Geographical variation in age–height relationships for dominant trees in Japanese cedar (Cryptomeria japonica D. Don) forests in Japan

In this study, we analyzed Japanese National Forest Inventory data to investigate the geographical variation in the relationship between tree height and age for dominant trees, and the effects of climatic conditions on these relationships. Our analysis focused on Cryptomeria japonica forests in 13 regions of Japan. The age–height relationships were classified into two regional groups that were distinguished by their climatic conditions. Several categories of climatic variables (warmth, solar radiation, precipitation, and snow depth) were significantly correlated with the parameters of a model for the age–height relationships. Our results also suggest the existence of a latitudinal cline for the maximum tree height of C. japonica in Japan. In regions with cold temperatures, deep snow, low solar radiation, and low summer precipitation, C. japonica shows a late-maturity pattern for height increase, with slow initial growth and a large maximum size. In regions with the opposite climatic conditions, it shows an early-maturity pattern with fast initial growth and small maximum size.

[1]  D. Bates,et al.  Mixed-Effects Models in S and S-PLUS , 2001 .

[2]  Jerome K. Vanclay,et al.  Forest site productivity: a review of the evolution of dendrometric concepts for even-aged stands , 2008 .

[3]  George W. Koch,et al.  The limits to tree height , 2004, Nature.

[4]  Y. Awaya,et al.  Age-related changes in stand volume growth of cryptomeria japonica plantations in akita district, Northeastern Japan , 2008 .

[5]  Mika Sulkava,et al.  Photosynthesis, temperature and radial growth of Scots pine in northern Finland: identifying the influential time intervals , 2011, Trees.

[6]  H. Oka,et al.  Climate risks and age-related damage probabilities - effects on the economically optimal rotation length for forest stand management in Japan , 2000 .

[7]  Y. Hirata,et al.  Allometric models of DBH and crown area derived from QuickBird panchromatic data in Cryptomeria japonica and Chamaecyparis obtusa stands , 2009 .

[8]  H. Salminen,et al.  Summer temperature affects the ratio of radial and height growth of Scots pine in northern Finland , 2009, Annals of Forest Science.

[9]  T. Seki,et al.  Long term growth records of old-aged sugi (Cryptomeria japonica) plantations in Akita Prefecture, North-eastern Japan. , 2000 .

[10]  J. H. Ward.  Hierarchical Grouping to Optimize an Objective Function , 1963 .

[11]  B. Enquist.  Forest annual carbon cost: comment. , 2011, Ecology.

[12]  Ronald E. McRoberts,et al.  Diagnostic tools for nearest neighbors techniques when used with satellite imagery , 2009 .

[13]  I. Maejima.  Natural seasons and weather singularities in japan , 1967 .

[14]  M. Ohsawa,et al.  Differentiation of vegetation zones and species strategies in the subalpine region of Mt. Fuji , 1984, Vegetatio.

[15]  Oscar García,et al.  A Stochastic Differential Equation Model for the Height Growth of Forest Stands , 1983 .

[16]  T. Eid,et al.  Site index prediction from site and climate variables for Norway spruce and Scots pine in Norway , 2012 .

[17]  日本学術会議国際生物学事業計画特別委員会,et al.  Primary productivity of Japanese forests : productivity of terrestrial communities , 1977 .

[18]  M. Abercrombie,et al.  A dictionary of biology , 1951 .

[19]  Edwin Benzel Steen,et al.  Dictionary of biology , 1971 .

[20]  B. Guan,et al.  Quantifying height growth and monthly growing degree days relationship of plantation Taiwan spruce. , 2009 .

[21]  E. Charnov,et al.  Evolution of life history parameters in animals with indeterminate growth, particularly fish , 2005, Evolutionary Ecology.

[22]  E. Mason,et al.  Towards a nationwide growth and yield model for radiata pine plantations in New Zealand , 2006 .

[23]  T. Kira.  A climatological interpretation of Japanese vegetation zones , 1977 .

[24]  Nobuya Mizoue,et al.  Evaluation of data quality in Japanese National Forest Inventory , 2009, Environmental monitoring and assessment.

[25]  K. Kikuzawa.  Geographical distribution of leaf life span and species diversity of trees simulated by a leaf-longevity model , 2004, Vegetatio.

[26]  Y. Awaya,et al.  Effects of thinning and site productivity on culmination of stand growth: results from long-term monitoring experiments in Japanese cedar (Cryptomeria japonica D. Don) forests in northeastern Japan , 2008, Journal of Forest Research.

[27]  S. Uemura.  Snowcover as a factor controlling the distribution and speciation of forest plants , 1989, Vegetatio.

[28]  Toshiro Iehara,et al.  Carbon stock estimates for sugi and hinoki forests in Japan , 2003 .

[29]  Tomokazu Takahashi,et al.  Genome Scan to Detect Genetic Structure and Adaptive Genes of Natural Populations of Cryptomeria japonica , 2007, Genetics.

[30]  S. Brody.  Relativity of physiologic time and physiologic weight. , 1937 .

[31]  R. Funada,et al.  Cambial sensitivity to rising temperatures by natural condition and artificial heating from late winter to early spring in the evergreen conifer Cryptomeria japonica , 2010, Trees.

[32]  Michelle Girvan,et al.  Predicting Maximum Tree Heights and Other Traits from Allometric Scaling and Resource Limitations , 2011, PloS one.

[33]  T. Nishizono.  Effects of thinning level and site productivity on age-related changes in stand volume growth can be explained by a single rescaled growth curve , 2010 .

[34]  Manfred Näslund,et al.  Skogsförsöksanstaltens gallringsförsök i tallskog , 1936 .

[35]  G. Campbell,et al.  An Introduction to Environmental Biophysics , 1977 .

[36]  James H. Brown,et al.  Effects of size and temperature on developmental time , 2002, Nature.

[37]  M. G. Ryan,et al.  Hydraulic Limits to Tree Height and Tree Growth , 1997 .

[38]  K. Zushi.  Regional estimation of Japanese cedar (Cryptomeria japonica D. Don) productivity by use of digital terrain analysis , 2007, Journal of Forest Research.