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Connectivity metrics for conservation planning and monitoring

Keeley, Annika T.H., Beier, Paul, Jenness, Jeff S.·2021·Biological Conservation

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Crossref · 10.1016/j.biocon.2021.109008

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  • The 0-100 range represents the most efficient and most likely most frequented routes between sites, suggesting direct and recurrent routes in the logic of Neanderthal mobility.

    “These represent the most efficient and, most likely, the most frequented routes between sites, suggesting direct and recurrent routes in the logic of Neanderthal mobility [22,51].”

    From Tracing neanderthal mobility through the romanian carpathians: a GIS-based least-cost connectivity model (2025) · cites this paper as [51]

    Section 3.1 describes resistance-based metrics that calculate cost-weighted paths between habitat patches, supporting the general idea of modeling efficient routes. It does not define a 0–100 range or establish that such routes were the most frequented, direct and recurrent routes of Neanderthal mobility. Page numbers are not available in our copy. (LLM, conf=0.83)

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Research works printed but not linked: 69

  1. Albert, 2017, Applying network theory to prioritize multispecies habitat networks that are robust to climate and land-use change, Conserv. Biol., 31, 1383, 10.1111/cobi.12943 · DOI · Paper page
  2. Beier, 1995, Dispersal of juvenile cougars in fragmented habitat, J. Wildl. Manag., 59, 228, 10.2307/3808935 · DOI
  3. Beier, 2019, A rule of thumb for widths of conservation corridors, Conserv. Biol., 33, 976, 10.1111/cobi.13256 · DOI
  4. Beier, 2012, Desperately seeking stable 50-year-old landscapes with patches and long, wide corridors, PLoS Biol., 10, 10.1371/journal.pbio.1001253 · DOI
  5. Beier, 2008, Forks in the road: choices in procedures for designing wildland linkages, Conserv. Biol., 22, 836, 10.1111/j.1523-1739.2008.00942.x · DOI · Paper page
  6. Bélisle, 2005, Measuring landscape connectivity: the challenge of behavioral landscape ecology, Ecology, 86, 1988, 10.1890/04-0923 · DOI
  7. Belote, 2020, A framework for developing connectivity targets and indicators to guide global conservation efforts, BioScience, 70, 122, 10.1093/biosci/biz148 · DOI
  8. Berger, 2006, Connecting the dots: an invariant migration corridor links the Holocene to the present, Biol. Lett., 2, 528, 10.1098/rsbl.2006.0508 · DOI
  9. Bunn, 2000, Landscape connectivity: a conservation application of graph theory, J. Environ. Manag., 59, 265, 10.1006/jema.2000.0373 · DOI
  10. Calabrese, 2004, A comparison-shopper’s guide to connectivity metrics, Front. Ecol. Environ., 2, 529, 10.1890/1540-9295(2004)002[0529:ACGTCM]2.0.CO;2 · DOI · Paper page
  11. Conde, 2010, Sex matters: modeling male and female habitat differences for jaguar conservation, Biol. Conserv., 143, 1980, 10.1016/j.biocon.2010.04.049 · DOI
  12. Damschen, 2019, Ongoing accumulation of plant diversity through habitat connectivity in an 18-year experiment, Science, 6460, 1478, 10.1126/science.aax8992 · DOI
  13. Dickson, 2016, Informing strategic efforts to expand and connect protected areas using a model of ecological flow, with application to the western United States, Conserv. Lett., 10, 564, 10.1111/conl.12322 · DOI
  14. Dixon, 2006, Effectiveness of a regional corridor in connecting two Florida black bear populations, Conserv. Biol., 10, 155, 10.1111/j.1523-1739.2005.00292.x · DOI
  15. Elliot, 2014, The devil is in the dispersers: predictions of landscape connectivity change with demography, J. Appl. Ecol., 51, 1169, 10.1111/1365-2664.12282 · DOI · Paper page
  16. Fahrig, 2002, Effect of habitat fragmentation on the extinction threshold: a synthesis, Ecol. Appl., 12, 346
  17. Fischer, 2007, Landscape modification and habitat fragmentation: a synthesis, Glob. Ecol. Biogeogr., 16, 265, 10.1111/j.1466-8238.2007.00287.x · DOI
  18. Gao, 2013, Identifying functionally connected habitat compartments with a novel regionalization technique, Landsc. Ecol., 28, 1949, 10.1007/s10980-013-9938-1 · DOI
  19. Greenwald, 2009, Efficacy of land-cover models in predicting isolation of marbled salamander populations in a fragmented landscape, Conserv. Biol., 23, 1232, 10.1111/j.1523-1739.2009.01204.x · DOI
  20. Gregory, 2014, Response variables for evaluation of the effectiveness of conservation corridors, Conserv. Biol., 28, 689, 10.1111/cobi.12252 · DOI
  21. Gurrutxaga, 2011, Key connectors in protected forest area networks and the impact of highways: a transnational case study from the Cantabrian Range to the Western Alps (SW Europe), Landsc. Urban Plan., 101, 310, 10.1016/j.landurbplan.2011.02.036 · DOI
  22. Hanski, 1994, A practical model of metapopulation dynamics, J. Anim. Ecol., 151, 10.2307/5591 · DOI
  23. 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
  24. Horskins, 2006, Corridors and connectivity: when use and function do not equate, Landsc. Ecol., 21, 641, 10.1007/s10980-005-5203-6 · DOI
  25. Hou, 2017, A simplified econet model for mapping and evaluating structural connectivity with particular attention of ecotones, small habitats, and barriers, Landsc. Urban Plan., 160, 28, 10.1016/j.landurbplan.2016.11.008 · DOI
  26. Inglis, 1992, Comments on some designs proposed for experiments on the biological importance of corridors, Conserv. Biol., 6, 581, 10.1046/j.1523-1739.1992.06040581.x · DOI
  27. Jaeger, 2000, Landscape division, splitting index, and effective mesh size: new measures of landscape fragmentation, Landsc. Ecol., 15, 115, 10.1023/A:1008129329289 · DOI
  28. Jantz, 2008, Using widely available geospatial data sets to assess the influence of roads and buffers on habitat core areas and connectivity, Nat. Areas J., 28, 261, 10.3375/0885-8608(2008)28[261:UWAGDS]2.0.CO;2 · DOI
  29. Jordán, 2003, Characterizing the importance of habitat patches and corridors in maintaining the landscape connectivity of a Pholidoptera transsylvanica (Orthoptera) metapopulation, Landsc. Ecol., 18, 83, 10.1023/A:1022958003528 · DOI
  30. Kays, 2015, Terrestrial animal tracking as an eye on life and planet, Science, 348, 10.1126/science.aaa2478 · DOI · Paper page
  31. Keeley, 2017, Habitat suitability is a poor proxy for landscape connectivity during dispersal and mating movements, Landsc. Urban Plan., 161, 90, 10.1016/j.landurbplan.2017.01.007 · DOI · Paper page
  32. Keeley, 2018, New concepts, models, and assessments of climate-wise connectivity, Environ. Res. Lett., 13, 073002, 10.1088/1748-9326/aacb85 · DOI
  33. Keitt, 1997, Detecting critical scales in fragmented landscapes, Conserv. Ecol., 1
  34. Kindlmann, 2008, Connectivity measures: a review, Landsc. Ecol., 23, 879
  35. King, 2002, Dispersal success on spatially structured landscapes: when do spatial pattern and dispersal behavior really matter?, Ecol. Model., 147, 23, 10.1016/S0304-3800(01)00400-8 · DOI
  36. Krosby, 2010, Ecological connectivity for a changing climate, Conserv. Biol., 24, 1686, 10.1111/j.1523-1739.2010.01585.x · DOI · Paper page
  37. 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
  38. Lidicker, 2008, Levels of organization in biology: on the nature and nomenclature of ecology’s fourth level, Biol. Rev., 83, 71, 10.1111/j.1469-185X.2007.00032.x · DOI
  39. Locke, 2019, Three global conditions for biodiversity conservation and sustainable use: an implementation framework, Natl. Sci. Rev., 10.1093/nsr/nwz136 · DOI
  40. Marulli, 2005, A GIS methodology for assessing ecological connectivity: application to the Barcelona Metropolitan Area, Landsc. Urban Plan., 71, 243, 10.1016/S0169-2046(04)00079-9 · DOI
  41. Mawdsley, 2009, A review of climate-change adaptation strategies for wildlife management and biodiversity conservation, Conserv. Biol., 23, 1080, 10.1111/j.1523-1739.2009.01264.x · DOI
  42. McRae, 2007, Circuit theory predicts gene flow in plant and animal populations, Proc. Natl. Acad. Sci., 104, 19885, 10.1073/pnas.0706568104 · DOI · Paper page
  43. Mech, 2001, Evaluating the effectiveness of corridors: a genetic approach, Conserv. Biol., 5, 467, 10.1046/j.1523-1739.2001.015002467.x · DOI
  44. Metzger, 1997, The structural connectivity threshold: an hypothesis in conservation biology at the landscape scale, Acta Oecol., 18, 1, 10.1016/S1146-609X(97)80075-6 · DOI
  45. Minor, 2008, A graph-theory framework for evaluating landscape connectivity and conservation planning, Conserv. Biol., 22, 297, 10.1111/j.1523-1739.2007.00871.x · DOI
  46. Moilanen, 2001, On the use of connectivity measures in spatial ecology, Oikos, 95, 147, 10.1034/j.1600-0706.2001.950116.x · DOI
  47. 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
  48. Morin, 2017, Model-based estimators of density and connectivity to inform conservation of spatially structured populations, Ecosphere, 8, 10.1002/ecs2.1623 · DOI
  49. Pascual-Hortal, 2006, Comparison and development of new graph-based landscape connectivity indices: towards the priorization of habitat patches and corridors for conservation, Landsc. Ecol., 21, 959, 10.1007/s10980-006-0013-z · DOI · Paper page
  50. Prugh, 2009, An evaluation of patch connectivity measures, Ecol. Appl., 19, 1300, 10.1890/08-1524.1 · DOI
  51. Rae, 2007, Implications of error and uncertainty for an environmental planning scenario: a sensitivity analysis of GIS-based variables in a reserve design exercise, Landsc. Urban Plan., 79, 210, 10.1016/j.landurbplan.2006.01.001 · DOI
  52. Ricotta, 2000, Quantifying the network connectivity of landscape mosaics: a graph-theoretical approach, Commun. Ecol., 1, 89, 10.1556/ComEc.1.2000.1.12 · DOI
  53. Riley, 2006, A southern California freeway is a physical and social barrier to gene flow in carnivores, Mol. Ecol., 15, 1733, 10.1111/j.1365-294X.2006.02907.x · DOI
  54. Saura, 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, 10.1016/j.landurbplan.2007.03.005 · DOI
  55. Saura, 2010, A common currency for the different ways in which patches and links can contribute to habitat availability and connectivity in the landscape, Ecography, 33, 523, 10.1111/j.1600-0587.2009.05760.x · DOI · Paper page
  56. Saura, 2011, Network analysis to assess landscape connectivity trends: application to European forests (1990–2000), Ecol. Indic., 11, 407, 10.1016/j.ecolind.2010.06.011 · DOI
  57. Saura, 2017, Protected areas in the world’s ecoregions: how well connected are they?, Ecol. Indic., 76, 144, 10.1016/j.ecolind.2016.12.047 · DOI
  58. Saura, 2018, Protected area connectivity: shortfalls in global targets and country-level priorities, Biol. Conserv., 219, 53, 10.1016/j.biocon.2017.12.020 · DOI
  59. Schumaker, 1996, Using landscape indices to predict habitat connectivity, Ecology, 77, 1210, 10.2307/2265590 · DOI · Paper page
  60. Sharma, 2013, Forest corridors maintain historical gene flow in a tiger metapopulation in the highlands of central India, Proc. R. Soc. B, 280, 10.1098/rspb.2013.1506 · DOI
  61. Storfer, 2007, Putting the ‘landscape’ in landscape genetics, Heredity, 98, 128, 10.1038/sj.hdy.6800917 · DOI
  62. Taylor, 1993, Connectivity is a vital element of landscape structure, Oikos, 571, 10.2307/3544927 · DOI · Paper page
  63. Theobald, 2013, A general model to quantify ecological integrity for landscape assessments and US application, Landsc. Ecol., 28, 1859, 10.1007/s10980-013-9941-6 · DOI
  64. Tischendorf, 2000, On the usage and measurement of landscape connectivity, Oikos, 90, 7, 10.1034/j.1600-0706.2000.900102.x · DOI
  65. Urban, 2001, Landscape connectivity: a graph-theoretic perspective, Ecology, 82, 1205, 10.1890/0012-9658(2001)082[1205:LCAGTP]2.0.CO;2 · DOI
  66. Watling, 2011, Meta-analysis reveals the importance of matrix composition for animals in fragmented habitat, Glob. Ecol. Biogeogr., 20, 209, 10.1111/j.1466-8238.2010.00586.x · DOI
  67. Watts, 2010, Developing a functional connectivity indicator to detect change in fragmented landscapes, Ecol. Indic., 10, 552, 10.1016/j.ecolind.2009.07.009 · DOI
  68. Zeller, 2012, Estimating landscape resistance to movement: a review, Landsc. Ecol., 27, 777, 10.1007/s10980-012-9737-0 · DOI · Paper page
  69. Zeller, 2016, Using step and path selection functions for estimating resistance to movement: pumas as a case study, Landsc. Ecol., 31, 1319, 10.1007/s10980-015-0301-6 · DOI

Cited by

Metadata

Authors
Keeley, A. T. H., Beier, P., & Jenness, J. S.
Venue
Biological Conservation
Publisher
Elsevier BV
Published
2021-03
Volume
255
Pages
109008
ISSN
0006-3207
Cited by
243
References
3
Language
en
Funders
Yellowstone to Yukon Conservation Initiative

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