Designing a sampling scheme to collect an organism of interest for a population genetic/genomic study can be fraught with difficulty. How best to sample? Randomly? Or, along a grid? How many individuals to sample? Thirty? Or, perhaps, the sample size employed in a recently published study on a similar organism?
This can be even more challenging if your pet organism(s) has a complex life cycle, whether that be a transition through different hosts (i.e., parasites), or the alternation of free-living phases which differ in ploidy (e.g., mosses or seaweeds). For example, the intertidal distribution of the red seaweed Gracilaria gracilis is much more discrete and, therefore, more survey-able than that of a co-occurring red, Chondrus crispus.
Discrete individuals of Gracilaria gracilis (left) in which it was easier to map individuals within a tide pool. A more dense distribution of Chondrus crispus (right, along with other seaweeds) in which a grid sampling approach was employed. © SA Krueger-Hadfield
However, both are haploid-diploid, complicating a sampling strategy, let alone subsequent popgen analyses which are not, yet, equipped to deal with haploid-diploids in a straight forward manner. Nevertheless, the mating system of the haploid-diploid red seaweed G. gracilis is allogamous, whereas inbreeding (specifically, inter-gametophytic selfing, see Klekowski 1969) dominates in C. crispus (Engel et al. 1999, Engel et al. 2004, Krueger-Hadfield et al. 2013, Krueger-Hadfield et al., in press). Thus, how best to sample, in order to investigate a chosen hypothesis? Sampling every single seaweed isn’t possible, logistically and financially. Even more complicated, how to sample over a distributional range to address broader questions about gene flow?
Simulations can help evaluate the genetic tools we, as molecular ecologists, already have at our disposal in our well-used toolboxes. As Hoban (2014) reviews:
[Simulators can help by] quantifying [the tools] performance in real-world conditions and help plan optimal sampling strategies. Moreover, simulations can help fully utilize large-scale genetic, geographical, pedigree, historical and ecological data sets … and help provide front-line advice and information increasingly sought by conservationists and natural resource policy-makers.
Hoban’s review details seven categories of simulation uses in molecular ecology: (i) infer species’ history, (ii) infer species’ biology, (iii) predict probable species’ response to environmental change, (iv) predict outcomes of management interventions, (v) evaluate methods performance, (vi) evaluate and optimize sampling strategy and (vii) explore the models of complex processes to discover new phenomena or develop theory. Case studies are provided for each category, but, as an example, for category six, despite stating the obvious:
One key task is to collect sufficient samples (numbers of observations) to ensure statistically significant and/or precise results (detection of an effect of interest), while also being efficient, as funding and time is limited.
Yet, few molecular ecologists have made use of these approaches. However, understanding which type of markers to apply to a specific question (SNPs or microsatellites?) is as fundamental as the knowledge of whether any marker x number of individuals combination will result in a detectable genetic pattern, if one exists. Simulations will, no doubt, become another powerful tool with which to explore the types of genetic patterns at a variety of scales that molecular ecologists crave.
References
Klekowski EJ (1969). Reproductive biology of the Pteridophyta. II. Theoretical considerations. Bot J Linn Soc. 62: 347-359. DOI: 10.1111/j.1095-8339.1969.tb01972.x
Engel CR, Wattier R, Destombe C, Valero M (1999). Performance of non-motile male gametes in the sea: analysis of paternity and fertilization success in a natural population of a red seaweed, Gracilaria gracilis. P Roy Soc Lond B Bio. 266:1879-1886. DOI: 10.1098/rspb.1999.0861
Engel CR, Destombe C, Valero M (2004). Mating system and gene flow in the red seaweed Gracilaria gracilis: effect of haploid–diploid life history and intertidal rocky shore landscape on fine-scale genetic structure. Heredity 92: 289-298. doi:10.1038/sj.hdy.6800407
Another excellent resource on simulations:
Hoban S, Bertorelle G, Gaggiotti OE (2012) Computer simulations: tools for population and evolutionary genetics. Nature Reviews Genetics 13: 110-122. doi:10.1038/nrg3130
Krueger-Hadfield SA, Roze D, Mauger S, Valero M. (2013). Intergametophytic sefling and microgeographic genetic structure shape populations of the intertidal red seaweed Chondrus crispus (Rhodophyta). Mol. Ecol. 22: 3242-3260. DOI: 10.1111/mec.12191
Hoban S (2014) An overview of the utility of population simulation software in molecular ecology. Molecular Ecology 23: 2383-2401. DOI: 10.1111/mec.12741
Krueger-Hadfield SA, Roze D, Correa JA, Destombe C, Valero M (in press) O father where art thou? Paternity analyses in a natural population of the haploid-diploid seaweed Chondrus crispus. Heredity doi:10.1038/hdy.2014.82