OHI Science | Citation policy
This script generates the trend of saltmarsh.
“We show a net salt marsh loss globally, equivalent to an area double the size of Singapore (719 km2), with a loss rate of 0.28% year from 2000 to 2019” (Campbell et al. 2022).
Apply a decreasing rate of .28% per year.
In 2023 we updated the rate of salt marsh decrease per year from the previous assessment. Previously we applied a decreasing rate of 1.5% per year, or -0.015 over each region. This was based on the reference: “The global loss rate of threatened coastal ecosystems is estimated at 4–9% year for corals (Gardner et al. 2003, Bellwood et al. 2004), a minimum of 1–2% year for saltmarshes (Adam 2002; Lotze et al. 2006)” https://link.springer.com/article/10.1007%2Fs12237-008-9038-7. We are now using a value of -0.28% per year of lost salt marsh habitat, based on the findings of Campbell et al. 2022.
Campbell, A.D., Fatoyinbo, L., Goldberg, L. et al. Global hotspots of salt marsh change and carbon emissions. Nature 612, 701–706 (2022). https://doi.org/10.1038/s41586-022-05355-z
Date Acessed: July 31 2023
Description: Estimate of a global percentage of loss of saltmarsh habitat per year.
Time range: NA, based on a study from 2000 to 2019
Apply a global trend for each region.. not satisfying.
## read in extent data
all <- read.csv(file.path(here("globalprep/hab_saltmarsh/", version_year, "data/habitat_extent_saltmarsh_updated.csv")))
# specify trend
all <- all %>%
filter(km2 > 0) %>%
filter(rgn_id < 255) %>%
dplyr::select(-year) %>%
mutate(trend = -0.0028)
# Save gapfilling
trend_gaps <- all %>%
mutate(variable = "trend") %>%
mutate(habitat = "saltmarsh") %>%
mutate(gapfill = 1)
write.csv(trend_gaps, here("globalprep/hab_saltmarsh/", version_year, "data/trend_saltmarsh_gf.csv"), row.names=FALSE)
### save trend data:
trend <- trend_gaps %>%
dplyr::select(rgn_id, habitat, trend) %>%
mutate(year = current_year)
write.csv(trend, here("globalprep/hab_saltmarsh/", version_year, "/data/habitat_trend_saltmarsh_updated.csv"), row.names=FALSE)