Advertisement

Landscape Ecology

, Volume 33, Issue 4, pp 625–640 | Cite as

Evaluating static and dynamic landscape connectivity modelling using a 25-year remote sensing time series

  • Robbi Bishop-Taylor
  • Mirela G. Tulbure
  • Mark Broich
Research Article

Abstract

Context

Despite calls for landscape connectivity research to account for spatiotemporal dynamics, studies have overwhelmingly evaluated the importance of habitats for connectivity at single or limited moments in time. Remote sensing time series represent a promising resource for studying connectivity within dynamic ecosystems. However, there is a critical need to assess how static and dynamic landscape connectivity modelling approaches compare for prioritising habitats for conservation within dynamic environments.

Objectives

To assess whether static landscape connectivity analyses can identify similar important areas for connectivity as analyses based on dynamic remotely sensed time series data.

Methods

We compared degree and betweenness centrality graph theory metric distributions from four static scenarios against equivalent results from a dynamic 25-year remotely sensed surface-water time series. Metrics were compared at multiple spatial aggregation scales across south-eastern Australia’s 1 million km2 semi-arid Murray–Darling Basin and three sub-regions with varying levels of hydroclimatic variability and development.

Results

We revealed large differences between static and dynamic connectivity metric distributions that varied by static scenario, region, spatial scale and hydroclimatic conditions. Static and dynamic metrics showed particularly low overlap within unregulated and spatiotemporally variable regions, although similarities increased at coarse aggregation scales.

Conclusions

In regions that exhibit high spatiotemporal variability, static connectivity modelling approaches are unlikely to serve as effective surrogates for more data intensive approaches based on dynamic, remotely sensed data. Although this limitation may be moderated by spatially aggregating static connectivity outputs, our results highlight the value of remotely sensed time series for assessing connectivity in dynamic landscapes.

Keywords

Spatiotemporal dynamics Graph theory Dynamic connectivity Static connectivity Network analysis Landscape connectivity 

Notes

Acknowledgements

This work was funded through an Australian Research Council Discovery Early Career Researcher Award (DE140101608) to Tulbure. We thank both anonymous reviewers for their valuable comments and suggestions.

References

  1. Albanese G, Haukos DA (2017) A network model framework for prioritizing wetland conservation in the Great Plains. Landscape Ecol 32:115–130CrossRefGoogle Scholar
  2. Avon C, Bergès L (2016) Prioritization of habitat patches for landscape connectivity conservation differs between least-cost and resistance distances. Landscape Ecol.  https://doi.org/10.1007/s10980-015-0336-8 Google Scholar
  3. Ballinger A, Mac Nally RC (2006) The landscape context of flooding in the Murray–Darling Basin. Adv Ecol Res 39:85–105CrossRefGoogle Scholar
  4. Bino G, Kingsford RT, Brandis K (2016) Australia’s wetlands—learning from the past to manage for the future. Pacific Conserv Biol 22:116–129CrossRefGoogle Scholar
  5. Bishop-Taylor R, Tulbure MG, Broich M (2015) Surface water network structure, landscape resistance to movement and flooding vital for maintaining ecological connectivity across Australia’s largest river basin. Landscape Ecol 30:2045–2065CrossRefGoogle Scholar
  6. Bishop-Taylor R, Tulbure MG, Broich M (2017a) Data from: Surface-water dynamics and land use influence landscape connectivity across a major dryland region. Dryad Digit Repos.  https://doi.org/10.5061/dryad.qf83q Google Scholar
  7. Bishop-Taylor R, Tulbure MG, Broich M (2017b) Surface water dynamics and land use influence landscape connectivity across a major dryland region. Ecol Appl 27:1124–1137CrossRefPubMedGoogle Scholar
  8. Bishop-Taylor R, Tulbure MG, Broich M (2017c) Impact of hydroclimatic variability on regional-scale landscape connectivity across a dynamic dryland region. Ecol Indic.  https://doi.org/10.1016/j.ecolind.2017.07.029 Google Scholar
  9. Blonder B, Wey TW, Dornhaus A, James R, Sih A (2012) Temporal dynamics and network analysis. Methods Ecol Evol 3:958–972CrossRefGoogle Scholar
  10. Bodin Ö, Norberg J (2006) A network approach for analyzing spatially structured populations in fragmented landscape. Landscape Ecol 22:31–44CrossRefGoogle Scholar
  11. Butts CT (2009) Revisiting the foundations of network analysis. Science 325:414–416CrossRefPubMedGoogle Scholar
  12. Carroll C, Wang T, Roberts DR, Michalak JL, Lawler JJ, Nielsen SE, Stralberg D, Wang T (2017) Scale-dependent complementarity of climatic velocity and environmental diversity for identifying priority areas for conservation under climate change. Glob Change Biol.  https://doi.org/10.1111/gcb.13679 Google Scholar
  13. Castorani MCN, Reed DC, Alberto F, Bell TW, Simons RD, Cavanaugh KC, Raimondi PT (2015) Connectivity structures local population dynamics: a long-term empirical test in a large metapopulation system. Ecology 96:3141–3152CrossRefPubMedGoogle Scholar
  14. 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
  15. Correa Ayram CA, Mendoza ME, Salicrup DR, Granados EL (2014) Identifying potential conservation areas in the Cuitzeo Lake basin, Mexico by multitemporal analysis of landscape connectivity. J Nat Conserv 22:424–435CrossRefGoogle Scholar
  16. Davis J, O’Grady AP, Dale A, Arthington AH, Gell PA, Driver PD, Capon SJ (2015) When trends intersect: the challenge of protecting freshwater ecosystems under multiple land use and hydrological intensification scenarios. Sci Total Environ 534:65–78CrossRefPubMedGoogle Scholar
  17. Drake JC, Griffis-Kyle KL, McIntyre NE (2017) Graph theory as an invasive species management tool: case study in the Sonoran Desert. Landscape Ecol.  https://doi.org/10.1007/s10980-017-0539-2 Google Scholar
  18. Drusch M, Del Bello U, Carlier S, Colin O, Fernandez V, Gascon F, Meygret A (2012) Sentinel-2: ESA’s optical high-resolution mission for GMES operational services. Remote Sens Environ 120:25–36CrossRefGoogle Scholar
  19. Estrada E, Bodin Ö (2008) Using network centrality measures to manage landscape connectivity. Ecol Appl 18:1810–1825CrossRefPubMedGoogle Scholar
  20. Fagan ME, DeFries RS, Sesnie SE, Arroyo-Mora JP, Chazdon RL (2016) Targeted reforestation could reverse declines in connectivity for understory birds in a tropical habitat corridor. Ecol Appl 26:1456–1474CrossRefPubMedGoogle Scholar
  21. Fortin M-J, James PMA, MacKenzie A, Melles SJ, Rayfield B (2012) Spatial statistics, spatial regression, and graph theory in ecology. Spat Stat 1:100–109CrossRefGoogle Scholar
  22. Fortuna MA, Gómez-Rodríguez C, Bascompte J (2006) Spatial network structure and amphibian persistence in stochastic environments. Proc R Soc B Biol Sci 273:1429–1434CrossRefGoogle Scholar
  23. Geoscience Australia (2006) GEODATA TOPO 250 K Series 3 (Packaged—Shape file format). http://www.ga.gov.au/metadata-gateway/metadata/record/gcat_63999. Accessed Nov 2016
  24. Heimhuber V, Tulbure MG, Broich M (2016) Modeling 25 years of spatio-temporal surface water and inundation dynamics on large river basin scale using time series of Earth observation data. Hydrol Earth Syst Sci 20:2227–2250CrossRefGoogle Scholar
  25. Heimhuber V, Tulbure MG, Broich M (2017) Modeling multidecadal surface water inundation dynamics and key drivers on large river basin scale using multiple time series of Earth-observation and river flow data. Water Resour Res 20:2227–2250Google Scholar
  26. Hernández A, Miranda M, Arellano EC, Saura S, Ovalle C (2015) Landscape dynamics and their effect on the functional connectivity of a Mediterranean landscape in Chile. Ecol Indic 48:198–206CrossRefGoogle Scholar
  27. Kingsford RT (2000) Ecological impacts of dams, water diversions and river management on floodplain wetlands in Australia. Austral Ecol 25:109–127CrossRefGoogle Scholar
  28. Kool JT, Moilanen A, Treml EA (2012) Population connectivity: recent advances and new perspectives. Landscape Ecol 28:165–185CrossRefGoogle Scholar
  29. Leblanc M, Tweed S, Van Dijk A, Timbal B (2012) A review of historic and future hydrological changes in the Murray–Darling Basin. Glob Planet Change 80–81:226–246CrossRefGoogle Scholar
  30. Littlefield CE, McRae BH, Michalak J, Lawler JJ, Carroll C (2017) Connecting today’s climates to future analogs to facilitate species movement under climate change. Conserv Biol.  https://doi.org/10.1111/cobi.12938 PubMedGoogle Scholar
  31. Lloyd MW, Widmeyer PA, Neel MC (2016) Temporal variability in potential connectivity of Vallisneria americana in the Chesapeake Bay. Landscape Ecol 31:2307–2321CrossRefGoogle Scholar
  32. Locher-Krause KE, Volk M, Waske B, Thonfeld F, Lautenbach S (2017) Expanding temporal resolution in landscape transformations: insights from a landsat-based case study in Southern Chile. Ecol Indic 75:132–144CrossRefGoogle Scholar
  33. Lymburner L, Tan P, Mueller N, Thackway R, Lewis A, Thankappan M, Senarath U (2010) The National Dynamic Land Cover Dataset. National Earth Observation Group, Geoscience Australia, CanberraGoogle Scholar
  34. Martensen AC, Saura S, Fortin M-J (2017) Spatio-temporal connectivity: assessing the amount of reachable habitat in dynamic landscapes. Methods Ecol Evol.  https://doi.org/10.1111/ijlh.12426 Google Scholar
  35. McIntyre NE, Wright CK, Swain S, Hayhoe K, Liu G, Schwartz FW, Henebry GM (2014) Climate forcing of wetland landscape connectivity in the Great Plains. Front Ecol Environ 12:59–64CrossRefGoogle Scholar
  36. McRae BH, Beier P (2007) Circuit theory predicts gene flow in plant and animal populations. Proc Natl Acad Sci USA 104:19885–19890CrossRefPubMedCentralPubMedGoogle Scholar
  37. McRae BH, Dickson BG, Keitt TH, Shah VB (2008) Using circuit theory to model connectivity in ecology, evolution, and conservation. Ecology 89:2712–2724CrossRefPubMedGoogle Scholar
  38. Minor ES, Urban DL (2008) A graph-theory framework for evaluating landscape connectivity and conservation planning. Conserv Biol 22:297–307CrossRefPubMedGoogle Scholar
  39. Morán-Ordóñez A, Pavlova A, Pinder AM, Sim L, Sunnucks P, Thompson RM, Davis J (2015) Aquatic communities in arid landscapes: local conditions, dispersal traits and landscape configuration determine local biodiversity. Divers Distrib 21:1230–1241CrossRefGoogle Scholar
  40. Mui AB, Caverhill B, Johnson B, Fortin MJ, He Y (2017) Using multiple metrics to estimate seasonal landscape connectivity for Blanding’s turtles (Emydoidea blandingii) in a fragmented landscape. Landscape Ecol 32:531–546CrossRefGoogle Scholar
  41. Murphy MA, Dezzani R, Pilliod DS, Storfer A (2010) Landscape genetics of high mountain frog metapopulations. Mol Ecol 19:3634–3649CrossRefPubMedGoogle Scholar
  42. Murphy AL, Pavlova A, Thompson R, Davis J, Sunnucks P (2015) Swimming through sand: connectivity of aquatic fauna in deserts. Ecol Evol 5:5252–5264CrossRefGoogle Scholar
  43. Newman MEJ (2003) The structure and function of complex networks. SIAM Rev 45:167–256CrossRefGoogle Scholar
  44. O’Connor P (1986) The biology of the Murray crayfish, Euastacus armatus (Decapoda: Parastacidae) and recommendations for the future management of the fishery (unpublished NSW Department of Agriculture data summary)Google Scholar
  45. O’Farrill G, Schampaert KG, Rayfield B, Bodin Ö, Calme S, Sengupta R, Gonzalez A (2014) The potential connectivity of waterhole networks and the effectiveness of a protected area under various drought scenarios. PLoS ONE 9:e95049CrossRefPubMedCentralPubMedGoogle Scholar
  46. Page K, Read A, Frazier P, Mount N (2005) The effect of altered flow regime on the frequency and duration of bankfull discharge: Murrumbidgee River, Australia. River Res Appl 578:567–578CrossRefGoogle Scholar
  47. Pascual-Hortal L, Saura S (2007) Impact of spatial scale on the identification of critical habitat patches for the maintenance of landscape connectivity. Landsc Urban Plan 83:176–186CrossRefGoogle Scholar
  48. Piquer-Rodríguez M, Torella S, Gavier-Pizarro G, Volante J, Somma D, Ginzburg R, Kuemmerle T (2015) Effects of past and future land conversions on forest connectivity in the Argentine Chaco. Landscape Ecol 30:817–833CrossRefGoogle Scholar
  49. Pittock B, Abbs D, Suppiah R, Jones R (2006) Climatic background to past and future floods in Australia. Adv Ecol Res 39:13–39CrossRefGoogle Scholar
  50. Pittock J, Finlayson CM (2011) Australia’s Murray–Darling Basin: freshwater ecosystem conservation options in an era of climate change. Mar Freshw Res 62:232CrossRefGoogle Scholar
  51. Puckridge JT, Sheldon F, Walker KF, Boulton A (1988) Flow variability and the ecology of large rivers. Mar Freshw Res 49:55–72CrossRefGoogle Scholar
  52. Roe JH, Brinton AC, Georges A (2009) Temporal and spatial variation in landscape connectivity for a freshwater turtle in a temporally dynamic wetland system. Ecol Appl 19:1288–1299CrossRefPubMedGoogle Scholar
  53. Rogers K, Ralph TJ (2010) Floodplain wetlands of the Murray–Darling Basin and their freshwater biota. In: Rogers K, Ralph TJ (eds) Floodplain wetl. Biota murray–darling basin water habitat requirements. CSIRO Publishing, Collingwood, pp 1–16Google Scholar
  54. Rubio L, Bodin Ö, Brotons L, Saura S (2014) Connectivity conservation priorities for individual patches evaluated in the present landscape: how durable and effective are they in the long term? Ecography (Cop) 38:782–791CrossRefGoogle Scholar
  55. Ruiz L, Parikh N, Heintzman LJ, Collins SD, Starr SM, Wright CK, McIntyre NE (2014) Dynamic connectivity of temporary wetlands in the southern Great Plains. Landscape Ecol 29:507–516CrossRefGoogle Scholar
  56. Saunders MI, Brown CJ, Foley MM, Febria CM, Albright R, Mehling MG, Burfeind DD (2015) Human impacts on connectivity in marine and freshwater ecosystems assessed using graph theory: a review. Mar Freshw Res 67:277–290Google Scholar
  57. 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
  58. Schaffer-Smith D, Swenson JJ, Barbaree B, Reiter ME (2017) Three decades of Landsat-derived spring surface water dynamics in an agricultural wetland mosaic; Implications for migratory shorebirds. Remote Sens Environ 193:180–192CrossRefPubMedCentralPubMedGoogle Scholar
  59. Schick RS, Lindley ST (2007) Directed connectivity among fish populations in a riverine network. J Appl Ecol 44:1116–1126CrossRefGoogle Scholar
  60. Schmit C, Rounsevell MDA, La Jeunesse I (2006) The limitations of spatial land use data in environmental analysis. Environ Sci Policy 9:174–188CrossRefGoogle Scholar
  61. Shah VB, McRae BH (2008) Circuitscape: a tool for landscape ecology. In: Varoquaux G, Vaught T, Millman J (eds) In: Proceedings of the 7th Python in Science Conference (SciPy 2008). Pasadena, CA, pp 62–66Google Scholar
  62. Smith MA, Green DM (2005) Dispersal and the metapopulation paradigm in amphibian ecology and conservation: are all amphibian populations metapopulations? Ecography (Cop) 28:110–128CrossRefGoogle Scholar
  63. Tulbure MG, Broich M (2013) Spatiotemporal dynamic of surface water bodies using Landsat time-series data from 1999 to 2011. ISPRS J Photogramm Remote Sens 79:44–52CrossRefGoogle Scholar
  64. Tulbure MG, Broich M, Stehman SV, Kommareddy A (2016) Surface water extent dynamics from three decades of seasonally continuous Landsat time series at subcontinental scale in a semi-arid region. Remote Sens Environ 178:142–157CrossRefGoogle Scholar
  65. Tulbure MG, Kininmonth SJ, Broich M (2014) Spatiotemporal dynamics of surface water networks across a global biodiversity hotspot—implications for conservation. Environ Res Lett 9:114012CrossRefGoogle Scholar
  66. Urban DL, Keitt TH (2001) Landscape connectivity: a graph-theoretic perspective. Ecology 82:1205–1218CrossRefGoogle Scholar
  67. Urban DL, Minor ES, Treml EA, Schick RS (2009) Graph models of habitat mosaics. Ecol Lett 12:260–273CrossRefPubMedGoogle Scholar
  68. Vanderhoof MK, Alexander LC, Todd MJ (2015) Temporal and spatial patterns of wetland extent influence variability of surface water connectivity in the Prairie Pothole Region, United States. Landsc Ecol.  https://doi.org/10.1007/s10980-015-0290-5 Google Scholar
  69. Verburg PH, Neumann K, Nol L (2011) Challenges in using land use and land cover data for global change studies. Glob Change Biol 17:974–989CrossRefGoogle Scholar
  70. Watts AG, Schlichting P, Billerman S, Jesmer B, Micheletti S, Fortin MJ, Murphy MA (2015) How spatio-temporal habitat connectivity affects amphibian genetic structure. Front Genet 6:1–13CrossRefGoogle Scholar
  71. Wright CK (2010) Spatiotemporal dynamics of prairie wetland networks: power-law scaling and implications for conservation planning. Ecology 91:1924–1930.  https://doi.org/10.1890/09-0865.1 CrossRefPubMedGoogle Scholar
  72. Zeigler SL, Fagan WF (2014) Transient windows for connectivity in a changing world. Mov Ecol 2:1CrossRefPubMedCentralPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Biological, Earth and Environmental SciencesThe University of New South WalesRandwickAustralia

Personalised recommendations