⌛ 2 hour

Rosewoods are the world’s most illegally trafficked wild product, which amounts to ~40% of the global illegal wildlife trade (more than all animal products added together). They are notoriously called the bloodwood, because the conflicts between the local poachers, often driven from extreme poverty, and the forest rangers cause bloodsheds.

We want to save this species and alleviate the poverty at the same time. We are teaching local people how to source seeds, grow them, and generate income. NGOs and governments buy these trees for restoration projects.

These all sound fantastic. However, it is known that traditionally forest conservation programmes ignore the standing genetic diversity and create genetic bottlenecks in the germplasms, for example, by sourcing seeds that are genetically closely related.

<aside> 💡 In this activity, we will analyse microsatellite data from a previous study by Hartvig et al. (2017) Ecol Evol to achieve these objectives:

1. Compare the genetic diversity among different populations
2. Delineate the population structure for the species
3. Make meaningful interpretations and implications for the species’ conservation </aside>

<aside> 💌 I wish to thank Prof Ida Hartvig (Copenhagen) and many collaborators for the rosewood project.

</aside>

Understanding and loading the data

<aside> 📥 Download

rosewood.csv

</aside>

• [ ] First, investigate the rosewood.csv using a simple spreadsheet software (e.g. Excel). This is known as a GenAlEx format.

  • This population study has microsatellite data based on 9 loci from 523 individuals across 26 populations. Where in the rosewood.csv store these metadata?
  • Roughly look at the sample and population columns (A and B). Also note that D1 (above ANG) and E1 (above BAN) say 4 and 28 respectively. What could those numbers mean?
  • Is this species hiploid or diploid? How do we know by judging the loci data?

• [ ] We will now start our session in R.

  rosewood <- read.genalex("rosewood.csv")
  rosewood
  

  What does it tell you? Do they match with what you observed from above?

• [ ] Now retrospectively thinking, are 9 loci really sufficient? We will compute a genotype accumulation curve to assess the power for discriminating between unique individuals given a random sample of $n$ loci.

  # For each number of loci (1, 2, 3...), randomly sample 1000 times to create the distribution of number of MLGs.
  gac <- genotype_curve(rosewood, sample = 1000, quiet = TRUE)
  

  • Has a plateau been reached with 9 loci?

• [ ] We will do some final checks on the data quality. We want to check if there are any loci with very rare alleles.

  (rw_lt <- locus_table(rosewood))
  

  • Which loci have the highest and lowest evenness?

• [ ] We also want to check if there are any loci with too much missing data.

  info_table(rosewood, type = "missing", plot = T)
  

  • Which population or loci have the most missing data? Can we drop that population or loci?

Testing HWE and LD

• [ ] We can simply get the summary of the genetic diversity by using the function poppr().
  • Oh Henry suddenly forgets how to use this function! Luckily, you can self-help by using the command ?poppr to check out the description and syntax of this function.