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International Journal of Biometeorology

, Volume 62, Issue 2, pp 273–285 | Cite as

Snowmelt timing, phenology, and growing season length in conifer forests of Crater Lake National Park, USA

  • Donal S. O’LearyIII
  • Jherime L. Kellermann
  • Chris Wayne
Original Paper

Abstract

Anthropogenic climate change is having significant impacts on montane and high-elevation areas globally. Warmer winter temperatures are driving reduced snowpack in the western USA with broad potential impacts on ecosystem dynamics of particular concern for protected areas. Vegetation phenology is a sensitive indicator of ecological response to climate change and is associated with snowmelt timing. Human monitoring of climate impacts can be resource prohibitive for land management agencies, whereas remotely sensed phenology observations are freely available at a range of spatiotemporal scales. Little work has been done in regions dominated by evergreen conifer cover, which represents many mountain regions at temperate latitudes. We used moderate resolution imaging spectroradiometer (MODIS) data to assess the influence of snowmelt timing and elevation on five phenology metrics (green up, maximum greenness, senescence, dormancy, and growing season length) within Crater Lake National Park, Oregon, USA from 2001 to 2012. Earlier annual mean snowmelt timing was significantly correlated with earlier onset of green up at the landscape scale. Snowmelt timing and elevation have significant explanatory power for phenology, though with high variability. Elevation has a moderate control on early season indicators such as snowmelt timing and green up and less on late-season variables such as senescence and growing season length. PCA results show that early season indicators and late season indicators vary independently. These results have important implications for ecosystem dynamics, management, and conservation, particularly of species such as whitebark pine (Pinus albicaulis) in alpine and subalpine areas.

Keywords

Climate change National parks NDVI Phenology Remote sensing Snowmelt 

Notes

Funding information

This material is based upon work supported by the National Science Foundation Graduate Research Fellowship Program under Grant No. DGE1322106. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. The authors also express their appreciation for support from the Young Leaders in Climate Change Fellowship, a partnership between The George Melendez Wright Foundation, The University of Washington College of the Environment, and The US National Park Service, as well as support from Crater Lake National Park and the Crater Lake National Park Science and Learning Center.

Supplementary material

484_2017_1449_MOESM1_ESM.docx (15 kb)
Appendix 1 (DOCX 14 kb)

References

  1. Abatzoglou JT, Rupp DE, Mote PW (2013) Seasonal climate variability and change in the Pacific Northwest of the United States. J Clim 27:2125–2142.  https://doi.org/10.1175/JCLI-D-13-002181 CrossRefGoogle Scholar
  2. Adamus PR, Odion DC, Jones V, Groshong LC, Reid R (2013) Crater Lake National Park natural resource condition assessment. Natural resource report NPS/NRSS/WRS/NRR-2013/724. National Park Service, Fort CollinsGoogle Scholar
  3. Ahl DE, Gower ST, Burrows SN, Shabanov NV, Myneni RB, Knyazikhin Y (2006) Monitoring spring canopy phenology of a deciduous broadleaf forest using MODIS. Remote Sens Environ 104:88–95.  https://doi.org/10.1016/j.rse.2006.05.003 CrossRefGoogle Scholar
  4. Ault TR, Schwartz MD, Zurita-Milla R, Weltzin JF, Betancourt JL (2015) Trends and natural variability of spring onset in the coterminous United States as evaluated by a new gridded dataset of spring indices. J Clim 28:8363–8378.  https://doi.org/10.1175/JCLI-D-14-007361 CrossRefGoogle Scholar
  5. Barnett TP, Pierce DW, Hidalgo HG, Bonfils C, Santer BD, Das T, Bala G, Wood AW, Nozawa T, Mirin AA, Cayan DR, Dettinger MD (2008) Human-induced changes in the hydrology of the western United States. Science 319:1080–1083.  https://doi.org/10.1126/science.1152538 CrossRefGoogle Scholar
  6. Baron JS, Gunderson L, Allen CD, Fleishman E, McKenzie D, Meyerson LA, Oropeza J, Stephenson N (2009) Options for national parks and reserves for adapting to climate change. Environ Manag 44:1033–1042.  https://doi.org/10.1007/s00267-009-9296-6 CrossRefGoogle Scholar
  7. Barry D, McDonald S (2012) Climate change or climate cycles? Snowpack trends in the Olympic and Cascade Mountains, Washington, USA. Environ Monit Assess 185:719–728.  https://doi.org/10.1007/s10661-012-2587-z CrossRefGoogle Scholar
  8. Bartholow JM (2005) Recent water temperature trends in the Lower Klamath River, California. North Am J Fish Manag 25:152–162.  https://doi.org/10.1577/M04-007.1 CrossRefGoogle Scholar
  9. Beck PSA, Atzberger C, Høgd KA, Johansen B, Skidmore AK (2006) Improved monitoring of vegetation dynamics at very high latitudes: a new method using MODIS NDVI. Remote Sens Environ 100:321–334.  https://doi.org/10.1016/j.rse.2005.10.021 CrossRefGoogle Scholar
  10. Beniston M, Diaz HF, Bradley RS (1997) Climatic change at high elevation sites: an overview. Clim Chang 36:233–251.  https://doi.org/10.1023/A:1005380714349 CrossRefGoogle Scholar
  11. Bentz BJ, Régnière J, Fettig CJ, Hansen EM, Hayes JL, Hicke JA, Kelsey RG, Negrón JF, Seybold SJ (2010) Climate change and bark beetles of the Western United States and Canada: direct and indirect effects. Bioscience 60:602–613.  https://doi.org/10.1525/bio20106086 CrossRefGoogle Scholar
  12. Both C, Van Asch M, Bijlsma RG, Van Den Burg AB, Visser ME (2009) Climate change and unequal phenological changes across four trophic levels: constraints or adaptations? J Anim Ecol 78:73–83.  https://doi.org/10.1111/j1365-2656200801458x CrossRefGoogle Scholar
  13. Burns CE, Johnston KM, Schmitz OJ (2003) Global climate change and mammalian species diversity in US national parks. Proc Natl Acad Sci 100:11474–11477.  https://doi.org/10.1073/pnas1635115100 CrossRefGoogle Scholar
  14. CaraDonna PJ, Iler AM, Inouye DW (2014) Shifts in flowering phenology reshape a subalpine plant community. Proc Natl Acad Sci 111:4916–4921.  https://doi.org/10.1073/pnas1323073111 CrossRefGoogle Scholar
  15. Carroll A, Taylor S, Regniere J, Safranyik L (2003) Effect of climate change on range expansion by the mountain pine beetle in British Columbia. In: Shore TL (ed) Mt Pine Beetle Symposium: challenges and solutions. Natural Resources Canada, Information Report, BC-X-399 Vic, Kelowna, pp 223–232Google Scholar
  16. Cayan DR (1996) Interannual climate variability and snowpack in the Western United States. J Clim 9:928–948.  https://doi.org/10.1175/1520-0442(1996)009<0928:ICVASI>2.0.CO;2 CrossRefGoogle Scholar
  17. Cayan DR, Maurer EP, Dettinger MD, Tyree M, Hayhoe K (2008) Climate change scenarios for the California region. Clim Chang 87:21–42.  https://doi.org/10.1007/s10584-007-9377-6 CrossRefGoogle Scholar
  18. Chapman DS, Haynes T, Beal S, Essl F, Bullock JM (2014) Phenology predicts the native and invasive range limits of common ragweed. Glob Chang Biol 20:192–202.  https://doi.org/10.1111/gcb12380 CrossRefGoogle Scholar
  19. Cleland EE, Chuine I, Menzel A, Mooney HA, Schwartz MD (2007) Shifting plant phenology in response to global change. Trends Ecol Evol 22:357–365CrossRefGoogle Scholar
  20. Dougherty PM, Whitehead D, Vose JM (1994) Environmental influences on the phenology of pine. Ecol Bull 43:64–75Google Scholar
  21. Durant J, Hjermann D, Ottersen G, Stenseth N (2007) Climate and the match or mismatch between predator requirements and resource availability. Clim Res 33:271–283.  https://doi.org/10.3354/cr033271 CrossRefGoogle Scholar
  22. Early R, Sax DF (2014) Climatic niche shifts between species’ native and naturalized ranges raise concern for ecological forecasts during invasions and climate change. Glob Ecol Biogeogr 23:1356–1365.  https://doi.org/10.1111/geb12208 CrossRefGoogle Scholar
  23. Enquist CA, Kellermann JL, Gerst KL, Miller-Rushing AJ (2014) Phenology research for natural resource management in the United States. Int J Biometeorol 58:579–589.  https://doi.org/10.1007/s00484-013-0772-6 CrossRefGoogle Scholar
  24. Fancy SG, Gross JE, Carter SL (2008) Monitoring the condition of natural resources in US national parks. Environ Monit Assess 151:161–174.  https://doi.org/10.1007/s10661-008-0257-y CrossRefGoogle Scholar
  25. Fontana F, Rixen C, Jonas T, Aberegg G, Wunderle S (2008) Alpine grassland phenology as seen in AVHRR, VEGETATION, and MODIS NDVI time series—a comparison with in situ measurements. Sensors 8:2833–2853.  https://doi.org/10.3390/s8042833 CrossRefGoogle Scholar
  26. Forrest J, Miller-Rushing AJ (2010) Toward a synthetic understanding of the role of phenology in ecology and evolution. Philos Trans R Soc Lond Ser B Biol Sci 365:3101–3112.  https://doi.org/10.1098/rstb20100145 CrossRefGoogle Scholar
  27. Ganguly S, Friedl MA, Tan B, Zhang X, Verma M (2010) Land surface phenology from MODIS: characterization of the collection 5 global land cover dynamics product. Remote Sens Environ 114:1805–1816.  https://doi.org/10.1016/jrse201004005 CrossRefGoogle Scholar
  28. Goldstein AH, Hultman NE, Fracheboud JM, Bauer MR, Panek JA, Xu M, Qi Y, Guenther AB, Baugh W (2000) Effects of climate variability on the carbon dioxide, water, and sensible heat fluxes above a ponderosa pine plantation in the Sierra Nevada (CA). Agric For Meteorol 101:113–129.  https://doi.org/10.1016/S0168-1923(99)00168-9 CrossRefGoogle Scholar
  29. Grunewald T, Schirmer M, Mott R, Lehning M (2010) Spatial and temporal variability of snow depth and ablation rates in a small mountain catchment. Cryosphere 4:215–225CrossRefGoogle Scholar
  30. Hall DK, Riggs GA (2007) Accuracy assessment of the MODIS snow products. Hydrol Process 21:1534–1547.  https://doi.org/10.1002/hyp.6715
  31. Hall DK, Salomonson VV, Riggs GA (2006) MODIS/Terra Snow Cover 8-Day L3 Global 500m Grid Version 5. (January 2001–December 2012) National Snow and Ice Data Center, Boulder, Colorado USA: National Snow and Ice Data Center. Digital mediaGoogle Scholar
  32. Hamlet AF, Lettenmaier DP (1999) Effects of climate change on hydrology and water resources in the Columbia River Basin. J Am Water Resour Assoc 35:1597–1623.  https://doi.org/10.1111/j.1752-1688.1999.tb04240.x CrossRefGoogle Scholar
  33. Harpold AA, Molotch NP, Musselman KN, Bales RC, Kirchner PB, Litvak M, Brooks PD (2015) Soil moisture response to snowmelt timing in mixed-conifer subalpine forests. Hydrol Process 29:2782–2798.  https://doi.org/10.1002/hyp10400 CrossRefGoogle Scholar
  34. Harrington R, Woiwod I, Sparks T (1999) Climate change and trophic interactions. Trends Ecol Evol 14:146–150.  https://doi.org/10.1016/S0169-5347(99)01604-3 CrossRefGoogle Scholar
  35. Hellmann JJ, Byers JE, Bierwagen BG, Dukes JS (2008) Five potential consequences of climate change for invasive species. Conserv Biol 22:534–543.  https://doi.org/10.1111/j1523-1739200800951x CrossRefGoogle Scholar
  36. Inouye DW (2008) Effects of Climate Change on phenology, frost damage, and floral abundance of montane wildflowers. Ecology 89:353–362.  https://doi.org/10.1890/06-2128.1
  37. IPCC (2007) Climate Change 2007: impacts, adaptation and vulnerability. In: Parry ML, Canziani OF, Palutikof JP, van der Linden PJ, Hanson CE (eds) Contribution of working group II to the fourth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, p 976Google Scholar
  38. IPCC (2014) Climate change 2014: impacts, adaptation, and vulnerability. Part A: global and sectoral aspects. In: Field CB, Barros VR, Dokken DJ, Mach KJ, Mastrandrea MD, Bilir TE, Chatterjee M, Ebi KL, Estrada YO, Genova RC, Girma B, Kissel ES, Levy AN, MacCracken S, Mastrandrea PR, White LL (eds) Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge 1132 ppGoogle Scholar
  39. Jönsson AM, Eklundh L, Hellström M, Bärring L, Jönsson P (2010) Annual changes in MODIS vegetation indices of Swedish coniferous forests in relation to snow dynamics and tree phenology. Remote Sens Environ 114:2719–2730.  https://doi.org/10.1016/jrse201006005 CrossRefGoogle Scholar
  40. Keane RE, Gray KL, Dickinson LJ (2007) Whitebark pine diameter growth response to removal of competition. Res. Note RMRS-RN-32. U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station, Fort CollinsGoogle Scholar
  41. Keane RE, Tomback DF, Aubry CA, Bower AD, Campbell EM, Cripps CL, Jenkins MB, Mahalovich MF, Manning M, McKinney ST, Murray MP, Perkins DL, Reinhart DP, Ryan C, Schoettle AW, Smith CM (2012) A range-wide restoration strategy for whitebark pine (Pinus albicaulis). Gen Tech Rep RMRS-GTR-279. U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station, Fort CollinsCrossRefGoogle Scholar
  42. Kendall KC, Keane RE (2001) Whitebark pine decline: infection, mortality, and population trends. In: Tomback DF, Arno SF, Keane RE (eds) Whitebark pine communities: ecology and restoration. Island Press, Washington, DC, pp 221–242Google Scholar
  43. Knight JF, Lunetta RS, Ediriwickrema J, Khorram S (2006) Regional scale land cover characterization using MODIS-NDVI 250 m multi-temporal imagery: a phenology-based approach. GISci Remote Sens 43:1–23.  https://doi.org/10.2747/1548-1603.43.1.1 CrossRefGoogle Scholar
  44. Knowles P, Grant MC (1983) Age and size structure analyses of Engelmann spruce, ponderosa pine, lodgepole pine, and limber pine in Colorado. Ecology 64:1–9.  https://doi.org/10.2307/1937322 CrossRefGoogle Scholar
  45. Körner C (2016) Plant adaptation to cold climates. F1000Res 5. doi: 10.12688/f1000research.9107.1
  46. Kriegler FJ, Malila WA, Nalepka RF, Richardson W (1969) Preprocessing transformations and their effects on multispectral recognition. Proc Sixth Int Symp Remote Sens Environ 97–131Google Scholar
  47. Lambert AM, Miller-Rushing AJ, Inouye DW (2010) Changes in snowmelt date and summer precipitation affect the flowering phenology of Erythronium grandiflorum (glacier lily; Liliaceae). Am J Bot 97:1431–1437.  https://doi.org/10.3732/ajb1000095 CrossRefGoogle Scholar
  48. Law BE, Waring RH (2014) Carbon implications of current and future effects of drought, fire and management on Pacific Northwest forests. For Ecol Manag 355:4–14.  https://doi.org/10.1016/jforeco201411023 CrossRefGoogle Scholar
  49. Leingärtner A, Krauss J, Steffan-Dewenter I (2014) Elevation and experimental snowmelt manipulation affect emergence phenology and abundance of soil-hibernating arthropods. Ecol Entomol 39:412–418.  https://doi.org/10.1111/een12112 CrossRefGoogle Scholar
  50. Leung LR, Qian Y, Bian X, Washington WM, Han J, Roads JO (2004) Mid-century ensemble regional climate change scenarios for the Western United States. Clim Chang 62:75–113.  https://doi.org/10.1023/B:CLIM00000136925064055 CrossRefGoogle Scholar
  51. Levy S (2003) Turbulence in the Klamath River Basin. Bioscience 53:315–320.  https://doi.org/10.1641/0006-3568(2003)053[0315:TITKRB]2.0.CO;2 CrossRefGoogle Scholar
  52. Loustau D, Pluviaud F, Bosc A, et al (2001) Sub-regional climate change impacts on the water balance, carbon balance and primary productivity of maritime pine in South-West France. Models Sustain Manag Temp Plant For 45Google Scholar
  53. Mayer TD, Naman SW (2011) Streamflow response to climate as influenced by geology and elevation. J Am Water Res Assoc 47:724–738.  https://doi.org/10.1111/j.1752-1688.2011.00537.x CrossRefGoogle Scholar
  54. McCullough IM, Davis FW, Dingman JR, Flint LE, Flint AL, Serra-Diaz JM, Syphard AD, Moritz MA, Hannah L, Franklin J (2015) High and dry: high elevations disproportionately exposed to regional climate change in Mediterranean-climate landscapes. Landsc Ecol 31:1–13.  https://doi.org/10.1007/s10980-015-0318-x Google Scholar
  55. Meier GA, Brown JF, Evelsizer RJ, Vogelmann JE (2015) Phenology and climate relationships in aspen (Populus tremuloides Michx.) forest and woodland communities of southwestern Colorado. Ecol Indic 48:189–197.  https://doi.org/10.1016/j.ecolind.2014.05.033 CrossRefGoogle Scholar
  56. Millar CI, Westfall RD, Delany DL, Bokach MJ, Flint AL, Flint LE (2012) Forest mortality in high-elevation whitebark pine (Pinus albicaulis) forests of eastern California, USA; influence of environmental context, bark beetles, climatic water deficit, and warming. Can J For Res 42:749–765CrossRefGoogle Scholar
  57. Miller NL, Bashford KE, Strem E (2003) Potential impacts of climate change on California hydrology. J Am Water Resour Assoc 39:771–784.  https://doi.org/10.1111/j.1752-1688.2003.tb04404.x CrossRefGoogle Scholar
  58. Monahan WB, Fisichelli NA (2014) Climate exposure of US national parks in a new era of change. PLoS One 9:e101302.  https://doi.org/10.1371/journalpone0101302 CrossRefGoogle Scholar
  59. Moore C, Kampf S, Stone B, Richer E (2014) A GIS-based method for defining snow zones: application to the Western United States. Geocarto Int 30:62–81.  https://doi.org/10.1080/101060492014885089 CrossRefGoogle Scholar
  60. Moritz C, Patton JL, Conroy CJ, Parra JL, White GC, Beissinger SR (2008) Impact of a century of climate change on small-mammal communities in Yosemite National Park, USA. Science 322:261–264.  https://doi.org/10.1126/science1163428 CrossRefGoogle Scholar
  61. Mote PW (2006) Climate-driven variability and trends in mountain snowpack in Western North America. J Clim 19:6209–6220.  https://doi.org/10.1175/JCLI39711 CrossRefGoogle Scholar
  62. Mote PW, Salathe EPS Jr (2010) Future climate in the Pacific Northwest. Clim Chang 102:29–50.  https://doi.org/10.1007/s10584-010-9848-z CrossRefGoogle Scholar
  63. Mountain Research Initiative EDW Working Group (2015) Elevation-dependent warming in mountain regions of the world. Nat Clim Chang 5:424–430.  https://doi.org/10.1038/nclimate2563 CrossRefGoogle Scholar
  64. Muhlfeld CC, Giersch JJ, Hauer FR, Pederson GT, Luikart G, Peterson DP, Downs CC, Fagre DB (2011) Climate change links fate of glaciers and an endemic alpine invertebrate. Clim Chang 106:337–345.  https://doi.org/10.1007/s10584-011-0057-1 CrossRefGoogle Scholar
  65. NatureServe (2016) NatureServe Web Service. Arlington. Available https://services.natureserve.org. Accessed 5 Oct 2016
  66. Nolin AW (2012) Perspectives on climate change, mountain hydrology, and water resources in the Oregon Cascades, USA. Mt Res Dev 32:S35–S46.  https://doi.org/10.1659/MRD-JOURNAL-D-11-00038.S1 CrossRefGoogle Scholar
  67. O’Leary D, Bloom T, Smith J, Zempf C, Medler M (2016) A new method comparing snowmelt timing with annual area burned. Fire Ecol 12:41–51.  https://doi.org/10.4996/fireecology.1201041 CrossRefGoogle Scholar
  68. O’Leary DSI, Hall DK, Medler MJ, Matthews RA, Flower A (2017) Snowmelt timing maps derived from MODIS for North America, 2001-2015. Oak Ridge Natl Lab.  https://doi.org/10.3334/ORNLDAAC/1504
  69. Parmesan C, Yohe G (2003) A globally coherent fingerprint of climate change impacts across natural systems. Nature 421:37–42.  https://doi.org/10.1038/nature01286 CrossRefGoogle Scholar
  70. Payne JT, Wood AW, Hamlet AF, Palmer RN, Lettenmaier DP (2004) Mitigating the effects of climate change on the water resources of the Columbia River basin. Clim Chang 62:233–256.  https://doi.org/10.1023/B:CLIM.0000013694.18154.d6 CrossRefGoogle Scholar
  71. Peterson AG, Abatzoglou JT (2014) Observed changes in false springs over the contiguous United States. Geophys Res Lett 41:2014GL059266.  https://doi.org/10.1002/2014GL059266 CrossRefGoogle Scholar
  72. Pettorelli N, Vik JO, Mysterud A, Gaillard JM, Tucker CJ, Stenseth NC (2005) Using the satellite-derived NDVI to assess ecological responses to environmental change. Trends Ecol Evol 20:503–510CrossRefGoogle Scholar
  73. Poff NL, Allan JD, Palmer MA, Hart DD, Richter BD, Arthington AH, Rogers KH, Meyer JL, Stanford JA (2003) River flows and water wars: emerging science for environmental decision making. Front Ecol Environ 1:298–306.  https://doi.org/10.1890/1540-9295(2003)001[0298:RFAWWE]2.0.CO;2 CrossRefGoogle Scholar
  74. Post E, Pedersen C, Wilmers CC, Forchhammer MC (2008) Warming, plant phenology and the spatial dimension of trophic mismatch for large herbivores. Proc R Soc Lond B Biol Sci 275:2005–2013CrossRefGoogle Scholar
  75. Rango A, van Katwijk V (1990) Climate change effects on the snowmelt hydrology of western North American mountain basins. IEEE Trans Geosci Remote Sens 28:970–974.  https://doi.org/10.1109/36.58987 CrossRefGoogle Scholar
  76. Rangwala I, Miller JR (2012) Climate change in mountains: a review of elevation-dependent warming and its possible causes. Clim Chang 114:527–547.  https://doi.org/10.1007/s10584-012-0419-3 CrossRefGoogle Scholar
  77. Rouse JW, Haas RH, Scheel JA, Deering DW (1974) Monitoring Vegetation Systems in the Great Plains with ERTS. Proc 3rd Earth Resour Technol Satell ERTS Symp 1:48–62Google Scholar
  78. Sambaraju KR, Carroll AL, Zhu J, Stahl K, Moore RD, Aukema BH (2012) Climate change could alter the distribution of mountain pine beetle outbreaks in western Canada. Ecography 35:211–223.  https://doi.org/10.1111/j1600-0587201106847x CrossRefGoogle Scholar
  79. Sedlacek J, Wheeler JA, Cortés AJ, Bossdorf O, Hoch G, Lexer C, Wipf S, Karrenberg S, van Kleunen M, Rixen C (2015) The response of the alpine dwarf shrub Salix herbacea to altered snowmelt timing: lessons from a multi-site transplant experiment. PLoS One 10:e0122395.  https://doi.org/10.1371/journal.pone.0122395 CrossRefGoogle Scholar
  80. Semmens K, Ramage J (2012) Investigating correlations between snowmelt and forest fires in a high latitude snowmelt dominated drainage basin. Hydrol Process 26:2608–2617.  https://doi.org/10.1002/hyp.9327 CrossRefGoogle Scholar
  81. Stewart IT (2009) Changes in snowpack and snowmelt runoff for key mountain regions. Hydrol Process 23:78–94.  https://doi.org/10.1002/hyp.7128 CrossRefGoogle Scholar
  82. Totland Ø, Alatalo JM (2002) Effects of temperature and date of snowmelt on growth, reproduction, and flowering phenology in the arctic/alpine herb, Ranunculus glacialis. Oecologia 133:168–175.  https://doi.org/10.1007/s00442-002-1028-z CrossRefGoogle Scholar
  83. U.S. Environmental Protection Agency (2016) Climate change indicators in the United States, 2016. Fourth edition. EPA 430-R-16-004Google Scholar
  84. Visser ME, Holleman LJM (2001) Warmer springs disrupt the synchrony of oak and winter moth phenology. Proc R Soc Lond B Biol Sci 268:289–294.  https://doi.org/10.1098/rspb20001363 CrossRefGoogle Scholar
  85. Visser ME, Holleman LJM, Gienapp P (2005) Shifts in caterpillar biomass phenology due to climate change and its impact on the breeding biology of an insectivorous bird. Oecologia 147:164–172.  https://doi.org/10.1007/s00442-005-0299-6 CrossRefGoogle Scholar
  86. Walther GR (2010) Community and ecosystem responses to recent climate change. Philos Trans R Soc Lond Ser B Biol Sci 365:2019–2024.  https://doi.org/10.1098/rstb.2010.0021 CrossRefGoogle Scholar
  87. Wang X, Xie H, Liang T (2008) Evaluation of MODIS snow cover and cloud mask and its application in Northern Xinjiang, China. Remote Sens Environ 112:1497–1513CrossRefGoogle Scholar
  88. Wang Q, Tenhunen J, Dinh NQ, et al (2004) Similarities in ground- and satellite-based NDVI time series and their relationship to physiological activity of a Scots pine forest in Finland. Remote Sens Environ 93:225–237.  https://doi.org/10.1016/j.rse.2004.07.006
  89. Westerling AL, Hidalgo HG, Cayan DR, Swetnam TW (2006) Warming and earlier spring increase western US forest wildfire activity. Science 313:940–943.  https://doi.org/10.1126/science.1128834 CrossRefGoogle Scholar
  90. Wipf S, Stoeckli V, Bebi P (2009) Winter climate change in alpine tundra: plant responses to changes in snow depth and snowmelt timing. Clim Change 94:105–121.  https://doi.org/10.1007/s10584-009-9546-x
  91. Wolf A, Zimmerman NB, Anderegg WRL, Busby PE, Christensen J (2016) Altitudinal shifts of the native and introduced flora of California in the context of 20th-century warming. Glob Ecol Biogeogr 25:418–429.  https://doi.org/10.1111/geb12423 CrossRefGoogle Scholar
  92. Yu X, Zhuang DF (2006) Monitoring forest phenophases of Northeast China based on MODIS NDVI data. Resour Sci 28:111–117Google Scholar
  93. Zhang X, Friedl MA, Schaaf CB, Strahler AH, Hodges JC, Ga F, Reed BC, Huete A (2003) Monitoring vegetation phenology using MODIS. Remote Sens Environ 84:471–475CrossRefGoogle Scholar
  94. Zhang X, Tan B, Friedl MA, Goldberg MD, Yu Y (2012) Long-term detection of global vegetation phenology from satellite instruments. INTECH Open Access Publisher, RijekaCrossRefGoogle Scholar

Copyright information

© ISB 2017

Authors and Affiliations

  • Donal S. O’LearyIII
    • 1
    • 2
  • Jherime L. Kellermann
    • 3
    • 4
  • Chris Wayne
    • 5
  1. 1.Department of Geographical Sciences, College of Behavioral and Social SciencesUniversity of MarylandCollege ParkUSA
  2. 2.Department of Geography, Huxley CollegeWestern Washington UniversityBellinghamUSA
  3. 3.Crater Lake National Park Science & Learning CenterCrater Lake National ParkCrater LakeUSA
  4. 4.Natural Sciences DepartmentOregon Institute of TechnologyKlamath FallsUSA
  5. 5.Division of Resource Preservation and ResearchCrater Lake National ParkCrater LakeUSA

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