Skip to main content

Advertisement

Log in

Green-up of deciduous forest communities of northeastern North America in response to climate variation and climate change

  • Research Article
  • Published:
Landscape Ecology Aims and scope Submit manuscript

Abstract

Temporal shifts in phenology are important biotic indicators of climate change. Satellite-derived Land Surface Phenology (LSP) offers data for the study of vegetation phenology at landscape to global spatial scales. However, the mechanisms of plant phenological responses to temperature are rarely considered at broad spatial scales, despite the potential improvements to spatiotemporal predictions. Geographical gradients in community species composition may also affect LSP spatially and temporally. Using a modified survival analysis, we reveal how weather and climate relate to physiological chilling and heating requirements and affect deciduous forest green-up in New England, USA over 9 years (2001–2009). While warm daily temperatures lead to earlier green-up of deciduous forests, chilling temperatures had a larger influence on green-up. We also found that the effects of community composition across the landscape were as important as the effects of weather. Greater oak dominance led to later green-up, while sites with more birch tended to have earlier green-up dates. Projection into the future (2046–2065) with statistically downscaled, bias corrected climate model output suggested advanced green-up (8–48 days) driven by higher heating and chilling accumulations, but green-up in coastal areas may be delayed due to reduced chilling accumulation. This study provides an innovative statistical method combining plant physiological mechanisms, topographic spatial heterogeneity, and species composition to predict how LSP responds to climate and weather variation and makes future projections.

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.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  • Ahmed KF, Wang G, Silander JA, Wilson AM, Allen JM, Horton R, Anyah R (2013) Statistical downscaling and bias correction of climate model outputs for climate change impact assessment in the US Northeast. Glob Planet Change 100:320–332

    Article  Google Scholar 

  • Alburquerque N, García-Montiel F, Carrillo A, Burgos L (2008) Chilling and heat requirements of sweet cherry cultivars and the relationship between altitude and the probability of satisfying the chill requirements. Environ Exp Bot 64:162–170

    Article  Google Scholar 

  • Allen JM, Terres MA, Katsuki T, Iwamoto K, Kobori H, Higuchi H, Primack RB, Wilson AM, Gelfand A, Silander JA (2014) Modeling daily flowering probabilities: expected impact of climate change on Japanese cherry phenology. Glob Change Biol 20:1251–2063

    Article  Google Scholar 

  • Augspurger CK (2013) Reconstructing patterns of temperature, phenology, and frost damage over 124 years. Ecology 94:41–50

    Article  PubMed  Google Scholar 

  • Baldocchi D, Wong S (2008) Accumulated winter chill is decreasing in the fruit growing regions of California. Clim Change 87:153–166

    Article  Google Scholar 

  • Bater CW, Coops NC, Wulder MA, Hilker T, Nielsen SE, McDermid G, Stenhouse GB (2011) Using digital time-lapse cameras to monitor species-specific understory and overstory phenology in support of wildlife habitat assessment. Environ Monit Assess 180:1–13

    Article  PubMed  Google Scholar 

  • Bennett JP (1949) Temperature and bud rest period. Calif Agric 3:9–12

  • Berg MP, Kiers ET, Driessen G, Heijden MVD, Kool BW, Kuenen F, Liefting M, Verhoef HA, Ellers J (2010) Adapt or disperse: understanding species persistence in a changing world. Glob Change Biol 16:587–598

    Article  Google Scholar 

  • Bertin R (2008) Plant phenology and distribution in relation to recent climate change. J Torrey Bot Soc 135:126–146

    Article  Google Scholar 

  • Caffarra A, Donnelly A (2011) The ecological significance of phenology in four different tree species: effects of light and temperature on bud burst. Int J Biometeorol 55:711–721

    Article  PubMed  Google Scholar 

  • Caffarra A, Donnelly A, Chuine I, Jones MB (2011) Modeling the timing of Betula pubescens bud burst. I. Temperature and photoperiod: a conceptual model. Clim Res 46:147–157

    Article  Google Scholar 

  • Cannell MGR, Smith RI (1983) Thermal time, chill days and prediction of budburst in Picea Sitchensis. J Appl Ecol 20:951–963

    Article  Google Scholar 

  • Chuine I (2000) A unified model for budburst of trees. J Theor Biol 207:337–347

    Article  CAS  PubMed  Google Scholar 

  • Clark JS, Melillo J, Mohan J, Salk C (2013) The seasonal timing of warming that controls onset of growing season. Glob Change Biol (in press). doi: 10.1111/gcb.12420

  • Cleland EE, Chuine I, Menzel A, Mooney HA, Schwartz MD (2007) Shifting plant phenology in response to global change. Trends Ecol Evol 22:358–365

    Article  Google Scholar 

  • Cox DR, Oakes D (1984) Analysis of survival data. CRC Press, New York

    Google Scholar 

  • Diez MJ, Ibanes I, Miller-Rushing AJ, Mazer SJ, Crimmins MA, Bertelsen CD, Inouye DW (2012) Forecasting phenology: from species variability to community patterns. Ecol Lett 15:545–553

    Article  PubMed  Google Scholar 

  • Diez JM, Ibáñez I, Silander J, Primack RB, Higuchi H, Kobori H, Sen A, James TY (2014) Beyond seasonal climate: statistical estimation of phenological responses to weather. Ecol Appl (In press)

  • Dunn AH, de Beurs KM (2011) Land surface phenology of North American mountain environments using moderate resolution imaging spectroradiometer data. Remote Sens Environ 115:1220–1233

    Article  Google Scholar 

  • Elmore AJ, Guinn SM, Minsley BJ, Richardson AD (2012) Landscape controls on the timing of spring, autumn, and growing season length in mid-Atlantic forests. Glob Change Biol 18:656–674

    Article  Google Scholar 

  • Fisher J, Richardson A, Mustard JF (2007) Phenology model from surface meteorology does not capture satellite-based greenup estimations. Glob Change Biol 13:707–721

    Article  Google Scholar 

  • Foster DR, Aber JD (2004) Forests in time: the environmental consequences of 1,000 years of change in New England. Yale University Press, New Heaven and London

    Google Scholar 

  • Fu YH, Campioli M, Deckmyn G, Janssens JA (2012) The impact of winter and spring temperatures on temperate tree budburst dates: results from an experimental climate manipulation. PLoS ONE 7(10):e47324. doi:10.1371/journal.pone.0047324

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Gienapp P, Hemerik L, Visser ME (2005) A new statistical tool to predict phenology under climate change scenarios. Glob Change Biol 11:600–606

    Article  Google Scholar 

  • Guy R (2014) The early bud gets to warm. New Phytol 202:7–9

    Article  PubMed  Google Scholar 

  • Heide OM (1993) Dormancy release in beech Buds (Fagus sylvatica) requires both chilling and long days. Physiol Plant 89:187–191

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Hwang T, Song C, Vose JM, Band LE (2011) Topography-mediated controls on local vegetation phenology estimated from MODIS vegetation index. Landscape Ecol 26:541–556

    Article  Google Scholar 

  • Ibanez I, Primack RB, Miller-Rushing AJ, Ellwood E, Higuchi H, Lee SD, Kobori H, Silander JA (2010) Forecasting phenology under global warming. Philos T Roy Soc B 365:3247–3260

    Article  Google Scholar 

  • Ide R, Oguma H (2013) A cost-effective monitoring method using digital time-lapse cameras for detecting temporal and spatial variations of snowmelt and vegetation phenology in alpine ecosystems. Ecol Inform 16:25–34

    Article  Google Scholar 

  • Isaacson BN, Serbin SP, Townsend PA (2012) Detection of relative differences in phenology of forest species using Landsat and MODIS. Landscape Ecol 27:529–543

    Article  Google Scholar 

  • Jeong S-J, Medvigy D, Shevliakova E, Brown ME (2013) Predicting changes in temperate forest budburst using continental-scale observations and models. Geophys Res Lett 40:1–6

    Article  Google Scholar 

  • Jiménez S, Reighard GL, Bielenberg DG (2010) Gene expression of DAM5 and DAM6 is suppressed by chilling temperatures and inversely correlated with bud break rate. Plant Mol Biol 73:157–167

    Article  PubMed  Google Scholar 

  • Kaduk JD, Los SO (2011) Predicting the time of greenup in temperate and boreal biomes. Clim Change 107:277–304

    Article  Google Scholar 

  • Keough GR (2012) Maple Syrup 2012. USDA, National Agricultural Statistics Service, New England Field Office, Concord, pp 1–8

    Google Scholar 

  • Kim Y, Wang G (2005) Modeling seasonal vegetation variation and its validation against moderate resolution imaging spectroradiometer (MODIS) observations over North America. J Geophys Res 110:D04106. doi:10.1029/2004JD005436

    Google Scholar 

  • Li Z, Reighard GL, Abbott AG, Bielenberg DG (2009) Dormancy associated MADS genes from the EVG locus of peach [Prunus persica (L.) Batsch] have distinct seasonal and photoperiodic expression patterns. J Exp Bot 60:3521–3530

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Luedeling E, Zhang M, Luedeling V, Girveta EH (2009) Sensitivity of winter chill models for fruit and nut trees to climatic changes expected in California’s Central Valley. Agr Ecosyst Environ 133:23–31

    Article  Google Scholar 

  • Mathews NS, Dwyer KW (1990) Floodplain vegetation phenology in southeastern USA: optimizing the timing of aerial imagery acquisition. Wetl Ecol Manag 1:65–72

    Article  Google Scholar 

  • Maurer EP, Wood AW, Adam JC, Lettenmaier DP, Nijssen B (2002) A long-term hydrologically-based data set of land surface fluxes and states for the conterminous United States. J Climate 15:3237–3251

    Article  Google Scholar 

  • Metzler KJ, Barrett JP (2006) The vegetation of Connecticut: a preliminary classification. State geological and natural history survey of Connecticut. Hartford, CT, pp 29–30

    Google Scholar 

  • Pau S, Wolkovich EM, Cook BI, Davies TJ, Kraft NJB, Blomgren K, Betancourt JL, Cleland EE (2011) Predicting phenology by integrating ecology, evolution and climate science. Glob Change Biol 17:3633–3643

    Article  Google Scholar 

  • Paul LK, Rinne PLH, van der Schoot C (2014) Shoot meristems of deciduous woody perennials: self-organization and morphogenetic transitions. Curr Opin Plant Biol 17:86–95

    Article  PubMed  Google Scholar 

  • Plummer M, Best N, Cowles K, Vines K (2006) CODA: convergence diagnosis and output analysis for MCMC. R News 6:7–11

    Google Scholar 

  • Polgar AC, Primack BR (2011) Leaf-out phenology of temperate woody plants: from trees to ecosystems. New Phytol 191:926–941

    Article  PubMed  Google Scholar 

  • Polgar AC, Gallinat A, Primack BR (2013) Drivers of leaf-out phenology and their implications for species invasions: insights from Thoreau’s Concord. New Phytol 202:106–115

    Article  PubMed  Google Scholar 

  • Pope KS, Dose V, Da Silva D, Brown PH, Leslie CA, Dejong TM (2013) Detecting nonlinear response of spring phenology to climate change by Bayesian analysis. Glob Change Biol 19:1518–1525

    Article  Google Scholar 

  • R Core Team (2011) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org/

  • Richardson EA, Seeley SD, Walker DR (1974) A model for estimating completion of rest for ‘Redhaven’ and ‘Elberta’ peach trees. HortScience 9:331–332

    Google Scholar 

  • Richardson AD, Bailey AS, Denny EG, Martin CW, O’Keefe J (2006) Phenology of a northern hardwood forest canopy. Glob Change Biol 12:1174–1188

    Article  Google Scholar 

  • Richardson AD, Braswell BH, Hollinger DY, Jenkins JP, Ollinger SV (2009) Near-surface remote sensing of spatial and temporal variation in canopy phenology. Ecol Appl 19:1417–1428

    Article  PubMed  Google Scholar 

  • Rinne PLH, Welling A, Vahala J, Ripel L, Ruonala R, Kangasjarvi J, van der Schoot C (2011) Chilling of dormant buds hyperinduces Flowering Locus T and recruits GA-inducible 1,3-b-Glucanases to reopen signal conduits and release dormancy in Populus. Plant Cell 23:130–146

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Sarvas R (1974) Investigation on the annul cycle of development of forest trees. II. Autumn dormancy and winter dormancy. Commun Inst For Fenn 76:1–110

    Google Scholar 

  • Shaltout AD, Unrath CR (1983) Rest completion prediction model for ‘Starkrimson Delicious’ apples. J Am Soc Hortic Sci 108:957–961

    Google Scholar 

  • Sherman R, Mullen R, Haomin L, Zhendong F, Yi W (2008) Spatial patterns of plant diversity and communities in alpine ecosystems of the Hengduan Mountains, northwest Yunnan, China. J Plant Ecol 1:117–136

    Article  Google Scholar 

  • Spiegelhalter DJ, Best NG, Carlin BP, van der Linde A (2002) Bayesian measures of model complexity and fit (with discussion). J R Stat Soc B 64:583–640

    Article  Google Scholar 

  • Sunley RJ, Atkinson CJ, Jones HG (2006) Chill unit models and recent changes in the occurrence of winter chill and Spring frost in the United Kingdom. J Hortic Sci Biotech 81:949–958

    Google Scholar 

  • Terres MA, Gelfand AE, Allen JM, Silander JA (2013) Analyzing first flowering event data using survival models with space and time-varying covariates. Environmetrics 24:317–331

    Article  Google Scholar 

  • Van de Pol M, Cockburn A (2011) Identifying the critical climatic time window that affects trait expression. Am Nat 177:698–707

    Article  PubMed  Google Scholar 

  • Vitasse Y, Francios C, Delpierre N, Dufrene E, Kremer A, Chuine I, Delzon S (2011) Assessing the effects of climate change on the phenology of European temperate trees. Agr Forest Meteorol 151:969–980

    Article  Google Scholar 

  • Vittoz P, Cherix D, Gonseth Y, Lubini V, Maggini R, Zbinden N, Zumbach S (2013) Climate change impacts on biodiversity in Switzerland: a review. J Nat Conserv 21:154–162

    Article  Google Scholar 

  • Walther G, Post E, Convey P, Menzel A, Parmesan C, Beebee TJC, Fromentin J, Hoegh-Guldberg O, Bairlein F (2002) Ecological responses to recent climate change. Nature 416:389–395

    Article  CAS  PubMed  Google Scholar 

  • Wesolowski T, Rowinski P (2006) Timing of budburst and tree-leaf development in a multispecies temperate forest. For Ecol Manag 237:387–393

    Article  Google Scholar 

  • White MA, Thornton PE, Running SW (1997) A continental phenology model for monitoring vegetation responses to interannual climatic variability. Glob Biogeochem Cycles 11:217–234

    Article  CAS  Google Scholar 

  • Wilson AM, Latimer AM, Silander JA, Gelfand AE, de Klerk H (2010) A hierarchical bayesian model of wildfire in a mediterranean biodiversity hotspot: implications of weather variability and global circulation. Ecol Model 221:106–112

    Article  Google Scholar 

  • Wilson AM, Silander JA, Gelfand AE, Glenn JH (2011) Scaling up: linking field data and remote sensing with a hierarchical model. Int J Geogr Inf Sci 25:509–521

    Article  Google Scholar 

  • Yang X, Mustard JF, Tang J, Xu H (2012) Regional-scale phenology modeling based on meteorological records and remote sensing observations. J Geophys Res 117:G03029. doi:10.1029/2012JG001977

    Google Scholar 

  • Yu H, Luedeling E, Xu J (2010) Winter and spring warming result in delayed spring phenology on the Tibetan Plateau. Proc Natl Acad Sci 107:22151–22156

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Zhang X, Friedl MA, Schaaf CB, Strahler AH, Hodges JCF, Gao F, Reed BC, Huete A (2003) Monitoring vegetation phenology using MODIS. Remote Sens Environ 84:471–475

    Article  Google Scholar 

  • Zhang X, Friedl MA, Schaaf CB (2009) Sensitivity of vegetation phenology detection to the temporal resolution of satellite data. Int J Remote Sens 30:2061–2074

    Article  Google Scholar 

  • Zhou L, Kaufmann RK, Tian Y, Myneni RB, Tucker CJ (2003) Relation between interannual variations in satellite measures of northern forest greenness and climate between 1982 and 1999. J Geophys Res 108(D1):4004. doi:10.1029/2002JD002510

    Article  Google Scholar 

Download references

Acknowledgments

This study was supported in part by an US National Science Foundation grant (DEB 0842465) to J.A.S. and by NASA headquarters under the NASA Earth and Space Science Fellowship Program Grant NNX09AN82H to AMW. We thank R. B. Primack for helpful comments and X. Wang and A. E. Gelfand for statistical advice.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yingying Xie.

Electronic supplementary material

Below is the link to the electronic supplementary material.

10980_2014_99_MOESM1_ESM.docx

Supplementary Material 1: Model R code for green-up dates of deciduous forest community. Supplementary material 1 (DOCX 29 kb)

Supplementary Material 2: Tables and figures. Supplementary material 2 (DOCX 920 kb)

10980_2014_99_MOESM3_ESM.docx

Supplementary Material 3: Spatio-temporal uncertainty in the MODIS phenology product and implications for the analyses and modeling thereof. Supplementary material 3 (DOCX 895 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xie, Y., Ahmed, K.F., Allen, J.M. et al. Green-up of deciduous forest communities of northeastern North America in response to climate variation and climate change. Landscape Ecol 30, 109–123 (2015). https://doi.org/10.1007/s10980-014-0099-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10980-014-0099-7

Keywords

Navigation