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* back to main and stashing as published version

* cleaning up README
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@DanOvando
DanOvando authored Jul 16, 2021
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# Data
*.pdf
*.png
*.eps
sraplus_tmb.*

*.zip
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9 changes: 5 additions & 4 deletions README.Rmd
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## Abstract

Implementation of the United Nations Sustainable Development Goals requires assessments of the global state of fish populations. While we have reliable estimates of stock status for fish populations accounting for approximately half of recent global catch, our knowledge of the state of the majority of the world's 'unassessed' fish stocks remains highly uncertain. Numerous publications 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. Here, we show that despite numerous efforts, our understanding of the status of global fish stocks remains incomplete, even when new sources of broadly available data are added. Estimates of fish populations based primarily on catch histories on average performed 25% better than a random guess. But, on average these methods assigned fisheries to the wrong FAO status category 57% of the time. Within these broad summaries the performance of models trained on our tested data sources varied widely across regions. Effective improvement in estimates of the state of the world's exploited fish populations depends more on expanded collection of new information and efficient use of existing data than development of new modeling methods.s exploited fish populations depends on prioritizing the collection of high-priority
Implementation of the United Nations Sustainable Development Goals requires assessments of the global state of fish populations. While we have reliable estimates of stock status for fish populations accounting for approximately half of recent global catch, our knowledge of the state of the majority of the world's 'unassessed' fish stocks remains highly uncertain. Numerous publications 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. Here, we show that despite numerous efforts, our understanding of the status of global fish stocks remains incomplete, even when new sources of broadly available data are added. Estimates of fish populations based primarily on catch histories on average performed 25% better than a random guess. But, on average these methods assigned fisheries to the wrong FAO status category 57% of the time. Within these broad summaries the performance of models trained on our tested data sources varied widely across regions. Substantial improvements to estimates of the state of the world's exploited fish populations depends more on expanded collection of new information and efficient use of existing data than development of new modeling methods.


*Mean classification accuracy (assignment to FAO stock status category) by FAO statistical area arising from different data sources. Data source panels are ordered in descending (starting from top left) mean accuracy at the FAO region level. RLSADB Index refers to catch and abundance index drawn from RLSADB. Effective CPUE refers to an index of abundance based on reconstructed effort data. Effective CPUE+ uses CPUE along with Fisheries Management Index (FMI) and/or swept area ratio (SAR) data. For both CPUE series 'nominal' assumes a 0% technology creep, for 'effective' a 2.6% technology creep is assumed. FMI uses FMI scores to develop a prior on recent fishing mortality rates, SAR does the same but based on swept area ratio. CMSY uses the methods from Froese et al. 2017 [@froese2017]. Guess assigns a random recent B/B~MSY~ of 0.4,1, or 1.6.*
```{r, echo = FALSE}
knitr::include_graphics("documents/figs/acc-map.png")
<!-- *Mean classification accuracy (assignment to FAO stock status category) by FAO statistical area arising from different data sources. Data source panels are ordered in descending (starting from top left) mean accuracy at the FAO region level. RLSADB Index refers to catch and abundance index drawn from RLSADB. Effective CPUE refers to an index of abundance based on reconstructed effort data. Effective CPUE+ uses CPUE along with Fisheries Management Index (FMI) and/or swept area ratio (SAR) data. For both CPUE series 'nominal' assumes a 0% technology creep, for 'effective' a 2.6% technology creep is assumed. FMI uses FMI scores to develop a prior on recent fishing mortality rates, SAR does the same but based on swept area ratio. CMSY uses the methods from Froese et al. 2017 [@froese2017]. Guess assigns a random recent B/B~MSY~ of 0.4,1, or 1.6.* -->

```{r, echo = FALSE, include=FALSE}
knitr::include_graphics("documents/figs/figure_4.eps")
```


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22 changes: 4 additions & 18 deletions README.md
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Expand Up @@ -31,26 +31,12 @@ added. Estimates of fish populations based primarily on catch histories
on average performed 25% better than a random guess. But, on average
these methods assigned fisheries to the wrong FAO status category 57% of
the time. Within these broad summaries the performance of models trained
on our tested data sources varied widely across regions. Effective
improvement in estimates of the state of the world’s exploited fish
on our tested data sources varied widely across regions. Substantial
improvements to estimates of the state of the world’s exploited fish
populations depends more on expanded collection of new information and
efficient use of existing data than development of new modeling
methods.s exploited fish populations depends on prioritizing the
collection of high-priority
efficient use of existing data than development of new modeling methods.

*Mean classification accuracy (assignment to FAO stock status category)
by FAO statistical area arising from different data sources. Data source
panels are ordered in descending (starting from top left) mean accuracy
at the FAO region level. RLSADB Index refers to catch and abundance
index drawn from RLSADB. Effective CPUE refers to an index of abundance
based on reconstructed effort data. Effective CPUE+ uses CPUE along with
Fisheries Management Index (FMI) and/or swept area ratio (SAR) data. For
both CPUE series ‘nominal’ assumes a 0% technology creep, for
‘effective’ a 2.6% technology creep is assumed. FMI uses FMI scores to
develop a prior on recent fishing mortality rates, SAR does the same but
based on swept area ratio. CMSY uses the methods from Froese et al. 2017
\[@froese2017\]. Guess assigns a random recent B/B<sub>MSY</sub> of
0.4,1, or 1.6.* <img src="documents/figs/acc-map.png" width="2400" />
<!-- *Mean classification accuracy (assignment to FAO stock status category) by FAO statistical area arising from different data sources. Data source panels are ordered in descending (starting from top left) mean accuracy at the FAO region level. RLSADB Index refers to catch and abundance index drawn from RLSADB. Effective CPUE refers to an index of abundance based on reconstructed effort data. Effective CPUE+ uses CPUE along with Fisheries Management Index (FMI) and/or swept area ratio (SAR) data. For both CPUE series 'nominal' assumes a 0% technology creep, for 'effective' a 2.6% technology creep is assumed. FMI uses FMI scores to develop a prior on recent fishing mortality rates, SAR does the same but based on swept area ratio. CMSY uses the methods from Froese et al. 2017 [@froese2017]. Guess assigns a random recent B/B~MSY~ of 0.4,1, or 1.6.* -->

# Reproducing Results

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198 changes: 4 additions & 194 deletions documents/fish-and-fisheries.csl
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