Book contents
- Frontmatter
- Contents
- Contributors
- Preface
- Acknowledgments
- Part IIntroduction
- Part IIAdvances in source–sink theory
- Part IIIProgress in source–sink methodology
- 9On estimating demographic and dispersal parameters for niche and source–sink models
- 10Source–sink status of small and large wetland fragments and growth rate of a population network
- 11Demographic and dispersal data from anthropogenic grasslands: what should we measure?
- 12Network analysis: a tool for studying the connectivity of source–sink systems
- 13Sources, sinks, and model accuracy
- 14Scale-dependence of habitat sources and sinks
- 15Effects of experimental population removal for the spatial population ecology of the alpine butterfly, Parnassius smintheus
- Part IVImprovement of source–sink management
- Part VSynthesis
- Index
- References
12 - Network analysis: a tool for studying the connectivity of source–sink systems
Published online by Cambridge University Press: 05 July 2011
Book contents
- Frontmatter
- Contents
- Contributors
- Preface
- Acknowledgments
- Part IIntroduction
- Part IIAdvances in source–sink theory
- Part IIIProgress in source–sink methodology
- 9On estimating demographic and dispersal parameters for niche and source–sink models
- 10Source–sink status of small and large wetland fragments and growth rate of a population network
- 11Demographic and dispersal data from anthropogenic grasslands: what should we measure?
- 12Network analysis: a tool for studying the connectivity of source–sink systems
- 13Sources, sinks, and model accuracy
- 14Scale-dependence of habitat sources and sinks
- 15Effects of experimental population removal for the spatial population ecology of the alpine butterfly, Parnassius smintheus
- Part IVImprovement of source–sink management
- Part VSynthesis
- Index
- References
Summary
In order to optimize landscape conservation efforts, the most important landscape elements (patches and corridors) need to be identified quantitatively. A major aspect of functional importance is the role of a landscape element in maintaining connectivity. From a network perspective, different habitat patches and corridors can be ranked according to suitable centrality indices. Choosing and applying different indices may reflect differences in the problems studied (e.g., whether dispersal is limited) or data type (e.g., whether corridor permeability can be quantified). Within this framework, it is possible to consider the quality of patches and corridors or to make a distinction between source and sink patches. Network analytical tools can be used to quantify the local and global properties of directed landscape graphs (depicting source–sink systems). I present two case studies for illustrating how to set quantitative priority ranks of landscape elements and how to match the suitable techniques to particular problems (an undirected network of flightless bush crickets and a directed network of forest-living carabids). In the second case, I also calculate where to build a new corridor and suggest which corridor qualities to improve in the directed landscape graph. I propose a plan for redesigning a future freeway across the studied area. Finally, I argue that setting quantitative priority ranks for landscape elements could increase the efficiency of conservation efforts.
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- Sources, Sinks and Sustainability , pp. 258 - 272Publisher: Cambridge University PressPrint publication year: 2011
References
and (2007). A network approach for analyzing spatially structured populations in fragmented landscape. Landscape Ecology 22: 31–44.CrossRefGoogle Scholar
(2006). Identifying sets of key players in a social network. Computational and Mathematical Organization Theory 12: 21–34.CrossRefGoogle Scholar
, and (1974). Quaternary refugia in tropical America: evidence from race formation in Heliconius butterflies. Proceedings of the Royal Society of London B 187: 369–378.CrossRefGoogle Scholar
and (1993). Landscape graphs: ecological modelling with graph theory to detect configurations common to diverse landscapes. Landscape Ecology 8: 239–255.CrossRefGoogle Scholar
, and (2004). Social networks in the guppy (Poecilia reticulata). Proceedings of the Royal Society of London Series B – Biological Sciences 271: S516–S519.CrossRefGoogle Scholar
and (1999). Mesopredator release and avifaunal extinctions in a fragmented system. Nature 400: 563–566.CrossRefGoogle Scholar
, , , and (2001). Habitat suitability modelling for red deer (Cervus elaphus L.) in south-central Slovenia with classification trees. Ecological Modelling 138: 321–330.CrossRefGoogle Scholar
(1996). Sources and sinks in population biology. Trends in Ecology and Evolution 11: 326–330.CrossRefGoogle ScholarPubMed
, and (1992). Ecological processes that affect populations in complex landscapes. Oikos 65: 169–175.CrossRefGoogle Scholar
and (2008). Using network centrality measures to manage landscape connectivity. Ecological Applications 18: 1810–1825.CrossRefGoogle ScholarPubMed
, , and (2001). A wolf habitat suitability prediction study in Valais (Switzerland). Landscape and Urban Planning 55: 55–65.CrossRefGoogle Scholar
(1961). A structural analysis of the situation in the Middle East in 1956. Journal of Conflict Resolution 5: 167–178.CrossRefGoogle Scholar
(2008). Predicting target selection by terrorists: a network analysis of the 2005 London underground attacks. International Journal for Critical Infrastructures 4: 206–214.CrossRefGoogle Scholar
, , , and (2003). Characterizing the importance of habitat patches and corridors in maintaining the landscape connectivity of a Pholidoptera transsylvanica (Orthoptera) metapopulation. Landscape Ecology 18: 83–92.CrossRefGoogle Scholar
, , , and (2007). Carabids (Coleoptera: Carabidae) in a forest patchwork: a connectivity analysis of the Bereg Plain landscape graph. Landscape Ecology 22: 1527–1539.CrossRefGoogle Scholar
and (1996). Ecology and behavior of ground beetles (Coleoptera: Carabidae). Annual Review of Entomology 41: 231–256.CrossRefGoogle Scholar
, , and (2006). The influence of matrix and edges on species richness patterns of ground beetles (Coleoptera, Carabidae) in habitat islands. Global Ecology and Biogeography 15: 283–289.CrossRefGoogle Scholar
, and (2001). Effects of habitat fragmentation on carabids in forest patches. Journal of Biogeography 28: 129–138.CrossRefGoogle Scholar
, and (2005). Spatial structure and dynamics in marine food webs. In Aquatic Food Webs (, , and , eds.). Oxford University Press, Oxford, UK.Google Scholar
and (2007). Graph theory as a proxy for spatially explicit population models in conservation planning. Ecological Applications 17: 1771–1782.CrossRefGoogle ScholarPubMed
and (2008). A graph-theory framework for evaluating landscape connectivity and conservation planning. Conservation Biology 22: 297–307.CrossRefGoogle ScholarPubMed
(2003). The structure and function of complex networks. SIAM Review 45: 167–256.CrossRefGoogle Scholar
(1997). A comparative study on grasshopper (Orthoptera) communities in the Aggtelek Biosphere Reserve. In Research in Aggtelek National Park and Biosphere Reserve ( and , eds.). ANP Directorate, Aggtelek, Hungary: 109–116.Google Scholar
and (2006). Comparison and development of new graph-based landscape connectivity indices: towards the prioritization of habitat patches for conservation. Landscape Ecology 21: 959–967.CrossRefGoogle Scholar
, and (1997). Toward an integration of landscape and food web ecology: the dynamics of spatially subsidized food webs. Annual Review of Ecology and Systematics 28: 289–231.CrossRefGoogle Scholar
(1988). Sources, sinks, and population regulation. American Naturalist 132: 652–661.CrossRefGoogle Scholar
, , and (2000). Quantifying the network connectivity of landscape mosaics: a graph-theoretical approach. Community Ecology 1: 89–94.CrossRefGoogle Scholar
and (2005). Working backwards to move forwards: graph-based connectivity metrics for reserve network selection. Environmental Modeling and Assessment 10: 107–113.CrossRefGoogle Scholar
, , , , and (1998). Inbreeding and extinction in a butterfly metapopulation. Nature 392: 491–494.CrossRefGoogle Scholar
, , and (2009). Estimating connectivity in marine populations: an empirical evaluation of assignment tests and parentage analysis under different gene flow scenarios. Molecular Ecology 18: 1765–1776.CrossRefGoogle ScholarPubMed
(2007). Evaluating forest landscape connectivity through Conefor Sensinode 2.2. In Patterns and Processes in Forest Landscapes: Multiple Use and Sustainable Management (, , and , eds.). Springer, Berlin: 403–422.Google Scholar
and (2007). Conefor Sensinode 2.2 User’s Manual: Software for Quantifying the Importance of Habitat Patches for Maintaining Landscape Connectivity through Graphs and Habitat Availability Indices. University of Lleida, Spain.Google Scholar
and (2007). Directed connectivity among fish populations in a riverine network. Journal of Applied Ecology 44: 1116–1126.CrossRefGoogle Scholar
, , , , , and (2004). Network analysis of potential migration routes for oriental white storks (Ciconia boyciana). Ecological Research 19: 683–698.CrossRefGoogle Scholar
(1998). Flagships, umbrellas, and keystones: is single-species management passé in the landscape area?Biological Conservation 83: 247–257.CrossRefGoogle Scholar
and (1997). Corridors as conduits for small animals: attainable distances depending on movement pattern, boundary reaction and corridor width. Oikos 79: 603–611.CrossRefGoogle Scholar
, , and (2008). Modeling population connectivity by ocean currents, a graph-theoretic approach for marine conservation. Landscape Ecology 23: 19–36.CrossRefGoogle Scholar
and (2001). Landscape connectivity: a graph-theoretic perspective. Ecology 82: 1205–1218.CrossRefGoogle Scholar
(1997). Biogeographical outline of the invertebrate fauna of the Aggtelek Karst and surrounding areas. In Research in Aggtelek National Park and Biosphere Reserve ( and , eds.). ANP Directorate, Aggtelek, Hungary: 87–94.Google Scholar
, , and (2009). Graph theory in action: evaluating planned highway tracks based on connectivity measures. Landscape Ecology 24: 581–586.CrossRefGoogle Scholar
and (1994). Social Network Analysis. Cambridge University Press, Cambridge, UK.CrossRefGoogle Scholar
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