Text by Lauri Laanisto
Seems like macroecology has finally arrived to a crossroads where it might split so fundamentally, that two or more new disciplines will emerge. Finding global patterns, that would explain the world in one graph, like JH Brown once hoped for: “The promise of macroecology is that very general statistical patterns provide clues the operation of equally general mechanistic processes which govern the structure and dynamics of complex ecological systems.” (Brown 1999). It has not really come up to expectations. You can collect all the stamps, but the collection is still not making sense, if to paraphrase another classic, John Lawton´s view from the park about community ecology (the whole series of these papers are now in Oikos´s virtual issue nr 8!).
But the top-down approach still looms large. Taxon grops, diversities, traits, trait groups etc are plotted against different diversities, environmental factors, indices etc. And there is hope. The hope will not go away, that´s for sure.
The other side, however, almost like wants to go back in time. When ecological publications were still so through that you could get sufficient information for each location, species and trait studied. It´s difficult to do it, when the sites are all across the globe and you have trait data for 10 000 species or sth. Big data can only take us to a point. Like self-help books would say: “What Got You Here Won’t Get You There”. Instead of self-help books I cite here a recent essay review by Angela Moles about this exact thing: “Being John Harper: Using evolutionary ideas to improve understanding of global patterns in plant traits“.
A recent global modelling paper (that includes Ülo Niinemets as a coauthor) in PNAS, based on big data on SLA and leaf nitrogen and phosporous content (from TRY database) is, at least in my mind, trying to take both paths in this crossroads. Whether they succeed or not, take a look!
Check also out this blog post that describes the same PNAS paper in a way that makes much more sense…
Citation: Butler, E. E., Datta, A., Flores-Moreno, H., Chen, M., Wythers, K. R., Fazayeli, F., … Niinemets, Ü., … & Blonder, B. (2017). Mapping local and global variability in plant trait distributions. Proceedings of the National Academy of Sciences, 201708984, doi.org/10.1073/pnas.1708984114. (link to full text)
Currently, Earth system models (ESMs) represent variation in plant life through the presence of a small set of plant functional types (PFTs), each of which accounts for hundreds or thousands of species across thousands of vegetated grid cells on land. By expanding plant traits from a single mean value per PFT to a full distribution per PFT that varies among grid cells, the trait variation present in nature is restored and may be propagated to estimates of ecosystem processes. Indeed, critical ecosystem processes tend to depend on the full trait distribution, which therefore needs to be represented accurately. These maps reintroduce substantial local variation and will allow for a more accurate representation of the land surface in ESMs.
Our ability to understand and predict the response of ecosystems to a changing environment depends on quantifying vegetation functional diversity. However, representing this diversity at the global scale is challenging. Typically, in Earth system models, characterization of plant diversity has been limited to grouping related species into plant functional types (PFTs), with all trait variation in a PFT collapsed into a single mean value that is applied globally. Using the largest global plant trait database and state of the art Bayesian modeling, we created fine-grained global maps of plant trait distributions that can be applied to Earth system models. Focusing on a set of plant traits closely coupled to photosynthesis and foliar respiration—specific leaf area (SLA) and dry mass-based concentrations of leaf nitrogen (Nm) and phosphorus (Pm), we characterize how traits vary within and among over 50,000 ∼50×50-km cells across the entire vegetated land surface. We do this in several ways—without defining the PFT of each grid cell and using 4 or 14 PFTs; each model’s predictions are evaluated against out-of-sample data. This endeavor advances prior trait mapping by generating global maps that preserve variability across scales by using modern Bayesian spatial statistical modeling in combination with a database over three times larger than that in previous analyses. Our maps reveal that the most diverse grid cells possess trait variability close to the range of global PFT means.