Analyses of Genetic Diversity, Differentiation and Geographic

Article Analyses of Genetic Diversity, Diﬀerentiation and Geographic Origin of Natural Provenances and Land Races of Casuarina equisetifolia Based on EST-SSR Markers

1, 2, 1, 1 1 1 Yong Zhang †, Pan Hu †, Chonglu Zhong *, Yongcheng Wei , Jingxiang Meng , Zhen Li , Khongsak Pinyopusarerk 3 and David Bush 3,*

1 Research Institute of Tropical Forestry, Chinese Academy of Forestry, Longdong, Guangzhou 510520, China; [email protected] (Y.Z.); [email protected] (Y.W.); [email protected] (J.M.); [email protected] (Z.L.) 2 Experimental Centre of Forestry in North China, Chinese Academy of Forestry, Beijing 102300, China; [email protected] 3 CSIRO Australian Tree Seed Centre, GPO Box 1600, Canberra, ACT 2601, Australia; [email protected] * Correspondence: [email protected] (C.Z.); [email protected] (D.B.) Co-ﬁrst author. † Received: 13 February 2020; Accepted: 8 April 2020; Published: 10 April 2020

Abstract: Research Highlights: High variation of genetic diversity and differentiation among 27 seed sources within 14 natural provenances and 13 land race samples of Casuarina equisetifolia were found. High proportions of monoecious individuals may be present in some populations, as indicated by severe heterozytote deficiency and inbreeding found in many provenances and land races. The most probable origins of the land races were inferred according to the values of pairwise provenance differentiation and Nei’s genetic distances. Targeted introductions and testing of unrelated new accessions of C. equisetifolia from the Pacific and Philippines was proposed to identify Ralstonia-resistant genotypes. Background and Objectives: Casuarina equisetifolia was introduced to China a hundred years ago and has become a critically important tree species in coastal protection since the 1950s. Despite its importance, patterns of genetic variation, genetic relationships among natural provenances and probable origins of the land races remain unresolved. This has become a concern in China where Ralstonia solanacearum bacterial wilt has devastated plantations that are known to be from a narrow genetic base that urgently needs to be broadened. Materials and Methods: Fourteen natural provenances from Australia, Pacific islands and Southeast Asia, and 13 land race samples from parts of Asia and Africa outside the natural range were genotyped using 13 SSR (Simple Sequence Repeats) markers to characterize their allelic variation and genetic relationship. Results: Significant genetic diversity and differentiation among 27 seed sources within 14 provenances and 13 land race samples of C. equisetifolia was indicated. Significant heterozygote deficiency and inbreeding was indicated for a number of provenances, perhaps indicating a high proportion of monoecious parents in these populations. The most probable origins of the land races of the introduced countries were suggested according to the values of pairwise provenance differentiation (FST) and Nei’s genetic distances. Conclusions: We found significant genetic diversity and genetic differentiation among seed sources of C. equisetifolia. While individual land races do not appear to lack diversity, we were able to infer the origins of some, allowing targeted introductions of unrelated material to be made. In the case of the Chinese land race, targeting and testing new accessions from the Pacific and the Philippines may be a good strategy to identify Ralstonia-resistant genotypes.

Forests 2020, 11, 432; doi:10.3390/f11040432 www.mdpi.com/journal/forests Forests 2020, 11, 432 2 of 17

Keywords: provenance; land race; allelic diversity; genetic relationship; microsatellite marker; Forestsgeographic 2020, 11, x origin; FOR PEERRalstonia REVIEW solanacearum 2 of 20

1. Introduction 1. Introduction Casuarina equisetifolia ssp. equisetifolia (hereafter referred to as C. equisetifolia) belongs to the CasuarinaceaeCasuarinafamily equisetifolia and has ssp. a wideequisetifolia natural (hereafter occurrence, referred with the to most as C. southerly equisetifolia part) belongs of the range to the in Casuarinaceaenorthern Australia, family throughoutand has a wide southern natural Thailand, occurrence Malaysia,, with the Indonesia, most southerly the Philippines, part of the Melanesia range in northernand Polynesia Australia, (Figure throughout1)[ 1]. C. equisetifolia southern Thailand,is a nitrogen-fixing Malaysia, tree Indonesia, of considerable the Philippines, social, economic Melanesia and andenvironmental Polynesia ( importanceFigure 1) [1] in. C tropical. equisetifolia and subtropical is a nitrogen regions-fixing of tree the world.of considerable Over two social, million economic hectares andof Casuarina environmentalplantation, importance most of in which tropical are andC. equisetifolia subtropical, have regions been of plantedthe world for. woodOver two production, million hcoastalectares shelterbelts, of Casuarina vegetation plantation, rehabilitation most of which and are for ornamentalC. equisetifolia purposes, have been around planted the world for wood [2]. It production,is an important coastal plantation shelterbelts, species vegetation in India rehabilitation where it has undergoneand for ornamental genetic improvement,purposes around and the an worldimportant [2]. It and is an a significantimportant plantation tree in the Pacific,species wherein India planting where it is has thought undergone to have genetic commenced improvement, around and2700 an years important before and present a significant time [3]. tree in the Pacific, where planting is thought to have commenced around 2700 years before present time [3].

Figure 1. Natural distribution of Casuarina equisetifolia. The range of subsp. equisetifolia is contained Figurewithin 1. the Natural dotted linedistribution [1]. of Casuarina equisetifolia. The range of subsp. equisetifolia is contained within the dotted line [1]. In southern China, C. equisetifolia plays a critically important role in coastal protection. An estimated 300,000In southern hectaresof China,Casuarina C. equisetifoliaplantations, plays predominantly a criticallyC. important equisetifolia ,role have in been coastal established protection over. An the estimatedpast 70 years 300,000 along coastlines hectares of ﬁveCasuarina provinces plantations, (Hainan, Guangxi, predominantly Guangdong, C. equisetifolia Fujian and, Zhijiang) have been [4]. establishedThe plantations over immediately the past 70 years adjacent along to thecoastlines coast are of typically five provinces not harvested (Hainan, but Guangxi, left intact Guangdong, more-or-less Fujianpermanently and Zhijiang) for environmental [4]. The plantations amelioration immediately and protection adjacent functions. to the coast Inland are oftypically the coastal not harvested protection butzones, left which intact are more typically-or-les abouts permanently 200 m wide, for aenvironmental second belt, that amelioration is periodically and harvested, protection is establishedfunctions. Inlandin some of regions. the coastal The protection two contiguous zones, planting which are belts typically are typically about established 200 m wide, using a second the same belt, planting that is periodicallystock at any harvested, given location. is established in some regions. The two contiguous planting belts are typically establishedAlmost using 90% the of the same plantations planting stock established at any overgiven the location. past 30 years are clonal [5]. However, the geneticAlmost base 90% of these of the plantations plantations is veryestablished narrow, over with the only past 22 3 clones0 years identiﬁed are clonal from [5]. However, plantations the of geneticcoastal baseCasuarina of theseshelterbelts plantations of is Guangdong, very narrow Hainan, with only and 22 Fujian clones provinces, identified many from ofplantations which were of coastalthemselves Casuarina closely shelterbelts related [6]. of This Guangdong, is of particular Hainan concern and Fujian given provinces, that the shelterbelt many of plantings which were are themselvesrequired to closely remain related resilient [6] to. This both is biotic of particular and abiotic concern stressors. given In that recent the years,shelterbelt disease, plantings especially are requiredbacterial to wilt remain caused resilient by Ralstonia to both solanacearum biotic and [abiotic7], and stressors insect pests. In [recent8] have years, been disease a serious, especially problem. bacterial wilt caused by Ralstonia solanacearum [7], and insect pests [8] have been a serious problem. They have caused extensive mortality, resulting in declining productivity in those plantations that are harvested and reduced protection from wind and erosion in littoral shelterbelts. The effects of climate change, resulting in stronger winds, together with the need to plant on degraded coastal and inland sites with saline, alkaline, waterlogged and drought-susceptible soils also require a highly

Forests 2020, 11, 432 3 of 17

They have caused extensive mortality, resulting in declining productivity in those plantations that are harvested and reduced protection from wind and erosion in littoral shelterbelts. The effects of climate change, resulting in stronger winds, together with the need to plant on degraded coastal and inland sites with saline, alkaline, waterlogged and drought-susceptible soils also require a highly resilient breed of tree with sufficient diversity to provide adaptation to these stressors. Clearly, the widely-used set of 22 clones is less than ideal in this regard. There is now a strong imperative to identify new clones or seedling-based planting materials for southern China. As the coastal plantations are mostly not harvested, high resilience to strong winds, resistance to biotic and abiotic pests and ease of propagation are important selection traits. Reasonable growth rates are also desirable, though genetic improvement of this trait is not necessarily required. Based on the current situation of coastal Casuarina shelterbelts in China mentioned above, a wide range of seed sources of C. equisetifolia from the species’ natural distribution and land races were obtained from the Australian Tree Seed Centre for establishment of a gene bank and genetic testing, aiming at broadening and enriching the genetic base of C. equisetifolia in southern China [9]. However, the genetic diversity of these seed sources, and the genetic relationship of the seed sources derived from different regions, especially those between native provenances and land races, remain unclear. Genetic diversity provides the basis for adaptation and resistance to abiotic and biotic stresses and changing environment, and is therefore crucial for the long-term survival and development of forests because high genetic diversity allows natural selection to result in adaptability [10–12]. Meanwhile, high genetic diversity in domestication and breeding populations provides opportunity for breeders to develop new and improved breeds with desirable characteristics. In artificial establishment of new forests, maintaining high genetic diversity sometimes results in reduction of productivity, but lack of genetic diversity can also lead to total failure of plantations. For example, thousands of hectares of clonal plantations of Eucalyptus species were killed by an epidemic of Cryptosporiopsis eucalypti leaf blight fungus in one year in Thailand [13], and pink disease caused by Corticium salmonicolor fungus occasionally infests stands of Acacia mangium and acacia hybrid, killing up to 70% of trees in stands in India [14]. In southern China, bacterial wilt caused by Ralstonia solanacearum damages large-scale clonal plantations of C. equisetifolia following typhoons (Y.Z., personal observation) that are becoming more intense as a result of climate change-induced ocean surface warming [15]. Indeed, abiotic stressors resulting from climate change (e.g., changes to annual temperature regimes, amount and seasonality of rainfall) are likely to act synergistically with pathogens under some circumstances, increasing the potential for widespread damage to agricultural crops [16] and plantations [17] by pathogens that have not previously been problematic. Increasing genetic diversity to maximize the likelihood of disease resistance, even if a penalty in terms of growth results, is a major objective of the Casuarina clonal selection program for southern China. Genetic diversity studies of C. equisetifolia have been documented in some countries and regions. Morphological, allozyme and diverse dominant and codominant molecular marker techniques have been used to analyze and assess geographic variation and genetic diversity of C. equisetifolia [7,18–21]. However, these studies either used less-precise marker techniques or a limited sample of provenances; in particular, some land races which have been naturalized for many decades were not included. In this study, we employed Simple Sequence Repeats (SSR) markers. These markers are proven to be powerful for elucidating phylogenetic relationships and estimating genetic diversity. SSR markers are codominant, highly polymorphic, selectively neutral, uniformly distributed in plant genomes, and are characterized by hypervariability, high abundance and high reproducibility. This makes them generally preferable to other molecular markers such as RAPD (Random amplified polymorphic DNA), AFLP (Amplified Fragment Length Polymorphism), ISSR (Inter-simple Sequence Repeat) and RFLP (Restriction Fragment Length Polymorphism) in population genetic applications such as ours. In this study, genetic parameters of 27 seed sources (14 natural provenances and 13 land races) of C. equisetifolia were determined using SSR markers, aiming to achieve the following objectives: (1) to elucidate the genetic diversity and variation among natural provenances and land races across Forests 2020, 11, 432 4 of 17 their distribution ranges around the world, (2) to explore the genetic structure and relationship of the 27 seed sources, and (3) to attempt to infer the probable origins of the land races.

2. Materials and Methods

2.1. Seed Sources Twenty-seven seed sources (14 provenances and 13 land races) of C. equisetifolia from throughout its natural range and land races from 18 countries were used in this study. These seed sources can be divided into four broad regions, namely, Oceania natural (seven provenances), Asia natural (seven provenances), Asia introduced (eight seed sources) and Africa introduced (five seed sources). The first two groups of seed sources (seeds) were collected from natural forests in their natural range. The latter two groups were considered likely to be land races (i.e., exhibiting some genetic differentiation from their ancestral populations), having been cultivated for many generations. The female parents of progenies (seeds) of each seed source varied from four to ten depending on the population size. Detailed information of the seed sources are given in Table1.

Table 1. Information of Casuarina equisetifolia provenances used in the study.

CSIRO Altitude Rainfall No. of Code Seed Source Location Country Latitude Longitude Seedlot (m) (mm) Parents Oceania Natural 17862 AU1 Wagait, Northern Territory Australia 12 25S 130 44E 3 1740 6 18345 AU2 Chili Beach, Queensland Australia 12 39S 143 25E 1 1600 5 21311 GU Inarajan Beach Guam 13 15N 144 44E 3 2100 9 Papua New 20586 PNG Horno Is. Manus 02 19S 147 49E 1 1800 Bulk Guinea Solomon 18402 SB Kolombangara 08 07S 157 08E 2 3500 10 Islands 18040 TO Navutoka, Tongatapu Tonga 21 04S 175 04E 1 1800 10 18312 VU Efate Vanuatu 17 45S 168 18E 30 2400 Bulk Asia Natural 18244 MY1 Bako, Sarawak Malaysia 01 44N 110 30E 50 4000 4 18376 MY2 Tangjong Balau, Johor Malaysia 01 36N 104 16E 15 2600 4 18357 PH1 Narra, Palawan Philippines 09 19N 118 29E 10 2500 6 18117 PH2 San Jose, Mindoro Philippines 12 25N 121 03E 20 2000 Bulk 18154 PH3 Aklan, Panay Island Philippines 11 55N 121 23E 30 2100 5 18297 TH1 Ban Kamphuam, Ranong Thailand 09 21N 98 16E 10 3000 8 18298 TH2 Had Chaomai, Trang Thailand 07 33N 100 37E 2 1600 9 Asia Introduced 18267 CN1 Yanjiang, Guangdong China 23 00N 113 03E 4 1500 10 18268 CN2 Daodong, Hainan China 19 58N 110 59E 10 1700 14 18013 IN1 Cuttack, Orissa India 20 12N 86 38E 7 1400 6 18015 IN2 Balasore, Orissa India 21 30N 84 53E 2 1600 4 18119 IN3 Rameswaram, Tamil Nadu India 09 15N 79 20E 5 900 8 18287 LK Hambantota Sri Lanka 06 08N 81 07E 16 1000 Bulk 18128 VN Hai Thinh, Ha NamNinh Vietnam 20 22N 106 21E 2 2000 7 21331 BD Parki Beach, Chittagong Bangladesh 21 11N 91 48E 5 1700 5 Africa Introduced 18355 BJ Cotonou Benin 06 24N 02 31E 8 1300 8 18122 EG Montazah Egypt 31 16N 30 05E 13 200 8 18135 KE1 Malindi Kenya 03 15S 40 09E 7 900 10 18142 KE2 Kiliﬁ Kenya 03 38S 39 95E 20 1000 5 18565 MU Isle D’Ambre Mauritius 20 03S 57 39E 2 1700 4 Note: CSIRO, Commonwealth Scientiﬁc and Industrial Research Organization, Australia.

2.2. DNA Extraction and SSR Markers Screening About 200 seeds of each seed source (mixture of seeds from 4 to 14 mother trees) (Table1) were sown in a tray containing potting media (1.5:1:2 vermiculite:peat:river sands by volume) for germination and seedling growth. Due to varying germination rates of diﬀerent seed sources, 15–65 seedlings (mostly around 30) of each seed source, totally 480 seedlings, were obtained for genomic DNA extraction (Table2). Genomic DNA of the 840 seedlings was extracted from twigs using a modiﬁed Forests 2020, 11, 432 5 of 17

CTAB (Cetyltrimethylammonium Ammonium Bromide) method [22]. Thirteen SSR markers were developed from EST (Expressed Sequence Tag) sequences of Casuarina genus downloaded from NCBI (National Center for Biotechnology Information, https://www.ncbi.nlm.nih.gov). The marker primer sequences and repeat motif of each primer pair are shown in Table3.

Table 2. Genetic diversity indices for the 27 seed sources of C. equisetifolia.

Provenance N Na Ne AR Ho He FIS AU1 31 6.00 3.32 1.64 g 0.74 0.63 g 0.17 − AU2 25 4.08 1.76 1.39 bcdef 0.39 0.39 cdefg 0.00 GU 57 3.92 1.67 1.33 bcde 0.09 0.33 bcdefg 0.73 PNG 30 2.23 1.17 1.13 a 0.11 0.13 a 0.13 SB 30 5.77 2.58 1.53 fg 0.49 0.52 fg 0.06 TO 30 3.54 1.71 1.29 abcd 0.21 0.27 abcd 0.22 VU 28 4.31 1.92 1.43 bcdef 0.41 0.42 bcdef 0.02 ON 231 13.85 2.71 1.39 0.42 0.58 0.28 MY1 29 6.54 2.66 1.59 g 0.46 0.57 fg 0.19 MY2 30 5.54 2.85 1.34 bcde 0.24 0.33 bcde 0.27 PH1 15 6.39 2.41 1.11 a 0.04 0.10 a 0.60 PH2 16 1.92 1.22 1.52 defg 0.30 0.49 defg 0.39 PH3 41 4.46 2.07 1.56 g 0.41 0.56 fg 0.27 TH1 15 6.00 2.69 1.16 ab 0.12 0.14 ab 0.14 TH2 32 4.23 1.67 1.59 g 0.65 0.58 fg 0.12 − AN 201 13.62 3.63 1.41 0.35 0.65 0.46 BD 13 1.77 1.28 1.22 abc 0.17 0.22 abc 0.23 CN1 30 5.00 2.51 1.53 efg 0.45 0.51 ef 0.12 CN2 30 5.31 2.64 1.51 defg 0.45 0.49 def 0.08 IN1 39 5.54 2.51 1.51 defg 0.50 0.50 defg 0.00 IN2 46 5.54 2.56 1.52 fg 0.32 0.52 fg 0.38 IN3 30 5.00 1.93 1.43 cdefg 0.38 0.42 cdef 0.10 LK 15 3.00 2.18 1.52 fg 0.61 0.47 cdef 0.30 − VN 30 4.54 1.89 1.41 cdef 0.39 0.40 cdef 0.03 AI 233 11.31 3.08 1.46 0.40 0.40 0.00 BJ 65 5.85 2.06 1.45 cdef 0.36 0.44 cdef 0.18 EG 30 4.31 1.87 1.38 bcdef 0.30 0.37 bcdef 0.19 KE1 30 4.00 1.69 1.36 bcdef 0.29 0.35 bcdef 0.17 KE2 30 6.15 3.11 1.66 g 0.87 0.65 g 0.34 − MU 30 5.00 2.45 1.55 fg 0.57 0.54 fg 0.06 − AFI 185 11.62 3.24 1.48 0.34 0.66 0.48 Overall/mean 840 4.66 2.16 1.44 0.38 0.41 0.07

Note: N, number of individuals sampled; Na, number of alleles per locus averaged across the 13 loci; Ne, number of effective alleles per locus averaged across loci; AR, allelic richness; Ho, observed heterozygosity; He, expected heterozygosity; FIS, inbreeding coefficient. ON, Oceania Natural; AN, Asia Natural; AI, Asia introduced; AFI, Africa introduced; For AR and He values, values followed by the same letter are not significantly different according to the Friedman multiple range test (p < 0.05).

2.3. PCR Amplification and Genotyping The polymerase chain reaction (PCR) system, 10 µL in volume, was composed of 10 ng DNA template, 1.0 buffer (100 mM Tris-HCl pH 9.0, 80 mM (NH ) SO , 100 mM KCl, 0.5% NP-40), 2.0 mM × 4 2 4 MgCl2, 200 µM dNTP, 10 pmol Fluorescent-dUTP (Fermentas International Inc.), 0.5 µM forward primer, 0.5 µM reverse primer and 1 U Taq DNA polymerase (Fermentas International Inc.). A touchdown PCR program was implemented on an Applied Biosystems 2720 Thermal Cycler (Applied Biosystems, Foster City, CA, USA). The touchdown amplification protocol was as follows: denaturation at 94 ◦C for 5 min, followed with 20 cycles touchdown program of 30 s denaturation at Forests 2020, 11, 432 6 of 17

94 ◦C, annealing from 60 to 50 ◦C for 30 s with a decrease of 0.5 ◦C per cycle, and 30 s extension at 72 ◦C, then, 26 cycles of normal PCR with 30 s denaturation at 94 ◦C, 30 s annealing at 60 ◦C, and 30 s extension at 72 ◦C, ending with a final extension at 72 ◦C for 10 min. Capillary electrophoresis detection of amplified fragments of each EST-SSR locus was performed on an ABI 3130XL Genetic Analyzer (Applied Biosystems, USA) when PCR products were confirmed through agarose gel electrophoresis.

Table 3. Thirteen EST-SSR (Expressed Sequence Tag-Simple Sequence Repeats) primer information used for PCR ampliﬁcation of 27 seed sources of C. equisetifolia.

Primer Sequence Annealing Mg2+Concen. Primer Accession No. SSR Type (50-30) Tm (◦C) (mM) F: TGCAGCATCATCACTACT P3 FQ324509 (AGA) 54 1.5 6 R: ACTCCAACCAACTCTATTC F: TTTGTCTTCCCTACTCCG P15 FQ326101 (CTTCT) 52 1.5 5 R:AACCCTTTTCCACTTTCTTA F: TTCAAAACCCTAGCATCT P19 FQ327279 (CTT) 50 1.5 6 R: CATACCATTAACCAAAGC F: GCTGGAGGTGGTGGTGTT P24 FQ327965 (CT) 56 1.5 14 R: TATGGAATAGACGAGAAGTGAG F: CATCTGAACTTTTGAAACCCTA P26 FQ328032 (TCGCAC) 56 1.5 3 R:GGCATGGCTTCGTCTTGG F: CCTCAAACCAAGACCACC P36 FQ363031 (CAACGACAA) 52 2.0 3 R: CCGACTTCCATGCTCAAT F: GCCGAGTTATGGGGACGA P48 FQ363175 (TAG) 58 2.5 6 R: GGTGTTTGTGACGACGCT F: GCACGGTCGTCTTATTCT P52 FQ365340 (CGT) 52 2.0 6 R:TCGCTTCCCATACAAATC F: TGCCGCTGAACAAAATGA P56 FQ365696 (TG) 56 2.0 9 R:ATGGTCTCGCCTGGAATG F: ATGGGACATTTTGGTGAT P79 FQ374531 (CATCTT) 50 1.5 3 R:CTTTGCTTTAGGCGTTTT F: GCTTTGTCCTACCGTTTC P80 FQ374771 (GAC) 56 1.5 12 R:ATCACCACCATCCTCGTC F: CCCTGCTTCTGGTCATTC P81 FQ374894 (TC) 50 1.5 9 R: GATCTGTGGCTTTGCTTG F: ACACGCCCTGTGATAGTT P93 FQ376339 (TC) 54 1.5 9 R: GAGGAATTGAGCTTGCTG Note: Accession No., accession number of primers generated by GeneBank after being uploaded; Annealing Tm, annealing temperature; Mg2+ Concen., Mg2+ concentration; mM, millimole litre 1. · −

2.4. Data Analyses Genotype data was collected using software GeneMapper 4.0 (Thermo Fisher Scientific, Waltham, MA, USA). As the samples of individuals from different provenances or land races were uneven, rarefaction was required to calculate allelic diversity indices [23]. Software FSTAT (version 2.9.4) [24] was used to calculate genetic diversity parameters of 840 accessions of C. equisetifolia based on the 13 EST-SSR markers, including number of alleles per locus (Na), number of effective alleles per locus (Ne), observed heterozygosity (Ho), expected heterozygosity (He), polymorphism information content (PIC) and Hardy–Weinberg equilibrium (HWE) was tested as well. Friedman test was used to undertake statistical significance tests and multiple-range comparison of allelic richness (AR) and expected heterozygosity (He) among the 27 seed sources using SPSS 20.0 (IBM-SPSS Inc. Chicago, IL, USA) software. Partitioning of genetic diversity within and between provenances was examined by analysis of molecular variance (AMOVA) in Arlequin 3.0 software [25]. Nei’s genetic distance among and within seed sources and pairwise genetic differentiation (FST) for the 27 seed sources were conducted using GenAlEx 6.5 [26], and a dendrogram was constructed using the Unweighted Pair Group Method with the Arithmetic Averaging (UPGMA) method based on POPTREE2 software [27] with 10,000 bootstraps. Isolation by distance (IBD) among the 27 seed sources was tested using the Mantel test implemented in online software IBD [28], to analyze the relationship between genetic distance (GD) and geographical distance (GGD). The pairwise GGD (km) among the 27 seed sources were calculated based on Forests 2020, 11, 432 7 of 17 their GPS (Global Positioning System) coordinates of collected locations using online software: http://jan.ucc.nau.edu/cvm/latlongdist.html. The Mantel test was applied to the matrices of pairwise population differentiation (calculated as FST/(1-FST)), and of log-transformed geographic distances between seed sources with 1000 random permutations [29]. The significance of IBD values was assessed using 9999 permutations. A Bayesian clustering analysis was performed to infer genetic structure of the 27 seed sources using the software STRUCTURE 2.3.4 [30]. The number of assumed clusters (K) was set for a range of 1 to 30. The analysis was undertaken under the Admixture model with a “burn-in” of 100,000 followed by 50,000 iterations of the Markov Chain Monte Carlo (MCMC) model, and 10 replications were run for each K. In order to detect the optimal value of K inferred clusters, the parameter ∆K was calculated using the online Structure Harvester software [31].

3. Results

3.1. Microsatellite Loci Diversity and Polymorphism In total, 279 alleles were identified across the 13 microsatellite loci examined in the 840 individuals representing 27 seed sources of C. equisetifolia (Table S1). The number of alleles per locus (Na) and the average effective number of alleles per locus (Ne) ranged from 9 to 46 and 1.53 to 7.03, respectively. Polymorphism information content (PIC) of the 13 loci across all the 840 provenance accessions ranged from 0.33 to 0.83 with an average of 0.60, indicating that 10 out of the 13 loci presented high PIC, and only 3 loci (P26, P52 and P81) presented moderate PIC, according to the suggested criterion of high (PIC > 0.5), moderate (0.25 < PIC < 0.5) and low (PIC < 0.25) [32]. Observed heterozygosity (Ho) and expected heterozygosity (He) per locus ranged from 0.24 to 0.64 and 0.35 to 0.85, with an average of 0.39 and 0.63, respectively. The inbreeding coefficient per locus ranged from 0.31 to 0.62, with an − average of 0.39, suggesting excess of homozygotes in most loci, except locus P26. Detailed information of the genetic diversity indices revealed by 13 SSR loci is given in Table4.

Table 4. Genetic diversity indices at 13 EST-SSR loci for the 840 accessions within 27 seed sources of C. equisetifolia.

Locus Na Ne I PIC Ho He FIS F(null) P3 14 7.03 2.14 0.83 0.38 0.85 0.55*** 0.12 P15 21 2.42 1.29 0.52 0.27 0.57 0.53*** 0.12 P19 10 4.46 1.66 0.71 0.30 0.75 0.60*** 0.18 P24 25 2.90 1.58 0.58 0.24 0.63 0.62*** 0.21 P26 13 2.02 0.94 0.42 0.64 0.49 0.31*** 0.06 − P36 46 3.20 1.97 0.62 0.34 0.64 0.47*** 0.09 P48 24 4.50 1.85 0.72 0.48 0.76 0.37*** 0.08 P52 9 1.53 0.69 0.36 0.31 0.40 0.23*** 0.04 P56 25 3.52 1.76 0.67 0.32 0.70 0.54*** 0.11 P79 18 1.65 0.89 0.33 0.34 0.35 0.03*** 0.05 P80 32 3.71 1.95 0.70 0.36 0.72 0.50*** 0.08 P81 18 2.32 1.34 0.54 0.46 0.57 0.19*** 0.03 P93 24 4.75 1.95 0.77 0.57 0.79 0.28*** 0.07 mean 21.46 3.39 1.54 0.60 0.39 0.63 0.39 0.10 Note: Na, number of alleles per locus averaged across the 13 loci; Ne, number of effective alleles per locus averaged across loci; I, Shannon’s information index across loci; PIC, polymorphism information content; Ho, observed heterozygosity; He, expected heterozygosity; FIS, inbreeding coefficient; F(null), estimated frequency of null alleles; Significance levels are indicated by *** p < 0.001.

3.2. Provenance Diversity and Variation

Among the 27 seed sources, number of alleles (Na) ranged from 1.77 (Bangladesh) to 6.54 (Sarawak, Malaysia), with an average of 4.66, and effective alleles (Ne) by provenance (over all loci) ranged from 1.17 (Papua New Guinea) to 3.32 (Northern Territory, Australia), with an average of 1.54, Forests 2020, 11, 432 8 of 17 respectively. Allelic richness (AR) ranged from 1.11 (Palawan, Philippines) to 1.66 (Kilifi, Kenya), with an average of 1.44, and statistically significant differences (p < 0.05) between the 27 seed sources were discovered. Observed heterozygosity ranged from 0.11 to 0.53, with an average of 0.39, expected heterozygosity (He) ranged from 0.47 to 0.89, with an average of 0.55, and also, statistically significant differences (p < 0.05) between the 27 seed sources were found. The inbreeding coefficient (FIS) of 27 provenances and land races ranged from 0.34 (Kenya) to 0.73 (Guam), with an average of 0.29, − indicating significant heterozygotic deficits and excesses of homozygotes for a number of seed sources. It is noteworthy that natural provenances of Guam and PH1 (Philippines) presented extremely low Ho (0.09 and 0.04) and extremely high FIS (0.73 and 0.60), suggesting that the two provenances may be producing inbred seedling offspring (Table2). AMOVA analysis showed moderate genetic differentiation among provenances and land races (28.31%), with most of the variation observed among individuals (70.12%), and variation derived from regions was low (1.57%) (Table5).

Table 5. Analysis of molecular variance (AMOVA) of 27 seed sources derived from four regions based on 13 SSR loci.

Source of SS VC V% F-Statistics Variation

Regions 398.557 0.067 1.57 FST = 0.299 Seedlots 1606.684 1.206 28.31 FSC = 0.288 Individuals 4445.605 2.987 70.12 F = 0.016 CT − Total 6450.845 4.259

SS, sum of squares; VC, variance components; V %, percent variation; FST, differentiation among regions; FSC, differentiation among provenances; FCT, differentiation among individuals.

3.3. Genetic Structure of 27 Seed Sources

The average genetic differentiation index value (FST) for all pairwise provenances was 0.325, and between any two provenances, the index values varied from 0.024 to 0.502. The smallest FST value was between CN1 and CN2, which are both introduced subpopulations. In contrast, the two natural provenances PH1 and TO, which originated from two geographically distant countries, Philippines and Tonga, had the highest genetic differentiation (Table6). Minimum genetic di fferentiation between putative land races and the natural seed sources (i.e., FST between a land race and the natural seed source from which it is least differentiated) ranged between 0.03 and 0.145. It was low (<0.05) for BD, BJ, CN1, CN2 and IN1. It was high (>0.1) for EG and KE1. These values might be indicative of (i) likely origins of the land races and (ii) whether a putative land race is genetically distinct from the source population. No significant correlation between genetic distances and geographic distances among the 27 seed sources was revealed by the Mantel test (p > 0.05, R2 = 0.021) (Figure2). STRUCTURE and Structure Harvester analysis of up to K = 30 potential clusters indicated that the ∆K reached a maximum at K = 3. All three clusters are present among the Oceania seedlots but only two are present among the Asian natural seedlots. However, two marked secondary peaks at ∆K = 12 and 15 were also found, providing evidence for finer clustering of the 27 seed sources (Figures3 and4). Forests 2020, 11, 432 9 of 17

Table 6. Pairwise genetic diﬀerentiation indices (FST) for the 27 seed sources of C. equisetifolia. The upper right quadrant indicates the natural provenance with the lowest FST for each land race.

Natural Provenances Land Races AU1 AU2 GU PNG SB TO VU MY1 MY2 PH1 PH2 PH3 TH1 TH2 BD CN1 CN2 IN1 IN2 IN3 LK VN BJ EG KE1 KE2 MU AU1 0 AU1 AU1 AU2 0.162 0 GU 0.241 0.170 0 PNG 0.320 0.198 0.124 0 SB 0.159 0.099 0.186 0.206 0 SB TO 0.240 0.305 0.359 0.465 0.328 0 TO TO TO VU 0.173 0.055 0.127 0.149 0.077 0.303 0 VU MY1 0.121 0.135 0.173 0.237 0.103 0.221 0.105 0 MY1 MY1 MY1 MY2 0.157 0.138 0.197 0.234 0.109 0.295 0.109 0.114 0 PH1 0.331 0.198 0.127 0.053 0.218 0.502 0.146 0.250 0.261 0 PH1 PH2 0.134 0.106 0.105 0.109 0.099 0.268 0.064 0.093 0.128 0.129 0 PH3 0.144 0.104 0.128 0.180 0.086 0.243 0.077 0.111 0.082 0.196 0.085 0 TH1 0.119 0.124 0.189 0.225 0.110 0.237 0.099 0.067 0.067 0.242 0.102 0.094 0 TH1 TH2 0.131 0.144 0.192 0.231 0.070 0.271 0.122 0.099 0.059 0.242 0.121 0.086 0.057 0 TH2 BD 0.334 0.321 0.149 0.321 0.347 0.499 0.271 0.299 0.311 0.042 0.260 0.244 0.284 0.288 0 CN1 0.136 0.126 0.200 0.246 0.094 0.212 0.099 0.036 0.130 0.251 0.093 0.135 0.079 0.115 0.342 0 CN2 0.143 0.123 0.203 0.240 0.102 0.215 0.105 0.050 0.137 0.248 0.110 0.131 0.081 0.119 0.338 0.024 0 IN1 0.168 0.132 0.183 0.218 0.030 0.312 0.077 0.089 0.096 0.222 0.107 0.093 0.098 0.065 0.323 0.084 0.094 0 IN2 0.112 0.116 0.174 0.229 0.140 0.110 0.121 0.089 0.131 0.241 0.100 0.084 0.103 0.122 0.274 0.097 0.090 0.140 0 IN3 0.158 0.186 0.254 0.337 0.224 0.077 0.209 0.131 0.193 0.349 0.178 0.159 0.149 0.189 0.346 0.123 0.134 0.219 0.051 0 LK 0.169 0.186 0.271 0.301 0.140 0.292 0.162 0.084 0.134 0.340 0.153 0.157 0.069 0.099 0.392 0.077 0.074 0.127 0.141 0.179 0 VN 0.166 0.155 0.251 0.303 0.205 0.119 0.187 0.124 0.180 0.320 0.162 0.151 0.132 0.172 0.354 0.105 0.105 0.216 0.054 0.037 0.147 0 BJ 0.176 0.102 0.088 0.091 0.089 0.280 0.049 0.103 0.088 0.096 0.053 0.066 0.088 0.085 0.226 0.112 0.117 0.073 0.102 0.190 0.147 0.164 0 EG 0.145 0.216 0.321 0.400 0.270 0.149 0.261 0.180 0.247 0.445 0.206 0.204 0.181 0.210 0.452 0.171 0.172 0.286 0.086 0.075 0.214 0.061 0.238 0 KE1 0.179 0.235 0.333 0.414 0.258 0.159 0.274 0.185 0.264 0.450 0.213 0.210 0.200 0.216 0.464 0.157 0.179 0.274 0.116 0.076 0.243 0.076 0.251 0.101 0 KE2 0.080 0.193 0.247 0.335 0.173 0.216 0.188 0.107 0.171 0.353 0.135 0.156 0.139 0.156 0.333 0.121 0.137 0.170 0.113 0.137 0.166 0.144 0.183 0.160 0.139 0 MU 0.113 0.120 0.179 0.235 0.071 0.324 0.107 0.099 0.109 0.234 0.110 0.101 0.104 0.068 0.315 0.110 0.117 0.071 0.124 0.208 0.150 0.184 0.091 0.204 0.243 0.139 0 Forests 2020, 11, x FOR PEER REVIEW 5 of 20 provenances PH1 and TO, which originated from two geographically distant countries, Philippines and Tonga, had the highest genetic differentiation (Table 6). Minimum genetic differentiation between putative land races and the natural seed sources (i.e., FST between a land race and the natural seed source from which it is least differentiated) ranged between 0.03 and 0.145. It was low (<0.05) for BD, BJ, CN1, CN2 and IN1. It was high (>0.1) for EG and KE1. These values might be indicative of (i) likely origins of the land races and (ii) whether a putative land race is genetically distinct from the source population. No significant correlation between genetic distances and geographic distances among the 27 seed sources was revealed by the Mantel test (p > 0.05, R2 = 0.021) (Figure 2). STRUCTURE and Structure Harvester analysis of up to K = 30 potential clusters indicated that the ΔK reached a maximum at K = 3. All three clusters are present among the Oceania seedlots but only two are present among the Asian natural seedlots. However, two marked secondary peaks at ΔForestsK = 122020 and, 11 15, 432 were also found, providing evidence for finer clustering of the 27 seed sources (Figure10 of 17s 3 and 4).

3.4. Genetic Relationship of 27 Seed Sources Relationships between the 27 seed sources from 18 countries are summarized in a UPGMA dendrogram based on Nei’s unbiased genetic distances (Figure 5). According to the dendrogram, two main clusters are evident. The first group comprised 8 seed sources, and the second group comprised 19 seed sources. Bootstrap support for further bifurcation is typically moderate, ranging from 17% to Figure 2. Relationship between pairwise genetic distances (GD) and geographic distances (GGD) for 85%.Figure Some 2. Relationship seed source betweens appear pairwise to be closelygenetic distancesrelated, such (GD) asand C Ngeographic1 and CN distances2 (Chinese (GGD) land for race s), PH1 the 27 seed sources. No signiﬁcant correlation was discovered between GD and GGD (p > 0.05). (Phthe 27ilippines) seed sources. and BDNo (significantBangladesh correlation), PNG (Papuawas discovered New Guinea) between and GD P andH1, GGD PNG (p and > 0.05). BD .

Figure 3. Genetic cluster number inferred by Structure Harvester software for K ranging from 1 to 30. Figure 3. Genetic cluster number inferred by Structure Harvester software for K ranging from 1 to The delta K shows a clear peak at K = 3, and two marked secondary peaks in delta K = 12 and 15 were 30. The delta K shows a clear peak at K = 3, and two marked secondary peaks in delta K = 12 and 15 found. were found.

Forests 2020, 11, x; doi: FOR PEER REVIEW www.mdpi.com/journal/forests Forests 2020, 11, 432 11 of 17

Figure 4. Population structure inferred by Bayesian cluster analyses (STRUCTURE) for 840 individual genotypes from 27 seed sources representing 7 Oceania naturalFigure provenances, 4. Population 7 Asia structure natural inferred provenances, by Bayesian 8 Asia cluster introduced analyses land (STRUCTURE) races and 5 Africa for 840 introduced individual land genotypes races. fromThe delta 27 seed K method sources representinggave K = 3 as 7 the Oceania optimal natural numberprovenances, of structures, 7 Asia large natural peaks provenances, corresponding 8 Asia to K introduced = 12 and 15 land clusters races are and also 5 Africa indicated introduced (Figure land 4). races. The delta K method gave K = 3 as the optimal number of structures, large peaks corresponding to K = 12 and 15 clusters are also indicated (Figure4).

Forests 2020, 11, x; doi: FOR PEER REVIEW www.mdpi.com/journal/forests Forests 2020, 11, 432 12 of 17

Figure 5. Dendrogram generated by the Unweighted Pair Group Method showing the 27 seed sources Figure 5. Dendrogram generated by the Unweighted Pair Group Method showing the 27 seed sources of C. equisetifolia clustered as two main groups, based on Nei’s of C. equisetifolia clustered as two main groups, based on Nei’s unbiased genetic distance derived from unbiased genetic distance derived from 13 EST-SSR markers. Different colour codes are used to differentiate the four regions of seed sources: red: Oceania natural, purple: 13 EST-SSR markers. Different colour codes are used to differentiate the four regions of seed sources: Asia natural, green: Asia introduced, black: Africa introduced. Group I and Subgroups IIa–c correspond to specific clusters in the K = 3 STRUCTURE analysis. red: Oceania natural, purple: Asia natural, green: Asia introduced, black: Africa introduced. Group I and Subgroups IIa–c correspond to specific clusters in the K = 3 STRUCTURE analysis.

4. Discussion Genetic differentiation among seed sources was substantial, with AMOVA indicating 28% of Forests 2020, 11, x; doi: FOR PEER REVIEW www.mdpi.com/journal/forests variance partitioned among natural provenances. UPGMA cluster analysis of the natural and introduced populations revealed two main clusters of subpopulations (Figure5). The first cluster comprised AU1 and TO natural provenances and putative land races from India (IN2, IN3), Vietnam, Egypt and Kenya (KE1, KE2). The second main cluster comprised a mix of Asian, Pacific and Australian (AU2) provenances along with Asian and African land races. The optimal Bayesian clustering generated by STRUCTURE indicated K = 3 clusters, a result that prima facie is at odds with the UPGMA cluster analysis. However, the membership of the STRUCTURE clusters corresponds closely with those delineated by UPGMA. UPGMA cluster I, comprising 8 seed Forests 2020, 11, 432 13 of 17 sources, corresponds exactly with one of the three STRUCTURE clusters, indicated by green bars in Figure4. The K = 3 “blue” cluster corresponds to UPGMA cluster IIa, while the “red” cluster corresponds with members of UPGMA clusters IIb and IIc, also noting that bootstrap support is in many cases only weak to moderate for many of the UPGMA clusters. Three genetic clusters are sufficient for interpretation of the probable broad regional origins of Asian and African land races. However, the finer of K = 12 and K = 15 clusters also revealed some potential affinities between wild and land race populations not resolved by the K = 3 model. An example is the affinity between AU1 and KE2, evident at both K = 12 and K = 15. The K = 12 and K = 15 models indicate Tonga (TO) as a more-specific source of introduction for some Asian (IN2, IN3, VN) and African (EG, KE1) land races than the K = 3 model, which resolves these land races to either Australia or Tonga. In fact, it would be surprising if these land races have originated from Tonga, as this nation has not been a major exporter of seed. It would seem more likely that another population in Australia, not included in this study, may be the actual origin. At K = 15, possible over-fitting of clusters is also evident, with splitting of the Guam material into two distinct clusters, for example. Overall, the clustering observed from both UGMA and STRUCTURE analyses was surprising given the previous findings of Hu et al. [9], who studied geographic patterns of seedling morphology and growth of many of the seedlots used in this study. Their study indicated marked clustering of (i) Australian and Pacific provenances (i.e., Oceania) and African land races, and (ii) Asian provenances and Asian land races. A possible reason for the difference might be that the growth and morphological traits studied by Hu et al. [9] are controlled by loci that are under selection, while the present study used markers that are assumed to be neutral. Quantitative trait and neutral marker divergence have been observed in a number of organisms [33], including tree species [34]. A previous study using AFLP dominant markers [20] also indicated mixed clustering of Asian and Oceania natural populations. In accord with the AMOVA analysis, the overall FST value of 0.29 (Table5) among the 27 seed sources is considered to be high. This is not surprising considering the low probability of gene flow among the seed sources due to geographic isolation. The pairwise genetic differentiation indices (Table6) of the 27 seed sources revealed that the most closely related pair of provenances were CN1 and CN2 (0.024), which are both samples of what could be considered to be a single land race. Gene flow between them is unlikely as they are separated by about 400 km. We therefore think it is probable that the two stands were established using the same seed source or one of them was established with seeds collected from the other. Similarly, very low pairwise FST values obtained between land races and natural provenances (for example China versus Malaysia, China versus Thailand, Benin versus Philippines) may imply that these natural provenances are the origins of the land races. Natural provenances PNG, PH1 and TO all appear to be quite strongly differentiated from the Chinese land race samples. These might provide good sources for further testing and potential widening of the genetic base in China. Allilic richness and heterozygosity are two the most important parameters for estimating genetic diversity [35]. Overall, genetic diversity among the wild and introduced seed lots was moderate (AR = 1.44, He = 0.41), though there was considerable variation among seedlots (Table2). Expected heterozygosity was markedly lower than estimates for natural provenances of two casuarina species, C. junghuhniana (He = 0.65) and C. cunninghamiana (He = 0.70), using EST-SSR markers [21]. The comparatively high diversity estimates for C. junghuhniana and C. cunninghamiana might be attributed to limited anthropogenic interference with the natural populations. Casuarina junghuhniana grows naturally on the slopes of volcanoes and undisturbed areas between 550 and 3100 m at altitude in Indonesia [36], and C. cunninghamiana grows naturally along stream banks and swampy areas in eastern Australia [37] and has become a protected species in NSW (New South Wales, Australia) due to past over-exploitation for fuelwood and building materials. Both possess less-extensive ranges than the cosmopolitan C. equisetifolia. Among seedlots from the Oceania region, PNG had particularly low genetic diversity (AR = 1.13, He = 0.13) while in the Asian natural group, PH1 and TH1 both lacked diversity (AR < 0.12, He < 0.15). Forests 2020, 11, 432 14 of 17

It is often the case that genetic diversity is low in land races due to introductions of a narrow range of genetic materials. In this case, however, genetic diversity in the introduced populations was moderate to high, with only the Bangladesh (BD) accession having AR and He below the range 1.36 to 1.66 and 0.35 to 0.65, respectively. Though heterozygosity was calculated over regions (Table2), interpretation of these values is difficult given the lack of observed regional clustering (Figure5). Low expected heterozygosity of some natural provenances and land races of C. equisetifolia may be indicative of genetic diversity loss within provenances, small sample sizes and/or introductions of a narrow initial genetic base in the case of land races. Genetic diversity loss can be caused by factors including breeding system, habitat fragmentation and artificial selection. Casuarina equisetifolia is mainly dioecious, but, within subpopulations, possesses varying proportions of monoecy from less than 10% [38–40] to as high as 80% on Guam [41]. Provenances with a high proportion of monoecious individuals are likely to produce a high proportion of self-offspring [42], and for this reason, they are commonly culled from open-pollinated breeding populations. The Guam population provides an excellent case-in-point, exhibiting low AR and He, a major deficit of observed heterozygotes and extremely high FIS, providing strong evidence of inbreeding in this population. Lack of genetic diversity and inbreeding in the Guam population may be a factor contributing to the widespread occurrence of Ralstonia bacterial wilt on the island [43], as there may be a lack of adaptive genes in the narrow genetic base. It is not known whether other seedlots with high FIS such as PH2 and the IN2 land race also have elevated monoecy. Inbreeding (FIS) appears to be elevated among the Asian natural populations, though TH2 is an exception. As inbreeding depression is very common among forest trees, and a number of Asian provenances have been shown to be very vigorous in plantation trials [4], it is probable that selection against homozygous individuals would be strong. This would mitigate the accumulation of homozygotes over successive generations from breeding among relatives that would be required for the observed high FIS values, and suggests that the observed inbreeding in these Asian seedlots might be the result of selfing, implying a proportion of monoecious individuals. Geographical origin-determination for land races of exotic tree species would help to guide further introductions and breeding program composition. Based on the UPGMA dendrogram (Figure5) and Bayesian clustering dendrogram (Figure4), together with pairwise genetic distances (Table2), we can infer the probable origins of some land races. For example, in addition to the aforementioned inferences that the most probable origin of land races of China was Southeast Asia, we can infer that the origin of the Bangladesh land race might be either PH1 or PNG, since both provenances had very close genetic distance with the Bangladesh subpopulation. But the lower pairwise FST values between Bangladesh and Philippines 1 (0.042) than that between Bangladesh and PNG (0.321) supported that the provenance of Philippines 1 is more likely the origin of land race of Bangladesh. Ralstonia solanacearum bacterial wilt is an ongoing problem that has caused extensive mortality in coastal shelterbelts of C. equisetifolia [7]. There is an urgent need to develop a diverse set of new clones, with superior disease resistance, for coastal plantings in southern China. These will be particularly important for those industrial plantations situated inland of those immediately adjacent to the sea, as selection for growth and other industrial properties as well as disease resistance will be necessary. A better alternative to clonal forestry for the permanent environmental plantations is establishment using diverse seedling-based stock. Significant provenance-level variation in resistance to Ralstonia solanacearum infection has been found, especially among some Oceania provenances in a field trial of international provenance trial (Research Institute of Tropical Forestry). These provenances do not grow as rapidly as provenances from South-east Asia or the Chinese land races [2,4], though in their coastal protection function, this is not as important as long-term survival, which requires disease resistance. Since it would appear improbable that Oceania and Philippines provenances are represented among the land races of China, it would be prudent to develop new clones combining the fast growth of plus trees of the South-east Asian provenances or China’s land races and disease-resistant traits of Oceania through artificial crossing or open-pollinated hybridizing orchards. Forests 2020, 11, 432 15 of 17

5. Conclusions This study revealed significant differentiation among 27 seed sources within 14 natural provenances and 13 land race samples of C. equisetifolia using 13 EST-SSR markers. Allelic diversity indices and inbreeding coefficient estimation indicated that there were significant heterozygotic deficits and excesses of homozygotes in many provenances and land races, implying that significant proportions of monoecious individuals may be present in some populations. The most probable origins of the land races were suggested according to the values of pairwise provenance differentiation (FST) and Nei’s genetic distances. The results of our neutral marker-based study only partially accorded with a previous morphology and growth study, suggesting that key traits may be under selection.

Supplementary Materials: The following are available online at http://www.mdpi.com/1999-4907/11/4/432/s1: Table S1: Genotyping data of 840 individuals of 27 C. equisetifolia seed sources. Author Contributions: Conceptualization and methodology, C.Z., K.P. and D.B.; validation, Y.Z., P.H. and D.B.; formal analysis, Y.Z.; investigation, Y.Z., P.H., Y.W., J.M. and Z.L.; resources, C.Z. and K.P.; data curation, Y.Z. and P.H.; writing—original draft preparation, Y.Z; writing—review and editing, D.B. and K.P.; supervision, project administration and funding acquisition, C.Z. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by the National Natural Science Foundation of China (Grant No. 31770716 and 31470634), the Fundamental Research Funds for the Central Non-profit Research Institution of CAF (No. CAFYBB2018SZ002 and CAFYBB2018ZB003). Acknowledgments: The survey and sample collection work was kindly supported by numerous local forestry sectors, such as Jinjiang Forestry Bureau and Chihu Forest Farm in Fujian province, Dianbai Forest Institute in Guangdong province, Daodong Forest Farm in Hainan province. We are particularly grateful to Gongfu Ye and Sen Nie of Fujian Academy of Forestry Science, for their key assistance in sample and relevant information collection. Conflicts of Interest: The authors declare no conflict of interest.

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