Revising report quarto book

.Rhistory

@@ -0,0 +1,28 @@
+rm(list = ls())
+dat <- read.table("../indicator_data/surface_omega_series.txt", skip = 0, header = F)
+head(dat)
+tail(dat)
+dat$dates <- paste0(month.abb[dat$V2], dat$V1)
+datdata <- dat$dates
+inddata <- data.frame(dat$V3)
+labs <- c("Ocean acidification", "Surface aragonite saturation", "")
+indnames <- data.frame(matrix(labs, nrow = 3, byrow = F))
+s <- list(labels = indnames, indicators = inddata, datelist = datdata)
+class(s) <-"indicatordata"
+plotIndicatorTimeSeries(s)
+library(plotTimeSeries)
+library(spam)
+rm(list = ls())
+dat <- read.table("../indicator_data/surface_omega_series.txt", skip = 0, header = F)
+head(dat)
+tail(dat)
+dat$dates <- paste0(month.abb[dat$V2], dat$V1)
+datdata <- dat$dates
+inddata <- data.frame(dat$V3)
+labs <- c("Ocean acidification", "Surface aragonite saturation", "")
+indnames <- data.frame(matrix(labs, nrow = 3, byrow = F))
+s <- list(labels = indnames, indicators = inddata, datelist = datdata)
+class(s) <-"indicatordata"
+plotIndicatorTimeSeries(s)
+inddata <- s
+save(inddata, file = "OA_test.RData")

.quarto/cites/index.json

@@ -1 +1 @@
-{"OA.qmd":[],"Lab-report\\OA.qmd":[],"index.qmd":[],"Report_book_files\\Report4.qmd":[]}
+{"index.qmd":[],"Lab-report\\OA.qmd":[],"Report_book_files\\Report4.qmd":[],"OA.qmd":[]}

Report_book_files/Discussion_material.qmd

@@ -0,0 +1,13 @@
+# 6. Integrated ecosystem perspectives
+
+Stoplight plot
+
+# 7. Research Recommendations
+
+## Data gaps
+
+# 8. Acknowledgements
+
+# 9. References
+
+# 10. Data source table

Report_book_files/FMP_objectives.qmd

@@ -0,0 +1,3 @@
+# 3. Fishery management plan objectives and conceptual models
+
+This report's indicator selection process sought to select indicators that corresponded to the island based fishery management plan (FMP) objectives in order to track performance, and also selected indicators related to risks to meeting these management objectives. The following figure shows indicators selected per FMP objective. Indicators were also sourced and considered from the conceptual model exercise completed by the Council's Science and Statistical Committee and District Advisory panels, which began in 2019. Top scored connections in ecosystem components were considered in the ESR indicator suite as well (Rivera et al, in publication).

Report_book_files/Intro_text.qmd

@@ -0,0 +1,15 @@
+# 2. Introduction
+
+Ecosystem-based management of fisheries and other marine resources has emerged as a priority in the U.S. (EPAP 1999, Fluharty et al. 2006, McFadden and Barnes 2009, NOAA 2016) and elsewhere (Browman et al. 2004, Sainsbury et al. 2014, Walther and Möllmann 2014, Long et al. 2015). The NOAA National Marine Fisheries Service (NOAA Fisheries) defines ecosystem-based fisheries management (EBFM) as 'a systematic approach to fisheries management in a geographically specified area that contributes to the resilience and sustainability of the ecosystem; recognizes the physical, biological, economic, and social interactions among the affected fishery-related components of the ecosystem, including humans; and seeks to optimize benefits among a diverse set of societal goals' (NOAA 2016).
+
+## 2.1 Indicator selection
+
+This report relied on both previously identified proposed indicators as well as expert vetting to select a suite of indicators that best address the fishery management plan (FMP) objectives for the U.S. Caribbean. The CFMC's Science and Statistical Committee, as well as the region's Ecosystem-Based Fishery Management Technical Advisory Panel (EBFM TAP), recently completed a series of conceptual models linking key components of the ecosystem and human activities related to fishing. This report used these conceptual models as a starting list of proposed indicators and matched the indicators to answer FMP objectives when possible. For those objectives that did not have an immediate conceptual model-identified indicator, this report used a decision matrix process for expert vetting (Fig x).
+
+insert fig
+
+This decision matrix was composed of a list of proposed indicators compiled from the conceptual models as well as proposed indicators provided via expert input. These potential indicators were vetted and edited by expert small working groups, who then scored a decision matrix (Fig. x) of potential indicators against the following decision criteria: long term data availability, measurability, sensitivity to environmental changes, specificity, spatial and temporal scalability, relevance to specific FMP objectives, and responsiveness to management actions.
+
+## 2.2 Notes on interpreting time series figures
+
+Time series data are plotted in a standardized format for ease of interpretation (e.g., Fig. 2.1). The x-axis represents the temporal dimension, which may be monthly, yearly, or irregular time steps, and the y-axis represents the indicator value in units specified in the axis label. The dashed horizontal line represents the mean indicator value across the entire time series, and the solid horizontal lines denote the mean plus or minus one standard deviation. Red shaded areas and green shaded areas show years for which the indicator value is below or above one standard deviation from the mean, respectively. The blue vertical shaded box highlights the last five years of indicator values, over which additional metrics are calculated. Black circles to the right of each figure indicate whether the indicator values over the last five years are greater (plus sign), less than (minus sign), or within (solid circle) one standard deviation from the mean of the overall time series. Arrows to the right of each figure indicate whether the least squares linear fit through the last five years of data produces a positive or negative slope that is greater than one standard deviation (upward or downward arrows respectively), or less than one standard deviation (left-right arrow).

Report_book_files/Performance_indicators.qmd

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+```{r, message = FALSE, warnings = FALSE}
+#devtools::install_github("https://github.com/MandyKarnauskas-NOAA/plotTimeSeries.git")
+
+library(plotTimeSeries)
+library(spam)
+```
+
+# 5. Tracking performance toward fishery management objectives
+
+## 5.1 Food production
+
+### Fishery independent surveys of economically important species
+
+Indicator 16
+
+### Commercial landings
+
+Indicator 17
+
+### Maximum length and size structure
+
+Indicator 18
+
+### Changes in target species / landing composition
+
+Indicator 20
+
+## 5.2 Socioeconomic health
+
+### Total, lobster and conch revenues
+
+Indicator 21
+
+### Total, lobster and conch trips
+
+Indicator 22
+
+### Ocean economy employment and wages
+
+Indicator 23
+
+```{r}
+load("../indicator_objects/OceanNAICS.RData")
+plotIndicatorTimeSeries(inddata, coltoplot = 1:2, plotrownum = 2, trendAnalysis = F, sublabel = T)
+```
+
+### GDP
+
+Indicator 24
+
+### Unemployment
+
+Indicator 25
+
+## 5.3 Equity
+
+### Gini coefficient for distribution of landings and revenue
+
+Indicator 26
+
+```{r}
+load("../indicator_objects/gini.RData")
+plotIndicatorTimeSeries(inddata, coltoplot = 1:2, plotrownum = 2, trendAnalysis = F, sublabel = T)
+```
+
+### Commercial fishing community engegement and reliance
+
+Indicator 27
+
+## 5.4 Engagement and participation
+
+### Recreational fishing engagement and participation
+
+Indicator 28
+
+### Commercial fishing engagement and participation
+
+Indicator 29
+
+## 5.5 Bycatch reduction
+
+### Changes in gear type
+
+Indicator 30
+
+## 5.5 Governance
+
+### Number of seasonal closures implemented
+
+Indicator 31
+
+### Number of education and outreach events
+
+Indicator 32
+
+### Number of enforcement actions
+
+Indicator 33
+
+## 5.6 Protection of ecosystems
+
+### Percent coral cover and coral species richness
+
+Indicator 34
+
+```{r, message = FALSE, warnings = FALSE}
+load("../indicator_objects/NCRMP_coral_cover_richness.RData")
+plotIndicatorTimeSeries(inddata, trendAnalysis = F)
+```
+
+### Coral species diversity
+
+Indicator 35

Report_book_files/Risk_indicators.qmd

@@ -0,0 +1,118 @@
+```{r, message = FALSE, warnings = FALSE}
+#devtools::install_github("https://github.com/MandyKarnauskas-NOAA/plotTimeSeries.git")
+
+library(plotTimeSeries)
+library(spam)
+```
+
+# 4. Risks to meeting fishery management objectives
+
+### Degree heating weeks
+
+Indicator 1
+
+### Ocean acidification via aragonite saturation state
+
+Indicator 2
+
+```{r, message = FALSE, warnings = FALSE}
+load("../indicator_objects/OA.RData")
+plotIndicatorTimeSeries(inddata, trendAnalysis = F)
+```
+
+### Hurricane activity
+
+Indicator 3
+
+```{r, message = FALSE, warnings = FALSE}
+load("../indicator_objects/ACEindex.RData")
+plotIndicatorTimeSeries(inddata, trendAnalysis = F)
+```
+
+### Turbidity
+
+Indicator 4
+
+turbidity.RData doesn't work
+
+### Sea surface temperature
+
+Indicator 5
+
+### Marine debris
+
+Indicator 6
+
+```{r, message = FALSE, warnings = FALSE}
+load("../indicator_objects/beach_litter_un_carib.RData")
+plotIndicatorTimeSeries(inddata, trendAnalysis = F)
+```
+
+```{r, message = FALSE, warnings = FALSE}
+load("../indicator_objects/marine_debris.RData")
+plotIndicatorTimeSeries(inddata, trendAnalysis = F)
+```
+
+### Identified point source pollution sites
+
+Indicator 7
+
+### Primary productivity via ocean color
+
+Indicator 8
+
+carib_Chl.RData doesn't work
+
+### Coastal development via land cover
+
+Indicator 9
+
+### Number of major earthquakes
+
+Indicator 10
+
+```{r, message = FALSE, warnings = FALSE}
+load("../indicator_objects/earthquakes.RData")
+plotIndicatorTimeSeries(inddata, trendAnalysis = F)
+```
+
+### Fishery/market disturbance indicator (maybe belongs in socioeconomic health)
+
+Indicator 11
+
+```{r, message = FALSE, warnings = FALSE}
+load("../indicator_objects/disturbance.RData")
+plotIndicatorTimeSeries(inddata, trendAnalysis = F)
+```
+
+### Sargassum inundation
+
+Indicator 12
+
+doesn't work
+
+load("indicator_objects/Sargassum.RData") 
+plotIndicatorTimeSeries(inddata, coltoplot = 1:2, plotrownum = 2, trendAnalysis = F, sublabel = T) 
+
+load("indicator_objects/sargassum_innundation_monthly_mean_hu.RData")
+plotIndicatorTimeSeries(inddata, coltoplot = 1:2, plotrownum = 2, trendAnalysis = F, sublabel = T)
+
+### Tourism via hotel occupancy
+
+Indicator 13
+
+doesn't work 
+
+load("indicator_objects/hotel_occupancy_rates_USVI_and_PR.RData")
+plotIndicatorTimeSeries(inddata, trendAnalysis = F) 
+
+load("indicator_objects/hotel_occupancy.RData") 
+plotIndicatorTimeSeries(inddata, trendAnalysis = F) 
+
+### Population density
+
+Indicator 14
+
+### Population change
+
+Indicator 15

Report_book_files/setting_up_ESR_report.qmd

@@ -8,12 +8,16 @@ editor: visual
 
 This report is for the Caribbean ESR that was compiled from 2023-2024. This is the first ESR for the Caribbean region. Here we can add more text about why this ESR is being compiled.
 
-## Some notes
+## Getting started
 
 The Caribbean ESR report was created the following way:
 
 1.  clone the Caribbean ESR repo into my R studio
-2.  create 2 files, one called "index.qmd" and another called "_quarto.txt" in the main Caribbean ESR folder
-3.  rename the "_quarto.txt" file to "_quarto.yml" in file explorer to change it into the book YAML
+2.  create 2 files, one called "index.qmd" and another called "\_quarto.txt" in the main Caribbean ESR folder
+3.  rename the "\_quarto.txt" file to "\_quarto.yml" in file explorer to change it into the book YAML
 4.  populate the YAML document
-5.  Now in order to be able to render the book as both an html and pdf (or whatever other format you want) you need to make sure all the appropriate files are in the main folder. You can't put them in sub-fold
+5.  Now in order to be able to render the book as both an html and pdf (or whatever other format you want) you need to make sure all the appropriate files are in the main folder. You can't put them in sub-folder.
+
+## Report design
+
+First, we needed to add a cover page. We used the NMFS titlepages quarto extension, which can be found [here](https://nmfs-opensci.github.io/quarto_titlepages/03-titlepage-themes.html). First you need to install the extension using the terminal. Then the YAML code from the example titlepages can be added to your quarto book YAML. May need to terminate R and restart for changes to go into affect. To edit the title page, see the source code at the above link and revise as desired.