ATFS will focus on four major science themes that integrate across biogeographic, ecological, remote sensing, and modeling disciplines and pose what we consider to be the most urgent challenges preventing a more complete understanding of the role of tropical forests in the Earth System.
- Tropical Forest Diversity and Distributions
- Drivers of Tropical Forest Dynamics
- Scaling up Tropical Forest Biomass and Diversity with Remote Sensing
- Predicting Tropical Forest Structure and Function
Tropical Forest Diversity and Distributions
A key impediment to species-level comparisons among forest plots or among networks of plots is the limited taxonomic knowledge for many tropical tree taxa. Individual plot networks maintain specimen collections and taxonomic information on hundreds to thousands of species, but these have never been compared. Multi-network collaboration is required to develop and apply new approaches and standards for resolving taxonomic issues both within and among tropical forest networks. A range of new tools in taxonomy, imaging spectroscopy, genomics, and artificial intelligence and machine learning, will facilitate progress in this area. With more than half of all tree species on Earth found within the ATFS plots, the opportunity exists to make major progress in understanding the species diversity of tropical forests. Resolving these taxonomic issues would provide essential baseline data on the diversity of tropical forests, the distributions of individual species, and is fundamental to addressing how species composition in tropical forests is changing.
Drivers of Tropical Forest Dynamics
Tropical forests vary in their structure and composition, and the dynamics of growth, mortality and recruitment, but the biotic and abiotic drivers of this variation remain incompletely understood. A robust understanding of how tropical forest dynamics depend on environmental drivers like climate, soil properties, disturbance, and interactions with other taxa, such as seed-dispersing animals, is a critical precursor to predicting the future of tropical forests under global change. Studies based on clusters of plots have documented patterns at local to regional scales, but results often diverge across studies. The degree to which these divergent results reflect differences in methodology, sampling error, or biologically meaningful variation among sites is unclear. This uncertainty impedes progress in tropical forest science because it reduces the ability to make more generally applicable inferences that are necessary to advance understanding of the role of tropical forests in the Earth System. Individual tropical forest plot networks have collected decades of observations on forest dynamics. ATFS will enable us to resolve these and other conflicting findings through the development and application of more powerful analytical techniques and the synthesis of much larger datasets.
Scaling up Tropical Forest Biomass and Diversity with Remote Sensing
Tropical forests play a key role in the global carbon cycle: intact tropical forests are major stores of terrestrial carbon, degraded and cleared forests are a major source of carbon to the atmosphere, and regrowing forests are major carbon sinks. Projections of the feedbacks between tropical forests and climate require accurate and precise estimates of current and future tropical forest carbon stocks and fluxes. Carbon stocks vary enormously across the tropics in relation to a range of environmental and anthropogenic drivers. While ground plots have advanced our understanding of tropical forest carbon stocks, they cover less than 1% of tropical forest area. Remote-sensing technologies provide the potential to scale up ground-based observations of forest biomass, structure, productivity, and mortality from local-to-global scales, and to be a key solution for estimating global carbon stocks and fluxes, and consequently, forest responses to anthropogenic change. US and international space agencies are making significant investments in space-based global biomass and diversity monitoring. Despite these investments, there has been limited coordination with coincident ground-based observations, which are essential for calibration and validation of remotely-sensed biomass and diversity estimates. By bringing together the tropical plot networks, including experts on plots and remote sensing, ATFS will catalyze major advances in tracking forest structure, biomass and diversity at a pantropical scale.
Predicting Tropical Forest Structure and Function
Improved representation of tropical forests in Earth System Models (ESMs), and thus improved predictions of the future of tropical forests under global change, requires better process-based knowledge and benchmark data for tropical forests. Historically, ESMs have done a poor job of capturing patterns in tropical forest structure and dynamics, and have diverged greatly in their predictions for tropical forest responses to novel atmospheric and climate scenarios. Resolving key uncertainties in vegetation demographic models requires strong integration between model development and testing with plot-based observations of vegetation structure, dynamics, and composition. In particular, these models, with their higher-fidelity representation of vegetation structure, need to be evaluated against benchmark data for tree size distributions, growth, mortality, and recruitment, and their variation with site conditions and species traits. These are the kind of data that ATFS plot networks maintain. Pantropical syntheses of ATFS data will result in key benchmarks for general patterns, for example, how tree growth and mortality vary with functional traits and environmental conditions. By bringing the plot networks together in the ATFS, the opportunity exists to vastly improve understanding of key processes in models and compare the performance of competing model formulations and parameterizations with the big data collected by the tropical forest plot networks.