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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

<strong>Makurhini</strong> (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:

<code> <i> Godínez-Gómez, O. and Correa Ayram C.A. 2020. Makurhini: Analyzing landscape connectivity. </code> </i>

Installation

• Depends: R (> 4.0.0), igraph (>= 1.2.6)
• Pre-install Rtools.
• Pre-install devtools (<code>install.packages(“devtools”)</code>) and remotes (<code>install.packages(“remotes”)</code>) 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

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)

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

ProtConn value:

Example incorporated in Makurhini

In this example we estimate the ProtConn for only one ecoregion of central Mexico (black line) using a vector file with the polygons of the country’s federal protected areas (green).

data("Protected_areas", package = "Makurhini")
data("regions", package = "Makurhini")
region <- regions[2,]

test_protconn <- MK_ProtConn(nodes = Protected_areas,
                             region = region,
                             area_unit = "ha",
                             distance = list(type= "centroid"),
                             distance_thresholds = 5000,
                             probability = 0.5,
                             transboundary = 50000,
                             plot = TRUE,
                             parallel = NULL,
                             protconn_bound = TRUE,
                             delta = TRUE,
                             write = NULL,
                             intern = FALSE)

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)
#> [1] "list"

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:

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 2.27e-17   NaN     0       1       1 ((3676911 589967.3, 3676931…
#> 2     2      1 0            1     0       2       2 ((3558044 696202.5, 3557972…
#> 3     3      1 0            1     0       3       3 ((3569169 687776.4, 3569146…
#> 4     4      1 0            1     0       2       2 ((3547317 685713.2, 3547363…
#> 5     5      1 0            1     0       3       3 ((3567471 684357.4, 3567380…
#> 6     6      0 2.27e-17   NaN     0       4       4 ((3590569 672451.7, 3590090…

Examples:

Moreover, you can change distance using the distance (<code>?distancefile</code>) 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")

<img src="man/figures/README-unnamed-chunk-23-1.png" width="60%" /><img src="man/figures/README-unnamed-chunk-23-2.png" width="60%" />

• 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)`

• 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     6.1496
#> 2  2   60.2227   11.9415   19.8289   167.982     80.1711 0.3585     6.1063
#> 3  3   48.8665    6.2099   12.7079   127.049     87.2921 0.3846     5.1270
#> 4  4   15.1875    7.4210   48.8626    18.536     51.1374 0.8194     1.3417
#> 5  5   33.2716   13.0877   39.3360    55.038     60.6640 0.6045     2.6917
#> 6  6   53.1344   11.3564   21.3730   111.123     78.6270 0.4782     4.3004
#>     FRAC                       geometry
#> 1 1.7389 POLYGON ((3676911 589967.3,...
#> 2 2.5006 POLYGON ((3558044 696202.5,...
#> 3 2.4914 POLYGON ((3569169 687776.4,...
#> 4 2.1465 POLYGON ((3547317 685713.2,...
#> 5 2.2872 POLYGON ((3567471 684357.4,...
#> 6 2.3714 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   68.18515
#> 4           200      Edge   31.81485
#> 5           300 Core Area   54.77234
#> 6           300      Edge   45.22766

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.