Landscape Ecology

, Volume 32, Issue 2, pp 249–263 | Cite as

Using landscape graphs to delineate ecologically functional areas

Research Article

Abstract

Context

Landscape graphs are widely used to model connectivity and to support decision-making in conservation planning. Compartmentalization methods applied to such graphs aim to define clusters of highly interconnected patches. Recent studies show that compartmentalization based on modularity is suitable, but it applies to non-weighted graphs whereas most landscape graphs involve weighted nodes and links.

Objectives

We propose to adapt modularity computation to weighted landscape graphs and to validate the relevance of the resulting compartments using demographic or genetic data about the patches.

Methods

A weighted adjacency matrix was designed to express potential fluxes, associating patch capacities and inter-patch distances. Eight weighting scenarios were compared. The statistical evaluation of each compartmentalization was based on Wilks’ Lambda. These methods were performed on a grassland network where patches are documented by annual densities of water voles in the Jura massif (France).

Results

The scenarios in which patch capacity is assigned a small weight led to the more relevant results, giving high modularity values and low Wilks’ Lambda values. When considering a fixed number of compartments, we found a significant negative correlation between these two criteria. Comparison showed that compartments are ecologically more valid than graph components.

Conclusions

The method proposed is suitable for designing ecologically functional areas from weighted landscape graphs. Maximum modularity values can serve as a guide for setting the parameters of the adjacency matrix.

Keywords

Landscape connectivity Compartmentalization Modularity Ecological network Montane water vole 

References

  1. Awade M, Boscolo D, Metger JP (2012) Using binary and probabilistic habitat availability indices derived from graph theory to model bird occurrence in fragmented forests. Landscape Ecol 27:185–198CrossRefGoogle Scholar
  2. Barrat A, Barthélemy M, Pastor-Satorras R, Vespignani A (2004) The architecture of complex weighted networks. Proc Natl Acad Sci USA 101:3747–3752CrossRefPubMedPubMedCentralGoogle Scholar
  3. Berthier K, Chaval Y, Galan M, Charbonnel N, Cosson JF (2009) Dispersion individuelle: conséquences. In: Delattre P, Giraudoux P (eds) Le campagnol terrestre: prévention et contrôle des populations. QUAE Edition, Versailles, pp 39–47Google Scholar
  4. Berthier K, Piry S, Cosson JF, Giraudoux P, Foltête JC, Defaut R, Truchetet D, Lambin X (2014) Dispersal, landscape and travelling waves in cyclic vole populations. Ecol Lett 17:53–64CrossRefPubMedGoogle Scholar
  5. Bjornstad ON, Ims RA, Lambin X (1999) Spatial population dynamics: analyzing patterns and processes of population synchrony. Trends Ecol Evol 14:427–432CrossRefPubMedGoogle Scholar
  6. Blant MA, Beuret B, Poitry R, Joseph E (2009) Influence of landscape and soil on the intensity of pullulations of vole (Arvicola terrestris scherman) in Swiss Jura. Rev Suisse Agric 41:301–307Google Scholar
  7. Bodea M, Burrage K, Possingham HP (2008) Using complex network metrics to predict the persistence of metapopulations with asymmetric connectivity patterns. Ecol Model 214:201–209CrossRefGoogle Scholar
  8. Bodin O, Norberg J (2007) A network approach for analyzing spatially structured populations in fragmented landscape. Landscape Ecol 22:31–44CrossRefGoogle Scholar
  9. Brandes U, Delling D, Gaertler M, Görke R, Hoefer M, Nikoloski Z, Wagner D (2008) On modularity clustering. IEEE Trans Knowl Data Eng 20:172–188CrossRefGoogle Scholar
  10. Cavanaugh KC, Siegel DA, Raimondi PT, Alberto F (2014) Patch definition in metapopulation analysis: a graph theory approach to solve the mega-patch problem. Ecology 95:316–328CrossRefPubMedGoogle Scholar
  11. Clauzel C, Bannwarth C, Foltête JC (2015) A planning tool for integrating broad-scale connectivity in habitat restoration: an application to pond creation in eastern France. J Nat Conserv 23:98–107CrossRefGoogle Scholar
  12. Duhamel R, Quéré JP, Delattre P, Giraudoux P (2000) Landscape effects on the population dynamics of the fossorial form of the water vole (Arvicola terrestris Sherman). Landscape Ecol 15:89–98CrossRefGoogle Scholar
  13. Everitt BS, Dunn G (1991) Applied multivariate data analysis. Edward Arnold, LondonGoogle Scholar
  14. Fall A, Fortin MJ, Manseau M, O’Brien D (2007) Spatial graphs: principles and applications for habitat connectivity. Ecosystems 10:448–461CrossRefGoogle Scholar
  15. Foltête JC, Clauzel C, Vuidel G, Tournant P (2012a) Integrating graph-based connectivity metrics into species distribution models. Landscape Ecol 27:557–569CrossRefGoogle Scholar
  16. Foltête JC, Couval G, Fontanier M, Vuidel G, Giraudoux P (2016) A graph-based approach to defend agro-ecological systems against water vole outbreaks. Ecol Indic 71:87–98CrossRefGoogle Scholar
  17. Foltête JC, Girardet X, Clauzel C (2014) A methodological framework for the use of landscape graphs in land-use planning. Landsc Urban Plan 124:140–150CrossRefGoogle Scholar
  18. Foltête JC, Giraudoux P (2012) A graph-based approach to investigating the influence of the landscape on population spread processes. Ecol Indic 18:684–692CrossRefGoogle Scholar
  19. Foltête JC, Vuidel G, Clauzel C (2012b) A software tool dedicated to the modelling of landscape networks. Environ Model Soft 38:316–327CrossRefGoogle Scholar
  20. Galpern P, Manseau M, Fall A (2011) Patch-based graphs of landscape connectivity: a guide to construction, analysis and application for conservation. Biol Conserv 144:44–55CrossRefGoogle Scholar
  21. Galpern P, Manseau M, Wilson P (2012) Grains of connectivity: analysis at multiple spatial scales in landscape genetics. Mol Ecol 21:3996–4009CrossRefPubMedGoogle Scholar
  22. Gao P, Kupfer JA, Guo D, Lei TL (2013) Identifying functionally connected habitat compartments with a novel regionalization technique. Landscape Ecol 28:1949–1959CrossRefGoogle Scholar
  23. Giraudoux P, Delattre P, Habert M, Quéré JP, Deblay S, Defaut R, Duhamel R, Moissenet MF, Salvi D, Truchetet D (1997) Population dynamics of fossorial water vole: a land use and landscape perspective. Agric Ecosyst Environ 66:47–60CrossRefGoogle Scholar
  24. Giraudoux P, Pradier B, Delattre P, Deblay S, Salvi D, Defaut R (1995) Estimation of water vole abundance by using surface indices. Acta Theriol 40:77–96CrossRefGoogle Scholar
  25. Girvan M, Newman MEJ (2002) Community structure in social and biological networks. Proc Natl Acad Sci USA 99:7821–7826CrossRefPubMedPubMedCentralGoogle Scholar
  26. Gustafson EJ, Gardner RH (1996) The effect of landscape heterogeneity on the probability of patch colonization. Ecology 77:94–107CrossRefGoogle Scholar
  27. Hall AL, Beissinger SR (2014) A practical toolbox for design and analysis of landscape genetics studies. Landscape Ecol 29:1487–1504CrossRefGoogle Scholar
  28. Lookingbill TR, Elmore AJ, Engelhardt KAM, Churchill JB, Gates E, Johnson JB (2010) Influence of wetland networks on bat activity in mixed-use landscapes. Biol Conserv 143:974–983CrossRefGoogle Scholar
  29. Luque S, Saura S, Fortin MJ (2012) Landscape connectivity analysis for conservation: insights from combining new methods with ecological and genetic data. Landscape Ecol 27:153–157CrossRefGoogle Scholar
  30. Marriott FHC (1971) Practical problems in a method of cluster analysis. Biometrics 27:501–514CrossRefPubMedGoogle Scholar
  31. McLachlan GJ (2004) Discriminant analysis and statistical pattern recognition. Wiley, HobokenGoogle Scholar
  32. Minor ES, Urban DL (2007) Graph theory as a proxy for spatially explicit population models in conservation planning. Ecol Appl 17:1771–1782CrossRefPubMedGoogle Scholar
  33. Moilanen A (2011) On the limitations of graph-theoretic connectivity in spatial ecology and conservation. J Appl Ecol 48:1543–1547CrossRefGoogle Scholar
  34. Morilhat C, Bernard N, Foltête JC, Giraudoux P (2008) Neighbourhood landscape effect on population kinetics of the fossorial water vole (Arvicola terrestris scherman). Landscape Ecol 23:569–579CrossRefGoogle Scholar
  35. Newman M (2004) Analysis of weighted networks. Phys Rev E 70:056131CrossRefGoogle Scholar
  36. Newman M, Girvan M (2004) Finding and evaluating community structure in networks. Phys Rev E 69:026113CrossRefGoogle Scholar
  37. Saura S, 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. Landsc Urban Plan 83:91–103CrossRefGoogle Scholar
  38. Schuetz P, Caflisch A (2008) Efficient modularity optimization by multistep greedy algorithm and vertex mover refinement. Phys Rev E 77(046):112Google Scholar
  39. Taylor P, Fahrig L, With W (2006) Landscape connectivity: a return to basics. In: Crooks KR, Sanjayan M (eds) Connectivity conservation. Cambridge University Press, Cambridge, pp 29–43CrossRefGoogle Scholar
  40. Tournant P, Afonso E, Giraudoux P, Roué S, Foltête JC (2013) Evaluating the effect of habitat connectivity on the distribution of lesser horseshoe bat maternity roosts using landscape graphs. Biol Conserv 164:39–49CrossRefGoogle Scholar
  41. Urban DL, Keitt TH (2001) Landscape connectivity: a graph theoretic approach. Ecology 82:1205–1218CrossRefGoogle Scholar
  42. Urban DL, Minor ES, Treml EA, Schick RS (2009) Graph models of land mosaics. Ecol Lett 12:260–273CrossRefPubMedGoogle Scholar
  43. Vogt P, Riiters KH, Iwanowski M, Estreguil C, Kozak J, Wade TG, Wickham JD (2007) Mapping spatial patterns with morphological image processing. Landscape Ecol 22:171–177CrossRefGoogle Scholar
  44. Webster R (1972) Wilks’s criterion: a measure for comparing the value of general purpose soil classifications. J Soil Sci 11:254–260Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  1. 1.ThéMA, UMR 6049 CNRS – Université Bourgogne-Franche-ComtéBesançonFrance

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