Using machine learning to understand species diversity in tropical forest


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.


Initially you will learn about tropical forest ecology and the protocols that have been used to collect forest tree data. You will also learn the theories that might explain the patterns and processes that drive forest structure, tree species distribution and dynamics.
After training in machine learning techniques and advanced statistics, you will apply at least two advanced types of deep learning architectures, such as Convolutional neural networks and Generative adversarial networks, to multiple datasets of tropical forest plot censuses. The spatial and temporal heterogeneity of tropical forests makes it very difficult to understand and evaluate the mechanisms and drivers of species community dynamics. The increasing threats to tropical forest means it is essential that we apply advanced analytical and decision-making tools in the forest ecology domain. After initial trials of deep learning techniques using already available data you will identify whether additional information is required to improve the forest models, and if necessary you will have the opportunity to collect the additional data on forest structure in Puerto Rico on the Luquillo Forest Dynamics plot or another forest site.

The Luquillo Forest Dynamics Plot is part of the network of large forest plots ForestGeo from whom you will obtain forest plot data, and have opportunities to develop collaborations with tropical forest scientists to compare different forest types, with different histories to look for common mechanisms

Project Timeline

Year 1

Literature review to learn the ecological theory involved in tropical forest ecology and the population models that have been used to understand the species community dynamics. Receive training in machine learning methods and advanced statistical techniques. Explore deep learning techniques using already available forest census data. Visit Puerto Rico to learn tropical forest census protocols and collect data on forest structure,

Year 2

Design multiple deep learning models and train them with the collected forest census data from Puerto Rico. Test the models produced with data from other forest census plots to investigate generality and measure robustness.

Year 3

Repeat forest structure data collection in Puerto Rico to look at temporal relationships and changes in forest structure over time. The forest in Puerto Rico is undergoing rapid structural changes while recovering from Hurricane Maria. Integrate the data collected on forest structural change into the initial models. Start thesis preparation.

Year 3.5

Continue thesis preparation and papers for publication. Present the results at national and international conferences.

& Skills

You will gain an appreciation of tropical ecology and the methods for collection of forest tree data, conduct tree species population modelling, and data analysis using advanced statistical techniques, deep learning and machine learning methods. You will experience field work in tropical forest in Puerto Rico and collaborate with forest scientists from across the world.

You will benefit from spending time at each project partner institution, networking and interacting with DTP students at IAPETUS2 conference and training events.

In addition, the student will benefit from transferable skills through IAPETUS2 DTP core training, and courses at CEH and Heriot-Watt University. These skills will include scientific writing, presentation skills scientific methodologies, statistics and research ethics. There will be opportunities for the student to present their work at national and international conferences.

References & further reading

Chen, L. et. al. (2017). Forest tree neighborhoods are structured more by negative conspecific density dependence than by interactions among closely related species. Ecography.
Feng, X. et. al. (2017). Improving predictions of tropical forest response to climate change through integration of field studies and ecosystem modelling Global Change Biology (doi/10.1111/gcb.13863/full)
Lamanna JA. et. al. (2017). Plant diversity increases with the strength of negative density dependence at the global scale. Science30 June 2017: 1389-1392
Leigh, EG. et. al. (2004). “Why Do Some Tropical Forests Have So Many Species of Trees?.” Biotropica (4): 447–473
Hu, W., Huang, Y., Wei, L., Zhang, F. and Li, H. (2015). Deep convolutional neural networks for hyperspectral image classification. Journal of Sensors 258619,
Johnson, DJ, et. al. (2018). Climate sensitive size-dependent survival in tropical trees. Nature Ecology & Evolution. Vol 2 pp 1436–1442. (
Le Cun, Y., Bengio, Y. and Hinton, G. (2015): Deep learning. Nature, 521(7553), 436.
Usinowicz, J. et. al. (2017). Temporal niches and the latitudinal gradient in forest diversity. Nature doi:10.1038/nature24038
See videos at:

Further Information

Dr. Jill Thompson +44 (0)131 445 8518. Jill has worked with the Luquillo Forest Dynamics plot and ForestGeo network since 1995 and has collaborated in producing many publications on tropical forest plant ecology.

Dr. Marta Vallejo +44 (0)131 451 3081. Marta has expertise in machine learning and predictive modelling and has worked on several research projects in the areas of deep learning for image analysis.

Dr. Michael Lones +44 (0)131 451 8434. Michael has worked in predictive modelling and machine learning for over 15 years, and has particular expertise in the area of biologically-motivated computing models.

Apply Now