bt.brennan.2019a.RdTo conserve species, we must conserve their habitat. This concept is well known, but the reality is much more complex than simply conserving a particular area. Habitats are dynamic and vary across both space and time. Such variation can help to facilitate long-term persistence of species by allowing local movement in search of the best conditions. Brennan et al. clearly demonstrate the benefit of the habitat mosaic to Pacific salmon by characterizing how both climate and population productivity vary over time and space in an Alaskan river system.
bt.brennan.2019aThe data frame 183,371 × 11 contains the following columns:
| file_id | character | file identifier |
| fish_id | character | fish identifier |
| species | character | chinook salmon and sockeye salmon |
| year | integer | year of caught |
| date | character | date of caught |
| sex | character | fish sex |
| size | numeric | age of caught |
| age | numeric | mid-eye to fork length (mm) |
| distance | numeric | distance from otolith core (proportion of total) |
| oto_sr8786 | numeric | sr isotopes |
| oto_sr8786_se | numeric | process error of sr isotopes |
The dataset contains state-space fits of otolith 87Sr/86Sr ratios from chinook salmon (Oncorhynchus tshawytscha) and Sockeye salmon (Oncorhynchus nerka) harvested Alaskan river system.
Instrument: LA-MC-ICP-MS (laser ablation–multicollector–inductively coupled plasma–mass spectrometry)
Brennan, S. R., Schindler, D. E., Cline, T. J., Walsworth, T. E., Buck, G., & Fernandez, D. P. (2019). Shifting habitat mosaics and fish production across river basins. Science, 364(6442), 783--786. https://doi.org/10.1126/science.aav4313
Traversing the paper's information via Semantic Scholar ID 11891bffbdb3db606baf1aea084a74d2c302b458 using S2miner package
otolith, stable isotope, Sr8786
### copy data into 'dat'
dat <- bt.brennan.2019a
tibble::tibble(dat)
#> # A tibble: 183,371 × 11
#> file_id fish_id species year date sex size age distance oto_sr8786
#> <chr> <chr> <chr> <dbl> <dttm> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 2014_K_1.csv nush_K_2014_1 Oncorhy… 2014 2014-06-25 00:00:00 F 885 1.4 1 0.704
#> 2 2014_K_1.csv nush_K_2014_1 Oncorhy… 2014 2014-06-25 00:00:00 F 885 1.4 2 0.704
#> 3 2014_K_1.csv nush_K_2014_1 Oncorhy… 2014 2014-06-25 00:00:00 F 885 1.4 3 0.704
#> 4 2014_K_1.csv nush_K_2014_1 Oncorhy… 2014 2014-06-25 00:00:00 F 885 1.4 4 0.704
#> 5 2014_K_1.csv nush_K_2014_1 Oncorhy… 2014 2014-06-25 00:00:00 F 885 1.4 5 0.704
#> 6 2014_K_1.csv nush_K_2014_1 Oncorhy… 2014 2014-06-25 00:00:00 F 885 1.4 6 0.704
#> 7 2014_K_1.csv nush_K_2014_1 Oncorhy… 2014 2014-06-25 00:00:00 F 885 1.4 7 0.704
#> 8 2014_K_1.csv nush_K_2014_1 Oncorhy… 2014 2014-06-25 00:00:00 F 885 1.4 8 0.704
#> 9 2014_K_1.csv nush_K_2014_1 Oncorhy… 2014 2014-06-25 00:00:00 F 885 1.4 9 0.704
#> 10 2014_K_1.csv nush_K_2014_1 Oncorhy… 2014 2014-06-25 00:00:00 F 885 1.4 10 0.704
#> # ℹ 183,361 more rows
#> # ℹ 1 more variable: oto_sr8786_se <dbl>
if (FALSE) {
### sulfur profile figure
library(dplyr)
library(ggplot2)
### Sr isotopes
dat <- dat |> group_by(fish_id) |>
mutate(distance_scale = distance/max(distance))
ggplot(data = dat, aes(distance_scale, oto_sr8786)) +
geom_line(aes(group = fish_id, colour = year), show.legend = F) +
facet_grid(.~ species, scales = "free_y") +
xlab("Distance from otolith core (proportion of total)") +
ylab(expression(paste(
{}^"87",
"Sr/",
{}^"86",
"Sr"
))) +
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")
)
}