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Landscape connectivity

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Title: Landscape connectivity  
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Landscape connectivity

Landscape connectivity is, broadly, "the degree to which the climate change.[6][7]

Contents

  • Connectivity metrics 1
  • Software 2
  • See also 3
  • References 4

Connectivity metrics

Although connectivity is an intuitive concept, there is no single consistently-used metric of connectivity. Theories of connectivity include consideration of both binary representations of connectivity through "corridors" and "linkages" and continuous representations of connectivity, which include the binary condition as a sub-set [8][9]

Generally, connectivity metrics fall into three categories:[10]

  1. Structural connectivity metrics are based on the physical properties of landscapes, which includes the idea of patches (size, number of patches, average distance to each other) and relative disturbance (human structures such as roads, parcelization, urban/agricultural land-use, human population).
  2. Potential connectivity metrics are based on the landscape structure as well as some basic information about the study organism's dispersal ability such as average dispersal distance, or dispersal kernel.
  3. Actual (also called realized, or functional) connectivity metrics are measured based on the actual movements of individuals along and across contours of connectivity, including among patches (where these exist). This takes into account the actual number of individuals born at different sites, their reproduction rates, and mortality during dispersal.[11] Some authors make a further distinction based on the number of individuals that not only disperse between sites, but that also survive to reproduce.[12]

Software

Typically, the "natural" form of connectivity as an ecological property perceived by organisms is modeled as a continuous surface of permeability, which is the corollary to disturbance. This can be accomplished by most geographic information systems (GIS) able to model in grid/raster format. A critical component of this form of modeling is the recognition that connectivity and disturbance are perceived and responded to differently by different organisms and ecological processes. This variety in responses is one of the most challenging parts of attempting to represent connectivity in spatial modeling. Typically, the most accurate connectivity models are for single species/processes and are developed based on information about the species/process.[13] There is little, and often no evidence that spatial models, including those described here, can represent connectivity for the many species or processes that occupy many natural landscapes. The disturbance-based models are used as the basis for the binary representations of connectivity as paths/corridor/linkages through landscapes described below.

  • Circuitscape

Circuitscape is an open source program that uses circuit theory to predict connectivity in heterogeneous landscapes for individual movement, gene flow, and conservation planning. Circuit theory offers several advantages over common analytic connectivity models, including a theoretical basis in random walk theory and an ability to evaluate contributions of multiple dispersal pathways. Landscapes are represented as conductive surfaces, with low resistances assigned to habitats that are most permeable to movement or best promote gene flow, and high resistances assigned to poor dispersal habitat or to movement barriers. Effective resistances, current densities, and voltages calculated across the landscapes can then be related to ecological processes, such as individual movement and gene flow.

  • Conefor Sensinode
  • FunConn
  • Graphab

Graphab is a software application devoted to the modelling of landscape networks. It is composed of four main modules: graph building, including loading the initial landscape data and identification of the patches and the links; computation of the connectivity metrics from the graph; connection between the graph and exogenous point data set; visual and cartographical interface. Graphab runs on any computer supporting Java 1.6 or later (PC under Linux, Windows, Mac...). It is distributed free of charge for non-commercial use.

  • PathMatrix

See also

Landscape Ecology

References

  1. ^ Taylor, P.D., Fahrig, L., Henein, K. and Merriam, G. 1993. Connectivity is a vital element of landscape structure. Oikos 68:571–573.
  2. ^ Fischer, J., Lindenmayer, D.B. and I. Fazey. 2004. Appreciating ecological complexity: habitat contours as a conceptual landscape model. Conservation Biology, 18: 1245–1253.
  3. ^ Fischer, J. and D.B. Lindenmayer. 2006. Beyond fragmentation: the continuum model for fauna research and conservation in human-modified landscapes. Oikos, 112: 473–480.
  4. ^ Brooks, C. P. 2003. A scalar analysis of landscape connectivity. Oikos 102:433-439.
  5. ^ Baguette, M., S. Blanchet, D. Legrand, V. M. Stevens, and C. Turlure. 2013. Individual dispersal, landscape connectivity and ecological networks. Biological Reviews Of The Cambridge Philosophical Society 88:310–326.
  6. ^ Hodgson, J.A., C.D. Thomas, B.A. Wintle, and A. Moilanen. 2009. Climate change, connectivity and conservation decision-making: back to basics. Journal of Applied Ecology, 46: 964-969.
  7. ^ McRae, B. H., Hall, S. A., Beier, P., Theobald, D. M. 2012. Where to Restore Ecological Connectivity? Detecting Barriers and Quantifying Restoration Benefits. PLoS ONE 7:e52604.
  8. ^ Fischer, J., Lindenmayer, D.B. and I. Fazey. 2004. Appreciating ecological complexity: habitat contours as a conceptual landscape model. Conservation Biology, 18: 1245–1253.
  9. ^ Fischer, J. and D.B. Lindenmayer. 2006. Beyond fragmentation: the continuum model for fauna research and conservation in human-modified landscapes. Oikos, 112: 473–480.
  10. ^ Calabrese, J. M., and W. F. Fagan. 2004. A comparison-shopper's guide to connectivity metrics. Frontiers in Ecology and the Environment 2:529-536.
  11. ^ Watson, J. R., S. Mitarai, D. A. Siegel, J. E. Caselle, C. Dong, and J. C. McWilliams. 2010. Realized and potential larval connectivity in the Southern California Bight. Marine Ecology Progress Series 401:31-48.
  12. ^ Pineda, J., J. A. Hare, and S. Sponaungle. 2007. Larval transport and dispersal in the coastal ocean and consequences for population connectivity. Oceanography 20:22-39.
  13. ^ LaPoint, S., P. Gallery, M. Wikelski, and R. Kays. 2013. Animal behavior, cost-based corridor models, and real corridors. Landscape Ecology, 28: 1615-1630.
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