Genetic variance and Ecological Niche Models (gENM)

Global atmospheric temperatures are currently following the worst case scenarios for climate projections. Studies of climate change impacts on ecosystems often use Species Distribution Models (SDM) to predict how altered climatic regimes will shift species' ranges; however, processes, such as local adaptation, are known to generate variation within species Parmesan (2006) that could have community level impacts (Ikeda et al. 2014; Record et al. 2013), which current SDMs employ species level data, which have historically averaged over this important intraspecific variation (Aitken and Bemmels 2016; Gotelli and Stanton-Geddes 2015; Fitzpatrick and Keller 2014). In this project, a collaborative team of researchers will use two study systems, the carnivorous plant Sarracenia purpurea (the Northern Pitcher Plant) and Eastern North American ant assemblages, to:

1. collect and process field samples for genetic analyses
2. test the sensitivity of SDM to intraspecific genetic variation through computer simulations
3. improve the transparency of SDM modeling through the application and development of data provenance software (Boose et al. 2007).

The students selected for this project will be co-mentored by Matthew Lau and Sydne Record. At the beginning of the project, mentors will have daily meetings advising individual projects for the first two weeks. Thereafter, we will have weekly group meetings. Matthew Lau will be based out of HF for the summer. Sydne Record will be based at HF and Bryn Mawr College for the summer. Sydne will Skype with the student 2-3 times per week when she is not physically at HF or in the field with the student.

Details on specific student components of the group project are as follows:

Collecting and processing field samples for genetic analyses: Sydne Record will be the primary mentor for this student. The student will learn field sampling methods, plant identification skills, and how to perform DNA extractions for field collected samples. The student will also begin to learn about species distribution modeling and how it would be extended to incorporate the data they collect on pitcher plant genetic variation. General requirements for this position include: being willing to participate in field activities sometimes for long hours, having a valid driver’s license and clean driving record, being able to hike with a 45-lb pack in rough forested or wetland terrain with standing water several feet deep, being willing to handle contents of pitcher plants (including spiders, ants, etc.), being willing to collect leaf tissue from pitcher plants, spending time in the laboratory processing genetics samples and handling chemicals, being able to spend many hours in the lab analyzing data, have or be willing to develop a basic understanding of Excel and R for graphical statistical analysis, be willing to collaborate and work with other members of the pitcher plant genomic distribution modeling team that extends beyond researchers at HF, and being willing to travel overnight for field excursions. On average the student can expect to spend about 50% of the time doing field work and 50% of the time doing lab work and data analysis. This student will be offsight for field excursions for 1-2 weeks and may spend 1 week at University of Vermont processing genetic samples in Dr. Stephen Keller’s laboratory. There will be opportunities for the student to develop an independent project that can be extended into a year-long independent project or senior thesis work.

Species distribution modeling: Matthew Lau will be the primary mentor for this student. This student’s main project would be to conduct simulations including genetic variation in SDMs for ants. Additionally, the student could explore the effects of how intraspecific variation is distributed spatially or temporally or conduct simulations with other species. This student will learn about SDMs and refine skills using R statistical software and GIS. The student may also learn about working on remote servers using BASH. General requirements for the project include: having experience using R statistical software and GIS, being willing to spend many hours working at a computer, and have or be willing to learn how to work remotely on servers using BASH. On average the student can expect to spend most of their time inside doing data analysis, but will have opportunities to accompany the field based student or students working on other field projects in the field.

Data provenance: Matthew Lau will be the primary mentor for this student. This student’s main project would be to apply the data provenance software ‘RData tracker’ to the SDM project and to the field samples, which will need to be spatially referenced. Further work could be done in developing specific software functions and data pipelines to improve data provenance software. This student will learn about data provenance and refine skills using R statistical software. General requirements for the project include: having experience using R statistical software and being willing to spend many hours working at a computer. On average the student can expect to spend most of their time inside doing data analysis, but will have opportunities to accompany the field based student or students working on other field projects in the field.

Useful Links:

Species distribution modeling 101: http://rcastilho.pt/SDM101/SDM_files/SDM101_v1.pdf

References:

Aitken, Sally N, and Jordan B Bemmels. 2016. “Time to get moving: assisted gene flow of forest trees.” Evolutionary Applications 9 (1): 271–90. doi:10.1111/eva.12293.

Boose, Emery R., Aaron M. Ellison, Leon J. Osterweil, Lori a. Clarke, Rodion Podorozhny, Julian L. Hadley, Alexander Wise, and David R. Foster. 2007. “Ensuring reliable datasets for environmental models and forecasts.” Ecological Informatics 2: 237–47. doi:10.1016/j.ecoinf.2007.07.006.

Fitzpatrick, Matthew C., and Stephen R. Keller. 2014. “Ecological genomics meets community-level modelling of biodiversity: mapping the genomic landscape of current and future environmental adaptation.” Edited by Mark Vellend. Ecology Letters 18 (1): n/a–/a. doi:10.1111/ele.12376.

Gotelli, Nicholas J., and John Stanton-Geddes. 2015. “Climate change, genetic markers and species distribution modelling.” Journal of Biogeography 42 (9): 1577–85. doi:10.1111/jbi.12562.

Ikeda, Dana H., Helen M. Bothwell, Matthew K. Lau, Gregory A. O’Neill, Kevin C. Grady, and Thomas G. Whitham. 2014. “A genetics-based Universal Community Transfer Function for predicting the impacts of climate change on future communities.” Edited by Joseph Bailey. Functional Ecology 28 (1): 65–74. doi:10.1111/1365-2435.12151.

Parmesan, Camille. 2006. “Ecological and Evolutionary Responses to Recent Climate Change.” Annual Review of Ecology, Evolution, and Systematics 37 (1): 637–69. doi:10.1146/annurev.ecolsys.37.091305.110100.

Record, S., N. D. Charney, R. M. Zakaria, and A. M. Ellison. 2013. “Projecting global mangrove species and community distributions under climate change.” Ecosphere 4 (3): art34. doi:10.1890/ES12-00296.1.