Spatial stochastic modeling of resin yield from pine stands

The spatial structure of resin-yield in maritime pine stands in central Spain was studied on two different scales and with data from two tapping periods (1998 and 1999). For the fine scale, Moran's I and the K function were used to study within-stand spatial variation. We found that in one plot, trees separated by distances of less than 5 m had similar production. The K function results showed that the distribution of trees did not depart significantly from complete spatial randomness. For a much larger scale, data was available from 37 and 59 ten-tree plots for years 1998 and 1999, respectively. Partial (monthly) yields were also measured. The experimental variograms for the mean plot production showed that a large percentage of the total variance was spatially structured. Furthermore, the experimental variograms for the partial yields revealed changes in the spatial structure of this phenomenon within the same year. Spatial stochastic modeling was shown to be an effective and economic modeling strategy....

[1]  J. Chilès,et al.  Geostatistics: Modeling Spatial Uncertainty , 1999 .

[2]  Jon D. Johnson,et al.  Borehole oleoresin production from slash pine , 1997 .

[3]  R. Alía,et al.  Genetic improvement of resin yield from maritime pine in Spain , 2001 .

[4]  Phaedon C. Kyriakidis,et al.  Geostatistical Space–Time Models: A Review , 1999 .

[5]  R. Olea.  Geostatistics for Natural Resources Evaluation By Pierre Goovaerts, Oxford University Press, Applied Geostatistics Series, 1997, 483 p., hardcover, $65 (U.S.), ISBN 0-19-511538-4 , 1999 .

[6]  B. Ripley.  Modelling Spatial Patterns , 1977 .

[7]  N. Oden,et al.  Spatial Pattern of Sex Expression in Silver Maple (Acer saccharinum L.): Morisita's Index and Spatial Autocorrelation , 1983, The American Naturalist.

[8]  Peter Holmgren,et al.  Applying objectively estimated and spatially continuous forest parameters in tactical planning to obtain dynamic treatment units , 1997 .

[9]  J. S. Samra,et al.  Spatial stochastic modeling of growth and forest resource evaluation , 1989 .

[10]  L. Salvador,et al.  Regiones de procedencia Pinus pinaster Aiton , 1996 .

[11]  G. Matheron.  Principles of geostatistics , 1963 .

[12]  Melinda Moeur,et al.  Spatial models of competition and gap dynamics in old-growth Tsuga heterophylla/Thuja plicata forests , 1997 .

[13]  A. Richards.  THE HISTORY OF THE CHROMOSOMAL VESICLES IN FUNDULUS AND THE THEORY OF GENETIC CONTINUITY OF CHROMOSOMES , 1917 .

[14]  R. Czaplewski,et al.  Spatial autocorrelation in growth of undisturbed natural pine stands across Georgia , 1994 .

[15]  R. Alía,et al.  Modelling resin production distributions for Pinus Pinaster Ait using two probability functions , 2000 .

[16]  Jerzy Szwagrzyk,et al.  Spatial patterns of trees in natural forests of East‐Central Europe , 1993 .

[17]  Michael Edward Hohn,et al.  An Introduction to Applied Geostatistics: by Edward H. Isaaks and R. Mohan Srivastava, 1989, Oxford University Press, New York, 561 p., ISBN 0-19-505012-6, ISBN 0-19-505013-4 (paperback), $55.00 cloth, $35.00 paper (US) , 1991 .

[18]  R. Alía,et al.  Genetic variation and migration pathways of maritime pine (Pinus pinaster Ait) in the Iberian peninsula , 2000, Theoretical and Applied Genetics.

[19]  H. Burkhart,et al.  Spatial autocorrelation of diameter and height increment predictions from two stand simulators for loblolly pine , 1994 .

[20]  Franco Biondi,et al.  Geostatistically modeling stem size and increment in an old-growth forest , 1994 .

[21]  Nikos Nanos,et al.  Spatial prediction of diameter distribution models , 2002 .

[22]  E. B. Moser,et al.  Fire-induced changes in Quercus laevis spatial pattern in Florida sandhills , 1989 .