lconnect

README code{white-space: pre-wrap;} span.smallcaps{font-variant: small-caps;} span.underline{text-decoration: underline;} div.column{display: inline-block; vertical-align: top; width: 50%;} div.hanging-indent{margin-left: 1.5em; text-indent: -1.5em;} ul.task-list{list-style: none;} # lconnect Simple tools to derive landscape connectivity metrics and prioritize habitat patches based on their contribution to overall connectivity. The objective of this package is to provide the simplest possible approach to derive landscape connectivity metrics. These are the landscape connectivity metrics currently provided: Number of components ’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 (dispersal distance). Number of links ’LNK’ - Number of links connecting the patches. Considering that the maximum distance is the species dispersal ability and that these graphs (landscapes) are binary, which means that the habitat patches are either connected or unconnected (Pascual-Hortal and Saura, 2006). Higher LNK implies higher connectivity. Threshold dependent (dispersal distance). Size of the Largest Component ’SLC’ - Area of the largest component (group of interconnected patches) (Pascual- Hortal and Saura, 2006). Threshold dependent (dispersal distance). Mean Size of Components ’MSC’ - Mean component area (Pascual-Hortal and Saura, 2006). Threshold dependent (dispersal distance). Class coincidence probability ’CCP’ - Class coincidence probability. It is defined as the probability that two randomly chosen points within the habitat belong to the same component. Ranges between 0 and 1 (Pascual-Hortal and Saura 2006). Higher CCP implies higher connectivity. Threshold dependent (dispersal distance). Landscape coincidence probability ’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. Ranges between 0 and 1 (Pascual-Hortal and Saura 2006). Threshold dependent (dispersal distance). Characteristic path length ’CPL’ - Characteristic path length. Mean of all the shortest paths between the network nodes (habitat patches) (Minor and Urban, 2008). The shorter the CPL value the more connected the patches are. Threshold dependent (dispersal distance). Expected cluster size ’ECS’ - Expected cluster (component) 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 (dispersal distance). Area-weighted flux ’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 (pij), required by the AWF formula, was computed using pij=exp(-k*dij), where k is a constant making pij=0.5 at half the dispersal distance defined by the user. Does not depend on any distance threshold (probabilistic). Integral index of connectivity ’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 (dispersal distance). 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.

Help for package lconnect const macros = { "\\R": "\\textsf{R}", "\\mbox": "\\text", "\\code": "\\texttt"}; function processMathHTML() { var l = document.getElementsByClassName('reqn'); for (let e of l) { katex.render(e.textContent, e, { throwOnError: false, macros }); } return; } Package {lconnect} Contents con_metric patch_imp plot.lconnect plot.pimp upload_land Title: Simple Tools to Compute Landscape Connectivity Metrics Version: 0.1.2 Description: Provides functions to upload vectorial data and derive landscape connectivity metrics in habitat or matrix systems. Additionally, includes an approach to assess individual patch contribution to the overall landscape connectivity, enabling the prioritization of habitat patches. The computation of landscape connectivity and patch importance are very useful in Landscape Ecology research. The metrics available are: number of components, number of links, size of the largest component, mean size of components, class coincidence probability, landscape coincidence probability, characteristic path length, expected cluster size, area-weighted flux and integral index of connectivity. Pascual-Hortal, L., and Saura, S. (2006) < doi:10.1007/s10980-006-0013-z > Urban, D., and Keitt, T. (2001) < doi:10.2307/2679983 > Laita, A., Kotiaho, J., Monkkonen, M. (2011) < doi:10.1007/s10980-011-9620-4 >. Depends: R (≥ 3.4.0) License: GPL-3 Encoding: UTF-8 LinkingTo: Rcpp Imports: sf, igraph, Rcpp, scales, methods BugReports: https://github.com/FMestre1/lconnect/issues RoxygenNote: 7.1.2 NeedsCompilation: yes Packaged: 2024-03-09 02:37:16 UTC; asus Author: Frederico Mestre [aut, cre], Bruno Silva [aut], Benjamin Branoff [ctb] Maintainer: Frederico Mestre <mestre.frederico@gmail.com> Repository: CRAN Date/Publication: 2024-03-09 03:10:02 UTC Landscape connectivity metrics Description Compute several landscape connectivity metrics. 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 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_di