Project title:
Quantifying landscape-level variation in tropical forest structure, function, and composition and their change over time
Mentor name:
Helene Muller-Landau, Senior Scientist
Co-mentor name and position Joe Wright, Senior Scientist (other co-mentors also possible)
Contact information:
Location of internship:
Barro Colorado Island, Panama, and/or Gamboa, Panama
Will mentor be at this location? Yes.
Project summary and objectives:
Tropical forests vary widely in their structure, function, and composition, variation that is associated with climate, soils, geomorphology, land use history, and biogeographic realm. Forest structure encompasses the horizontal and vertical distribution of vegetation, which depends on the abundances, spatial arrangement, and morphology of trees and lianas (woody vines) of different forms and sizes. Forest function refers to the ecological roles of forests, including carbon storage, woody productivity, photosynthesis, evapotranspiration, and nutrient cycling, among others. By forest composition, we mean which species with what traits and abundances are present in an area; we focus especially on woody plant functional composition and diversity, as well as woody plant species composition and diversity.
A mechanistic understanding of this variation is crucial to accurately predicting the future of tropical forests under changing climates, disturbance regimes, and nutrient deposition. The high plant diversity of tropical forests offers the potential for high resilience to anthropogenic global change because species vary widely in their responses to environmental variation and the most negatively affected species will invariably become less common. However, this very diversity presents a tremendous challenge to our ability to understand tropical forest function today and to predict how it will respond to global change, as it means an understanding of compositional variation among forests and functional variation among species is critical.
This project aims to improve our understanding of patterns, causes, and consequences of landscape-scale variation in tropical forest structure, function, and woody plant composition, and how they are changing (or not) over time. The specific objectives are the following:
· Contribute to the development of methods to quantify tropical forest structure, function, and composition over large areas using ground-based, drone-based, airborne, and satellite remote sensing.
· Quantify variation in forest structure, function, and composition in Panama at high spatial and temporal resolution over large areas.
· Investigate how spatial variation in forest structure, function, and composition relate to each other and to soils, climate, topography, stand age, and other factors.
· Evaluate temporal variation in forest structure, function, and composition and its relation to climate cycles and trends, disturbance timing and intensity, anthropogenic influences, and other hypothesized drivers.
We pursue these objectives through a combination of field work, remote sensing, data analysis, literature review, and modeling, together with a large network of collaborating scientists and students. Our contributions to methods development include collection of high-quality datasets suitable for training models, publication of detailed protocols, and development and publication of algorithms. Our data collection encompasses both traditional and novel methods, with ground-based, drone-based, and airborne instruments and observations. Mapped forest plots on Barro Colorado Island and elsewhere in central Panama are a particular focus, as these enable linkage of new data to pre-existing datasets on forest structure, dynamics, tree species composition, and more. These sites thereby provide a basis for development and calibration of models that can then be applied to other sites for which fewer data are available.
Mentorship goals including benefits to the intern
The intern will gain experience in tropical forest field research, data management using R and GitHub, statistical analyses and figure preparation using the R programming language, reading and discussing scientific literature in English, working in a team with lab members and collaborators from diverse backgrounds, preparation of scientific presentations, preparation of data publications, and the scholarly publishing process. Interns who commit for 9 months or longer will have the opportunity to contribute as coauthors to a scholarly publication.
Intern’s role, desired background time commitment
Each intern is expected to participate in field work, data management, data analysis, and literature review. The exact balance of time allocated to these and other tasks will be determined by the intern and mentors to fit the skills and interests of the intern and the needs of the project at the time of the internship.
This internship is full-time, with a minimum commitment of 6 months. Each intern will be awarded a stipend of US$1250/month, as well as support for round-trip travel to Panama in the case of candidates not already in the country.
The ideal candidate has a bachelor’s degree in a relevant field, ability to conduct tropical forest field work in rugged terrain, strong organizational skills, ability to work well with team members from diverse backgrounds, strong quantitative skills including programming experience, strong English oral and written communication skills, and good Spanish communication skills. For full consideration, please email a (1) CV, (2) an unofficial undergraduate transcript (with an explanation of the grading scheme if from a non-US university), (3) a scientific writing sample, preferably in English but can be in Spanish (e.g., undergraduate thesis, lab report, research paper), (4) a sample of code you have written with commenting (preferably in R or Python, but can be another language), and (5) a cover letter describing your qualifications, interest in the position, potential start dates, and contact information for 3 references to mullerh@si.edu, in addition to submitting an application through the SOLAA platform.
Expected products: Each intern is expected to contribute to one or more data publications, protocols, or reports. Interns who commit for 9 months or longer will have the opportunity to contribute as coauthors to a scholarly publication.
What are the regularly held occasions for group discussions, attendance at lectures, career counseling, and other educational and experiential opportunities for your interns?
The intern will participate in weekly lab meetings of the Muller-Landau lab, and will have the opportunity to attend weekly seminars on BCI (Thursdays) and at Tupper (Tuesdays), as well as STRI and SI training opportunities (e.g., periodic offerings in GIS, R programming, career development). The intern will meet every 1-2 weeks with the primary mentor and will also have the opportunity to informally interact with other STRI staff scientists, employees, visiting scientists, fellows, and interns.
List of suggested publication or reading related to this project:
Muller-Landau, H. C., K. C. Cushman, E. E. Arroyo, I. Martinez Cano, K. J. Anderson-Teixeira, and B. Backiel. 2021. Patterns and mechanisms of spatial variation in tropical forest productivity, woody residence time, and biomass. New Phytologist, 229: 3065-3087. https://doi.org/https://doi.org/10.1111/nph.17084
Lee, C. K. F., G. Song, H. C. Muller-Landau, S. Wu, S. J. Wright, K. C. Cushman, R. F. Araujo, S. Bohlman, Y. Zhao, Z. Lin, Z. Sun, P. C. Y. Cheng, M. K.-P. Ng, and J. Wu. 2023. Cost-effective and accurate monitoring of flowering across multiple tropical tree species over two years with a time series of high-resolution drone imagery and deep learning. ISPRS Journal of Photogrammetry and Remote Sensing, 201: 92-103. https://doi.org/https://doi.org/10.1016/j.isprsjprs.2023.05.022
Cushman, K. C., M. Detto, M. García, and H. C. Muller-Landau. 2022. Soils and topography control natural disturbance rates and thereby forest structure in a lowland tropical landscape. Ecology Letters 25:1126-1138. https://doi.org/10.1111/ele.13978
Araujo, R. F., S. Grubinger, C. H. S. Celes, R. I. Negrón-Juárez, M. Garcia, J. P. Dandois, and H. C. Muller-Landau. 2021. Strong temporal variation in treefall and branchfall rates in a tropical forest is related to extreme rainfall: results from 5 years of monthly drone data for a 50-ha plot. Biogeosciences 18:6517-6531. https://doi.org/10.5194/bg-18-6517-2021
Park, John Y., H. C. Muller-Landau, J. W. Lichstein, S. W. Rifai, J. P. Dandois, and S. A. Bohlman. 2019. Quantifying leaf phenology of individual trees and species in a tropical forest using unmanned aerial vehicle (UAV) images. Remote Sensing 11:1534. https://doi.org/10.3390/rs11131534
Baldeck, C. A., G. P. Asner, R. E. Martin, C. B. Anderson, D. E. Knapp, J. R. Kellner, and S. J. Wright. 2015. Operational Tree Species Mapping in a Diverse Tropical Forest with Airborne Imaging Spectroscopy. Plos One, 10. https://doi.org/10.1371/journal.pone.0118403
