1. Low Input Tree Breeding Strategies Dag Lindgren 1 and Run-Peng Wei 2,3 1Department of Forest Genetics and Plant Physiology Swedish University of Agricultural Sciences SE-901 83,Umeå, Sweden 2 South China Agricultural University, Wushan, Guangzhou 510642, China 3 Sino-Forest Corporation, Sun Hung Kai Centre, Wanchai, Hong Kong October 9, 2006, Turkey

2. Thinking low input helps making high input more efficient Input level can vary between tiers (elite vs main)… Other factors than budget important… No strict limit between high input and low!

3. High-input techniques  Breeding values estimated from offspring or relatives;  Test plantations;  Clone archives;  Controlled crosses;  Known pedigrees;  Orchards intensively managed exclusively for seed production;  Grafts for seed production.

4. Low input situations  Poor;  Unstable organisation;  Uncertain continuity;  No specialists;  Minor program.

5. Low-input techniques  Selection on phenotypes instead of testing of genotypes;  No records of tree ID or pedigree;  Wind pollination;  Seeds derived from stands used for other purposes;  “Cheap” plantations created for future seed production and long term improvement.

6. Low-input techniques  Thin stands rather intense to get better pollen;  Harvest seeds from best trees for production and long term improvement. But…try to make predictions of inbreeding, coancestry and diversity.

7. Plantations combining objectives Plantations which look and are managed similar to "normal" plantations:  Limited need of specialised competence and organisational stability;  Multiple use (options for seeds, improvement, wood, conservation...);  Can function as seed collection area; “cheap” trees may be cut for seed collection (climbing often too expensive);  Not too long rotation time (to keep cones harvestable and to speed up improvement;  Close to local organisation, enterprise and people = better and cheaper management.

8. Phenotypic selection  No tree identities required;  No computer required;  No strict objective measures required;  Transparent (not black box);  Can be executed immediately in field;  A type of selection forwards;  Also called mass-selection;  Similar to Nature, sustainable and environmental.

9. Phenotypic selection  No separate test populations needed!

10. Testing doubtful low-input  more complicated;  more demanding on temporal and organisational stability;  requires trust in future;  Not needed if relaying on phenotypic selection.

11. Phenotypic selection  Phenotypic selection may be as powerful or more powerful than BLUP (selection for best estimate of breeding values), as I will show.  For following slides: Combined index selection is a breeding value estimate based on performance of an individual as its sibs.  There are procedures for finding the most efficient selection at a certain diversity in a population of a large number of large families. I’ll show:

12. Maximising gain at a given diversity by selection in infinite normal distributions. h2=0.25 and P=0.1 Modified From Lindgren and Wei 1993 0 1 Combined index (maximizes gain) Note that phenotypic selection is on the optimising curve, thus no way to get more gain without giving up diversity! Phenotypic selection (easy) Between family (exhausts diversity) Within family (conserves diversity) Gain 0.5 Diversity Max Min

13.  This was ”theoretical mathematical”. To make it more realistic a simulator (POPSIM, Mullin) was used. Input close to operative Swedish conifer breeding.

14. 12 18 24 30 468101214 Effective number (Ns) Gain Restricted selection for Phenotypic and Combined index, conciders both individual and family) in a population created by 20 parents with family size 20, h 2 =0.5. Points correspond to restriction intensity. Simulation (POPSIM). Balanced selection means 2 selections per parent Andersson 1999 and others Combined index Phenotypic Balanced

15. Note:  Phenotypic selection as good as restricted combined index compared at same gene diversity!  Now let’s consider without restrictions:

16. 12 18 24 30 468101214 Effective number (Ns) Gain Andersson 1999 and others Combined index Phenotypic Balanced

17. Comparing Three Selection strategies 12 18 24 30 468101214 Effective number (Ns) Gain Combined index Phenotypic Balanced

18.  In the following diversity is measured as loss of gene diversity since tree improvement started. This equivalent to status number as used in earlier figures, but scale and direction on the diversity axis changes;  Phenotypic selection works with multigenerational programs also:

19. Restricted selection for Phenotypic and Combined index during multiple generations A population with a family structure, h 2 =0.5, family size 20 00.10.20.30.40.5 Loss of gene diversity 5 generations 1 generation Phenotype Combined index Selection criteria: Andersson 1999 and others Gain

20.  Phenotypic selection is compatible also in a multi-generation program;  For unrestricted selection genetic variation get exhausted. In the long run phenotypic selection give more gain;  However, if breeding population large and heritability small, this exhaustion takes long time (next figure).

21. 0 10 20 30 40 00.10.20.30.40.50.60.7 Loss of gene diversity Gain PhenotypeCombined index 5 generations 1 generation One and five generations of restricted selection in a population with a family structure, h 2 =0.05, family size 500. Low heritability and large families favor combined index

22. 0 10 20 30 40 00.10.20.30.40.50.60.7 Loss of gene diversity Gain After first generation Combined index Phenotypic Balanced After five generations Development of Gain and Gene Diversity over five generations of selection in a population with a family structure, h 2 =0.05, family size = 500, for three selection strategies.

23.  Combined index selection does not seem to give a superior gain and erodes diversity. However, comparisons can be made at variable breeding population size!

24. Comparison under variable breeding population size  The size of the breeding population is under the breeders control and could be different (optimized) for different strategies;  Comparisons were done under a fixed plant number (1280) as fixed resource;  Simulations by POPSIM similar to earlier. Li and Lindgren 2006

25. Combined index selection seems not inferior, and thus gets rehabilitated. Combined index is much better only when heritability is low Li and Lindgren 2006 Gain for phenotypic selection compared to combined index selection after first generation under fixed test plant number

26. Gain for phenotypic selection compared to combined index selection after five generations Phenotypic selection is better at high heritability; The alternatives become similar efficient when the gene diversity is high; Low heritability favours combined index selection. At moderate or high heritabilities phenotypic selection seems equal or slightly superior after some breeding generations

27.  These comparisons assume the size of the breeding population is a free resource, and that is certainly not the case.

28. Multigenerational comparison of testing strategies in Swedish conifer breeding Danusevicius and Lindgren 2002 Clonal testing is much superior to progeny-testing Phenotypic testing better than progeny-testing at low budget

29. Clone trial of Eucalyptus camaldulensis converted to seed orchard based on clonal performance in the trial How may clonal testing look like in practice in low budget? Verghese et al 2004

30. Fertility variation matters for accumulation of coancestry over generations  It can be predicted  Female contributions can be controlled

31. The development over generations in a closed population of 154 teak trees based on their observed fertility variations (Bila et al. 1999) 0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07 12345678910 Generations Coancestry (inbreeding) Female and male varies Female constant Equal-tree fertility Fertility variation matters for accumulation of relatedness over generations Control over female is powerful and easy (count seeds)