THE GENETIC ARCHITECTURE OF REPRODUCTIVE ISOLATION IN ...gato-docs.its.txstate.edu/biology/faculty-s... · genomic regions may be selectively neutral or adaptive in hybrids and thus

ORIGINAL ARTICLE

doi:10.1111/j.1558-5646.2009.00742.x

THE GENETIC ARCHITECTURE OFREPRODUCTIVE ISOLATION IN LOUISIANAIRISES: HYBRID FITNESS IN NATURESunni J. Taylor,1,2 Michael Arnold,3 and Noland H. Martin 1

1Department of Biology, Texas State University, San Marcos, Texas 786662E-mail: [email protected]

3Department of Genetics, University of Georgia, Athens, Georgia 30602

Received November 5, 2008

Accepted April 23, 2009

Negative epistasis in hybrid genomes commonly results in postzygotic isolation between divergent lineages. However, some

genomic regions may be selectively neutral or adaptive in hybrids and thus may potentially cross species barriers. We examined

postzygotic isolation between ecologically similar species of Louisiana Iris: Iris brevicaulis and I. fulva to determine the potential

for adaptive introgression in nature. Line-cross analyses allowed us a general overview of the gene action responsible for fitness-

related traits. We then used a QTL mapping approach to detect genomic regions responsible for variation in these traits. Although

hybrid classes suffered reduced fitness for many traits, hybrid means were equivalent to at least one of the parental species in

overall estimates of maternal and paternal fitness during the two years of the field study. The genetic architecture underlying

the fitness-related traits varied across field site and year of the study, thus emphasizing the importance of the environment in

determining the degree of postzygotic isolation and potential for introgression across natural hybrid zones.

KEY WORDS: Genetic architecture, hybrid fitness, introgression, QTL mapping, speciation, postzygotic isolation.

Speciation involves the evolution of prezygotic and postzygotic

isolating mechanisms between genetically divergent populations

(Dobzhansky 1937; Grant 1981; Coyne and Orr 2004). Although

individual isolating barriers may be incomplete, these barriers act

sequentially to restrict gene flow between divergent lineages. De-

spite the relative importance of prezygotic isolation in preventing

current gene flow (Ramsey et al. 2003; Coyne and Orr 2004;

Martin and Willis 2007), postzygotic isolation is undoubtedly a

major factor in population divergence and reinforcement of isola-

tion in most systems (Coyne and Orr 2004) wherein interspecific

nuclear–nuclear (Orr 1995; Turelli and Orr 2000) and cytonu-

clear (Levin 2003) gene interactions can result in maladapted or

otherwise unfit hybrids.

Darwin (1859), initially perplexed by the evolution of postzy-

gotic isolation, noted that the degree of hybrid sterility or inviabil-

ity is not uniform across hybrid classes or among hybrids between

different species. Indeed, many interspecific matings yield F1 off-

spring with high fitness (e.g., Emms and Arnold 1997; Burke

et al. 1998a; Campbell and Waser 2001; Milne et al. 2003). How-

ever, this high fitness is often a poor predictor of the fitness of

later-generation hybrids (e.g., Milne et al. 2003), as heterosis in

predominantly outbreeding species is primarily due to dominance

(Grant 1975), which quickly decays to reveal hybrid breakdown in

later generations. The evolutionary consequence of hybridization

between genetically divergent populations will thus not only de-

pend on the fitness of F1 generation hybrids, but later-generation

hybrids as well. Furthermore, a complex genetic architecture often

underlies many components of fitness in hybrids (e.g., Edmands

1999; Fritz et al. 2006), and the consequence of hybridization

will be greatly affected by the nature of this genetic architecture

(Rieseberg et al. 1999; Barton 2001; Burke and Arnold 2001;

Johansen-Morris and Latta 2006).

2 5 8 1C© 2009 The Author(s). Journal compilation C© 2009 The Society for the Study of Evolution.Evolution 63-10: 2581–2594

SUNNI J. TAYLOR ET AL.

In hybridizing species pairs, certain genomic regions have

been shown to be quite resistant to introgression of foreign alleles

due to negative heterospecific gene–gene interactions. Linkage

mapping studies in plants, for instance, show large regions of seg-

regation distortion wherein heterospecific alleles are disfavored

(e.g., Fishman et al. 2001; Kuittinen et al. 2004; Bouck et al.

2005). However, a “genic view of speciation” specifically allows

for large swaths of the genome to be permeable to introgression,

with species differentiation potentially occurring in the presence

of only a small number of “speciation genes” (Wu 2001). This

view suggests that not all heterospecific alleles decrease the fit-

ness of hybrids, and numerous studies have revealed that at least

some portions of the genome may be permeable to introgression

of advantageous and/or neutral genomic regions (Sweigart and

Willis 2003; Bouck et al. 2005; Martin et al. 2005, 2006; see

Arnold 2006, 2008 for reviews).

The permeability of a species’ genome is likely to depend on

a number of ecological and genetic factors. Namely, the potential

for the formation of fit F1 offspring, followed by the formation

of fit backcross genotypes, will vary depending on the underlying

genetic architecture of hybrid fitness and any potential genotype ×environment interactions. Although F1 hybrids, for example, may

reveal extremely high fitness due to heterosis, the breakup of coad-

apted gene complexes in later-generation hybrids will likely yield

offspring with low fitness in the parental environments. Such re-

duced fitness will prevent a large portion of heterospecific DNA

(including neutral genomic regions linked to those genes caus-

ing hybrid incompatibilities) from crossing species boundaries.

Thus, to gain a clear understanding of the evolutionary dynam-

ics underlying the formation and maintenance of hybrid zones,

it is necessary to know (1) the fitness of F1 and later-generation

hybrids in a variety of habitats in nature, (2) the genetic architec-

ture of this fitness, and (3) how this genetic architecture varies in

different environments.

In the present study, we use Louisiana irises as a model

system to examine the genetic architecture of hybrid fitness in

different natural habitats and across years. The Louisiana Iris

system consists of three widespread species, Iris brevicaulis, I.

fulva, and I. hexagona. Iris fulva and I. brevicaulis are the most

ecologically similar (Viosca 1935) and are found sympatrically

in southern Louisiana (Cruzan and Arnold 1993; Johnston et al.

2001). The formation of F1 individuals is extremely rare, likely

due to strong prezygotic isolation (Cruzan and Arnold 1994;

Emms et al. 1996; Martin et al. 2007, 2008) and abortion of in-

termediate seeds (Cruzan and Arnold 1994, 1999; Burke et al.

1998b). However, the few F1 individuals that are formed are

both viable and fertile and able to facilitate the formation of

later-generation hybrid classes, resulting in a widespread intro-

gression between species of this complex (Arnold et al. 1990a,b,

1992).

In the current study, we assayed fitness of parental species

and artificially created hybrids in the natural habitat of Louisiana

irises. Using both line-cross analysis and a quantitative trait loci

(QTL) mapping approach, we were able to describe the underly-

ing genetic architecture of a number of fitness traits. Specifically,

we used line-cross analyses to compare the fitness of F1 and BC1

hybrids with that of parental I. brevicaulis and I. fulva genotypes.

This enabled us to identify modes of gene action that act in a

nonadditive fashion—dominance and epistasis—as well as the

direction of epistatic effects (i.e., positive or negative) that con-

tribute to overall hybrid fitness. In addition to line-cross analyses,

we used a QTL mapping approach to identify genomic regions

responsible for much of the observed variation in hybrid fitness,

and we were also able to estimate the effect on hybrid fitness

of pairwise interactions between specific regions of the genome.

QTL that revealed increased fitness when heterospecific DNA was

introgressed are expected to introgress across species boundaries

when the species hybridize in nature. Those genomic regions that

revealed reduced hybrid fitness when heterospecific DNA was

introgressed are likely resistant to introgression, whereas regions

wherein no significant fitness-QTL were detected are expected to

act in a neutral fashion with respect to interspecific introgression.

MethodsCONSTRUCTION OF MAPPING POPULATIONS AND

LINKAGE MAPS

To create the backcross mapping populations, pure-species

parental plants were collected from their native habitats in south-

ern Louisiana (M. L. Arnold, unpubl. data). The one I. fulva

genotype (If174) that was used in the crossing design was col-

lected from bayou margins in Terrebonne Parish, Louisiana. The

single I. brevicaulis genotype (lb72) that was used in the crossing

design was collected from the understory of an oak hardwood

forest in St. Martin Parish, Louisiana. The mapping parents are

not highly inbred, as estimates of heterozygosity for a number

of unlinked markers were high for both genotypes (Bouck et al.

2005). If174 (paternal parent) and Ib72 (maternal parent) were

crossed in the greenhouse to produce F1 hybrids. To minimize

the within-species genetic variation segregating in the mapping

population, clones of these parental plants were then used as the

recurrent parent in subsequent years, such that multiple clones of

a single F1 hybrid were backcrossed to clones of If174 to produce

the BCIf mapping population, whereas multiple clones of a dif-

ferent F1 genotype were backcrossed to clones of Ib72 to produce

the BCIb mapping population. In both backcross mapping popula-

tions, the F1 hybrids served as the pollen parent. Backcross seeds

were produced and planted in the greenhouse in 1999, and ulti-

mately 225 BCIf and 230 BCIb individuals were used to develop

the linkage maps needed for the present study (Bouck et al. 2005).

2 5 8 2 EVOLUTION OCTOBER 2009

POSTZYGOTIC ISOLATION IN IRIS

These two independent linkage maps were constructed

(Bouck et al. 2005, updated by Martin et al. 2007) using dominant

Iris retroelement (IRRE) transposon display markers (Kentner

et al. 2003) in Mapmaker 3.0 (Lander et al. 1987; Lincoln et al.

1992). Bouck et al. (2005) and Martin et al. (2007) provide de-

tailed protocols of the map construction. In brief, two separate

maps were constructed, one using the BCIb mapping population

and another using the BCIf mapping population. Because IRRE

markers reveal dominant inheritance, the maps cannot be linked at

present. Currently, codominant microsatellite markers are being

developed to link these maps at a later date (S. Tang, R. Okashah,

S. Knapp, M. Arnold, N. Martin, unpubl. data). The presence or

absence of dominant I. fulva markers was ascertained in the BCIb

mapping population, whereas the presence/absence of dominant

I. fulva markers was ascertained in the BCIf mapping population.

Linkage groups were identified in Mapmaker 3.0, ordered, and

labeled in the order of largest to smallest (LG1–LG22). Again,

linkage groups that share an identification (e.g., LG1) does not

imply homology. The map derived from BCIb hybrids consists of

142 framework markers, and has an average marker spacing of 13

cM, whereas the map derived from BCIf hybrids consists of 108

framework markers and has an average marker spacing of 12 cM

(Bouck et al. 2005; Martin et al. 2007). Given this large marker

spacing, a number of genes may underlie each QTL detected in

this study.

ASSAYING FITNESS IN THE FIELD

Two experimental plots were selected in southern Louisiana to

represent the general habitat of both species (cypress-mixed hard-

wood forests, Viosca 1935). Plots (ca. 1 km apart) are located

near the Chopique Bayou in the U.S. Army Corps of Engineers

Atchafalaya Basin Floodway in south-central Louisiana, USA.

These are the same plots that were observed for flowering phe-

nology (Martin et al. 2007) and pollinator visitation (Martin et al.

2008), but not the plots described in Martin et al. (2006), as ex-

tensive flooding resulted in high mortality in those sites. We refer

to the current plots as either the “dry” plot or the “wet” plot to re-

flect the fact that “wet” plot remains inundated after heavy rains

and retains moisture for much longer than does the “dry” plot.

This pattern held true for both the 2006 and 2007 field seasons

of the current study. In all analyses, plot is treated as a fixed

effect.

Clonal reproduction in Iris allowed planting of the same

genotype into both environments. In October 2005, up to five

clones (i.e., ramets) of each genotype of the mapping populations

(BCIf : 172 genotypes; BCIf : 243 genotypes), the parental species

(I. brevicaulis: 62 clones of seven wild-collected individuals; I.

fulva: 43 clones of five wild-collected individuals), and the F1

hybrids used in the crossing design (47 clones) were planted

randomly at 0.5-m intervals into each experimental plot. A total

of 1000 individual ramets were planted and subsequently assayed

for fitness during the 2006 and 2007 field seasons.

FITNESS COMPONENTS

Lifetime fitness in long-lived perennial plants, such as irises, is

difficult to capture. Here, we assayed components of postseedling

fitness, as Johnston et al. (2003) found that hybrids between these

Iris species germinate at rates equal or superior to those of the

parents, followed by high fitness in early life-history stages. These

observations suggested that a large proportion of lifetime selection

in this system is associated with adult life-history stages (but also

see Cruzan and Arnold 1994).

To assess the fitness of the parental species and all hybrid

classes, we recorded for each ramet the: (1) number of ramets pro-

duced before the flowering season (January 2006, March 2007),

(2) presence/absence of flowering stalks, (3) number of flowering

stalks, (4) number of flower nodes, (5) number of flowers, (6)

presence/absence of fruit, and (7) number of fruits. Seed viability

was not assayed, as Johnston et al. (2003) found that hybrid and

parental seeds did not differ in germination or early life-history

fitness.

Although we have analyzed the above fitness components

separately, a discussion of total fitness for each of the years ex-

amined must include all components listed above. As such, we

have calculated a summary of paternal fitness for both years sep-

arately as follows:

Fitness(P) = [Growth Points/Initial Weight (g)] × [Stalks

(including zeroes if the plant did not flower)/Growth Point] ×[Nodes/Stalk] × [Flowers/Node].

This represents the total number of flowers produced, corrected

for the initial weight of the rhizome planted in October 2005. No

attempts to examine pollen viability or pollen number were made,

and thus all flowers are assumed to have equal paternal fitness (see

Bouck 2004). We then calculated an estimate of maternal fitness

as:

Fitness(M) = [Fitness(P)] × [Fruits (including zeroes if the plant

did not fruit/Flower].

This represents the total number of fruits produced, corrected for

the initial weight of the rhizome planted in October 2005. All

fruits are assumed to have equal maternal fitness, as the number

of seeds per fruit was not scored.

DATA ANALYSIS

For continuous variables, we used a fully saturated three-way

analysis of covariance that included the main effects of: ‘‘cross

type’’ (I. brevicaulis, BCIb, F1, BCIf , and I. fulva), ‘‘habitat’’

(“wet” or “dry” site), and initial rhizome weight (covariate), as

well as all possible two- and three-way interactions between the

EVOLUTION OCTOBER 2009 2 5 8 3


main effects. This model was also used in logistic regressions for

the two nominal variables (presence/absence of flowering stalk

and presence/absence of fruit). Post hoc Tukey’s HSD tests were

used to detect differences between cross types for all traits in

which a significant “cross-type” effect was detected. Due to the

large variation in year-to-year environmental conditions (temper-

ature, rainfall, and inundation patterns: N. H. Martin, pers. obs.,

Army Corps of Engineers weather data) and the potential for the

first-year study to be subject to transplant effects, we chose to

analyze the data separately for each year.

We used planned linear contrasts to detect deviations from

expected cross-type means given the null assumption of various

models of gene action (Mather and Jinks 1982). To test whether

the hybrid classes conformed to a purely additive genetic model,

we compared the mean of the F1 generation to the mid-parent

value and further compared the backcross means to the mean of the

recurrent parent and the mid-parent value. An additive-dominance

model was suggested for traits in which the F1 mean differed

significantly from the mid-parent value (at P = 0.05). To test

whether the means of the backcrosses conformed to an additive-

dominance model, we compared the mean of each BC1 hybrid

population to the expectation of BC1 = 0.5(F1) + 0.5(recurrent

parent). Significant deviations from expectations of the additive-

dominance model suggested the effect of epistasis on the fitness

of hybrids for that component.

For each fitness trait examined, four separate QTL analyses

were performed within each of the BCIf and BCIb mapping pop-

ulations: separately for each site (“dry” and “wet”) and separately

for each year of the study. In each site, up to five copies of each of

the BC1 genotypes were planted and assayed for each fitness trait,

and the means of each genotype were calculated to perform QTL

mapping. No transformations were performed on any of the traits

that revealed nonnormal distributions. Because quantitative traits

are determined by a number of genes (in this case QTL), each of

which affects the phenotype to varying degrees, the distribution

of a quantitative trait is ultimately affected by the distributions

of several quantitative trait loci. Thus, transformations that make

quantitative data normal are not appropriate for quantitative trait

mapping studies, and furthermore make QTL effect sizes difficult

to interpret (R. Doerge and Z.-B. Zeng, pers. comm.).

Composite interval mapping (CIM, Zeng 1994) was per-

formed in Windows QTL Cartographer version 2.5 (Wang et al.

2007) at 2-cM intervals using a forward and backward regression

method separately on both maps. A 10-cM window size was used

to exclude closely linked cofactors, with the number of control

markers set to five (the program’s default setting). Experiment-

wise threshold values for declaring the significance of a QTL (P =0.05) were determined using 1000 permutation tests (as suggested

by Churchill and Doerge 1994; Doerge and Churchill 1996). A

drop below the permutation threshold (or a change in the direc-

tionality of the QTL effect) was used as an indicator of a boundary

between multiple QTL peaks on the same linkage group. Signifi-

cant QTL were assigned based on these permutation-test criteria.

We further refined our CIM QTL models using multiple in-

terval mapping (MIM, Kao et al. 1999) to (1) detect additional

significant QTL (because MIM has greater power and precision

for detecting significant QTL; Kao et al. 1999) and (2) search for

epistatic interactions between detected QTL. Specifically, MIM

was performed for all traits using default settings as follows. First,

potential QTL that were initially detected by CIM (inclusively de-

fined as peaks exceeding two logarithm of odds (LOD) thresholds,

regardless of whether those peaks were significant as defined by

CIM) were used as the initial model in MIM. We searched for

new QTL in a stepwise fashion, keeping the model that decreased

the Bayesian information criterion (BIC). We then searched for

QTL × QTL interactions that further reduced the BIC.

Windows QTL Cartographer was also used to search for

genotype by environment interactions using the G × E hypothesis

test in multiple trait mapping (Jiang and Zeng 1995). The four

measures of the same trait were combined into one analysis to

test for G × E interactions. Significance thresholds for the joint

trait (i.e., all four measures of the same trait) were determined

by 1000 permutations (Churchill and Doerge 1994; Doerge and

Churchill 1996).

Windows QTL Cartographer is currently unable to identify

interactions between nonadditive QTL. As such, we searched for

epistasis between genomic regions in R/QTL (Broman et al. 2003)

with a two-dimensional scan (“scantwo” function). Threshold lev-

els of significance were determined by 1000 permutations of this

two-dimensional scan (α = 0.05). For each significant interact-

ing pair of loci, the effect sizes and the proportion of the pheno-

typic variance explained were estimated by a multiple QTL model

(“fitqtl” function).

ResultsCOMPARISON OF CROSS-TYPE MEANS REVEALS

DIFFERENTIAL HYBRID AND PARENTAL FITNESS

Unlike the field plots assayed for survivorship by Martin et al.

(2006), 97% of our plants survived during the two years of the

study and survival did not differ by cross type (χ2 = 0.131,

P = 0.998) or site (χ2 = 0.002, P = 0.968). All seven fitness

components assayed differed significantly by cross type, as did

composite measures of paternal and maternal fitness (at P <

0.05). Test statistic values and associated P-values are detailed in

Table S1.

F1 hybrids were consistently equivalent or superior to I. brevi-

caulis and I. fulva individuals in components of clonal and sexual

fitness, and were not significantly inferior to the least-fit species

for any fitness component (Fig. 1). For most traits (excluding



Figure 1. Least squares means (±SE) for seven fitness components during 2006 and 2007. Lines represent assumptions given additivity.

Significant (P < 0.05) deviations from expectations (as determined by line-cross analysis) are denoted by an asterisk. If the F1 mean

differed from the midparent, BC1 means were compared to expectations of an additive-dominance model (BC1 = 0.5(F1) + 0.5 (recurrent

parent)). Significant differences between cross-type means (as determined by Tukey’s HSD test) are noted as follows. The means of cross

types that share a letter designation do not differ; the means of cross types that do not share a letter designation are significantly

different (P < 0.05) from one another. Only “flowers per node 2007” was influenced by a site × cross-type interaction. As such, the means

for each site are presented in that panel.



Figure 2. Summary of paternal (flower production) and maternal (fruit production) fitness (LSM ± SE), given for 2006 and 2007. Line-cross

and Tukey’s HSD comparisons are shown as in Figure 1.

presence/absence of flowering stalk 2006, stalks per growth point

2006, fruits per flower 2007), the backcross hybrid classes as a

whole exhibited significant breakdown (Fig. 1).

Overall, I. fulva and the F1 hybrids were the most paternally

fit in 2006, whereas I. fulva was superior to all other genotypic

classes in 2007 (Fig. 2). The two BC1 classes produced the fewest

numbers of flowers (corrected for initial rhizome weight) in 2007,

but were superior to I. brevicaulis in 2006 (Fig. 2). Maternal fitness

of cross types varied over the two years of the study as well as

across sites in 2006 (F4,1860 = 8.386, P < 0.001; Fig. 2). Of all

genotypic classes, I. fulva and the F1 hybrids produced the highest

number of fruits (per gram of tissue initially planted) in the wet

site during 2006 (Fig. 2). However, I. fulva suffered from greatly

reduced fruit set in the dry site in that of all plants of this species

that flowered, only one successfully sets fruit. In the dry site,

the species and BC1 generations did not differ (Fig. 2). In 2007,

maternal fitness was highest in F1 hybrids, followed by the two

species (Fig. 2).

LINE-CROSS ANALYSES REVEAL VARYING MODES

OF GENE ACTION AND EVIDENCE FOR G × E

INTERACTIONS

Mode of gene action differed across the fitness traits that were

examined, and even between sites and years for many of the

traits (Figs. 1 and 2). No traits, in either year or in either habitat,

revealed a simple additive mode of inheritance. Patterns of over-

dominance were observed for five of the seven fitness traits for

at least one of the study-seasons, such that F1 hybrids revealed

higher fitness than additive expectations. Patterns of underdom-

inance were observed for two traits, “flowering nodes per stalk”

and “flowers per node”, but such patterns of significantly re-

duced F1 fitness emerged in only one of the two seasons (2006

and 2007 respectively). Strikingly, a simple additive-dominance

model was insufficient to explain the mode of inheritance for any

of these fitness traits (Fig. 1). For all traits, in at least one of the

study years, at least one backcross hybrid cross-type (and in many

cases both reciprocal crosses) revealed significant deviations from

additive-dominance expectations (Fig. 1). In all cases, the back-

cross hybrids revealed lower fitness than expected, indicating that

antagonistic epistatic interactions between heterospecific alleles

act to reduce the relative fitness of late-generation hybrids.

Modes of gene action differed across years for many traits,

as well as between sites within a single year (but only for “flowers

per node” and “maternal fitness”) (Figs. 1 and 2). For example,

the proportion of plants that flowered conformed to an additive

dominance model in 2006 (i.e., backcross hybrids did not signif-

icantly differ from the F1-parental midparent); however, in 2007,

the backcross generation means were lower than expected under

this model, suggestive of the presence of epistatic effects reduc-

ing backcross hybrid fitness in that year (Fig. 1). Accordingly,

significant cross-type × site interactions were found for maternal

fitness in 2006 (F4,1832 = 8.36, P < 0.001) and flowers per node

in 2007 (F4,1099 = 4.746, P = 0.012). For these two traits, line-

cross analyses were conducted separately for each site and charts



for both sites are reported separately (Figs. 1 and 2). In all, these

different modes of gene action across years, and across habitats

indicate that the genetic architecture of fitness traits can be greatly

modified depending on the environmental conditions wherein the

plants are growing.

QTL MAPPING REVEALS COMPLEX MODES OF

INHERITANCE

Using both CIM and MIM QTL mapping approaches, we esti-

mated the minimum number of loci responsible for variation in

each trait. The number of QTL identified should be considered

a minimum, as there are likely other QTL that we were unable

to detect due to either small sample size or small effect size of

the undetected QTL. Due to the limited detection power for some

fitness traits, we focus solely on the direction of QTL, as the mag-

nitude of the QTL effects (both additive effects and proportion of

the variance explained) is potentially inflated (Beavis 1994). The

positions of additive QTL are shown on Figure 3. The direction of

effects and the percentage of variance explained (R2) are shown

in Table 1. The total number of additive QTL detected for each

of the 28 traits in the BCIb mapping population ranged from 0

to 4, with a maximum of three epistatic interactions per trait de-

tected by MIM (Table 1; Fig. 3A), whereas the number of QTL

detected per trait in the BCIf mapping population ranged from

0 to 5, with a maximum of one epistatic interaction detected by

MIM (Table 1B; Fig. 3B).

Of the traits for which more than one significant additive

QTL was detected, a majority (BCIb: ∼69%; BCIf : ∼54%) were

affected by QTL with mixed directions of effect on the trait.

Furthermore, the direction of epistatic effects varied, even when

more than one interaction was found for the same trait. For ex-

ample, three significant QTL × QTL interactions were detected

for the trait “fruit/not” for the BCIb mapping population in the

dry site during the 2006 field season. One of these interactions

was in the same direction as the additive effects of the QTL

(LG4:LG10), one was opposite the direction of the additive ef-

fects (LG10:LG17), and the last was between QTL with opposing

additive effects (LG3:LG10). Additionally, due to the prevalence

of epistasis in the line-cross analysis, we searched for epistasis be-

tween QTL without additive effects in R/QTL. These interactions

were found in the BCIb mapping population for “stalk/not.wet06”

and “stalk/not.wet07” (Table 1).

G × E interactions suggested by the line-cross analysis, AN-

COVA, and environment-specific detection of QTL were explic-

itly tested by multiple trait mapping in QTL Cartographer. Al-

though many significant QTL were environment specific (only

found in one site or year), we did not find any significant G × E

interactions in our dataset.

The 99 QTL detected in this study are dispersed throughout

the genome, yet a number of QTL shared common nearest markers

and had overlapping confidence intervals (Fig. 3). Many of the

overlapping QTL were associated with variation in the same trait,

but in different sites or different years. For example, QTL for

flowers per node in each site overlapped on LG 6 in the BCIb

mapping population and LG 11 in the BCIf mapping population

(Fig. 3).

DiscussionIrises have become a model system for the genic view of speciation

(Lexer and Widmer 2008) largely due to the extensive work that

has been performed on these experimental backcross populations

(Bouck et al. 2005; Martin et al. 2005, 2006; Bouck et al. 2007;

Martin et al. 2007, 2008; reviewed by Arnold et al. 2008a,b).

By using these experimental genotypes, we have been able to

investigate the evolutionary dynamics of hybrid zone formation

between I. brevicaulis and I. fulva. A substantial portion of the Iris

genome appears to be porous to introgression, as heterospecific

alleles are actually favored in a majority of regions that experience

segregation distortion in these backcross mapping populations

(Bouck et al. 2005). Furthermore QTL associated with increased

survival of the introgressed genotypes in greenhouse conditions

(Martin et al. 2005), and in naturally flooded conditions (Martin

et al. 2006) have been detected that are likely candidates for

introgression in natural populations. QTL have also been detected

that, when introgressed, allow hybrids to attract a wider array

of pollinators than the parental species (Martin et al. 2008). In

addition to those regions that increase fitness, parts of the iris

genome may be selectively neutral, implying that large swaths

of the iris genome are potentially permeable to introgression.

In this study, we assayed the fitness of pure-species, F1, and

reciprocal backcross hybrids to detect these regions that are likely

and unlikely to introgress across species boundaries in natural

hybrid zones.

HYBRID FITNESS IN NATURE

We assayed five genotypic classes (I. brevicaulis, I. fulva, F1,

BCIb, and BCIf ) for both clonal and sexual growth during two

years of a field study in two separate field sites. To determine the

genetic architecture of hybrid fitness and potential for adaptive

introgression across natural hybrid zones, we chose to plant our

populations into two plots, well within the ranges of I. brevicaulis

and I. fulva in southern Louisiana. In many perennial species,

overall fitness is a function of both clonal and sexual reproduc-

tion. In the present study, Louisiana Iris hybrids were able to pro-

duce as many (in BCIf or BCIb hybrids), or substantially more (in

F1 hybrids), clonal growth points than I. brevicaulis and I. fulva

(Fig. 1). Clonal growth increases both the potential for long-

term survival of a genotype (Cook 1979; Gardner and Mangel

1999), and the genotype’s sexual reproduction by facilitating



Figure 3. QTL locations for fitness components in the BCIb (A) and BCIf (B) mapping populations. QTL are shown with 2-LOD confidence

intervals and labeled. Color denotes trait as follows: black, growth points; red, stalk/not; green, stalk/GP; blue, nodes/stalk; yellow,

flr/node; pink, fruit/not; light green, fruit/flower. Filled and empty boxes represent QTL detected in the wet and dry sites, respectively. A

positive sign above a QTL indicates that the introgressed allele increases the trait value whereas a negative sign above a QTL indicates

that the introgressed allele decreases the trait value.



Table 1. (A) BCIb QTL summary report. QTL underlying the fitness of hybrids for seven fitness components in two plots in the Atchafalaya

River basin of Louisiana in 2006 and 2007. QTL locations are given by map position estimates with 2-LOD confidence intervals (in Kosambi

cM) on the linkage group (LG). Additive QTL effects were estimated by CIM. Epistatic effects were estimated by MIM and R/QTL. (B) BCIf

QTL summary report. QTL underlying the fitness of hybrids for seven fitness components in two plots in the Atchafalaya River basin of

Louisiana in 2006 and 2007. QTL locations are given by map position estimates with 2-LOD confidence intervals (in Kosambi cM) on the

linkage group (LG). Additive QTL effects were estimated by CIM. Epistatic effects were estimated by MIM and R/QTL. Fruits/flower.dry06

was not included due to low sample size.

(A) BCIb QTL

Trait Year Site Linkage group (at cM) R2 Additive

Growth points/Wt 2006 Dry LG 5 at 63 0.140 0.040Wet No QTL detected

2007 Dry LG 1 at 87 0.172 0.118LG 1 at 106 0.124 −0.102

Wet LG 17 at 0 0.109 0.093Stalk/not 2006 Dry LG 3 at 75 0.056 −0.193

Wet LG 4 at 90: LG 9 at 65 0.096 0.1662007 Dry LG 9 at 14 0.165 0.328

LG 9 at 35 0.225 −0.377LG 21 at 7 0.084 0.216

Wet LG 2 at 80: LG 4 at 85 0.235 −0.298Stalk/GP 2006 Dry LG 5 at 25 0.103 0.143

Wet LG 2 at 112 0.099 −0.125LG 7 at 60 0.129 0.154LG 2 at 112: LG 7 at 60 0.081 −0.219

2007 Dry LG 6 at 62 0.098 −0.044Wet No QTL detected

Node/stalk 2006 Dry LG 1 at 0 0.149 −0.654LG 13 at 27 0.120 −0.536LG 15 at 0 0.142 −0.574

Wet LG 13 at 53 0.126 −0.474LG 17 at 0 0.213 0.606

2007 Dry LG 6 at 45 0.164 −0.493Wet LG 2 at 96 0.143 0.570

Flower/node 2006 Dry LG 7 at 88 0.197 −0.144LG 11 at 37 0.105 −0.109

Wet No QTL detected2007 Dry LG 6 at 45 0.095 0.060

LG 19 at 30 0.105 −0.063Wet LG 2 at 106 0.110 −0.105

LG 6 at 45 0.085 0.093Fruit/not 2006 Dry LG 3 at 0 0.620 −0.230

LG 4 at 91 0.097 0.228LG 10 at 10 0.012 0.249LG 17 at 0 0.063 0.259LG 3 at 0: LG 10 at 10 0.048 0.460LG 4 at 91: LG 10 at 10 0.634 1.545LG 10 at 10: LG 17 at 0 0.056 −0.517

Wet LG 14 at 41 0.693 0.7992007 Dry LG 2 at 28 0.329 0.455

LG 9 at 65 0.360 0.504LG 2 at 28: LG 9 at 65 0.259 −0.918

Wet LG 2 at 41 0.146 0.341

Continued.



Table 1. Continued.

Fruit/flower 2006 Dry LG 6 at 73 0.208 0.261LG 13 at 32 0.111 −0.192LG 15 at 14 0.176 0.244LG 22 at 0 0.283 −0.302

Wet LG 7 at 74 0.256 0.257LG 11 at 0 0.342 −0.307LG 13 at 53 0.283 0.267LG 14 at 39 0.374 −0.304LG 17 at 27 0.231 0.299LG 4 at 81 0.372 0.279

2007 Dry LG 21 at 0 0.264 0.240Wet LG 17 at 35 0.303 −0.328

(B) BCIf QTL

Fitness component Year Site Linkage group (at cM) R2 Additive

Growth points/Wt 2006 Dry LG 10 at 0 0.109 −0.039Wet No QTL detected

2007 Dry LG 11 at 8 0.123 0.152LG 10 at 0 0.092 −0.075

Wet LG 13 at 30 0.107 0.106Stalk/not 2006 Dry LG 7 at 50 0.102 −0.269

Wet LG 3 at 18 0.308 0.4682007 Dry LG 2 at 40 0.111 0.276

Wet No QTL detectedStalk/GP 2006 Dry LG 13 at 11 0.171 −0.228

Wet No QTL detected2007 Dry LG 1 at 0 0.339 0.100

LG 1 at 80 0.165 −0.071Wet No QTL detected

Node/stalk 2006 Dry LG 1 at 10 0.133 −0.471LG 9 at 40 0.117 −0.590LG 15 at 0 0.237 −0.788LG9 at 40: LG 15 at 0 0.070 −0.775

Wet LG 3 at 61 0.140 −0.791LG 1 at 28 0.182 −0.839LG 9 at 20 0.287 −0.735LG 9 at 40 0.090 −0.346LG 17 at 0 0.220 0.944LG 1 at 28: LG 9 at 40 0.021 −0.450

2007 Dry LG 1 at 39 0.137 −0.569LG 16 at 0 0.138 0.581LG 15 at 0 0.110 −0.473

Wet LG 8 at 57 0.187 −0.509Flower/node 2006 Dry LG 18 at 12 0.139 −0.199

Wet LG 3 at 61 0.147 0.138LG 9 at 40 0.179 0.147

2007 Dry LG 11 at 10 0.212 −0.316Wet LG 4 at 100 0.255 0.311

LG 11 at 10 0.206 −0.304Fruit/not 2006 Dry LG 4 at 21 0.075 0.217

LG 11 at 25 0.238 0.338LG 4 at 21: LG 11 at 35 0.087 0.357

Continued.



Table 1. Continued.

2006 Wet LG 12 at 12 0.339 −0.518LG 2 at 45 0.215 0.529

2007 Dry LG 3 at 105 0.102 0.194LG 6 at 0 0.089 0.240LG3 at 105: LG6 at 0 0.227 −0.580

Wet No QTL detectedFruit/flower 2006 Dry No QTL detected

Wet LG 6 at 42 0.074 −0.125LG 11 at 35 0.192 0.187LG 2 at 65 0.107 −0.147LG 20 at 0 0.158 0.186LG 15 at 14 0.300 0.240

2007 Dry LG 4 at 17 0.164 0.188LG 9 at 64 0.155 0.174

Wet LG 5 at 66 0.144 0.143LG 8 at 0 0.133 0.139LG 16 at 14 0.175 0.158

the production of multiple flowering stalks, flowers, and fruits

(Watson 1984; Gardner and Mangel 1999). The latter benefit of

increased asexual reproduction is potentially most important for

introgression, as hybrids that persist by vegetative means may

increase their probability of mating, even if the hybrid genotype

suffers reduced fertility (Ellstrand et al. 1996).

Sexual fitness in plants is a combination of both paternal

output (pollen) and maternal output (seeds). Our measure of total

paternal fitness was the number of flowers produced, corrected

for the initial weight of the rhizome planted. We recognize that

cross types potentially differ in male fitness due to differences in

siring success not assayed in the current study. For instance, BCIb

hybrids exhibit 34% lower pollen viability in greenhouse condi-

tions than pure-species parents (Bouck 2004). Thus, our estimate

of hybrid breakdown in male fitness should be considered con-

servative. In both sites during both years of our field study, the F1

hybrids had high estimates of male fitness such that they exceeded

that of at least one (or both) of the pure-species means. Backcross

hybrids produced significantly fewer flowers than expected, given

the performance of the F1. However, in 2006, the BC1 means (and

maximum values) exceeded those of I. brevicaulis and were not

significantly different than those of I. fulva. As long-distance gene

flow in this system is predominantly pollen mediated (Arnold et al.

1991, 1992), the potential for heterospecific alleles to introgress

into new populations is one likely consequence of the production

of such fertile hybrids.

We estimated maternal fitness as the number of fruits pro-

duced, again corrected for the weight of the rhizome planted in

the field. As with paternal fitness, the F1 hybrids exhibited higher

maternal fitness than the parents, but these patterns varied across

years and, in 2006, across sites. The high fitness of the F1 in

all aspects of fitness presents the opportunity to facilitate gene

flow between these divergent lineages. However, as shown here

(Figs. 1 and 2), the recombinant hybrid offspring vary in fitness

such that only a minority of offspring are fit and potentially suited

to novel or parental habitats. Indeed, hybrid classes that resemble

late-generation backcrosses occupy different habitats in natural

hybrid zones than do either parental species (Cruzan and Arnold

1993).

GENETIC ARCHITECTURE

Genetic architecture is often investigated by either line-cross anal-

ysis (Mather and Jinks 1982; Lynch and Walsh 1998) or QTL

analysis. Line-cross analysis can elucidate the general model of

gene action responsible for hybrid fitness, including epistasis and

hybrid breakdown, whereas QTL analysis allows for the detection

of specific genomic regions associated with variation in the trait.

In combination, these techniques provide a comprehensive view

of the genetic architecture that underlies fitness.

In the present study, line-cross analysis allowed an overview

of the general model of gene action responsible for all fitness

components. The most common model included heterosis in F1

hybrids followed by a significant hybrid breakdown in the first-

generation backcross hybrids (Figs. 1 and 2). This highlights the

importance of nonadditive modes of gene action affecting a variety

of fitness traits, an observation also seen in line-cross experiments

in unrelated taxa (e.g., Lair et al. 1997; Kelly 2005; Rhode and

Cruzan 2005; Demuth and Wade 2007; Wegner et al. 2008).

Consistent with the line-cross analyses, our QTL analyses

demonstrated that introgression of heterospecific QTL was most

often associated with a decrease in the fitness value (Table 1).

For these genomic regions, introgression of heterospecific DNA

would likely be strongly disfavored. An examination of the BCIf

and BCIb linkage maps reveals QTL affecting both prezygotic



(Bouck et al. 2005, 2007; Martin et al. 2007, 2008) and postzy-

gotic barriers (Bouck et al. 2005; Martin et al. 2005, 2006; Fig. 3)

widely dispersed across the entire genome (in both linkage maps).

Despite detecting such regions that reduced fitness when intro-

gressed, we also detected other regions that increased fitness when

introgressed (Table 1).This genetic architecture supports the hy-

pothesis that the genome likely acts as a “genetic sieve,” allowing

for the introgression of certain selectively advantageous regions,

while preventing the introgression of deleterious regions (Kim

and Rieseberg 1999; Rieseberg et al. 1999; Wu 2001).

Iris hybrid zones consist of I. brevicaulis-like and I. fulva-like

hybrids interspersed with seemingly “pure” I. brevicaulis and I.

fulva genotypes (Cruzan and Arnold 1993; Johnston et al. 2001).

In these “mosaic” hybrid zones (Harrison 1986; Howard 1986), I.

fulva-like hybrids are generally found in wet, low-lying habitats,

whereas I. brevicaulis-like hybrids are predominately found in

drier habitats of slightly higher elevation, habitats characteristic

of the pure-species plants (Cruzan and Arnold 1993; Johnston

et al. 2001). Genotypic clines corresponding to elevation (i.e.,

moisture) gradients have also been found to exist between the

different species and between different hybrid classes (Cruzan and

Arnold 1993), likely reflecting the importance of the environment

(especially moisture levels in this case) in determining hybrid and

parental fitness.

Habitats are variable, both across broadscale and small-scale

spatial landscapes and across short and long timescales. This

spatial and temporal variability can have important fitness con-

sequences to the organisms that occupy these variable habitats.

Indeed, our multiyear fitness studies of Louisiana Iris in their

native Atchafalaya swamp have revealed striking differences in

fitness across years, likely due to different weather/water condi-

tions. For example, in 2004, a long-term flood event resulted in the

death of over 90% of all experimental plants planted into nature

(Martin et al. 2006). Of those that survived, I. fulva and I. fulva-

like hybrids did so at significantly higher rates than I. brevicaulis

(none of which survived) and I. brevicaulis-like hybrids (Martin

et al. 2006). In contrast, the present study (years 2006 and 2007)

revealed no significant difference in survivorship (over 97% of all

genotypes survived during both of these relatively “mild” years),

although there were dramatic differences in other measures of

fitness observed across years (Figs. 1 and 2).

Habitat-specific (i.e., site-specific) variation in fitness was

also observed across the two sites in both years of this study.

Overall, plants in the wet site performed better than those in

the dry site, regardless of cross type (data not reported). Site ×cross-type interactions resulted from variation in the fitness of I.

fulva and BCIf hybrids presumably due to differences in water

availability between the two sites. Thus, habitat variability can

have important implications on the fitness of the organisms ex-

posed to these various conditions, which may in turn reflect the

environment dependence of hybrid fitness and the potential for

introgression.

Although environmental conditions may have dramatic phe-

notypic effects on organisms, the environment has also been found

to affect the underlying genetic architecture of many traits (e.g.,

Johanson-Morris and Latta 2006; Wegner et al. 2008; Fig. 1).

As such, both the general mode of gene action and the QTL

detected in a population potentially vary across environments.

Environment-specific QTL have been detected in a variety of

crop plants (e.g., Li et al. 2003 and references therein), as well

as in Arabidopsis (Stratton 1998). In our experimental setup, all

of the QTL that we detected in the mapping populations were in

fact environment-specific, meaning that they were not detected

in all four environmental conditions (both sites in both years).

QTL mapping studies are notoriously conservative in “calling”

QTL (resulting in the lack of detecting QTL of small effect due

to sampling error), which may explain a lack of detection in some

habitats. Furthermore, an examination of the line-cross analysis

reveals that only a single trait, clonal growth, followed the same

model for gene action across all habitats and across all years,

leading us to conclude that our habitat-specific QTL are “real”

and not a statistical artifact resulting from lack of detection. Al-

though we show that these QTL are environment specific, direct

tests for QTL × environment interactions only approached and

did not surpass the significance threshold. Again, we note that

differences in sample size between years (more flowered in the

second year of the study) reduced our sample sizes and ability to

detect significant interactions.

The QTL reported in the present and previous studies of

Louisiana irises (Martin et al. 2005, 2006; Bouck et al. 2007;

Martin et al. 2007, 2008) form good “hypotheses” to test in natu-

ral hybrid zones. For example, we can ask whether the patterns of

introgression predicted by these QTL analyses are detected in a

variety of natural hybrid populations across multiple growing sea-

sons. The body of data concerning the biology and genetic archi-

tecture of pre- and postzygotic reproductive isolation in Louisiana

irises thus represents a unique and powerful resource for testing

the process of speciation in the face of gene flow (Arnold 2006).

ACKNOWLEDGMENTSThis work would not have been possible without the help of Y. Z. Mar-tin and R. H. Martin. S. Sandilos, K. Cummings, H. Goins, and M.Taylor assisted in data collection. B. Weckerly provided valuable statis-tical advice. M. Dobson, J. Ott, C. Nice, N. Dharmasiri, T. Juenger, andtwo anonymous reviewers made improvements to earlier versions of thismanuscript. We thank B. Osberghaus and all of the rangers of the ArmyCorps of Engineers Atchafalaya Basin Floodway System for field sitesand assistance. This study was supported by two National Science Foun-dation grants to MLA (DEB-0074159 and DEB-0345123). In addition,the work was funded by startup funds from Texas State University—SanMarcos, and grants from the American Iris Society Foundation and theSociety for Louisiana Iris to NHM.



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