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|Title:||Patterns of Functional Connectivity|
|Authors:||RODRIGUEZ FREIRE Monica; ESTREGUIL Christine; VOGT Peter|
|Citation:||Proceedings of the International Conference Landscape Ecology and Forest Management p. 147-148|
|Publisher:||Chinese Academy of Forestry|
|Type:||Articles in periodicals and books|
|Abstract:||Despite the growing awareness on the importance of landscape connectivity for the conservation of biodiversity there is still a lack of models which show the role the different areas play for the maintenance of connectivity. This paper present a new methodology to extract information about connectivity from continuous cost surface models (Dijkstra 1959; Douglas 1994), and extracting spatial patterns at all possible analysis scales. Cost surface models allow to visualize functional landscape connectivity and to quantify the probability (cost) of reaching each part of the territory from the source areas. By thresholding a cost map (median cost reached at the median dispersal distance) for a forest-dwelling species in Galicia (NW Spain) (Rodríguez Freire 2006; Rodríguez Freire and Crecente 2006) we map the networks, or functional dispersal range, allowing detecting functionally connected forests. The spatial patterns of the networks were analyzed by morphological image processing (Vogt et al. 2007a; Vogt et al. 2007b). The pattern frequency map was derived by monitoring the pattern classification of the networks over a sequence of increasing buffer widths. ¿Core¿ dispersal areas, surrounded by ¿edge¿ and ¿perforations¿, indicated the inner part of the networks, where the connectivity is highest. From this area ¿branches¿ expanded into the surrounding landscape, acting as principal radiating routes of movement. Flecks are isolated small areas disconnected from the remaining dispersal range and as such more vulnerable to extinctions. The area between branches and flecks is related to disruptions in the dispersal range where measures to improve connectivity would be most efficient. The specific main contributions of this methodology was in bridging two gaps: (1) the pass from continuous cost surface models to a binary image showing the space available for the population¿s dispersal ¿here called networks or functional dispersal range¿ and (2) the finding of a significant and scale-independent spatial pattern image (pattern frequency map) that describes and synthesize previous information. This methodology allows the combination of cost surfaces and morphological image processing to quantitatively analyze functional connectivity, classifying the areas in which the connectivity is sufficient and detecting areas in which is necessary the increasing of connectivity.|
|JRC Institute:||Institute for Environment and Sustainability|
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