You get a better model of the forest when you look at the individual trees

The computing principle GIGO states that if the data you put into a model is garbage, then the output will be too. Jie Yang and colleagues have been tackling the problem of modeling of tropical tree growth. What data is the best data for these models?

One approach that you could expect is to take an average of traits as data. “While collecting trait data from a small number of individuals may be a pragmatic approach, particularly in diverse systems, the analysis of population- or species-mean trait data is conceptually misaligned with the vast evolutionary ecology literature relating traits to individual performance and it may lead to weak or misleading models and inferences,” write Yang and colleagues.

The team decided to take a different approach and build their simulated forests from individual simulated trees, with variability built in. They based their model on the Xishuangbanna forest dynamics plot in a seasonal tropical rainforest of Southwest China. The team measured the trees from August 2009 to August 2018. Yang and colleagues measured seven functional traits (Leaf area, leaf chlorophyll content, leaf dry matter content, leaf thickness, leaf toughness, leaf mass per area and wood specific resistance) for each tree with a dendrometer band) for each tree, and measured over five hundred trees.

The scientists found this approach yields more accurate results. “The results provided in this paper show that models of individual-level tree growth in a tropical rainforest were greatly improved when using individual-level trait data and when growth models are built upon first principles. These results not only inform us on how we should model tree growth upon the basis of traits in future work, but they also indicate that traitbased ecology should rethink how it conceptually and analytically aligns with evolutionary ecology,” they write.

“The key advantages of functional trait-based ecology are that the traits measured are representative of fundamental tradeoffs and they are relatively easy to measure across systems. However, these advantages would be weakened if the traits collected did not convey information regarding the performance of individuals and, therefore, populations. The trait literature frequently utilizes mean trait values to represent all individuals in a population or species. This approach greatly reduces the resources needed for trait inventories, but the negative consequences of such data aggregation are not well established.”

“This indicates that trait-based approaches are particularly powerful for modeling tree growth when collected and analyzed at the individual-level. Data collection or analyses that aggregate data to the population- or species-level will provide some insights, but these insights will be limited in most cases when modeling plant performance and, in some cases, misleading.”