Douglas Sheil, Forest Ecology and Forest Management Chair Group, Wageningen University and Research, The Netherlands, discusses his article: A simple competition model can predict rainforest tree diversity, species abundance and ecosystem functions
Many tropical forests are remarkably diverse, often supporting hundreds of tree species in just a few hectares. But how can so many species coexist without a few of the most competitive species taking over and replacing the rest? I had assumed the answer was too complex for any simple model to helpfully capture. Surely, I thought, there were just too many factors and mechanisms at play—likely pathogens, dispersal distances, seed predators, disturbances, and many more. So, I was surprised when our study proved me wrong.
I likely wasn’t alone in my previous scepticism. Ecologists have long doubted that simple mathematical models, like the Lotka-Volterra competition model, could be applied to species-rich systems. These models are elegant in their simplicity but were traditionally used for revealing the dynamics of very simple communities with just one or two species. Trying to scale them up to ecosystems with hundreds of species seemed, at least to me, to be implausible. And yet, that’s exactly what we—an international team of ecologists and theoretical biologists from Japan, Malaysia, the USA, and the Netherlands—have done.
Using data from a 50-hectare tropical rainforest plot in Malaysia, we explored whether we could develop a simple competition model and if this could explain the coexistence of hundreds of tree species while also shedding light on their biomass, productivity, and diversity. To my genuine astonishment, our approach using a Lotka-Volterra competition model not only worked but offered fresh insights into how these remarkable ecosystems function.
Our approach assumes that trees compete more strongly with their own kind (what we ecologists call “conspecifics”) than with other species (or “heterospecifics”). We used estimated leaf biomass as a proxy for competition—after all, leaves are the main engine for the light capture that determines each species productivity and growth, and there is finite space in the forest canopy to add more. Put simply our assumption was that each tree’s growth is suppressed more by the leafy canopy of its own species than by the leaves of others. We were able to formulate and implement this in a relatively pragmatic matter. This stabilises diversity because it keeps any one species from dominating.
But why does this stabilisation happen? Here’s where we propose something we call the “ontogeny hypothesis”: species with fewer large, mature trees (and therefore lower overall leaf biomass) are often represented by smaller, faster-growing stems. These small trees can typically grow more quickly than the big, slow-growing individuals of species with lots of foliage in the canopy. In short, tree size matters. And this size-related dynamic allows species with different sizes and related strategies to coexist over time.
What We Found
Our main analyses predicted that 361 of the 487 tree species in the forest plot with sufficient data to characterise would stably coexist. A staggering level of diversity—especially for such a simple model. But that wasn’t all. The model also revealed that species richness is related to forest productivity. Productivity (essentially, how much the forest grows) and aboveground biomass (the total mass of all those trees) both increase with the number of coexisting species when species numbers are low, peaking at around 150 species.
In theory the model should also be able to capture, simulate, and predict dynamics beyond just coexistence. For instance, it offers a framework to explore how forests might change over time through succession (when one group of species gradually replaces another) or even monodominance (when a single species takes over). We predict that stable monodominance could occur if one species grows enough leaf biomass to suppress all others—a hypothesis we hope to test in forests where this phenomenon has been reported. In our article, drawing on previous work by theorists, we were able to determine the relationships and thresholds that govern these processes and link these to our plot based measurements, thus inferring (and predicting) outcomes.
Keeping It Real
Challenges with applying and calibrating this model remain. There are always uncertainties when some species are scarce and their properties are hard to determine. However, we find it reassuring that the general patterns and predictions are consistently reproduced when we use different data and make alternative analytical choices, for example, using forest census data from other years.
Though not addressed explicitly by the model, which is not spatial, our results appear to underline the importance of spatial heterogeneity—the idea that forests are patchy, with local conditions (like light, water, and soil) varying. This variation appears crucial for maintaining diversity. Similarly, demographic variation—differences in tree sizes and growth rates within species as we describe as our ontogeny hypothesis —plays a big role too. We believe these factors, can explain why these ecosystems remain so diverse.
What It Means
Perhaps the most surprising takeaway from our study is how much can be explained without reference to more complex ideas such as niche differentiation, dispersal limitation, or neutral dynamics. By focusing on competition within species and using straightforward metrics like leaf biomass, we show that many of the mechanisms often invoked to explain rainforest diversity may be unnecessary. That said, our model isn’t about excluding other mechanisms. Rather, it offers a baseline: a simple framework for understanding how species interact and coexist, even in the absence of more complex factors. It also raises new questions. For instance, it seems able to predict how forests may respond to species losses or gains.
Although there is still a lot to learn by testing other forests and sites to prove the wider applicability of our approach, our preliminary findings indicate that at least some of nature’s complexity may be distilled into simple models. For me, this study challenged my beliefs about what simple models can achieve. It might do the same for others. This study was a surprising tale of how classic theory, paired with impressive data, can offer remarkable analytical tractability and new insights concerning these complex ecosystems.
