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Advancing Endangered Species Act consultations – Use of an Automated, Computational Pipeline to Extract Points of Departure from Public Data Sources
Citation:
Hazemi, M., Dan Villeneuve, A. Latier, M. Jankowski, C. LaLone, C. Schaupp, M. Chan, D. Haggard, AND K. Mattingly. Advancing Endangered Species Act consultations – Use of an Automated, Computational Pipeline to Extract Points of Departure from Public Data Sources. SETAC North America, Pittsburgh, PA, November 13 - 17, 2022. https://doi.org/10.23645/epacomptox.21559146
Impact/Purpose:
Presentation to the Society of Environmental Toxicology and Chemistry (SETAC) annual meeting November 2022. Endangered and threatened plant and animal species can be protected under the Endangered Species Act (ESA), a federal law which prohibits any action that imperils the continued existence of any species listed under the ESA. If an action proposed by the federal government overlaps the habitat range for a listed species, federal agencies are required to complete an ESA consultation that investigates the proposed action and the adverse impacts it may have on the imperiled species. As part of this consultation process, the U.S. Environmental Protection Agency develops toxicity analyses for each species and chemical included in the proposed action to assess how toxic the activity may be to each imperiled species affected by the action. Conducting toxicity analyses, however, is a an often multi-year process involving substantial workloads that needs to turned around quicker than is often realistically possible. As such, there is a need to streamline the toxicity assessment process of sorting through available toxicity data and determining the impact of chemical pollutants on ESA-listed species and their habitats. This research sought to determine if using an automated application that extracts, categorizes, scores, and filters toxicity data would yield toxicity threshold values that are lower than, and just as protective from adverse effects, as those identified through manual selection. To test this theory, four ESA-listed species and twelve chemicals were input into the automated application and relevant data quality filtering was used to extract toxicity threshold values in three different ways. Values from each method were compared to toxicity data generated from manual selection. Comparisons indicated that automated values tended to be lower or approximately equal to the manual values, thereby supporting our theory that the automated application can be used to produce protective toxicity thresholds and streamline toxicity analyses. While there is still room for improvement with the automated application, this tool can help expedite toxicity assessments and standardize the screening of chemicals for evaluation.
Description:
Endangered Species Act (ESA) consultations are required when a proposed federal action overlaps a listed species range or designated critical habitat. As part of its ESA consultations, the U.S. Environmental Protection Agency develops species-specific toxicity analyses for each chemical included in the proposed action. Due to substantial workloads, tight regulatory timelines, and the often-protracted length of ESA consultations, there is a need to streamline the development of biological evaluation (BE) toxicity assessments for determining the impact of chemical pollutants on ESA-listed species. Moreover, there is limited availability of species-specific toxicity data for many contaminants, further complicating the consultation process. The current study tested the hypothesis that an automated computational pipeline (i.e., QlikSense application or “app”) that extracts, categorizes, scores, and filters data from the ECOTOX knowledgebase would yield toxicity benchmark values that are protective (i.e., lower) compared to those identified through manual search and curation. A combination of 4 species (vernal pool fairy shrimp [Branchinecta lynchi], steelhead [Oncorhynchus mykiss], Foskett speckled dace [Rhinichthys osculus ssp.], and bocaccio rockfish [Sebastes paucispinis]) and 12 pesticides, for which BEs have been conducted and toxicity reference values (TRVs) derived, were queried in the app. Within the app, records were scored based on pre-defined data quality characteristics (e.g., taxonomic relatedness to query species). Points of departure (PODs), selected using three different analyses, were collected for each chemical-species pair, and compared to manually curated TRVs. Results indicated that app-derived POD estimates tended to be at lower or approximately equal concentrations relative to manually derived reference values, providing evidence that the app method for automated extraction, filtering, and scoring of effect values reported in ECOTOX yielded generally protective PODs for the evaluated ESA-listed species. However, given that app-derived PODs were not always lower than TRVs, future work may include application of adjustment factors commonly used in BEs. Applying automated data extraction and filtering tools can help standardize the screening of chemicals for evaluation and expedite assessment of available toxicity data to prioritize and select PODs for BE development. This abstract neither constitutes nor necessarily reflects US EPA policy.
URLs/Downloads:
DOI: Advancing Endangered Species Act consultations – Use of an Automated, Computational Pipeline to Extract Points of Departure from Public Data Sources
HAZEMI_POD EXTRACTION-R10 OBJ 1-SETAC2022_DRAFT6 - FORSHARING.PDF (PDF, NA pp, 11357.419 KB, about PDF)