Longo edits (#8)

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DanOvando · Oct 20, 2020 · 1474a41 · 1474a41
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diff --git a/documents/ovando-etal-assessing-global-fisheries-long.Rmd b/documents/ovando-etal-assessing-global-fisheries-long.Rmd
@@ -17,7 +17,7 @@ output:
     reference_docx: word-template.docx
   bookdown::pdf_document2: default
 params:
-  results_name: ["v0.5"]
+  results_name: ["v1.0"]
   min_years_catch: [25]
 abstract: |
   Assessments of the global state of fisheries play an important role in shaping the public narrative around ocean health, motivating future directions of research and funding, formulating evidence-based policy and tracking the implementation of the United Nations Sustainable Development Goals. While we have reliable estimates of stock status for fisheries accounting for 50% of global catch, our knowledge of the state of the remaining 50%, the worlds 'unassessed' fisheries, is poor. Numerous high-profile publications featuring a range of statistical methods have produced estimates of the global status of these unassessed fisheries, but limited quantity and quality of data along with methodological differences have produced counterintuitive and conflicting results. This is especially true in areas such as Southeast Asia and Africa which are also regions that have high dependence on fishery resources. How can we effectively estimate the state of global fishery sustainability and track progress towards the Sustainable Development Goals targets? We developed a flexible assessment model to assess the value of different kinds, quantities, and quality of data in improving estimates of fishery stock status. We then explore avenues for obtaining potentially impactful data, including through the use of local expert opinion through Fisheries Management Index scores, and increasingly available but historically underutilized data such as trawl footprints and effort data. These data are then used to illustrate how different types of information paint starkly different pictures of the state of fisheries around the world, and to identify priority data types for future collection. Our results provide further evidence that relying on catch data alone results in inaccurate classification of fishery status, often performing only as well or worse than a random guess. Obtaining accurate estimates of stock status for the world's unassessed fisheries depends on prioritizing the collection of high-priority data at a global scale, not on the development of new modeling methods alone.