A next-generation approach for quantifying tropical plant diversity across scales

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Tropical forests hold much of Earths plant species. We know that this diversity of life is important, both in its own right as one of the great natural wonders, but also for underpinning global biogeochemical cycles (e.g. the carbon cycle), and determining resilience to climate change. Yet, despite centuries of research, we still don't know with any certainty how many of species of plants there are in the tropics, which areas have the most species, or how the abundance of these different species are changing through time, for example due to climate change.There are two main reasons for the uncertainty surrounding tropical biodiversity: First, there are thousands of plant species in tropical forests (e.g. 120,000 plant species in tropical Latin America), many of which look extremely similar, making it difficult (sometimes impossible) to identify which species an individual plant belongs too. Current approaches for species identification based on morphology are inherently subjective and difficult to standardize, meaning that identification errors are high and mostly unquantified. Second, tropical forests are vast, and often remote, meaning that ecologists are only able to sample a tiny fraction of the total forest area and most tropical forests remain unknown to science and are likely to remain so in coming decades. These two challenges cannot be overcome by collecting more data in the same way that we have for the past decades, instead a fundamental change in approach is required.The overarching goal of this fellowship is to establish a suite of unified, quantitative, and scalable approaches that use new technologies and existing datasets to measure plant diversity across Amazonia, Earth's largest and most diverse tropical forest. I propose to realize this goal using four independent yet complementary approaches. The scale of this challenge is huge; therefore, I plan to initially focus only on the most common tree species and families. Because these common species account for nearly 20% of all trees in Amazonia, reducing uncertainty in a few hundred species will have a profound impact on our understanding of Amazonian plant biodiversity.First, I will develop a new automated approach for identifying plant species by measuring reflected light spectra of leaf samples and classifying plants into species based on these spectra using artificial intelligence (AI) techniques. I will apply this approach to five common Amazonian plant families that together account for approximately 19% of individual trees in Amazonia. This will provide a framework for standardized quantitative species identifications at Amazon-wide scales.Second, I will map 25 common species at landscape scales (250ha) using a drone-based sensor that measures reflected light spectra of tree canopies. I will combine this drone imagery with field-verified locations of dominant canopy tree species, and then use AI approaches to learn and map these common tree species based on their canopy spectra across the landscape.Third, I will test if we can use the distribution of these common species as proxies for the distribution of rarer tree species. Using a new modelling approaches that explicitly for the covariation among species, I plan to predict the abundance of rare tree species using the distribution of common species.Fourth, I will test the extent to which we can scale-up our understanding of the distribution of common canopy tree species using satellite imagery. Satellite imagery can provide continuous information across the entire Amazon basin that relates to plant biodiversity. I propose to use massive existing forest inventory plot datasets to untangle the satellite biodiversity signal. By focusing on the same large common canopy tree species, I will be isolating the portion of plant communities that are actually detected by satellite sensors.