OHI Science | Citation policy
This script generates the health condition of seagrass for each OHI region for the latest year of data. We do this by assigning a global average to those regions with decreasing trends. Any region with a stable or increasing trend receives a condition of 1.
Updated the health with more general values.
Reference: Waycott, M., Duarte, C.M., Carruthers, T.J.B., Orth, R.J., Dennison, W.C., Olyarnik, S., Calladine, A., Fourqurean, J.W., Heck, K.L., Hughes, A.R., Kendrick, G.A., Kenworthy, W.J., Short, F.T., Williams, S.L., 2009. Accelerating loss of seagrasses across the globe threatens coastal ecosystems. PNAS 106, 12377–12381. https://doi.org/10.1073/pnas.0905620106
Description: Contains data regarding the trends of seagrasses since 1879.
Time range: 1879 - 2007
Taken from Waycott et al. 2009: - Our comprehensive global assessment of 215 studies found that seagrasses have been disappearing at a rate of 110 km2 yr−1 since 1980 and that 29% of the known areal extent has disappeared since seagrass areas were initially recorded in 1879.
Based on this, we will score all regions health as 1 - 0.29 = 0.71.
To add to methods: “This reflects global loss since 1879; however, most of the loss is expected to have occurred since 1990. There is likely to be much spatial variation that isn’t accounted for by using a global value, but we are limited by the availability of data.”
seagrass_trend <- read_csv("data/habitat_trend_seagrass_updated.csv") ## read in extent data
## give all regions a health score of 0.71
seagrass_health <- seagrass_trend %>%
mutate(health = ifelse(trend >=0, 1, 0.71)) %>%
mutate(year = 2021) %>%
dplyr::select(-trend)
write.csv(seagrass_health, "data/habitat_health_seagrass_updated.csv", row.names = FALSE)
seagrass_health_gf <- seagrass_health %>%
mutate(gap_fill = ifelse(health == 0.71, "global value", "none")) %>%
dplyr::select(-health)
write.csv(seagrass_health_gf, "data/health_seagrass_gf.csv", row.names = FALSE)seagrass_health <- read_csv("data/habitat_health_seagrass_updated.csv")
old_seagrass_health <- read_csv(file.path("../v2012/data/habitat_health_seagrass_updated.csv"))
compare_habitat_health <- seagrass_health %>%
left_join(old_seagrass_health, by = "rgn_id") %>%
# mutate(km2.y = ifelse(
# km2.x >0 & is.na(km2.y) ,0, #assign 0 values to old data km2 that have new data so that we can properly graph these differences.
# km2.y
# )) %>%
mutate(difference = health.x - health.y) %>%
left_join(rgns_eez)