data [@un-fao2023]. Finally, to determine the total production in tonnes for fish oil and fish meal, we used Sea Around Us Project (2020) global marine fisheries catch data [@pauly2020] and B/Bmsy data [@ricard2012examining; @costello_status_2016; @martell2016simple; @thorson_new_2016; @rosenberg2014developing; @costello2016global] calculated from our fisheries sub-goal (methods can be found in section 6.6.1), and subsetted the stocks for stocks used for fish oil production (Table 6.12) [@froehlich2020avoiding]. The tonnes of harvest of fish oil and fish meal species were multiplied by 0.9 to reflect the amount of the fish actually going to production of feed or oil, while the other 10% of fish is used in other ways, such as direct human consumption [@froehlich2020avoiding].

      +

      To determine the total production in tonnes for seaweed we summed seaweed production provided in the FAO global aquaculture production data (United Nations 2023). To determine total production in tonnes of ornamental fish we summed the products provided in the FAO commodities data (UN-FAO 2023). Finally, to determine the total production in tonnes for fish oil and fish meal, we used Sea Around Us Project (2020) global marine fisheries catch data (D. Pauly, Zeller, and Palomareas 2020) and B/Bmsy data (Ricard et al. 2012; C. Costello et al. 2012; Martell and Froese 2013; Thorson et al. 2013; Rosenberg et al. 2014; Christopher Costello et al. 2016) calculated from our fisheries sub-goal (methods can be found in section 6.6.1), and subsetted the stocks for stocks used for fish oil production (Table 6.12) (Froehlich 2018). The tonnes of harvest of fish oil and fish meal species were multiplied by 0.9 to reflect the amount of the fish actually going to production of feed or oil, while the other 10% of fish is used in other ways, such as direct human consumption (Froehlich 2018).

      Table 6.12. Natural product categories. List of species and FAO products included in each of the three natural product categories.

      @@ -1668,9 +1688,9 @@

      Current status

      S_{c} = 1- average({ E_c+R_c }), (Eq. 6.16) \]

      where \({E_c}\) is the exposure term and \({R_c}\) is the risk term for ornamentals.

      -

      The exposure term, \(E_c\), is the ln-transformed intensity of harvest for ornamental fish calculated as tonnes of harvest per km2 of coral and rocky reef, relative to the global maximum. We ln 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) [@halpern2008global], 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) [@unep-wcmc2018global].

      +

      The exposure term, \(E_c\), is the ln-transformed intensity of harvest for ornamental fish calculated as tonnes of harvest per km2 of coral and rocky reef, relative to the global maximum. We ln 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) (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) (UNEP-WCMC et al. 2018).

      The risk term, \(R_c\), is based on whether ornamental fishing has unsustainable harvest practices. In specific, we used the intensity of cyanide and dynamite fishing as a proxy. 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.

      -

      For seaweed commodity sustainability, we used the mariculture sustainability scores represented in the Monterey Bay Aquarium Seafood Watch aquaculture recommendations [@2023]. Ten mariculture practice criteria from the Monterey Bay Aquarium Seafood Watch 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). 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 possible raw SFW score of 10 and minimum of 1. The sustainability score for seaweeds in the Seafood Watch recommendations was a global score, 0.79, which was applied to the seaweed harvest. Seaweed is widely regarded as a very sustainable aquaculture harvest, however, it does poorly on 3 of the 10 criteria that Seafood Watch uses, bringing its average down to 0.79. In specific, it scores poorly on the data quality (having robust and up-to-date information on production practices and their impacts available for analysis), escapes (preventing population-level impacts to wild species or other ecosystem-level impacts from farm escapes), and habitat (maintaining the functionality of ecological valuable habitat) criteria.

      +

      For seaweed commodity sustainability, we used the mariculture sustainability scores represented in 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 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). 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 possible raw SFW score of 10 and minimum of 1. The sustainability score for seaweeds in the Seafood Watch recommendations was a global score, 0.79, which was applied to the seaweed harvest. Seaweed is widely regarded as a very sustainable aquaculture harvest, however, it does poorly on 3 of the 10 criteria that Seafood Watch uses, bringing its average down to 0.79. In specific, it scores poorly on the data quality (having robust and up-to-date information on production practices and their impacts available for analysis), escapes (preventing population-level impacts to wild species or other ecosystem-level impacts from farm escapes), and habitat (maintaining the functionality of ecological valuable habitat) criteria.

      The fish oil commodity sustainability was estimated using the B/Bmsy values which were calculated in our fisheries sub-goal (section 6.6.1).

      To estimate the status score, \(x_{np}\), for each region and year we took the weighted average of the individual product scores, \(P_c\), such that:

      \[ @@ -1709,7 +1729,7 @@

      Current status

      There are several important caveats about the natural product status model. First, our approach for ornamentals is supply (export) based. If declining demand for ornamentals causes a decline in production, the producing country’s score declines even if it could (sustainably) produce more. Similarly, if a country chose to reduce or halt production of ornamentals in order to improve conservation or sustainability, its score would decline. Second, we do not have Maximum Sustainable Yield (MSY) estimates for seaweeds or ornamental fish production. When such estimates become available in the future they can easily be incorporated. These scenarios may lead to decreases in the score for a region despite maintenance or even improvement of the sustainable harvest of natural products. Finally, our estimate of the sustainability of the harvest practices of ornamental fish are likely overly optimistic. For example, fishing for ornamental trade often employs unsustainable techniques such as cyanide fishing, but we have few data to inform such an estimate of sustainability in the status calculation for ornamental fish.

      -

      This model requires both harvest tonnes and value data. However, because of inconsistencies with how data are reported to FAO, there are many cases where harvest data but no value data are reported, and vice versa. We gapfilled these data because otherwise these mismatches in reporting would result in losing real data, especially for producing the weight contributions of each natural product commodity. We used a linear regression model to estimate missing tonnes or US dollar values [@frazier2016mapping]. For countries that never harvested a product, we assumed they cannot produce it and treat that as a ‘no data’ rather than a zero value. For countries that harvested a product at any point in time, empty values are treated as zeros since the country has the capacity to harvest that product.

      +

      This model requires both harvest tonnes and value data. However, because of inconsistencies with how data are reported to FAO, there are many cases where harvest data but no value data are reported, and vice versa. We gapfilled these data because otherwise these mismatches in reporting would result in losing real data, especially for producing the weight contributions of each natural product commodity. We used a linear regression model to estimate missing tonnes or US dollar values (Frazier, Longo, and Halpern 2016). For countries that never harvested a product, we assumed they cannot produce it and treat that as a ‘no data’ rather than a zero value. For countries that harvested a product at any point in time, empty values are treated as zeros since the country has the capacity to harvest that product.

      Trend

      @@ -1769,12 +1789,12 @@

      I

      Iconic species are those that are relevant to local cultural identity through their relationship to one or more of the following: 1) traditional activities such as fishing, hunting or commerce; 2) local ethnic or religious practices; 3) existence value; and 4) locally-recognized aesthetic value (e.g., touristic attractions/common subjects for art such as whales). Ultimately, almost any species can be iconic to someone, and so the intent with this goal was to focus on those species widely seen as iconic from a cultural or existence value (rather than a livelihoods or extractive reason). Habitat-forming species were not included, nor were species harvested solely for economic or utilitarian purposes (even though they may be iconic to a sector or individual).

      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 [@iucn2022]:

      +

      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 2022a):

      \[ x_{ico} = \frac { \displaystyle\sum_{ i=EX }^{ LC }{ S_{i}\times w_{i} } }{ \displaystyle\sum_{ i=EX }^{ LC }{ S_{i} } }, (Eq. 6.18) \]

      -

      where for each IUCN threat category \(i\), \(S_{i}\) is the number of assessed species and \(w_{i}\) is the status (Table 6.3) following the methods described by Butchart et al. [-@butchart2007improvements]. 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 (Table 7.5. Iconic species resources), 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). Additional culturally important species species, available at the continent level [@garcia2023], were added to supplement the original iconic species list.

      +

      where for each IUCN threat category \(i\), \(S_{i}\) is the number of assessed species and \(w_{i}\) is the status (Table 6.3) 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 (Table 7.5. Iconic species resources), 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). Additional culturally important species species, available at the continent level (Reyes-García et al. 2023), were added to supplement the original iconic species list.

      Trend

      @@ -1815,8 +1835,8 @@

      Data

      Lasting special places (subgoal of sense of place)

      -

      The lasting special places sub-goal focuses on geographic locations that hold particular value for aesthetic, spiritual, cultural, recreational or existence reasons [@trc_inventory_2004]. This sub-goal is particularly hard to quantify. Ideally one would survey every community around the world to determine the top list of special places, and then assess how those locations are faring relative to a desired state (e.g., protected or well managed). The reality is that such lists do not exist. Instead, we assume areas that are protected indicate special places (i.e., the effort to protect them suggests they are important places). Clearly this is an imperfect assumption but in many cases it will be true.

      -

      The identification of protected areas does not indicate the proportion of special places in a region that are protected. To solve this problem we make two important assumptions. First, we assume that all countries have roughly the same percentage of their coastal waters and coastline that qualify as lasting special places. In other words, they all have the same reference target (as a percentage of the total area). Second, we assume that the target reference level is 30% of area protected [@hughes2003climate].

      +

      The lasting special places sub-goal focuses on geographic locations that hold particular value for aesthetic, spiritual, cultural, recreational or existence reasons (TRC 2004). This sub-goal is particularly hard to quantify. Ideally one would survey every community around the world to determine the top list of special places, and then assess how those locations are faring relative to a desired state (e.g., protected or well managed). The reality is that such lists do not exist. Instead, we assume areas that are protected indicate special places (i.e., the effort to protect them suggests they are important places). Clearly this is an imperfect assumption but in many cases it will be true.

      +

      The identification of protected areas does not indicate the proportion of special places in a region that are protected. To solve this problem we make two important assumptions. First, we assume that all countries have roughly the same percentage of their coastal waters and coastline that qualify as lasting special places. In other words, they all have the same reference target (as a percentage of the total area). Second, we assume that the target reference level is 30% of area protected (Hughes 2003).

      Current status

      We calculate the status of this goal as:

      @@ -1825,7 +1845,7 @@

      Current status

      \]

      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-wcmcandiucn2023]. 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.

      +

      We use the United Nation’s World Database on Protected Areas (WDPA) to identify protected areas (UNEP-WCMC and IUCN 2022). 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.

      Trend

      @@ -1871,8 +1891,8 @@

      Current status

      T_{r} = { A }\times { S }, (Eq. 6.21) \]

      where, \(A\) is the proportion of international overnight visitor arrivals to total international arrivals, and \(S\) is sustainability.

      -

      Ideally there would be data available specifically for arrivals, \(A\), related to coastal tourism; however, the best data available at a global scale reports the total international arrivals, which does not solely reflect coastal tourism [@unwto2023arrivals].

      -

      Unfortunately it was not possible to determine the proportion of international arrivals affiliated with strictly leisure tourism. However, some (unknown) proportion of business travelers also enjoy the coast for leisure during their visit to coastal areas, such that we assumed all tourist arrivals were related to tourism and recreation values. Regional applications of the Index can make use of better-resolved data and more direct measures of tourism, as has been done within the US West Coast [@halpern2014assessing], where data for participation in coastal recreational activities across 19 different sectors were available.

      +

      Ideally there would be data available specifically for arrivals, \(A\), related to coastal tourism; however, the best data available at a global scale reports the total international arrivals, which does not solely reflect coastal tourism (UNWTO 2022).

      +

      Unfortunately it was not possible to determine the proportion of international arrivals affiliated with strictly leisure tourism. However, some (unknown) proportion of business travelers also enjoy the coast for leisure during their visit to coastal areas, such that we assumed all tourist arrivals were related to tourism and recreation values. Regional applications of the Index can make use of better-resolved data and more direct measures of tourism, as has been done within the US West Coast (Halpern et al. 2014), where data for participation in coastal recreational activities across 19 different sectors were available.

      Measures of sustainability are data from the World Economic Forum’s Travel & Tourism Development Index (TTDI). This index 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. We use scores for the Travel and Tourism Sustainability Subindex which encompasses three pillars:

      Pillar 15: Environmental Sustainability

        @@ -1912,7 +1932,7 @@

        Current status

      • Geographically dispersed tourism
      • Quality of town and city centre
      -

      The sustainability factor, \(S\), is the Travel and Tourism Sustainability Subindex score, which is the unweighted average of its three component pillars. Missing sustainability data were gapfilled using per capita GDP (World Bank data with gaps filled using CIA data) based on a linear regression model. For regions without per capita GDP data, remaining missing data were gapfilled using averages of UN geopolitical regions, [@unitednations2013statistics] with sustainability data.

      +

      The sustainability factor, \(S\), is the Travel and Tourism Sustainability Subindex score, which is the unweighted average of its three component pillars. Missing sustainability data were gapfilled using per capita GDP (World Bank data with gaps filled using CIA data) based on a linear regression model. For regions without per capita GDP data, remaining missing data were gapfilled using averages of UN geopolitical regions, (Nations 2013b) with sustainability data.

      Trend

      @@ -1935,12 +1955,212 @@

      Data

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

      Social Progress Index (res_spi): Social Progress Index scores

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

      +
      +
      +
      + + -

      - +

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      - + @@ -30,7 +49,7 @@ - + @@ -180,6 +199,7 @@

      On this page

    3. Pressure
    4. Resilience
      5. +
    5. References
      6. @@ -522,7 +542,7 @@

      Resilience

      \[ r\quad =\quad \gamma *(\frac { { Y }_{ E }+ {Y}_{R} }{ 2 } )+(1-\gamma )*{ Y }_{ S }, (Eq. 5.10) \]

      -

      We chose \(\gamma = 0.5\) so the weight of resilience components that address ecological systems (ecosystem and regulatory) vs. social systems would be equivalent to the proportions used in the model to calculate pressure. Resilience indicators are intended to directly address, as much as possible, specific pressures. Consequently, within a pressure category, resilience scores should not exceed pressure scores, otherwise likely future status scores will be inflated. For the 2021 OHI assessment, in a significant modification from past OHI methods [@halpern2012index], where total resilience scores were allowed to exceed total pressures scores, we have capped resilience such that it will not exceed the corresponding pressures, e.g., \((r−p) ≤ 0\) (i.e. \(r≤p\)), when calculating the likely future status for a given goal [@ohara2020changes].

      +

      We chose \(\gamma = 0.5\) so the weight of resilience components that address ecological systems (ecosystem and regulatory) vs. social systems would be equivalent to the proportions used in the model to calculate pressure. Resilience indicators are intended to directly address, as much as possible, specific pressures. Consequently, within a pressure category, resilience scores should not exceed pressure scores, otherwise likely future status scores will be inflated. For the 2021 OHI assessment, in a significant modification from past OHI methods (Halpern et al. 2012), where total resilience scores were allowed to exceed total pressures scores, we have capped resilience such that it will not exceed the corresponding pressures, e.g., \((r−p) ≤ 0\) (i.e. \(r≤p\)), when calculating the likely future status for a given goal (O’Hara et al. 2020).

      Figure 5.3. Resilience components Resilience includes both ecological and social resilience categories. Ecological resilience includes an ecosystem and regulatory category. The regulatory category includes 5 subcategories that mirror the pressure categories (fishing pressure, habitat destruction, climate change, water pollution, and species/genetic introductions) as well as a goal-specific category.

      @@ -755,14 +775,28 @@

      Social integri Y_{ S}\quad =\quad \frac { \displaystyle\sum _{ i=1 }^{ N }{ { y }_{ S,i } } }{ N }, (Eq. 5.13) \]

      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 [@kaufmann2010worldwide] partly measures their effectiveness through its inclusion of corruption indices.

      - - +

      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, Kraay, and Mastruzzi 2010) partly measures their effectiveness through its inclusion of corruption indices.

      +

    +
    + + + +
    - +

    References

    +
    +Halpern, Benjamin S., Catherine Longo, Darren Hardy, Karen L. McLeod, Jameal F. Samhouri, Steven K. Katona, Kristin Kleisner, et al. 2012. “An Index to Assess the Health and Benefits of the Global Ocean.” Nature. https://doi.org/10.1038/nature11397. +
    +
    +Kaufmann, Daniel, Aart Kraay, and Massimo Mastruzzi. 2010. “The Worldwide Governance Indicators: Methodology and Analytical Issues.” {SSRN} {Scholarly} {Paper} ID 1682130. Rochester, NY: Social Science Research Network. http://info.worldbank.org/governance/wgi/index.aspx#home. +
    +
    +O’Hara, Casey C., Courtney Scarborough, Karen L. Hunter, Jamie C. Afflerbach, Karin Bodtker, Melanie Frazier, Julia S. Stewart Lowndes, R. Ian Perry, and Benjamin S. Halpern. 2020. “Changes in Ocean Health in British Columbia from 2001 to 2016.” Edited by Andrea Belgrano. PLOS ONE 15 (1): e0227502. https://doi.org/10.1371/journal.pone.0227502. +
    +
    @@ -30,7 +49,7 @@ - + @@ -191,8 +210,8 @@

    Regions

    -

    One of the first steps of conducting an OHI assessment is defining regions of interest. These can be based on political and/or ecological boundaries. The definition of a “region” varies depending on the goals and scale of the OHI assessment. For the global OHI, each region is defined as the Exclusive Economic Zone boundaries [EEZ, @claus2012marine] area (300 nm offshore) for all coastal countries and territories (e.g., US Virgin Islands).

    -

    There are 220 global coastal countries and territorial regions (Table 4.1). Regions are based on EEZ boundaries. However, we aggregate some EEZ regions to the level of country (e.g., Hawaii is estimated as part of the larger U.S.). We have also modified some boundaries [@halpern2012index; @halpern2015patterns]. We do not estimate OHI values for disputed or unclaimed areas.

    +

    One of the first steps of conducting an OHI assessment is defining regions of interest. These can be based on political and/or ecological boundaries. The definition of a “region” varies depending on the goals and scale of the OHI assessment. For the global OHI, each region is defined as the Exclusive Economic Zone boundaries (EEZ, Claus et al. 2012) area (300 nm offshore) for all coastal countries and territories (e.g., US Virgin Islands).

    +

    There are 220 global coastal countries and territorial regions (Table 4.1). Regions are based on EEZ boundaries. However, we aggregate some EEZ regions to the level of country (e.g., Hawaii is estimated as part of the larger U.S.). We have also modified some boundaries (Halpern et al. 2012; Halpern et al. 2015). We do not estimate OHI values for disputed or unclaimed areas.

    Figure 4.1. Global regions Map of the OHI regions (with color corresponding to 2022 regional index scores). Mollweide coordinate reference system is used because it accurately represents area.

    @@ -1776,7 +1795,18 @@

    Regions

    - + +

    References

    +
    +Claus, S, N De Hauwere, B Vanhoorne, F Souza Dias, P Oset Garcia, F Hernandez, and J Mees. 2012. “Marine Regions: Exclusive Economic Zones (V7).” http://www.marineregions.org/. +
    +
    +Halpern, Benjamin S., Catherine Longo, Darren Hardy, Karen L. McLeod, Jameal F. Samhouri, Steven K. Katona, Kristin Kleisner, et al. 2012. “An Index to Assess the Health and Benefits of the Global Ocean.” Nature. https://doi.org/10.1038/nature11397. +
    +
    +Halpern, Benjamin S., Catherine Longo, Julia S. Stewart Lowndes, Benjamin D. Best, Melanie Frazier, Steven K. Katona, Kristin M. Kleisner, Andrew A. Rosenberg, Courtney Scarborough, and Elizabeth R. Selig. 2015. “Patterns and Emerging Trends in Global Ocean Health.” PLoS ONE 10 (3): e0117863. https://doi.org/10.1371/journal.pone.0117863. +
    +