Linking functional traits and demography to model species-rich communities
Abstract
It has long been anticipated that relating functional traits to species demography would be a cornerstone for achieving large-scale predictability of ecological systems. If such a relationship existed, species demography could be modeled only by measuring functional traits, transforming our ability to predict states and dynamics of species-rich communities with process-based community models. Here, we introduce a new method that links empirical functional traits with the demographic parameters of a process-based model by calibrating a transfer function through inverse modeling. As a case study, we parameterize a modified Lotka–Volterra model of a high-diversity mountain grassland with static plant community and functional trait data only. The calibrated trait–demography relationships are amenable to ecological interpretation, and lead to species abundances that fit well to the observed community structure. We conclude that our new method offers a general solution to bridge the divide between trait data and process-based models in species-rich ecosystems.
What the paper shows and why it matters
(AI-generated)
Process-based community models predict community structure from population-level processes such as growth and competition and have been argued to be a step towards forecasting biodiversity change. Their demographic parameters, the growth and competition rates of each species, increase rapidly with the number of species, so such models have mostly been used for low-diversity or laboratory communities, where measuring the rates directly takes less effort. Functional traits (measurable properties such as plant height) are much cheaper to record and act on fitness through demography. The paper takes on the problem that no predictive link from traits to demographic rates has been established, so traits cannot simply be translated into model parameters.
The paper proposes that the demographic parameters of a community model be predicted from functional traits by a “transfer function” whose few parameters are fitted to observed community data, and illustrates how this makes models of species-rich communities possible to calibrate. Rather than assuming how traits relate to demography, the method estimates the relationship by inverse modelling, that is, by searching for parameter values that reproduce observed abundances; the parameters to fit scale with the number of traits, not species. The paper calibrates a modified Lotka–Volterra competition model for 118 grassland plant species along a temperature gradient in the French Alps. Eleven transfer-function parameters predicted 354 demographic parameters from eight traits. The calibrated model reproduced species turnover along the gradient (pseudo R² of 0.59, against below 0.18 for 200 null models with randomised traits), and tall plants were associated with a higher minimum tolerated temperature and a lower sensitivity to competition, relationships the paper calls sensible. Species distribution models, which have many more parameters, reached a higher pseudo R².
The paper reads these results as showing that traits and static community data can parameterise process-based models of species-rich systems, so that the main challenge shifts from missing demographic data to statistical frameworks linking traits to model parameters. It sees the method as paving the way to more complex community models, including dynamic ones, and speculates that alternative transfer functions and models could be compared. It states that a link between traits and the modelled processes must exist, and that the equilibrium assumption forced one demographic rate to be fixed. The case study rests on a single grassland data set.