metrics.graph {MetaLandSim}R Documentation

Computes landscape connectivity metrics

Description

Computes several landscape metrics, mostly derived from graph theory or assuming a graph representation of the landscape.

Usage

metrics.graph(rl, metric, dispersal.dist = NULL)

Arguments

rl

Object of class 'landscape'.

metric

one of the following connectivity metrics:

  • 'NC' - Number of components.

  • 'LNK' - Number of links connecting the patches.

  • 'SLC' - Area (in hectares) of the largest group of patches.

  • 'MSC'- Mean area (in hectares) of the components.

  • 'HI' - Harary Index.

  • 'NH' - Normalized Harary Index.

  • 'ORD' - Landscape (graph) order.

  • 'GD' - Landscape (graph) diameter.

  • 'CCP' - Class coincidence probability.

  • 'LCP' - Landscape coincidence probability.

  • 'ECS' - Expected cluster size.

  • 'AWF' - Area-weighted flux.

  • 'IIC' - Integral index of connectivity.

  • 'PC' - Probability of connectivity.

  • 'ECA' - Equivalent connected area.

dispersal.dist

Maximum dispersal distance for the binary indexes (NC, LNK, SLC, MSC, HI, NH, ORD, GD, CCP, LCP, ECS, IIC) and mean dispersal distance for the probabilistic indexes (AWF, PC, ECA). When no value is provided the function will assume the dispersal value provided by the 'landscape' object.

Details

These metrics assume different types of links between nodes (patches). Some assume probabilistic connections between nodes (e.g. PC) while others assume binary connections (e.g. NC, SLC, LNK, IIC). Also, these metrics have several degrees of complexity, from the simpler ones (such as NC and LNK) to the more complex (such as IIC and PC). Some are purely structural; the same landscape has the same index whatever the species, while others are measures of functional, where the connectivity of a given landscape is dependent on the species (dispersal ability). Precaution must be taken when looking at the outputs produced by some of these metrics (particularly the simpler, structural ones). Regardless of being simpler to compute, the outputs might be misleading. This metrics can however be used as exploratory tools.
This function was improved by the collaboration of Dr. Santiago Saura (Universidad Politecnica de Madrid).

Detail about each of the metrics:

Value

Returns the numeric value(s), corresponding to the chosen connectivity metric(s) for a given landscape.

Author(s)

Frederico Mestre and Fernando Canovas

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.

Ivanciuc, O., Balaban, T. S., and Balaban, A. T. (1993). Design of topological indices. Part 4. Reciprocal distance matrix, related local vertex invariants and topological indices. Journal of Mathematical Chemistry, 12(1): 309-318.

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. (2007). Graph theory as a proxy for spatially explicit population models in conservation planning. Ecological Applications, 17(6): 1771-1782.

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.

Plavsic, D., Nikolic, S., Trinajstic, N., and Mihalic, Z. (1993). On the Harary index for the characterization of chemical graphs. Journal of Mathematical Chemistry, 12(1): 235-250.

Ricotta, C., Stanisci, A., Avena, G. C., and Blasi, C. (2000). Quantifying the network connectivity of landscape mosaics: a graph-theoretical approach. Community Ecology, 1(1): 89-94.

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.

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 Ambito Forestal 106: 15-21

Urban, D., and Keitt, T. (2001). Landscape connectivity: a graph-theoretic perspective. Ecology, 82(5): 1205-1218.

See Also

rland.graph

Examples


data(rland)

#Compute the Integral index of connectivity of a landscape:

metrics.graph (rl=rland, metric="AWF")


[Package MetaLandSim version 2.0.0 Index]