Periodic Reporting for period 1 - TropDemTrait (Tree growth and mortality in the face of climate change: A pantropical journey at the crossroad of trait-based and demographic approaches)
Okres sprawozdawczy: 2021-11-01 do 2023-10-31
The project overarching goal was to understand how the demography of different tropical forests and tree species respond to stresses caused by climate change. It aimed to provide causal insights into the processes shaping forest dynamics across multiple spatial scales: from the local neighbourhood of trees to differences and commonalities among and across species and tropical regions and continents – up to a pantropical scale.
The project had three overarching objectives.
The first objective consisted in understanding the effects of climatic anomalies (temporal climatic variations around a local historical baseline) and the neighbourhood crowding of trees on the community-level growth and mortality of tropical forests. It also aimed to characterise differences among forests, regions, and continents with respect to responses to heat- and water-related stresses, using up to 50 years of permanent plot monitoring in Central Africa, South America, Southeast Asia, and Oceania. The work also examined how tree growth sensitivity to climate anomalies may be moderated by local historical climatic conditions.
The second objective aimed to leverage a unique international dataset of tree species’ functional traits related to their resource acquisition strategies to provide physiological insights into the variation of growth and mortality sensitivity to climate among tropical forests’ species. This would allow mechanistically grounded forecasts of future floristic and functional tree compositions under contrasting future climate scenarios. This objective further aimed to test for temporal trends of change in species demography over the past decades, and to use species traits to understand these potential changes.
Finally, the project aimed to define whether proxies of whole-plant relative allocation to photosynthesis can improve the capacity of leaf traits to predict growth and survival variations, by combining leaf traits with tree crown metrics. This would yield higher prediction accuracy of demographic rates than the commonplace use of leaf traits alone.
The project developed a data analysis framework aimed to deal with the complexity of the questions and data, statistically, while ensuring a transparent set of ecological assumptions were defined to allow causal inference based on observational data, that is, using a formal framework to approach cause-effect relations. This analytical framework is an important feature of the project, as it greatly reduces risks of otherwise frequent problematic statistical biases arising when not differentiating cause-effect relations from other non-causal associations. TropDemTrait therefore developed a theoretical and causal framework responding to the need of a formal and reproducible approach to transparently derive statistical models from a set of interdependent causal assumptions about the studied system, to justify a causal interpretation of model outputs, linking the statistical model to the biological/ecological question of the work. A series of advanced Bayesian growth and survival models were developed, used and compared, to integrate the causal modelling framework and questions of the project into statistical analyses to respond to the project’s questions. This workflow is a product of the project and will hopefully contribute to pushing Global Change Ecology and the timely questions it must address towards increasing reproducibility and more theoretically grounded advances. Simulated increases in climatic anomalies were then combined to the fitted model outputs to derive causal predictions of species and forests demographic responses for varying climate scenarios, average climates, and species phenotypes.