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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).
 
+<br/>
+
 ## 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].

_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

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].

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) %>%
 <!-- ## Need to run CombineLayers.R to create the relevant Rmd file -->
 <!-- # 'https://raw.githubusercontent.com/OHI-Science/ohi-global/draft/documents/methods/layers_all.Rmd' -->
 
+
+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

docs/OHI-score-anatomy.html

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+<link href="./media/favicon-ohi-test.png" rel="icon" type="image/png">
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@@ -176,7 +176,6 @@ <h2 id="toc-title">On this page</h2>
   <li><a href="#goals-of-ohi" id="toc-goals-of-ohi" class="nav-link active" data-scroll-target="#goals-of-ohi">10 Goals of OHI</a></li>
   <li><a href="#subgoals" id="toc-subgoals" class="nav-link" data-scroll-target="#subgoals">Subgoals</a></li>
   <li><a href="#dimensions" id="toc-dimensions" class="nav-link" data-scroll-target="#dimensions">Dimensions</a></li>
-  <li><a href="#data-inclusion-and-data-gaps" id="toc-data-inclusion-and-data-gaps" class="nav-link" data-scroll-target="#data-inclusion-and-data-gaps">Data inclusion and data gaps</a></li>
   </ul>
 </nav>
     </div>
@@ -201,6 +200,7 @@ <h1 class="title">Brief Anatomy of OHI Scores</h1>
 </header>
 
 <p>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).</p>
+<p><br></p>
 <section id="goals-of-ohi" class="level2">
 <h2 class="anchored" data-anchor-id="goals-of-ohi">10 Goals of OHI</h2>
 <p><strong>Table 2.1. The 10 goals of the Ocean Health Index</strong></p>
@@ -349,11 +349,11 @@ <h2 class="anchored" data-anchor-id="dimensions">Dimensions</h2>
 <p><strong>Table 2.3. Dimension used to calculate an OHI goal score</strong> 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.</p>
 <table class="table">
 <colgroup>
-<col style="width: 19%">
-<col style="width: 19%">
-<col style="width: 22%">
-<col style="width: 19%">
-<col style="width: 19%">
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@@ -408,14 +408,6 @@ <h2 class="anchored" data-anchor-id="dimensions">Dimensions</h2>
 <figcaption class="figure-caption"><strong>Figure 2.2. Example flowerplot of goal scores for a region.</strong> Goal and subgoal scores for Canada. The middle value is the regional Index score, and is calculated by averaging the goal scores.</figcaption>
 </figure>
 </div>
-</section>
-<section id="data-inclusion-and-data-gaps" class="level1">
-<h1>Data inclusion and data gaps</h1>
-<p>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 <span class="citation" data-cites="frazier2016mapping">[@frazier2016mapping]</span>.</p>
-<p>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.</p>
-<p>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 <span class="citation" data-cites="burgass2017navigating">[@burgass2017navigating]</span>. 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.</p>
-<p>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.</p>
-<p>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 <span class="citation" data-cites="frazier2016mapping">[@frazier2016mapping]</span>.</p>
 
 
 </section>

docs/OHI-theory.html

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docs/about.html

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