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Ethics in ecological forecasting: four case-based teaching modules

Author(s): Abigail S.L. Lewis1, Dexter W. Howard1, Gerbrand Koren2, Cazimir Kowalski3, Jason McLachlan3, Jody Peters4, Georgia Smies5, Olivia Tabares6

1. Department of Biological Sciences, Virginia Tech, Blacksburg, VA, USA 2. Copernicus Institute of Sustainable Development, Utrecht University, Utrecht, the Netherlands 3. Department of Biological Sciences, University of Notre Dame, Notre Dame, IN, USA 4. University of Notre Dame 5. Division of Natural Resources, Salish Kootenai College, Pablo, Montana, USA 6. The American School Foundation, Ciudad de México, México

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Summary:
Working with ecological data often involves ethical considerations, particularly when data are applied to address societal needs. However, data science ethics are rarely included as part of undergraduate and graduate training programs. Here, we…

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Working with ecological data often involves ethical considerations, particularly when data are applied to address societal needs. However, data science ethics are rarely included as part of undergraduate and graduate training programs. Here, we present four modules for teaching ethics in data science, with real-world case studies related to ecological forecasting.  See the module topics in the description below. The material was originally published in Teaching Issues and Experiments in Ecology (TIEE), Vol 19, Practice #13. Dec 2023. https://tiee.esa.org/vol/v19/issues/case_studies/lewis/abstract.html In addition to having this material in TIEE, we are sharing these resources in QUBES to expand the scope of potential users.  TIEE approved us sharing the material in both places.  Note that TIEE does not provide DOIs for the published materials, but QUBES does. We include both pdfs and word documents for the essay assignments, class handouts, and pre-reading materials. The Full Article Text provides the student-active approaches and cognitive skills, the case studies, the background materials, and instructions for the instructors and the students for each of the four modules.

Licensed under CC Attribution-NonCommercial 4.0 International according to these terms

Version 1.0 - published on 07 Feb 2024 doi:10.25334/5D99-Y019 - cite this

Keywords

  1. data science
  2. remote sensing
  3. ethics
  4. uncertainty
  5. ecological forecasting
  6. indigenous knowledge
  7. tropical forests
  8. cultural competence
  9. conflicts of interest
  10. marine fisheries
  11. flying foxes
  12. freshwater quality
  13. inequities

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Contents:
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Description

COURSE CONTEXT:
These stand-alone modules are designed to be flexible to a wide range of course contexts. We anticipate that in most cases, an instructor will choose to use only one of the four modules, and will choose the module that best matches their classes interests and needs (see descriptions below). However, each module covers a different topic related to ethics in ecological forecasting, and using multiple modules as part of a single course would provide increased training in the ethics of environmental data science.  All modules use a think-pair-share format for discussion, making them flexible to a wide range of class sizes. In very large courses, we note that it may be helpful to have teaching assistants who can help answer questions and engage with students during small group discussions.

Module 1: Flying foxes and uncertainty

This module asks students to discuss ethical issues associated with the quantification and presentation of forecast uncertainty. As such, this module would connect well with data analysis lessons in an introductory ecology course or lab. In a forecasting-specific course, this module could be used as an introductory lesson to highlight the sources of forecast uncertainty and why uncertainty is necessary to include.

Module 2: Marine Fisheries and conflicts of interest

This module focuses on the idea that forecasts may have multiple end users with different and potentially conflicting interests. This module would be beneficial in a general ecology or natural resources management class for discussing the benefits and challenges of public-facing research. In forecasting and data science classes this module would benefit discussions on end user engagement as a component of the forecast development process.

Module 3: Water Quality and Indigenous Knowledge

This module focuses on the topic of scientific engagement with communities, with a case study of Indigenous communities and water quality. As such, this module would best be implemented in an introductory data science, forecasting, or ecology course, providing a discussion of how scientists can engage with impacted and marginalized communities. Due to the focus of this module, it may be most relevant for courses taught in the United States of America.

Module 4: Tropical forests and data availability

This module addresses the (lack of) data availability to develop, validate or score ecological forecasts on a global scale. In particular, the module addresses underrepresentation of tropical ecosystems for studying vegetation dynamics. The themes of this module are broadly applicable to many classes, including courses in ecology and data science. The module is written to target advanced undergraduate students. 

STUDENT ASSESSMENTS:
Each module includes an optional essay assignment to assess student learning, with a suggested rubric.  

CLASS TIME:
Four independent modules, one hour each 

STUDENT-ACTIVE APPROACHES:
Each of the modules are structured in “Think, pair, share” discussions. Students take the time to reflect individually, then discuss in small groups and share with the class. 

 

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