Universal transfer-response function: modelling the impacts of climate on lodgepole pine maximum height in British Columbia, Canada

Kate F. Peterson , Tongli Wang , Gregory A. O’Neill , Derek F. Sattler , Bianca N. I. Eskelson

Journal of Forestry Research ›› 2026, Vol. 37 ›› Issue (1) : 125

PDF
Journal of Forestry Research ›› 2026, Vol. 37 ›› Issue (1) :125 DOI: 10.1007/s11676-026-02070-7
Original Paper
research-article
Universal transfer-response function: modelling the impacts of climate on lodgepole pine maximum height in British Columbia, Canada
Author information +
History +
PDF

Abstract

This study addresses a critical gap in forest management by introducing a Universal Transfer-Response Function (UTRF) to predict Lodgepole Pine (Pinus contorta var. latifola Dougl.) maximum height under varying climate conditions in British Columbia. Using long-term provenance trial data, we developed a two-stage modeling approach: first, fitting a logistic height-age curve to estimate individual tree growth potential, and second, applying a cubic polynomial UTRF incorporating site, provenance, and climate transfer variables. The model explained approximately 62% of variation in maximum height and was used to simulate future growth under multiple climate scenarios. Results indicate that southern lodgepole pine populations will experience severe declines in height growth under warming conditions, while northern populations may benefit temporarily, reflecting a northward shift in the species’ climatic niche. Spatial predictions further highlight regions becoming unsuitable for lodgepole pine by 2100, emphasizing the urgency of climate-based seed transfer and assisted migration strategies. By integrating UTRF into operational growth and yield models, forest managers can simulate adaptive strategies to mitigate timber yield losses. This finding represents a significant advancement in climate-sensitive forestry modeling and provides a foundation for sustainable management under future climate uncertainty.

Keywords

Forestry / Climate change / Genecology / Growth and yield

Cite this article

Download citation ▾
Kate F. Peterson, Tongli Wang, Gregory A. O’Neill, Derek F. Sattler, Bianca N. I. Eskelson. Universal transfer-response function: modelling the impacts of climate on lodgepole pine maximum height in British Columbia, Canada. Journal of Forestry Research, 2026, 37(1): 125 DOI:10.1007/s11676-026-02070-7

登录浏览全文

4963

注册一个新账户 忘记密码

References

[1]

Aitken SN, Whitlock MC. Assisted gene flow to facilitate local adaptation to climate change. Annu Rev Ecol Evol Syst, 2013, 44: 367-388

[2]

Aitken SN, Yeaman S, Holliday JA, Wang TL, Curtis-McLane S. Adaptation, migration or extirpation: climate change outcomes for tree populations. Evol Appl, 2008, 1(1): 95-111

[3]

Alberto FJ, Aitken SN, Alía R, González-Martínez SC, Hänninen H, Kremer A, Lefèvre F, Lenormand T, Yeaman S, Whetten R, Savolainen O. Potential for evolutionary responses to climate change–evidence from tree populations. Glob Change Biol, 2013, 19(6): 1645-1661

[4]

Bates D, Mächler M, Bolker B, Walker S. Fitting linear mixed-effects models Usinglme4. J Stat Soft, 2015, 67(1): 1-48

[5]

BC Ministry of Forests (2022) Economics and trade branch. Economic State of British Columbia’s Forest Sector.

[6]

Berlin M, Persson T, Jansson G, Haapanen M, Ruotsalainen S, Bärring L, Andersson Gull B. Scots pine transfer effect models for growth and survival in Sweden and Finland. Silva Fenn, 2016

[7]

Chhin S, Hogg EH, Lieffers VJ, Huang S. Potential effects of climate change on the growth of lodgepole pine across diameter size classes and ecological regions. For Ecol Manag, 2008, 256(10): 1692-1703

[8]

Crookston NL, Rehfeldt GE, Dixon GE, Weiskittel AR. Addressing climate change in the forest vegetation simulator to assess impacts on landscape forest dynamics. For Ecol Manag, 2010, 260(7): 1198-1211

[9]

Davis MB, Shaw RG, Etterson JR. Evolutionary responses to changing climate. Ecology, 2005, 86(7): 1704-1714

[10]

Dumroese RK, Williams MI, Stanturf JA, St Clair JB. Considerations for restoring temperate forests of tomorrow: forest restoration, assisted migration, and bioengineering. New for, 2015, 46(5): 947-964

[11]

GADM (2025) GADM: Global Administrative Areas. University of California, Berkely. [digital geospatial data]. Available online: http://www.gadm.org

[12]

Hijmans R (2023a) raster: Geographic Data Analysis and Modeling. R package version 3.6–26. https://CRAN.R-project.org/package=raster

[13]

Hijmans R (2023b) terra: Spatial Data Analysis. R package version 1.7–65. https://CRAN.R-project.org/package=terra

[14]

Illingworth K (1978) Study of lodgepole pine genotype-environment interactions in British Columbia, in: Proceedings of the IUFRO Joint Meeting of Working Parties. BC Ministry of Forests, Information Services Branch, Vancouver, Canada, pp. 151–158.

[15]

Johnson S (2008) The NLopt nonlinear-optimization package. https://github.com/stevengj/nlopt

[16]

Joo S, Maguire DA, Jayawickrama KJS, Ye TZ, St Clair JB. Estimation of yield gains at rotation-age from genetic tree improvement in coast Douglas-fir. For Ecol Manag, 2020, 466: 117930

[17]

Lenoir J, Bertrand R, Comte L, Bourgeaud L, Hattab T, Murienne J, Grenouillet G. Species better track climate warming in the oceans than on land. Nat Ecol Evol, 2020, 4(8): 1044-1059

[18]

Lesven JA, Druguet Dayras M, Cazabonne J, Gillet F, Arsenault A, Rius D, Bergeron Y. Future impacts of climate change on black spruce growth and mortality: review and challenges. Environ Rev, 2024, 32(2): 214-230

[19]

Liao CY, Podrázský VV, Liu GB. Diameter and height growth analysis for individual White Pine trees in the area of Kostelec nad Černými lesy. J for Sci, 2003, 49(12): 544-551

[20]

Littell RC, Milliken GA, Stroup WW, Wolfinger RD, Schabenberger OE. SAS for mixed models. SAS Institute, 2006, 61(2): 184-185

[21]

Long JS, Ervin LH. Using heteroscedasticity consistent standard errors in the linear regression model. Am Stat, 2000, 54(3): 217-224

[22]

Luo DW, O’Neill GA, Thomas BR, Ukrainetz N, Wang TL. Evaluating the effectiveness of climate-based seed transfer and assisted migration: a case study of lodgepole pine (Pinus contorta var. latifolia Dougl.) and interior spruce (Picea engelmanii x glauca (Moench) Voss and their hybrids) in western Canada. Ann for Sci, 2025, 82(1): 7

[23]

MacKenzie WH, Mahony CR. An ecological approach to climate change-informed tree species selection for reforestation. For Ecol Manag, 2021, 481: 118705

[24]

Mahony CR, Wang TL, Hamann A, Cannon AJ. A global climate model ensemble for downscaled monthly climate normals over North America. Int J Climatol, 2022, 42(11): 5871-5891

[25]

Matyas C. Modeling climate change effects with provenance test data. Tree Physiol, 1994, 14(7–9): 797-804

[26]

McLane SC, Daniels LD, Aitken SN. Climate impacts on lodgepole pine (Pinus contorta) radial growth in a provenance experiment. For Ecol Manag, 2011, 262(2): 115-123

[27]

McLane SC, LeMay VM, Aitken SN. Modeling lodgepole pine radial growth relative to climate and genetics using universal growth-trend response functions. Ecol Appl, 2011, 21(3): 776-788

[28]

Metsaranta JM, Fortin M, White JC, Sattler D, Kurz WA, Penner M, Edwards J, Hays-Byl W, Comeau R, Roy V. Climate sensitive growth and yield models in Canadian forestry: challenges and opportunities. For Chron, 2024, 100(1): 88-106

[29]

Mitchell KJ. Dynamics and simulated yield of Douglas fir. For Sci Monogr, 1975, 17: 39

[30]

Nigh G. Mitigating the effects of climate change on lodgepole pine site height in British Columbia, Canada, with a transfer function. Forestry (Lond), 2014, 87(3): 377-387

[31]

O’Neill G, Wang T, Ukrainetz N, Charleson L, McAuley L, Yanchuk A, Zedel S (2017) A proposed climate-based seed transfer system for British Columbia. Tech Rep 099. Prov. BC, Victoria, BC. http://www.for.gov.bc.ca/hfd/pubs/Docs/Tr/Tr099.htm

[32]

O’Neill GA, Nigh G. Linking population genetics and tree height growth models to predict impacts of climate change on forest production. Glob Change Biol, 2011, 17(10): 3208-3217

[33]

Park A, Talbot C. Information underload: ecological complexity, incomplete knowledge, and data deficits create challenges for the assisted migration of forest trees. Bioscience, 2018, 68(4): 251-263

[34]

Peterson KF, Wang TL. Iterative nearest neighbour age-height curve adjustment: addressing the impact of spatial heterogeneity on longitudinal forestry provenance trial data. For Ecol Manag, 2024, 557: 121749

[35]

Pinheiro JC, Bates DMMixed-effects models in sand S-PLUS, 2000New YorkSpringer

[36]

Pinheiro JC, Bates DM, R Core Team (2025) nlme: Linear and Nonlinear Mixed Effects Models. https://doi.org/10.32614/CRAN.package.nlme R package version 3.1–168. https://cran.r-project.org/package=nlme

[37]

Pörtner HO, Roberts DC, Tignor M, Poloczanska K, Mintenback K, Alegría A, Craig M, Langsdorf S, Löscke S, Möller V, Okem A, Rama B (eds.) (2022) Climate change 2022: impacts, adaptation and vulnerability. https://doi.org/10.1017/9781009325844

[38]

Powell MJDThe BOBYQA algorithm for bound constrained optimization without derivatives, 2009Cambridge EnglandDepartment of Applied Mathematics and Theoretical Physics

[39]

R Core TeamR: a language and an environment for statistical computing, 2022Vienna, AustriaR Foundation for Statistical Computing

[40]

R Core TeamR: a language and an environment for statistical computing, 2025Vienna, AustriaR Foundation for Statistical Computing

[41]

Raymond CA, Lindgren D. Genetic flexibility—a model for determining the range of suitable environments for a seed source. Silvae Genet, 1990, 39: 112-120

[42]

Rehfeldt GE. Ecological adaptations in Douglas-fir (Pseudotsuga menziesii var. glauca) populations. Heredity, 1979, 43(3): 383-397

[43]

Rehfeldt GE, Ying CC, Spittlehouse DL, Hamilton DAJr. Genetic responses to climate in Pinus contorta: niche breadth, climate change, and reforestation. Ecol Monogr, 1999, 69(3): 375-407

[44]

Rehfeldt GE, Leites LP, Joyce DG, Weiskittel AR. Role of population genetics in guiding ecological responses to climate. Glob Change Biol, 2018, 24(2): 858-868

[45]

Reich PB, Oleksyn J. Climate warming will reduce growth and survival of Scots pine except in the far north. Ecol Lett, 2008, 11(6): 588-597

[46]

Sattler DF, Peterson KF, Wang T, O’Neill GA (2024) A transfer function for survival and its use in developing climate-sensitive projections of volume for lodgepole pine. Technical Report 151. Province of British Columbia, Canada. Victoria, B.C.

[47]

Savolainen O, Pyhäjärvi T, Knürr T. Gene flow and local adaptation in trees. Annu Rev Ecol Evol Syst, 2007, 38: 595-619

[48]

Spittlehouse D (2008) Climate change, impacts and adaptation scenarios: climate change and forest and range management in British Columbia. Forest Science.

[49]

St Clair JB, Mandel NL, Vance-Borland KW. Genecology of Douglas fir in western Oregon and Washington. Ann Bot, 2005, 96(7): 1199-1214

[50]

Wang T. ClimateNAr: the R version of ClimateNA and some related functions. R Package Version, 2024, 2: 1

[51]

Wang T, Hamann A, Spittlehouse DL, Aitken SN. Development of scale-free climate data for Western Canada for use in resource management. Int J Climatol, 2006, 26(3): 383-397

[52]

Wang T, Hamann A, Yanchuk A, O’Neill GA, Aitken SN. Use of response functions in selecting lodgepole pine populations for future climates. Glob Change Biol, 2006, 12(12): 2404-2416

[53]

Wang TL, O’Neill GA, Aitken SN. Integrating environmental and genetic effects to predict responses of tree populations to climate. Ecol Appl, 2010, 20(1): 153-163

[54]

Wang TL, Hamann A, Spittlehouse DL, Murdock TQ. ClimateWNA—high-resolution spatial climate data for western North America. J Appl Meteorol Climatol, 2012, 51(1): 16-29

[55]

Wang TL, Hamann A, Spittlehouse D, Carroll C. Locally downscaled and spatially customizable climate data for historical and future periods for North America. PLoS ONE, 2016, 11(6): e0156720

[56]

Wei RP, Lindgren K, Lindgren D. Parental environment effects on cold acclimation and height growth in lodgepole pine seedlings. Silvae Genet, 2001, 50: 252-257

[57]

Weiskittel AR, Hann DW, Kershaw JA, Vanclay JK. Forest growth and yield modeling. Wiley-Blackwell Online Books, Wiley, 2011

[58]

Zhao YR, Wang TL. Predicting the global fundamental climate niche of lodgepole pine for climate change adaptation. Front for Glob Change, 2023, 6: 1084797

RIGHTS & PERMISSIONS

Northeast Forestry University

PDF

14

Accesses

0

Citation

Detail

Sections
Recommended

/