salps.qmd

---
title: "Salpsae"
format:
  html:
    code-fold: true
---

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

```{r read_ecomon, message=FALSE}
#| code-fold: false
thisgroup = "Salps"
x = read_ecomon_spp(form = 'sf', groups = thisgroup, agg = FALSE) |>
  ecomon_to_long() |>
  dplyr::mutate(year = format(date, "%Y") ,
                month = factor(format(date, "%b"), levels = month.abb))
nonz = dplyr::filter(x, value > 0)
zero = dplyr::filter(x, value <= 0)
n = nrow(x)
nz = zero |> nrow()
coast = sf::st_crop(coast, x)
```

There are `r n` records for `salps` of which `r nz` are zeroes (about `r sprintf("%0.2f%%", nz/n*100)`) leaving `r n-nz` non-zero salp abundance records.

# Non-zero abundances

Here's an abudnance scaled plot of the non-zero abundances.
```{r}
plot(nonz['value'],
     pch = ".",
     logz = TRUE,
     main = glue::glue("Non-zero abundance of {thisgroup}"),
     axes = TRUE,
     reset = FALSE)
plot(coast, add = TRUE, col = "black")
```


```{r, warning=FALSE}
ggplot(data = dplyr::group_by(nonz, 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 = glue::glue("Non-zero abundance {thisgroup} Records by Year"))

ggplot(data = dplyr::group_by(nonz, 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 = glue::glue("Non-zero abundance {thisgroup} Records by Month"))
```



# Zero Abundance (*i.e.* absences)

The distribution of zero abundance records is much more extensive.

```{r}
plot(zero['value'] |>
       dplyr::filter(value <= 0) |>
       sf::st_geometry(),
     pch = ".",
     main = glue::glue("Zero abundance presences {thisgroup}"),
     axes = TRUE,
     reset = FALSE)
plot(coast, add = TRUE, col = "black")
```

```{r, warning=FALSE}
ggplot(data = dplyr::group_by(zero, 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 = glue::glue("Zero abundance {thisgroup} Records by Year"))

ggplot(data = dplyr::group_by(zero, 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 = glue::glue("Zero abudnance {thisgroup} Records by Month"))
```


# A closer look at non-zero abundances

We can bin the data into abundance quantiles.

```{r}
#| code-fold: false
quants = seq(from = 0, to = 0.95, by = 0.05)
qs = quantile(nonz$value, quants)
ix = findInterval(nonz$value, qs)
nonz = dplyr::mutate(nonz,
                     quantile = quants[ix],
                     percentile = factor(names(qs)[ix], levels = names(qs)))
```


```{r}
ggplot(data = nonz) + 
  geom_sf(alpha = 0.1, size = 0.3) + 
  geom_sf(data = coast, 
          color = "orange") +
  facet_wrap(~percentile)
```

# A closer look at the top 5% abundances

```{r}
top5 = dplyr::filter(nonz, quantile >= 0.95)

ggplot(data = top5) + 
  geom_sf(alpha = 0.1, size = 0.4) + 
  geom_sf(data = coast, 
          color = "orange") +
  facet_wrap(~month, drop = FALSE)
```