OHI Science | Citation policy
https://cdnsciencepub.com/doi/10.1139/anc-2017-0001
This script generates the condition of saltmarsh. Based on the following references, global saltmarsh has decreased by 25-50% from historical extent (no idea of the timeframe this refers to) and there is an estimated 1-2% decrease every year. Given this, we will apply a condition of 0.75 globally (using the more conservatinve 25% decline estimate).
Mcowen, C., Weatherdon, L., Bochove, J.-W., Sullivan, E., Blyth, S., Zockler, C., Stanwell-Smith, D., Kingston, N., Martin, C., Spalding, M., Fletcher, S., 2017. A global map of saltmarshes. BDJ 5, e11764. https://doi.org/10.3897/BDJ.5.e11764
Downloaded: October 5, 2021
Description: “Despite their importance, saltmarsh areas are declining around the world, having lost between 25% and 50% of their global historical coverage (Crooks et al. 2011, Duarte et al. 2008).”
Time range: NA
## This file makes it easier to process data for the OHI global assessment
## by creating the following objects:
##
## * dir_M = identifies correct file path to Mazu (internal server) based on your operating system
## * mollCRS = the crs code for the mollweide coordinate reference system we use in the global assessment
## * regions_shape() = function to load global shapefile for land/eez/high seas/antarctica regions
## * ohi_rasters() = function to load two rasters: global eez regions and ocean region
## * region_data() = function to load 2 dataframes describing global regions
## * rgn_syns() = function to load dataframe of region synonyms (used to convert country names to OHI regions)
## * low_pop() = function to load dataframe of regions with low and no human population
## * UNgeorgn = function to load dataframe of UN geopolitical designations used to gapfill missing data## loads 2 dataframes: rgns_all and rgns_eez
## rgns_all = includes eez/high seas/antarctica regions, IDs correspond with region shapefile and raster
## rgns_eez = includes only eez regions## read in extent data
all <- read.csv(file.path(here("globalprep/hab_saltmarsh/v2021/data/habitat_extent_saltmarsh_updated.csv")))
# specify trend
all <- all %>%
filter(km2 > 0) %>%
filter(rgn_id < 255) %>%
dplyr::select(-year) %>%
mutate(health = 0.75)
# Save gapfilling
health_gaps <- all %>%
mutate(variable = "health") %>%
mutate(habitat = "saltmarsh") %>%
mutate(gapfill = 1) %>%
dplyr::select(-km2)
write.csv(health_gaps, here('globalprep/hab_saltmarsh/v2021/data/health_saltmarsh_gf.csv'), row.names=FALSE)
### save trend data:
health <- trend_gaps %>%
dplyr::select(rgn_id, habitat, health) %>%
mutate(year = 2021)
write.csv(health, here('globalprep/hab_saltmarsh/v2021/data/habitat_health_saltmarsh_updated.csv'), row.names=FALSE)