Genetic diversity, core collection construction and genotype–environment associations of Eucalyptus cloeziana F. Muell. (Myrtaceae) germplasm based on EST-SSR markers

doi:10.21203/rs.3.rs-4917616/v1

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Genetic diversity, core collection construction and genotype–environment associations of Eucalyptus cloeziana F. Muell. (Myrtaceae) germplasm based on EST-SSR markers

https://doi.org/10.21203/rs.3.rs-4917616/v1

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Conservation and assessment of germplasm resources play a crucial role in forest genetics and breeding. Eucalyptus cloeziana F. Muell is esteemed as a valuable timber tree species in China owing to its substantial economic and ecological significance. Nevertheless, there has been a dearth of research on functional genomics and molecular breeding within this species. In this study, 20 fluorescent-labeled polymorphic EST-SSR markers were utilized to genotype 448 E. cloeziana individuals from 118 families originating from 19 provenances within the State-owned Dongmen Forest Farm of Guangxi province, one of the largest gene pools of Eucalyptus in Asia. The results unveiled a relatively high genetic diversity of E. cloeziana germplasm (I = 2.310; He = 0.855) and low genetic differentiation among provenances, with the majority of genetic variation occurring within provenances (96.73%). Bayesian structure analysis grouped the tested accessions into three clusters: northern provenances, northern high-altitude provenances, and southern provenances. A core collection comprising 85 individuals (18.97% of the total breeding population of 448 individuals) was established through random non-repetitive sampling to diminish redundancy while uploading a high level of genetic diversity (I = 2.470; He = 0.890). Three loci associated with environment (EC-e039, EC-e015, and EC-e089) were identified using latent factor mixed model (LFMM) and redundancy analysis (RDA). Two temperature variables (maximum temperature of the warmest month and mean temperature of the driest quarter) and two precipitation variables (annual precipitation and precipitation of the driest month) were identified as significant environmental factors influencing adaptive variation in the species. These findings highlight the close relationship between climate conditions and genetic variability, providing valuable insights for the management of forest species in the face of a swiftly changing environment.

Eucalyptus cloeziana F. Muell.

Genetic diversity

Genetic structure

Microsatellite markers

Core collection

Genotype–environment associations

Research on genetic diversity and genotype–environment associations is valuable for understanding the distribution of genetic variation, elucidating the evolution of species and developing appropriate conservation and utilization strategies for wide populations. Genetic diversity is the driving force behind adapting to the environment, resulting from the continuous evolution of organisms (Chen, 2018; Hughes et al. 2008). By revealing the interaction between the environment and genotype, we can infer the adaptability of plants to climate change (Pritchard and Di Rienzo, 2010). The genetic diversity of a breeding population is crucial for their ability to counteract the effects of environmental changes. Therefore, it is important to maintain genetic variation at a high level or at a level similar to natural populations and understand the adaptation mechanisms after introduction (Chaisurisri and El-Kassaby, 1994). There are a few studies about assessing genetic diversity between breeding populations and natural populations of timber species. It was found that the assessment results varied due to the components of the breeding population or the types of makers used (Barros et al. 2022; Lv et al. 2020; Tinio et al. 2014).

Germplasm resources are the foundation of genetic improvement, and a core collection is an efficient way to manage and utilize these resources (Liu et al. 2020). The key to constructing a core collection lies in scientific methodology. The concept of core collection was pioneered by the Australian scientist Frankel, where the genetic diversity of a crop species and its relatives is represented with minimal repetition, and some germplasm materials not included in the core collection are gathers as a reserve collection (Balfourier et al. 2007). Recently, stable molecular makers have been utilized in core collections for several valuable timber species, such as Fraxinus (Yan et al. 2019), Dalbergia Odorifera (Liu et al. 2019), Ginkgo biloba (Yao et al. 2023), Phoebe bournei (Zhang et al. 2024), Pinus koraiensis (Yan et al. 2024) and Eucalyptus cloeziana (Lv et al. 2020). This approach allows for better retention of the full genetic diversity of tree species, leading to improved accuracy and reliability. However, in terms of determining sample size of core collection in perennial woody species, the majority of studies subjectively selected sampling ratio before extracting core collection, and few studies objectively select sampling size based on the changing pattern of genetic diversity indexes with the increasing sampling size through a massive non-repetitive random sampling.

Microsatellites (also known as simple sequence repeats, SSR) markers are a popular tool for the identification of forest genetic resources due to their co-dominant inheritance, with stability, high polymorphism, relative simplicity, and relatively low cost. EST-SSRs developed from the expressed sequence tags (ESTs) have higher informativeness and cross-species transferability than genomic SSRs (gSSRs) because the primers of EST-SSRs are designed from more conserved coding regions of the genome (Li et al. 2022; Varshney et al. 2006; Vašek et al. 2019). Currently, EST-SSR markers are widely used for genetic diversity analysis, germplasm identification, quantitative trait locus analysis, and core germplasm construction (Bian et al. 2023; Choi et al. 2018; Liu et al. 2023; Yao et al. 2023) and understanding the adaptive mechanisms of forest species (Castellana et al. 2021). In the genus Eucalyptus, more than 700 EST-SSR markers have been developed (Grattapaglia et al. 2015; Li et al. 2015; Liang et al. 2022). Moreover, genetic resource evaluation of Eucalyptus has been carried out for E. tereticornis (Song et al. 2016), Corymbia citriodora (Liu et al. 2016), E. pellita (Li et al. 2016), E. globulus (Costa et al. 2017), E. urophylla (Barros et al. 2022; Lu et al. 2018), E. grandis (Miranda et al. 2019), E. cloeziana (Lv et al. 2020) based on SSR markers in the past ten years, showing high genetic diversity of these tree species and providing useful information for assessing genetic resource.

Eucalyptus cloeziana F. Muell., the sole species of the Eucalyptus subgenus Queensland Eucalyptus in the myrtle family, is native to Queensland, Australia (Qin et al. 2020) and possesses outstanding biological properties. Due to its rapid growth, superior timber qualities, significant growth potential, strong resilience, rich genetic resources, and genetic structure, E. cloeziana has become a highly valuable medium to large diameter tree species for cultivation. It is an ideal species for sawn timber widely used in furniture, mining pillars, construction, pit wood, and various other fields. The Guangxi State-owned Dongmen Forest Farm in China boasts one of the largest Eucalyptus gene pools in Asia, with a history of planted eucalypts dating back to 1965. E. cloeziana has been successfully introduced from Queensland, Australia to Dongmen since the 1980s. Currently the farm preserves over100 families of E. cloeziana germplasm resources and their superior progeny, providing ample foundational materials for genetic enhancement. However, after 40 years of environmental adaptation, free pollination, and artificial selection, the germplasm resources exhibit complex relationships. The abundant genetic variation may lead to significant genetic redundancy, posing challenges for systematic management and reducing utilization efficiency. This situation is not conducive to long-term breeding programs and population stability.

In this study, twenty highly polymorphic EST-SSR markers developed based on the transcriptome of E. cloeziana were initially employed to analyze the genetic variation, establish a core collection of E. cloeziana breeding population, and understand the relationship between genetic variability and environmental variables in E. cloeziana provenances based on common garden tests. The main objectives of this study were to (1) explore the genetic diversity and population structure of E. cloeziana germplasms experiencing long-term environmental adaptation and artificial phenotypic selection; (2) construct a core collection for E. cloeziana; (3) uncover the main geoclimatic factors influencing the adaptive differentiation of E. cloeziana provenances. The results aim to provide a molecular basis for understanding E. cloeziana genetic diversity, effectively preserving and utilizing E. cloeziana germplasm resources, which provide better materials for E. cloeziana breeding and ensure the population inheritance of important traits.

Plant materials

The study material was planted in the State-owned Dongmen Forest Farm of Guangxi province in China. It is located in Dongmen Town, Fusui County, Chongzuo City, Guangxi province. The experimental material was a test stand of E. cloeziana seedstock in forest class 12 at Lekha Branch (107°55′50″E, 22°23′31″N), planted in 2001. The afforestation material was seedlots of 120 free-pollinated families from 14 provenances originated in Australian Tree Seed Centre (Canberra, Australia), and 5 provenances were secondary provenances from the State-owned Dongmen Forest Farm (Table 1). Of the five secondary provenances, D47 (B47) was taken from the free-pollinated offspring of the first batch of E. cloeziana test forest (B47) planted in 1983, and B85, S14127, S14425, and S12195 were taken from the free-pollinated offspring of the second batch of E. cloeziana test forest (4 native provenances in Queensland) planted in 1989 at Dongmen Forest Farm (Xiang et al. 2012). The experiment was designed in a grouped randomized block design with 120 families. The 120 families were randomly divided into two groups A and B, with single plots, 30 replications, and 2 m × 4 m plant spacing. After natural culling and repeated thinning, 118 families belonging to 19 provenances, each containing 3 to 10 trees, were preserved until now. In August 2021, we collected a total of 448 E. cloeziana accessions for DNA analysis from the 118 families. Using an overhead sampler, 100 to 200 g of fresh healthy leaves from each of the 448 individual plants were collected into self-sealed bags containing silica gel, then stored in an ultra-low-temperature refrigerator at -80℃. All voucher specimens were preserved in the Forestry College of Guangxi University.

Table 1

Number list of 19 provenances of *E. cloeziana*
Seedlot	Provenance	Sample size	Latitude	Longitude	Alt (m)
B85	Dongmen (East of Watsonville QLD)	3	N22°20′	E107°50′	110
D47(B47)	Dongmen (Cardwell QLD)	101	N22°20′	E107°50′	110
S10104	Mount Mulligan QLD	2	S16°54′	E144°51′	700
S11194	Goomboorian QLD	19	S26°07′	E152°47′	80
S12195	Dongmen (Hungry Hills Eidsvold QLD)	8	N22°20′	E107°50′	110
S14127	Dongmen (South of Ravenshoe QLD)	30	N22°20′	E107°50′	110
S14212	5–12 km S Helenvale	2	S15°45′	E145°15′	500
S14217	5–12 km S Helenvale	6	S15°45′	E145°15′	500
S14425	Dongmen (Gympie QLD)	4	N22°20′	E107°50′	110
S19155	Paluma Range QLD	17	S19°01′	E146°17′	300
S19157	Scrubby CK QLD	18	S15°41′	E145°16′	250
S4355	SF502 Gympie Veteran QLD	22	S26°07′	E152°40′	100
S4363	SF502 Gympie Dwonfield QLD	32	S26°05′	E152°41′	110
S4367	SF627 Toolara Wolvi QLD	14	S26°07′	E152°47′	120
S4369	SF952 Pomona 12 Yurol QLD	17	S26°22′	E152°55′	40
S4642	SF461 Cardwell QLD	23	S18°16′	E146°00′	20
S4672	SF194 Barron QLD	18	S17°15′	E145°30′	830
S4690	SF251 Ravenshoe QLD	19	S17°42′	E145°31′	1000
S5200	Cardwell QLD	93	S18°16′	E146°02′	6
Note: The seedlots were as allocated by Australian Tree Seed Centre (Canberra, Australian); SF: State Forest

DNA extraction and SSR genotyping

The total genomic DNA of E. cloeziana leaves was extracted using a modified cetyltrimethyl ammonium bromide (CTAB) method (Zheng et al. 2020). DNA extraction was completed by 1% agarose gel electrophoresis after 12 hours of incubation, and the results were visualized using an ultra-sensitive multifunctional imager (Amersham Imager 600, General Electric Company, USA). The total DNA content was quantified using an ultra-microprotein nucleic acid assay (ScanDrop100, Analytik Jena AG, Germany), and the qualified DNA samples were stored at -20℃.

A total of 20 informative polymorphic EST-SSR markers were used for genotyping the 448 samples. These markers were selected from 30 candidate EST-SSR markers that were developed based on the transcriptome sequencing data of E. cloeziana (Liang et al. 2022). The primer information and their fluorescent types are shown in the Supplementary file (Table S1). Polymerase chain reaction (PCR) amplification was performed in a 12.5 µL total reaction volume containing 1 µL template DNA (final concentration 3 ~ 5 ng), 0.25 µL of forward and reverse primers (final concentration 0.2 µM) respectively, 6.3 µL 2 × Easy Taq PCR Super Mix (TSE006, Beijing Tsingke Biotech Co., Ltd., China), and 4.7 µL double-distilled water with the following conditions: pre-denaturation at 98°C for 3 min, then denaturation at 98°C for 10 s, annealing at 58°C for 30 s, extension at 72°C for 1 min with 32 cycles, final extension at 72°C for 2 min, and then stored at 10°C. All the 20 microsatellite loci were amplified independently with 20 separate PCRs for each sample. The PCR products were separated by using the DNA sequencer ABI3730 XL (Applied Biosystems, Foster City, CA, USA) with a ROX-500 internal size standard, and allele sizes were scored using ABI GENEMAPPER v4.0. The output profiles were checked to confirm the allelic size.

Data analysis

Genetic diversity and structure

Genetic diversity parameters, including the number of alleles (Na), number of effective alleles (Ne), the fixation index (F), genetic differentiation among provenances (Fst), standard genetic distance (Ds), Nei’s Distance, the number of migrants (Nm), were estimated using GenAlEx 6.51 (Peakall and Smouse, 2012). Shannon’s information index (I), observed heterozygosity (Ho), expected heterozygosity (He), and evenness of alleles (E) were calculated using the R package poppr (Kamvar et al. 2013). Polymorphic information content (PIC) was calculated using the software CERVUS 3.07 (Kalinowski et al. 2007). R package poppr was used to perform molecular analysis of variance (AMOVA). Genetic common ancestry analysis between individuals was conducted using the R package LEA (Frichot and François, 2015). R package factoextra was utilized for cluster analysis (Kassambara and Mundt, 2020). The R package ggtree was employed to visualize the cluster tree (Yu et al. 2017).

Construction of the Core Collection

Random non-repetitive sampling was performed at different sampling sizes (5, 10, 15, 20, ..., 400) in 448 individuals of E. cloeziana in R. Each sample size was randomly selected 500 times. The genetic diversity parameters (Ar, I, Ho, He) of the sample population obtained from each sampling were calculated. The size of selections for the core breeding population is determined based on the trend of the Shannon’s information index (I) and standard errors of the expected heterozygosity (He). After obtaining the optimal sample size, corehunter in R was used to establish the core collection (Beukelaer et al. 2023). Finally, the permutation tests and principal coordinate analysis between the core collection and original germplasms were also performed in R.

Genotype–environment Associations

A total of 22 environmental variables (19 climatic variables and 3 topographical variables) were collected in this study (Supplementary file, Table S2). The climatic variables were downloaded using DIVA-GIS, an open computer program for mapping and geographic data analysis (Hijmans et al. 2005; Tian et al. 2015). The details of the 3 topographical variables (longitude, latitude, altitude) were provided by Dongmen Forest Farm. Genotype–environment associations were performed using two methods in R. Meanwhile, the 22 environmental variables were divided into 3 groups: 3 topographical variables (Lat-Alt), 11 temperature variables (bio1 - bio11) and 8 precipitation variables (bio12-bio19). We utilized the latent factor mixed model (LFMM) in the LEA package and classical redundancy analysis in the VEGAN package to identify loci potentially associated with some environmental variables (Caye et al. 2019; Frichot and François, 2015; Oksanen et al. 2001). SSR loci with p-values ≤ 1.0 E-5 or q-values ≤ 1.0 E-5 were considered environment-associated loci (EAL). Subsequently, a partial redundancy analysis (PRDA) was conducted using the R package “VEGAN” to determine how loci co-varied in response to the multivariate environment (Capblancq et al. 2018; Oksanen et al. 2001). Additionally, allele frequency was calculated in R using the hierfstat package (Goudet et al. 2022).

Genetic diversity

High polymorphism was detected at 20 microsatellite loci in the E. cloeziana provenances population (PIC = 0.840, 0.635 ~ 0.944) (Table 2). A total of 405 alleles were detected by 20 EST-SSRs. Single-locus parameters showed differences among loci, with Na ranging from 10 to 32 (mean 20.25), Ne from 3.127 to 17.520 (mean 8.292), and I from 1.453 to 3.035 (mean 2.310). The average observed heterozygosity (Ho = 0.744, 0.574 ~ 0.850) was lower than the expected heterozygosity (He = 0.855, 0.681 ~ 0.944), indicating that there were more homozygotes than heterozygotes in the breeding population of E. cloeziana. In summary, the parameters showed relatively high genetic diversity in the breeding population.

Table 2

Genetic diversity parameters of 20 EST-SSR markers
Primer name	Number of alleles (Na)	Effective number of alleles (Ne)	Shannon's information index (I)	Polymorphism information content (PIC)	Observed heterozygosity (Ho)	Expected heterozygosity (He)	Fixed index (F)
EC-e003	32	17.520	3.035	0.944	0.850	0.944	0.099
EC-e005	13	5.281	1.895	0.787	0.716	0.812	0.117
EC-e012	20	9.474	2.464	0.885	0.835	0.895	0.067
EC-e013	30	9.359	2.685	0.885	0.649	0.894	0.274
EC-e015	25	5.793	2.262	0.814	0.736	0.828	0.111
EC-e019	13	7.849	2.217	0.86	0.645	0.874	0.261
EC-e020	21	8.387	2.460	0.871	0.834	0.882	0.053
EC-e024	21	9.772	2.427	0.889	0.827	0.899	0.079
EC-e029	27	3.522	1.967	0.702	0.635	0.717	0.113
EC-e031	13	5.254	1.905	0.786	0.795	0.811	0.018
EC-e033	23	12.218	2.689	0.912	0.783	0.919	0.147
EC-e034	14	8.642	2.328	0.874	0.792	0.885	0.104
EC-e039	27	10.561	2.626	0.898	0.791	0.906	0.126
EC-e044	13	4.314	1.770	0.74	0.599	0.769	0.22
EC-e047	19	6.944	2.193	0.842	0.592	0.857	0.308
EC-e057	27	11.235	2.703	0.905	0.803	0.912	0.119
EC-e063	13	6.162	2.108	0.821	0.808	0.839	0.035
EC-e064	10	3.127	1.453	0.635	0.574	0.681	0.156
EC-e082	14	6.807	2.154	0.837	0.785	0.854	0.08
EC-e089	30	13.616	2.866	0.922	0.838	0.928	0.096
Mean	20.25	8.292	2.310	0.840	0.744	0.855	0.129

Of the 19 different E. cloeziana provenances (Tables 3), the D47(B47) source had the highest genetic diversity (Na = 15.30, Ne = 7.20, I = 2.14), followed by S5200 and S14127, with the lowest in S10104 and S14212. Among the 19 provenances, 16 provenances (84.21%) were found to have higher He (ranging from 0.65 to 0.90, with a mean of 0.82) than Ho (ranging from 0.76 to 0.85, with a mean of 0.757). This indicates that the provenances tested are not sufficiently heterozygous in their native habitat. It should be noted that, except for a few provenances (B85, S10104, S14212, S14217, and S14425) with small sample sizes (n = 2 to 6), the mean fixation indices (F) were greater than zero, and the F values of S4367 and S4690 were slightly higher than those of other provenances.

Table 3

Genetic diversity parameters of *E. cloeziana* provenances of EST-SSR markers
Seedlot	Sample Size (n)	Alleles Number (Na)	Effective number of alleles (Ne)	Shannon's information index (I)	Observed heterozygosity (Ho)	Expected heterozygosity (He)	Fixed index (F)
B85	3	3.90	3.43	1.27	0.78	0.83	-0.14
D47(B47)	101	15.30	7.20	2.14	0.75	0.83	0.10
S10104	2	3.10	2.92	1.05	0.83	0.82	-0.36
S11194	19	9.20	5.31	1.80	0.69	0.79	0.11
S12195	8	6.70	4.78	1.64	0.74	0.80	0.02
S14127	30	11.40	6.36	2.01	0.77	0.83	0.06
S14212	2	3.15	2.94	1.07	0.90	0.83	-0.45
S14217	6	6.05	4.66	1.62	0.80	0.83	-0.04
S14425	4	4.35	3.46	1.29	0.85	0.76	-0.30
S19155	17	10.25	6.50	2.00	0.75	0.84	0.08
S19157	18	10.10	6.30	1.99	0.75	0.85	0.09
S4355	22	10.80	6.11	1.98	0.74	0.83	0.09
S4363	32	11.40	5.95	1.96	0.74	0.82	0.09
S4367	14	9.00	5.73	1.90	0.65	0.84	0.20
S4369	17	7.75	4.51	1.68	0.74	0.78	0.02
S4642	23	10.65	5.96	1.94	0.74	0.82	0.07
S4672	18	10.00	6.12	1.94	0.75	0.82	0.07
S4690	19	9.80	5.90	1.93	0.66	0.82	0.19
S5200	93	15.00	7.05	2.13	0.77	0.83	0.07
Mean		8.837	5.325	1.754	0.757	0.82	-0.002

Genetic structure

Genetic differentiation and genetic structure

Molecular analysis of variance (AMOVA) (Tables 4) showed that the variance component accounted for only 3.24% among populations (provenances), indicating that the majority of genetic variation (96.73%) occurred within the populations. This reveals that genetic differentiation among provenances was not obvious. The random permutation test demonstrated that all levels of variation were significantly non-zero (p < 0.01) (Fig. 1), conforming the high reliability of the degree of differentiation at all levels.

Table 4

AMOVA result of EST-SSR markers
Source of variation	Degrees of freedom	Sum of squares	Variance component	Ratio of variance
Among provenances	18	806.418	0.5433	3.24
Among families within provenances	101	2098.744	0.5227	3.11
Among individuals within families	328	5549.504	1.1995	7.15
Within individuals	448	6505.099	14.5203	86.50
Total	895	14959.766	16.7858	100.00

Individual ancestry coefficients were estimated for all 448 samples, and the optimal number of ancestral populations (K) appeared to be three based on the minimal cross-entropy criterion (Fig. 2A). A bar plot illustrating individual ancestry coefficients was created for K values ranging from two to four (Fig. 2B). When K = 3, the majority of individuals from S14212, S4217, S19155, S19157, and S4355 shared a high proportion of ancestry coefficients (Cluster 1, red bar), while Cluster 3 (green bar) mainly consisted of individuals from S4363, S4367, S4369, S4642, S4672, S4690, S5200, S10104, S11194, D47(B47), S14127, S12195, B85, and S14425. Some individuals from provenances S4642, S5200, and D47(B47) exhibited alternative common ancestral characteristics (blue bar). These findings further suggest that the level of differentiation among E. cloeziana provenances was low, and gene flow was relatively frequent.

In order to illustrate the spatial genetic structure of the tested provenances more clearly, the co-ancestry of the 19 provenances with K of 3 was depicted on the map (Fig. 3). It was observed that the nearby provenances S14212, S14217, S19157, and the slightly distant S19155 share the green color as the primary ancestor and the dark blue color as the secondary ancestor. The early introductions of seedlings from Dongmen Forest Farm in 1983, the descendants of B85, D47(B47), S12195, S14127, and S14425, exhibit a similar ancestral composition to S4363, S11194, S4367, S4363, and S4369 from southern Queensland, with light blue as the primary ancestor and green and dark blue as secondary ancestors. The remaining five provenances showed similarity in terms of their common ancestry, with light blue as the primary common ancestor and dark blue as the secondary ancestor.

Clustering analysis of provenances

Nei's genetic distance matrix (Supplementary file Table S3) was calculated for 19 provenances, and the genetic distances ranged from 0.081 to 1.012 (mean 0.474). The genetic distance between S10104 and S11194 was the largest (1.012), while that between D47 (B47) and S5200 was the smallest (0.081). The UPGMA analysis also divided the 19 provenances into three clusters (Fig. 4). The five provenances (S11194, S4355, S4367, S4363, and S4369) in Cluster 1 were all from southeastern Australia. The five secondary provenances from Dongmen Forest Farm (B85, S12195, S14127, S14425, D47 (B47)) and the eight provenances (S14212, S14217, S19157, S4672, S4690, S19155, S4642, and S5200) from northern Queensland belonged to Cluster 2. Meanwhile, S10104 oriented from Mount Mulligan, Queensland, formed a separate cluster.

NJ clustering analysis was performed on the K-mean values of 448 E. cloeziana individuals based on the genetic distance among individuals (Fig. 5), and the individuals can be mainly divided into three groups. The results are the same as UPGMA clustering. Cluster 1 contains 13 provenances (including D47 (B47), S5200, S14127, S4642…), Cluster 2 contains a smaller number of provenances, of which S10104 is fully clustered in this group. Cluster 3 mainly contains individuals from S4355, S4363, S4367, S4369, and S11194.

Principal coordinates analysis

Principal Coordinate Analysis (PCoA) also categorized the 448 individuals of E. cloeziana into three distinct clusters, as illustrated in Fig. 6. The first two components explained 78% of the overall genetic variation across all provenances (Fig. 6A). The PCoA results were in alignment with previous analysis of genetic ancestry coefficients and clustering. Notably, it was observed that S10104 exhibited close genetic relatedness to S14212, S14425, B85, and S14217.

Extraction of a core collection

Sample size determination

The Shannon-Wiener index (I) trends and the standard errors of expected heterozygosity (He) were analyzed based on random samples collected at various sampling sizes. The analysis revealed that both I and the standard errors of He exhibited a plateau between sample sizes of 70 to 100 (Fig. 7). Consequently, a sample size of 85 was chosen as it demonstrated an allele retention rate of 91.60%, providing a more accurate depiction of the genetic makeup of the entire population.

Inspection of core collection

To assess the authenticity of the core collection, a comparison was made between the genetic diversity of the core germplasms and the original germplasm (Table 5). The data presented in Table 5 indicate that three genetic parameters (I, He, E) of the core collection exceeded those of the original germplasm, suggesting that the core collection is more effective in representing the genetic diversity of the original population.

Table 5

Comparison of genetic parameters between core collection and original collection
Collection	Number of samples	Number of Seedlots	Number of alleles (N)	Observed heterozygosity (Ho)	Expected heterozygosity (He)	Shannon-wiener index (I)	Alleles evenness (E)
Original collection	448	19	20.25	0.744	0.86	2.31	0.73
Core collection	85	17	18.55	0.712	0.89	2.47	0.78

To assess significant differences in genetic diversity between the core collection and the original collection, permutation tests were conducted 5000 times (Fig. 8). The observed values of Ho, I, and E fell within the confidence intervals, supporting the acceptance of the original hypothesis, which suggests that there are no significant differences between the core collection and the original collection. However, the observed value of He is outside the confidence interval, indicating a high genetic diversity within the core collection. Principal Coordinate Analysis (PCoA) was then performed to further examine the distinctions and distribution of individuals between the core collection and the breeding population (Fig. 9). The density distribution of the core collection generally mirrored that of the original population, thus reflecting original population’s distribution. Consequently, the core collection effectively minimized redundancy while preserving a substantial level of genetic diversity.

Genotype–environment Associations

Essential environment factors

To investigate the specific temperature and precipitation factors influencing the distribution of the species, considering the variation attributed to three topographical variables (longitude, latitude, altitude), a partial Redundancy Analysis (RDA) was conducted using R software. The selection of environmental variables was based on the criterion that the Variance Inflation Factor (VIF) should be less than 10 to reduce covariance and simplify the model (Guo et al. 2023).

The partial RDA indicated that the model was statistically significant for both temperature and precipitation variables. In the case of temperature variables, the adjusted R-squared value was 0.019 with a p-value of 0.001 for the 11 variables, while for precipitation variables, the adjusted R-squared value was 0.021 with a p-value of 0.001 for the 8 variables. The RDA axes 1 and 2 for the 11 temperature variables explained 76.82% and 11.73% of the genetic variation, respectively. Specifically, the temperature variables bio5 and bio9 exhibited significant loadings on RDA axis 1 (Fig. 10A). Provenances in subgroup 3 were predominantly located in regions characterized by higher bio5 and bio9 values, whereas subgroup 1 and subgroup 2 were situated in areas with lower bio5 and bio9 values. Regarding the 8 precipitation variables, RDA axes 1 and 2 explained 84.22% and 13.32% of the genetic variation, respectively. Among the precipitation variables, bio12 was associated with RDA axis 1, while bio14 was linked to RDA axis 2 (Fig. 10B).

Environment-associated loci (EAL)

The latent factor mixed model (LFMM) identified 1, 3, 3 SSRs significantly associated with various environmental variables, including 3 topographical variables (Lat-Alt), 11 temperature variables (bio1-bio11), and 8 precipitation variables (bio12-bio19), with a significant level of p < 1.0 E-5 (Supplementary file Fig. S1 and Table S4). Meanwhile, classical RDA identified 0, 5, 3 SSRs associated with three groups of environmental variables at a significance level of q < 1.0 E-5 (Supplementary file Fig. S2 and Table S4). One specific locus (EC-e024-165 bp) detected by LFMM, which is a BTB/POZ domain-containing protein known for regulating plant disease resistance, was found to be associated with the topographical variables (Zhang et al. 2019; Zhao et al. 2022). Several SSRs, such as EC-e039, EC-e015 and EC-e089, identified by both LFMM and classical RDA, were classified as environment-associated loci (EAL). To investigate the impact of essential environmental factors on these functional loci, allele frequencies were calculated (Table 6). The analysis revealed that the EAL ‘EC-e039-242 bp’ was notably associated with 11 temperature variables and exhibited a significant outlier pattern across all temperature and precipitation factors. Additionally, the allele frequency of EAL ‘EC-e015-132 bp’ was found to be relatively higher in four northern provenances (S4642, S5200, S10104, S19155).

Table 6

Allele frequency of 3 EALs and data of 2 essential precipitation factors (except 5 secondary provenances in Dongmen Forest Farm)
Region	Provenance	Altitude	bio5	bio9	bio12	bio14	Allele Frequency of EC-e039-242	Allele Frequency of EC-e015-132	Allele Frequency of EC-e089-102
North (Subgroup 3)	S10104	599	31.6	19.78	1165	9	0.25	0.5	0.25
	S4672	769	29.1	17.72	1707	33	0	0	0
	S4690	993	28.5	18.13	1426	32	0	0	0.03
	S4642	82	31.6	20.45	2159	29	0	0.02	0
	S5200	55	31.6	20.60	2199	29	0	0.05	0
Northern high-altitude (Subgroup 2)	S19157	317	30.9	23.05	1839	23	0	0	0
	S19155	471	30.6	18.33	1074	16	0	0.03	0.03
	S14212	209	31.4	23.67	1838	21	0	0	0
	S14217	209	31.4	23.67	1838	21	0	0	0
South (Subgroup 1)	S4367	136	29.7	16.07	1291	38	0	0	0
	S4355	121	30.2	16.03	1189	36	0	0	0
	S4363	121	30.2	16.03	1189	36	0	0	0.02
	S4369	79	28.9	18.08	1631	45	0	0	0
	S11194	136	29.7	16.07	1291	38	0	0	0

Genetic diversity

The observation of high genetic diversity within the E. cloeziana breeding population aligns with previous research findings (Deng et al. 2019; Lv et al. 2020). However, it is important to note that the markers utilized in earlier studies were sourced from closely related Eucalyptus species, potentially leading to reduced discriminatory power and affecting the precision of genetic diversity estimates. For instance, Wang et al. (2019) reported that across 14 natural populations of E. cloeziana, the average observed heterozygosity (Ho ) and expected heterozygosity (He) were 0.749 and 0.780, respectively, using 11 neutrally tested g-SSR makers. Deng et al. (2019) found that E. cloeziana in the four primary distribution areas exhibited Ho and He values of 0.508 and 0.825, respectively, with 14 neutrally untested Eucalyptus EST-SSR markers. Additionally, Lv et al. (2020) reported that across 17 E. cloeziana breeding populations, the average Ho and He were 0.432 and 0.682, respectively, using 12 SSR markers (comprising four g-SSRs prefixed with EMBRA and eight EST-SSRs prefixed with EUCeSSR). Notably, all these values were lower than the Ho (0.744) and He (0.855) observed in present study. Furthermore, the Ne and I in the current study were higher compared to previous research, suggesting that the breeding population under investigation holds significant importance for forest tree breeding programs. The EST-SSR makers derived from E. cloeziana were found to be highly polymorphic and discriminatory. It should be pointed out that the higher I and He values of the D47 (B47) and S5200 (both from Cardwell) populations may be attributed to their larger sample size (101 individuals), indicating superior growth performance and adaptability. This finding is consistent with Wang et al. (2019)’s study, where populations from Cardwell exhibited the highest genetic diversity and sample size among the 14 populations studied. Additionally, Turnbull (1979) noted increased genetic diversity in northern natural populations, particularly those from Cardwell or in close proximity to Ravenshoe, based on isozyme analysis.

Genetic differentiation

The Co-ancestry analysis classified 448 samples into three taxa (K = 3), with S14212, S14217, S19155, and S19157 forming a distinct group. These four provenances, when considering their geographic distribution, were found to cluster together. The proximity of S14212, S14217, and S19157 suggests frequent gene pool exchange among these provenances. Despite being in a coastal area, S19155 also clustered with them, possibly due to their shared high-altitude locations. It is plausible that S19155 is either not indigenous or is experiencing significant environmental disturbances. Additionally, both UPGMA clustering and NJ clustering methods categorized the 448 E. cloeziana individuals into three main groups. Among these, S4355, S4363, S4367, S4369, and S11194 formed a major cluster, while the remaining 13 provenances constituted another major group. S10104 stood out as a single taxon, potentially due to its unique growth environment at a higher altitude or its limited sample size (n = 2). In contrast to the previous analyses, PCoA revealed that S10104 clustered with S14212, S14217, S14425, and B85, likely because of their shared high-altitude origins. In summary, the 19 provenances were categorized into three subgroups. Subgroup1 comprised five southern provenances (S4367, S4355, S4363, S4369, S11194). Subgroup2 consisted of four northern high-altitude provenances (S14212, S14217, S19157, S19155). Subgroup3 included ten provenances: five secondary provenances in Dongmen Forest Farm (D47(B47), B85, S12195, S14127, S14425) and five northern provenances (S10104, S4672, S4690, S4642, S5200). Based on the geographical distribution structure, it can be inferred that the E. cloeziana provenances were divided into northern and southern clusters, aligning with previous studies (Deng et al. 2019; Lv et al. 2020).

Core collection

The core collection is a limited subset designed to reduce genetic redundancy while maintaining the highest possible genetic diversity of the entire population. In this study, the final sampling size consisted of 85 samples, which accounted for 18.97% of the original germplasm. The core collection was composed of 17 provenances, excluding two provenances that potentially exhibited low genetic diversity. The proportion of the core collection relative to the entire germplasm collection was comparable to that observed in certain woody plant species, such as Schima superba (15.25%, 115/754) (Yang et al. 2017), Catalpa bungei (23.96%, 46/192) (Fang et al. 2017), and Phoebe bournei (25%, 59/237) (Huang et al. 2020), but lower than that in Robinia pseudoacacia (32.07%, 338/1054) (Guo et al. 2022)d cloeziana (35%, 247/707) (Lv et al. 2020). This variation may be attributed to differences in sampling methodologies. In this research, the specific sample size of the core collection was determined based on the trends of I and the standard errors of He. I and He are genetic diversity metrics that consider the allelic composition of the sample as a whole. Additionally, I value aims to retain rare alleles when forming the core collection, while He specifically evaluates diversity within each locus. The standard errors of He provide a more precise estimation compared to He alone (Pruett and Winker, 2008; Thachuk et al. 2009). Therefore, the trends of I and the standard errors of He can serve as indicators of changes in genetic diversity within sample populations of varying sampling size. It is important to note that these sample populations were derived from a large pool of non-repeated random samples.

The determination of the appropriate sampling size is contingent upon identifying the point at which the curves reach a plateau. Subsequently, the corehunter package in R was utilized to extract a core collection following the identification of the optimal sampling size. The core collection developed in this study preserved the genetic variability present in the entire breeding population and effectively represented the original germplasm. This underscores the efficacy of the approach employed for core collection establishment, which could serve as a model for creating core collections for timber tree species. Furthermore, it is essential for core collections to maximize genetic variation, as the process of core collection establishment is an ongoing endeavor aimed at continual enhancement. Therefore, integrating morphological/phenotypic data with genetic data can provide valuable insights.

Genotype–environment Associations

The majority of provenances in subgroup 1 were found in regions characterized by lower annual precipitation and higher precipitation during the driest month. In contrast, subgroup 2 tended to be located in areas with higher annual precipitation and lower precipitation during the driest month. Turbull (1979) previously discussed that the distribution of E. cloeziana in Australia is primarily constrained by moisture availability during the dry season, which is directly linked to the temperature and precipitation patterns of the dry season. The findings of the Partial Redundancy Analysis (PRDA) in this study align with the conclusions of the earlier research. Among the three Environment-Associated Loci (EAL), the ‘EC-e039-242 bp’ locus, which is associated with all temperature and precipitation variables, encodes a folate-biopterin transporter. This transporter plays a role in transporting folate monoglutamate in tetrahydrofolate biosynthesis, thereby influencing plant responses to drought stress through photorespiration (Eudes et al. 2010; Foyer et al. 2009; Jiang et al. 2013). This suggests that the EAL ‘EC-e039-242 bp’, linked to all environmental factors, may be influenced by the combined effects of temperature and precipitation on photorespiration. The high allele frequency of ‘EC-e039-242 bp’ in S10104, compared to other provenances, may be attributed to the lowest bio14 levels in the provenance, coupled with relatively high bio5 and bio9 levels leading to drought conditions in the area, thereby enhancing photorespiration (Table 6). It has been suggested that S10104 has undergone adaption to drought conditions through positive selection. The second EAL, ‘EC-e015-132 bp’, which is associated with precipitation factors, is likely to encode prolyl 4-hydroxylase, an enzyme involved in prolyl hydroxylation leading to the formation of hydroxyproline. Hydroxyproline is a common component of a group of proteins known as hydroxyproline-rich glycoproteins (HPRGs), which play various roles in plant growth, development, cell-to-cell interactions, and cellular communication (Gorres and Raines, 2010). Some HPRGs have been found to be closely linked to plant disease resistance (Deepak et al. 2010), flooding stress (Mareri et al. 2019; Vlad et al. 2007), and drought resistance (Zhao et al. 2012). The allele frequency of the EAL ‘EC-e015-132 bp’ is relatively higher in four northern provenances (S4642, S5200, S10104, S19155). While S4642 and S5200 originate from Cardwell, a region with high rainfall, S10104 and S19155 come from areas with low rainfall. Therefore, it is possible that the two provenances in Cardwell have adapted to flooding conditions, while the others have adapted to drought, both through positive selection. The last EAL ‘EC-e089-102 bp’ associated with precipitation factors is a potential ADP-ribosylation factor (ARF) GTPase-activating protein. This protein is involved in regulating vesicular traffic and organelle structure (D’Souza-Schorey and Chavrier, 2006) and improving tolerance to salt and drought stress (Joshi et al. 2014). The allele frequency of this locus in four provenances (S10104, S4690, S19155, S4363) is higher compared to other provenances. The relatively low bio12 levels in these provenances suggest the presence of drought stress, but the provenances have adapted to it. These environment-associated loci play an indirect role in plant growth and development. Additionally, the allele frequency across the three EALs in S10104 is significantly higher than in other provenances, indicating that this provenance is unique and requires protection.

The study revealed that the EST-SSR markers derived from the transcriptome sequencing data of E. cloeziana serve as a robust tool for uncovering the relatively high genetic diversity and population structure of E. cloeziana. The 19 E. cloeziana provenances were categorized into three distinct subgroups. A core collection comprising 85 individuals was established to preserve a high level of genetic diversity and represent the original collection. Three environment-associated loci were identified through LFMM and classical RDA. Specifically, two temperature variables (maximum temperature of the warmest month and mean temperature of the driest quarter) and two precipitation variables (annual precipitation and precipitation of the driest month) were found to be significant environmental factors influencing the adaptive variation of the species. This research contributes to the establishment of a theoretical framework for germplasm resource management, as well as the conservation and utilization of E. cloeziana germplasm resources.

It should be pointed out that these findings are based on single SSR markers. Due to excellent timber properties of E. cloeziana, SNP loci associated with its timber traits can be developed based on transcriptome or reduced representation genome sequencing in subsequent studies for promoting improvement of tree breeding. Besides, soil properties are the another limited conditions for the distribution of E. cloeziana except environmental factors (Turnbull, 1979), thus clarifying the soil properties of planting ground is necessary.

Supplementary Information The online version contains supplementary material available at https://doi.org/XXXXX.

Acknowledgements The authors are deeply grateful to the staff of State-Dongmen Forest Farm of Guangxi Zhuang Autonomous Region in sample collection. The authors are deeply grateful to the editor and all anonymous reviewers for their helpful feedback and valuable suggestions.

Authors' contributions Hui Lu contributed to formal analysis, software, validation, investigation, writing - original draft, writing - review & editing. Xinyuan Liang contributed to methodology, investigation and visualization. Jianzhong Wang contributed to investigation and visualization. Tiandao Bai contributed to writing - review & editing. Weixin Jiang contributed to conceptualization, methodology, validation, writing - review & editing, and funding acquisition. All authors have read and agreed to the published version of the manuscript.

Funding This work was supported by the Key Research and Development Program of Guangxi Zhuang Autonomous Region (2021JBGS003).

Conflicts of interest The authors declare that they have no conflict of interest.

Ethics approval This article does not contain any studies involving animals or human participants performed by any of the authors.

Consent for publication (include appropriate statements) The authors consent for publication.

Availability of data and material (data transparency) Unavailable.

Code availability Not applicable.

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Genetic diversity, core collection construction and genotype–environment associations of Eucalyptus cloeziana F. Muell. (Myrtaceae) germplasm based on EST-SSR markers

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Abstract

Figures

Introduction

Materials and methods

Plant materials

DNA extraction and SSR genotyping

Data analysis

Genetic diversity and structure

Construction of the Core Collection

Genotype–environment Associations

Results

Genetic diversity

Genetic structure

Genetic differentiation and genetic structure

Clustering analysis of provenances

Principal coordinates analysis

Extraction of a core collection

Sample size determination

Inspection of core collection

Genotype–environment Associations

Essential environment factors

Environment-associated loci (EAL)

Discussion

Genetic diversity

Genetic differentiation

Core collection

Genotype–environment Associations

Conclusions and limitations

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1