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DC Field | Value | Language |
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dc.contributor.advisor | Bolker, Benjamin | - |
dc.contributor.author | Freeman, Jennifer | - |
dc.date.accessioned | 2023-06-07T17:39:08Z | - |
dc.date.available | 2023-06-07T17:39:08Z | - |
dc.date.issued | 2023 | - |
dc.identifier.uri | http://hdl.handle.net/11375/28626 | - |
dc.description | All data and code for this research has been made publicly available at doi:10.5281/zenodo.8002390. | en_US |
dc.description.abstract | We present a general statistical framework to infer processes (underlying ecological mechanisms) from patterns (observed arrangements in nature) in spatial ecology. We demonstrate our method by investigating the process of plant recruitment, how new individuals join a plant community, combining seed dispersal and environmental factors that determine the success of seeds germinating and surviving to juvenile maturity (establishment). Observed data includes seed and seedling counts at discrete spatial locations for the tree species slash pine (Pinus elliottii). The patterns in the data are described by their spatial correlation and we incorporate these correlations into historically used spatial models. We use a Bayesian simulation-based inference algorithm (Approximate Bayesian Computation, ABC) to estimate model parameters. Interest in ABC and Bayesian inference methods is growing in ecology, but they still remain behind classic approaches. Our results highlight techniques to validate the method to ensure accuracy and detect issues. Simulation tools are discussed to improve computational efficiency. We conclude with ABC parameter estimates that capture valuable spatial information ecologists can interpret. A small comparison study with classic likelihood-based parameter estimates is performed to illustrate the flexibility and informativeness of ABC. Our method is purposefully kept general to make it applicable to many spatial ecological problems. | en_US |
dc.language.iso | en | en_US |
dc.title | Estimating the Spatial Dynamics of Plant Recruitment using Approximate Bayesian Computation | en_US |
dc.type | Thesis | en_US |
dc.contributor.department | Computational Engineering and Science | en_US |
dc.description.degreetype | Thesis | en_US |
dc.description.degree | Master of Science (MSc) | en_US |
dc.description.layabstract | Ecosystems can be characterized by underlying processes that create observed patterns in space; by understanding these patterns we can learn about the ecological mechanisms at play. Plant recruitment, the process by which new individuals join a plant community, can be decomposed into two parts: (1) the dispersal of seeds across the landscape and (2) the environmental conditions that determine the success of those seeds becoming seedlings. We learn about the spatial dynamics behind recruitment by analyzing observed seed and seedling patterns with a computational statistics tool (Approximate Bayesian Computation, ABC). This tool can provide strong advantages over more classic statistical methods. Our method can serve as a general framework for understanding unobserved spatial processes that give rise to observed spatial patterns in any ecological system. | en_US |
Appears in Collections: | Open Access Dissertations and Theses |
Files in This Item:
File | Description | Size | Format | |
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Freeman_Jennifer_C_2023June_MSc.pdf | 705.22 kB | Adobe PDF | View/Open |
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