Determination of climatic predictors influencing seed production in seed orchards of Korean red pine based on different regression models

Abstract

Pinus densiflora is a pine species native to the Korean peninsula, and seed orchards have supplied material needed for afforestation in South Korea. Climate variables affecting seed production have not been identified. The purpose of this study was to determine climate variables that influence annual seed production of two seed orchards using multiple linear regression (MLR), elastic net regression (ENR) and partial least square regression (PLSR) models. The PLSR model included 12 climatic variables from 2003 to 2020 and explained 74.3% of the total variation in seed production. It showed better predictive performance (R ² = 0.662) than the EN (0.516) and the MLR (0.366) models. Among the 12 climatic variables, July temperature two years prior to seed production and July precipitation after one year had the strongest influence on seed production. The time periods indicated by the two variables corresponded to pollen cone initiation and female gametophyte development. The results will be helpful for developing seed collection plans, selecting new orchard sites with favorable climatic conditions, and investigating the relationships between seed production and climatic factors in related pine species.

References

Abdi H. Partial least squares regression and projection on latent structure regression (PLS Regression). Wiley Interdiscip Rev Comput Stat, 2010, 2(1): 97-106

Bisi F, von Hardenberg J, Bertolino S, Wauters LA, Imperio S, Preatoni DG, Provenzale A, Mazzumoto MV, Martinoli A. Current and future conifer seed production in the Alps: testing weather factors as cues behind masting. Eur J Forest Res, 2016, 135(4): 743-754

Cox I, Gaudard M. Discovering partial least squares with jmP^®, 2013 Cary, NC SAS Institute Inc., USA

Crain BA, Cregg BM. Regulation and management of cone induction in temperate conifers. For Sci, 2017, 64(1): 82-101

Das B, Nair B, Reddy VK, Venkatesh P. Evaluation of multiple linear, neutral network and penalized regression models for prediction of rice yield based on weather parameters for west coast of India. Int J Biometeorol, 2018, 62(10): 1809-1822

Duan LX, Xie HX, Li ZW, Yuan H, Guo YD, Xiao X, Zhou Q. Use of partial least squares regression to identify factors controlling rice yield in Southern China. Agron J, 2020, 112(3): 1502-1516

Feng C, Wang H, Lu N, Tu XM. Log transformation: application and interpretation in biomedical research. Stat Med, 2002, 32(2): 230-239

Fernando DD. The pine reproductive process in temperate and tropical regions. New for, 2014, 45(3): 333-352

Fober H. Relation between climatic factors and Scots pine (Pinus silvestris L.) cone crops in Poland. Arboretum Kórnickie, 1976, 21: 367-331

Gernandt DS, López GG, García SO, Liston A. Phylogeny and Classification of Pinus. TAXON, 2005, 54(1): 29-42

Greene DF, Johnson EA. Modelling the temporal variation in the seed production of North American trees. Can J for Res, 2003, 34(1): 65-75

Hu YT, Wei XR, Hao MD, Fu W, Zhao J, Wang Z. Partial least squares regression for determining factors controlling winter wheat yield. Agron J, 2018, 110(1): 281-292

Journe V, Hacket-Pain A, Oberklammer I, Pesendorfer MB, Bogdziewicz M. Forecasting seed production in perennial plants: identifying challenges and charting a path forward. New Phytol, 2023, 239: 466-476

Korea Forest Service (2021) Statistical yearbook for forest forestry, No. 51. (in Korean). https://kfss.forest.go.kr/stat/ptl/fyb/frstyYrBookList.do?curMenu=9854

Krannitz PG, Duralia TE. Cone and seed production in Pinus ponderosa: a review. West N Am Nat, 2004, 64(2): 208-218

Krebs CJ, LaMontagne JM, Kenney AJ, Boutin S. Climatic determinants of white spruce cone crops in the boreal forest of southwestern Yukon. Botany, 2012, 90(2): 113-119

Kuhn M, Johnson K. Applied predictive modeling, 2013 New York, NY Springer 505p

Lee CS, Chun YM, Lee H, Pi JH, Lim CH (2018) Establishment, regeneration, and succession of Korean red pine (Pinus densiflora S. et Z.) forest in Korea. In: Conifers. Edited by A.C. Gonçalves. London: IntechOpen, pp 47–76

Lester DT. Variation in cone production of red pine in relation to weather. Can J Bot, 1967, 45(9): 1683-1691

O’Brien RM. A caution regarding rules of thumb for variance inflation factors. Qual Quant, 2007, 41(5): 673-690

Owens JN, Blake MD (1985) Forest tree seed production: a review of the literature and recommendation for future research. Petawawa Nat For Inst Inf Rep, PI-X-53,pp 22–23

Owens JN (2006) The reproductive biology of lodgepole pine. For Genet Counc, British Colombia Extension Note 07. 62p. https://forestgeneticsbc.ca/wp-content/uploads/bsk-pdf-manager/2020/07/ExtNote7-Final-web.pdf

Ozolinčius R, Stakênas V, Serafinavičiũtė B, Buožytė R. Effects of artificial soil drought on Scots pine fruiting, seed vitality, and pollen germination. Ekologija, 2009, 55(3–4): 189-195

Pukkala T, Hokkanen T, Nikkanen T. Prediction models for the annual seed crop of Norway spruce and Scots pine in Finland. Silva Fenn, 2010, 44(4): 629-642

Rudolf PO (1990) Pinus resinosa Ait. Red pine. In: Silvics of North America: Vol. 1. Conifers. Agriculture Handbook 654. Edited by R.M. Burns and B.H. Honkala. USDA Forest Service. pp. 442–455

SAS Institute Inc. (2020a) JMP^® 16 Fitting Linear Models. Cary, NC. https://support.sas.com/content/dam/SAS/support/en/books/jmp-for-mixed-models/72967_excerpt.pdf

SAS Institute Inc. (2020b) JMP^® 16 Multivariate Methods. Cary, NC

Sharif B, Makowski D, Plauborg F, Olesen J. Comparison of regression techniques to predict response of oilseed rape yield to variation in climatic conditions in Denmark. Europ J Agron, 2017, 82: 11-20

Thabeet A, Vennetier M, Gadbin-Henry C, Denelle N, Roux M, Caraglio Y, Vila B. Response of Pinus sylvestris L. to recent climatic events in the French mediterranean region. Trees, 2009, 23(4): 843-853

Vinzi VE, Russolillo G. Partial least squares algorithms and methods. Wiley Interdiscip Rev Comput Stat, 2013, 5(1): 1-19

Wion AP, Pearse IS, Rodman KC, Veblen TT, Redmond MD. Masting is shaped by tree-level attributes and stand structure, more than climate, in a rocky mountain conifer species. For Ecol Manage, 2023, 531: 120794

Wold S, Sjöström M, Eriksson L. PLS-regression: a basic tool of chemometrics. Chemom Intell Lab Syst, 2001, 58(2): 109-130

Zeng WZ, Xu C, Gang Z, Wu JW, Huang JS. Estimation of sunflower seed yield using partial least squares regression and artificial neural network models. Pedosphere, 2018, 28(5): 764-774

Funding

Seoul National University