Makurhini: Analyzing landscape connectivity.

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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:https://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:

Use the Linux command line to install the unit package:

sudo apt-get install -y libudunits2-dev
Use the Linux command line to install gdal:

sudo apt install libgdal-dev
Use the Linux command line to install libfontconfig and libharfbuzz:

sudo apt install libfontconfig1-dev

sudo apt install libharfbuzz-dev libfribidi-dev
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'))
Use the Linux command line to install igraph:

sudo apt-get install libnlopt-dev

sudo apt-get install r-cran-igraph
You can now install the gdistance, graph4lg and ggpubr packages directly in your R or RStudio.

install.packages(c('gdistance', 'graph4lg', 'ggpubr'))
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

Function	Purpose
MK_Fragmentation	Calculate patch and landscape statistics (e.g., mean size patches, edge density, core area percent, shape index, fractal dimension index, effective mesh size).
distancefile	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).
MK_RMCentrality	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.
MK_BCentrality	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
MK_dPCIIC	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()’.
MK_dECA	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()’.
MK_ProtConn	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)
MK_ProtConnMult	Estimate the ProtConn indicator and fractions for multiple regions. It uses the ‘distancefile()’.
MK_ProtConn_raster	Estimate Protected Connected (ProtConn) indicator and fractions for one region using raster inputs (nodes and region). It uses the ‘distancefile()’.
MK_Connect_grid	Compute the ProtConn indicator and fractions, PC or IIC overall connectivity metrics (ECA) in a regular grid. It uses the ‘distancefile()’.
test_metric_distance	Compare ECA or ProtConn connectivity metrics using one or up to four types of distances, computed in the ‘distancefile()’ function, and multiple dispersion distances.

Example

This is a basic example which shows you how to solve some common problems:

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)

In the following example, we will calculate the connectivity of the protected areas network in four ecoregions of the Colombian Amazon neighboring countries using the ProtConn indicator and its fractions. We considered a transboundary distance of 50 km.

test_protconn <- MK_ProtConnMult(nodes = Protected_areas, 
                                 region = ecoregions,
                                 area_unit = "ha",
                                 distance = list(type= "centroid"),
                                 distance_thresholds = 10000,
                                 probability = 0.5, 
                                 transboundary = 50000,
                                 plot = TRUE, 
                                 CI = NULL, 
                                 parallel = 4, 
                                 intern = FALSE)
test_protconn[[1]][[1]]

ProtConn value:

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")
class(list_forest_patches)
#[1] "list"
data("study_area", package = "Makurhini")
class(study_area)[1]
#[1] "SpatialPolygonsDataFrame"

Max_attribute <- unit_convert(gArea(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"))
dECA_test

ECA table:

ECA plot:

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)

Example with 142 old-growth vegetation fragments in southeast Mexico (?vegetation_patches).

data("vegetation_patches", package = "Makurhini")
nrow(vegetation_patches) # Number of patches
#> [1] 142
class(vegetation_patches)[1]
#> [1] "sf"
#[1] "sf"

IIC <- MK_dPCIIC(nodes = vegetation_patches, attribute = NULL,
                distance = list(type = "centroid"),
                metric = "IIC", distance_thresholds = 10000) #10 km
head(IIC)
#> Simple feature collection with 6 features and 5 fields
#> Geometry type: POLYGON
#> Dimension:     XY
#> Bounding box:  xmin: 3542152 ymin: 498183.1 xmax: 3711426 ymax: 696540.5
#> Projected CRS: +proj=lcc +lat_1=17.5 +lat_2=29.5 +lat_0=12 +lon_0=-102 +x_0=2500000 +y_0=0 +datum=WGS84 +units=m +no_defs
#>   id       dIIC  dIICintra  dIICflux dIICconnector
#> 1  1 88.6878612 88.6878612 0.0000000   0.00000e+00
#> 2  2  0.0228809  0.0182727 0.0046082   0.00000e+00
#> 3  3  0.0202227  0.0120311 0.0081916   0.00000e+00
#> 4  4  0.0057703  0.0011621 0.0046082   0.00000e+00
#> 5  5  0.0137690  0.0055774 0.0081916   2.91434e-15
#> 6  6  0.0142244  0.0142244 0.0000000   0.00000e+00
#>                         geometry
#> 1 POLYGON ((3676911 589967.3,...
#> 2 POLYGON ((3558044 696202.5,...
#> 3 POLYGON ((3569169 687776.4,...
#> 4 POLYGON ((3547317 685713.2,...
#> 5 POLYGON ((3567471 684357.4,...
#> 6 POLYGON ((3590569 672451.7,...

Probability of connectivity (PC) and fractions (Intra, Flux and Connector)

PC <- MK_dPCIIC(nodes = vegetation_patches, attribute = NULL,
                distance = list(type = "centroid"),
                metric = "PC", probability = 0.05,
                distance_thresholds = 10000)
head(PC)
#> Simple feature collection with 6 features and 5 fields
#> Geometry type: POLYGON
#> Dimension:     XY
#> Bounding box:  xmin: 3542152 ymin: 498183.1 xmax: 3711426 ymax: 696540.5
#> Projected CRS: +proj=lcc +lat_1=17.5 +lat_2=29.5 +lat_0=12 +lon_0=-102 +x_0=2500000 +y_0=0 +datum=WGS84 +units=m +no_defs
#>   id        dPC   dPCintra   dPCflux dPCconnector
#> 1  1 89.0768714 89.0760927 0.0007786  0.00000e+00
#> 2  2  0.0192790  0.0183527 0.0009263  0.00000e+00
#> 3  3  0.0136652  0.0120837 0.0015814  0.00000e+00
#> 4  4  0.0017528  0.0011672 0.0005856  0.00000e+00
#> 5  5  0.0069526  0.0056018 0.0013508  4.42528e-15
#> 6  6  0.0143397  0.0142867 0.0000531  0.00000e+00
#>                         geometry
#> 1 POLYGON ((3676911 589967.3,...
#> 2 POLYGON ((3558044 696202.5,...
#> 3 POLYGON ((3569169 687776.4,...
#> 4 POLYGON ((3547317 685713.2,...
#> 5 POLYGON ((3567471 684357.4,...
#> 6 POLYGON ((3590569 672451.7,...

Centrality measures

centrality_test <- MK_RMCentrality(nodes = vegetation_patches,
                                distance = list(type = "centroid"),
                                 distance_thresholds = 10000,
                                 probability = 0.05,
                                 write = NULL)
head(centrality_test)
#> Simple feature collection with 6 features and 7 fields
#> Geometry type: POLYGON
#> Dimension:     XY
#> Bounding box:  xmin: 3542152 ymin: 498183.1 xmax: 3711426 ymax: 696540.5
#> Projected CRS: +proj=lcc +lat_1=17.5 +lat_2=29.5 +lat_0=12 +lon_0=-102 +x_0=2500000 +y_0=0 +datum=WGS84 +units=m +no_defs
#> # A tibble: 6 × 8
#>      id degree    eigen close   BWC cluster modules                     geometry
#>   <int>  <dbl>    <dbl> <dbl> <dbl>   <dbl>   <dbl>                <POLYGON [m]>
#> 1     1      0 0          NaN     0       1       1 ((3676911 589967.3, 3676931…
#> 2     2      1 4.54e-17     1     0       2       2 ((3558044 696202.5, 3557972…
#> 3     3      1 9.08e-17     1     0       3       3 ((3569169 687776.4, 3569146…
#> 4     4      1 6.81e-17     1     0       2       2 ((3547317 685713.2, 3547363…
#> 5     5      1 6.81e-17     1     0       3       3 ((3567471 684357.4, 3567380…
#> 6     6      0 0          NaN     0       4       4 ((3590569 672451.7, 3590090…

Examples:

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 level.

Example:

data("vegetation_patches", package = "Makurhini")
nrow(vegetation_patches) # Number of patches
#> [1] 142

To define the edge of the patches we can use, for example, a distance of 500 m from the limit of the patches.

Fragmentation_test <- MK_Fragmentation(patches = vegetation_patches, edge_distance = 500,
                                       plot = TRUE, min_patch_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)
#> [1] "list"
names(Fragmentation_test)
#> [1] "Summary landscape metrics (Viewer Panel)"
#> [2] "Patch statistics shapefile"
Fragmentation_test$`Summary landscape metrics (Viewer Panel)`

Metric	Value
Patch area (km2)	12792.2046
Number of patches	142.0000
Size (mean)	90.0859
Patches \< minimum patch area	126.0000
Patches \< minimum patch area (%)	30.8017
Total edge	12297.5330
Edge density	0.9613
Total Core Area (km2)	7622.3940
Cority	1.0000
Shape Index (mean)	138.4898
FRAC (mean)	1.4680
MESH (km2)	1543.1460

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]])
#> Simple feature collection with 6 features and 9 fields
#> Geometry type: POLYGON
#> Dimension:     XY
#> Bounding box:  xmin: 3542152 ymin: 498183.1 xmax: 3711426 ymax: 696540.5
#> Projected CRS: +proj=lcc +lat_1=17.5 +lat_2=29.5 +lat_0=12 +lon_0=-102 +x_0=2500000 +y_0=0 +datum=WGS84 +units=m +no_defs
#>   id      Area        CA CAPercent Perimeter EdgePercent   PARA ShapeIndex
#> 1  1 4195.5691 3541.3806   84.4076  1412.046     15.5924 2.9713  8212.7666
#> 2  2   60.2227   11.9415   19.8289   167.982     80.1711 0.3585   117.0545
#> 3  3   48.8665    6.2099   12.7079   127.049     87.2921 0.3846    79.7484
#> 4  4   15.1875    7.4210   48.8626    18.536     51.1374 0.8194     6.4864
#> 5  5   33.2716   13.0877   39.3360    55.038     60.6640 0.6045    28.5066
#> 6  6   53.1344   11.3564   21.3730   111.123     78.6270 0.4782    72.7339
#>     FRAC                       geometry
#> 1 1.4065 POLYGON ((3676911 589967.3,...
#> 2 1.8241 POLYGON ((3558044 696202.5,...
#> 3 1.7785 POLYGON ((3569169 687776.4,...
#> 4 1.1273 POLYGON ((3547317 685713.2,...
#> 5 1.4961 POLYGON ((3567471 684357.4,...
#> 6 1.6735 POLYGON ((3590569 672451.7,...

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%).

#>   Edge.distance      Type Percentage
#> 1           100 Core Area   83.50499
#> 2           100      Edge   16.49501
#> 3           200 Core Area   68.18516
#> 4           200      Edge   31.81484
#> 5           300 Core Area   54.77231
#> 6           300      Edge   45.22769

The average core area percentage (average patch area that has the least possible edge effect) for all patches decreases by more than 70% when considering an edge effect with an edge depth distance of 1 km.

Edge depth distance (m)	Core Area (%)
100	83.5%
500	34.14%
1000	9.78%

Name		Name	Last commit message	Last commit date
Latest commit History 265 Commits
R		R
data		data
docs		docs
inst/extdata		inst/extdata
man		man
vignettes		vignettes
.RDataTmp		.RDataTmp
.Rbuildignore		.Rbuildignore
.gitignore		.gitignore
.travis.yml		.travis.yml
DESCRIPTION		DESCRIPTION
LICENSE		LICENSE
LICENSE.md		LICENSE.md
Makurhini.Rproj		Makurhini.Rproj
NAMESPACE		NAMESPACE
README.Rmd		README.Rmd
README.md		README.md
_pkgdown.yml		_pkgdown.yml
appveyor.yml		appveyor.yml

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Makurhini: Analyzing landscape connectivity.

NEWS

Overview

Citing Makurhini package

Installation

Makurhini on Linux

Summary of main Makurhini functions

Example

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

Fragmentation statistics

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Makurhini: Analyzing landscape connectivity.

NEWS

Overview

Citing Makurhini package

Installation

Makurhini on Linux

Summary of main Makurhini functions

Example

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

Fragmentation statistics

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