bt.cordoleani.2021.RdResults from chinook salmon otolith strontium isotope profiles separated into early, intermediate and late migrants.
bt.cordoleani.2021The data frame 2,969 × 9 contains the following columns:
| sample | character | fish identifier |
| year | integer | year of capture |
| watershed | character | watershed of captured |
| distance | numeric | distance from otolith core to edge (um) |
| oto_sr | numeric | otolith strontium isotope data (Sr8786) |
| se1 | numeric | standard error of otolith strontium isotope data (Sr8786) |
| sr_v | numeric | strontium signal (v) |
| sr_vcol | numeric | strontium signal (vcol) |
| reartype | character | three life history types of chinook salmon |
The dataset contains chinook salmon otolith strontium isotope profiles. Otolith isotope profiles revealed three distinct juvenile life history types (referred to as 'early', 'intermediate' and 'late' migrants).
In threatened spring-run chinook salmon spawning at the southern edge of the species range, this study found that late-migrating juveniles are critical to cohort success in years characterized by droughts and ocean heatwaves. Late migrants rely on cool river temperatures over summer, increasingly rare due to the combined effects of warming and impassable dams.
Instrument: LA-ICP-MS (laser ablation-inductively coupled plasma mass spectrometry)
Cordoleani, F., Phillis, C. C., Sturrock, A. M., FitzGerald, A. M., Malkassian, A., Whitman, G. E., ... & Johnson, R. C. (2021). Threatened salmon rely on a rare life history strategy in a warming landscape. Nature Climate Change, 11(11), 982--988. https://doi.org/10.1038/s41558-021-01186-4
Data and code availability are available at https://github.com/floracordoleani/MillDeerOtolithPaper
Traversing the paper's information via Semantic Scholar ID e71577a8327b918628b43a1964ef212ee802e9a4 using S2miner package
otolith, stable isotope, Sr8786
### copy data into 'dat'
dat <- bt.cordoleani.2021
tibble::tibble(dat)
#> # A tibble: 2,969 × 9
#> sample year watershed distance oto_sr se1 sr_v sr_vcol reartype
#> <chr> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
#> 1 DC07_01 2007 Deer Creek 26.3 0.707 0.00009 1.02 41.3 IntermediateOutmigrant
#> 2 DC07_01 2007 Deer Creek 82.5 0.707 0.00008 0.91 23.8 IntermediateOutmigrant
#> 3 DC07_01 2007 Deer Creek 150. 0.706 0.00012 0.92 25.4 IntermediateOutmigrant
#> 4 DC07_01 2007 Deer Creek 203. 0.705 0.00013 0.76 0 IntermediateOutmigrant
#> 5 DC07_01 2007 Deer Creek 268. 0.705 0.00012 0.93 27.0 IntermediateOutmigrant
#> 6 DC07_01 2007 Deer Creek 293. 0.704 0.00012 1.13 58.7 IntermediateOutmigrant
#> 7 DC07_01 2007 Deer Creek 328. 0.704 0.00012 0.8 6.35 IntermediateOutmigrant
#> 8 DC07_01 2007 Deer Creek 358. 0.704 0.00012 0.87 17.5 IntermediateOutmigrant
#> 9 DC07_01 2007 Deer Creek 387. 0.705 0.00008 0.79 4.76 IntermediateOutmigrant
#> 10 DC07_01 2007 Deer Creek 408. 0.704 0.00014 0.78 3.17 IntermediateOutmigrant
#> # ℹ 2,959 more rows
if (FALSE) {
### Sr profile figure
library(dplyr)
library(ggplot2)
ggplot(data = dat,aes(distance, oto_sr))+
geom_line(aes(col = reartype),show.legend = F,linewidth = 0.02, na.rm = T)+
facet_grid(.~ reartype)+
labs(
x = (expression(paste("Otolith radius (", mu, "m)", sep = ""))),
y = expression(paste(
{}^"87",
"Sr/",
{}^"86",
"Sr"
))
)+
scale_x_continuous(limits = c(0, 1200), breaks = seq(0, 1200, 200)) +
scale_y_continuous(limits = c(0.7035, 0.710), breaks = seq(0.704, 0.710, 0.001)) +
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")
)
}