Data Science Coast To Coast — Biodiversity

Data Science Coast to Coast

May 19, 2021
12:00pm to 1:00pm
Virtual Participation

Data Science Coast To Coast — Biodiversity
Date: Wednesday, May 19, 2021
Time: 12:00–1:00 PM Pacific 

The Data Science Coast to Coast (DS C2C) seminar series is hosted jointly by seven academic data science institutes — BIDS, NYU’s Center for Data Science, Rice University’s Ken Kennedy Institute, Stanford Data Science, the University of Michigan’s Michigan Institute for Data Science (MIDAS), and the University of Washington’s eScience Institute, and Johns Hopkins University's Institute of Data Intensive Engineering and Science (IDIES) — to provide a unique opportunity to foster a broad-reaching data science community. In the first half of 2021, DS C2C will host five seminars, each featuring one faculty member and one postdoctoral fellow from two universities. Each speaker will give a 20-minute talk about ongoing projects and motivating issues, followed by 20 minutes of discussion with the audience. These seminars will be the launching point for follow-on research discussion meetings that will hopefully lead to fruitful collaborative research.

Data Integration Across Space and Time to Infer Biodiversity Dynamics

Rosemary GillespieProfessor & Chair in Systematic Entomology, University of California, Berkeley 
Abstract: The world’s ecosystems are under serious threat due to ongoing stressors of the Anthropocene, notably habitat destruction, climate change, loss of biodiversity, disease, and the spread of invasive species. Biodiversity in particular is suffering catastrophic decline and tracking and understanding the factors affecting change is a major challenge that we are currently not meeting. Unless we develop new approaches, it will take centuries to document biodiversity and identify attributes that render ecological communities robust and resilient to change, and by then it will likely be too late. Here, we examine insights we can gain into biodiversity dynamics by looking at ways that we can first assess spatial patterns of diversity, abundance, and foodwebs, and determine the response of the organisms within these communities, to the changing environments that surround them. We have piloted an environmental DNA approach to generate estimates of abundance and interactions of macroorganisms in terrestrial systems across different spatial scales. By applying various theoretical and modeling approaches to the vast amounts of genetic data, we can encapsulate the “status” of a biological community in terms of its integrity and potential resilience to change. Moreover, by analyzing these data through slices in time (months, years, decades, or longer), we can assess how the community might accommodate, adapt, or collapse in response to change. These changes include habitat transformation, climate modification, fire, or disease. The critical data challenge is to integrate data that characterize the biological community, genomic data that reveal the response of any given taxon to that change, with past, present, and modeled climate change data. We highlight the role of historic collections from museums, and the physical record they provide of past environments.

Diversity in Animal Response to Environmental Change

Shelly TriggData Science Postdoctoral Fellow, University of Washington 
Abstract - How will ecosystems tolerate the climate and ocean change occurring now and predicted for the future? To begin addressing this question, we can subject different animals to different anthropogenic pressures and evaluate their responses. We can more sensitively and comprehensively assess responses by performing molecular surveys using omics technologies (e.g. genomics, proteomics, metabolomics, etc.), which allow us to more clearly see the cellular processes that underlie environmental tolerance and intolerance. This data can also help us compare between species since all species have these general molecules (DNA, proteins, metabolites) in common. I'm going to present data from different studies on marine invertebrates exposed to different environmental conditions, and describe how I used multiple data science approaches to distill large omics datasets into dominant biological pathways associated with environmental tolerance and intolerance. After summarizing responses across species and conditions, I will propose future directions and data science applications for the wealth of environmental omics data being generated.

All events in the series are free to attend, and all who are interested are welcome and encouraged to participate. Questions may be directed to Jing Liu (ljing@umich.edu), Managing Director of MIDAS.

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Speaker(s)

Rosemary Gillespie

Professor; Environmental Science, Policy, and Management; UC Berkeley

Rosemary G. Gillespie holds the Schlinger Chair in Systematic Entomology. She is president elect of the American Genetics Association, past president of the International Biogeography Society, trustee and fellow of the California Academy of Sciences, senior editor for Molecular Ecology, and associate editor for Journal of Biogeography. Her primary research uses islands as model systems to understand ecological and evolutionary processes. Hotspot archipelagoes, such as Hawaii, provide a temporal framework of islands that allows one to synthesize ecological and evolutionary perspectives. She uses this framework to integrate macroecological (interaction networks and maximum entropy inference) and evolutionary (population genetics and phylogenetics) approaches to build a predictive understanding of the dynamic interplay between ecology and evolution in shaping the ecology of complex ecosystems.

As part of her interest in biodiversity dynamics, she is co-lead of the UC Berkeley Institute for Global Change Biology (BiGCB), which aims to develop a universal protocol for guiding global change research, for example, in characterizing the early warning signs that precede irreparable damage to ecosystems. This effort involves building the informatics infrastructure needed to access, visualize, and analyze the rich data associated with museum collections, field stations, and other data sources. One component involves ways to accelerate the rate of digitization of label data through crowdsourcing (Notes from Nature).

Shelly Trigg

Data Science Postdoctoral Fellow, University of Washington