In OscarGOGO/Makurhini: Makurhini: Analyzing landscape connectivity
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "60%",
message=FALSE, warning=FALSE
)
Makurhini: Analyzing landscape connectivity.
NEWS
Thank you for using Makurhini. We have a new version Makurhini 3.0!
An update was made in the estimation of short distances between nodes,
which can improve the processing of the functions that estimate
connectivity indices.
Two new functions have been added: MK_dPCIIC_links and
MK_Focal_nodes. The first one is used to estimate the link importance
for conservation and restoration. The second estimates the focal
Integral Index of Connectivity (IIC~f~) or the focal Probability of
Connectivity (PC~f~) under one or more distance thresholds. Furthermore,
this function estimates the composite connectivity index (CCI~f~; for
further details, please see Latorre-Cárdenas et al., 2023.
https://doi.org/10.3390/land12030631).
Overview
Makurhini (Connect in Purépecha language) is an R
package for calculating fragmentation and landscape connectivity indices
used in conservation planning. Makurhini provides a set of functions to
identify connectivity of protected areas networks and the importance of
landscape elements for maintaining connectivity. This package allows the
evaluation of scenarios under landscape connectivity changes and
presents an additional improvement, the inclusion of landscape
heterogeneity as a constraining factor for connectivity.
The network connectivity indices calculated in Makurhini package have
been previously published (e.g., Pascual-Hortal & Saura, 2006.
Landscape ecology, https://doi.org/10.1007/s10980-006-0013-z; Saura
& Pascual-Hortal, 2007. Lanscape and urban planning,
https://doi.org/10.1016/j.landurbplan.2007.03.005; Saura & Rubio,
2010. Ecography, https://doi.org/10.1111/j.1600-0587.2009.05760.x;
Saura et al., 2011. Ecological indicators,
https://doi.org/10.1016/j.ecolind.2010.06.011; Saura et al., 2017.
Ecological indicators,
http://dx.doi.org/10.1016/j.ecolind.2016.12.047; Saura et al., 2018.
Biological conservation,
https://doi.org/10.1016/j.biocon.2017.12.020), and it allows the
integration of efficient and useful workflow for landscape management
and monitoring of global conservation targets.
Citing Makurhini package
A formal paper detailing this package is forthcoming, but until it is
published, please use the something like the following to cite if you
use it in your work:
Godínez-Gómez, O. and Correa Ayram C.A. 2020. Makurhini:
Analyzing landscape connectivity.
Installation
- Depends: R (> 4.0.0), igraph (>= 1.2.6)
- Pre-install
Rtools.
- Pre-install devtools (
install.packages("devtools")) and
remotes (install.packages("remotes")) packages.
- It is recommended to install the R igraph package (>= 1.2.6)
beforehand.
You can install the released version of Makurhini from
GitHub with:
library(devtools)
library(remotes)
install_github("connectscape/Makurhini", dependencies = TRUE, upgrade = "never")
In case it does not appear in the list of packages, close the R session
and reopen.
If the following error occurs during installation:
Using github PAT
from envvar GITHUB_PAT Error: Failed to install 'unknown package' from
GitHub: HTTP error 401. Bad credentials
Then you can try the following:
Sys.getenv("GITHUB_PAT")
Sys.unsetenv("GITHUB_PAT")
Makurhini on Linux
To install Makurhini on linux consider the following steps:
1) Use the Linux command line to install the unit package:
`sudo apt-get install -y libudunits2-dev`
2) Use the Linux command line to install gdal:
`sudo apt install libgdal-dev`
3) Use the Linux command line to install libfontconfig and
libharfbuzz:
`sudo apt install libfontconfig1-dev`
`sudo apt install libharfbuzz-dev libfribidi-dev`
4) You can now install the devtools and remotes packages, and the
terra, raster and sf packages directly in your R or
RStudio.
`install.packages(c('remotes', 'devtools', 'terra', 'raster', 'sf'))`
5) Use the Linux command line to install igraph:
`sudo apt-get install libnlopt-dev`
`sudo apt-get install r-cran-igraph`
6) You can now install the gdistance, graph4lg and ggpubr
packages directly in your R or RStudio.
`install.packages(c('gdistance', 'graph4lg', 'ggpubr'))`
7) Now you can install Makurhini directly in your R or RStudio.
library(devtools)
library(remotes)
install_github("connectscape/Makurhini", dependencies = TRUE, upgrade = "never")
Note that the installation of Makurhini on Linux depends on your version
of operating system and that you manage to install the packages that
Makurhini depends on.
Summary of main Makurhini functions
library(formattable)
functions_MK <- data.frame(Function = c("MK_Fragmentation", "distancefile", "MK_RMCentrality", "MK_BCentrality", "MK_dPCIIC", "MK_dECA", "MK_ProtConn", "MK_ProtConnMult", "MK_ProtConn_raster", "MK_Connect_grid",
"MK_dPCIIC_links", "MK_Focal_nodes", "test_metric_distance"), Purpose = c("Calculate patch and landscape statistics (e.g., mean size patches, edge density, core area percent, shape index, fractal dimension index, effective mesh size).", "Get a table or matrix with the distances between pairs of nodes. Two Euclidean distances ('centroid' and 'edge') and two cost distances that consider the landscape heterogeneity ('least-cost' and 'commute-time, this last is analogous to the resistance distance of circuitscape, see ’gdistance’ package).", "Estimate centrality measures under one or several dispersal distances (e.g., betweenness centrality, node memberships, modularity). It uses the 'distancefile ()' to calculate the distances of the nodes so they can be calculated using Euclidean or cost distances that consider the landscape heterogeneity.", "Calculate the BC, BCIIC and BCPC indexes under one or several distance thresholds using the command line of CONEFOR. It uses the 'distancefile ()' to calculate the distances of the nodes so they can be calculated using Euclidean or cost distances that consider the landscape heterogeneity", "Calculate the integral index of connectivity (IIC) and probability of connectivity (PC) indices under one or several dispersal distances. It computes overall and index fractions (dPC or dIIC, intra, flux and connector) and the effect of restauration in the landscape connectivity when adding new nodes (restoration scenarios). It uses the 'distancefile()'.", "Estimate the Equivalent Connected Area (ECA) and compare the relative change in ECA (dECA) between time periods using one or several dispersal distances. It uses the 'distancefile()'.", "Estimate the Protected Connected (ProtConn) indicator and fractions for one region using one or several dispersal distances and transboundary buffer areas (e.g., ProtConn, ProtUnconn, RelConn, ProtConn[design], ProtConn[bound], ProtConn[Prot], ProtConn[Within], ProtConn[Contig], ProtConn[Trans], ProtConn[Unprot]). It uses the 'distancefile(). This function estimates what we call the ProtConn delta (dProtConn) which estimates the contribution of each protected area to connectivity in the region (ProtConn value)", "Estimate the ProtConn indicator and fractions for multiple regions. It uses the 'distancefile()'.", "Estimate Protected Connected (ProtConn) indicator and fractions for one region using raster inputs (nodes and region). It uses the 'distancefile()'.", "Compute the ProtConn indicator and fractions, PC or IIC overall connectivity metrics (ECA) in a regular grid. It uses the 'distancefile()'.", "Estimate the link importance for conservation and restoration. It calculates the contribution of each individual link to maintain (mode: link removal) or improve (mode: link change) the overall connectivity.", "Estimate the focal Integral Index of Connectivity or the focal Probability of Connectivity and the Composite Connectivity Index under one or more distance thresholds.", "Compare ECA or ProtConn connectivity metrics using one or up to four types of distances, computed in the 'distancefile()' function, and multiple dispersion distances."))
formattable(functions_MK, align =c("l","l"), list(`Function` = formatter(
"span", style = ~ style(font.style = "italic"))))
Examples
Protected Connected Land
(ProtConn) Equivalent Connectivity
Area (ECA) Integral index of
connectivity (IIC) and fractions (Intra, Flux and
Connector)
Probability of connectivity (PC) and fractions (Intra, Flux and
Connector)
[Centrality measures] (e.g., betweenness centrality, node memberships,
and modularity) [Fragmentation statistics]
Protected Connected Land (ProtConn) {#protected-connected-land-protconn}
library(Makurhini)
library(sf)
library(tmap)
library(mapview)
library(classInt)
library(ggplot2)
library(rmapshaper)
In this example, we assess the connectivity of Colombia's protected
areas network in 33 ecoregions of great importance to the country using
the Protected Connected Indicator (ProtConn). Particularly, we have
1,530 polygons of protected areas. The spatial information utilized in
this example is derived from the connectivity assessment study of
protected areas in the Andean Amazon region, as conducted by Castillo et
al., (2020). In order to estimate the ProtConn index, we employ the
MK_ProtConn() and MK_ProtConn_mult() functions. In this example, we
will utilize an organism median dispersal distance threshold of 10 km, a
connection probability pij = 0.5, and a transboundary PA search radius
of 50 km (for further details, please refer to Castillo et al., 2020;
Saura et al., 2017). We used Euclidean distances, particularly the
distances between edges to establish the connections between nodes
(PAs).
#Protected areas
load(system.file("extdata", "Protected_areas.rda",
package = "Makurhini", mustWork = TRUE))
nrow(Protected_areas)
#Ecoregions
data("Ecoregions", package = "Makurhini")
nrow(Ecoregions)
#For practicality we will use the first three columns.
Ecoregions <- Ecoregions[,1:3]
mask_ecoregions <- ms_dissolve(Ecoregions)
PAs_national <- ms_clip(Protected_areas, mask_ecoregions)
PAs_transnational <- ms_erase(Protected_areas, mask_ecoregions)
PAs_transnational$Type <- "PAs in neighboring countries"
PAs_subnational <- PAs_national[PAs_national$ESCALA_2 == "Subnacional",]
PAs_subnational$Type <- "Subnational PAs"
PAs_national <- PAs_national[PAs_national$ESCALA_2 == "Nacional",]
PAs_national$Type <- "National PAs"
PAs <- rbind(PAs_national, PAs_subnational, PAs_transnational)
PAs$Type <- factor(PAs$Type, levels = c("National PAs", "Subnational PAs", "PAs in neighboring countries"))
ggplot() +
geom_sf(data = Ecoregions, aes(fill = "Ecoregions"), color = "black") +
geom_sf(data = PAs, aes(fill=Type), color = NA) +
scale_fill_manual(name = "Type", values = c("#1DAB80", "#FF00C5", "#E06936", "#8D8BBE"))+
theme_minimal()
MK_ProtConn()
This function calculates the Protected Connected indicator (ProtConn)
for a region, its fractions and the importance (contribution) of each
protected area to maintain connectivity in the region under one or more
distance thresholds.
#Select first ecoregion
Ecoregion_1 <- Ecoregions[1,]
#keep = 0.6 simplify the geometry and reduce the number of vertices
ProtConn_1 <- MK_ProtConn(nodes = Protected_areas, region = Ecoregion_1,
area_unit = "ha",
distance = list(type= "edge", keep = 0.6),
distance_thresholds = 10000, probability = 0.5,
transboundary = 50000, plot = TRUE,
delta = TRUE, intern = FALSE)
A dynamic table is generated, displaying the ProtConn values and their
fractions. Additionally, a graph is produced, illustrating the ProtConn
values and comparing them with the percentage of protected and connected
area recommended for a region in the Aichi and Kumming-Montreal targets.
class(ProtConn_1)
names(ProtConn_1)
ProtConn_1$`Protected Connected (Viewer Panel)`
ProtConn_1$`ProtConn Plot`
ProtConn delta or the higher contribution to ProtConn value in the
ecoregion (grey polygon):
ggplot()+
geom_sf(data = Ecoregion_1)+
geom_sf(data = ProtConn_1$ProtConn_Delta,
aes(fill = cut(dProtConn, breaks = classIntervals(ProtConn_1$ProtConn_Delta$dProtConn, 5, "jenks")[[2]])), color = NA)+
scale_fill_brewer(type = "qual",
palette = "RdYlGn",
name = "dProtConn",
na.translate = FALSE)+
theme_minimal() +
theme(
legend.position.inside = c(0.1,0.21),
legend.key.height = unit(0.4, "cm"),
legend.key.width = unit(0.5, "cm")
)
MK_ProtConnMult()
In order to facilitate the estimation of the ProtConn index for a
variety of geographical regions, the MK_ProtConnMult function has been
incorporated into Makurhini, which enables the estimation of the
ProtConn indicator and fractions for different regions.
ProtConn_2 <- MK_ProtConnMult(nodes = Protected_areas,
region = Ecoregions,
area_unit = "ha",
distance = list(type= "edge"),
distance_thresholds = 10000,
probability = 0.5, transboundary = 50000,
plot = TRUE, parallel = 4)
ProtConn_2 <- readRDS("C:/Users/Usuario/Documents/R/TEST_Folder/ProtConn_1b.rds")
A dynamic table and vector (sf class) are generated, displaying the
ProtConn values and their fractions. Additionally, a graph is produced,
illustrating the ProtConn values and comparing them with the percentage
of protected and connected area recommended for a region in the Aichi
and Kumming-Montreal targets.
class(ProtConn_2)
names(ProtConn_2)
Table:
ProtConn_2$ProtConn_10000$ProtConn_overall10000
Plot showing the mean and standard deviation values:
ProtConn_2$ProtConn_10000$`ProtConn Plot`
Vector file of class sf:
head(ProtConn_2$ProtConn_10000$ProtConn_10000)
Visualize using ggplot2:
interv <- c(0.0701, 1.9375, 4.2690, 6.6786, 10.7244, 17.8158, 25.6303, 41.8570, 45.4735, 97.7425)
#We can use some package to get intervals for example classInt R Packge:
#library(classInt)
#interv <- classIntervals(ProtConn_2$ProtConn_10000$ProtConn_10000$ProtConn, 9, "jenks")[[2]]
ggplot()+
geom_sf(data = Ecoregions)+
geom_sf(data = ProtConn_2$ProtConn_10000$ProtConn_10000,
aes(fill = cut(ProtConn, breaks = interv)), color = NA)+
scale_fill_brewer(type = "qual",
palette = "RdYlGn",
name = "ProtConn",
na.translate = FALSE)+
theme_minimal() +
theme(
legend.position.inside = c(0.1,0.21),
legend.key.height = unit(0.4, "cm"),
legend.key.width = unit(0.5, "cm")
)
Equivalent Connectivity Area (ECA) {#equivalent-connectivity-area-eca}
Example in the Biosphere Reserve Mariposa Monarca, Mexico, with
old-growth vegetation fragments of four times (?list_forest_patches).
data("list_forest_patches", package = "Makurhini")
data("study_area", package = "Makurhini")
class(list_forest_patches)
Max_attribute <- unit_convert(st_area(study_area), "m2", "ha")
dECA_test <- MK_dECA(nodes= list_forest_patches, attribute = NULL, area_unit = "ha",
distance = list(type= "centroid"), metric = "PC",
probability = 0.05, distance_thresholds = 5000,
LA = Max_attribute, plot= c("1993", "2003", "2007", "2011"), intern = FALSE)
ECA table:
{width="504"}
Another way to analyze the ECA (and ProtConn indicator) is by using the
'MK_Connect_grid()' that estimates the index values on a grid. An
example of its application is the following, on the Andean-Amazon
Piedmont. The analysis was performed using a grid of hexagons each with
an area of 10,000 ha and a forest/non-forest map to measure changes in
Andean-Amazon connectivity.
Integral index of connectivity (IIC) and fractions (Intra, Flux and Connector) {#integral-index-of-connectivity-iic-and-fractions-intra-flux-and-connector}
In this example, the MK_dPCIIC() function was applied to estimate the
connectivity of 404 remnant habitat patches, which were modeled to 40
non-volant mammal species of the Trans-Mexican Volcanic System (TMVS) by
Correa Ayram et al., (2017). The landscape resistance to dispersal was
estimated at a 100-meter resolution using a spatial human footprint
index, land use intensity, time of human landscape intervention,
biophysical vulnerability, fragmentation, and habitat loss (Correa Ayram
et al., 2017). The raster was aggregated by a factor of 5 to change its
original resolution from 100m to 500m. To represent different dispersal
capacities of multiple species we considered the following median
(associated to a probability of 0.5) distance thresholds: 250, 1500,
3000, and 10,000 meters. These four distances group the 40 species
according to their dispersal distance requirements
#Habitat nodes
data("habitat_nodes", package = "Makurhini")
nrow(habitat_nodes)
#Study area
data("TMVS", package = "Makurhini")
#Resistance
data("resistance_matrix", package = "Makurhini")
raster_map <- as(resistance_matrix, "SpatialPixelsDataFrame")
raster_map <- as.data.frame(raster_map)
colnames(raster_map) <- c("value", "x", "y")
ggplot() +
geom_tile(data = raster_map, aes(x = x, y = y, fill = value), alpha = 0.8) +
geom_sf(data = TMVS, aes(color = "Study area"), fill = NA, color = "black") +
geom_sf(data = habitat_nodes, aes(color = "Habitat nodes"), fill = "forestgreen") +
scale_fill_gradientn(colors = c("#000004FF", "#1B0C42FF", "#4B0C6BFF", "#781C6DFF",
"#A52C60FF", "#CF4446FF", "#ED6925FF", "#FB9A06FF",
"#F7D03CFF", "#FCFFA4FF"))+
scale_color_manual(name = "", values = "black")+
theme_minimal() +
theme(axis.title.x = element_blank(),
axis.title.y = element_blank())
PC_example_2 <- MK_dPCIIC(nodes = habitat_nodes,
attribute = NULL,
distance = list(type = "least-cost",
resistance = resistance_matrix),
parallel = NULL,
metric = "PC",
probability = 0.5,
distance_thresholds = c(250, 1500, 3000, 10000))
PC_example_2 <- readRDS("C:/Users/Usuario/Documents/R/TEST_Folder/PC_example_2.rds")
We obtain a list object where each element is a result for each
distance threshold.
class(PC_example_2)
names(PC_example_2)
head(PC_example_2$d10000)
Each element of the list is a vector type object that can be exported
using the sf functions and in its vector formats (e.g., shp, gpkg) using
the sf package (Pebesma et al., 2024), for example:
write_sf(PC_example_2$d10000, “.../dPC_d0000.shp”)
We can use, for example, ggplot2 or tmap R packages, to map the results:
interv <- c(0.0000021, 0.0596058, 0.1612625, 0.2943665, 0.4937340, 0.8902072, 1.1303198, 1.7556675, 3.4064392, 80.7958156)
#Keep the same range of values of PC_example_1 for comparison, only the highest range changes.
interv[length(interv)] <- max(PC_example_2$d10000$dPC)
ggplot()+
geom_sf(data = TMVS)+
geom_sf(data = PC_example_2$d10000, aes(fill = cut(dPC, breaks = interv)), color = NA)+
scale_fill_brewer(type = "qual",
palette = "RdYlGn",
name = "dPC",
na.translate = FALSE)+
theme_minimal() +
theme(
legend.position = "inside",
legend.position.inside = c(0.1, 0.21),
legend.key.height = unit(0.2, "cm"),
legend.key.width = unit(0.3, "cm"),
legend.text = element_text(size = 5.5),
legend.title = element_text(size = 5.5)
)+ labs(title="Least-cost distance")
tmap_mode("plot")
c <-9
dPC <- tm_shape(PC_example_2$d10000) + tm_fill("dPC", palette = RColorBrewer::brewer.pal(c, "RdYlGn"),
breaks = classIntervals(PC_example_2$d10000$dPC, c, "jenks")[[2]])+
tm_style("cobalt")+ tm_layout(legend.width = 0.43, legend.height = 0.43)
dintra <- tm_shape(PC_example_2$d10000) + tm_fill("dPCintra", palette = RColorBrewer::brewer.pal(c, "RdYlGn"), breaks = classIntervals(PC_example_2$d10000$dPCintra, c, "jenks")[[2]])+
tm_style("cobalt")+ tm_layout(legend.width = 0.43, legend.height = 0.43)
dflux <- tm_shape(PC_example_2$d10000) + tm_fill("dPCflux", palette = RColorBrewer::brewer.pal(c, "RdYlGn"), breaks = classIntervals(PC_example_2$d10000$dPCflux, c, "jenks")[[2]])+ tm_style("cobalt")+ tm_layout(legend.width = 0.43, legend.height = 0.43)
dconect <- tm_shape(PC_example_2$d10000) + tm_fill("dPCconnector", palette = RColorBrewer::brewer.pal(c, "RdYlGn"), breaks = classIntervals(PC_example_2$d10000$dPCconnector, c, "jenks")[[2]])+ tm_style("cobalt")+ tm_layout(legend.width = 0.43, legend.height = 0.43)
tmap_arrange(dPC, dintra, dflux, dconect)
Centrality measures
centrality_test <- MK_RMCentrality(nodes = habitat_nodes,
distance = list(type = "centroid"),
distance_thresholds = 10000,
probability = 0.05,
write = NULL)
head(centrality_test)
Examples:
clustertest <- tm_shape(centrality_test) + tm_fill("cluster", palette = RColorBrewer::brewer.pal(c, "PuOr"), breaks = classIntervals(centrality_test$cluster, c, "jenks")[[2]])+ tm_style("cobalt")+ tm_layout(legend.width = 0.43, legend.height = 0.43)
modules <- tm_shape(centrality_test) + tm_fill("modules", palette = RColorBrewer::brewer.pal(c, "PuOr"), breaks = classIntervals(centrality_test$modules, c, "jenks")[[2]])+ tm_style("cobalt")+ tm_layout(legend.width = 0.43, legend.height = 0.43)
tmap_arrange(clustertest, modules)
Moreover, you can change distance using the distance
(?distancefile) argument:
Euclidean distances:
- distance = list(type= "centroid")
- distance = list(type= "edge")
Least cost distances:
- distance = list(type= "least-cost", resistance = "resistance
raster")
- distance = list(type= "commute-time", resistance = "resistance
raster")
Fragmentation statistics
'MK_Fragmentation()' estimates fragmentation statistics at the
landscape and patch/node level.
Example:
data("habitat_nodes", package = "Makurhini")
nrow(habitat_nodes) # Number of nodes
To define the edge of the nodes we can use, for example, a distance of
500 m from the limit of the nodes.
Fragmentation_test <- MK_Fragmentation(nodes = habitat_nodes, edge_distance = 500,
plot = TRUE, min_node_area = 100,
landscape_area = NULL, area_unit = "km2",
perimeter_unit = "km")
- The results are presented as a list, the first result is called
"Summary landscape metrics (Viewer Panel)" and it has
fragmentation statistics at landscape level.
class(Fragmentation_test)
names(Fragmentation_test)
Fragmentation_test$`Summary landscape metrics (Viewer Panel)`
- The second output "Patch statistics shapefile" is a shapefile with
patch level fragmentation statistics that can be saved using
write_sf() from 'sf' package
(https://cran.r-project.org/web/packages/sf/index.html).
head(Fragmentation_test[[2]])
library(classInt)
tmap_mode("plot")
c <-9
CAP <- tm_shape(Fragmentation_test[[2]]) + tm_fill("CAPercent", palette = RColorBrewer::brewer.pal(c, "RdYlGn"),
breaks = classIntervals(Fragmentation_test[[2]]$CAPercent, c, "jenks")[[2]])+
tm_style("cobalt")+ tm_layout(legend.width = 0.43, legend.height = 0.43)
Per <- tm_shape(Fragmentation_test[[2]]) + tm_fill("Perimeter", palette = rev(RColorBrewer::brewer.pal(c, "RdYlGn")),
breaks = classIntervals(Fragmentation_test[[2]]$Perimeter, c, "jenks")[[2]])+
tm_style("cobalt")+ tm_layout(legend.width = 0.43, legend.height = 0.43)
SH <- tm_shape(Fragmentation_test[[2]]) + tm_fill("ShapeIndex", palette = rev(RColorBrewer::brewer.pal(c, "PRGn")),
breaks = classIntervals(Fragmentation_test[[2]]$ShapeIndex, c, "jenks")[[2]])+
tm_style("cobalt")+ tm_layout(legend.width = 0.43, legend.height = 0.43)
Frac <- tm_shape(Fragmentation_test[[2]]) + tm_fill("FRAC", palette = rev(RColorBrewer::brewer.pal(c, "PRGn")),
breaks = classIntervals(Fragmentation_test[[2]]$FRAC, c, "quantile")[[2]])+
tm_style("cobalt")+ tm_layout(legend.width = 0.43, legend.height = 0.43)
tmap_arrange(CAP, Per, SH, Frac)
We can make a loop where we explore different edge depths. In the
following example, We will explore 10 edge depths (edge_distance
argument): 100, 200, 300, 400, 500, 600, 700, 800, 900 and 1000 meters.
We will apply the 'MK_Fragmentation' function using the previous
distances and then, we will extract the core area percentage and edge
percentage statistics. Finally, we will plot the average of the patch
core area percentage and edge percentage (% core area + % edge = 100%).
library(purrr)
Fragmentation_test.2 <- map_dfr(seq(100, 1000, 100), function(x){
x.1 <- MK_Fragmentation(nodes = habitat_nodes,
edge_distance = x, plot = FALSE)[[2]]
CA <- mean(x.1$CAPercent)
Edge <- mean(x.1$EdgePercent)
x.2 <- rbind(data.frame('Edge distance' = x, Type = "Core Area", Percentage = CA),
data.frame('Edge distance' = x, Type = "Edge", Percentage = Edge))
return(x.2)
})
library(ggplot2)
ggplot(Fragmentation_test.2, aes(x = Edge.distance, y = Percentage, group = Type)) +
geom_line(aes(color = Type))+
geom_point(aes(color = Type))+ ylim(0,100)+
scale_x_continuous("Edge depth distance (m)",
labels = as.character(Fragmentation_test.2$Edge.distance), breaks = Fragmentation_test.2$Edge.distance)+
scale_color_brewer(palette="Dark2")+
theme_classic()
The mean core area percentage (the mean node/patch area that exhibits
the least possible edge effect) for all patches is observed to decline
by over 60% when an edge depth distance of 1 km is considered.
| Edge depth distance (m) | Core Area (%) |
|-------------------------|:-------------:|
| 100 | 65.76% |
| 500 | 12.86% |
| 1000 | 3.63% |
OscarGOGO/Makurhini documentation built on Jan. 9, 2025, 1:20 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.path = "man/figures/README-", out.width = "60%", message=FALSE, warning=FALSE )
Makurhini: Analyzing landscape connectivity.
NEWS
Thank you for using Makurhini. We have a new version Makurhini 3.0!
An update was made in the estimation of short distances between nodes, which can improve the processing of the functions that estimate connectivity indices.
Two new functions have been added: MK_dPCIIC_links and MK_Focal_nodes. The first one is used to estimate the link importance for conservation and restoration. The second estimates the focal Integral Index of Connectivity (IIC~f~) or the focal Probability of Connectivity (PC~f~) under one or more distance thresholds. Furthermore, this function estimates the composite connectivity index (CCI~f~; for further details, please see Latorre-Cárdenas et al., 2023. https://doi.org/10.3390/land12030631).
Overview
Makurhini (Connect in Purépecha language) is an R package for calculating fragmentation and landscape connectivity indices used in conservation planning. Makurhini provides a set of functions to identify connectivity of protected areas networks and the importance of landscape elements for maintaining connectivity. This package allows the evaluation of scenarios under landscape connectivity changes and presents an additional improvement, the inclusion of landscape heterogeneity as a constraining factor for connectivity.
The network connectivity indices calculated in Makurhini package have been previously published (e.g., Pascual-Hortal & Saura, 2006. Landscape ecology, https://doi.org/10.1007/s10980-006-0013-z; Saura & Pascual-Hortal, 2007. Lanscape and urban planning, https://doi.org/10.1016/j.landurbplan.2007.03.005; Saura & Rubio, 2010. Ecography, https://doi.org/10.1111/j.1600-0587.2009.05760.x; Saura et al., 2011. Ecological indicators, https://doi.org/10.1016/j.ecolind.2010.06.011; Saura et al., 2017. Ecological indicators, http://dx.doi.org/10.1016/j.ecolind.2016.12.047; Saura et al., 2018. Biological conservation, https://doi.org/10.1016/j.biocon.2017.12.020), and it allows the integration of efficient and useful workflow for landscape management and monitoring of global conservation targets.
Citing Makurhini package
A formal paper detailing this package is forthcoming, but until it is published, please use the something like the following to cite if you use it in your work:
Godínez-Gómez, O. and Correa Ayram C.A. 2020. Makurhini:
Analyzing landscape connectivity.
Installation
- Depends: R (> 4.0.0), igraph (>= 1.2.6)
- Pre-install Rtools.
- Pre-install devtools (
install.packages("devtools")) and remotes (install.packages("remotes")) packages. - It is recommended to install the R igraph package (>= 1.2.6) beforehand.
You can install the released version of Makurhini from GitHub with:
library(devtools) library(remotes) install_github("connectscape/Makurhini", dependencies = TRUE, upgrade = "never")
In case it does not appear in the list of packages, close the R session and reopen.
If the following error occurs during installation:
Using github PAT from envvar GITHUB_PAT Error: Failed to install 'unknown package' from GitHub: HTTP error 401. Bad credentials
Then you can try the following:
Sys.getenv("GITHUB_PAT") Sys.unsetenv("GITHUB_PAT")
Makurhini on Linux
To install Makurhini on linux consider the following steps:
1) Use the Linux command line to install the unit package:
`sudo apt-get install -y libudunits2-dev`
2) Use the Linux command line to install gdal:
`sudo apt install libgdal-dev`
3) Use the Linux command line to install libfontconfig and libharfbuzz:
`sudo apt install libfontconfig1-dev` `sudo apt install libharfbuzz-dev libfribidi-dev`
4) You can now install the devtools and remotes packages, and the terra, raster and sf packages directly in your R or RStudio.
`install.packages(c('remotes', 'devtools', 'terra', 'raster', 'sf'))`
5) Use the Linux command line to install igraph:
`sudo apt-get install libnlopt-dev` `sudo apt-get install r-cran-igraph`
6) You can now install the gdistance, graph4lg and ggpubr packages directly in your R or RStudio.
`install.packages(c('gdistance', 'graph4lg', 'ggpubr'))`
7) Now you can install Makurhini directly in your R or RStudio.
library(devtools) library(remotes) install_github("connectscape/Makurhini", dependencies = TRUE, upgrade = "never")
Note that the installation of Makurhini on Linux depends on your version of operating system and that you manage to install the packages that Makurhini depends on.
Summary of main Makurhini functions
library(formattable) functions_MK <- data.frame(Function = c("MK_Fragmentation", "distancefile", "MK_RMCentrality", "MK_BCentrality", "MK_dPCIIC", "MK_dECA", "MK_ProtConn", "MK_ProtConnMult", "MK_ProtConn_raster", "MK_Connect_grid", "MK_dPCIIC_links", "MK_Focal_nodes", "test_metric_distance"), Purpose = c("Calculate patch and landscape statistics (e.g., mean size patches, edge density, core area percent, shape index, fractal dimension index, effective mesh size).", "Get a table or matrix with the distances between pairs of nodes. Two Euclidean distances ('centroid' and 'edge') and two cost distances that consider the landscape heterogeneity ('least-cost' and 'commute-time, this last is analogous to the resistance distance of circuitscape, see ’gdistance’ package).", "Estimate centrality measures under one or several dispersal distances (e.g., betweenness centrality, node memberships, modularity). It uses the 'distancefile ()' to calculate the distances of the nodes so they can be calculated using Euclidean or cost distances that consider the landscape heterogeneity.", "Calculate the BC, BCIIC and BCPC indexes under one or several distance thresholds using the command line of CONEFOR. It uses the 'distancefile ()' to calculate the distances of the nodes so they can be calculated using Euclidean or cost distances that consider the landscape heterogeneity", "Calculate the integral index of connectivity (IIC) and probability of connectivity (PC) indices under one or several dispersal distances. It computes overall and index fractions (dPC or dIIC, intra, flux and connector) and the effect of restauration in the landscape connectivity when adding new nodes (restoration scenarios). It uses the 'distancefile()'.", "Estimate the Equivalent Connected Area (ECA) and compare the relative change in ECA (dECA) between time periods using one or several dispersal distances. It uses the 'distancefile()'.", "Estimate the Protected Connected (ProtConn) indicator and fractions for one region using one or several dispersal distances and transboundary buffer areas (e.g., ProtConn, ProtUnconn, RelConn, ProtConn[design], ProtConn[bound], ProtConn[Prot], ProtConn[Within], ProtConn[Contig], ProtConn[Trans], ProtConn[Unprot]). It uses the 'distancefile(). This function estimates what we call the ProtConn delta (dProtConn) which estimates the contribution of each protected area to connectivity in the region (ProtConn value)", "Estimate the ProtConn indicator and fractions for multiple regions. It uses the 'distancefile()'.", "Estimate Protected Connected (ProtConn) indicator and fractions for one region using raster inputs (nodes and region). It uses the 'distancefile()'.", "Compute the ProtConn indicator and fractions, PC or IIC overall connectivity metrics (ECA) in a regular grid. It uses the 'distancefile()'.", "Estimate the link importance for conservation and restoration. It calculates the contribution of each individual link to maintain (mode: link removal) or improve (mode: link change) the overall connectivity.", "Estimate the focal Integral Index of Connectivity or the focal Probability of Connectivity and the Composite Connectivity Index under one or more distance thresholds.", "Compare ECA or ProtConn connectivity metrics using one or up to four types of distances, computed in the 'distancefile()' function, and multiple dispersion distances.")) formattable(functions_MK, align =c("l","l"), list(`Function` = formatter( "span", style = ~ style(font.style = "italic"))))
Examples
Protected Connected Land (ProtConn) Equivalent Connectivity Area (ECA) Integral index of connectivity (IIC) and fractions (Intra, Flux and Connector) Probability of connectivity (PC) and fractions (Intra, Flux and Connector) [Centrality measures] (e.g., betweenness centrality, node memberships, and modularity) [Fragmentation statistics]
Protected Connected Land (ProtConn) {#protected-connected-land-protconn}
library(Makurhini) library(sf) library(tmap) library(mapview) library(classInt) library(ggplot2) library(rmapshaper)
In this example, we assess the connectivity of Colombia's protected
areas network in 33 ecoregions of great importance to the country using
the Protected Connected Indicator (ProtConn). Particularly, we have
1,530 polygons of protected areas. The spatial information utilized in
this example is derived from the connectivity assessment study of
protected areas in the Andean Amazon region, as conducted by Castillo et
al., (2020). In order to estimate the ProtConn index, we employ the
MK_ProtConn() and MK_ProtConn_mult() functions. In this example, we
will utilize an organism median dispersal distance threshold of 10 km, a
connection probability pij = 0.5, and a transboundary PA search radius
of 50 km (for further details, please refer to Castillo et al., 2020;
Saura et al., 2017). We used Euclidean distances, particularly the
distances between edges to establish the connections between nodes
(PAs).
#Protected areas load(system.file("extdata", "Protected_areas.rda", package = "Makurhini", mustWork = TRUE)) nrow(Protected_areas) #Ecoregions data("Ecoregions", package = "Makurhini") nrow(Ecoregions) #For practicality we will use the first three columns. Ecoregions <- Ecoregions[,1:3]
mask_ecoregions <- ms_dissolve(Ecoregions) PAs_national <- ms_clip(Protected_areas, mask_ecoregions) PAs_transnational <- ms_erase(Protected_areas, mask_ecoregions) PAs_transnational$Type <- "PAs in neighboring countries" PAs_subnational <- PAs_national[PAs_national$ESCALA_2 == "Subnacional",] PAs_subnational$Type <- "Subnational PAs" PAs_national <- PAs_national[PAs_national$ESCALA_2 == "Nacional",] PAs_national$Type <- "National PAs" PAs <- rbind(PAs_national, PAs_subnational, PAs_transnational) PAs$Type <- factor(PAs$Type, levels = c("National PAs", "Subnational PAs", "PAs in neighboring countries")) ggplot() + geom_sf(data = Ecoregions, aes(fill = "Ecoregions"), color = "black") + geom_sf(data = PAs, aes(fill=Type), color = NA) + scale_fill_manual(name = "Type", values = c("#1DAB80", "#FF00C5", "#E06936", "#8D8BBE"))+ theme_minimal()
MK_ProtConn()
This function calculates the Protected Connected indicator (ProtConn) for a region, its fractions and the importance (contribution) of each protected area to maintain connectivity in the region under one or more distance thresholds.
#Select first ecoregion Ecoregion_1 <- Ecoregions[1,] #keep = 0.6 simplify the geometry and reduce the number of vertices ProtConn_1 <- MK_ProtConn(nodes = Protected_areas, region = Ecoregion_1, area_unit = "ha", distance = list(type= "edge", keep = 0.6), distance_thresholds = 10000, probability = 0.5, transboundary = 50000, plot = TRUE, delta = TRUE, intern = FALSE)
A dynamic table is generated, displaying the ProtConn values and their fractions. Additionally, a graph is produced, illustrating the ProtConn values and comparing them with the percentage of protected and connected area recommended for a region in the Aichi and Kumming-Montreal targets.
class(ProtConn_1) names(ProtConn_1)
ProtConn_1$`Protected Connected (Viewer Panel)`
ProtConn_1$`ProtConn Plot`
ProtConn delta or the higher contribution to ProtConn value in the ecoregion (grey polygon):
ggplot()+ geom_sf(data = Ecoregion_1)+ geom_sf(data = ProtConn_1$ProtConn_Delta, aes(fill = cut(dProtConn, breaks = classIntervals(ProtConn_1$ProtConn_Delta$dProtConn, 5, "jenks")[[2]])), color = NA)+ scale_fill_brewer(type = "qual", palette = "RdYlGn", name = "dProtConn", na.translate = FALSE)+ theme_minimal() + theme( legend.position.inside = c(0.1,0.21), legend.key.height = unit(0.4, "cm"), legend.key.width = unit(0.5, "cm") )
MK_ProtConnMult()
In order to facilitate the estimation of the ProtConn index for a variety of geographical regions, the MK_ProtConnMult function has been incorporated into Makurhini, which enables the estimation of the ProtConn indicator and fractions for different regions.
ProtConn_2 <- MK_ProtConnMult(nodes = Protected_areas, region = Ecoregions, area_unit = "ha", distance = list(type= "edge"), distance_thresholds = 10000, probability = 0.5, transboundary = 50000, plot = TRUE, parallel = 4)
ProtConn_2 <- readRDS("C:/Users/Usuario/Documents/R/TEST_Folder/ProtConn_1b.rds")
A dynamic table and vector (sf class) are generated, displaying the ProtConn values and their fractions. Additionally, a graph is produced, illustrating the ProtConn values and comparing them with the percentage of protected and connected area recommended for a region in the Aichi and Kumming-Montreal targets.
class(ProtConn_2) names(ProtConn_2)
Table:
ProtConn_2$ProtConn_10000$ProtConn_overall10000
Plot showing the mean and standard deviation values:
ProtConn_2$ProtConn_10000$`ProtConn Plot`
Vector file of class sf:
head(ProtConn_2$ProtConn_10000$ProtConn_10000)
Visualize using ggplot2:
interv <- c(0.0701, 1.9375, 4.2690, 6.6786, 10.7244, 17.8158, 25.6303, 41.8570, 45.4735, 97.7425)
#We can use some package to get intervals for example classInt R Packge: #library(classInt) #interv <- classIntervals(ProtConn_2$ProtConn_10000$ProtConn_10000$ProtConn, 9, "jenks")[[2]]
ggplot()+ geom_sf(data = Ecoregions)+ geom_sf(data = ProtConn_2$ProtConn_10000$ProtConn_10000, aes(fill = cut(ProtConn, breaks = interv)), color = NA)+ scale_fill_brewer(type = "qual", palette = "RdYlGn", name = "ProtConn", na.translate = FALSE)+ theme_minimal() + theme( legend.position.inside = c(0.1,0.21), legend.key.height = unit(0.4, "cm"), legend.key.width = unit(0.5, "cm") )
Equivalent Connectivity Area (ECA) {#equivalent-connectivity-area-eca}
Example in the Biosphere Reserve Mariposa Monarca, Mexico, with old-growth vegetation fragments of four times (?list_forest_patches).
data("list_forest_patches", package = "Makurhini") data("study_area", package = "Makurhini") class(list_forest_patches) Max_attribute <- unit_convert(st_area(study_area), "m2", "ha")
dECA_test <- MK_dECA(nodes= list_forest_patches, attribute = NULL, area_unit = "ha", distance = list(type= "centroid"), metric = "PC", probability = 0.05, distance_thresholds = 5000, LA = Max_attribute, plot= c("1993", "2003", "2007", "2011"), intern = FALSE)
ECA table:
{width="504"}
Another way to analyze the ECA (and ProtConn indicator) is by using the 'MK_Connect_grid()' that estimates the index values on a grid. An example of its application is the following, on the Andean-Amazon Piedmont. The analysis was performed using a grid of hexagons each with an area of 10,000 ha and a forest/non-forest map to measure changes in Andean-Amazon connectivity.
Integral index of connectivity (IIC) and fractions (Intra, Flux and Connector) {#integral-index-of-connectivity-iic-and-fractions-intra-flux-and-connector}
In this example, the MK_dPCIIC() function was applied to estimate the
connectivity of 404 remnant habitat patches, which were modeled to 40
non-volant mammal species of the Trans-Mexican Volcanic System (TMVS) by
Correa Ayram et al., (2017). The landscape resistance to dispersal was
estimated at a 100-meter resolution using a spatial human footprint
index, land use intensity, time of human landscape intervention,
biophysical vulnerability, fragmentation, and habitat loss (Correa Ayram
et al., 2017). The raster was aggregated by a factor of 5 to change its
original resolution from 100m to 500m. To represent different dispersal
capacities of multiple species we considered the following median
(associated to a probability of 0.5) distance thresholds: 250, 1500,
3000, and 10,000 meters. These four distances group the 40 species
according to their dispersal distance requirements
#Habitat nodes data("habitat_nodes", package = "Makurhini") nrow(habitat_nodes) #Study area data("TMVS", package = "Makurhini") #Resistance data("resistance_matrix", package = "Makurhini")
raster_map <- as(resistance_matrix, "SpatialPixelsDataFrame") raster_map <- as.data.frame(raster_map) colnames(raster_map) <- c("value", "x", "y") ggplot() + geom_tile(data = raster_map, aes(x = x, y = y, fill = value), alpha = 0.8) + geom_sf(data = TMVS, aes(color = "Study area"), fill = NA, color = "black") + geom_sf(data = habitat_nodes, aes(color = "Habitat nodes"), fill = "forestgreen") + scale_fill_gradientn(colors = c("#000004FF", "#1B0C42FF", "#4B0C6BFF", "#781C6DFF", "#A52C60FF", "#CF4446FF", "#ED6925FF", "#FB9A06FF", "#F7D03CFF", "#FCFFA4FF"))+ scale_color_manual(name = "", values = "black")+ theme_minimal() + theme(axis.title.x = element_blank(), axis.title.y = element_blank())
PC_example_2 <- MK_dPCIIC(nodes = habitat_nodes, attribute = NULL, distance = list(type = "least-cost", resistance = resistance_matrix), parallel = NULL, metric = "PC", probability = 0.5, distance_thresholds = c(250, 1500, 3000, 10000))
PC_example_2 <- readRDS("C:/Users/Usuario/Documents/R/TEST_Folder/PC_example_2.rds")
We obtain a list object where each element is a result for each
distance threshold.
class(PC_example_2) names(PC_example_2) head(PC_example_2$d10000)
Each element of the list is a vector type object that can be exported using the sf functions and in its vector formats (e.g., shp, gpkg) using the sf package (Pebesma et al., 2024), for example:
write_sf(PC_example_2$d10000, “.../dPC_d0000.shp”)
We can use, for example, ggplot2 or tmap R packages, to map the results:
interv <- c(0.0000021, 0.0596058, 0.1612625, 0.2943665, 0.4937340, 0.8902072, 1.1303198, 1.7556675, 3.4064392, 80.7958156)
#Keep the same range of values of PC_example_1 for comparison, only the highest range changes. interv[length(interv)] <- max(PC_example_2$d10000$dPC) ggplot()+ geom_sf(data = TMVS)+ geom_sf(data = PC_example_2$d10000, aes(fill = cut(dPC, breaks = interv)), color = NA)+ scale_fill_brewer(type = "qual", palette = "RdYlGn", name = "dPC", na.translate = FALSE)+ theme_minimal() + theme( legend.position = "inside", legend.position.inside = c(0.1, 0.21), legend.key.height = unit(0.2, "cm"), legend.key.width = unit(0.3, "cm"), legend.text = element_text(size = 5.5), legend.title = element_text(size = 5.5) )+ labs(title="Least-cost distance")
tmap_mode("plot") c <-9 dPC <- tm_shape(PC_example_2$d10000) + tm_fill("dPC", palette = RColorBrewer::brewer.pal(c, "RdYlGn"), breaks = classIntervals(PC_example_2$d10000$dPC, c, "jenks")[[2]])+ tm_style("cobalt")+ tm_layout(legend.width = 0.43, legend.height = 0.43) dintra <- tm_shape(PC_example_2$d10000) + tm_fill("dPCintra", palette = RColorBrewer::brewer.pal(c, "RdYlGn"), breaks = classIntervals(PC_example_2$d10000$dPCintra, c, "jenks")[[2]])+ tm_style("cobalt")+ tm_layout(legend.width = 0.43, legend.height = 0.43) dflux <- tm_shape(PC_example_2$d10000) + tm_fill("dPCflux", palette = RColorBrewer::brewer.pal(c, "RdYlGn"), breaks = classIntervals(PC_example_2$d10000$dPCflux, c, "jenks")[[2]])+ tm_style("cobalt")+ tm_layout(legend.width = 0.43, legend.height = 0.43) dconect <- tm_shape(PC_example_2$d10000) + tm_fill("dPCconnector", palette = RColorBrewer::brewer.pal(c, "RdYlGn"), breaks = classIntervals(PC_example_2$d10000$dPCconnector, c, "jenks")[[2]])+ tm_style("cobalt")+ tm_layout(legend.width = 0.43, legend.height = 0.43) tmap_arrange(dPC, dintra, dflux, dconect)
Centrality measures
centrality_test <- MK_RMCentrality(nodes = habitat_nodes, distance = list(type = "centroid"), distance_thresholds = 10000, probability = 0.05, write = NULL) head(centrality_test)
Examples:
clustertest <- tm_shape(centrality_test) + tm_fill("cluster", palette = RColorBrewer::brewer.pal(c, "PuOr"), breaks = classIntervals(centrality_test$cluster, c, "jenks")[[2]])+ tm_style("cobalt")+ tm_layout(legend.width = 0.43, legend.height = 0.43) modules <- tm_shape(centrality_test) + tm_fill("modules", palette = RColorBrewer::brewer.pal(c, "PuOr"), breaks = classIntervals(centrality_test$modules, c, "jenks")[[2]])+ tm_style("cobalt")+ tm_layout(legend.width = 0.43, legend.height = 0.43) tmap_arrange(clustertest, modules)
Moreover, you can change distance using the distance
(?distancefile) argument:
Euclidean distances:
- distance = list(type= "centroid")
- distance = list(type= "edge")
Least cost distances:
- distance = list(type= "least-cost", resistance = "resistance raster")
- distance = list(type= "commute-time", resistance = "resistance raster")
Fragmentation statistics
'MK_Fragmentation()' estimates fragmentation statistics at the landscape and patch/node level.
Example:
data("habitat_nodes", package = "Makurhini") nrow(habitat_nodes) # Number of nodes
To define the edge of the nodes we can use, for example, a distance of 500 m from the limit of the nodes.
Fragmentation_test <- MK_Fragmentation(nodes = habitat_nodes, edge_distance = 500, plot = TRUE, min_node_area = 100, landscape_area = NULL, area_unit = "km2", perimeter_unit = "km")
- The results are presented as a list, the first result is called "Summary landscape metrics (Viewer Panel)" and it has fragmentation statistics at landscape level.
class(Fragmentation_test) names(Fragmentation_test) Fragmentation_test$`Summary landscape metrics (Viewer Panel)`
- The second output "Patch statistics shapefile" is a shapefile with patch level fragmentation statistics that can be saved using write_sf() from 'sf' package (https://cran.r-project.org/web/packages/sf/index.html).
head(Fragmentation_test[[2]])
library(classInt) tmap_mode("plot") c <-9 CAP <- tm_shape(Fragmentation_test[[2]]) + tm_fill("CAPercent", palette = RColorBrewer::brewer.pal(c, "RdYlGn"), breaks = classIntervals(Fragmentation_test[[2]]$CAPercent, c, "jenks")[[2]])+ tm_style("cobalt")+ tm_layout(legend.width = 0.43, legend.height = 0.43) Per <- tm_shape(Fragmentation_test[[2]]) + tm_fill("Perimeter", palette = rev(RColorBrewer::brewer.pal(c, "RdYlGn")), breaks = classIntervals(Fragmentation_test[[2]]$Perimeter, c, "jenks")[[2]])+ tm_style("cobalt")+ tm_layout(legend.width = 0.43, legend.height = 0.43) SH <- tm_shape(Fragmentation_test[[2]]) + tm_fill("ShapeIndex", palette = rev(RColorBrewer::brewer.pal(c, "PRGn")), breaks = classIntervals(Fragmentation_test[[2]]$ShapeIndex, c, "jenks")[[2]])+ tm_style("cobalt")+ tm_layout(legend.width = 0.43, legend.height = 0.43) Frac <- tm_shape(Fragmentation_test[[2]]) + tm_fill("FRAC", palette = rev(RColorBrewer::brewer.pal(c, "PRGn")), breaks = classIntervals(Fragmentation_test[[2]]$FRAC, c, "quantile")[[2]])+ tm_style("cobalt")+ tm_layout(legend.width = 0.43, legend.height = 0.43) tmap_arrange(CAP, Per, SH, Frac)
We can make a loop where we explore different edge depths. In the following example, We will explore 10 edge depths (edge_distance argument): 100, 200, 300, 400, 500, 600, 700, 800, 900 and 1000 meters. We will apply the 'MK_Fragmentation' function using the previous distances and then, we will extract the core area percentage and edge percentage statistics. Finally, we will plot the average of the patch core area percentage and edge percentage (% core area + % edge = 100%).
library(purrr) Fragmentation_test.2 <- map_dfr(seq(100, 1000, 100), function(x){ x.1 <- MK_Fragmentation(nodes = habitat_nodes, edge_distance = x, plot = FALSE)[[2]] CA <- mean(x.1$CAPercent) Edge <- mean(x.1$EdgePercent) x.2 <- rbind(data.frame('Edge distance' = x, Type = "Core Area", Percentage = CA), data.frame('Edge distance' = x, Type = "Edge", Percentage = Edge)) return(x.2) })
library(ggplot2) ggplot(Fragmentation_test.2, aes(x = Edge.distance, y = Percentage, group = Type)) + geom_line(aes(color = Type))+ geom_point(aes(color = Type))+ ylim(0,100)+ scale_x_continuous("Edge depth distance (m)", labels = as.character(Fragmentation_test.2$Edge.distance), breaks = Fragmentation_test.2$Edge.distance)+ scale_color_brewer(palette="Dark2")+ theme_classic()
The mean core area percentage (the mean node/patch area that exhibits the least possible edge effect) for all patches is observed to decline by over 60% when an edge depth distance of 1 km is considered.
| Edge depth distance (m) | Core Area (%) | |-------------------------|:-------------:| | 100 | 65.76% | | 500 | 12.86% | | 1000 | 3.63% |
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.