Low Input Tree Breeding Strategies

Dag Lindgren1 and Run-Peng Wei2,3

1Department of Forest Genetics and Plant PhysiologySwedish University of Agricultural SciencesSE-901 83,Umeå, Sweden2 South China Agricultural University, Wushan, Guangzhou 510642, China3 Sino-Forest Corporation, Sun Hung Kai Centre, Wanchai, Hong Kong

October 9, 2006, Turkey

• 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!

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.

Low input situations

Poor;Unstable organisation;Uncertain continuity;No specialists;Minor program.

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.

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.

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.

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.

Phenotypic selection No separate test populations needed!

Testing doubtful low-input more complicated;

more demanding on temporal and organisational stability;

requires trust in future;

Not needed if relaying on phenotypic selection.

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:

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)

Gai

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0.5Diversity MaxMi

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

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Effective number (Ns)

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Restricted selection for Phenotypic and Combined index, conciders both individual and family) in a population created by 20 parents with family size 20, h2=0.5. Points correspond to restriction intensity. Simulation (POPSIM). Balanced selection means 2 selections per parent

Andersson 1999 and others

Combined index

Phenotypic

Balanced

Note: Phenotypic selection as good as restricted

combined index compared at same gene diversity!

Now let’s consider without restrictions:

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4 6 8 10 12 14

Effective number (Ns)

Gai

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Andersson 1999 and others

Combined index

Phenotypic

Balanced

Comparing Three Selection strategies

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4 6 8 10 12 14

Effective number (Ns)

Gai

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Combined index

Phenotypic

Balanced

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:

Restricted selection for Phenotypic and Combined index during multiple generations A population with a family structure, h2=0.5, family size 20

0 0.1 0.2 0.3 0.4 0.5 Loss of gene diversity

5 generations

1 generation

Phenotype

Combined index

Selection criteria:

Andersson 1999 and others

Gai

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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).

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0 0.1 0.2 0.3 0.4 0.5 0.6 0.7Loss of gene diversity

Gai

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5 generations

1 generation

One and five generations of restricted selection in a population with a family structure, h2=0.05, family size 500. Low heritability and large families favor combined index

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0 0.1 0.2 0.3 0.4 0.5 0.6 0.7Loss of gene diversity

Gai

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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, h2=0.05, family size = 500, for three selection strategies.

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

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

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

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Status number

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h2=0.01 h2=0.20 h2=0.50 h2=1.0

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Status number

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h2=0.01 h2=0.20 h=0.50 h2=1.0

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

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

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

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

Fertility variation matters for accumulation of coancestry over generations

It can be predictedFemale contributions can be controlled

The development over generations in a closed population of 154 teak trees based on their observed fertility variations (Bila et al. 1999)

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1 2 3 4 5 6 7 8 9 10Generations

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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)