United States

Department of

Agriculture

Forest Service

Pacific Northwest

Research Station

General Technical

Report

PNW-GTR-411

February 1998

Breeding Design Considerations

for Coastal Douglas-Fir

Randy Johnson
Author RANDY JOHNSON is a research geneticist, Forestry Sciences Laboratory,

3200 SW Jefferson Way, Corvallis, OR 97331-4401.
Abstract Johnson, Randy. 1998. Breeding design considerations for coastal Douglas-fir. Gen.

Tech. Rep. PNW-GTR-411. Portland, OR: U.S. Department of Agriculture, Forest

Service, Pacific Northwest Research Station. 34 p.

The basic principles of designing forest tree breeding programs are reviewed for

Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) in the Pacific Northwest.

Breeding populations are discussed given current and future breeding zone sizes

and seed orchard designs. Seed orchard composition is discussed for potential

genetic gain and maintaining genetic diversity in the forest. Mating and field testing

designs are described and compared. Recommendations of the Breeding Zone

Evaluation and Restructuring Cooperatives Working Group of the Northwest Tree

Improvement Cooperative are presented.

Keywords: Douglas-fir, multiple populations, sublines, breeding population, gene

resource populations, mating designs, selection, seed orchard.

Contents 1 Introduction

1 Population Structure

1 Subpopulations

4 Considerations When Structuring a Breeding Population

11 Conclusions

11 Crossing Designs

11 Types of Designs

14 Number of Crosses to Use in the Breeding Populations

16 Field Testing Design Considerations

18 Conclusions

18 Recommendations

18 Population Structure

20 Mating and Crossing

20 Field Design

20 Acknowledgments

21 Literature Cited

25 Appendix 1

25 Modeling the Difference in Efficiency Between Testing Full-Sib Families and

Using Midparent Values From GCA Tests

28 Appendix 2

28 Modeling the Value of Different Mating Designs in Preserving LowFrequency Alleles

31 Glossary
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Document continues on next page.
Introduction Tree improvement activities started in the Pacific Northwest in the 1950s, with largescale operational breeding programs for coastal Douglas-fir (Pseudotsuga menziesii

(Mirb.) Franco) beginning in Oregon and Washington in the 1960s (Adams and others

1990). Many of these programs are entering the second generation of breeding and

are now starting to develop or revise tree improvement strategies. This paper reviews

some of the basic principles of forest tree breeding so that foresters can better understand the challenges in developing the next generation of breeding in the Pacific

Northwest. Also included in this report are the general recommendations of Breeding

Zone Evaluation and Restructuring Cooperatives Working Group of the Northwest

Tree Improvement Cooperative (NWTIC). (A glossary of terms is provided for words

in bold print at the end of this report; also terminology used in this paper is from

Burdon and Namkoong [1983]).

The two main objectives of a breeding program are to (1) improve economic traits

and (2) ensure that the resulting breeding populations are well adapted and have

sufficient genetic variation for gain to continue in subsequent generations. These

two objectives are contradictory because to increase gain, selection intensity must

be increased (fewer selections made of only the best), which in turn reduces genetic

variation.

Breeders overcome this dilemma by structuring their selections so that both objectives

can be met. Burdon (1988) describes three populations to consider when designing a

breeding program. The breeding population is the group of selections that will be

used to produce offspring for the next generation of selection. Usually selections are

control-pollinated to produce full-sib families, but in many first-generation programs,

open-pollinated families were used. The production population produces propagules

for the forest, either as seed or clonal donor stock. The production population is a

subset of the best breeding population selections. The gene resource population is

all the extant individuals of a species that might potentially be selected for inclusion

in the breeding population. It is usually large enough to maintain alleles that are at

low frequency so that they are not lost for future use. The gene resource population

includes the breeding population but often encompasses a much larger population.

Besides providing improved plants, a breeding program also must supply information

for making decisions. Therefore, besides (1) providing improved selections for the following generation and (2) ensuring sufficient genetic variation is available for the future, a breeding program also must (3) provide breeding value estimates for the next

generation of selections, and (4) provide information on the parental population for

roguing orchards. No one mating design will maximize these four objectives simultaneously (Burdon and Shelbourne 1971). Therefore, one must prioritize the objectives before designing a breeding program. The breeding program must address

every facet of breeding, including the mating design; the number, size, locations,

and field design of progeny tests; timing of operations; and methods of selection.

Campbell (1989) developed a decision tree to help design breeding programs. This

paper will examine specific aspects of the decision tree presented by Campbell

(1989); in particular, this paper discusses the structure of the breeding population,

crossing design considerations, and field test design considerations.

Breeding populations can be subsetted into subpopulations to (1) conserve genetic

variation, (2) increase gain by putting more effort into the better selections, and (3)

restrict inbreeding in the production population (seed orchard).

Population Structure

Subpopulations

1
Multiple populations—Multiple populations refer to subpopulations designed to

maintain genetic diversity in the breeding population. The concept was introduced to

forestry by Namkoong (1976) and was developed because of the uncertainty of the

future value of the traits selected. The idea is that each multiple population is selected for different traits (or different weightings), thereby providing more options

(genetic variation) in the future.

Genetic diversity can be of two types: intrapopulation diversity or interpopulation diversity. The current NWTIC breeding programs are using intrapopulation (i.e.,

intrastand and intrabreeding zone variation) to improve growth genetically. For traits

controlled by many genes (polygenic), such as growth, gains should be possible for

many generations. For example, in corn breeding, increases (and decreases) in oil

content have continued after 76 generations of breeding (Dudley 1977). The key to

continued gains is in maintaining genetic variation. Mutation will maintain this at some

level (Lande 1995, Lynch 1995), the level being dependent on population size.

Interpopulation diversity, that associated with variation among breeding zones

(populations), is associated with adaptation to different environments and to

processes of genetic drift (i.e., random loss of genes). Currently, this type of

diversity is being maintained by restricting breeding programs to relatively small

breeding zones.

Both types of variation (diversity) may supply genetic variation for unknown traits that

may be needed in the future (e.g., resistance to a new pest or the need for a different wood property). A recent example is resistance to Swiss needle cast. Resistance

is associated with distance to the coast (though not exclusively). If the disease becomes a problem farther inland and resistance is needed, it may be predominately

found in more coastal breeding zones (interpopulation), yet some selections (although at a lower frequency) may be present farther inland (intrapopulation for those

zones).

Namkoong and others (1989) point out that multiple populations, each selecting for

different traits, will conserve genes better than one single breeding population. In fact,

this is the only way to ensure the conservation of interpopulation diversity, because

interpopulation diversity may require the conservation of gene combinations in addition

to the conservation of individual genes. Multiple populations, however, also can be

used to maintain intrapopulation diversity when the multiple populations are being

selected for different sets of traits. This intrapopulation diversity can provide the variation needed to improve new traits that may be needed. A drawback to multiple populations is that overall gain for a single trait is normally greater for a single large population than the average of the multiple populations. If sufficient multiple populations are

present, it would be possible that one of the populations was better than the large single population. This superior single population, however, would have to have enough

unrelated clones to stock a seed orchard fully (or other production option) to achieve

all the gain in that population. More important, if the multiple populations are selected

for different criteria (different weightings of traits), there is a greater possibility that one

population has the appropriate weightings for future economic weightings, as these

can change with time (Namkoong 1976).

2
Stratified breeding populations—Nucleus breeding refers to the stratification of

the breeding population into two groups, an elite and a main population, based on

estimated genetic value. The key idea of this strategy is to concentrate more of the

breeding effort on the elite population, where maximum gain is expected, with less

emphasis placed on the main population, which is the primary source of genetic

variation for the long term. This system was initially used in sheep breeding (James

1977) and has since been incorporated into forest trees by Cotterill (1989). The main

population serves in both breeding population and gene resource population roles.

An example is the North Carolina State University-Industry Tree Breeding Cooperative where they have a main breeding program and small elite populations for

accelerated short-term gain.

Lindgren and Matheson (1986) proposed a strategy using a similar concept regarding

seed orchards, suggesting that clones be used in proportion to their breeding value.

In the context of breeding populations, the better clones (parents) would be used to

make more crosses than the poorer clones.

Sublines—Sublining refers to the partitioning of the breeding population into unrelated groups (sublines). Each group is bred for a similar purpose (unlike multiple

populations). The idea was first introduced to forestry by van Buijtenen and Lowe

(1979) when they put forward the idea of “breeding groups.” Unrelatedness across

sublines is maintained generation-to-generation in the breeding population by only

crossing within sublines. Production seed is produced by crossing among sublines,

thus inbreeding depression cannot occur because related individuals do not mate in

the production of commercial seed. The alternative to avoid inbreeding depression in

an unsublined population is to carefully monitor the crossing in the breeding population and place restrictions on related crossing (distance between clones in a seed

orchard) in production populations. Eventually, after many generations of breeding,

inbreeding will occur in an unsublined population; the key is to allow it to happen

gradually and practice selection along the way.

Choice of how one uses the production population impacts the feasibility of sublining

a breeding population. In open-pollinated seed orchards, one must have as many

sublines as the number of unrelated clones desired for the orchard. This can result

in many small sublines that may restrict making specific cross combinations in

breeding. For example, if sublines had less than 10 individuals, crosses among high

wood density selections might not be possible, because more than one high-density

selection might not be present in a subline if volume is the primary selection trait. If

the production option is control-pollinated seed or clonal forestry, one would need

only two sublines to allow for unrelated crosses. The Cooperative Forest Genetic

Research Program breeding program (University of Florida) and the North Carolina

State University-Industry Cooperative Tree Improvement Program use sublines for

open-pollinated orchard strategies, whereas the New Zealand Radiata Pine Breeding

Cooperative and Southern Tree Breeding Association (Australia) have fewer sublines

because they use control-pollinated orchard strategies.

Sublining has both advantages and disadvantages. Disadvantages are (1) it may be

difficult to divide the breeding population into unrelated groups; this is especially true

for programs that are already in their second or third generation of breeding, (2) any

new selections brought into a breeding population must maintain the integrity of the

sublines, (3) inbreeding will occur at a faster rate in sublined populations because

relatedness is proportional to the number of selections; therefore (4) loss of fecundity

3
can be a more severe problem within a subline than within a larger unsublined

population, and (5) the crosses made for breeding may not be suitable for making

selections for clonal or full-sib family deployment because of increased inbreeding

depression within sublines.

The advantages to sublining are (1) the operational simplicity in production of commercial seed and (2) complete control of inbreeding depression. In sublining, the

option always exists to unsubline the population; the converse is seldom true. This

flexibility is one reason many programs have opted to subline their breeding populations as they begin their second or third generation of breeding.

Considerations When

Structuring a Breeding

Population

Questions that must be asked before structuring breeding populations include the

following:

• What should be considered a breeding zone?

• What is considered as the gene resource population?

• How large of a breeding population is needed?

• What will the commercial seed-plant propagation system be?

• How many clones must be in a seed orchard to maintain diversity in the

commercial forest, yet achieve genetic gain?

• What traits or trait combinations will be selected on?

Breeding zone size—The NWTIC breeding programs were initially established

with relatively small breeding zones to ensure the adaptiveness of the resulting improved material. The complex environmental patterns of the region did not provide

large areas of uniform climate to allow for large breeding zones as are found in most

other breeding programs in the United States. Subsequent research has shown that

Douglas-fir shows considerable local adaptation based on variation in seedling and

tree characteristics (Campbell 1986, Campbell 1991, Campbell and Sugano 1993,

Silen and Mandel 1983, Sorensen 1983). Although research confirmed that local

adaptation is present, other results suggest that the original breeding zones may have

been too small. Randall (1996) has reviewed the data and proposed new seed transfer

zones that are longer in the north-south direction than the old ones. The small breeding zones ensured adaptiveness but also limited the return on investment because the

results of any one breeding program had limited application.

The return on investment opportunities have been diminished further because the

USDA Forest Service and USDI BLM reduced harvest on their lands. Land owners are

now combining breeding zones to decrease breeding costs and increase the area over

which gains can be capitalized. Indirect evidence has suggested that this could be

possible in some areas. Weyerhaeuser1 studies (Stonecypher and others 1996) have

shown that, on their lower elevation sites, there is a set of improved families that

performed well in all four of their Washington breeding zones, at least in the short

1 The use of trade or firm names in this publication is for

reader information and does not imply endorsement by the

U.S. Department of Agriculture of any product or service.

4
term. Data from NWTIC trials also have shown significant correlations among zones;

full-sib progeny performance at the J.E. Schroeder Seed Orchard near St. Paul,

Oregon, was reasonably correlated with progeny performance in the field (midparent

values of open-pollinated (trials) in four cooperatives (Snow Peak r = 0.68, Molalla

r = 0.59, Umpqua Coast r = 0.43, and Vernonia r = 0.41) (Silen 1995). In another

study (Johnson, in press), distance of plantation separation was not correlated with

the genetic correlation among pairs of sites. Conversely, Campbell (1992) showed

significant genotype-by-environment interaction in several breeding zones throughout

Oregon.

Care must be taken in interpreting data from these studies because none used trees

of rotation age. Maladaptation can take many years before it shows itself in the field.

The Stonecypher and others (1996) data only examined mild sites, and the other data

are limited to relatively small breeding zones (Johnson, in press).

Gene resource population—To alter a trait through traditional breeding, genetic variation must be present. For example, one can only breed for resistance if the genes

(alleles) for resistance are present. The land area that could offer potentially beneficial

genes to a breeding zone include more than the breeding zone being considered.

Neighboring zones could offer genes of interest with perhaps minimal impact on

adaptability. In addition, gene resource populations are more than just the progeny

tests and parental selections currently in the breeding programs. Trees in the forest

are also potential contributors of favorable genes.

To a limited extent, the whole species range could be considered as a source for

potential genes, but this would not be the norm. At this point, transferring single

genes is impossible; therefore for the Pacific Northwest to use a gene from the Southwest, we also must incorporate the maladaptation associated with trees from there. In

this paper, a gene resource population will be considered as the land area from which

the breeding population was drawn. This includes existing stands and ex situ resources available through breeding programs (e.g., progeny tests, seed orchards,

and breeding orchards) throughout an ecoregion. An ecoregion is defined as a group

of first-generation breeding zones that have similar climatic and ecological conditions.

Breeding population size—Before determining the size of a breeding population,

“breeding population” must first be clearly defined. In the past, a breeding population

was considered as the selections made in a single breeding zone. Some breeding cooperatives have already combined zones, and breeding populations are now composed of multiple first-generation breeding zones. For this discussion, we will consider

a breeding population to be the pool of individuals from which we would consider

selecting a clone to use in advanced generation breeding, not just for use as a seed

orchard candidate.

Breeding population size is important because the rate at which genes (alleles) are

lost to random drift is a function of population size. Genes that are at low frequency in the population are most prone to being lost if population sizes are too small.

Loss of genes can affect potential gain in current traits of interest and in traits that

may be of interest sometime in the future.

5
Gain from selection is affected by initial gene frequencies; breeding gains come predominately from genes with moderate frequencies (Namkoong 1979). In later generations, it will be important that low-frequency genes have increased in frequency to take

the place of what were once genes with moderate frequency, genes that have since

increased in frequency such that they no longer provide the bulk of the gain.

To have the flexibility to breed for new traits, it also is important to maintain neutral

alleles because they may become valuable in the future. If a program has to find

genes for a new trait in the gene resource population, any gain achieved in the breeding population for other traits will be reduced because the gene resource population

will have a reduced level of gain (sometimes no gain at all).

Eventually, most low-frequency alleles will be lost from the population through random

drift. Kang (1979) computed the necessary effective population size needed to maintain neutral alleles (those not under selection pressure) for 30 generations for varying

allele frequencies (table 1). Effective population size represents the number of individuals that would give rise to the rate of inbreeding for the population in question. If individuals do not produce equal numbers of offspring or individuals are closely related,

the effective population size is less than the census number.

It is virtually impossible to conserve all genes; even nature loses genes to random

drift. A population with an effective population size (Ne) of 50, however, would conserve most genes with a frequency of 0.05 or greater for an extremely long time. The

30-generation time period in table 1 would encompass more than 300 years for most

forest tree breeding programs.

Namkoong and Roberds (1982) point out that neutral alleles may be linked to alleles

being selected against, in which case, the rate of allele loss would increase. They suggest doubling the population number needed to maintain unlinked neutral alleles to account for this. An effective population size of 100 to 200 should be adequate to maintain most neutral alleles.

Forest tree breeding programs tend to have 300 to 400 parents (White 1992). These

numbers are larger than what is available in breeding programs for some agronomic

crops, but much less than that suggested for conserving populations in the wild. An

Ne of 500 proposed by Franklin (1980) and Soulé (1980) has traditionally been accepted as the number needed to maintain variability in quantitative traits by those in

the conservation biology realm. Recently, Lynch (1995) has proposed increasing this

number to 1,000, and Lande (1995) has proposed 5,000. These estimates are based

on the effects of mutation, selection, and random drift.

White (1992) provides an excellent review of the literature and a discussion on population sizes and concludes that effective population sizes of 20 to 50 can sustain several

generations of breeding (see also Namkoong and others 1989). Several hundred to

more than a thousand parents, however, may be required to sustain long-term (20 to

50 generations) gain. One-hundred-fifty to two hundred selections (census number, not

Ne) should maintain sufficient diversity to maintain rate of gain in the breeding program for at least 10 generations if the selections are made with some forethought; i.e.,

by limiting the number of selections from any one family, the resulting Ne of the breeding population can be maintained at a reasonable level (50 to 100).

6
Production population—The current production populations for most Douglas-fir programs in the Pacific Northwest are open-pollinated seed orchards containing more

than 30 unrelated selections (clones) or families. Some orchards have in excess of

100 clones. Having such a large number of clones reduces the potential genetic gain

but ensures diversity. Thirty sublines would be required to ensure 30 unrelated clones

in future generations. If one can rely on supplemental mass pollination (SMP) or controled-pollinated orchards in the future, then much fewer (two) sublines are necessary to control inbreeding in the production population.

Production population size—The size of the production population impacts a breeding program in many ways. The most obvious impact is on realized gain. It is important for an organization to realize the impact that increasing the number of orchard

clones has on potential genetic gain and resulting genetic diversity. Secondly, as previously mentioned, the number of sublines is affected by size of the production population. Elite population sizes also are influenced by the size of the production population. If the production population is considered a subset of an elite breeding population, then larger production populations can result in larger elite breeding populations.

One way to look at genetic diversity is to examine the genetic variation that comes

from a seed orchard. The theoretical genetic variation resulting from a given number

of clones in the orchard can be calculated. The formula for calculating the percentage

of the potential additive genetic variation for any number of clones is (Falconer

1960, p. 266):

Percent of total additive genetic variation = 1-F , (1)

where

F = the inbreeding coefficient.

If each clone in an orchard contributes equal numbers of seed and pollen, and all

matings are at random (panmixous), then (Falconer 1960, p. 69),

F = 1/(2N) , (2)

where

N = the number of orchard clones (if all clones are unrelated).

Thus the expected percentage of all possible additive genetic variation expected from

a clonal orchard is,

Percent of total additive genetic variation = 1-(1/(2N)) . (3)

Table 1—Effective population size (Ne) necessary to maintain neutral alleles

for 30 generations with initial frequency p

p 0.005 0.01 0.02 0.03 0.04 0.05 0.10 0.20 0.50

Ne 282 161 94 69 56 48 29 19 11

Source: Kang 1979.

7
Equation (3) assumes that selfing takes place and that there is no inbreeding depression. If one assumes no selfing, the reduction in variation is greater because certain

gene combinations are limited (i.e., a very large proportion of homozygotes for rare

alleles are the result of selfing). To approximate this relation, the F-value is replaced

with the probability of an individual being inbred for a monoecious species, resulting in,

Percent of total additive genetic variation = 1-(1/N) . (4)

The results from equation (4) are more realistic, in that a two-clone orchard (a full-sib

family) has half the expected additive genetic variation of that found in the population.

This is in line with quantitative genetics because the additive genetic variation within a

full-sib family is one-half the population total.

Comparable formulae for nonadditive genetic variation are complex because they are

dependent on gene frequencies. Because most of the genetic variation is additive

(Yanchuck 1996), this discussion will not address the nonadditive portion of genetic

variation.

The equations above assume all mating is at random and all clones provide equal

seed and pollen to the final seed crop; this does not occur in orchards. The effective

population size (Ne) needs to be used for N in the equations to adjust for the imbalance. This imbalance must be considered when discussing the “optimum” number

of orchard clones. By using data approximated from El-Kassaby and Askew (1991),

the effective inbreeding population size (Ne) of a Douglas-fir orchard was estimated

by using Robertson’s (1961) formula,

(5)

where

ui = the number of offspring contributed by clonei (familyi in this case).

The effective population size (Ne) of the full-sib family orchard examined by ElKassaby and Askew (1991) was about two-thirds of the actual population size. The

reduction calculated in the full-sib orchard may underestimate the reduction for clonal

orchards because clonal seed production has been shown to be more skewed than

open-pollinated family orchards (El-Kassaby and others 1989). The amount of skewness also is dependent on crop year. El-Kassaby and others (1989) found that in good

cone years, the genetic contribution of each parent was more similar to one another.

Kjær (1996) found that the variance effective population number (Ne relative to genetic

drift) decreased in poor flowering years in a Norway spruce (Picea excelsa Link) orchard, thereby resulting in less genetic variation than good flowering years. Variance

Ne varied from 37 percent of the census number (N) in the poor flowering year to over

100 percent of N in the year with abundant flowering. In Scots pine (Pinus sylvestris

L.), Muona and Harju (1989) found Ne to be 66 percent and 93 percent of N for two

seed orchards in Finland. Siegismund and others (1996) estimated variance Ne to be

121 percent of N (the inbreeding Ne was 65 percent of N) for a noble fir (Abies

procera Rehd.) orchard in Denmark.

Ne = ( u u i

2

i

2

∑ )/ , ∑

8
A conservative estimate of Ne in an orchard would be half the census number (N)

based on the literature. This differs greatly, however, from year to year. If we want

90 percent of the potential genetic variation in an orchard, therefore, we would want

2 x 10 = 20 clones. Figure 1 shows the relation between gain and genetic variation

when selecting from 1,000 individuals and setting Ne = 1⁄2N. After 20 to 25 clones,

the genetic variation in the resulting seed changes little, but gain still decreases

noticeably.

Rare (low-frequency) genes (alleles) are valuable in breeding populations but have

limited benefits in plantations. If the gene is in the breeding population, it may or may

not be in the production population. A low-frequency gene in the breeding population

also will be at a low frequency in a seed orchard (if at all) and therefore also will be at

a low frequency in the forest. An optimistic case study would be to assume that one

clone in a 33-clone orchard was homozygous for a rare desirable gene. Theoretically,

the gene would be transmitted to 6 percent of the orchard progeny (3 percent of orchard seed from the clone and 3 percent of the pollen). If we assume this is a dominant gene and the only source of resistance to a new disease, then resulting stands

will have a maximum stocking after a major disease outbreak of only 6 percent. From

a practical standpoint, 6 percent stocking is not any better than 0 percent; the stand

would have to be replanted in either case. The gene will be of much more value in

the next generation where it can be used in the breeding population to produce many

resistant selections for a new seed orchard.

It has been well documented that selfing is detrimental in most coniferous species

and should be avoided in the production of commercial seed. The theoretical selfing

rate under panmixia is 1/n, where n is the number of orchard clones. Table 2 shows

Figure 1—Genetic gain and percentage of total additive genetic variation as a function of number of

seed orchard parents under the assumption that Ne = 0.5 N.

50

60

70

80

90

100

Possible gain (%)

0

20

40

60

80

100

Possible genetic variation (%)

0 10 20 30 40 50 60 70

Number of selections (out of 1,000)

Genetic variation

Gain

9
that when the number of clones is below six, the percentage of inbred seed is 20 percent or more. Not until the number of clones is above 10 does the percentage drop

to below 10 percent. Because the effective population size is probably less than the

actual size, the clonal numbers in table 2 may need to be increased to approximate

a more realistic model. Many selfed seeds, however, are not filled (Sorensen 1982),

and orchard management techniques can reduce inbreeding rates. These factors must

be considered when examining potential inbreeding in an orchard (El-Kassaby and

Davidson 1991). Inbreeding will pose more of a problem in orchards where related

individuals are included in the orchard. In such cases, the inbred seed resulting from

sib-crossing would have less seed abortion and may not be culled in standard nursery

practices. Proper orchard design can mitigate this problem to some degree. For a

more thorough discussion, see Sorensen and Miles (1982).

Orchards with relatively short lifespans would need fewer clones than orchards with

long lifespans because they would contribute to a smaller fraction of the reforestation

base over time. If rotation age is 60 years and the orchard production life is 15 years,

then four separate orchards will contribute to the genetic variation in the operational

forest. If the orchard production life was 30 years, then only two orchards would contribute to the genetic variation. Orchards will be related from generation to generation,

but not entirely. Genetic variation across the landscape, therefore, will be larger when

orchards have shorter lifespans.

A final orchard (production population) of 25 clones after roguing should be adequate

given the above considerations. It would produce forest stands with over 90 percent

of the potential genetic variation of a 200-clone orchard and should produce less than

10 percent selfed seed. Twenty-five sublines would be required to ensure complete

unrelatedness in future seed orchards if open-pollinated orchards will be the standard.

If the future holds the possibility of control-pollinated orchards by using some aspect

of supplemental mass pollination, then only two sublines would be needed.

Table 2—Theoretical percentage of self-pollinations from a seed orchard

with varying numbers of unrelated clones and panmixia

Self- Self- SelfClones pollination Clones pollination Clones pollination

Percent Percent Percent

1 100.0 11 9.1 21 4.8

2 50.0 12 8.3 22 4.5

3 33.3 13 7.7 23 4.3

4 25.0 14 7.1 24 4.2

5 20.0 15 6.7 25 4.0

6 16.7 16 6.3 26 3.8

7 14.3 17 5.9 27 3.7

8 12.5 18 5.6 28 3.6

9 11.1 19 5.3 29 3.4

10 10.0 20 5.0 30 3.3

10
Possible multiple populations—With current information, multiple populations could

be generated in most programs for growth, wood density, and to some degree, stem

form. Other traits now being assessed by companies include forking, straightness,

and resistance to Swiss needle cast. Additional multiple populations will be possible

in the future.

If one were to view Douglas-fir breeding in western Oregon, Washington, and British

Columbia as one large breeding program, the current breeding zones would represent

multiple breeding populations. Each breeding zone is breeding for volume in a particular zone. This will maintain the interpopulation diversity associated with adaptability.

Conclusions The breeding population for a local cooperative should include a minimum of 100

selections but will probably be more than 150 because genetic diversity is of high

importance. The effective population would probably be close to 100 if total population size is near 150 selections because many advanced-generation selections are

apt to be related.

Breeding zones will be combined for biological and economic reasons to form

deployment zones (combinations of old breeding zones with the intention that one

production population will suffice for the area) and testing zones (area over which a

group of organizations test the same families). Additional testing will be needed to

confirm that increased maladaptation does not result. The new seed transfer zones

(Randall 1996) can provide some general guidelines to establishing deployment

zones. Combining old breeding zones will increase genetic diversity because breeding

populations will contain the variation from multiple first-generation breeding zones.

However, this does not necessarily translate to a better adapted population. This can

only be determined with long-term testing. In nature, gene flow occurs among seed

zones through the movement of pollen; there is no reason we should eliminate this

natural process. Any maladapted selections would be removed from the breeding

population through testing. The frequency of maladapted reforestation seedlings

should be extremely rare because only tested clones will be allowed in the production populations.

Crossing Designs The primary purpose of the crossing design is to produce a population (pedigreed

preferred) from which selections can be made, and from which breeding value data

can be estimated. It also can provide information on genetic parameters, but this information is only crucial when the parameters are unknown. Information on most genetic parameters is readily available for Douglas-fir (heritabilities, general combining

ability/specific combining ability (GCA/SCA) ratios, etc.), but the availability of

information on genetic correlations across breeding zones is extremely limited.

Crossing designs fall into four broad categories:

1. Random matings

2. Assortative and nucleus matings

3. Structured designs (diallels, factorials)

4. Complementary mating designs

Types of Designs

11
Random matings produce families for selection purposes but provide relatively poor

information on parental performance. Assortative and nucleus mating designs,

which use the better parents in more crosses, can improve gain minimally (increase

efficiency by 5 to 10 percent), but also can reduce effective population size drastically

(King and Johnson 1993). A preferable way to increase gain in the breeding population is to increase the number of families produced in the crossing design. Increased

use of better parents is more beneficial in the seed production population where effective population size need not be as large. Random designs usually do not have the

appropriate structure needed to estimate the GCA and SCA variance components.

Variance component estimation, however, is usually a secondary objective, and openpollinated orchards cannot use SCA to generate gain.

Diallel and factorial mating designs are structured designs that provide an adequate

selection population and can estimate GCA and SCA variance components. In forest

tree breeding, these designs are limited to partial and disconnected diallels and disconnected factorials because complete designs would be too large for practical programs. The major drawback to these designs is that they can take a considerable

amount of time to complete. Factorials also have the problem in that unbiased parental

breeding values cannot be produced. In a factorial, the tester parents differ among disconnected groups and between females and males, thus limiting unbiased comparisons to within small groupings. Comparable, unbiased parental breeding values are

difficult (and sometimes impossible) to produce with incomplete disconnected diallels.

Balanced partial diallels appear to be relatively efficient in ranking parents (Burdon and

van Buijetenen 1990).

Complementary mating designs are being used by an increasing number of

breeding programs because a single mating design cannot simultaneously optimize

all objectives (Burdon and Shelbourne 1971). The most common combination of designs is a polycross (male or female testers) combined with full-sib families planted

either in replicated trials or family blocks. The replicated polycross trials provide information on breeding values of parents and can be used to estimate breeding values of

the control-pollinated families by use of midparent values. Only programs that can

capitalize on SCA must test full-sib families in replicated trials.

The complementary design offers advantages and disadvantages. A major disadvantage lies in the fact that the polycross trees are not readily available for use as selections for the next generation. This is because (1) the tester parentage is commonly

unknown (unless genetic markers are used or full-sibs are bulked to make the half-sib

tester family), (2) only a limited number of unrelated selections would be available, and

(3) the tester parents are usually not part of the subline. This reduces overall selection

intensity because a large percentage of the population is not available for selection.

Another potential drawback is that there is no possibility to examine gains by using

SCA if the full-sib families are only planted in family blocks.

The advantages of a complementary design are as follows:

1. Accurate GCA estimates of parents

2. Parents are tested as outcrossed individuals

3. The potential for more precise within-family selection in family blocks

4. Simple data analysis

12
Use of a polycross test ensures that each parent is tested with similar, if not identical,

tester parents. This leads to comparable breeding values for all parents. Breeding

values would then be used to estimate the genetic value of the full-sib crosses that

will provide selections for the next generations. If the “polycross” is comprised of

bulked full-sib families (as is the case for female testers), each tester also can be

monitored and any tester exhibiting unusually high SCA effects can be replaced.

Female testers also would eliminate any bias from seed size or other maternal

effects.

Testing parents as outcrossed individuals is especially important in sublined breeding

populations. Because inbreeding depression differs by clone, it is possible that breeding values established by using inbred families will not accurately represent the true

breeding value of an outcrossed individual. This problem can be overcome with an

outcrossed GCA test. Tester parents could be chosen that are unrelated to individuals

in all sublines or use different testers for each subline. The former offers the advantage of using poorer genotypes as testers. This can allow for better GCA precision

for some traits if dominance masks GCA values (e.g., rust resistance in loblolly pine

[Pinus taeda L.], see Byram and others 1987). Using unique testers for each subline

would allow one to use superior testers. These crosses would produce trials of superior trees that could be used for clonal selections and provide for better public relations.

The effectiveness of GCA testing relative to directly testing the full-sib families are

shown in appendix 1. When dominance is not present, midparent values are 90 to 95

percent as effective as directly testing the full-sib families. When dominance variation

is half the additive variation, the two methods are identical. If the relative effectiveness is about equal, then the testing design that provides for the highest selection

intensity (i.e., produces the most full-sib families while maintaining reasonable withinfamily selection) would yield the highest gain.

Intuitively it seems that choosing a single mating design (such as disconnected

diallels) would produce more full-sib families than if a complementary design were

used (two mating designs with only one for making selections). If one assumes a

constant number of trees per design, this is always the case. The operational constraints, however, are far greater on the single mating design.

Regardless of the number of full-sib families, the number of families to be tested

in replicated field trials remains constant with the complementary design (i.e., the

number of parents). If one were to produce twice as many families as parents, the

replicated field trials for the complementary design would be half the size of a single

trial design. This also could lead to better test precision because replication size

would be reduced. Because pollen or female testers are usually readily available, time

required to complete the crossing for the GCA test should be less than that for the

complete full-sib test. Because full-sib blocks for the complementary design do not

have to be replicated and the field layout is simple, more seedlings can be planted

as full-sib blocks than as replicated trials.

Within-family selection in blocks is much simpler than when members of a family are

scattered among sites and replications. One can visually compare the members of a

family, which is a tremendous aid in selection.

13
Cotterill and Jackson (1989) examined seed orchard gains arising from several breeding strategies. They found that half-diallels produced the greatest gains but were followed closely by single-pair mating with open-pollinated progeny tests (a complementary design), then by single-pair mating. They emphasized the fact that if one were

to consider the time needed to complete crossing, the more simple designs may result

in greatest gain per unit of time.

The decision on the crossing design is dependent on the economic model one wishes

to use. If one wishes to maximize the return on a given “minimal” investment, the single design may be the one of choice, especially if land resources were not sufficient

to establish both replicated field tests and full-sib blocks. The capital costs of establishing full-sib blocks would be minimal because replication is not necessary, but suitable land is still a requirement. If one wished to invest until the marginal return was

below a certain level, the complementary design may be the option of choice.

Number of Crosses to

Use in the Breeding

Populations

Gain can be more readily achieved through family selection than through within-family

selection. The heritability of family means for most traits ranges from 0.6 to 0.9. This

indicates that between 60 and 90 percent of the variation observed in family means is

attributable to genetic differences. The heritability of within-family selection usually

ranges from 0.05 to 0.20. The efficiency of within-family selection is considerably lower

than that of family selection. Despite the lower heritability for within-family selection,

the intensity of selection (a function of the proportion selected) is usually much

greater. Genetic gain is a function of both heritability and selection intensity, so the

interaction of these two must be considered when selecting the number of families

and number of individuals per family to produce in a crossing design. Other important

considerations include practical limitations and the need to maintain the effective

population size at a reasonable level.

Table 3 shows how gain is affected by increasing the level of family selection for three

levels of heritability. The example illustrates a fixed progeny test population size of

6,000 trees that are the result of crossing 60 selections. These 60 selections are the

best two individuals from the best 30 families. A total of 30 families is created if a

single-pair mating design is used. Because 30 families are chosen in the next generation, there is no family selection, and all the gain is from within-family selection (the

best 2 out of 200 trees per family). As the number of families produced increases, the

overall gain increases; but at a decreasing rate. At some point, there are so few individuals within a family that the decrease in family-mean heritability results in a

lowering of gain (family means of only a few individuals are relatively unstable because of the small sample size). By doubling the number of families from 30 to 60,

gain increases 82 to 117 percent (1.49/0.82, 1.24/0.58). Doubling the number of

families again (60 to 120) only increases gain 21 to 23 percent. Increasing families

from 125 to 250 only increases gain 5 to 7.7 percent. If one were to use a model that

assumed a constant family size and allowed for increasing the number of tested

trees, the gain would be somewhat larger for additional crosses.

Increasing families comes at a cost, because more breeding is usually required. An

appropriate economic analysis is required to optimize the number of families. Intuitively, it would seem that the minimum number of families to be produced is somewhere between the number of selections (parents) and twice the number of selections.

This is equivalent to two to four crosses per selection.

14
Table 3—Gain associated with family and within-family selection by selecting the best 2 individuals from

the best 30 families (60 selections)a

h2 = 0.20 h2 = 0.15 h2 = 0.10

Number of

Number of trees per Within Relative Relative Relative

crosses cross Family ib family i Gain gain Gain gain Gain gain

30 200 0.000 2.580 0.82 0.55 0.71 0.51 0.58 0.46

40 150 .414 2.478 1.19 .80 1.08 .78 .94 .76

50 120 .634 2.400 1.37 .92 1.26 .91 1.13 .91

60 100 .788 2.328 1.49 1.00 1.38 1.00 1.24 1.00

70 86 .906 2.267 1.58 1.06 1.47 1.06 1.33 1.07

80 75 1.001 2.217 1.65 1.10 1.53 1.11 1.39 1.12

90 67 1.081 2.174 1.70 1.14 1.59 1.15 1.44 1.16

100 60 1.149 2.127 1.74 1.17 1.63 1.18 1.48 1.19

120 50 1.258 2.052 1.81 1.21 1.69 1.22 1.53 1.23

125 48 1.286 2.035 1.82 1.22 1.70 1.23 1.54 1.24

150 40 1.390 1.957 1.87 1.26 1.75 1.27 1.58 1.27

175 34 1.475 1.889 1.91 1.28 1.78 1.29 1.61 1.29

200 30 1.545 1.829 1.93 1.30 1.80 1.30 1.62 1.30

225 27 1.606 1.781 1.95 1.31 1.82 1.31 1.63 1.31

250 24 1.658 1.726 1.96 1.31 1.82 1.32 1.62 1.31

300 20 1.746 1.638 1.97 1.32 1.82 1.32 1.62 1.30

a The number of progeny tested trees is held to 6,000. Assumes a single progeny test site. b i represents selection intensity.

15
One must also consider that in addition to increasing gain, increased family selection

results in increased inbreeding and decreased effective population sizes. One way to

overcome this problem is to increase the family selection intensity by increasing the

total number of families and leaving the number of families selected constant. The

inbreeding problem can be addressed through sublining to some extent (see above),

and multiple populations can assist in maintaining low-frequency alleles. In the end,

the costs and benefits of increased family selection must be considered.

Appendix 2 examines gain over generations for several options. If low-frequency

alleles are not necessary in the next eight generations, then intense family selection

maximizes gain. If, through high family selection, a low-frequency allele is lost that is

later needed, then more expensive options such as open-nucleus breeding or multiple

populations need to be considered.

Field Testing Design

Considerations

Cotterill and James (1984) examined field testing designs in some detail, and a review

was done by Mikola (1993). Cotterill and James (1984) examined the tradeoffs between increased family selection intensity and decreased family-mean heritabilities

given a fixed number of progeny to test. Their results showed that the optimum was

between 10 and 20 trees per family depending on heritability. This provided sufficient

numbers to estimate family means and provided many families for selection. The

NWTIC progeny tests serve not only to rank families but also to provide information

for delineating breeding zones. Each test, therefore, must provide reliable estimates

of family means on each site; each site must be able to stand alone. The earliest

NWTIC trials did not have sufficient numbers of trees per family to characterize sites,

but the large number of sites allowed for a reliable ranking of families overall. The

Cotterill and James optimum also implies undamaged, living trees. Progeny tests,

therefore, should have a minimum of 20 trees per family per site.

It is important that progeny tests be established on multiple sites to ensure that family

rankings are stable over sites (i.e., test for the presence of genotype-by-environmental

interaction). Multiple sites also are needed to help delineate appropriate breeding

zones; the new NWTIC testing zones have not been shown to represent appropriate

deployment zones. From a selection point of view, the benefits from adding additional

sites decreases as site numbers increase. Figure 2 shows the expected gain from

using different numbers of progeny test sites (see Jognson, in press). The figure was

generated by using data from five breeding units. After four sites, the additional gain

from adding additional sites is minimal. The minimum four-site gain was 80 percent of

the average four-site gain; the minimum five-site combination was not far off the fivesite mean. Based on this data, four sites seem adequate for an existing breeding unit.

If one site is lost, there still would be three sites, which, on average, is over 85 percent

as efficient as six sites. Where multiple first-generation breeding zones have been

combined and new deployment zones cannot be verified, it would be wise to have at

least two progeny test sites in each first-generation breeding zone. This would ensure

a minimum of two sites in a deployment zone if first-generation breeding zones are, in

fact, the most appropriate deployment zones. The other progeny test sites would still

contribute some information because the correlation across sites would not be zero;

therefore, total gain would reflect more than the two-site gain in figure 2.

The sets-in-reps field design allows for better comparisons among sets of families

than the reps-in-sets design originally used. Unbalanced designs, as developed by

Patterson and Williams (1976), could further improve the comparisons among all

families.

16
Noncontiguous and single-tree plots are the most efficient for ranking families

(Cotterill and James 1984, Lambeth and others 1983, Loo-Dinkins and Tauer 1987)

and should be continued by the NWTIC.

The noncontiguous plots are not optimal for examining gains on a per-acre basis or

for within-family selection. The primary purpose of progeny testing is to rank families,

next of importance is within-family selection, and lastly is the quantification of gain.

Full-sib blocks could simplify within-family selection because all trees of a family could

be viewed at a single location. To use full-sib blocks to quantify gain, they would need

to be replicated. Blocks for within-family selection would not need replication, except

for ensurance purposes.

Figure 2—Mean, minimum, and maximum gain from using different numbers of

progeny test sites. Source: Johnson, in press.

0

20

40

60

80

100

120

G

ain

1 2 3 4 5 6 7 8

Number of sites

Max

Mean

Min

17
Conclusions The major question yet to be answered in the Pacific Northwest is the appropriate size

of deployment zones. Until more information is available, breeding designs should provide flexibility for the future. Now is the time to design flexibility into a program; by the

third generation, designating sublines will be nearly impossible. If second-generation

testing zones are too large and as a result third-generation population sizes are too

small, additional selections will have to be made in first-generation tests, thereby reducing gain in the later generations.

Flexibility costs money, and to offset these costs, testing and deployment zones will

be larger than in the first generation. Organizations will try to obtain more gain from

their investment by spreading their breeding results over a larger land base—perhaps

even more so if the Federal agencies continue to reduce their harvest levels and

inputs into tree breeding. Testing costs also could be reduced by combining firstgeneration breeding zones because fewer trials and smaller breeding populations

would be required for one breeding program than for many. Again a lack of information exists about which zones could be combined into a single testing program. If the

testing zone is too large and a seed orchard is developed for the whole testing zone,

maladapted growing stock will result.

The key to the next generation will be to develop programs that are structured so that,

as more information becomes available, the programs can be easily modified to accurately represent well-founded deployment zones. This requires having sufficiently

large population sizes and enough trials in the ground.

Obviously, providing flexibility conflicts with maximizing short-term gain, especially

when limited breeding resources are available. The former requires large investments

into maintaining large populations, whereas the latter depends on emphasizing only

the best selections (although at a higher risk). A compromise can be reached by structuring a reasonably sized breeding population and establishing progeny tests on a

sufficient number of sites to allow for the worst case scenario of going back to the old

breeding zones as future deployment zones.

Recommendations The following overall recommendations were developed by the Breeding Zone Evaluation and Restructuring Cooperatives Working Group of the NWTIC.

Population Structure The general recommendations for population structure are as follows:

• Within each first-generation breeding zone, construct a minimum of two unrelated breeding groups from second-generation selections and the best firstgeneration parents.

• Testing zones will incorporate larger areas than previous breeding zones; new

zones are to be based on any available data or observations.

• Breeding populations will utilize breeding groups from a wide range within an

ecoregion. These will include selections from outside the testing zone. As

selections are considered from areas outside the testing zone, only the best

parents and families will be considered.

• Elite populations can be developed to increase gain in the next few generations.

Within each of the first-generation breeding zones, the best available first- and

second-generation selections should be placed in unrelated breeding groups based

on parental origin. These breeding groups will function in a dual role as both sublines

and multiple populations. Crossing to produce full-sib families for the next generation

18
will be limited to crosses within a breeding group. They will serve as multiple populations because breeding groups from different breeding zones may have somewhat

different sets of genes that influence growth and other traits. They also will function

as sublines that can be used in control-pollinated production populations. In the worst

case scenario of having to use the old breeding zones as deployment zones, the

multiple populations can serve as elite populations.

New testing zones will test families produced from breeding groups that span a relatively broad range within an ecoregion and be “rolling zones.” Breeding groups that

originate near a progeny test site will be completely represented on the site. Breeding

groups located farther away from a progeny test site would have only their better

crosses tested. Such a test design should provide a sufficient number of superior

parents for a breeding zone. The design is similar to the continuous zone suggestion

of Rehfeldt (1990).

By using the best selections from breeding groups outside the testing zone, a more

intensely selected group of parents is tested within a testing zone. This will increase

selection intensity and maintain a relatively large effective population size. The risk of

producing maladapted families will increase because some families will be produced

from parents that originated from outside the testing zone. Selections from these

families, however, would never be used in the production population until they had

been tested in the testing zone. If after testing, some breeding population selections

are not suitable for a breeding zone, additional second-generation selections could

be infused into third-generation programs to increase the effective population sizes of

third-generation breeding populations. This would reduce gain in the breeding population, but the production populations in the next few generations could still capitalize

on gain made by the high selection intensity in the families that were adapted.

An added advantage of testing the best parents and families over a broad range is

that information will be available for the next generation to help better define breeding

and testing zones. A subset of families will be tested over many testing zones and

provide an excellent population from which to examine the stability of performance

over the landscape.

Breeding group size should be between 20 and 30. This provides sufficient genetic

variation for many generations and is large enough that inbreeding depression should

not be a problem in the next few generations. Because there is a minimum of two

breeding groups from each first-generation breeding zone, actual breeding numbers

from an old zone would be 40 or more. In the worst case scenario, when only the old

local zone is appropriate for breeding, sufficient third-generation seed orchard selections would be available from the two local breeding populations.

Additional elite populations could be formed if there is interest in increasing the rate

of genetic gain over the next three to four generations. These elite populations would

be used for intense breeding over the next few generations to increase gains in the

production population. Greater gains could arise from a rapid turnover of generations,

a different crossing design, more intense field testing, and testing for adaptive traits

in the nursery. Elite populations could be based on specific combinations of traits

(e.g., fast growth, high wood density, disease resistance, and form). An elite population would consist of the best 30 to 40 selections from various breeding groups. This

19
would allow for two sublines to be formed to prevent the occurrence of inbreeding in

the production population. Thirty to forty selections would provide a sufficient number

of production population (seed orchard) selections and allow for at least five generations of breeding. Any fewer selections would require that additional selections from

the “main” populations be used in future seed orchards to maintain an effective population size of > 20 in the orchard. The use of too many “main” population selections in

an orchard would dilute the gain made from the elite breeding.

Mating and Crossing Each selection should be used in a minimum of two crosses so that some level of

family selection can be practiced. Any selection used in a seed orchard should be

used in a minimum of three crosses if a GCA test is not used and roguing information is needed. Three crosses per parent should provide sufficient parental GCA

information in a balanced crossing design (Burdon and van Buijtenen 1990) but four

crosses may be necessary if there is a large degree of imbalance. Crossing should

be limited to within breeding groups and be dictated by operational constraints, not by

preplanned designs such as diallels or factorials. Although preplanned designs provide

good estimates of genetic variation patterns and provide better breeding values for

parents, they often take considerably more time to complete.

Complementary crossing designs will be necessary in future generations to test parents as outcrossed individuals but are not necessary in this generation because inbreeding is not yet a problem in the breeding population. To use GCA tests this generation, different testers would be required for each testing zone. This would limit the

usefulness of these tests in examining family performance over a wide geographic

range because a tester family would be restricted to a single testing zone.

A complementary design is recommended for elite populations because gain could be

increased by several methods: (1) a polycross mating design could be completed

faster than the full-sib crosses, thus reducing generation interval; (2) nursery tests

could screen GCA families for some adaptive traits (bud set, flushing, cold hardness,

etc.) to provide additional information that could be used in helping deploy the next

generation of commercial seed; (3) more trials (of smaller size) could be planted in the

region to increase the precision of family rankings; and (4) a quicker and more accurate assessment of parental breeding values would be available to rogue secondgeneration seed orchards.

Field Design Previously it had been suggested that a cross should be tested on at least four sites

overall. Assuming that a cross will be tested in its first-generation breeding zone of

origin and in at least two others, it is recommended that each first-generation breeding

zone have at least two progeny test sites. This should ensure adequate testing even

if breeding zones cannot be combined because the four test sites outside the old zone

still provide some information for ranking families within an old breeding zone.

Instead of a “reps-in-sets” or “sets-in-reps” design, an imbalanced (alpha) design

(Patterson and Williams 1976) could improve comparisons among families. Software

is readily available for setting up and analyzing such designs (CSIRO/BioSS 1996).

Acknowledgments Many persons have commented on this paper, including Frank Sorensen, Brad

St. Clair, Nancy Mandel, Jess Daniels, Dan Cress, Bill Randall, and Roy Silen. The

recommendations come from meetings of the NWTIC Breeding Zone Evaluation and

Restructuring Cooperatives Working Group, whose members include Dan Cress, Jess

Daniels (Chair), Jim Hamlin, Rich Kelly, Rick Quam, Brad St.Clair, Joe Weber, and

20
the author. Many of the ideas presented here were first thought through in an organized manner while working with Claire Williams (presently with Texas A&M

University) and Cheryl Talbert (Weyerhaeuser Company) on a review of breeding

strategies for Weyerhaeuser Company.

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24
Appendix 1 Complementary mating designs must depend on selecting full-sib families based on

midparent values that come from polycross trials if the full-sib families are only

planted in family blocks. The objective of this section is to examine the efficiencies of

midparent values.

Three crossing designs are examined for selecting the best full-sib families:

1. Full-sib families produced at random; individual family means are used to select

families.

2. Full-sib families produced at random; midparent values from GCA tests are used

to select families.

3. Double pair-matings, both full-sib and half-sib families values are used to select

families.

Gain was computed by examining predicted gain as a result of using selection

indices. Two sets of genetic parameters were examined: no SCA (dominance) variation and SCA variation = 1⁄2 GCA (additive). This exercise assumes 78 progeny are

planted in progeny tests and no GxE interactions. The variance components were set

to:

Variance

component No SCA SCA = 1⁄2 GCA

σ2

a 560 560

σ2

d 0 280

σ2

e 2000 2000

σ2

full-sib family 309 382

σ2

half-sib family 171 175

Gain was calculated as,

Gain = i Gai Vi

-1⁄2 = i b’ G (b’Pb)-1⁄2 = i (b’Pb)1⁄2 , (6)

where

i = selection intensity,

Gai = covariance between the breeding value (a) and the index (I),

Vi = variance of the index,

b = the index coefficients = P-1G, and

P = the variance covariance matrix of the family means used to predict the breeding

value.

Modeling the Difference

in Efficiency Between

Testing Full-Sib Families

and Using Midparent

Values From GCA Tests

25
The matrices for the indices were as follows:

Test design

FS family GCA Testing Double-pair crosses

P matrix 309 171 0 309 0 140

(no SCA) 0 171 0 309 140

140 140 309

P matrix 382 175 0 382 0 140

(SCA = 1⁄2 GCA) 0 175 0 382 140

140 140 382

G’ array 280 140 140 140 140 280

The results show little difference in estimated gain for a given selection intensity (i)

(table 4). Therefore the option that could produce the most families for a given cost

would produce the most gain.

The random full-sib crosses were examined in more detail by Monte-Carlo simulation

in a manner similar to that described in King and Johnson (1993). Sixty parents were

generated with additive genetic variance (σ2

a) of 560. These 60 selections were

crossed in a double-pair crossing design. Combinations were selected at random. Fullsib and half-sib family (phenotypic) means were generated for full-sib and half-sib

families as follows,

Full-sib mean = ga3 + gd3 + fsvar, and

Half-sib mean = (ga2 / 2) + hsvar , (7)

where

ga3 = additive genetic value of the full-sib family in generation 3 = (gm + gf)/2;

gm = additive genetic value of mother (ga2);

gf = additive genetic value of father (ga2);

ga2 = additive genetic value of parent selections in generation 2

(it is divided by 2 to represent a pollen mean of 0);

Table 4—Gains estimates calculated as (b’Pb)1⁄2 for 3 breeding options and 2 levels of dominance (SCA) variationa

GCAb-SCA variation

Crossing design No. of SCA SCA = 1⁄2 GCA

Random full sib 15.92 14.33

Random GCA 15.14 14.98

Double pair 15.98 14.67

a SCA = Specific combining ability.

b GCA = General combining ability.

26
gd3 = dominance genetic value of the full-sib family, this number was produced

randomly as (1⁄4σ2

d)

1⁄2 * rannor, where rannor is a normal random variable

with mean 0 and variance 1;

fsvar = deviation of a full-sib family mean from its actual value; this was produced

as [(1⁄2σ2

a + 3⁄4σ2d + σ2e) / 78]1⁄2 * rannor; and

hsvar =deviation of half-sib family mean from its actual value, this was produced

as [(3⁄4σ2

a + σ2d + σ2e) / 78]1⁄2 * rannor.

Midparent values were estimated for each full-sib family as the average of the parental half-sib family means. The efficiency of the GCA tests (half-sib families) and the

full-sib family means were compared by examining the correlations of the phenotypic

means (full-sib mean and midparent value) with the actual additive genotypic value of

the full-sib family (ga3) and the genotypic value of the full-sib family (ga3 + gd3).

Twenty-five simulations were run to produce 1,500 family means for which correlations were calculated. Specific combining ability (dominance) variation was examined

at the values of 0 and 1⁄2 GCA variation as in the previous example.

When SCA variation was not present, the correlation between the full-sib family mean

and the full-sib breeding value was marginally larger than the correlation between the

midparent value and the full-sib breeding value (table 5). When SCA variation was

one-half the GCA variation the midparent value and full-sib family mean had identical

correlations with the full-sib breeding value (table 5). This corresponds to the index

selection estimates of gain (table 4).

Table 5—Correlation of full-sib family means and midparent values

from a GCA test with the additive and total genetic value of the familya

No SCAb SCA variance = 1⁄2 GCA

Additive Genetic Additive Genetic

Estimate type value value value value

Full-sib 0.966 0.966 0.894 0.965

Midparent .905 .905 .909 .835

a GCA = general combining ability.

b SCA = specific combining ability.

27
Appendix 2 How much gain is lost if a needed gene is lost from the breeding population through

the process of intense selection over generations? This exercise will examine the

gains from breeding scenarios and the impact of gene loss. The four breeding scenarios are:

1. Five subpopulations, each with 30 parents (selections) that produce 30 families by

way of double pair matings. The best individual is selected from each full-sib family.

Gain is only made from within-family selection. Effective population size (Ne) is 30

in each subpopulation. Total Ne = 150.

2. One elite breeding population of 30 parents. These 30 parents make 150 full-sib

families. Selection is made by using family and within-family selection. The best

individual is chosen from the best 30 families. It is assumed that the effective population size is reduced to 15 because of the family selection; this implies that several

half-sibs are selected.

3. This scenario is similar to scenario 1, in that there are five subpopulations, each

with 30 parents. These 30 selections produce 60 full-sib families. Only half as many

individuals are tested per family. Selection is then based on family (30/60) and withinfamily (1/45) selection. Ne is estimated as 25 per subpopulation because minimal

family selection is applied. Total Ne = 100.

4. This scenario is a nucleus breeding program that uses scenario 2 as its nucleus

and has a 200-family open-pollinated breeding population as the main population. The

best two individuals from the best 95 families are chosen out of the main (the other

10 families come from the nucleus top 10 selections). The best three main population

selections are added to the nucleus each generation. For the nucleus, Ne was estimated at Ne = 15; a conservative estimate as main selections are introduced to the

nucleus each generation. The main Ne was estimated at 150.

The genetic variation (σ2

a) was reduced (1-F) each generation. This reduction was

spread equally among and within families. This is an oversimplification but will suffice

for this comparison. Inbreeding values (F) for each generation were calculated as

(Falconer 1960, p. 62),

Ft = 1 / 2Ne + (1 - 1/Ne)Ft-1 , (8)

where

Ft = inbreeding value in generation t, and

Ft-1 = inbreeding value in generation t-1.

Inbreeding values for each generation were estimated as follows:

Generation

Inbreeding values 1 2 3 4 5 6 7 8

F for (1) 0 0.017 0.033 0.049 0.065 0.081 0.096 0.111

F for (2) and nucleus (4) 0 .033 .066 .097 .127 .156 .184 .211

F for (3) 0 .020 .040 .059 .078 .096 .114 .132

F for main (4) 0 .003 .007 .010 .013 .017 .020 .027

Modeling the Value

of Different Mating

Designs in Preserving

Low-Frequency Alleles

28
The initial variance components were set to σ2

a = 560 and σ2e = 2000. Gain calculations assumed three progeny test sites of 30 individuals per family in single-tree plots

for options 1 and 2, and no genotype-by-environmental interaction. The variance of

full-sib family means would therefore equal 0.5σ2

a + (0.5 σ2a + σ2e )/n, where n is the

number of individuals per full-sib family. The within full-sib family variation would equal

0.5 σ2

a + σ2e. Gains from family selection were estimated as i 0.5 σ2a / σp , where σp

is the square root of the variance of family means for family selection and the square

root of the within-family variation for within-family selection. Gains for the half-sib

families in option 4 required modifying the equations to represent 1/4 of the additive

genetic variation being among families and 3/4 being within families. Total gains were

calculated as the sum of the family and within-family gains (i.e., not index selection,

but family and within-family selection). Gains for open-nucleus breeding were estimated by first calculating gains in the nucleus and main without accounting selections being moved between populations. Final gains were calculated by weighting the

gains by the contribution each population made to the subsequent population; i.e.,

final gains for the nucleus were 27/30 of the nucleus gain plus 3/30 of the main gains.

Cumulative gain over 8 generations was estimated as follows:

Generation

Options 1 2 3 4 5 6 7 8

1 14.5 28.7 42.8 56.6 70.2 83.6 96.7 109.7

2 36.8 72.7 107.7 142.0 175.4 208.1 240.0 271.2

3 24.7 49.1 73.0 96.6 119.9 142.8 165.3 187.5

4 nucleus 34.6 68.3 99.6 128.8 156.3 182.2 206.7 230.0

4 main 16.2 33.2 50.9 69.1 87.7 106.6 125.8 145.0

In generation eight, a disease is devastating the Douglas-fir plantations of the Pacific

Northwest. A single gene gives resistance, but it is at a low frequency in the population. Because of their relative large Ne, options 1 and 3 still have the gene in one of

their subpopulations, but it was lost from option 2. In option 4, the gene is still in the

main population but has been lost from the nucleus. Where does each breeding

option stand?

Options 1 and 3 use the existing gene in the population and do not reduce gain.

Option 2 must go back to the wild and find the gene, thus reducing its gain by half.

Option 4 must go to the main for half of the gain for the next generation. Gain is

therefore the average of the two.

The resulting estimated gains are as follows:

Option Gain

1 109.7

2 135.6

3 187.5

4 187.5

29
Option 1 with no family selection is the big loser. If the disease had never come, it

gave 40 percent of the option 2 gain and 57 percent of the option 3 gain. Even with

the disease, it will produce only 81 percent of the option 2 gain. This points out the

need for family selection; it is the major source of gain. If the gene had occurred by

mutation in either the breeding population or plantations established from seed orchard

seed, gain would be reduced by half the amount of improvement in the last generation, thus making option 2 the better choice because gain would be 255.6.

The choice between options 2 through 4 requires a study in risk management and is

not addressed here. Questions that must be asked are What is the probability of a

devastating disease? and At what frequencies would we find resistant genes? Option

4 is the highest cost and maximizes gain in the most severe case and is the second

best in the no-disease scenario. Is the added cost worth the ensurance?

The risk of losing genes could have been reduced if option 2 had been the nucleus

of a nucleus breeding program.

30
Glossary Definitions come from various sources, including Falconer (1960), Wright (1962), and

Snyder (1959).1

Additive genetic variation—The variation associated with the additive gene action

effects. The additive gene action effects are those associated with offspring being like

their parents. For example, the additive variance is the variation associated with regressing full-sib family means on the midparent value of the parents.

Alleles—Members of a series of genes producing different effects on the same developmental process. An allele (gene) is located at a particular locus (place on a

chromosome). Because Douglas-fir has two of each chromosome, it has two alleles

(genes) that affect the outcome of a trait at each locus.

Assortative mating designs—Mating designs that use the breeding value of a

selection to determine with whom it will be mated. In positive assortative mating, the

best individuals are mated together, thereby increasing the probability of producing a

superior full-sib family. It also can mean that the number of times a selection is used

in a crossing program is related to its breeding value.

Breeding population—The group of selections that will be crossed to produce

offspring for the next generation of selection for a particular deployment zone.

Breeding group—Within the context of the NWTIC, this is a group of 20 to 30 selections that will be used to generate full-sib families. Selections within a breeding group

are from the same geographic area; therefore, they are expected to have similar

adaptational characteristics because, presumably, they have evolved under similar

selection pressures. Breeding population crosses are made within breeding groups;

therefore, breeding groups also serve as sublines.

Breeding value—The estimated genetic value of a selection. The breeding value for

a parent is judged by the performance of its progeny. For open-pollinated families,

the parental breeding value is calculated as twice the deviation of the progeny mean

from the population mean. The value of the progeny is half due to the identified

parent and half due to the population mean (for open-pollinated families), thus the

need to multiply the deviation by two.

Breeding zone—A geographic-elevational subdivision of a local first-generation

NWTIC cooperative program.

Clone—A group of plants derived from a single individual (ortet) by asexual reproduction (e.g., grafts or rooted cuttings). All members (ramets) of a clone have the same

genotype.

Complementary mating design—A combination of mating designs, different designs

for different objectives. The most common combination is a polycross (male or female

testers) to estimate general combining abilities, combined with full-sib families planted

either in replicated trials or family blocks to be used as the selection population.

1 Definitions also were obtained from the document

“Northwest Tree Improvement Cooperative–BZERC Working

Groug proposal for Restructuring the Molalla and Snow Peak

Co-ops for a second-generation–Northern Oregon Cascades

Tree Improvement Cooperative (”NOCTIC")." The document

is available from Jess Daniels, Daniels and Associates, Inc.,

1143 West Roanoke Street, Centralia, WA 98531-2023.

31
Deployment zone—The planting area (i.e., a set of planting environments) for which

a landowner (or group of cooperators) chooses to develop a production population

(e.g., a seed orchard).

Diallel mating design—A mating design in which a group of parents are crossed in

every way possible. Modifications include half-diallels, where reciprocal crosses are

ignored, and partial diallels which only have a subset of the crosses. Below is an example of a five-parent half-diallel (without selfs); the x’s are created crosses:

< - - - - - - Parent - - - - - - >

Parent A B C D E

A xxxx

B x xx

C xx

D x

Ecoregion—Within the context of the NWTIC, it is a subdivision of the range of

coastal Douglas-fir, which is perceived to be significantly different from other such

subdivisions in terms of overall climatic and ecological conditions influencing the

general adaptational character or status of resident populations. Although there is

genetic variation among populations within an ecoregion, the range of variation is

expected to be much narrower within than among ecoregions. For example, the

coastal areas of Oregon and Washington might be considered as one ecoregion.

Effective population size—The number of individuals that would give rise to the

sampling variance or rate of inbreeding appropriate to the population and mating design being considered, if they were to randomly mate and contribute equal numbers

of gametes (seed and pollen). For an overview of these concepts, see Caballero 1994.

As an example, if 40 percent of the seed (and pollen) in an orchard seed lot came

from each of two unrelated parents, and four other unrelated parents each contributed

5 percent of the seed (and pollen), the census number (N) would be 6, but the

calculated effective population size (Ne) would be 3.

Elite populations—A subset of the breeding population, which is comprised of the

best selections. Elite populations generally are used to accelerate breeding efforts by

putting more resources into breeding the elite population relative to the larger breeding

population (sometimes referred to as the main population).

Factorial mating design—A mating design in which one group of parents (selections)

is used as females and another group is used as males. For example:

<-- a Parents - - >

`ÿParents A B C D

E xxxx

F xxx x

G xxxx

H xxxx

I xx x x

32
Full sibs—Trees with both parents in common.

General combining ability (GCA)—The average performance of parents in crosses.

The GCA variation is associated with a full-sib family mean relative to the breeding

values of its parents.

Gene resource population—All of the extant individuals of a species which might

potentially be selected for inclusion in a breeding population.

Genetic diversity—In this paper, genetic diversity means the variation in a character

that is associated with the different genotypes in a population.

Genotype—The genetic constitution of an individual (or clone) as determined by its

genes.

Half sibs—Trees with one parent in common.

Heritability—The proportion of the observed variation associated with genetic factors;

a measure of the relative degree to which a character is influenced by heredity as

compared to environment; the ratio of genetic variation to phenotypic variation. The

higher the heritability, the more an individual’s phenotype (what it looks like) is indicative of its genotype (its genetic makeup).

Heritability of family means—The proportion of the variation of family means associated with genetic variation.

Interpopulation diversity—genetic diversity associated with the genetic variation

among breeding zones (populations).

Intrapopulation diversity—Genetic diversity associated with the within-breeding

zone genetic variation.

Linkage—The association of alleles from one generation to the next because they

are located near one another on a chromosome.

Multiple populations—Groupings of the breeding population designed to maintain

genetic diversity.

Nucleus mating design—A mating design that places more emphasis on an elite

population.

Nucleus breeding—Nucleus breeding places more emphasis on the very best selections (the nucleus population [elite population]) by using two different mating and

testing programs, one for the nucleus and one for the main (a larger, more genetically

diverse population). The first nucleus-breeding program in forestry (Cotterill and

others 1989) used an intensive control-pollinated mating design in the elite population

and an open-pollinated mating design to breed the larger main population. Genetic

variation was maintained in the elite population by bringing up the best selections

from the main population every generation.

33
Panmixia—Panmixia means that any individual has an equal chance of mating with

any other individual within the population; random mating.

Production population—A group of selections used to produce improved operational

reforestation stock (e.g., seed orchard clones and clonal donor stock).

Random drift—The change in gene frequency resulting from sampling of small

populations.

Specific combining ability—The part of the genetic variation that represents the

variation associated with a full-sib family mean being different from the parental

midparent value.

Sublining—Refers to the partitioning of the breeding population into unrelated groups

(sublines). Unrelatedness across sublines is maintained generation-to-generation in

the breeding population by only crossing within sublines. Production seed is produced

by crossing among sublines, thus inbreeding depression cannot occur in the production of commercial seed because related individuals do not mate.

34
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Pacific Northwest Research Station

333 S.W. First Avenue

P.O. Box 3890

Portland, Oregon 97208-3890

Johnson, Randy. 1998. Breeding design considerations for coastal Douglas-fir. Gen.

Tech. Rep. PNW-GTR-411. Portland, OR: U.S. Department of Agriculture, Forest

Service, Pacific Northwest Research Station. 34 p.

The basic principles of designing forest tree breeding programs are reviewed for

Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) in the Pacific Northwest. Breeding populations are discussed given current and future breeding zone sizes and seed

orchard designs. Seed orchard composition is discussed for potential genetic gain

and maintaining genetic diversity in the forest. Mating and field testing designs are

described and compared. Recommendations of the Breeding Zone Evaluation and

Restructuring Cooperatives Working Group of the Northwest Tree Improvement

Cooperative are presented.

Keywords: Douglas-fir, multiple populations, sublines, breeding population, gene

resource populations, mating designs, selection, seed orchard.
U.S. Department of Agriculture

Pacific Northwest Research Station

333 S.W. First Avenue

P.O. Box 3890

Portland, OR 97208

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