Tutorial
1 Overview
The following section provides an example of possible workflow. It is important to note that these are indeed examples of the software’s capabilities and are not intended to be used as scientific advice in a spatial conservation planning process. It is the user’s responsibility to ensure that all analysis decisions are valid.
library(sf)
library(leaflet)
library(tmap)
library(tidyverse)
library(DT)
# set default projection for leaflet
proj <- "+proj=merc +a=6378137 +b=6378137 +lat_ts=0.0 +lon_0=0.0 +x_0=0.0 +y_0=0 +k=1.0 +units=m +nadgrids=@null +wktext +no_defs"1.1 Demographic Data using Spatial Dependencies
Download the example project folder. This folder contains the Marxan Connect Project file, the input data, and the output data from this example. Feel free to follow along using Marxan Connect by loading tutorial.MarCon.
Before adding connectivity to the mix, let’s have a look at the ‘traditional’ Marxan files which contain only representation targets. The files include reef planning units that cover the Great Barrier Reef and we’ve identified a few bioregion types for which we’ve set conservation targets.
1.1.1 spec.dat
spec <- read.csv("tutorial/CSD_demographic/input/spec.dat")
datatable(spec,rownames = FALSE, options = list(searching = FALSE))1.1.2 puvspr.dat
The table shown here is a trimmed version showing the first 30 rows as an example of the type of data in the puvspr.dat file. The original dataset has 701 entries.
puvspr <- read.csv("tutorial/CSD_demographic/input/puvspr2.dat")
datatable(puvspr[1:30,],rownames = FALSE, options = list(searching = FALSE))1.1.3 pu.dat
The table shown here is a trimmed version showing the first 30 rows as an example of the type of data in the pu.dat file. The original dataset has 321 entries.
pu <- read.csv("tutorial/CSD_demographic/input/pu.dat")
datatable(pu[1:30,],rownames = FALSE, options = list(searching = FALSE))1.1.4 Inital Conservation Features
This map shows the bioregions and depth classes, which serve as conservation features in the Marxan analysis with no connectivity.
puvspr_wide <- puvspr %>%
left_join(select(spec,"id","name"),
by=c("species"="id")) %>%
select(-species) %>%
spread(key="name",value="amount")
# planning units with output
output <- read.csv("tutorial/CSD_demographic/output/pu_no_connect.csv") %>%
mutate(geometry=st_as_sfc(geometry,"+proj=longlat +datum=WGS84"),
best_solution = as.logical(best_solution)) %>%
st_as_sf() %>%
left_join(puvspr_wide,by=c("pu_id"="pu"))map <- leaflet(output) %>%
addTiles()
groups <- names(select(output,-best_solution,-select_freq, -fa_included, -aa_included, -id, -pu_id, -Y_COORD, -X_COORD, -TARGET_FID, -GAZ_LOC_CO, -GBR_NAME, -Join_Count, -PERIMETER, -QLD_NAME, -REEFS_, -REEFS_ID, -REEF_ID, -SUB_ID))[c(-1,-2)]
groups <- groups[groups!="geometry"]
for(i in groups){
z <- unlist(data.frame(output)[i])
if(is.numeric(z)){
pal <- colorBin("YlOrRd", domain = z)
}else{
pal <- colorFactor("YlOrRd", domain = z)
}
map = map %>%
addPolygons(fillColor = ~pal(z),
fillOpacity = 0.6,
weight=0.5,
color="white",
group=i,
label = as.character(z)) %>%
addLegend(pal = pal,
values = z,
title = i,
group = i,
position="bottomleft")
}
map <- map %>%
addLayersControl(overlayGroups = groups,
options = layersControlOptions(collapsed = FALSE))
for(i in groups){
map <- map %>% hideGroup(i)
}
map %>%
showGroup("BIORE_102")