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)
Reporting period: 2021-11-01 to 2023-10-31
Tropical moist forests affect the terrestrial carbon cycle through the processes of tree growth and mortality, and are home to the majority of terrestrial biodiversity. Understanding tropical forest dynamics – how trees grow and die – is essential as tropical trees are the largest living carbon storage and reabsorption system of terrestrial ecosystems, but they are also major sources of uncertainties in Earth System Models. To better predict the role of these forests in climate change, complex processes must be quantified, which is possible through diverse datasets that use both traditional and novel approaches to measuring environmental, biological, and physiological data. Sophisticated statistical analyses, and computationally intense programming are required to integrate these diverse data sources, capture uncertainty, and build robust causal inference of the future of tropical forests processes.
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 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 centralized multiple sources of forest inventories covering decades of tropical forest observations across four continents (part of the Global Ecosystem Monitoring – GEM – network), multiple datasets of anatomical, chemical, and physiological leaf and wood traits collected in the inventoried plots, following a single standardized protocol, as well as detailed climatic data covering the period 1970 – 2019. This resulted in reproducible commented R scripts as well as a final dataset that formed the basis of the whole project, a deliverable of the project that will ease future collaborations within and outside the GEM plot network within and across different tropical regions. The resulting database integrates 50 years of biological and environmental data in 102 tropical forest plots (71,291 trees, 3892 species – 649 with functional traits –, 253,159 tree growth values).
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.
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.
Main findings: The project emphasises a marked increase in tree mortality in the Australian moist tropics that has occurred since the mid 1980’s, mostly due to climate change, with 70% of the dominant tree species and 99% of the forest regions impacted. While previous research in the Amazon and Congo basins showed a slowdown of the carbon sink, the project findings indicate that tropical forests can switch from being carbon sinks to becoming net carbon sources, hence stressing that while tropical forests are major nature-based solutions to mitigate carbon emissions, they are also more vulnerable than previously thought to climate change. The project further showed that not all species nor tropical forests and communities are equal in the face of climate change. Species fast resource acquisition traits tended to have competitive advantages in term of growth and access to resources, but appeared to be more vulnerable to heat and atmospheric evaporative demand stresses, growth-wise; the very constraints expected to increase in intensity and frequency with climate change. This effect on species’ demography is expected to cause changes in tropical forests’ floristic and functional composition, which may then affect a series of ecosystem-level processes. The project found that warmer tropical moist forests tend to suffer the most marked growth slowdowns, when compared to colder tropical forests. This has implications for forests’ capacity to mitigate anthropogenic carbon emissions, as these warmer forests also are among the most productive for the long-term sequestration of atmospheric carbon. The project main findings and causal analytical approaches are expected to improve the way the tropical forests component of Dynamic Global Vegetation Models is modelled, hence contributing to enhanced forecast of future climate change trajectories under alternative scenarios in Earth System Models.