Landscape connectivity metrics

Description

Compute several landscape connectivity metrics.

Usage

con_metric(landscape, metric)

Arguments

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 chosen 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 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 information 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 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.

Value

Numeric vector with the landscape connectivity metrics selected.

Author(s)

Frederico Mestre

Bruno Silva

Benjamin Branoff

References

Bunn, A. G., Urban, D. L., and Keitt, T. H. (2000). Landscape connectivity: a conservation application 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 landscape connectivity indices: towards the priorization of habitat patches and corridors for conservation. Landscape Ecology, 21(7): 959-967.

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"))

Prioritization of patches

Description

Prioritization of patches according to individual contribution to overall connectivity.

Usage

patch_imp(landscape, metric, vector_out = FALSE)

Arguments

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 or AWF.

Value

An object of class "pimp". This object is a list with the following values:

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 object of class "lconnect"

Description

Method of the generic plot for objects of class "lconnect".

Usage

## S3 method for class 'lconnect'
plot(x, ...)

Arguments

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

Plot pimp object

Description

Method of the generic plot for objects of class "pimp".

Usage

## S3 method for class 'pimp'
plot(x, ..., main)

Arguments

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

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)

Arguments

Value

An object of class "lconnect". This object is a list with the following values:

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)