Lindgren, Dag.(Swedish University of Agricultural Sciences, Sweden), Danusevicius, Darius. (Lithuanian Forest Research Institute. Lithuania), Högberg, Karl-Anders. (The Forestry Research Institute of Sweden, Sweden), Weng, Yuhui. (New Brunswick Dept. of Nat. Resources, Canada), Hallingbäck, H.R. (Swedish University of Agricultural Sciences, Sweden) A clone deployment and selective harvest model and algorithms for seed orchards and clonal mixtures. IUFRO conference Seoul August 28,

 In advanced generations, avoiding relatives becomes problematic.  Relatives may be beneficial.  Avoidance of relatives becomes costly in gain and requires structuring breeding populations.  Seedling seed orchards are common and always contain relatives, so relatedness occurs in seed orchards today.  Natural seeds in natural forests contain matings of relatives, relatives are normal in nature.

CONSPROS  Inbreeding  Lower diversity at same clone number  Unrelated clones require structuring the breeding population (e.g. many sublines), which reduces gain.  Higher gain  More options  More clones can be chosen, thus less selfing and more diversity.

 “Group coancestry” = “Average relatedness” (large full sib family =0.25)  Note self-coancestry included  Effective number = Status number = half the inverse of group coancestry  Group coancestry = loss of gene diversity since breeding started

If comparing (maximizing) net gain at the same group coancestry (status number) the last term becomes constant and the weight not needed!

 The weight (W) considers both inbreeding depression and loss of diversity.  Inbreeding seems generally to be a less important constraint for relatives than gene diversity.  Self-coancestry (relatedness of clones with themselves) and Cross-coancestry may be assigned different weights.  Selfing yields few planted seedlings for conifers and wherefore selfing causes little production loss, while mating among relatives may give higher production loss.  In results presented here these weighting options has not been used, but studying weights indicated that inbreeding was a less important constraint on relatives than gene diversity.

 Maximizing net gain (optimal selection). By definition best, but other strategies may be good enough and simpler.  Linear deployment (proportions proportional to breeding value) allowing relatives or restricting against relatives.  Truncation selection allowing relatives or restricting against relatives.

The algorithm is able to optimize  Number of families  Number of family members  Number of copies (ramets) of each family member Candidate entries can be combined to return highest net gain (=BV - coancestry)

 Individuals are deployed in the proportions maximizing net gain. Fam1 Fam3 Fam4 Fam2 Fam5 Inputs Results feed a computer (”solver” in EXCEL)

 With many candidates, calculations may become difficult, uncertain and non transparent.  Short-listing the candidates before the final maximization helps!

“Linear deployment” restricting against relatives was good enough if there are more than double as many unrelated candidates as needed!“Linear deployment” restricting against relatives was good enough if there are more than double as many unrelated candidates as needed! Other deployment alternatives were clearly inferior to optimal!Other deployment alternatives were clearly inferior to optimal! Optimal proportions Linear unrelated Truncation unrelated Linear related Truncation related Ordered by rank Status number = 12, Swedish Scots pine values

 Deployment was performed for two new Norway spruce seed orchards in southern Sweden (replacer of current orchards Runesten and Gälltofta, Finnvid Prescher responsible).  The candidates were tested clones from controlled crosses.  Problem with candidates: Different degree of relatedness, in particular the best parents are heavily represented in progeny with high BV!  Breeding value for different characteristics were weighted to get a value index BV for the selections.

 A shortlist was made with 30 candidates with high BV, but not sharing the same parent more than 3 times. The short- listed population status number was  Deployment to seed orchard maximizing gain at status number 13 was made by EXCEL function “Solver”.  Both growth and wood density could be considerably improved by the optimization procedure.  Cross-coancestry at optimum was 0.01, thus some relatives.  90% (27) of the short-listed clones were deployed in varying proportions, those selected against would have added much to relatedness.  Seems the optimum clonal number become higher if some relatives are selected.

 Harvesting only the best clones become a common praxis in Sweden as progeny-test results become available and orchards become mature and produce surplus seeds. There is also a need for two genetic fractions, the best for plant production and the second for direct seeding.  The number of ramets per clone often varies considerable, which increases the demand for suitable tools when genetic thinning is applied.  Suggestions were made for selective harvest procedures in the mature Scots pine seed orchards of a Swedish forest company.

 An assumption of pollen contamination 50% was made. Each ramet was assumed to give the same pollen and seed contribution.  The number of ramets harvested for each clone can be input. Seed production, genetic gain and gene diversity appear as outputs.  The algorithms made it possible to identify solutions where the clone representation is heterogeneous and non optimal (due to establishment constraints or to calamities).  The number of ramets per clone can be optimized maximizing genetic value for required seed need at a given status number in seed orchard crop by using the EXCEL function (solver).  Optimizing selective harvest in a seed orchard with very different clone contributions have the potential to increase both the gain and gene diversity,  Based on such runs of the relevant worksheet recommendations to the company was given.

 To deploy clones to plantations can be made in almost the same way as for a seed orchard. The penalty for high diversity may be chosen lower as there is no inbreeding and less concerns with diversity.  This application was planning clone mixtures for New Brunswick, Canada with tested clones from known families. Figures refers to ten year volume.  The data was a black spruce clonal test. The trial includes 17 full-sib families, on average 10 clones per family. These families were created by crossings among 12 trees.  In New Brunswick where are strict restrictions on clonal mixtures, thus status number must be high and not relaxed. Only part of an acceptable clonal mixture could be derived from the material analyzed.

Efficiencies of selection methods  Shortlist: the top 50 (BV) clones without restriction on relatedness.  Optimization was considerable superior. At Ns=5, Optimization gave 9% larger gain than the Truncation and 6% larger than Linear.  For Ns=5, Truncation choose12, while Optimization 15 and Linear 18 clones.

Effects by restrictions on family contribution For a low status number (Ns) - around half of the maximal - the shortlisted number per family did not matter for gain, but for a status number near the maximum, it was beneficial to have some families well-represented. Cross-Coancestry is relatedness among different clones and was not small (first cousin =0.0625), thus it can be beneficial to allow some related clones. RestrictionClonesNs=5 Gain CrossCo Ns=8.6 Gain CrossCo 1Clone/Fam1737, Clone/Fam Clone/Fam No Shortlists can limit the contributions from individual families

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