Tropical forests have many more species, and many more rare species compared to temperate forests. Despite much research, there are still many competing theories about the mechanisms that are involved in maintaining tropical forest diversity, why tropical forests have many rare species (Leigh et. al. 2004) and why there is such a latitudinal gradient of species richness (Lammana et. al. 2017; Usinowicz et. al. 2017). Understanding the underlying drivers of species diversity and structure in forests is a significant challenge. Overcoming this challenge will help us to predict the impact of climate change, extreme weather events, and pests and disease, and may also inform the potential for sustainable management and forest restoration.
We propose that you will investigate the use of different machine learning approaches to help us understand the drivers of the patterns and processes that structure tropical forest, and improve our understanding of the ecological theories such as niche differentiation, regeneration niche, gap dynamic, and negative density dependence (Wright et. al. 2002), which might explain tropical forest diversity. Machine learning methods have been used to identify areas of disturbance in forests, analyse hyperspectral images taken from aerial surveys of forest canopies, and estimate biomass, but as yet these methods have not been applied to study forest community dynamics and forest structure. One successful machine learning method is deep learning. Deep learning models are especially suitable to work with large datasets and are capable of learning visual and temporal hierarchical dependencies and predict future patterns. In addition, deep learning provides ways to combine multiple modes of information into a unified model for prediction, segmentation or classification.
At first you will use deep learning techniques to investigate already collected forest census data on tree species identity, location, growth and survival data from the Luquillo forest dynamics plot in Puerto Rico. Later there will be an opportunity for field work in Puerto Rico to collect forest data.
The overarching question is: What determines species distribution patterns and dynamics in tropical forests? The focus of the study will be determined by the candidate through discussions with the supervisors.
As a result of this collaborative project, you will:
1. Understand theories of plant and forest ecology and their application to forest community dynamics.
2. Gain practical experience in tropical forest and data collection protocols.
3. Develop skills in handling large data sets.
4. Interact with an international community of forest ecologists involved in large forest plots
5. Understand different machine learning methods and modelling techniques.
6. Undertake research related to the design of advanced deep learning models to discover patterns hidden in the data and automatically search for key drivers of forest dynamics.
7. Apply these models to similar data from other forest census plots.