con_metric: Landscape connectivity metrics
In lconnect: Simple Tools to Compute Landscape Connectivity Metrics

Description Usage Arguments Details Value Author(s) References Examples

View source: R/con_metric.R

Compute several landscape connectivity metrics.

1	con_metric(landscape, metric)

`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".

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.

Numeric vector with the landscape connectivity metrics selected.

Frederico Mestre

Bruno Silva

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.

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"))