Skip to main content

Advertisement

Log in

Gyrfalcon nest distribution in Alaska based on a predictive GIS model

  • Original Paper
  • Published:
Polar Biology Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  • Andersen DE (2007) Survey techniques. In: Bird D, Bildstein K (eds) Raptor research and management techniques. Hancock House Publishers, Blaine, WA, pp 89–100

    Google Scholar 

  • Anderson DR, Burnham KP, Thompson WL (2000) Null hypothesis testing: problems, prevalence, and an alternative. J Wildl Manage 64:912–923

    Article  Google Scholar 

  • Araujo MB, Guisan A (2006) Five (or so) challenges for species distribution modeling. J Biogeo 33:1677–1688

    Article  Google Scholar 

  • Araujo MB, Williams PH (2000) Selecting areas for species persistence using occurrence data. Biol Conserv 96:331–345

    Article  Google Scholar 

  • Beikman HM (1980) Geologic map of Alaska. U.S. Geological Survey special publication # SG0002-1T and SG0002-2T, Washington, DC

  • Beyer HL (2008) Hawth’s analysis tools for ArcGIS. http://www.spatialecology.com/htools. Accessed 12 Dec 2008

  • 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. http://bna.birds.cornell.edu/bna/species/114. Accessed 10 Jan 2009

  • Boyce MS, McDonald LL (1999) Relating populations to habitats using resource selection functions. Trends Ecol Evol 14:268–272

    Article  PubMed  Google Scholar 

  • 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 39:296–302

    Google Scholar 

  • Breiman L (2001) Random forests. Mach Learn 45:5–32

    Article  Google Scholar 

  • Britten MW, McIntyre CL, Kralovec M (1995) Satellite radio telemetry and bird studies in national parks and preserves. Park Sci 15:20–24

    Google Scholar 

  • Cade TJ (1960) Ecology of the peregrine and gyrfalcon populations in Alaska. Univ Calif Pub Zool 63:151–290

    Google Scholar 

  • Cade TJ (1982) The falcons of the world. Cornell University Press, Ithaca, NY

    Google Scholar 

  • 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 65–84

    Google Scholar 

  • Crick HQ (2004) The impact of climate change on birds. Ibis 146:48–56

    Article  Google Scholar 

  • 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 303–314

    Google Scholar 

  • Elith J, Graham C, Anderson RP et al (2006) Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29:129–151

    Article  Google Scholar 

  • 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 41:263–274

    Article  Google Scholar 

  • Environmental Systems Research Institute (2008) ArcMap 9.3 resource center. http://www.resources.esri.com. Accessed 12 Dec 2008

  • 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 303–314

    Google Scholar 

  • Fielding AH, Bell JF (1997) A review of methods for the assessment of prediction errors in conservation presence/absence models. Environ Conserv 24:38–49

    Article  Google Scholar 

  • 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 25–26

  • Friedman JH (2002) Stochastic gradient boosting. Comput Stat Data Anal 38:367–378

    Article  Google Scholar 

  • 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 13:332–340

    Article  Google Scholar 

  • Hastie T, Tibshirani R, Friedman J (2001) The elements of statistical learning: data mining, inference, and prediction. Springer, New York

    Google Scholar 

  • 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 35–42

    Google Scholar 

  • 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 291–302

    Google Scholar 

  • Higmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A (2005) Very high resolution interpolated climate surfaces for global land areas. Int J Climat 25:1965–1978

    Article  Google Scholar 

  • Huettmann F, Diamond AW (2006) Large-scale effects on the spatial distribution of seabirds in the Northwest Atlantic. Landsc Ecol 21:1089–1108

    Article  Google Scholar 

  • 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 Cruz

    Google Scholar 

  • Hutchinson GE (1957) A treatise on limnology. Wiley, New York

    Google Scholar 

  • 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, MN

    Google Scholar 

  • Karlstrom TNV, Coulter HW, Fernald AT et al (1964) Surface geology of Alaska. Miscellaneous geologic investigations map I-357. Washington, DC

  • Keating KA, Cherry S (2004) Use and interpretation of logistic regression in habitat selection studies. J Wildl Manage 68:774–789

    Article  Google Scholar 

  • Lehner B, Doll P (2004) Development and validation of a global database of lakes, reservoirs and wetlands. J Hydrol 296:1–22

    Article  Google Scholar 

  • 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-Kamchatskiy

  • 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 120:337–347

    Article  Google Scholar 

  • Manel S, Williams HC, Ormerod SJ (2001) Evaluating presence–absence models in ecology: the need to account for prevalence. J Appl Ecol 38:921–931

    Article  Google Scholar 

  • 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, London

    Google Scholar 

  • 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 9:279–294

    Article  Google Scholar 

  • Mladenoff DJ, Sickley TA, Wydeven AP (1999) Predicting gray wolf landscape recolonization: logistic regression models vs. new field data. Ecol Appl 9:37–44

    Article  Google Scholar 

  • Nielsen ÓK (1991) Age of first breeding and site fidelity of gyrfalcons. Náttúrufæðingurinn 60:135–143

    Google Scholar 

  • Nielsen ÓK (1999) Gyrfalcon predation on ptarmigan: numerical and functional responses. J Anim Ecol 68:1034–1050

    Article  Google Scholar 

  • Nielsen ÓK, Cade TJ (1990a) Annual cycle of the gyrfalcon in Iceland. Nat Geo Res 6:41–62

    Google Scholar 

  • Nielsen ÓK, Cade TJ (1990b) Seasonal changes in food habits of gyrfalcons in northeast Iceland. Ornis Scand 21:202–211

    Article  Google Scholar 

  • 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 141:2193–2207

    Article  Google Scholar 

  • 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 152–161

  • Palmer RS (1988) Handbook of North American birds, vol 5. Vail-Ballou Press, Binghamton, NY

    Google Scholar 

  • Pearce JL, Boyce MS (2006) Modeling distribution and abundance with presence-only data. J Appl Ecol 43:405–412

    Article  Google Scholar 

  • Peterson AT (2001) Prediction species’ geographic distributions based on ecological niche modeling. Condor 103:599–605

    Article  Google Scholar 

  • Potapov E, Sale R (2005) The gyrfalcon. Yale University Press, New Haven, CT

    Google Scholar 

  • Rieger S, Schoephorster DB, Furbush CE (1979) Exploratory soil survey of Alaska. U.S. Department of Agriculture. Natural Resource Conservation Service, Fort Worth, Texas

    Google Scholar 

  • Salford Systems (2002) TreeNet Version 2.0. http://www.salford-systems.com/treenet. Accessed 30 Oct 2008

  • Sanchez GH (1993) The ecology of wintering gyrfalcons (Falco rusticolus) in central South Dakota. Master’s Thesis, Boise State University

  • Sanderson EW, Jaiteh M, Levy MA, Redford KH, Wannebo AV, Woolmer G (2003) The human footprint and the last of the wild. Biosci 52:891–904

    Article  Google Scholar 

  • Schroeder B (2004) ROC plot. http://brandenburg.geoecology.uni-potsdam.de/users/schroeder/download.html. Accessed 30 Oct 2008

  • Seavy NE, Dybala KE, Snyder MA (2008) Climate models and ornithology. Auk 125:1–10

    Article  Google Scholar 

  • 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, CA

  • Stafford J, Wendler G, Curtis J (2000) Temperature and precipitation of Alaska: 50 year trend analysis. Theor Appl Climat 67:33–44

    Article  Google Scholar 

  • 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 437–444

  • Swets JA (1988) Measuring the accuracy of diagnostic systems. Science 240:1285–1293

    Article  CAS  PubMed  Google Scholar 

  • Tape K, Sturm M, Racine C (2006) The evidence for shrub expansion in northern Alaska and the pan-Arctic. Glob Change Biol 12:686–702

    Article  Google Scholar 

  • 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 147:13–23

    Article  Google Scholar 

  • US Census Bureau (2004) 2000 census of population and housing, United States summary. Report PHC-3-1, Washington DC

  • US Department of Energy (2008) U.S. wind resources map. http://www.windpoweringamerica.gov/wind_maps.asp. Accessed 22 Oct 2008

  • US Geological Survey (2008) Circum-Arctic resource appraisal: estimates of undiscovered oil and gas north of the Arctic Circle. USGS Fact Sheet 2008–3049

  • US Geological Survey (1997) Alaska 300 m digital elevation model. US Geological Survey Alaska Field Office, Anchorage, Alaska

    Google Scholar 

  • Verbyla DL, Litaitis JA (1989) Resampling methods for evaluating classification accuracy of wildlife habitat models. Environ Manage 13:783–787

    Article  Google Scholar 

  • 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, AK

    Google Scholar 

  • 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 739–749

    Google Scholar 

  • Wu J, Hobbs R (2002) Key issues and research priorities in landscape ecology: an idiosyncratic synthesis. Landsc Ecol 17:355–365

    Article  Google Scholar 

  • Wu XB, Smeins FE (2000) Multiple-scale habitat modeling approach for rare plant conservation. Landsc Urban Plan 51:11–28

    Article  Google Scholar 

Download references

Acknowledgments

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.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Travis L. Booms.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Booms, T.L., Huettmann, F. & Schempf, P.F. Gyrfalcon nest distribution in Alaska based on a predictive GIS model. Polar Biol 33, 347–358 (2010). https://doi.org/10.1007/s00300-009-0711-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00300-009-0711-5

Keywords

Navigation