DOI: 10.5061/dryad.dz08kps3c
title: Lambertia multiflora microsatellite data

description: The restoration of diverse self-sustaining ecosystems requires
 re-establishment of functional interactions among species. For plant
 communities, pollinators are usually essential for pollination, seed set,
 and seed quality. A common assumption in ecological restoration for plants
 pollinated by animals is one of “build it and they will come”, which is
 rarely tested. Beyond seed set, there may be negative genetic consequences
 for seed quality if pollinators and their behaviour do not reflect those
 in reference populations. Here, we conduct an ecological genetic
 assessment of seed quality via mating system parameters in Lambertia
 multiflora(Proteaceae), a species dependent on nectar-feeding birds for
 pollination. Four populations of L. multiflora in disturbed sites that
 were rehabilitated following mineral sand mining were compared to four
 natural reference populations, near Eneabba, Western Australia. In each
 population, approximately 10 offspring from each of 10 maternal plants
 were genotyped with 11 highly polymorphic microsatellite markers. From
 these data, genetic diversity and mating system parameters were assessed
 and found to be equivalent across all populations. Mean allelic diversity
 and heterozygosity across loci were very high. All populations were
 completely outcrossing with no bi-parental inbreeding. Mean correlated
 paternity, sibship, and effective population size estimates for restored
 and natural populations were not significantly different and reflected
 uniformly high paternal diversity and wide outcrossing. Results suggest
 self-incompatibility,  a surprising result given high levels of
 selfing detected in other lambertias. Equivalent genetic results for
 restored and natural reference populations indicate successful restitution
 of bird-pollinator services for L. multiflora in these post-mining
 rehabilitation sites. Synthesis and applications: Reviewing our results
 with other published studies suggests a resilience of bird-pollinator
 services in restored plant communities, a finding of broad reassurance to
 restoration practitioners working in these global south systems where bird
 pollination is a feature. Our study also highlights the global
 contribution of ecological genetics to the objective assessment of
 functional species interactions in ecological restoration, an increasingly
 important goal of land managers and regulators seeking to improve
 restoration standards.
description: <em>Study populations and
 sampling</em> We
 identified eight populations of <em>L. multiflora </em>where
 plants were common and of equivalent size, and populations were of
 equivalent size and density (range 0.1-0.6
 plants/m<sup>2</sup>). Four populations were in post-mining
 restoration sites, two older (established 30 and 38 years old) and two
 younger (established 10 and 11 years old), as well as four nearby
 undisturbed natural (reference) populations (Suppl Table 1; Fig 2). At all
 restoration sites, seed of <em>L. multiflora</em> have
 originated from topsoil or mulch, which has originated from multiple
 sources across the mine lease. From each population, we arbitrarily
 selected 10 maternal plants that were spaced &gt;5 m apart and were
 fruit bearing, from which we collected at least 20 seed-bearing fruit per
 maternal. Leaves were sampled from all maternal plants and stored in
 activated silica for extraction of DNA. Seed were extracted from fruits,
 weighed and germinated, and leaves sampled from seedlings grown in the
 glasshouses at Kings Park for DNA extraction and genotyping.
 <em>Data collection and
 analysis</em> Microsatellite markers were developed for
 mating system analyses from DNA sequences generated by an Ion Torrent
 next-generation sequencer and the QDD3 pipeline (Meglecz et al 2014). A
 short list of 35 loci were identified for screening on 8 offspring from
 each of 3 families, from which 11 reliable polymorphic microsatellite loci
 exhibiting Mendelian inheritance were identified and scored for all
 samples. Null alleles were identified and re-scored on the basis of family
 arrays (supplementary data). Parameters of genetic
 variation were analysed with Genalex v6.51b2 (Peakall and Smouse 2006,
 2012) for maternal and offspring cohorts that were corrected for null
 alleles. Mating system parameters (i) multilocus outcrossing rate
 (<em>t<sub>m</sub></em>), (ii) single-locus
 outcrossing rate (<em>t<sub>s</sub></em>), (iii)
 bi-parental inbreeding (<em>t<sub>m</sub> –
 t<sub>s</sub></em>), and (iv) correlation of paternal
 plants (<em>r<sub>p</sub></em>), were estimated
 with MLTR (Ritland 2002). MLTR parameters were set at SE based on 1000
 bootstraps, entire families resampled, Expectation-Maximisation method,
 pollen=ovule gene frequency, initial value of <em>t </em>set
 at 1, all other parameters at 0.1. The Windows version of the program
 COLONY (Jones and Wang 2010) was then used to implement maximum-likelihood
 estimation of the proportion of full- and half-sib relationships among
 offspring from each population, and likelihood estimates of effective
 population size from the estimated frequency of siblings (Wang 2009). The
 number of different sires for each population was estimated from ML
 configurations of each known offspring-maternal genotypes, from which
 paternal genotype was inferred. COLONY parameters included allowing
 polygamy for all males and females, mating system with inbreeding,
 full-likelihood analysis method, inclusion of known maternal sibships and
 maternal genotypes, estimate of maternal sibship size of 9, and all other
 parameters were program defaults. 
description: Excel
description: Data file is an Excel spreadsheet containing microsatellite data for 682
 samples of Lambertia multiflora offspring from 8 populations, 4 natural
 reference populations (N1-4) and 4 restored populations (R1-4). Data are
 formatted for analysis in the software Genalex (Peakall, R. & Smouse,
 P. E. (2006). GENALEX 6: genetic analysis in Excel. Population genetic
 software for teaching and research. *Molecular Ecology Notes, 6*, 288-295;
 Peakall, R.& Smouse, P.E. (2012). GenAlEx 6.5: genetic analysis in
 Excel. Population genetic software for teaching and research – an update.
 *Bioinformatics 28*, 2537-2539);
 [https://biology-assets.anu.edu.au/GenAlEx/Welcome.html](https://biology-assets.anu.edu.au/GenAlEx/Welcome.html) Data are as per Genalex formatting requirements: Row 1: # of loci (11), # of individuals (682), # of populations (8), then number of individuals from each of 8 populations. Row 2: title, then names of each of 8 populations (R1-4, N1-4), where R refers to a restored population, N refers to a natural reference population. Row 3: name of each of 11 loci. Rows 4-695: microsatellite data for each of 11 microsatellite loci for each of 682 samples. Column A: sample name, eg R1M3 is maternal plant 3 from restored population 1. Column B: restored population name Columns C-X: Allele size at each of 11 microsatellite loci for each sample. Note yellow shaded cells indicate the inference of a maternal null allele. Note this table can be read and analysed in Genalex. From Genalex, data can be exported as a csv file for analysis in MLTR.