Las plantaciones forestales producen un sinnúmero de bienes y servicios, con diferentes niveles de procesamiento y valor agregado. Cuando pensamos en el tronco de los árboles, en un extremo tenemos fibra y productos químicos, que actúan como componentes de otros productos. Muchas veces es difícil reconocer el árbol. En el otro extremo tenemos madera de alta calidad, en un instrumento musical o de apariencia, en que la madera y su aspecto orgánico juegan un rol primordial.
Corte. Aquí pasamos a estar caminando en Santiago (Chile) al frente de un Easy (una tienda de DIY, mejoramiento de hogar, construcción, etc) en que vendían tablas como las de la foto. Todas vienen ya sea de muy cerca de la médula del árbol, con las peores propiedades de la madera (baja estabilidad dimensional, stiffness, densidad, etc) o del exterior de trozas pequeñas, con toda clase de defectos evidentes.
Bajo uso estas tablas se van a doblar, torcer, rajar, etc en muy poco tiempo. Y yo no podía evitar pensar que con estos productos estamos matando la “marca” de la madera. La estamos asociando con un producto de calidad pobre, todo lo contrario con el empuje para usar más madera en la construcción. Como para meditar un rato.
The end (of the semester) is nigh, involving the typical drama of assignments, grades, etc. On top of that, Duncan McLean (one of our PhD students) successfully defended his thesis, which now means including a few suggested changes so he can be formally called Dr McLean. And working with Juan Carlos Valverde (a visiting PhD student from Universidad de Concepción), plus preparing a presentation for the IX Chilean Congress of Forest Sciences, this October in Valdivia, which happens to be roughly 9,000 km to the East.
The presentation introduces a paper that we are writing with my colleague Clemens Altaner, looking at the genetic control of pith-to-bark stiffness trajectories.
A few posts ago I was talking about heritabilities (like here) and it’s time to say something about genetic correlations. This is how I explain correlations to myself or in meetings with colleagues. Nothing formal, mostly an analogy.
Say we have to draw a distribution of breeding values for one trait (X) and, rather than looking from the side, we look at it from the top. It looks like a straight line, where the length gives an idea of variability and the cross marks the mean. We can have another distribution (Y), perhaps not as long (so not so variable) or maybe longer.
Often variables will vary together (co-vary, vary at the same time) and we can show that by drawing the lines at an angle, where they cross at their means. If you look at the formula for the covariance (co-variance, because traits co-vary, get it?), we grab the deviation from the mean for the two traits for each of the observations, multiply them, add them all up and get their average. We get positive values for the product when both traits are above or below the mean; we get negative values when one trait is below the mean and the other above it. Covariances are a pain, as they can take any value. Instead we can use “standardised” covariances, that vary between -1 and 1: we call these things *correlations*.
If the angle between the distributions is less than 90 degrees, increasing the values of one of the traits is (on average) accompanied by an increase on the other trait. then we have a positive covariance and, therefore, a positive correlation. The smaller the angle, the closer to a correlation of 1.
If the angle is 0 degrees (or close to it), changing the value of one trait has no (or very little) effect on the other trait. Zero correlation.
If the angle is greater than 90 degrees, changing the value of one trait tends to reduce the values of the other trait. The closer the angle to 180 degrees (so the positive values of one distribution are closer to the negative values of the other distribution), the closer to a -1 correlation.
Why do we care about these correlations? We use them all over the place in breeding. Sometimes as a measure of trade-off, as in “if I increase X, what will happen with Y?” or correlated response to selection. We also use them to understand how much information in one trait is contained in another trait, as in “can I use X as a selection criteria for Y?”. And a bunch of other uses, as well. But that’s another post.
I like food. I mean well beyond eating to survive: I do enjoy flavours. Thai is my favourite cuisine but I have a rule: I will try any food that I’m offered at least once. Sometimes the surprise is positive, sometimes it’s horrible; but that’s my rule.
I don’t know how I ended up in this Our World in Data page, but it is a good comparison. There will be quite a bit of variability depending on the specifics of the production system (beef in country X vs country Y, grass fed vs grain fed, etc), but I am interested in the rough scale. And the difference is huge.
Of course greenhouse gasses are only one of the environmental considerations when comparing different types of food. Water use, fertiliser use, soil conservation, pollution runoffs, etc come to mind. This is even before considering animal welfare; I like animals walking around and I pay more for that type of product.
I like the taste of beef but I have to say that I am eating it much less frequently than I used to five years ago. I am not asking you to be vegetarian or vegan—I am neither. In fact I am not asking you to do anything, but I just keep on looking at the graph thinking what I can do better while still enjoying food as much as I do.
I was reading a LinkedIn post that said “heritability is the extent to which differences in observed phenotypes can be attributed to genetic differences”.
There is this idea floating around assuming that if a trait is highly heritable, therefore genetics explains most differences we observe. I have seen it many times, both when people discuss breeding and even in political discussions. I vividly remember a think tank commentator stating that given IQ was highly heritable it is likely that millionaires make more money because their parents were more intelligent, or something along those lines.
I created the figure below using a dataset with wood basic density measurements (how much solid “stuff” you have in a set volume of wood) for trees growing in 17 different environments. The heritability of wood density is around 0.6; however, the differences between some environments are larger than the differences within environments.
We have to remember that heritabilities apply to specific populations and specific environments. Moreover, if we think of the mixed model analysis, we are fitting both fixed and random effects, so we are “correcting/controlling/putting individuals on the same footing” with our fixed effects, before having a look at the variation that is left over. We are then saying that out of that left over genetics explains a proportion of the variation (this is much smaller than the variation before accounting for other sources of variability).
In the case of wood density of radiata pine, the environment (particularly temperature explained by latitude and elevation and soil nutrients like boron) has a larger effect than genetics when looking across multiple trials. The trials with higher density are farther North in New Zealand, which is warmer. Once we are inside one of the trials, genetics explains 60% of the variability. In the same way, once we account for all other social differences, we are left with a much smaller level of variability to try explaining income differences with genetics.
Wood variability for trees in 17 progeny trials in New Zealand.
