Variation in Volume Production Through Clonal Deployment: Results From a Simulation Model to Minimize Risk for Both a Currently Known and Unknown Future Pest

Author:

Yanchuk A. D.1,Bishir J.2,Russell J. H.3,Polsson K. R.1

Affiliation:

1. Research Branch, British Columbia Ministry of Forests, 1st Floor, 722 Johnson Street, Victoria B.C. V8W 1N1 Canada

2. North Carolina State University, Department of Mathematics, Raleigh, North Carolina , USA

3. Research Branch, British Columbia Ministry of Forests, Cowichan Lake Research Station, 7060 Forestry Road, Mesachie Lake , Canada

Abstract

Abstract A simulation model was developed to examine optimum patterns of deploying selected clones in the hypothetical situations of both a currently known pest and an unknown future pest. We modelled the interactions between Sitka spruce (Picea sitchensis (Bong.) Carr.), an economically important forest tree in British Columbia and the northwestern U.S., and the spruce terminal weevil (Pissodes strobi (Peck)), a major pest in western spruces. The model is combined with the Province of British Columbia’s Tree and Stand Simulator (TASS) model to drive individual tree growth and stand establishment and development. Two clonal-sampling strategies are examined: a randomly drawn set of genotypes or clones, to depict the potential consequences of a new (e.g., exotic) or a previously unimportant natural pest attacking a ‘random’ set of genotypes, and a ‘fixed’ set of clones, emulating a ‘commercial’ or known set of clones for growth and resistance mechanisms. Simulations use a range of numbers of genotypes or clones (2, 6, 18 and 30), and three deployment patterns (a random mixture of ramets, single- clone blocks, and a mosaic of smaller clonal blocks), in one and five hectare (Ha) stands. Total merchantable timber volume on a per Ha basis at harvest age 80 is used to compare the various combinations and schemes. With both random and fixed chosen sets of clones, the random planting pattern (i.e., random mixture of ramets from the clonal set) produced the most volume. Eighteen randomly chosen clones generally produced more volume, than 2, 6 and 30 clones, but differences among 6, 18 and 30 clones were small in most cases, irrespective of planting pattern. For fixed clones, the use of more resistant clones with higher growth potential produced more volume; however, pure clonal blocks of the best clone were not better than a mixture of that clone and an inferior one. Reducing the effects of insect activity and attack on trees, by lowering the average annual temperature in the model, or turning off all insect ‘activity’, increased merchantable volume but did not change the optimum number of clones (~18) or deployment pattern (random mixture). Forestry agencies can weigh these findings against economic advantages of block plantings of similar genotypes, in the choice of an appropriate number of clones and a deployment strategy.

Publisher

Walter de Gruyter GmbH

Subject

Genetics,Forestry

Reference47 articles.

1. ALFARO, R. I. (1996): Feeding and oviposition preferences of white pine weevil (Coleoptera: Curculionidae) on resistant and susceptible Sitka spruce clones in laboratory bioassays. Environ. Entomol. 25(5): 1012-1019.10.1093/ee/25.5.1012

2. ALFARO, R. I., R. G. BROWN, K. J. MITCHELL, K. R. POLSSON and R. N. MACDONALD (1996): SWAT: A decision support system for spruce weevil management. In: Decision Support Systems for Forest Pest Management - Proceedings of a workshop of the Entomological Societies of Canada and British Columbia, Oct. 17, 1995, Victoria, B.C. (Eds. Shore, T.L. and Maclean, D.A.). FRDA Report No. 260. pp 31-41.

3. ALFARO, R. I., F. HE, E. TOMLIN and G. KISS (1997): White spruce resistance to white pine weevil related to bark resin canal density. Can. J. Bot. 75: 568-573.10.1139/b97-063

4. BISHIR, J. and J. H. ROBERDS (1995): Analysis of time to failure in clonally propagated plant populations. Math. Biosci. 125: 109-125.10.1016/0025-5564(94)00030-4

5. BISHIR, J. and J. H. ROBERDS (1997): Limit theorems and a general framework for risk analysis in clonal forestry. Math. Biosci. 142: 1-11.

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