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Understanding the Importance of Dynamic Landscape Connectivity

Zeller, Katherine, Lewison, Rebecca, Fletcher, Robert, Tulbure, Mirela, Jennings, Megan·2020·Land

Abstract

Landscape connectivity is increasingly promoted as a conservation tool to combat the negative effects of habitat loss, fragmentation, and climate change. Given its importance as a key conservation strategy, connectivity science is a rapidly growing discipline. However, most landscape connectivity models consider connectivity for only a single snapshot in time, despite the widespread recognition that landscapes and ecological processes are dynamic. In this paper, we discuss the emergence of dynamic connectivity and the importance of including dynamism in connectivity models and assessments. We outline dynamic processes for both structural and functional connectivity at multiple spatiotemporal scales and provide examples of modeling approaches at each of these scales. We highlight the unique challenges that accompany the adoption of dynamic connectivity for conservation management and planning in the context of traditional conservation prioritization approaches. With the increased availability of time series and species movement data, computational capacity, and an expanding number of empirical examples in the literature, incorporating dynamic processes into connectivity models is more feasible than ever. Here, we articulate how dynamism is an intrinsic component of connectivity and integral to the future of connectivity science.

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  • Recent work in connectivity modelling, such as dynamic landscape connectivity, has developed in response to spatiotemporal limitations of resistance surfaces.

    “Recent work in connectivity modelling, such as dynamic landscape connectivity46, has developed in response to these spatiotemporal limitations of resistance surfaces.”
  • Moving the field of connectivity science toward dynamic assessments has been identified as an important research priority.

    “Moving the field of connectivity science toward dynamic assessments has been identified as an important research priority, a goal that motivated this Special Issue [27][28][29][30].”
  • Dynamic connectivity research has the power to produce models of the big landscape ecology questions of our time: habitat loss, fragmentation, and climate change.

    “Dynamic connectivity research has the power to produce models of the big landscape ecology questions of our time: habitat loss, fragmentation, and climate change [30].”

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  1. Kennedy, 2019, Managing the middle: A shift in conservation priorities based on the global human modification gradient, Glob. Chang. Biol., 25, 811, 10.1111/gcb.14549 · DOI
  2. Betts, 2017, Global forest loss disproportionately erodes biodiversity in intact landscapes, Nature, 547, 441, 10.1038/nature23285 · DOI
  3. Newbold, 2016, Has land use pushed terrestrial biodiversity beyond the planetary boundary? A global assessment, Science, 353, 288, 10.1126/science.aaf2201 · DOI
  4. Crooks, 2017, Quantification of habitat fragmentation reveals extinction risk in terrestrial mammals, Proc. Natl. Acad. Sci. USA, 114, 7635, 10.1073/pnas.1705769114 · DOI
  5. Damschen, 2019, Ongoing accumulation of plant diversity through habitat connectivity in an 18-year experiment, Science, 365, 1478, 10.1126/science.aax8992 · DOI
  6. Wilson, 2010, A Meta-Analytic Review of Corridor Effectiveness, Conserv. Biol., 24, 660, 10.1111/j.1523-1739.2010.01450.x · DOI
  7. Resasco, 2019, Meta-analysis on a Decade of Testing Corridor Efficacy: What New Have we Learned?, Curr. Landsc. Ecol. Rep., 4, 61, 10.1007/s40823-019-00041-9 · DOI
  8. Fletcher, 2016, Divergent Perspectives on Landscape Connectivity Reveal Consistent Effects from Genes to Communities, Curr. Landsc. Ecol. Rep., 1, 67, 10.1007/s40823-016-0009-6 · DOI
  9. Chen, 2011, Rapid Range Shifts of Species Associated with High Levels of Climate Warming, Science, 333, 1024, 10.1126/science.1206432 · DOI
  10. Hilty, J., Lidicker, W.Z.J., and Merenlender, A.M. (2012). Corridor Ecology: The Science and Practice of Linking Landscapes for Biodiversity Conservation, Island Press.
  11. Brown, 1977, Turnover rates in insular biogeography: Effect of immigration on extinction, Ecology, 58, 445, 10.2307/1935620 · DOI
  12. Keeley, 2019, Thirty years of connectivity conservation planning: An assessment of factors influencing plan implementation, Environ. Res. Lett., 14, 103001, 10.1088/1748-9326/ab3234 · DOI
  13. Levins, 1969, Some demographic and genetic consequences of environmental heterogeneity for biological control, Am. Entomol., 15, 237
  14. Taylor, 1993, Connectivity Is a Vital Element of Landscape Structure, Oikos, 68, 571, 10.2307/3544927 · DOI · Paper page
  15. Wiens, 1993, Ecological Mechanisms and Landscape Ecology, Oikos, 66, 369, 10.2307/3544931 · DOI
  16. Howell, 2018, Increasing connectivity between metapopulation ecology and landscape ecology, Ecology, 99, 1119, 10.1002/ecy.2189 · DOI
  17. Hanski, 2001, Spatially realistic theory of metapopulation ecology, Naturwissenschaften, 88, 372, 10.1007/s001140100246 · DOI
  18. Moilanen, 2002, Simple Connectivity Measures in Spatial Ecology, Ecology, 83, 1131, 10.1890/0012-9658(2002)083[1131:SCMISE]2.0.CO;2 · DOI · Paper page
  19. Brandt, J., and Agger, P. (1984). Connectivity: A fundamental ecological characteristic of landscape pattern. Methodology in Landscape Ecological Research and Planning, Proceedings of the First Seminar, International Association of Landscape Ecology, Roskilde, Denmark, 15–19 October 1984, Roskilde University Centre.
  20. Tischendorf, 2000, On the usage and measurement of landscape connectivity, Oikos, 90, 7, 10.1034/j.1600-0706.2000.900102.x · DOI
  21. Hanski, I., and Gaggiotti, O.E. (2004). Metapopulation Dynamics: Perspectives from Landscape Ecology. Ecology, Genetics, and Evolution of Metapopulations, Elsevier Academic Press. · DOI
  22. Zeller, 2012, Estimating landscape resistance to movement: A review, Landsc. Ecol., 27, 777, 10.1007/s10980-012-9737-0 · DOI · Paper page
  23. Hanski, 1999, Habitat Connectivity, Habitat Continuity, and Metapopulations in Dynamic Landscapes, Oikos, 87, 209, 10.2307/3546736 · DOI
  24. Martensen, 2017, Spatio-temporal connectivity: Assessing the amount of reachable habitat in dynamic landscapes, Methods Ecol. Evol., 8, 1253, 10.1111/2041-210X.12799 · DOI
  25. Huang, 2020, Importance of spatio–temporal connectivity to maintain species experiencing range shifts, Ecography, 43, 591, 10.1111/ecog.04716 · DOI · Paper page
  26. Tulbure, 2018, Evaluating static and dynamic landscape connectivity modelling using a 25-year remote sensing time series, Landsc. Ecol., 33, 625, 10.1007/s10980-018-0624-1 · DOI · Paper page
  27. Wilson, 2009, Setting Conservation Priorities, Ann. N. Y. Acad. Sci., 1162, 237, 10.1111/j.1749-6632.2009.04149.x · DOI
  28. Turner, 1995, Ecological Dynamics at Broad Scales, BioScience, 45, 29, 10.2307/1312440 · DOI
  29. Fortuna, 2006, Spatial network structure and amphibian persistence in stochastic environments, Proc. R. Soc. B Biol. Sci., 273, 1429, 10.1098/rspb.2005.3448 · DOI
  30. O’Farrill, G., Schampaert, K.G., Rayfield, B., Bodin, Ö., Calmé, S., Sengupta, R., and Gonzalez, A. (2014). The Potential Connectivity of Waterhole Networks and the Effectiveness of a Protected Area under Various Drought Scenarios. PLoS ONE, 9. · DOI
  31. Tulbure, 2015, Surface water network structure, landscape resistance to movement and flooding vital for maintaining ecological connectivity across Australia’s largest river basin, Landsc. Ecol., 30, 2045, 10.1007/s10980-015-0230-4 · DOI
  32. Uden, 2014, The role of reserves and anthropogenic habitats for functional connectivity and resilience of ephemeral wetlands, Ecol. Appl., 24, 1569, 10.1890/13-1755.1 · DOI
  33. Wright, 2010, Spatiotemporal dynamics of prairie wetland networks: Power-law scaling and implications for conservation planning, Ecology, 91, 1924, 10.1890/09-0865.1 · DOI
  34. Wimberly, 2006, Species Dynamics in Disturbed Landscapes: When does a Shifting Habitat Mosaic Enhance Connectivity?, Landsc. Ecol., 21, 35, 10.1007/s10980-005-7757-8 · DOI
  35. Mui, 2017, Using multiple metrics to estimate seasonal landscape connectivity for Blanding’s turtles (Emydoidea blandingii) in a fragmented landscape, Landsc. Ecol., 32, 531, 10.1007/s10980-016-0456-9 · DOI
  36. Graham, 2010, Dynamic refugia and species persistence: Tracking spatial shifts in habitat through time, Ecography, 33, 1062, 10.1111/j.1600-0587.2010.06430.x · DOI
  37. Baudry, 2003, Temporal variability of connectivity in agricultural landscapes: Do farming activities help?, Landsc. Ecol., 18, 303, 10.1023/A:1024465200284 · DOI
  38. Zeigler, 2014, Transient windows for connectivity in a changing world, Mov. Ecol., 2, 1, 10.1186/2051-3933-2-1 · DOI
  39. Saunders, 2016, Human impacts on connectivity in marine and freshwater ecosystems assessed using graph theory: A review, Mar. Freshw. Res., 67, 277, 10.1071/MF14358 · DOI
  40. Kininmonth, 2010, Graph theoretic topology of the Great but small Barrier Reef world, Theor. Ecol., 3, 75, 10.1007/s12080-009-0055-3 · DOI
  41. Jacobson, 2010, Quantifying and disentangling dispersal in metacommunities: How close have we come? How far is there to go?, Landsc. Ecol., 25, 495, 10.1007/s10980-009-9442-9 · DOI
  42. Lett, 2010, Biophysical modelling to investigate the effects of climate change on marine population dispersal and connectivity, Prog. Oceanogr., 87, 106, 10.1016/j.pocean.2010.09.005 · DOI
  43. Soria, 2014, Dynamic connectivity patterns from an insular marine protected area in the Gulf of California, J. Mar. Syst., 129, 248, 10.1016/j.jmarsys.2013.06.012 · DOI
  44. Halpern, 2003, The Impact of Marine Reserves: Do Reserves Work and Does Reserve Size Matter?, Ecol. Appl., 13, 117, 10.1890/1051-0761(2003)013[0117:TIOMRD]2.0.CO;2 · DOI
  45. Beger, 2010, Conservation planning for connectivity across marine, freshwater, and terrestrial realms, Biol. Conserv., 143, 565, 10.1016/j.biocon.2009.11.006 · DOI
  46. McIntyre, 2018, The challenge of assaying landscape connectivity in a changing world: A 27-year case study in the southern Great Plains (USA) playa network, Ecol. Indic., 91, 607, 10.1016/j.ecolind.2018.04.051 · DOI
  47. Tulbure, 2014, Spatiotemporal dynamics of surface water networks across a global biodiversity hotspot—implications for conservation, Environ. Res. Lett., 9, 114012, 10.1088/1748-9326/9/11/114012 · DOI
  48. Tulbure, 2018, Impact of hydroclimatic variability on regional-scale landscape connectivity across a dynamic dryland region, Ecol. Indic., 94, 142, 10.1016/j.ecolind.2017.07.029 · DOI
  49. Ruiz, 2014, Dynamic connectivity of temporary wetlands in the southern Great Plains, Landsc. Ecol., 29, 507, 10.1007/s10980-013-9980-z · DOI
  50. McIntyre, 2014, Climate forcing of wetland landscape connectivity in the Great Plains, Front. Ecol. Environ., 12, 59, 10.1890/120369 · DOI
  51. Gurarie, 2011, Characteristic Spatial and Temporal Scales Unify Models of Animal Movement, Am. Nat., 178, 113, 10.1086/660285 · DOI
  52. Zelnik, Y.R., Arnoldi, J.-F., and Loreau, M. (2018). The Impact of Spatial and Temporal Dimensions of Disturbances on Ecosystem Stability. Front. Ecol. Evol., 6. · DOI
  53. Newman, E.A., Kennedy, M.C., Falk, D.A., and McKenzie, D. (2019). Scaling and Complexity in Landscape Ecology. Front. Ecol. Evol., 7. · DOI
  54. Leibowitz, 2003, Temporal connectivity in a prairie pothole complex, Wetlands, 23, 13, 10.1672/0277-5212(2003)023[0013:TCIAPP]2.0.CO;2 · DOI
  55. Bowne, 2006, Connectivity in an Agricultural Landscape as Reflected by Interpond Movements of a Freshwater Turtle, Conserv. Biol., 20, 780, 10.1111/j.1523-1739.2006.00355.x · DOI
  56. Ptacnik, 2019, Habitat loss over six decades accelerates regional and local biodiversity loss via changing landscape connectance, Ecol. Lett., 22, 1019, 10.1111/ele.13260 · DOI
  57. Greenwood, 1982, The Natal and Breeding Dispersal of Birds, Annu. Rev. Ecol. Syst., 13, 1, 10.1146/annurev.es.13.110182.000245 · DOI
  58. Reichert, 2016, Consistent scaling of population structure across landscapes despite intraspecific variation in movement and connectivity, J. Anim. Ecol., 85, 1563, 10.1111/1365-2656.12571 · DOI
  59. Lowe, 2010, What can genetics tell us about population connectivity?, Mol. Ecol., 19, 3038, 10.1111/j.1365-294X.2010.04688.x · DOI
  60. Anderson, 2010, Considering spatial and temporal scale in landscape-genetic studies of gene flow, Mol. Ecol., 19, 3565, 10.1111/j.1365-294X.2010.04757.x · DOI
  61. Talluto, 2017, Extinction debt and colonization credit delay range shifts of eastern North American trees, Nat. Ecol. Evol., 1, 0182, 10.1038/s41559-017-0182 · DOI
  62. Perry, 2019, How does temporal variation in habitat connectivity influence metapopulation dynamics?, Oikos, 128, 1277, 10.1111/oik.06052 · DOI
  63. Taylor, C.M. (2019). Effects of Natal Dispersal and Density-Dependence on Connectivity Patterns and Population Dynamics in a Migratory Network. Front. Ecol. Evol., 7. · DOI
  64. Kahilainen, 2018, Metapopulation dynamics in a changing climate: Increasing spatial synchrony in weather conditions drives metapopulation synchrony of a butterfly inhabiting a fragmented landscape, Glob. Chang. Biol., 24, 4316, 10.1111/gcb.14280 · DOI
  65. Wiens, 1989, Spatial Scaling in Ecology, Funct. Ecol., 3, 385, 10.2307/2389612 · DOI · Paper page
  66. Jarvis, 2019, Road mitigation using tunnels and fences promotes site connectivity and population expansion for a protected amphibian, Eur. J. Wildl. Res., 65, 27, 10.1007/s10344-019-1263-9 · DOI
  67. Cormont, 2011, Effect of local weather on butterfly flight behaviour, movement, and colonization: Significance for dispersal under climate change, Biodivers. Conserv., 20, 483, 10.1007/s10531-010-9960-4 · DOI
  68. Zeller, 2019, Black bears alter movements in response to anthropogenic features with time of day and season, Mov. Ecol., 7, 19, 10.1186/s40462-019-0166-4 · DOI
  69. Gaynor, 2019, Landscapes of Fear: Spatial Patterns of Risk Perception and Response, Trends Ecol. Evol., 34, 355, 10.1016/j.tree.2019.01.004 · DOI
  70. Aikens, 2017, The greenscape shapes surfing of resource waves in a large migratory herbivore, Ecol. Lett., 20, 741, 10.1111/ele.12772 · DOI
  71. Heller, 2009, Biodiversity management in the face of climate change: A review of 22 years of recommendations, Biol. Conserv., 142, 14, 10.1016/j.biocon.2008.10.006 · DOI · Paper page
  72. Krosby, 2010, Ecological Connectivity for a Changing Climate, Conserv. Biol., 24, 1686, 10.1111/j.1523-1739.2010.01585.x · DOI · Paper page
  73. Keeley, 2018, New concepts, models, and assessments of climate-wise connectivity, Environ. Res. Lett., 13, 073002, 10.1088/1748-9326/aacb85 · DOI
  74. Lawler, 2013, Projected climate-driven faunal movement routes, Ecol. Lett., 16, 1014, 10.1111/ele.12132 · DOI · Paper page
  75. Fleishman, 2014, Projecting current and future location, quality, and connectivity of habitat for breeding birds in the Great Basin, Ecosphere, 5, 82, 10.1890/ES13-00387.1 · DOI
  76. Littlefield, 2017, Connecting today’s climates to future climate analogs to facilitate movement of species under climate change, Conserv. Biol., 31, 1397, 10.1111/cobi.12938 · DOI · Paper page
  77. Veloz, 2012, Identifying climatic analogs for Wisconsin under 21st-century climate-change scenarios, Clim. Chang., 112, 1037, 10.1007/s10584-011-0261-z · DOI
  78. Lawler, 2013, Connectivity Planning to Address Climate Change, Conserv. Biol., 27, 407, 10.1111/cobi.12014 · DOI
  79. Carroll, 2018, Climatic, topographic, and anthropogenic factors determine connectivity between current and future climate analogs in North America, Glob. Chang. Biol., 24, 5318, 10.1111/gcb.14373 · DOI
  80. Epps, 2006, Elevation and connectivity define genetic refugia for mountain sheep as climate warms, Mol. Ecol., 15, 4295, 10.1111/j.1365-294X.2006.03103.x · DOI
  81. Morelli, 2017, Climate change refugia and habitat connectivity promote species persistence, Clim. Chang. Responses, 4, 8, 10.1186/s40665-017-0036-5 · DOI
  82. Brost, 2012, Use of land facets to design linkages for climate change, Ecol. Appl., 22, 87, 10.1890/11-0213.1 · DOI
  83. Krosby, 2015, Focal species and landscape “naturalness” corridor models offer complementary approaches for connectivity conservation planning, Landsc. Ecol., 30, 2121, 10.1007/s10980-015-0235-z · DOI
  84. Theobald, 2011, Assessing effects of land use on landscape connectivity: Loss and fragmentation of western U.S. forests, Ecol. Appl., 21, 2445, 10.1890/10-1701.1 · DOI
  85. Acevedo, 2015, Conservation under uncertainty: Optimal network protection strategies for worst-case disturbance events, J. Appl. Ecol., 52, 1588, 10.1111/1365-2664.12532 · DOI
  86. Jennings, 2016, Puma response to the effects of fire and urbanization, J. Wildl. Manag., 80, 221, 10.1002/jwmg.1018 · DOI
  87. Pressey, 2007, Conservation planning in a changing world, Trends Ecol. Evol., 22, 583, 10.1016/j.tree.2007.10.001 · DOI
  88. Vos, 2012, Species in a dynamic world: Consequences of habitat network dynamics on conservation planning, Biol. Conserv., 153, 239, 10.1016/j.biocon.2012.05.001 · DOI
  89. Pinto, 2009, Beyond the least-cost path: Evaluating corridor redundancy using a graph-theoretic approach, Landsc. Ecol., 24, 253, 10.1007/s10980-008-9303-y · DOI · Paper page
  90. Peterson, 2003, Scenario Planning: A Tool for Conservation in an Uncertain World, Conserv. Biol., 17, 358, 10.1046/j.1523-1739.2003.01491.x · DOI
  91. Loarie, 2009, The velocity of climate change, Nature, 462, 1052, 10.1038/nature08649 · DOI
  92. Michalak, 2018, Distribution and protection of climatic refugia in North America, Conserv. Biol., 32, 1414, 10.1111/cobi.13130 · DOI
  93. Anderson, 2015, Case studies of conservation plans that incorporate geodiversity, Conserv. Biol., 29, 680, 10.1111/cobi.12503 · DOI
  94. Jennings, M.K., Zeller, K.A., and Lewison, R.L. (2020). Supporting Adaptive Connectivity in Dynamic Landscapes. Land, 9. · DOI
  95. Gregory, 2006, Deconstructing Adaptive Management: Criteria for Applications to Environmental Management, Ecol. Appl., 16, 2411, 10.1890/1051-0761(2006)016[2411:DAMCFA]2.0.CO;2 · DOI

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Metadata

Authors
Zeller, K., Lewison, R., Fletcher, R., Tulbure, M., & Jennings, M.
Affiliations
  • Aldo Leopold Wilderness Research Institute, Rocky Mountain Research Station, United States Forest Service, 790 East Beckwith Ave, Missoula, MT 59801, USA
  • Department of Biology, San Diego State University, San Diego, CA 92182, USA
  • Department of Wildlife Ecology and Conservation, University of Florida, Gainesville, FL 32611, USA
  • Center for Geospatial Analytics, North Carolina State University, Raleigh, NC 27695, USA
Venue
Land
Publisher
MDPI AG
Published
2020-08-29
Volume
9
Issue
9
Pages
303
ISSN
2073-445X
Cited by
96
References
3
Language
en

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