Population genetics of a marine bivalve, Pinctada maxima, throughout the Indo‐Australian Archipelago shows differentiation and decreased diversity at range limits

Abstract

Intraspecific genetic diversity governs the potential of species to prevail in the face of environmental or ecological challenges; therefore, its protection is critical. The Indo-Australian Archipelago (IAA) is a significant reservoir of the world's marine biodiversity and a region of high conservation priority. Yet, despite indications that the IAA may harbour greater intraspecific variation, multiple-locus genetic diversity data are limited. We investigated microsatellite DNA variation in Pinctada maxima populations from the IAA to elucidate potential factors influencing levels of genetic diversity in the region. Results indicate that genetic diversity decreases as the geographical distance away from central Indonesia increases, and that populations located towards the centre of P. maxima's range are more genetically diverse than those located peripherally (P < 0.01). Significant partitioning of genetic variation was identified (F(ST) = 0.027; R(ST) = 0.023, P < 0.001) and indicates that historical biogeographical episodes or oceanographic factors have shaped present population genetic structure. We propose that the genetic diversity peak in P. maxima populations may be due to (i) an abundance of suitable habitat within the IAA, meaning larger, more temporally stable populations can be maintained and are less likely to encounter genetic bottlenecks; and/or (ii) the close proximity of biogeographical barriers around central Indonesia results in increased genetic diversity in the region because of admixture of genetically divergent populations. We encourage further genetic diversity studies of IAA marine biota to confirm whether this region has a significant role in maintaining intraspecific diversity, which will greatly assist the planning and efficacy of future conservation efforts.

Phylogeography of the bivalve Tegillarca granosa in coastal China: implications for management and conservation

“…In addition, hybridization occurring between diverged populations could have detrimental consequences such as genetic swamping and outbreeding depression (Utter 2000, Frankham 2010, which may indirectly increase the risk of reduced intraspecific genetic diversity. As a fundamental component of biodiversity, intraspecific genetic diversity is a driving force behind adaptation and evolutionary success (Allendorf & Luikart 2007, Lind et al 2007). These issues have been addressed in freshwater and terrestrial phylogeography studies (e.g.…”

Microsatellite marker analysis reveals the distinction between the north and south groups of hard clam (Meretrix meretrix) in China

“…Populations are connected by continuous migration, but the geographical distance can weaken the effect of migration until it is no longer strong enough to counter the effect of random genetic drift (Lind et al, 2007). However, oceanographic factors, biogeographical factors, or even anthropogenic activities (larval release) can complicate the isolation-by-distance model.…”

Interpopulation differences in shell forms of the pearl oyster, Pinctada imbricata radiata (Bivalvia: Pterioida), in the northern Persian Gulf inferred from principal component analysis and elliptic Fourier analysis

“…Genetic and morphometric techniques have been widely used to identify stocks of aquatic organisms (Palmer et al, 2004;Aguirre et al, 2006;Konan et al, 2010;Marquez et al, 2010;Tlig-Zouari et al, 2010;Garcia-Rodriguez et al, 2011). Allozyme (Apte and Gardner, 2001), isozyme (Hofstra et al, 1995), simple sequence repeat (Benzie and Smith-Keune, 2006;Lind et al, 2007;Yu et al, 2008), restriction fragment length polymorphism (Itoi et al, 2011), mitochondrial DNA (Cheng et al, 2011;Iwamoto et al, 2012), and random amplification of polymorphic DNA (Kim et al, 2008) are among the genetic techniques used. There are various morphometric approaches for detecting differences in the form of organisms (Cadrin and Friedland, 1999), e.g., landmark-based analyses (James Rohlf and Marcus, 1993;García-Rodríguez et al, 2011) and elliptic Fourier analysis (EFA) (Laurie et al, 1997;Palmer et al, 2004;Neto et al, 2006;Andrade et al, 2010;de Aranzamendi et al, 2010;Marquez et al, 2010).…”