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Package ‘lconnect’
March 24, 2019
Title Simple Tools to Compute Landscape Connectivity Metrics
Version 0.1.0
Description Simple tools to compute landscape connectivity metrics. The objective
of this package is to provide a simple to use approach to compute landscape
connectivity metrics.
Depends R (>= 3.4.4)
License GPL-3
Encoding UTF-8
LazyData true
Imports sf,
igraph
BugReports https://github.com/FMestre1/lconnect/issues
RoxygenNote 6.1.1
Rtopics documented:
con_metric ......................................... 1
patch_imp.......................................... 4
plot.lconnect......................................... 5
plot.pimp .......................................... 6
upload_land......................................... 6
Index 8
con_metric Landscape connectivity metrics
Description
Compute several landscape connectivity metrics.
1
2con_metric
Usage
con_metric(landscape, metric)
Arguments
landscape Object of class ’lconnect’ created by upload_land.
metric Character vector of landscape metrics to be computed. Can be one or more of
the metrics currently available: "NC", "LNK", "SLC", "MSC", "CCP", "LCP",
"CPL", "ECS", "AWF" and "IIC".
Details
The landscape connectivity metrics currently available are:
• NC – Number of components (groups of interconnected patches) in the landscape (Urban and
Keitt, 2001). Patches in the same component are considered to be accessible, while patches in
different components are not. Highly connected landscapes have less components. Threshold
dependent, i.e., maximum distance for two patches to be considered connected, which can be
interpreted as the maximum dispersal distance for a certain species.
• LNK – Number of links connecting the patches. The landscape is interpreted as binary, which
means that the habitat patches are either connected or not (Pascual-Hortal and Saura, 2006).
Higher LNK implies higher connectivity. Threshold dependent, i.e., maximum distance for
two patches to be considered connected, which can be interpreted as the maximum dispersal
distance for a certain species.
• SLC – Area of the largest group of interconnected patches (Pascual-Hortal and Saura, 2006).
Threshold dependent, i.e., maximum distance for two patches to be considered connected,
which can be interpreted as the maximum dispersal distance for a certain species.
• MSC – Mean area of interconnected patches (Pascual-Hortal and Saura, 2006). Threshold
dependent, i.e., maximum distance for two patches to be considered connected, which can be
interpreted as the maximum dispersal distance for a certain species.
• CCP – Class coincidence probability. It is defined as the probability that two randomly cho-
sen points within the habitat belong to the same component (or cluster). Ranges between 0
and 1 (Pascual-Hortal and Saura 2006). Higher CCP implies higher connectivity. Threshold
dependent, i.e., maximum distance for two patches to be considered connected, which can be
interpreted as the maximum dispersal distance for a certain species.
• LCP – Landscape coincidence probability. It is defined as the probability that two randomly
chosen points in the landscape (whether in an habitat patch or not) belong to the same habitat
component (or cluster). Ranges between 0 and 1 (Pascual-Hortal and Saura 2006). Threshold
dependent, i.e., maximum distance for two patches to be considered connected, which can be
interpreted as the maximum dispersal distance for a certain species.
• CPL – Characteristic path length. Mean of all the shortest paths between the habitat patches
(Minor and Urban, 2008). The shorter the CPL value the more connected the patches are.
Threshold dependent, i.e., maximum distance for two patches to be considered connected,
which can be interpreted as the maximum dispersal distance for a certain species.
• ECS – Expected component (or cluster) size. Mean cluster size of the clusters weighted by
area. (O’Brien et al., 2006 and Fall et al, 2007). This represents the size of the component in
con_metric 3
which a randomly located point in an habitat patch would reside. Although it is informative
regarding the area of the component, it does not provide any ecologically meaningful informa-
tion regarding the total area of habitat. As an example: ECS increases with less isolated small
components or patches, although the total habitat decreases (Laita et al. 2011). Threshold
dependent, i.e., maximum distance for two patches to be considered connected, which can be
interpreted as the maximum dispersal distance for a certain species.
• AWF – Area-weighted Flux. Evaluates the flow, weighted by area, between all pairs of patches
(Bunn et al. 2000 and Urban and Keitt 2001). The probability of dispersal between two
patches, was computed using pij=exp(-k * dij), where k is a constant making pij (the dispersal
probability between patches) 50 defined by the user. Although k, as was implemented, is
dependent on the dispersal distance, AWF is a probabilistic index and not directly dependent
on the threshold.
• IIC – Integral index of connectivity. Index developed specifically for landscapes by Pascual-
Hortal and Saura (2006). It is based on habitat availability and on a binary connection model
(as opposed to a probabilistic). It ranges from 0 to 1 (higher values indicating more con-
nectivity). Threshold dependent, i.e., maximum distance for two patches to be considered
connected, which can be interpreted as the maximum dispersal distance for a certain species.
Value
Numeric vector with the landscape connectivity metrics selected.
Author(s)
Frederico Mestre
Bruno Silva
References
Bunn, A. G., Urban, D. L., and Keitt, T. H. (2000). Landscape connectivity: a conservation appli-
cation of graph theory. Journal of Environmental Management, 59(4): 265-278.
Fall, A., Fortin, M. J., Manseau, M., and O’ Brien, D. (2007). Spatial graphs: principles and
applications for habitat connectivity. Ecosystems, 10(3): 448-461.
Laita, A., Kotiaho, J.S., Monkkonen, M. (2011). Graph-theoretic connectivity measures: what do
they tell us about connectivity? Landscape Ecology, 26: 951-967.
Minor, E. S., and Urban, D. L. (2008). A Graph-Theory Framework for Evaluating Landscape
Connectivity and Conservation Planning. Conservation Biology, 22(2): 297-307.
O’Brien, D., Manseau, M., Fall, A., and Fortin, M. J. (2006). Testing the importance of spatial
configuration of winter habitat for woodland caribou: an application of graph theory. Biological
Conservation, 130(1): 70-83.
Pascual-Hortal, L., and Saura, S. (2006). Comparison and development of new graph-based land-
scape connectivity indices: towards the priorization of habitat patches and corridors for conserva-
tion. Landscape Ecology, 21(7): 959-967.
Saura, S., and Pascual-Hortal, L. (2007). A new habitat availability index to integrate connectivity
in landscape conservation planning: comparison with existing indices and application to a case
study. Landscape and Urban Planning, 83(2): 91-103.
4patch_imp
Saura, S., Estreguil, C., Mouton, C. & Rodriguez-Freire, M. (2011a). Network analysis to assess
landscape connectivity trends: application to European forests (1990-2000). Ecological Indicators
11: 407-416.
Saura, S., Gonzalez-Avila, S. & Elena-Rossello, R. (2011b). Evaluacion de los cambios en la
conectividad de los bosques: el indice del area conexa equivalente y su aplicacion a los bosques de
Castilla y Leon. Montes, Revista de Âmbito Forestal 106: 15-21
Urban, D., and Keitt, T. (2001). Landscape connectivity: a graph-theoretic perspective. Ecology,
82(5): 1205-1218.
Examples
vec_path <- system.file("extdata/vec_projected.shp", package = "lconnect")
landscape <- upload_land(vec_path, bound_path = NULL,
habitat = 1, max_dist = 500)
metrics <- con_metric(landscape, metric = c("NC", "LCP"))
patch_imp Prioritization of patches
Description
Prioritization of patches according to individual contribution to overall connectivity.
Usage
patch_imp(landscape, metric, vector_out = FALSE)
Arguments
landscape Object of class "lconnect" created by upload_land.
metric String indicating the connectivity metric to use in the prioritization.
vector_out TRUE/FALSE indicating if the resulting spatial object should be recorded to
file.
Details
Each patch is removed at a time and connectivity metrics are recalculated without that specific
patch. Patch importance value indicates the percentage of reduction in the connectivity metric that
the loss of that patch represents in the landscape. The current version only allows the use of IIC.
Value
An object of class "pimp". This object is a list with the following values:
landscape Spatial polygon object of class "sf" (package "sf") with cluster identity and im-
portance of each polygon.
prioritization Vector with patch importance in percentage.
plot.lconnect 5
Author(s)
Frederico Mestre
Bruno Silva
References
Saura, S., Pascual-Hortal, L. (2007). A new habitat availability index to integrate connectivity in
landscape conservation planning: Comparison with existing indices and application to a case study.
Landscape and Urban Planning, 83(2-3):91-103.
Examples
vec_path <- system.file("extdata/vec_projected.shp", package = "lconnect")
landscape <- upload_land(vec_path, bound_path = NULL,
habitat = 1, max_dist = 500)
importance <- patch_imp(landscape, metric = "IIC")
plot(importance)
plot.lconnect Plot object of class "lconnect"
Description
Method of the generic plot for objects of class "lconnect".
Usage
## S3 method for class 'lconnect'
plot(x, ...)
Arguments
xObject of class "lconnect" created by upload_land.
... Other options passed to plot or or plot.sf.
Details
Plot patches with different colours representing cluster membership. Additional arguments accepted
by ’plot or plot.sf can be included.
Value
Plot depicting patches and cluster membership (distinct colours per cluster).
Author(s)
Bruno Silva
Frederico Mestre
6upload_land
plot.pimp Plot pimp object
Description
Method of the generic plot for objects of class "pimp".
Usage
## S3 method for class 'pimp'
plot(x, ..., main)
Arguments
xObject of class "pimp" created by patch_imp.
... Other options passed to plot or plot.sf.
main String with plot title.
Details
Plot patch importance with percentage value per patch. This value indicates the percentage of
reduction in the connectivity metric that the loss of that patch represents in the landscape. Additional
arguments accepted by plot or plot.sf can be included.
Value
Patch importance plot.
Author(s)
Bruno Silva
Frederico Mestre
upload_land Import and convert a shapefile to an object of class "lconnect"
Description
Import and convert a shapefile to an object of class "lconnect". Some landscape and patch metrics
which are the core of landscape connectivity metrics are calculated. The shapefile must be projected,
i.e., in planar coordinates and the first field must contain the habitat categories.
Usage
upload_land(land_path, bound_path = NULL, habitat, max_dist = NULL)
upload_land 7
Arguments
land_path String indicating the full path of the landscape shapefile.
bound_path String indicating the full path of the boundary shapefile. If NULL (default op-
tion) a convex hull will be created and used as boundary.
habitat Vector with habitat categories. The categories can be numeric or character.
max_dist Numeric indicating the maximum distance between patches in the same cluster.
Value
An object of class "lconnect". This object is a list with the following values:
landscape Spatial polygon object of class "sf" (package "sf") with cluster membership of
each polygon.
max_dist Numeric indicating the maximum distance between patches of the same cluster.
clusters Numeric vector indicating cluster identity of each polygon.
distance Object of class "dist" (package "stats") with eucledian distances between all
pairs of polygons.
boundary Spatial polygon of class "sfc" (package "sf") representing the boundary of the
landscape.
area_l Numeric with the total area of the boundary, in square units of landscape units.
Author(s)
Bruno Silva
Frederico Mestre
Examples
vec_path <- system.file("extdata/vec_projected.shp",
package = "lconnect")
landscape <- upload_land(vec_path, bound_path = NULL,
habitat = 1, max_dist = 500)
plot(landscape)
