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Article

Geographic Cline and Genetic Introgression Effects on Seed Morphology Variation and Germination Fitness in Two Closely Related Pine Species in Southeast Asia

by
Zheng-Ren Zhang
1,2,†,
Wei-Ying Li
3,†,
Yi-Yi Dong
1,2,
Jing-Xin Liu
1,
Qin-Ying Lan
4,5,
Xue Yang
6,
Pei-Yao Xin
3,* and
Jie Gao
1,5,7,*
1
CAS Key Laboratory of Tropical Forest Ecology, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Menglun 666303, China
2
University of the Chinese Academy of Sciences, Beijing 100049, China
3
Southwest Research Center for Landscape Architecture Engineering, State Forestry and Grassland Administration, Southwest Forestry University, Kunming 650224, China
4
Center for Integrative Conservation, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Menglun 666303, China
5
Center of Conservation Biology, Core Botanical Gardens, Chinese Academy of Sciences, Mengla 666303, China
6
College of Life Sciences, Jilin Agricultural University, Changchun 130118, China
7
State Key Laboratory of Tree Genetics and Breeding, Chinese Academy of Forestry, Beijing 100091, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Forests 2022, 13(3), 374; https://doi.org/10.3390/f13030374
Submission received: 13 December 2021 / Revised: 16 February 2022 / Accepted: 18 February 2022 / Published: 23 February 2022
(This article belongs to the Special Issue Genetic Relationships and Artificial Hybridization within the Forest Trees)
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Abstract

:
There is still limited information on how genetic introgression impacts morphological variation and population fitness in long-lived conifer species. Two closely related pine species, Pinus kesiya Royle ex Gordon and Pinus yunnanensis Franch. are widely distributed over Southeast Asia and Yunnan province of China, with a large spatial scale of asymmetric genetic introgression and hybridization, and form a hybrid lineage, P. kesiya var. langbianensis, where their ranges overlap in southeast Yunnan. We compared seed trait variation and germination performance between hybrids and parental species and characterized environmental gradients to investigate the genetic and ecological evolutionary consequences of genetic introgression. We found that seed width (SW) differed significantly among the three pines, and all the seed traits were significantly negatively correlated with latitude and associated with the mean temperatures of the driest and wettest quarters. A higher germination fitness of hybrids was detected at a low temperature, indicating that they had better adaptability to temperature stress than their parental species during the germination process. Our results suggest that environmental factors shape seed phenotypic variation in the pine species and that genetic introgression significantly affects seed germination fitness. Therefore, assisting gene flow in natural forest populations might facilitate their adaptation to climate change.

1. Introduction

Evaluation of the geographic variation in adaptive traits in long-lived forest tree species with a broad geographic distribution is necessary for understanding the relative importance of the effects of different environmental variables and genetic evolutionary factors, such as genetic drift, demographic history, natural selection, and genetic introgression [1,2,3,4]. Previous studies on intraspecific variation have focused predominantly on the correlation between intra- and inter-species phenotypic variation and local environmental factors [1,5,6,7,8]. In recent years, few studies have paid attention to the impacts of evolutionary factors, such as demographic history [1], hybridization, and introgression [9,10,11,12,13].
There has been a continuing debate about the consequences of genetic introgression: it is often considered a threat to biodiversity, as it will cause dilution of the local gene pool and reduce the fitness of offspring [14,15,16]. On the other hand, introgression may increase genetic diversity by bringing together novel allelic combinations, thereby facilitating the colonization of novel ecological niches that cannot be reached by either parental species [17,18]. Therefore, assessing the relative influence of genetic introgression on the phenotypic variation and fitness of species is essential to understanding the formation of species diversity and the impact of introgression on species’ evolutionary trajectories [19,20]. Aitken et al. have reviewed the method of assisted gene flow (AGF—a kind of artificial genetic introgression) to facilitate population’s adaptation to new climate conditions through the managed movement of individuals or gametes between populations or closely related species [21]. However, due to the long life history of conifers, the effect of the AGF on natural populations is hard to evaluate. In light of these considerations, it is reasonable to ask two questions: Does genetic introgression affect the reproductive traits of the hybrid populations? And even affect the population fitness?
Thus, we addressed the above questions using a closely related pine system. Pinus represents an essential group in the world’s forest ecosystem, often spanning large geographical regions and containing much genetic variation. Wind pollination leads to extensive hybrid zones among related species, making for ideal natural laboratories in which to gain an understanding of the effect of adaptive introgression on species’ adaptability to climate change [22,23]. Pinus kesiya Royle ex Gordon and its sister species Pinus yunnanensis Franch. are widespread in the natural forests of Southeast Asia. P. kesiya has a broad geographic range, including Laos, Vietnam, Myanmar, Thailand, the Philippines, and India [24,25], whereas P. yunnanensis is primarily distributed in southwestern Yunnan. The two species interact at the northern edge of P. kesiya’s range, forming a large contact zone, and the pine population here was considered a hybrid varietas of the P. kesiya and named P. kesiya var. langbianensis (A. Chev.) Gaussen ex Bui (Szemao pine) [26]. Moreover, the two pines and the large contact populations covering large distributions, gradually transitioned from tropical to subtropical regions with significant environmental differences. The two pine species likely diverged during the late Miocene and came into secondary contact during the Pliocene [27]. Genetic investigations using chloroplast (cp) and mitochondrial (mt) DNA markers have provided evidence that P. kesiya var. langbianensis populations from the contact zone are significantly introgressed by P. yunnanensis in the mitochondrial genome and dominantly introgressed by P. kesiya in the chloroplast genome. Asymmetric introgression from the two cytoplasmic genomes has given the population of P. kesiya var. lanbianensis a diverse genetic composition, creating a new “gene pool” for adaptation to heterogeneous environments. Our previous study on needle anatomy characters of the two species demonstrated that the populations from the contact zone exhibit both intermediate and extreme phenotypes, and adaptive needle traits showed clear clinal variation along latitude and climate gradients [28] (Jie Gao et al., unpublished work). In contrast to vegetative organs, which tend to exhibit more adaptive variation with respect to environmental variables, the reproductive organs are considered to be more affected by genetic variation [29,30,31].
Usually, the general idea of population fitness includes the ability of populations to survive and reproduce in the environment where they exist [32]. Thus, reproductive fitness is an essential part of population fitness. Reproductive fitness metrics, such as fruit and seed set, ovule abortion, seed germination, growth potential, and flowering ratios of hybrid lineages, have been used extensively to assess the role of hybridization and genetic introgression in evolution [17]. However, it is difficult to evaluate all the components of reproductive fitness during each life stage for long-lived forest tree species. Instead, seed germination can influence the seedling quality and growth traits, which are crucial for persistence and adaptive potential during later life stages [33,34,35], and which is also the first step in determining whether a plant can successfully colonize a new habitat. Further comparing seed germination in multiple experimental environments among parental species and populations from the contact zone with varying genetic backgrounds, a major gap in our understanding of the relationships between genetic background and plant fitness at early life stages can be filled.
In this study, seed morphology variation was investigated across a broad distribution range, and seed germination performance was measured under different temperature treatments in 28 representative populations. The objectives of the study were: (1) to investigate the variation patterns of seed traits in the two pine species; (2) to dissect the relative importance of environmental factors that contribute to seed trait variation; and (3) to compare seed germination fitness between the hybrid populations and their parental species to infer the effects of the genetic backgrounds.

2. Materials and Methods

2.1. Sample Collection

From 2015 to 2018, we collected samples from 12, 8, and 8 representative populations of P. yunnanensis, P. kesiya var. langbianensis, and P. kesiya, respectively (Figure 1, Table S1). Our sampling covered nearly the entire geographic range of the two pine species and we collected cones from 15–20 individual trees spaced at least 100 m apart from each local population. Cones were transported to the laboratory and stored; when the cones had fully burst, the seeds were removed.

2.2. Seed Morphology and Environmental Data Analysis

We measured the seed length (SL) and seed width (SW) of 30 random seeds from each population using an electronic vernier calliper. We used a digital electronic balance to measure the seed weight for 100 seeds per population and calculated the thousand seed weight (TSW). We obtained the current (1960–present) climatic data from the WorldClim database (http://www.worldclim.org/, accessed on 17 November 2019) [36] and soil data (soil pH, soil organic carbon stock, and soil cation exchange capacity) from the SoilGrids global soil database (https://soilgrids.org/, accessed on 17 November 2019). We selected climatic variables that had a relatively low correlation at r < 0.8. We used two-way ANOVA to test for significant differences in each trait among species and used Tukey’s HSD test to determine whether individual means differed significantly from one another (p < 0.05). We evaluated whether each trait was correlated with latitude using Spearman rank correlations. We used three methods to reveal the environmental contribution to seed variation among natural populations of the pine complex. First, we performed a redundancy analysis (RDA) on seed traits and environmental variables for populations of each species [37]. Second, we ran multiple regression models to find the best set of environmental predictors for each trait. We re-scaled all predictors to a mean of zero and a variance of one so that the coefficients could be compared directly. We used forward selection to maximize the degrees of freedom for error. Parameters were added into the model only if the Akaike information criterion (AIC) decreased. All analyses were performed in R [38]. Third, in cases for which the relationship between response variables and predictors was nonlinear and independent, we used the gradient boosting machine (GBM), which is a nonparametric machine learning approach for sequentially constructing an additive expansion of fitted weak learner models [39], to determine the relative importance of each environmental factor and to obtain environmental factor importance scores for all traits.

2.3. Seed Germination Experiment and Data Analysis

According to our unpublished study of the ecological niche divergence of the two pines, the most important climate covariates for both P. yunnanensis and P. kesiya are temperature seasonality and annual mean temperature, and the pines may have specific adaptations to these stresses. We therefore carried out a series of experiments to investigate seed germination fitness in the three pine species. Following [40,41], we sowed seeds on 0.8% agar in Petri dishes and incubated them at constant temperatures of 10, 20, or 30 °C with a 12 h photoperiod of 20 μmol m−2 s−1 irradiance provided by white fluorescent lamps. The assay was organized in a completely randomized design with four replicates of each temperature condition and 40 seeds in each replicate. Germination was recorded every five days. As all the seeds in this experiment were collected from natural populations and some were in limited supply, we sowed only 20 seeds per replicate from populations collected from Thailand and 35 seeds per replicate from populations collected from Vietnam. When the experiments were completed, we cut open the ungerminated seeds to assess their germination capacity and all ungerminated seeds with brown and soft embryos.
We used a meta-analytic approach to analyze the seed germination data. This method provides a more appropriate appreciation of the sources of variation in hierarchically structured germination experiments than the commonly used method of linear mixed models [42]. This approach consists of two steps. First, we fitted separate Weibull-1 event time models to the data from each seed germination test unit, i.e., each replicate of each population at a specific temperature condition [43,44]. In total, 327 models were fitted. We then extracted three estimated parameters and their standard errors from each of the 327 models. The three parameters were: G, the germination percentage (%); T50, the time required to reach 50% germination (days); and D, the slope of T50, which reflects the steepness of the germination curve at time T50. The steeper the slope, the more rapid the germination at T50 [44]. We consider the three parameters to all have represented the germination fitness, as they are widely used in germination experiments to evaluate the germination abilities of seeds. Second, we fitted meta-analytic random effects models [45] for each of the three parameters to test the effects of species (represented by the genetic background), temperature, seed traits, and mtDNA and cpSSR diversity of each population. In each model, “population” and “dish ID”, which were nested within “population”, were assigned as random factors. Additional details of this approach may be found in references [42,44]. Statistical analyses were performed using Rstudio Cloud alpha (https://rstudio.cloud/, accessed on 1 July 2019 ) with the packages “drc” [46], “multcomp” [47], and “metaphor” [48] for the event time models, multiple comparisons, and meta-analytic approach, respectively.

3. Results

3.1. Patterns of Variation in Seed Morphology

For the environmental variables, the ANOVA results showed that the geographic distributions of the three species differed significantly in eight environmental variables: bio03 (isothermality), bio08 (mean temperature of wettest quarter), bio09 (mean temperature of driest quarter), bio13 (precipitation of wettest month), bio14 (precipitation of driest month), bio15 (precipitation of driest month), PHIHOX (soil pH × 10 in H2O at depth), and OCSTHA (soil organic carbon stock). Six of the variables differed significantly between P. yunnanensis and P. kesiya. For P. kesiya var. langbianensis, bio03 and bio14 were similar to P. yunnanensis, and bio09 was similar to P. kesiya. For the other variables, P. kesiya var. langbianensis did not differ significantly from the two parental pine species (Table 1).
Of the seed morphology characters, only seed width differed significantly among the three pines (p < 0.01, Figure 2), and this difference was only found between the two parental species. There was no difference between P. kesiya var. langbianensis and its parents. Seed traits exhibited similar variance components among individuals and species, and both values were slightly higher than that among populations (Table 2). Among the three traits, seed mass showed the highest variance components among individuals, populations, and species.
All the seed traits were significantly negatively correlated with latitude (Figure 3); specifically, seed size and seed mass were greater at lower latitudes. The multiple regression models analysis showed that the best AIC candidate model included the environmental variables bio08 for seed mass, and bio09 and CECSOL for seed width. The model for seed length included more variables: bio03, bio08, bio09, bio14, PHIHOX, and OCSTHA (Table 3). Seed width was significantly positively correlated with the mean temperature of the driest quarter (bio09), whereas seed length and seed mass were positively correlated with the mean temperature of the wettest quarter (bio08).
Seed traits, including width, length, and mass, did not show significant clusters among the three pines based on the RDA analysis (Figure 4A). However, bio08, bio09, bio15, and PHIHOX determined 58.06% of the variation among the three species. The GBM analysis also revealed that bio08 made the largest contribution to the variation in all traits. Bio02, bio03, and bio09 contributed more to seed width; bio02, bio08, and bio13 contributed more to seed length; and bio02, bio03, and bio08 contributed more to seed mass (Figure 4B).

3.2. Seed Germination Tests

The two parental species (P. kesiya and P. yunnanensis) showed the same pattern: germination percentages (G) were highest at 20 °C and lowest at 10 °C. By contrast, germination percentage of the hybrid populations did not differ significantly among the three temperatures (Figure 5A–C). Both T50 and D differed significantly among the three temperatures within a species. At the inter-species level, there were significant differences in T50 at 10 °C and in D at 10 and 30 °C. The hybrid pine showed significantly higher D than the parental species at 10 °C but lower D than the parental species at 30 °C (Figure 5D–F).
Mitochondrial DNA and cpSSR diversity had greater effects on the germination parameters, and cpSSR diversity had a stronger impact than mtDNA diversity. Increased cpSSR diversity was associated with a higher germination percentage (G) in all three pine species (Figure 6A). Increased cpSSR diversity was associated with lower T50 in P. yunnanensis but higher T50 in P. kesiya var. langbianensis (Figure 6C). For P. kesiya, higher cpSSR diversity was associated with higher T50 at 20 and 30 °C but lower T50 at 10 °C. For D, increased cpSSR diversity was associated with a higher D value at 10 and 20 °C but a lower value at 30 °C in all three pines (Figure 6B). The effect of mtDNA diversity on the three parameters was relatively consistent at 20 and 30 °C but stronger at 10 °C (Figure 6D–F). At 10 °C, increased mtDNA diversity was associated with a higher germination percentage in P. yunnanensis and the hybrid pine but a lower germination percentage in P. kesiya (Figure 6D). Finally, it is worth noting that within the contacted zone, increased genetic diversity was associated with a higher germination percentage at all temperatures (Figure 6).

4. Discussion

4.1. Seed Morphology Variation and Its Association with Environmental Variables

This study showed that seed morphology traits, except for seed width, did not differ significantly among species in the closely related pine species, indicating that genetic background had little influence on seed morphology variation. However, seed traits exhibited a clinal shift along latitudinal and environmental gradients that appeared to reflect acclimation to local ecological conditions, suggesting that seed morphology was mainly affected by the environment rather than the genetic background in the three pines. The introgressive populations (P. kesiya var. langbianensis) from the hybrid zone showed intermediate values between P. yunnanensis and P. kesiya for all three traits. It has been reported that the stands of morphologically intermediate pines and other hybrids are common in areas of sympatry [49,50,51,52]. However, we cannot conclude with certainty that the phenotypically intermediate individuals resulted from interspecific gene flow, as the populations from the contact zone were also located in the middle of the latitudinal gradient. The niches of P. yunnanensis and P. kesiya differed significantly in six of the investigated climate variables. P. kesiya var. langbianensis populations in the overlap area were at the margin of the distributions of the parental species. Isothermality and precipitation of the driest month were similar to P. yunnanensis, whereas the mean temperature of the driest quarter was similar to P. kesiya. Intermediate seed morphology may have also resulted from the adaptation to the medium environmental conditions of the hybrid zone. This result contrasts with another hybrid pine, P. densata, most of whose seed morphometric traits differed from the parental species P. yunnanensis and P. tabuliformis, as the hybrid occupied a new niche that its parental species could not reach [53].
The pattern of variation in seed traits increased linearly with decreasing latitude, consistent with a common adaptive phenomenon in most forest tree species in which seed size and mass increase with increasing proximity to tropical areas [54]. In line with these results, the mean temperatures of the wettest and driest quarters had the most significant effect on seed morphological traits and a strong negative association with increasing latitude. The mean temperature of the wettest quarters increased linearly from 16.48 to 25.47 °C, and for the driest quarters increased linearly from 6.11 to 21.34 °C as latitude decreased. Precipitation during the driest and wettest months was also shown to make less of a contribution to seed trait variation based on the RDA and GBM analyses, but the effects of these variables cannot be ruled out. The tropics have higher mean temperatures during the wettest and driest quarters, leading to a longer growing season than in subtropical areas [54]. In addition, the trees in tropical regions have higher net primary productivity, which leads to higher total seed production with larger seeds [6,54]. Furthermore, the combined effects of the temperature and precipitation variables identified above may influence plant evapotranspiration and affect drought responses [55]. During the active growing season, drought stress is widely recognized as a limiting factor to plant growth in forest trees [56,57].

4.2. Seed Fitness of Populations from the Hybrid Zone and the Parental Species

The first step in understanding the mechanisms that underlie hybrid zone evolution is to examine the role of specific environmental factors in determining the fitness potential of hybrids relative to their parents [17]. Temperature factors contributed the most to the phenotypic variation in our analysis, indicating that the three pine species may have divergent adaptations to temperature stresses that may appear in an early life stage. The hybrid populations from the contact zone differed in germination performance from the parental species in several ways. First, the germination percentage differed significantly among the three temperature treatments in P. yunnanensis and P. kesiya but not in the hybrid populations. This suggests that germination performance was more stable in the hybrid pine and that the hybrid populations were more tolerant of temperature variation. It has been reported that hybrid genotypes possess a wide range of fitness relative to parental species [58,59]. Second, the hybrid populations exhibited better germination performance than the parental species at low temperatures. We observed the highest germination percentage in the hybrid pine and the lowest germination percentage in P. kesiya at 10 °C, although the difference was not significant. In addition, the hybrid pine had a significantly faster germination rate than its parental species at 10 °C. These results indicate that the hybrid populations had an adaptive advantage over the parental species at lower temperatures at the seed germination stage. That hybrid individuals have better adaptability than their parental species has also been demonstrated in other hybrid species [59,60,61,62].
The associations between genetic diversity, population size, and seed morphology traits related to seed germination have been investigated in several plants, and these correlations appear to vary among species [63]. Seed mass has long been regarded as an important aspect of plant reproductive biology, and it is known to influence germination percentage [64,65]. In a recent survey of seed germination, four of the ten species examined showed a positive relationship between germination and genetic diversity [66]. In our study, maternal and paternal genetic diversity made greater contributions to seed germination performance than seed traits. First, increased genetic diversity was associated with more germination within the hybrid populations under all temperature treatments. Increased cpSSR diversity was associated with a higher germination percentage in all three pine species. By contrast, mtDNA diversity showed a relatively constant effect at 20 and 30 °C and became stronger at 10 °C. Higher genetic variation may provide a sufficient “gene pool” for the population’s evolutionary potential to adapt to changing environments [67,68]. Second, the correlation between genetic diversity and germination fitness varied among species and temperatures. For example, increased cpSSR diversity was associated with a faster germination rate in P. yunnanensis but a slower germination rate in hybrid populations. Under the low-temperature treatment, increased mtDNA diversity was associated with more germination in P. yunnanensis and the hybrid pine but less germination in P. kesiya. The different correlation patterns of paternally inherited cpSSR and maternally inherited mtDNA diversity with seed germination fitness among species may arise from interspecific gene flow between species. In Pinus, the mitochondrial genome is maternally inherited and therefore reflects seed colonization patterns, whereas the chloroplast genome is paternally inherited and reveals pollen introgression between species [69,70]. This pattern is consistent with the asymmetric introgressive pattern in the hybrid populations, with significant introgression in P. yunnanensis in the mitochondrial genome and predominantly pollen gene flow in P. kesiya in the chloroplast genome. The subtropical pine of P. yunnanensis was better adapted to the cold environmental conditions and had passed this on to the hybrid populations through genetic inheritance.

5. Conclusions

The two closely related pine species P. kesiya and P. yunnanensis provide a perfect natural laboratory to assess the effects of AGF on wild forest tree populations. Our study demonstrated that assisted gene flow from the parental species had improved the hybrid populations’ seed germination fitness, which might further facilitate the adaptation of these populations to changing environmental conditions for a long time. These results also provide essential information for the delineation of seed collection zones for germplasm conservation, afforestation, and tree improvement programs. However, our experiments only partially identified the adaptive features of the three pine species. To better understand the genetic basis of genetic introgression operating in the pine populations, nuclear genomic data would be more informative. We also conducted reciprocal transplant experiments in the typical habitats of the two pine species to evaluate phenotype and fitness differentiation among introgressed populations and parental species in a long-term survey, which will continue to provide essential data on the genetic basis of adaptation in the pine populations.

Supplementary Materials

The following are available online at https://www.mdpi.com/article/10.3390/f13030374/s1: Table S1: Geographic locations, genetic diversity, and mean seed traits of the sampled populations from the pine populations; Figure S1: Germination percentage estimated based on the meta-analytic approach; Figure S2: The D (slope of T50) estimate based on the meta-analytic approach; Figure S3: T50 estimate based on the meta-analytic approach.

Author Contributions

J.G. and P.-Y.X. conceived the study; J.G. and J.-X.L. collected the samples; J.-X.L., Q.-Y.L., W.-Y.L., X.Y., Y.-Y.D. and Z.-R.Z. analyzed the data; J.G., W.-Y.L., X.Y. and Z.-R.Z. wrote the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This study was supported by grants from the National Natural Science Foundation of China (NSFC 31770701) and the Open Fund of State Key Laboratory of Tree Genetics and Breeding (Chinese Academy of Forestry) (Grant No. TGB2021002).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available in Table S1.

Conflicts of Interest

The authors declare that they have no known competing financial interest or personal relationships that could have appeared to influence the work reported in this paper.

Ethical Statements

Pinus kesiya, P. yunnanensis and P. kesiya var. langbianensis plants were used in this study. Pinus seeds were selected from China, Laos and Thailand (Table S1). The voucher specimens were deposited in the Lab of Coevolution Research Group in Xishuangbanna Tropical Botanic Garden (contact person: Zhengren Zhang, email: [email protected]). Our plant collection has followed the guidelines of the Xishuangbanna botanic garden.

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Forests 13 00374 g001 550
Figure 1. Sample locations of the three pine populations.
Figure 1. Sample locations of the three pine populations.
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Figure 2. Comparison of seed morphology between the P. yunnanensis, P. kesiya, and P. kesiya var. langbianensis populations. Significant differences are indicated by asterisks (** p < 0.01). Different lowercase letters indicate significant differences (Tukey’s HSD mean separation test).
Figure 2. Comparison of seed morphology between the P. yunnanensis, P. kesiya, and P. kesiya var. langbianensis populations. Significant differences are indicated by asterisks (** p < 0.01). Different lowercase letters indicate significant differences (Tukey’s HSD mean separation test).
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Figure 3. Latitudinal gradients of seed traits and the significant correlation between the seed traits and climatic variables. The climatic variables selected from Table 2 and only with the p-value < 0.05 are presented.
Figure 3. Latitudinal gradients of seed traits and the significant correlation between the seed traits and climatic variables. The climatic variables selected from Table 2 and only with the p-value < 0.05 are presented.
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Figure 4. Attempt for revealing the environmental contribution to seed variation among natural populations of the pine complex. (A) The redundancy analysis (RDA) based on the seed traits and environmental variables of each location. (B) The gradient boosting machine learning (GBM) for the relative importance of each environmental variable on the seed traits. The values of relative influence greater than 0.04 are shown.
Figure 4. Attempt for revealing the environmental contribution to seed variation among natural populations of the pine complex. (A) The redundancy analysis (RDA) based on the seed traits and environmental variables of each location. (B) The gradient boosting machine learning (GBM) for the relative importance of each environmental variable on the seed traits. The values of relative influence greater than 0.04 are shown.
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Figure 5. Germination parameters of the three pine species at 10, 20, and 30 °C. (A–C) show G (temperature comparisons of germination percentage), T50 (the time required to reach 50% germination (days)), and the slope of T50 of each species, respectively. (D–F) show species comparisons of G, D, and T50 at each temperature, respectively. Error bars indicate SE. Different letters indicate significant differences at p < 0.05. kxys: Pinus kesiya; sms: P. kesiya var. lanbianensis; yns: P. yunnanensis.
Figure 5. Germination parameters of the three pine species at 10, 20, and 30 °C. (A–C) show G (temperature comparisons of germination percentage), T50 (the time required to reach 50% germination (days)), and the slope of T50 of each species, respectively. (D–F) show species comparisons of G, D, and T50 at each temperature, respectively. Error bars indicate SE. Different letters indicate significant differences at p < 0.05. kxys: Pinus kesiya; sms: P. kesiya var. lanbianensis; yns: P. yunnanensis.
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Figure 6. Relationships of germination parameters and genetic diversity of the three pine species populations at 10, 20, and 30 °C. (A–C) show the relationships of germination percentage, slope at T50, and T50 with cpDNA diversity of each species, respectively. (D–F) show the relationships of germination percentage, the slope at T50, and T50 with mtDNA diversity of each species, respectively. kxys: Pinus kesiya; sms: P. kesiya var. langbianensis; yns: P. yunnanensis.
Figure 6. Relationships of germination parameters and genetic diversity of the three pine species populations at 10, 20, and 30 °C. (A–C) show the relationships of germination percentage, slope at T50, and T50 with cpDNA diversity of each species, respectively. (D–F) show the relationships of germination percentage, the slope at T50, and T50 with mtDNA diversity of each species, respectively. kxys: Pinus kesiya; sms: P. kesiya var. langbianensis; yns: P. yunnanensis.
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Table
Table 1. Comparison of environmental variables between the P. yunnanensis, P. kesiya, and P. kesiya var. langbianensis populations. Significant differences are indicated by asterisks (* p < 0.05; ** p < 0.01; *** p < 0.001). Different lowercase letters indicate significant differences (Tukey’s HSD mean separation test).
Table 1. Comparison of environmental variables between the P. yunnanensis, P. kesiya, and P. kesiya var. langbianensis populations. Significant differences are indicated by asterisks (* p < 0.05; ** p < 0.01; *** p < 0.001). Different lowercase letters indicate significant differences (Tukey’s HSD mean separation test).
Environmental Variable and
Seed Morphology		UnitsF-StatisticdfadjR2P.yunnanensisP. kesiyaP.kesiya. var.
langbianensis
p-Value
Bio02: Mean diurnal temperature range(°C × 10)0.193260.0288.681a9.108a8.714a
Bio03: Isothermality (Bio2/Bio7) (×100)(%)0.000 ***260.58239.450b53.635a44.586b
Bio08: Mean temperature of wettest quarter(°C × 10)0.005 **260.24120.794b22.998a22.784ab
Bio09: Mean temperature of driest quarter(°C × 10)0.000 ***260.6749.272b17.299a14.852a
Bio13: Precipitation of wettest month(mm)0.016 *260.174210.077b327.625a284.714ab
Bio14: Precipitation of driest month(mm)0.000 ***260.51613.539a3.875b15.286a
Bio15: Precipitation seasonality(CV3)0.024 *260.15079.253a86.933a86.169a
Bio18: Precipitation of warmest quarter(mm)0.258260.012586.539a700.750a761.429a
PHIHOX: Soil PH × 10 in H2OPH × 100.007 **260.21759.615a54.375b56.286ab
OCSTHA: Soil organic carbon stockTon/hectare0.049 *260.10826.385a21.375a19.857a
CECSOL: Cation exchange capacity of soilcmolc/kg0.56926−0.02520.308a21.500a18.714a
Table
Table 2. Results of ANOVA for the analyzed traits in the three pine species.
Table 2. Results of ANOVA for the analyzed traits in the three pine species.
TraitsBetween SpeciesBetween PopulationsBetween Individuals
MeanStandard DeviationCoefficient of Variation (%)MeanStandard DeviationCoefficient of Variation (%)MeanStandard DeviationCoefficient of Variation (%)
Width2.310.2410.252.290.198.482.290.2410.63
Length5.690.610.635.690.518.975.690.6210.92
Mass1.860.2514.561.850.2714.641.85--
Table
Table 3. Environmental variables that predict variation in measured seed traits of the pine populations. Model predictor variables were selected using forward selection with the criterion that variables were included if the model Akaike information criterion (AIC) decreased.
Table 3. Environmental variables that predict variation in measured seed traits of the pine populations. Model predictor variables were selected using forward selection with the criterion that variables were included if the model Akaike information criterion (AIC) decreased.
Seed TraitsInterceptbio03 1bio08 2bio09 3bio14 4PHIHOX 5CECSOL 6OCSTHA 7
Mass−2.415 × 10−16 0.6443
Width−2.563 × 10−16 0.7248 −0.2285
Length−4.141 × 10−161.5912.321−2.8360.2987−0.7044 0.2151
1 Bio03, isothermality; 2 bio08, mean temperature of wettest quarter; 3 bio09, mean temperature of driest quarter; 4 bio14, precipitation of driest quarter; 5 OCSTHA, soil organic carbon stock; 6 PHIHOX, soil PH; 7 CECSOL, cation exchange capacity.
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Zhang, Z.-R.; Li, W.-Y.; Dong, Y.-Y.; Liu, J.-X.; Lan, Q.-Y.; Yang, X.; Xin, P.-Y.; Gao, J. Geographic Cline and Genetic Introgression Effects on Seed Morphology Variation and Germination Fitness in Two Closely Related Pine Species in Southeast Asia. Forests 2022, 13, 374. https://doi.org/10.3390/f13030374

AMA Style

Zhang Z-R, Li W-Y, Dong Y-Y, Liu J-X, Lan Q-Y, Yang X, Xin P-Y, Gao J. Geographic Cline and Genetic Introgression Effects on Seed Morphology Variation and Germination Fitness in Two Closely Related Pine Species in Southeast Asia. Forests. 2022; 13(3):374. https://doi.org/10.3390/f13030374

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Zhang, Zheng-Ren, Wei-Ying Li, Yi-Yi Dong, Jing-Xin Liu, Qin-Ying Lan, Xue Yang, Pei-Yao Xin, and Jie Gao. 2022. "Geographic Cline and Genetic Introgression Effects on Seed Morphology Variation and Germination Fitness in Two Closely Related Pine Species in Southeast Asia" Forests 13, no. 3: 374. https://doi.org/10.3390/f13030374

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