Polar Biology

, Volume 33, Issue 3, pp 347–358 | Cite as

Gyrfalcon nest distribution in Alaska based on a predictive GIS model

  • Travis L. BoomsEmail author
  • Falk Huettmann
  • Philip F. Schempf
Original Paper


The gyrfalcon (Falco rusticolus) is an uncommon, little studied circumpolar Arctic bird that faces conservation concerns. We used 455 historical nest locations, 12 environmental abiotic predictor layers, Geographic Information System (ArcGIS), and TreeNet modeling software to create a spatially explicit model predicting gyrfalcon breeding distribution and population size across Alaska. The model predicted that 75% of the state had a relative gyrfalcon nest occurrence index value of <20% (where essentially no nests are expected to occur) and 7% of the state had a value of >60%. Areas of high predicted occurrence were in northern and western Alaska. The most important predictor variable was soil type, followed by sub-surface geology and vegetation type. Nine environmental factors were useful in predicting nest occurrence, indicating complex multivariate habitat relationships exist. We estimated the breeding gyrfalcon population in Alaska is 546 ± 180 pairs. The model was 67% accurate at predicting nest occurrence with an area under the curve score of 0.76 when assessed with independent data; this is a good result when considering its application to the entire state of Alaska. Prediction accuracy estimates were as high as 97% using 10-fold cross validation of the training data. The model helps guide science-based management efforts in times of increasing and global pressures for this species and Arctic landscapes.


Alaska Arctic Breeding distribution Conservation biology Falco rusticolus Gyrfalcon Predictive modeling 



This research was possible because of our collaborators’ massive investment of field effort, money, time, personal interest, and dedication over the past 36+ years. We heartily thank T. Swem, C. McIntyre, R. Ritchie, B. McCaffery, T. Cade, C. White, and others for their tireless dedication to surveying breeding raptors in Alaska. This work was primarily funded by the U.S. Fish and Wildlife Service Migratory Bird Raptor Management Office. T. B. was supported by a National Science Foundation Graduate Research Fellowship, a U.S. Environmental Protection Agency Science to Achieve Results Graduate Fellowship, a University of Alaska Fairbanks Thesis Completion Fellowship, and the Alaska Department of Fish and Game Nongame Program. The EPA has not officially endorsed this publication and the views expressed herein may not reflect the views of the EPA. We thank P. Liedberg, M. Swaim, and the Togiak National Wildlife Refuge; D. Carlson, D. Payer, S. Kendall, and the Arctic National Wildlife Refuge; and N. Olsen and the Selawik National Wildlife Refuge for providing essential support and logistics to conduct model accuracy assessment surveys. We also thank the UAF thesis committee, M. Lindberg, D. Piepenburg, N. Chernetsov, D. Boyce, and T. Gottschalk for helpful revisions. This is the University of Alaska Fairbanks EWHALE lab publication #55.


  1. Andersen DE (2007) Survey techniques. In: Bird D, Bildstein K (eds) Raptor research and management techniques. Hancock House Publishers, Blaine, WA, pp 89–100Google Scholar
  2. Anderson DR, Burnham KP, Thompson WL (2000) Null hypothesis testing: problems, prevalence, and an alternative. J Wildl Manage 64:912–923CrossRefGoogle Scholar
  3. Araujo MB, Guisan A (2006) Five (or so) challenges for species distribution modeling. J Biogeo 33:1677–1688CrossRefGoogle Scholar
  4. Araujo MB, Williams PH (2000) Selecting areas for species persistence using occurrence data. Biol Conserv 96:331–345CrossRefGoogle Scholar
  5. Beikman HM (1980) Geologic map of Alaska. U.S. Geological Survey special publication # SG0002-1T and SG0002-2T, Washington, DCGoogle Scholar
  6. Beyer HL (2008) Hawth’s analysis tools for ArcGIS. Accessed 12 Dec 2008
  7. Booms TL, Cade TJ, Clum NJ (2008) Gyrfalcon (Falco rusticolus). In: Poole A (ed) The birds of North America online. Cornell Lab of Ornithology. doi: 10.2173/bna.114. Accessed 10 Jan 2009
  8. Boyce MS, McDonald LL (1999) Relating populations to habitats using resource selection functions. Trends Ecol Evol 14:268–272CrossRefPubMedGoogle Scholar
  9. Boyce DA, Kennedy PL, Beier P, Ingraldi MF, MacVean SR, Siders MS, Squires JR, Woodbridge B (2005) When are goshawks not there? Is a single visit enough to infer absence at occupied nest areas? J Raptor Res 39:296–302Google Scholar
  10. Breiman L (2001) Random forests. Mach Learn 45:5–32CrossRefGoogle Scholar
  11. Britten MW, McIntyre CL, Kralovec M (1995) Satellite radio telemetry and bird studies in national parks and preserves. Park Sci 15:20–24Google Scholar
  12. Cade TJ (1960) Ecology of the peregrine and gyrfalcon populations in Alaska. Univ Calif Pub Zool 63:151–290Google Scholar
  13. Cade TJ (1982) The falcons of the world. Cornell University Press, Ithaca, NYGoogle Scholar
  14. Craig E, Huettmann F (2008) Using blackbox algorithms such as TreeNet and random forests for data-mining and for finding meaningful patterns, relationships, and outliers in complex ecological data: an overview, an example using golden eagle satellite data and an outlook for a promising future. In: Hsiao-fan W (ed) Intelligent data analysis: developing new methodologies through pattern discovery and recovery. IGI Global, Hershey, PA, pp 65–84Google Scholar
  15. Crick HQ (2004) The impact of climate change on birds. Ibis 146:48–56CrossRefGoogle Scholar
  16. Elith J, Burgman M (2002) Predictions and their validation: rare plants in the Central Highlands, Victoria, Australia. In: Scott JM, Heglund PJ, Morrison ML, Haufler JB, Raphael MG, Wall WA, Samson FB (eds) Predicting species occurrences. Island Press, Washington, pp 303–314Google Scholar
  17. Elith J, Graham C, Anderson RP et al (2006) Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29:129–151CrossRefGoogle Scholar
  18. Engler R, Guisan A, Rechsteiner L (2004) An improved approach for predicting the distribution of rare and endangered species from occurrence and pseudo-absence data. J Appl Ecol 41:263–274CrossRefGoogle Scholar
  19. Environmental Systems Research Institute (2008) ArcMap 9.3 resource center. Accessed 12 Dec 2008
  20. Fielding AH (2002) What are the appropriate characteristics of an accuracy measure? In: Scott JM, Heglund PJ, Morrison ML, Haufler JB, Raphael MG, Wall WA, Samson FB (eds) Predicting species occurrences. Island Press, Washington, pp 303–314Google Scholar
  21. Fielding AH, Bell JF (1997) A review of methods for the assessment of prediction errors in conservation presence/absence models. Environ Conserv 24:38–49CrossRefGoogle Scholar
  22. Fleming M (1997) A statewide vegetation map of Alaska using phenological classification of AVHRR data. In: Walker DA, Lillie AC (eds) The second circumpolar Arctic vegetation mapping workshop and the CAVM-North American workshop, pp 25–26Google Scholar
  23. Friedman JH (2002) Stochastic gradient boosting. Comput Stat Data Anal 38:367–378CrossRefGoogle Scholar
  24. Guisan A, Graham CH, Elith J, Huettmann F (2007) Sensitivity of predictive species distribution models to change in grain size: insights from a multi-models experiment across five continents. Divers Distrib 13:332–340CrossRefGoogle Scholar
  25. Hastie T, Tibshirani R, Friedman J (2001) The elements of statistical learning: data mining, inference, and prediction. Springer, New YorkGoogle Scholar
  26. Heglund PJ (2002) Foundations of species-environment relations. In: Scott JM, Heglund PJ, Morrison ML, Haufler JB, Raphael MG, Wall WA, Samson FB (eds) Predicting species occurrences. Island Press, Washington, pp 35–42Google Scholar
  27. Henebry GM, Merchant JW (2002) Geospatial data in time: limits and prospects for predicting species occurrences. In: Scott JM, Heglund PJ, Morrison ML, Haufler JB, Raphael MG, Wall WA, Samson FB (eds) Predicting species occurrences. Island Press, Washington, pp 291–302Google Scholar
  28. Higmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A (2005) Very high resolution interpolated climate surfaces for global land areas. Int J Climat 25:1965–1978CrossRefGoogle Scholar
  29. Huettmann F, Diamond AW (2006) Large-scale effects on the spatial distribution of seabirds in the Northwest Atlantic. Landsc Ecol 21:1089–1108CrossRefGoogle Scholar
  30. Hunt WG, Jackman RE, Hunt TL, Driscoll DE, Culp L (1999) A population study of golden eagles in the Altamont Pass wind resource area 1994–1997. Report to National Renewable Energy Laboratory, Subcontract XAT-6-16459-01. University of California, Santa CruzGoogle Scholar
  31. Hutchinson GE (1957) A treatise on limnology. Wiley, New YorkGoogle Scholar
  32. Johnson GD, Erickson WP, Strickland MD, Shepherd MF, Shepherd DA (2001) Avian monitoring studies at the Buffalo Ridge Wind Resource area, Minnesota: results of a 4-year study. Technical report prepared for Northern States Power Co, Minneapolis, MNGoogle Scholar
  33. Karlstrom TNV, Coulter HW, Fernald AT et al (1964) Surface geology of Alaska. Miscellaneous geologic investigations map I-357. Washington, DCGoogle Scholar
  34. Keating KA, Cherry S (2004) Use and interpretation of logistic regression in habitat selection studies. J Wildl Manage 68:774–789CrossRefGoogle Scholar
  35. Lehner B, Doll P (2004) Development and validation of a global database of lakes, reservoirs and wetlands. J Hydrol 296:1–22CrossRefGoogle Scholar
  36. Lobkov EG (2000) Illegal trapping and export of gyrfalcons from Kamchatka is a threat to the very existence of the Kamchatka population. Abstracts of the first conference on conservation of biodiversity in Kamchatka and its coastal waters. Kamchatsk NIRO, Petropavlosk-KamchatskiyGoogle Scholar
  37. Manel S, Dias JM, Ormerod SJ (1999) Comparing discriminate analysis, neural networks and logistic regression for predicting species distributions: a case study with a Himalayan river bird. Ecol Model 120:337–347CrossRefGoogle Scholar
  38. Manel S, Williams HC, Ormerod SJ (2001) Evaluating presence–absence models in ecology: the need to account for prevalence. J Appl Ecol 38:921–931CrossRefGoogle Scholar
  39. Manly BFJ, McDonald LL, Thomas DL, McDonald TL, Erickson WP (2002) Resource selection by animals, statistical design and analysis for field studies, 2nd edn. Kluwer Academic Publishers, LondonGoogle Scholar
  40. Mladenoff DJ, Sickley TA, Haight RG, Wydeven AP (1995) A regional landscape analysis and prediction of favorable gray wolf habitat in the northern great lakes region. Conserv Biol 9:279–294CrossRefGoogle Scholar
  41. Mladenoff DJ, Sickley TA, Wydeven AP (1999) Predicting gray wolf landscape recolonization: logistic regression models vs. new field data. Ecol Appl 9:37–44CrossRefGoogle Scholar
  42. Nielsen ÓK (1991) Age of first breeding and site fidelity of gyrfalcons. Náttúrufæðingurinn 60:135–143Google Scholar
  43. Nielsen ÓK (1999) Gyrfalcon predation on ptarmigan: numerical and functional responses. J Anim Ecol 68:1034–1050CrossRefGoogle Scholar
  44. Nielsen ÓK, Cade TJ (1990a) Annual cycle of the gyrfalcon in Iceland. Nat Geo Res 6:41–62Google Scholar
  45. Nielsen ÓK, Cade TJ (1990b) Seasonal changes in food habits of gyrfalcons in northeast Iceland. Ornis Scand 21:202–211CrossRefGoogle Scholar
  46. Nielsen SE, Stenhouse GB, Beyer HL, Huettmann F, Boyce MS (2008) Can natural disturbance-based forestry rescue a declining population of grizzly bears? Biol Conserv 141:2193–2207CrossRefGoogle Scholar
  47. Onyeahialam A, Huettmann F, Bertazzon S (2005) Modeling sage grouse: progressive computational methods for linking a complex set of local biodiversity and habitat data towards global conservation statements and decision support systems. Lecture Notes in Computer Science 3482, International Conference on Computational Science and its Applications Proceedings Part III, pp 152–161Google Scholar
  48. Palmer RS (1988) Handbook of North American birds, vol 5. Vail-Ballou Press, Binghamton, NYGoogle Scholar
  49. Pearce JL, Boyce MS (2006) Modeling distribution and abundance with presence-only data. J Appl Ecol 43:405–412CrossRefGoogle Scholar
  50. Peterson AT (2001) Prediction species’ geographic distributions based on ecological niche modeling. Condor 103:599–605CrossRefGoogle Scholar
  51. Potapov E, Sale R (2005) The gyrfalcon. Yale University Press, New Haven, CTGoogle Scholar
  52. Rieger S, Schoephorster DB, Furbush CE (1979) Exploratory soil survey of Alaska. U.S. Department of Agriculture. Natural Resource Conservation Service, Fort Worth, TexasGoogle Scholar
  53. Salford Systems (2002) TreeNet Version 2.0. Accessed 30 Oct 2008
  54. Sanchez GH (1993) The ecology of wintering gyrfalcons (Falco rusticolus) in central South Dakota. Master’s Thesis, Boise State UniversityGoogle Scholar
  55. Sanderson EW, Jaiteh M, Levy MA, Redford KH, Wannebo AV, Woolmer G (2003) The human footprint and the last of the wild. Biosci 52:891–904CrossRefGoogle Scholar
  56. Schroeder B (2004) ROC plot. Accessed 30 Oct 2008
  57. Seavy NE, Dybala KE, Snyder MA (2008) Climate models and ornithology. Auk 125:1–10CrossRefGoogle Scholar
  58. Smallwood KS, Thelander CG (2004) Developing methods to reduce bird mortality in the Altamont Pass Wind Resource Area. PIER-EA contract no. 500-01-019, Sacramento, CAGoogle Scholar
  59. Stafford J, Wendler G, Curtis J (2000) Temperature and precipitation of Alaska: 50 year trend analysis. Theor Appl Climat 67:33–44CrossRefGoogle Scholar
  60. Swem T, McIntyre C, Ritchie RJ, Bente PJ, Roseneau DG (1994) Distribution, abundance, and notes on the breeding biology of gyrfalcons (Falco rusticolus) in Alaska. In: Meyburg B-U, Chancellor RD (eds) Raptor conservation today: proceedings of the IV world conference on birds of prey and owls, Berlin, Germany, May 10–17, 1992. World Working Group on Birds of Prey and Owls, London, pp 437–444Google Scholar
  61. Swets JA (1988) Measuring the accuracy of diagnostic systems. Science 240:1285–1293CrossRefPubMedGoogle Scholar
  62. Tape K, Sturm M, Racine C (2006) The evidence for shrub expansion in northern Alaska and the pan-Arctic. Glob Change Biol 12:686–702CrossRefGoogle Scholar
  63. Urios G, Martinez-Abrain A (2006) The study of nest-site preferences in Eleonora’s falcon (Falco eleonorae) through digital terrain models on a western Mediterranean island. J Ornithol 147:13–23CrossRefGoogle Scholar
  64. US Census Bureau (2004) 2000 census of population and housing, United States summary. Report PHC-3-1, Washington DCGoogle Scholar
  65. US Department of Energy (2008) U.S. wind resources map. Accessed 22 Oct 2008
  66. US Geological Survey (2008) Circum-Arctic resource appraisal: estimates of undiscovered oil and gas north of the Arctic Circle. USGS Fact Sheet 2008–3049Google Scholar
  67. US Geological Survey (1997) Alaska 300 m digital elevation model. US Geological Survey Alaska Field Office, Anchorage, AlaskaGoogle Scholar
  68. Verbyla DL, Litaitis JA (1989) Resampling methods for evaluating classification accuracy of wildlife habitat models. Environ Manage 13:783–787CrossRefGoogle Scholar
  69. White CM, Boyce DA (1977) Distribution and ecology of raptor habitat studies for the Kilbuck Mountains, Anvik, Unalakleet, and northwestern Arctic regions of Alaska. US Bureau of Land Management Report, Anchorage, AKGoogle Scholar
  70. Wiens JA (2002) Predicting species occurrences: progress, problems, and prospects. In: Scott JM, Heglund PJ, Morrison ML, Haufler JB, Raphael MG, Wall WA, Samson FB (eds) Predicting species occurrences. Island Press, Washington, pp 739–749Google Scholar
  71. Wu J, Hobbs R (2002) Key issues and research priorities in landscape ecology: an idiosyncratic synthesis. Landsc Ecol 17:355–365CrossRefGoogle Scholar
  72. Wu XB, Smeins FE (2000) Multiple-scale habitat modeling approach for rare plant conservation. Landsc Urban Plan 51:11–28CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2009

Authors and Affiliations

  • Travis L. Booms
    • 1
    • 3
    Email author
  • Falk Huettmann
    • 1
  • Philip F. Schempf
    • 2
  1. 1. Department of Biology and Wildlife, Institute of Arctic BiologyUniversity of Alaska Fairbanks BiologyFairbanksUSA
  2. 2.U.S. Fish and Wildlife Service, Migratory Bird Management-RaptorsJuneauUSA
  3. 3.Alaska Department of Fish and Game, Nongame ProgramFairbanksUSA

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