Inspired by a recent post by Joern Fischer I have decided to share one of my (many) half baked ideas. It’s based on a paper I read recently that I have a few issues with and want to work up into a letter to the editor, so please see all this as a work in progress and if you want to co-author it with me feel free, because that way I’m less likely to get a bad rep.So to the paper.It’s this one by Liu et al in Global Ecology and Biogeography on how climate and age determine biomass in global mature forests.It sounded right up my street.I like forests.I like carbon. So I gave it a look.In the paper Liu et al aim to:
- Investigate the relationship between aboveground biomass and climatic conditions and stand age in mature forests across the globe.
- Identify an age threshold at which forests should be considered ‘mature.’
The first question is interesting because people have done similar things with secondary forests in the past but I’m not sure I’ve ever seen this done looking at stand age in mature forests as a factor affecting biomass.The second I’m not a big fan of, but I will come to why later in the post.So Liu et al carry out a few analyses looking at the effects of mean annual temperature, mean annual rainfall, and stand age on biomass. However, the graphs of the analysis looks like this: The figures had me worried and on closer inspection my suspicions were confirmed. They considered each explanatory variable independently in separate models. This is bad statistics but also doesn’t take account of the fact that previous studies have suggested that age, precipitation and temperature may interact to determine carbon accumulation rates. In addition the paper fails to account for spatial autocorrelation or differences between datasets that may be purely because of different methods used in their collection, rendering the results they present as questionable.To their credit Liu et al provide the data they used as supplementary materials so I thought I’d have a play with it to try and fix some of their errors.First I created a distance matrix and used that to look at spatial autocorrelation in biomass – surprise, surprise there were signs of spatial autocorrelation.I built a model that accounted for this and used a random effect to distinguish between each of the different datasets used in the study to account for between study error. I then did some model averaging so that all possible combinations of precipitation, temperature and age were included. I’ve put all of the code on github so feel free to look there and comment if you have any suggestions about the technical aspects of what I was up to.To cut a long story short the results suggest that all the variables considered by Liu et al are important, with one model that included all of them, as well as an interaction between temperature and age coming out as by far and away the best model.
This model was much better than those of Liu et al (Table 1) – suggesting their approach was overly simplistic, as well as being statistically flawed. So, the models I developed explained much more variability than the equivalent ones in the Liu et al paper and changes the spin they put on their results.
Table 1 – Comparison of my top model and the models of Liu. AICc indicates relative parsimony of the model.
|Model||AICc||AICc delta||R squared|
|Liu – Precipitation only||678.51||54.53||0.11|
|Liu – Temperature only||698.88||74.90||0.08|
|Liu – Age only||733.94||109.96||0.02|
This model has an R squared 0.28, which is very good given the scale of the analysis but also suggests that there is quite a lot going on that we aren’t capturing in this model. Part of this is probably because of the noise inherently added by using data collected in different ways. In what I think is the best study of it’s type to date suggests that biomass in mature forests is only weakly related to commonly used climate metrics like mean temperature and mean precipitation. Instead, James Stegen and colleagues suggest that total biomass is well predicted by the biomass of the largest individual tree and that this is constrained by water deficit.
Now I to the second aim of Liu et al: to define a threshold age for mature forest.
This I have a big problem with. Even mature forests subject to relatively similar climatic conditions can vary massively in biomass and the reasons for this are not completely clear. Given this it is unwise to try to define a global threshold. It would be a much better idea to use chronosequence studies or long-term monitoring to try to discern dynamics at a landscape scale and build upon that to determine when forest should be classed as mature (and I’m only partly saying that because that’s what we did with secondary forest data…). I also have fears about defining ‘mature forest’ only using the biomass of these forests, and would be interested in seeing how biodiversity varies along with age in these old growth forests. Given that secondary forest carbon can get close to recovery quite quickly while biodiversity lags behind similar relationships may be seen for old growth forest. Any policy definition of what mature forest is could potentially have big implications for global biodiversity, so it important we get it right.
So those are my ideas. Critique them or add to them as you wish. And as I said, all code is available on github along with the data from the paper. I’m serious about writing a response, but like I said it needs more work so if you want to join me drop me a message below or in an email.