bt.belltilcock.2021a.RdStable isotope analysis in eye lenses using three dietary isotopes (d13C, d15N and d34S) has significant potential for answering critical questions about migration, diet, foraging ecology and life history of migratory aquatic animals on earth.
bt.belltilcock.2021aThe data frame 33 × 11 contains the following columns:
| layer_no | integer | this is the which lens layer the data references, numeric value |
| d13c | numeric | carbon isotope value, per mille |
| d15n | numeric | nitrogen isotope value, per mille |
| d34s | numeric | sulfur isotope value, per mille |
| habitat | character | habitat the fish was throughout their life cycle |
| c_ug | numeric | concentration of carbon, micrograms |
| n_ug | numeric | concentration of nitrogen, micrograms |
| s_ug | numeric | concentration of sulfur, micrograms |
| c_n | numeric | carbon to nitrogen ratio, no units |
| predicted_dm | numeric | predicted diameter of this fish's eye lense created from a model |
| life_history | character | life history stage |
Bulk eye-lens stable isotope (d13C, d15N and d34S) used to reconstruct habitat use in an adult Chinook Salmon and a juvenile Chinook Salmon that had reared for 39 days on the floodplain.
Instrument: IRMS (isotope ratio mass spectrometer)
Bell-Tilcock, M. Jeffres, C. A. Rypel, A. L. Sommer, T. R. Katz, J. V. Whitman, G. & Johnson, R. C. (2021). Advancing diet reconstruction in fish eye lenses. Methods in Ecology and Evolution, 12(3), 449-457. http://dx.doi.org/10.1111/2041-210X.13543
Data availability are available at https://doi.org/10.25338/B8WW5D
Traversing the paper's information via Semantic Scholar ID 3ab4f1fe1d9e953caafa7ed0873b3b55e24d7e85 using S2miner package
eye lenses, stable isotope, d13C, d15N, d34S
### copy data into 'dat'
dat <- bt.belltilcock.2021a
tibble::tibble(dat)
#> # A tibble: 34 × 11
#> layer_no d13c d15n d34s habitat c_ug n_ug s_ug c_n predicted_dm life_history
#> <int> <dbl> <dbl> <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
#> 1 0 -15.9 14.3 14.5 Maternal 36.3 16.1 3.03 2.26 0.254 Adult
#> 2 1 -16.1 14.5 13.5 Maternal 1.84 5.19 0.57 0.355 0.383 Adult
#> 3 2 -16.6 13.9 14 Maternal 33.4 15.0 2.8 2.23 0.512 Adult
#> 4 3 -18.6 12.0 12.6 Maternal 39.2 16.9 3.27 2.32 0.642 Adult
#> 5 4 -19.0 11.8 11.4 Freshwater 42.0 17.9 3.72 2.35 0.771 Adult
#> 6 5 -18.7 12.4 13.4 Estuary 43.2 17.8 3.15 2.43 0.900 Adult
#> 7 6 -19.1 12.0 13.5 Estuary 58.4 22.8 4.38 2.56 1.03 Adult
#> 8 7 -19.6 11.4 13.4 Estuary 53.6 21.4 4.44 2.51 1.16 Adult
#> 9 8 -20.0 11.9 14.3 Ocean 200. 67.6 13.8 2.96 1.29 Adult
#> 10 9 -19.6 11.8 13.6 Ocean 80.5 29.8 5.79 2.70 1.42 Adult
#> # ℹ 24 more rows
if (FALSE) {
library(dplyr)
library(ggplot2)
library(tidyr)
### Adult Chinook Salmon Lens CNS isotope
dat[which(dat$life_history == "Adult"),] |>
pivot_longer(cols = c("d13c","d15n","d34s"),
names_to = "isotope",
values_to = "values") |>
ggplot(aes(layer_no,values))+
geom_point(aes(colour = isotope),size = 2, show.legend = F, na.rm = T)+
facet_grid(isotope~.,scales = "free_y")+
xlab("Laminae")+
scale_x_continuous(breaks = 0:28)+
theme_bw() +
theme(
panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
panel.background = element_blank(), axis.line = element_line(colour = "black"),
text = element_text(size = 10), legend.title = element_blank(),
plot.title = element_text(face = "bold")
)
### Juvenile Chinook Salmon Lens CNS isotope
dat[which(dat$life_history == "Juvenile"),] |>
pivot_longer(cols = c("d13c","d15n","d34s"),
names_to = "isotope",
values_to = "values") |>
ggplot(aes(layer_no,values))+
geom_point(aes(colour = isotope),size = 2, show.legend = F, na.rm = T)+
facet_grid(isotope~.,scales = "free_y")+
xlab("Laminae")+
scale_x_continuous(breaks = 0:4)+
theme_bw() +
theme(
panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
panel.background = element_blank(), axis.line = element_line(colour = "black"),
text = element_text(size = 10), legend.title = element_blank(),
plot.title = element_text(face = "bold")
)
}