index.qmd

---
title: "Jellycast"
---

```{r setup, echo = FALSE}
source("setup.R")
groups = names(default_groups())
coast = read_coast()
```

For the [Jellyscape](https://github.com/BigelowLab/jellyscape) project observations are drawn from a recent release of unstaged [ECOMON](https://www.fisheries.noaa.gov/about/northeast-fisheries-science-centern) data. We choose the following groups to belong to the gelatinous "jellyscape" although the list is easily modified.

  + *Siphonophores* (siph)
  + *Hydromedusea* (hydrom)
  + *Coelenterates* (coel)
  + *Ctenophores* (ctenop)
  + *Salps* (salps)

These we can read from the [ecomon package](https://github.com/BigelowLab/ecomon) and aggregate these species into a count per unit area (10m^2) or count per unit volume (100m^3). Accepting the defaults for the `read_ecomon_spp()` function yields an aggregated (summed) table for areal abundance for the `r length(groups)` groups listed.

# The Ecomon observation data

Here we show the general distribution of records for the `r length(groups)` groups listed.  Later we show more detailed per-group analyses.

```{r read_ecomon, message=FALSE, echo = FALSE}
x = read_ecomon_spp(form = 'sf') |>
  ecomon_to_long() |>
  dplyr::filter(name == "total_10m2") |>
  dplyr::mutate(value = value + 1)
dates = range(x$date)
plot(x['value'],
     pch = ".",
     logz = TRUE,
     main = "Ecomon Jellyscape Abundance by Area",
     axes = TRUE,
     reset = FALSE)
plot(coast, add = TRUE, col = "black")
```

To study just the listed groups, we reduce the data set by discarding all non-listed groups and provide an aggregate abundance as shown above.  For the selected groups the data covers `r dates[1]` through `r dates[2]` with `r nrow(x)` records of observation.


Over the years the number of observations has decreased (that isn't to say that the effort has decreased.)  The survey effort is concentrated in spring and autumn months.

```{r show_by_date, echo = FALSE}
x = dplyr::mutate(x,
                  year = format(date, "%Y") ,
                  month = factor(format(date, "%b"), levels = month.abb))

ggplot(data = dplyr::group_by(x, year), 
       mapping = aes(x = year)) +
  geom_bar() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) + 
  labs(y = "Record count", title = "Ecomon Jellyscape Records by Year")

ggplot(data = dplyr::group_by(x, month), 
       mapping = aes(x = month)) +
  geom_bar() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) + 
  labs(y = "Record count", title = "Ecomon Jellyscape Records by Month")
```