bt.belltilcock.2021b.RdMeasured d34S in otoliths of juvenile salmon to test if these archival biominerals could be used to reconstruct floodplain use.
bt.belltilcock.2021bThe data frame 99 × 8 contains the following columns:
| fish_id | character | fish identifier |
| sample | character | sample identifier |
| spot_number | character | spot number |
| age | integer | age of fish |
| distance | numeric | distance from otolith ventral edge to dorsal edge |
| spot_designation | character | spot designation |
| d34scor_vcdt | numeric | otolith sulfur isotope (d34S) |
| w_std_err_95t_permil | numeric | standard error of otolith sulfur isotope(d34S) |
It is suggested that otolith d34S can be used to differentiate floodplain and river rearing habitats used by native fishes, such as chinook salmon, across different hydrologic conditions and tissues, and provide a toolset to quantify the role of floodplains as fish habitats.
Instrument: EA-IRMS (elemental analyzer–isotope ratio mass spectrometry)
Bell-Tilcock, M., Jeffres, C. A., Rypel, A. L., Willmes, M., Armstrong, R. A., Holden, P., ... & Johnson, R. C. (2021). Biogeochemical processes create distinct isotopic fingerprints to track floodplain rearing of juvenile salmon. PloS One, 16(10), e0257444. https://doi.org/10.1371/journal.pone.0257444
Data and code availability are available at https://doi.org/10.5281/zenodo.5514074
Traversing the paper's information via Semantic Scholar ID ea858d445cd8148d36b138a4602ea0a084d4b070 using S2miner package
otolith, stable isotope, d34S
### copy data into 'dat'
dat <- bt.belltilcock.2021b
tibble::tibble(dat)
#> # A tibble: 99 × 8
#> fish_id sample spot_number age distance spot_designation d34scor_vcdt w_std_err_95t_permil
#> <chr> <chr> <chr> <int> <int> <chr> <dbl> <dbl>
#> 1 NP163500 CH-1 CH-1-EDGE NA -440 River 5.67 0.879
#> 2 NP163500 CH-1 CH-1.2 NA -400 Transition 8.26 0.956
#> 3 NP163500 CH-1 CH-1.3 NA -360 Hatchery 13.5 1.06
#> 4 NP163500 CH-1 CH-1.4 NA -320 Hatchery 13.6 1.23
#> 5 NP163500 CH-1 CH-1.5 NA -280 Hatchery 13.9 0.898
#> 6 NP163500 CH-1 CH-1.6 NA -240 Hatchery 14.1 1.50
#> 7 NP163500 CH-1 CH-1.7 NA -200 Hatchery 15.8 1.21
#> 8 NP163500 CH-1 CH-1.8 NA -160 Hatchery 15.1 1.13
#> 9 NP163500 CH-1 CH-1.9 NA -120 Hatchery 13.4 1.02
#> 10 NP163500 CH-1 CH-1.10 NA -80 Hatchery 16.5 0.984
#> # ℹ 89 more rows
if (FALSE) {
### sulfur profile figure
library(dplyr)
library(ggplot2)
library(lemon)
### Prepare profiles for figure
dat |>
group_by(fish_id, spot_designation) |>
mutate(
region_average = mean(d34scor_vcdt, na.rm = TRUE),
region_sd = sd(d34scor_vcdt, na.rm = TRUE)
) |>
ungroup() -> dat
ggplot(data = dat) +
annotate("rect",
ymin = -0.29, ymax = 4.71,
xmin = -Inf, xmax = Inf, fill = "steelblue3", alpha = .1
) +
annotate("rect",
ymin = -5.74, ymax = -1.2,
xmin = -Inf, xmax = Inf, fill = "forestgreen", alpha = .1
) +
geom_smooth(aes(x = distance, y = d34scor_vcdt, group = Fish_ID),
span = 0.2, color = "grey95", fill = "grey95", alpha = 0.5
) +
geom_pointrange(aes(
x = distance, y = d34scor_vcdt, ymax = d34scor_vcdt + w_std_err_95t_permil,
ymin = d34scor_vcdt - w_std_err_95t_permil,
fill = spot_designation
), shape = 21, color = "black") +
theme_classic() +
theme(
panel.background = element_rect(colour = "black"),
legend.position = "bottom",
legend.title = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
strip.background = element_blank()
) +
scale_x_continuous("Distance from otolith ventral edge to dorsal edge(µm)") +
scale_y_continuous(
name = expression(paste(delta^"34", "S"["Otolith"], " [‰ VCDT]")),
breaks = scales::pretty_breaks(n = 5)
) +
scale_fill_manual(values = c(
"firebrick", "palegreen3",
"orange", "steelblue", "grey"
)) +
facet_rep_wrap(~ fish_id, ncol = 1, repeat.tick.labels = "all")
}