DOI: 10.5061/dryad.nzs7h451m
title: Datasets for microsatellite genotype for natural populations and seedlings from six mother trees, and seedling survival and growth records

description: To assess genetic factors that affect the fitness of seedlings of
 Rubroshorea curtisii, a dominant canopy tree species in hill dipterocarp
 forests, the inter- and intra-population genetic structure of individuals
 with reproductive stage and, survival rate and seedling growth performance
 in relation to the bi-parental genetic relationship were studied. A
 Bayesian based clustering analysis revealed that three genetically
 distinct clusters were observed in almost all populations throughout the
 distributional range of the species in Malay Peninsula and provided the
 optimum explanation for the genetic structure of 182 mature individuals in
 two permanent plots in a hill dipterocarp forest. The two clusters showed
 larger genetic differentiation from the ancestral admixture population,
 but the other one was not differentiated. A total of 460 seedlings derived
 from six mother trees in the plot were raised in a nursery, and their
 pollen donors were identified using genetic marker based paternity
 assignment. Seed weight, bi-parental genetic relatedness, and bi-parental
 genetic heterogeneity based on the clustering analysis were used to
 analyze their effects on seedling fitness. The bi-parental larger genetic
 heterogeneity was associated with a significantly higher probability of
 seedling survivorship, and likewise, higher performance of vertical growth
 of the seedlings; but the seed weight and genetic relatedness did not
 significantly affect those. This evidence suggests that fitter seedlings
 derived from mating between parents with different genetic clusters
 contribute to maintaining genetic diversity through negative
 frequency-dependent selection and may have an important role in adaptation
 in the tropical forest plant community.
description: Leaf or inner bark tissue was collected from 134
 <em>R.</em><em> curtisii</em> individuals,
 representing nine natural populations throughout the natural distribution
 across Malay Peninsula (Table 1, Fig 1). The tissues were collected from
 <em>R. curtisii</em> individuals that were at least 20 m
 apart, to avoid collecting samples from genetically related individuals,
 regardless of age or size of trees. Samples were stored at -20 ˚C, prior
 to DNA extraction. Semangkok Forest Reserve, a designated hill dipterocarp
 forest conservation area, is in and governed by the Selangor state; it is
 60 km north of Kuala Lumpur, on the Malay Peninsula and is a designated
 hill dipterocarp forest conservation area. In 1993, Niiyama <em>et
 al. </em>(1999) established a 6-ha permanent plot (200 m × 300 m) in
 an undisturbed forest on a narrow ridge and steep slope, ranging from 340
 to 450 m above sea level (3°37'07''N,
 101°44'15''E). Another ca. 4-ha (100 m × 400 m) permanent
 plot was established within a selectively logged area of forest in 1994
 and was extended to about 5.4-ha (ca. 140 m × 400 m) in 2007
 (3°37'23''N, 101°44'15''E); this is only 200
 m away from the 6-ha permanent plot (Yagihashi et al., 2010). Leaf or
 inner bark samples were collected from 144 and 38 <em>R.
 curtisii</em> individuals (with dbh more than 20 cm) from the
 undisturbed plot and the logged plot, respectively, of which 17 and three
 trees were growing in areas of the study plots adjacent to the undisturbed
 and the logged plots, respectively. A total of 17 three trees growing in
 areas of the study plots adjacent to the undisturbed plot have previously
 been identified as the candidate pollen donors (Tani et al., 2012; Tani et
 al., 2015). Samples were also stored at -20 ˚C, before DNA
 extraction. A sporadic synchronized flowering event was
 observed in Semangkok Forest Reserve in October 2011, and fruit set
 occurred around February 2012. Seeds were collected from six selected
 mother trees in the undisturbed plot. After the removal of wings from the
 seeds, seed weight was measured, and then the seeds were placed on
 seedbeds consisting of river sand on February 20th, 2012 to investigate
 germination. The result of the germination test is presented in Table 2.
 The germinated seedlings were potted in a mixture of river sand and soil
 without fertilizer on March 26<sup>th</sup>, 2012, and
 maintained in the nursery of the Forest Research Institute Malaysia (FRIM,
 3°14'01''N, 101°38'00''E) under 50% shade,
 with a water sprinkler system for irrigation. Height and survival of
 seedlings were monitored weekly before and monthly after
 transplantation. Genomic DNA was extracted, using the
 method described by Murray & Thompson (1980). The material
 analyzed came from inner bark tissue samples of the adult trees from the
 research plots, material from either the inner bark or leaf tissues for
 the population samples and from the leaves of seedlings. The extracted DNA
 from the adult trees and population samples was further purified using a
 High Pure PCR Template Preparation Kit (Roche). After RNA digestion, the
 DNA was diluted to a concentration of about 2 ng/mL. All samples were
 genotyped based on ten microsatellite markers previously developed by
 Ujino <em>et al.</em> (1998), Lee et al. (2004) and Lee et al.
 (2006). Polymerase chain reaction (PCR) amplifications were carried out in
 total reaction volumes of 10 μL using a GeneAmp 9700 (Applied Biosystems).
 The PCR mixture contained 0.2 μM of each primer, 1x QIAGEN Multiplex PCR
 Master Mix (Qiagen), and 0.5-3 ng of template DNA. The temperature profile
 used was: 15 min at 95 ºC, then 30-35 cycles of 30 sec at 94 ºC, 90 sec at
 50-57 ºC and 90 sec at 72 ºC, with a 10 min final extension step at 72 ºC.
 Amplified PCR fragments were electrophoretically separated using a 3100
 genetic analyzer (Applied Biosystems) with a calibrated internal size
 standard (GeneScan ROX 400HD). The genotype of each individual was
 determined from the resulting electropherograms using GeneMarker
 (SoftGenetics). The microsatellite genotypic data from population samples,
 adult trees from the plots, and seedlings from the nursery are referred as
 population genotype, plot genotype and seedling genotype, respectively.
 The plot genotypes were obtained in previous studies (Tani et al., 2012;
 Tani et al., 2015), and were already deposited in Dryad (http://dx.doi.org/10.5061/dryad.7k434).
description: # Datasets for microsatellite genotype for natural populations and
 seedlings from six mother trees, and seedling survival and growth records
 [https://doi.org/10.5061/dryad.nzs7h451m](https://doi.org/10.5061/dryad.nzs7h451m) ## Description of the data and file structure Population Genotype Data Set: A csv file named "ShoreaCurtisiiPopulationGenotypeSTRUCTURE.csv" contains 10 microsatellite markers' genotypes from 163 Shorea curtisii DNAs collected from 9 natural populations in the Malay Peninsula. This csv file is optimized for the software "STRUCTURE", the first line shows names of microsatellite markers from Ujino et al. (1998), Lee, Tani, Ng, and Tsumura (2004), and Lee et al. (2006), then followed by genotype data of each individual. The first row shows individual names which represent the abbreviation of the population name on the left side of the under-bar and individual tag numbers on the right side of the under-bar. The abbreviation of the population name corresponds to Tani et al. (2022). The second and third rows are PopData and PopFlag of STRUCTURE, respectively. Microsatellite genotypes of each individual are recorded from the forth row with PCR fragment length form. Missing data are scored as "-9". Due to setting DRYAD file uploading system, the first line indicates contents of rows. This should be replaced to microsatellite loci names, such as shc04, shc07, shc09, sle074, sle384, sle293, sle562, sle566, slu044, slu175. Seedling Genotype Data Set: A csv file named as "ShoreaCurtisiiSeedlingGenotypeCERVUS.csv" contains 10 microsatellite markers' genotypes from 145 mature trees (more than 20 cm in diameter of breast height) and 481 seedlings derived from the six mother trees in a plot of undisturved forest at Semangkok Forest Reserve, Malay Peninsula.?The first line shows microsatellite marker names followed by the mature individuals genotype (from 2nd to 146th line) and followed by the seedling genotype (from 147th to 627th line). The first row shows names of the mature individuals and seedling followed by microsatellite allele with PCR fragment length style from 2nd row. Missing data are scored as "0". Seedling Survival Growth Record: A csv file named as gShoreaCurtisiiSurvivalGrowthRecord.csv h is input file for survival analysis performed by the R statistical software using the package esurvival f (Andersen & Gill, 1982; R Core Team, 2014) to fit maternal (seed weight represented as w) and biparental effects (r, qdis and fdis) to the survival of seedlings. This input file is also used for growth analysis using the generalized linear mixed model (GLMM) with normal distribution error structure on in stan 2.5 (Carpenter et al., 2017) using the package eRstan f in R 3.1.1 (Gelman, Lee, & Guo, 2015; R Core Team, 2014; Stan Development Team, 2020). The first line shows title of data, such as Seedling ID, Indium (consecutive number of seedlings), MT ID (mother tree ID), Mtnum (consecutive number of mother trees), CanF ID (candidate pollen donor ID), CanFnum (consecutive number of candidate pollen donors), w (seed weight of each seedling), r (relatedness between mother tree and pollen donor of each seedling), mk1~mk3 (Qk values of mother tree of each seedling), fk1fk3 (Qk values of pollen donor of each seedling), qdis (qdis statics of each seedling), fdis (fdis statics of each seedling), days (survival days of each seedling), cens (censorship information for each seedling, where 1 indicates death, and 0 indicates censored at the end of observation). In the rows followed by cens, height of each seedling in the observation date is recorded until death of each seedling or the end of the observation date. The survival, growth and explanatory variables for each seedling are represented from second line. Survival analysis: survival_coxph.r is R script for the survival analysis. Growth analysis: growth_glmmstan.r is R script for the growth analysis.