SEES Fellows: Developing Semi-parametric Models, Algorithms, and Tools for Ecological Analysis of Species Biodiversity

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The goal of this project is to develop statistical methods to enable ecologists to address critical problems in biodiversity modeling. The research will advance a general semi-parametric methodology that applies to species distribution models (SDMs) of species occupancy, abundance, demographics, and dynamics. The approach will address two major challenges for SDMs: 1) statistically correcting for missed detections in field observations of the species to attain unbiased estimates of ecological quantities of interest, and 2) capturing complex relationships between environmental inputs and model variables. While current techniques in SDMs include parametric models that address the first challenge and nonparametric methods that address the second challenge, the proposed methodology will be the first to simultaneously address both. Hierarchical models are one important class of species distribution models, which often contain unobserved variables of ecological interest. These models have parameters with biological meaning (e.g. extinction probability, etc.) that can be linked to a set of input variables describing various environmental characteristics (e.g. habitat, land use, climate, etc.). Once fit, the models can be examined to understand the effects of the inputs on the parameters. However, this fitting process is often difficult due to the need to carefully select which inputs to include in the model and what to assume about the functional form of their effects. The methods to be developed will incorporate flexible nonparametric methods into these hierarchical models. The resulting methodology will retain the semantics of the hierarchical model but allow the input effects to be fitted flexibly, automatically capturing nonlinearities and interactions without extensive model selection.Rapid declines in habitat for native species are a global problem due to increasingly human-dominated land-use, habitat fragmentation, and climate change. To design reserves, conservation easements, and similar policy measures, there is a need to understand habitat requirements and population dynamics of threatened species across time and space. Hence, accurate models of the demographics and distribution of species are needed. The methodology to be developed by this project will advance understanding of key aspects of species distributions that can guide conservation efforts. Modeling methodologies will be applied to a variety of ecological frameworks, and feedback from collaborators in ecology will help ensure usability by decision-makers. Moreover, the interdisciplinary research efforts of this project will be highlighted in outreach programs to elementary, middle, and high school students.This project is supported under the NSF Science, Engineering and Education for Sustainability Fellows (SEES Fellows) program, with the goal of helping to enable discoveries needed to inform actions that lead to environmental, energy and societal sustainability while creating the necessary workforce to address these challenges. With SEES Fellows support, this project will enable a promising early career researcher to establish themselves in an independent research career related to sustainability.