Applying circuit theory and landscape linkage maps to reintroduction planning for California Condors

Autoři: Jesse D’Elia aff001;  Joseph Brandt aff002;  L. Joseph Burnett aff003;  Susan M. Haig aff004;  Jeff Hollenbeck aff005;  Steve Kirkland aff002;  Bruce G. Marcot aff006;  Arianna Punzalan aff007;  Christopher J. West aff008;  Tiana Williams-Claussen aff008;  Rachel Wolstenholme aff010;  Rich Young aff001
Působiště autorů: Pacific Regional Office, U.S. Fish and Wildlife Service, Portland, Oregon, United States of America aff001;  California Condor Recovery Office, U.S. Fish and Wildlife Service, Ventura, California, United States of America aff002;  Ventana Wildlife Society, Monterey, California, United States of America aff003;  Forest and Rangeland Ecosystem Science Center, U.S. Geological Survey, Corvallis, Oregon, United States of America aff004;  The Northwest Habitat Institute, Corvallis, Oregon, United States of America aff005;  Pacific Northwest Research Station, U.S. Forest Service, Portland, Oregon, United States of America aff006;  Department of Ecosystem Science and Sustainability, Colorado State University, Fort Collins, Colorado, United States of America aff007;  Wildlife Program, Yurok Tribe, Klamath, California, United States of America aff008;  Department of Wildlife, Humboldt State University, Arcata, California, United States of America aff009;  Pinnacles National Park, U.S. National Park Service, Paicines, California, United States of America aff010
Vyšlo v časopise: PLoS ONE 14(12)
Kategorie: Research Article


Conservation practitioners are increasingly looking to species translocations as a tool to recover imperiled taxa. Quantitative predictions of where animals are likely to move when released into new areas would allow managers to better address the social, institutional, and ecological dimensions of conservation translocations. Using >5 million California condor (Gymnogyps californianus) occurrence locations from 75 individuals, we developed and tested circuit-based models to predict condor movement away from release sites. We found that circuit-based models of electrical current were well calibrated to the distribution of condor movement data in southern and central California (continuous Boyce Index = 0.86 and 0.98, respectively). Model calibration was improved in southern California when additional nodes were added to the circuit to account for nesting and feeding areas, where condor movement densities were higher (continuous Boyce Index = 0.95). Circuit-based projections of electrical current around a proposed release site in northern California comported with the condor’s historical distribution and revealed that, initially, condor movements would likely be most concentrated in northwestern California and southwest Oregon. Landscape linkage maps, which incorporate information on landscape resistance, complement circuit-based models and aid in the identification of specific avenues for population connectivity or areas where movement between populations may be constrained. We found landscape linkages in the Coast Range and the Sierra Nevada provided the most connectivity to a proposed reintroduction site in northern California. Our methods are applicable to conservation translocations for other species and are flexible, allowing researchers to develop multiple competing hypotheses when there are uncertainties about landscape or social attractants, or uncertainties in the landscape conductance surface.

Klíčová slova:

California – Conservation science – Electrical circuits – Habitats – Linkage mapping – Mountains – Oregon – Valleys


1. Seddon PJ, Armstrong DP, Maloney RF. Developing the science of reintroduction biology. Conservation Biology. 2007;21:303–12. doi: 10.1111/j.1523-1739.2006.00627.x 17391180

2. Brichieri-Colombi TA, Moehrenschlager A. Alignment of threat, effort, and perceived success in North American conservation translocations. Conservation Biology. 2016;30:1159–72. doi: 10.1111/cobi.12743 27119768

3. Griffith B, Scott JM, Carpenter JW, Reed C. Translocation as a species conservation tool: status and strategy. Science. 1989;245:477–80. doi: 10.1126/science.245.4917.477 17750257

4. Letty J, Marchandeau S, Aubineau J. Problems encountered by individuals in animal translocations: lessons from field studies. Ecoscience. 2007;14:420–31.

5. Dunham JB, White R, Allen CS, Marcot BG, Shively D. The reintroduction landscape: finding success at the intersection of ecological, social, and institutional dimensions. In: Jachowski DS, Millspaugh JJ, Angermeier PL, Slotow R, editors. Reintroduction of fish and wildlife populations. Berkeley, California: University of California Press; 2016. p. 79–103.

6. Torres RT, Carvalho J, Serrano E, Helmer W, Acevedo P, Fonseca C. Favourableness and connectivity of a Western Iberian landscape for the reintroduction of the iconic Iberian ibex Capra pyrenaica. Oryx. 2017;51:709–17.

7. Bar-David S, Saltz D, Dayan T. Predicting the spatial dynamics of a reintroduced population: the Persian fallow deer. Ecological Applications. 2005;15:1833–46.

8. Bar-David S, Saltz D, Dayan T, Shkedy Y. Using spatially expanding populations as a tool for evaluating landscape planning: the reintroduced Persian fallow deer as a case study. Journal for Nature Conservation. 2008;16:164–74.

9. Kuemmerle T, Perzanowski K, Akcakaya HR, Beaudry F, Van Deelen TR, Parnikoza I, et al. Cost-effectiveness of strategies to establish a European bison metapopulation in the Carpathians. Journal of Applied Ecology. 2011;48:317–29.

10. Carroll C, Phillips MK, Lopez-Gonzalez CA, Schumaker NH. Defining recovery goals and strategies for endangered species: The wolf as a case study. BioScience. 2006;56:25–37.

11. Snyder NFR, Snyder HA. The California Condor: A Saga of Natural History and Conservation. San Diego, CA: Academic Press; 2000.

12. Mee A, Hall LS. California Condors in the 21st Century. Washington, D.C. and Cambridge, MA: American Ornithologists’ Union and Nuttall Ornithological Club; 2007.

13. Walters JR, Derrickson SR, Fry DM, Haig SM, Marzluff JM, Wunderle JM Jr. Status of the California Condor (Gymnogyps californianus) and efforts to achieve its recovery. Auk. 2010;127:969–1001.

14. Finkelstein ME, Doak DF, George D, Burnett J, Brandt J, Church M, et al. Lead poisoning and the deceptive recovery of the critically endangered California condor. Proceedings of the National Academy of Sciences of the United States of America. 2012;109:11449–54. doi: 10.1073/pnas.1203141109 22733770

15. D’Elia J, Haig SM. California Condors in the Pacific Northwest. Corvallis, OR: OSU Press; 2013.

16. Meretsky VJ, Snyder NFR. Range use and movements of California condors. Condor. 1992;94:313–35.

17. D’Elia J, Haig SM, Johnson M, Marcot B, Young R. Activity-specific ecological niche models for planning reintroductions of California condors (Gymnogyps californianus). Biological Conservation. 2015;184:90–9.

18. McRae BH, Dickson BG, Keitt TH, Shah VB. Using circuit theory to model connectivity in ecology, evolution, and conservation. Ecology. 2008;89:2712–24. doi: 10.1890/07-1861.1 18959309

19. Cianfrani C, Maiorano L, Loy A, Kranz A, Lehmann A, Maggini R, et al. There and back again? Combining habitat suitability modeling and connectivity analyses to assess a potential return of the otter to Switzerland. Animal Conservation. 2013;16:584–94.

20. Jarchow CJ, Hossack BR, Sigafus BH, Schwalbe CR, Muths E. Modeling habitat connectivity to inform reintroductions: a case study with the Chiricahua Leopard frog. Journal of Herpetology. 2016;50:63–9.

21. Ziółkowska E, Perzanowski K, Bleyhl B, Ostapowicz K, Kuemmerle T. Understanding unexpected reintroduction outcomes: why aren’t European bison colonizing suitable habitat in the Carpathians? Biological Conservation. 2016;195:106–17.

22. Phillips SJ, Anderson RP, Schapire RE. Maximum entropy modeling of species geographic distributions. Ecological Modelling. 2006;190:231–59.

23. Phillips S. A brief tutorial on Maxent. Lessons in Conservation. 2010;3:108–35.

24. Elith J, Phillips SJ, Hastie T, Dudík M, Chee YE, Yates CJ. A statistical explanation of MaxEnt for ecologists. Diversity and Distributions. 2011;17:43–57.

25. Cianfrani C, Le Lay G, Hirzel AH, Loy A. Do habitat suitability models reliably predict the recovery areas of threatened species? Journal of Applied Ecology. 2010;47:421–30.

26. Merow C, Smith MJ, Edwards TC Jr., Guisan A, McMahohn SM, Normand S, et al. What do we gain from simplicity versus complexity in species distribution models? Ecography. 2014;37:1267–81.

27. U.S. Fish and Wildlife Service. Hopper Mountain National Wildlife Refuge Complex California Condor Recovery Program 2016 Annual Report. Ventura, CA: California Condor Recovery Office; 2017.

28. Robertson PA, Aebischer NJ, Kenward RE, Hanski IK, Williams NP. Simulation and jack-knifing assessment of home-range indices based on underlying trajectories. Journal of Applied Ecology. 1998;35:928–40.

29. Gavashelishvili A, McGrady M, Ghasabian M, Bildstein KL. Movements and habitat use by immature Cinereous vultures from the Caucasus. Bird Study. 2012;59:449–62.

30. Phillips SJ, Dudík M, Elith J, Graham CH, Lehmann A, Leathwick J, et al. Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data. Ecological Applications. 2009;19:181–97. doi: 10.1890/07-2153.1 19323182

31. Phillips SJ, Dudík M. Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography. 2008;31:161–75.

32. Pennycuick CJ. Power requirements for horizontal flight in the pigeon Columba livia. Journal of Experimental Biology. 1968;49:527–55.

33. Poessel SA, Brandt J, Miller TA, Katzner TE. Meteorological and environmental variables affect flight behavior and decision-making of an obligate soaring bird, the California Condor Gymnogyps californianus. Ibis. 2017;160:36–53.

34. Ruxton GD, Houston DC. Obligate vertebrate scavengers must be large soaring fliers. Journal of Theoretical Biology. 2004;228:431–6. doi: 10.1016/j.jtbi.2004.02.005 15135041

35. Duriez O, Kato A, Tromp C, Dell’Omo G, Vyssotski AL, Sarrazin F, et al. How cheap is soaring flight in raptors? A preliminary investigation in free-flying vultures. PLoS ONE [Internet]. 2014;9(1):e84887. Available from: doi: 10.1371/journal.pone.0084887 24454760

36. Koford CB. The California Condor. Research Report No. 4 ed. New York, NY: National Audubon Society; 1953.

37. Snyder NFR, Ramey RR, Sibley FC. Nest-site biology of the California condor. Condor. 1986;88:228–41.

38. Rivers JW, Johnson JM, Haig SM, Schwarz CJ, Glendening JW, Burnett LJ, et al. Resource selection by the California condor (Gymnogyps californianus) relative to terrestrial-based habitats and meteorological conditions. PLoS ONE [Internet]. 2014;9(2):e88430. Available from: doi: 10.1371/journal.pone.0088430 24523893

39. Donázar JA, Hiraldo F, Bustamante J. Factors influencing nest site selection, breeding density and breeding success in the bearded vulture (Gypaetus barbatus). Journal of Applied Ecology. 1993;30:504–14.

40. Poirazidis K, Goutner V, Skartsi T, Stamou G. Modelling nesting habitat as a conservation tool for the Eurasian black vulture (Aegypius monachus) in Dadia Nature Reserve, northeastern Greece. Biological Conservation. 2004;118:235–48.

41. Gavashelishvili A, McGrady MJ. Breeding site selection by bearded vulture (Gypaetus barbatus) and Eurasian griffon (Gyps fulvus) in the Caucasus. Animal Conservation. 2006;9:159–70.

42. Mateo-Tomás P, Olea PP. Combining scales in habitat models to improve conservation planning in an endangered vulture. Acta Oecologica. 2010;35:489–98.

43. Neveda-Rodríguez A, Vargas FH, Kohn S, Zapata-Ríos G. Andean Condor (Vultur gryphus) in Ecuador: geographic distribution, population size and extinction risk. PLoS ONE [Internet]. 2016;11(3):e0151827. Available from: doi: 10.1371/journal.pone.0151827 26986004

44. Spiegel O, Getz WM, Nathan R. Factors influencing foraging search efficiency: why do scarce lappet-faced vultures outperform ubiquitous white-backed vultures. American Naturalist. 2013;181:E102–E115. doi: 10.1086/670009 23594555

45. Lisney TJ, Stecyk K, Kolominsky J, Graves GR, Wylie DR, Iwaniuk AN. Comparison of eye morphology and retinal topography in two species of New World vultures (Aves: Cathartidae). Anatomical Record. 2013;296:1954–70.

46. Warren DL, Seifert SN. Ecological niche modeling in Maxent: the importance of model complexity and the performance of model selection criteria. Ecological Applications. 2011;21:335–42. doi: 10.1890/10-1171.1 21563566

47. Trainor AM, Walters JR, Morris WF, Sexton J, Moody A. Empirical estimation of dispersal resistance surfaces: a case study with red-cockaded woodpeckers. Landscape Ecology. 2013;28:755–67.

48. McRae BH, Shah VB. Circuitscape User’s Guide Version 3.5. Santa Barbara, CA: The University of California, Santa Barbara; 2011.

49. Pennycuick CJ, Scholey KD. Flight behavior of Andean condors Vultur gryphys and Turkey Vultures Cathartes aura around the Paracas Peninsula, Peru. Ibis. 1984;126:253–6.

50. Bildstein KL, Bechard MJ, Farmer C, Newcomb L. Narrow sea crossings present major obstacles to migrating Griffon Vultures Gyps fulvus. Ibis. 2009;151:382–91.

51. Nourani E, Yamaguchi NM. The effects of atmospheric currents on migratory behavior of soaring birds: a review. Ornithological Science. 2017:5–15.

52. Hirzel AH, Le Lay G, Helfer V, Randin C, Guisan A. Evaluating the ability of habitat suitability models to predict species presences. Ecological Modelling. 2006;199:142–52.

53. Di Cola V, Broennimann O, Petitpierre B, Breiner FT, D’Amen M, Randin C, et al. ecospat: an R package to support spatial analyses and modeling of species niches and distributions. Ecography. 2017;40:774–87.

54. Koen EL, Bowman J, Sadowski C, Wapole AA. Landscape connectivity for wildlife: development and validation of multispecies linkage maps. Methods in Ecology and Evolution. 2014;5:626–33.

55. McRae BH, Kavanagh DM. Linkage Mapper connectivity analysis software. The Nature Conservancy, Seattle WA. [Internet].

56. Austin MP. Spatial prediction of species distribution: an interface between ecological theory and statistical modeling. Ecological Modelling. 2002;157:101–18.

57. Randin CF, Dirnbock T, Dullinger S, Zimmermann NE, Zappa M, Guisan A. Are niche-based species distribution models transferable in space? Journal of Biogeography. 2006;33:1689–703.

58. Elith J, Kearney M, Phillips S. The art of modelling range-shifting species. Methods in Ecology and Evolution. 2010;1:330–42.

59. Mesgaran MB, Cousens RD, Webber BL. Here be dragons: a tool for quantifying novelty due to covariate range and correlation change when projecting species distribution models. Diversity & Distributions. 2014;20:1147–59.

60. Moon JB, Dewitt TH, Errend MN, Bruins RJF, Kentula ME, Chamberlain SJ, et al. Model application niche analysis: assessing the transferability and generalizability of ecological niche models. Ecosphere [Internet]. 2017;8(10):e01974. Available from: 30237908

61. Elith J, Leathwick JR. Species distribution models: ecological explanation and prediction across space and time. The Annual Review of Ecology, Evolution, and Systematics. 2009;40:677–97.

62. Austin M. Species distribution models and ecological theory: a critical assessment and some possible new approaches. Ecological Modelling. 2007;200:1–19.

63. Dzialak MR, Olson CV, Harju SM, Webb SL, Winstead JB. Temporal and hierarchical spatial components of animal occurrence: conserving seasonal habitat for greater sage-grouse. Ecosphere [Internet]. 2012;3(4):30. Available from:

64. Alarcón PAE, Lambertucci SA. A three-decade review of telemetry studies on vultures and condors. Movement Ecology [Internet]. 2018;6:13. Available from: 30202527

65. Blazquez-Cabrera S, Gaston A, Beier P, Garrote G, Simon MA, Saura S. Influence of separating home range and dispersal movements on characterizing corridors and effective distances. Landscape Ecology. 2016;31:2355–66.

66. Santos CD, Hanssen F, Munoz A, Onrubia A, Wikelski M, May R, et al. Match between soaring modes of black kites and the fine-scale distribution of updrafts. Scientific Reports [Internet]. 2017;7:6421. Available from: 10.1038/s41598-017-05319-8. 28743947

67. Shamoun-Baranes JZ, Liechti F, Vansteelant WMG. Atmospheric conditions create detours and tailbacks for migrating birds. Journal of Comparative Physiology A Neuroethology Sensory Neural and Behavioral Physiology. 2017;203:509–29.

68. Leshem Y, Yom-Tov Y. Routes of migrating soaring birds. Ibis. 1998;140:41–52.

69. Alpert P, Tannhauser DS, Leshem Y, Kravitz A, Rabinovitch-Hadar M. Migrating soaring birds align along sea-breeze fronts; first evidence from Israel. Bulletin of the American Meteorological Society. 2000;81:1599–601.

70. Bogliani G, Viterbi R, Nicolino M. Habitat use by a reintroduced population of bearded vultures (Gypaetus barbatus) in the Italian Alps. Journal of Raptor Research. 2011;45:56–62.

71. Monsarrat S, Benhamou S, Sarrazin F, Bessa-Gomes C, Bouten W, Duriez O. How predictability of feeding patches affects home range and foraging habitat selection in avian social scavengers? PLoS ONE [Internet]. 2013;8(1):e53077. Available from: doi: 10.1371/journal.pone.0053077 23301024

72. Grigg NP, Krilow JM, Gutierrez-Ibanez C, Wylie DR, Graves GR, Iwaniuk AN. Anatomical evidence for scent guided foraging in the turkey vulture. Scientific Reports [Internet]. 2017;7:17408. Available from: doi: 10.1038/s41598-017-17794-0 29234134

73. Lambertucci SA, Alarcón PAE, Hiraldo F, Sanchez-Zapata JA, Blanco G, Donázar JA. Apex scavenger movements call for transboundary conservation policies. Biological Conservation. 2018;170:145–150.

74. Johnstone JA, Dawson TE. Climatic context and ecological implications of summer fog decline in the coast redwood region. Proceedings of the National Academy of Sciences of the United States of America. 2010;107:4533–8. doi: 10.1073/pnas.0915062107 20160112

75. Ryan BC. A mathematical model for diagnosis and prediction of surface winds in mountainous terrain. Journal of Applied Meteorology. 1977;16:571–84.

76. Ryan BC. WNDCOM: estimating surface winds in mountainous terrain. General Technical Report PSW-73. Berkeley, CA: Pacific Southwest Forest and Range Experiment Station; 1983.

77. Bakker VJ, Smith DR, Copeland H, Brandt J, Wolstenholme R, Burnett J, et al. Effects of lead exposure, flock behavior, and management actions on the survival of California condors (Gymnogyps californianus). EcoHealth [Internet]. 2016; doi: 10.1007/s10393-015-1096-2 26769426

78. Galef BG, Laland KN. Social learning in animals: empirical studies and theoretical models. BioScience. 2005;55:489–99.

79. Morandini V, Ferrer M. Natal philopatry: local experience or social attraction? An experiment with Spanish imperial eagles. Animal Behaviour. 2017;130:153–7.

80. Ventana Wildlife Society. Ventana Wildlife Society’s California Condor Program 2015 Annual Report. Salinas, CA: Ventana Wildlife Society; 2016.

81. Ventana Wildlife Society. Ventana Wildlife Society’s California Condor Program 2016 Annual Report. Salinas, CA: Ventana Wildlife Society; 2017.

82. Álvarez M, Gálvez M, Millet A, Marco X, Álvarez E, Rafa M, et al. “Vulturnet” connectivity of the European populations of Cinereous Vulture: A programme to reintroduce the species into Catalonia. In: Zuberogoitia I, Martínez JE, editors. Ecology and Conservation of European Forest-Dwelling Raptors. Bilbao, Spain: Departamento de Agricultura de la Diputación Foral de Bizkaia; 2011. p. 356–61.

83. Stoynov E, Peshev H, Grozdanov A, Vangelova N. Five years overview of the reintroduction of Griffon Vulture Gyps fulvus in Kresna Gorge, Bulgaria. Vulture News. 2015;69:33–9.

84. Schumaker NH, Brookes A. HexSim: a modeling environment for ecology and conservation. Landscape Ecology. 2018;33:197–211. doi: 10.1007/s10980-017-0605-9 29545713

85. Nathan R, Getz WM, Revilla E, Holyoak M, Kadmon R, Saltz D, et al. A movement ecology paradigm for unifying organismal movement research. Proceedings of the National Academy of Sciences of the United States of America. 2008;105:19052–9. doi: 10.1073/pnas.0800375105 19060196

86. Miller ML, Ringelman KM, Eadie JM, Schank JC. Time to fly: A comparison of marginal value theorem approximations in an agent-based model of foraging waterfowl. Ecological Modelling. 2017;351:77–86.

87. Mihoub J, Le Gouar P, Sarrazin F. Breeding habitat selection behaviors in heterogeneous environments: implications for modeling reintroduction. Oikos. 2009;118:663–74.

88. Rytteri S, Kuussaari M, Saastamoinen M, Ovaskainen O. Can we predict the expansion rate of a translocated butterfly population based on a priori estimated movement rates? Biological Conservation. 2017;215:189–95.

89. Gilroy JJ, Lockwood JL, Both C. Simple settlement decisions explain common dispersal patterns in territorial species. Journal of Animal Ecology. 2016;85:1182–90. doi: 10.1111/1365-2656.12545 27155215

90. Ovaskainen O, Rekola H, Meyke E, Arjas E. Bayesian methods for analyzing movements in heterogeneous landscapes from mark-recapture data. Ecology. 2008;89:542–54. doi: 10.1890/07-0443.1 18409443

91. Plank MJ, Codling EA. Sampling rate and misidentification of Lévy and non-Lévy movement paths. Ecology. 2009;90:3546–53. doi: 10.1890/09-0079.1 20120821

92. Lookingbill TR, Gardner RH, Ferrari JR, Keller CE. Combining a dispersal model with network theory to assess habitat connectivity. Ecological Applications. 2010;20:427–41. doi: 10.1890/09-0073.1 20405797

93. Richardson KM, Doerr V, Ebrahimi M, Lovegrove TG, Parker KA. Chapter 6: Considering dispersal in reintroduction and restoration planning. In: Armstrong DP, Hayward MW, Moro D, Seddon PJ, editors. Advances in Reintroduction Biology of Australian and New Zealand Fauna. Melbourne, Australia: CSIRO Publishing; 2015. p. 59–72.

94. U.S. Fish and Wildlife Service. Endangered and threatened wildlife and plants; experimental populations. Federal Register. 1984;49:33885–94.

95. Sainsbury AW, Vaughan-Higgins RJ. Analyzing disease risks associated with translocations. Conservation Biology. 2012;26:442–52. doi: 10.1111/j.1523-1739.2012.01839.x 22533691

96. Weeks AR, Sgro CM, Young AG, Frankham R, Mitchell NJ, Miller KA, et al. Assessing the benefits and risks of translocations in changing environments: a genetic perspective. Evolutionary Applications. 2011;4:709–25. doi: 10.1111/j.1752-4571.2011.00192.x 22287981

97. D’Elia J, Haig SM, Mullins T, Miller MP. Ancient DNA reveals substantial genetic diversity in the California Condor (Gymnogyps californianus) prior to a population bottleneck. Condor. 2016;118:703–14.

98. Southwest Condor Working Group. California Condor Recovery Program in the Southwest; Fourth Review (2012–2016). Phoenix, AZ: U.S. Fish and Wildlife Service Arizona Ecological Services Office; 2017.

99. Zamboni T, Di Martino S, Jiménez-Pérez I. A review of multispecies reintroduction to restore a large ecosystem: The Iberá Rewilding Program (Argentina). Perspectives in Ecology and Conservation. 2017;15:248–56.

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