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Article

Bi-Directional Learning: Identifying Contaminants on the Yurok Indian Reservation

by
Beth Rose Middleton
1,*,
Sabine Talaugon
1,
Thomas M. Young
2,
Luann Wong
2,
Suzanne Fluharty
3,
Kaitlin Reed
4,
Christine Cosby
3 and
Richard Myers II
3
1
Department of Native American Studies, University of California, Davis, CA 95616, USA
2
Department of Civil and Environmental Engineering, University of California, Davis, CA 95616, USA
3
Yurok Tribe Environmental Program, Klamath, CA 95548, USA
4
Native American Studies, Humboldt State University, CA 95521, USA
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2019, 16(19), 3513; https://doi.org/10.3390/ijerph16193513
Submission received: 11 August 2019 / Revised: 1 September 2019 / Accepted: 11 September 2019 / Published: 20 September 2019
(This article belongs to the Special Issue New Directions in Environmental Communication Research)
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Abstract

The Yurok Tribe partnered with the University of California Davis (UC Davis) Superfund Research Program to identify and address contaminants in the Klamath watershed that may be impairing human and ecosystem health. We draw on a community-based participatory research approach that begins with community concerns, includes shared duties across the research process, and collaborative interpretation of results. A primary challenge facing University and Tribal researchers on this project is the complexity of the relationship(s) between the identity and concentrations of contaminants and the diversity of illnesses plaguing community members. The framework of bi-directional learning includes Yurok-led river sampling, Yurok traditional ecological knowledge, University lab analysis, and collaborative interpretation of results. Yurok staff and community members share their unique exposure pathways, their knowledge of the landscape, their past scientific studies, and the history of landscape management, and University researchers use both specific and broad scope chemical screening techniques to attempt to identify contaminants and their sources. Both university and tribal knowledge are crucial to understanding the relationship between human and environmental health. This paper examines University and Tribal researchers’ shared learning, progress, and challenges at the end of the second year of a five-year Superfund Research Program (SRP) grant to identify and remediate toxins in the lower Klamath River watershed. Our water quality research is framed within a larger question of how to best build university–Tribal collaboration to address contamination and associated human health impacts.
Keywords: Indigenous; contamination; community; bi-directional; health Indigenous; contamination; community; bi-directional; health

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MDPI and ACS Style

Middleton, B.R.; Talaugon, S.; Young, T.M.; Wong, L.; Fluharty, S.; Reed, K.; Cosby, C.; Myers, R., II. Bi-Directional Learning: Identifying Contaminants on the Yurok Indian Reservation. Int. J. Environ. Res. Public Health 2019, 16, 3513. https://doi.org/10.3390/ijerph16193513

AMA Style

Middleton BR, Talaugon S, Young TM, Wong L, Fluharty S, Reed K, Cosby C, Myers R II. Bi-Directional Learning: Identifying Contaminants on the Yurok Indian Reservation. International Journal of Environmental Research and Public Health. 2019; 16(19):3513. https://doi.org/10.3390/ijerph16193513

Chicago/Turabian Style

Middleton, Beth Rose, Sabine Talaugon, Thomas M. Young, Luann Wong, Suzanne Fluharty, Kaitlin Reed, Christine Cosby, and Richard Myers, II. 2019. "Bi-Directional Learning: Identifying Contaminants on the Yurok Indian Reservation" International Journal of Environmental Research and Public Health 16, no. 19: 3513. https://doi.org/10.3390/ijerph16193513

APA Style

Middleton, B. R., Talaugon, S., Young, T. M., Wong, L., Fluharty, S., Reed, K., Cosby, C., & Myers, R., II. (2019). Bi-Directional Learning: Identifying Contaminants on the Yurok Indian Reservation. International Journal of Environmental Research and Public Health, 16(19), 3513. https://doi.org/10.3390/ijerph16193513

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