Palimpsest

Evolving notes, images and sounds by Luis Apiolaza

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All that glitters (still) is not GxE interaction

Five months ago I was saying all that glitters is not GxE interaction, putting forward the idea that, at least in forestry, some of the reported GxE interaction was not interaction at all but poor connectedness.

Today Forest Ecology and Management published the work by Duncan McLean (our PhD student, congratulations!), Jaroslav Klápště, Mark Paget and myself (Open Access article). We have eight well-replicated and connected trials, SNP-based pedigree, covering a range of environmental conditions with little signs of interaction.

For wood basic density, a typically highly heritable & low interacting trait, out of the 28 cross-site genetic correlations (Type B in forestry parlance) the lowest value was 0.86. For stem diameter at breast height, a typically low heritability & high interacting trait, 20/28 cross-site correlations were greater or equal to 0.7, often used to define interacting sites. The lowest correlations came from a single South Island site (lowest value was 0.59).

Trees live for a long time and tree breeding programmes are long-lived endeavours. Our genetic evaluations often include trees from many selection series, with many trials connected by tenuous relationships. There are publications that show we only need small connections to compare genotypes across trials: the predicted error variance of the comparison is OK. We need to remember though, that those comparisons are assuming we know the true genetic correlations across sites (as in animal breeding); instead, we are estimating them from data. Data coming from poorly connected trials, subject to biases.

Now imagine we start explaining the estimated GxE interaction, Are we explaining real interaction or just estimation noise? We still have to continue revisiting this topic, which I hope it leads to updating the genetic evaluation system.

Genetic correlations for DBH and wood density under three genetic models.

Taylor & Francis made me do it

Today I received an email from Taylor & Francis letting me know that the final volume and pagination for one of our papers was available, and telling me that I should share this paper with the world. I should, as the open access (OA) costs are USD 3,000+. The article is here, by the way.

Today Elsevier sent me an email as well, confirming that OA fees of USD 3,400+ for our new accepted article were covered by our university’s Read and Publish Agreement.

Also today (it was a busy day!), MDPI sent me an email, stating that the authors of a new review were sharing their new OA article with me. It cost them 2,600 Swiss Francs or roughly USD 2,900 to do so. I consider MDPI Forests borderline predatory, so I wouldn’t pay to go there, but “cada loco con su tema”, as we say in Spanish.

I am part of a priviledged group, who works at one of the members of CAUL, an organisation for university libraries in Australia and New Zealand. We have access to big bucket agreements with publishers (the usual suspects like Elsevier, Springer Nature, Taylor & Francis, etc). We have a quota of articles, first-in, first-served, that are published open access “for free”. Not quite, the universities pay for that quota, but researchers are not charged individually.

This situation creates funny incentives: OA publishing in journals run by big publishers has no direct cost to me. OA publishing in journals that I like—Annals of Forest Science, for example—but that are not part of my university agreement is unaffordable. I literally have no funding for it. As Annals of Forest Science only publishes OA articles, that’s bye, bye for me. A good alternative, in forestry at least, is to publish for free in an OA journal like the New Zealand Journal of Forestry Science. Give them a  try.

Today I was left with the horrible feeling that we are burning money for no clear purpose in the current publication environment. We could easily pay for better PhD scholarships or postdoc salaries with that money, although is not available for those purposes. We can only use it to keep on feeding publishers with insanely high profit rates. Crazy.

Anyway, if you are interested in essential oils from eucalypts, read the article. I mentioned this work before but now comes with fresh, shiny, cineole-smelling page numbers. Either that or the article smells like burning money.

I do not work in that topic, except when I do

—We are planning a conference on changes to silviculture because of forest fires and climate change… Do you wanna come?
—But I don’t work in that topic.
—Don’t you?

To be perfectly honest, I have never seen myself as dealing with forest fires in my research. I do work, sometimes obsess, on the within- and between-tree variability of wood properties and its genetic control. BUT and, this is an important but, one of the ideas of working in my topic is to identify, domesticate and generate new varieties with “good” within-tree wood property trends. Trends that could allow for shorter rotation (time to harvest) plantations or that could have better wood with lower stocking (fewer trees per hectare).

And here comes the connection: one silvicultural response to increased fire frequency is to use lower stockings, reducing fire risk. Therefore, I DO work in that topic and, perhaps, should go to the conference. 😎 

P.S. In the late 1970s and early 1980s there was a fantastic British TV series called Connections, hosted by James Burke. Burke’s aim was to show the interconnection of ideas in history of science. He is also responsible of doing what has been called “the best-timed piece to camera” or “the greatest shot in television” (starting in second 0:43, if you are impatient). Just another connection.

Screenshot of James Burke’s best-timed piece inn camera

Not a miracle worker

—The genetic correlations are very high. Can you check them?
—Sure

The additive genetic variance for one of the traits was essentially zero, which pushed the correlation to get stuck at 0.99. Looking with more attention at the results of the univariate analyses, showed that the experimental design features (replicates, incomplete blocks) were also close to zero.

A cross tabulation showed that some of the replicates had only a single observation (and many of the incomplete blocks had none), although the families had five observations each.

More importantly, and easier, checking the size of the dataset showed 150 observations. One hundred and fifty observations to estimate the degree of genetic control and associations. Way too small!

The small sample size was not done on purpose; it was just left overs from another project that we are sending for publication soon. Small leftovers, small sample size; enough for a pilot study at the phenotypic level, but not for genetic parameters. There are no miracles in data analysis.

A few things that I expect to find in a well-functioning breeding programme

A good understanding of the biological traits that have an effect on profit. Can we identify new, more efficient selection criteria?

A clear awareness of trade-offs between: traits, genetic evaluation options, deployment systems, etc. Trade-offs are everywhere in breeding programmes.

A database system that contains all the data.

A well-documented genetic evaluation system: we can rerun the evaluation and get exactly the same results. The system can be developed in-house or can use commercial software (like asreml, SAS, Bolt, etc) but the code must be available.

Reproductive biology: essential to sort out the best deployment.

Figured/figuring out what are the main environmental drivers affecting performance. It is easy for some species, very difficult for others.

Personnel continuity BUT the programme can survive the proverbial bus running over the breeder.

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