Abstract
Spatial autocorrelation analysis is a well-established technique for detecting spatial structures and patterns in ecology. However, compared to inter-population genetic structure, much less studies examined spatial genetic structure (SGS) within a population by means of spatial autocorrelation analysis. More SGS analysis that compares the robustness of genome-wide single nucleotide polymorphisms (SNPs) and traditional population genetic markers in detecting SGS, and direct comparison between the estimated dispersal range based on SGS and the larval dispersal range of corals directly surveyed in the field would be important. In this study, we examined the SGS of a reef-building coral species, Heliopora coerulea, in two different reefs (Shiraho and Akaishi) using genome-wide SNPs derived from Multiplexed inter-simple sequence repeat (ISSR) genotyping by sequencing (MIG-seq) analysis and nine microsatellite loci for comparison. Microsatellite data failed to reveal significant spatial patterns when using the same number of samples as MIG-seq, whereas MIG-seq analysis revealed significant spatial autocorrelation patterns up to 750 m in both Shiraho and Akaishi reefs based on the maximum significant distance method. However, detailed spatial genetic analysis using fine-scale distance classes (25–200 m) found an x-intercept of 255–392 m in Shiraho and that of 258–330 m in Akaishi reef. The latter results agreed well with a previously reported direct field observation of larval dispersal, indicating that the larvae of H. coerulea settled within a 350 m range in Shiraho reef within one generation. Overall, our results empirically demonstrate that the x-intercept of the spatial correlogram agrees well with the larval dispersal distance that is most frequently found in field observations, and they would be useful for deciding effective conservation management units for maintenance and/or recovery within an ecological time scale.
Highlights
Coral reefs are highly diverse marine ecosystems, they are facing threats such as degradation due to climate change, ocean acidification, frequent outbreaks of corallivore, and other anthropogenic stressors (Wilkinson, 2006)
This study aimed (i) to examine the spatial genetic structure (SGS) of a reef building coral, H. coerulea, using traditional microsatellite and genome-wide single nucleotide polymorphisms (SNPs) data for evaluating the effectiveness of using genome-wide SNPs for SGS analysis and (ii) to compare the dispersal distances estimated by SGS with the direct larval dispersal measurement in the field reported in a previous study (Harii and Kayanne, 2003) to reveal which dispersal estimation method of SGS best matches the direct larval dispersal measurement in the field within ecological time scales
Spatial autocorrelation analysis is affected by several variances caused by population genetics, stochastic processes, and spatial processes (Slatkin and Arter, 1991), similar patterns of SGS were observed for the two different populations (Shiraho and Akaishi) with different microhabitat environments
Summary
Coral reefs are highly diverse marine ecosystems, they are facing threats such as degradation due to climate change, ocean acidification, frequent outbreaks of corallivore, and other anthropogenic stressors (Wilkinson, 2006). The adults are sessile, reef-building corals release gametes and/or larvae that disperse from their native habitats (Fisk and Harriott, 1990) Because such gamete and larval dispersal results in genetic connectivity among population forming metapopulation structure, estimating the range of gamete and larval dispersal is essential for conservation management (Connolly and Baird, 2010). Compared to intensively studied inter-population genetic structure, much less studies have examined the spatial genetic structure (SGS) of reef-building coral species within a population Such individual-based SGS within a population have provided important insights into the micro-evolutionary processes and sampling strategies of coral populations (Underwood et al, 2007, 2020; Gorospe and Karl, 2013; Chan et al, 2019; Dubé et al, 2020). The detailed genotype information for each sample using genomewide SNPs would improve the efficiency of detecting significant SGSs, as stochastic error in genotype is one of the challenges in spatial autocorrelation analysis (Slatkin and Arter, 1991)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
