Table 3.

Impediments to decision making based on the best available science

Impediment	Type of effect or outcome	Specific example
Lack of institutional capacity, including Insufficient agency expertise, insufficient financial resources, or both, contributing to poor quality decision making	• Failure to develop and incorporate a life-cycle model into an agency jeopardy determination • Failure to perform quality control with respect to a data set provided in a listing petition	For example, whereas in a biological opinion analyzing effects of a proposed action on Sacramento River winter-run Chinook salmon shortly after the species was listed identified the need to develop a life-cycle model for the species as a conservation recommendation, for the subsequent two decades the National Marine Fisheries Service failed to develop and apply such a model in part due to a lack of institutional capacity (National Marine Fisheries Service 1993, 2009)
Incomplete presentation of information that is relevant to the determination or decision under consideration	• Failure to consider and report on relevant and readily available data, analyses, or conclusions	For example, the Fish and Wildlife Service failed to consider recent survey data that provided evidence of a decline in the relative abundance of delta smelt when preparing a biological opinion (NRDC v. Kempthorne, 506 F. Supp. 2d 322 (E.D. Cal. 2007))
Misinterpretation of findings that accompany available research, monitoring, and modeling efforts	• Failure to adequately take into account assumptions that accompany analyses or limitations reported in association with findings	For example, where the Fish and Wildlife Service withdrew a proposed rule to list the flat-tailed horned lizard on the grounds the species is persisting in the vast majority of its range; that was based on a single capture-mark-recapture study that found no evidence of a large decline in population in two discrete sections of the species’ range (Tucson Herpetological Society v. Salazar, 566 F.3d 870 (9th Cir. 2009))
Misrepresentation of findings that accompany available research, monitoring, and modeling	• Representing population estimates that are based on sampling within a fraction of a species’ habitat as census data • Use of findings regarding the response of one species to an environmental change or disturbance to draw inference regarding the response of a different species without disclosing and accounting for pertinent differences between the species	For example, the National Marine Fisheries Service used data regarding survival of hatchery Chinook salmon to predict the behavioral responses of steelhead and green sturgeon absent any effort to first ascertain whether the former is an effective surrogate for the latter and despite the substantially different life histories of the two species (Murphy and Weiland 2011)
Inappropriate emphasis resulting from the presentation of findings out of full landscape or temporal context, or adequately considering the ecology and behavior of the listed species	• Not interpreting available demographic data using life history information and understanding of environmental stressors • Reporting short-term data on trends in a species’ relative abundance without disclosing available long-term trend data	For example, the California Fish and Game Commission designated the tricolored blackbird a candidate for listing under the State’s Endangered Species Act based on a decline in estimates of the species abundance recorded in surveys completed in 2008, 2011, and 2014, disregarding a dozen other surveys completed during the previous four decades (California Department of Fish and Wildlife 2015)
Assumption that published conclusions are valid, resulting from a failure to critically analyze conclusions that are presented as part of an empirical study	• Reliance on a publication regarding the effects of an environmental change or certain types of disturbance on a species, even where other scientific information is inconsistent with the results or inferences reported in the article	For example, in response to an administrative appeal from denial of an Information Quality Act request for correction with respect to certain information in a biological opinion regarding operations of water export projects in California, the Fish and Wildlife Service stated that it “accepts the peer review processes of scientific journals and thus, the scientific validity of the paper’s conclusions” (U.S. Fish and Wildlife Service 2009)
Use of agency staff as researchers, analysts, and advocates for agency determinations in regulatory and judicial proceedings	• Use of a staff biologist who had published research directly relevant to the agency decision to evaluate the available scientific information and craft the decision	For example, a science review panel asked to review a proposed rule with respect to the status of the gray wolf on the list of threatened and endangered species noted that the rule relied heavily on an article authored by four Fish and Wildlife Service biologists and accepted the conclusions in the article uncritically (National Center for Ecological Analysis and Synthesis 2014)
Use of peer review as a substitute for rigorous structured decision-making by agency staff or consultants, who should possess adequate expertise, resources, and time to complete their task	• Using peer review of a completed determination to replace structured effects analysis as the vehicle to identify and incorporate scientific knowledge into an agency decision-making process	For example, the Secretaries of the Interior and Commerce asked the National Research Council to review the draft Bay Delta Conservation Plan in terms of its use of science and adaptive management, and the panel found the Plan to be incomplete or unclear in a variety of attributes and approaches, including due to the absence of an effects analysis (National Research Council 2011)
Bias based on prejudice or unreasoned judgment that forecloses objective identification, presentation, and application of data, analyses, and interpretations	• Agency use of staff to evaluate or craft determinations, despite knowing a priori that such personnel are advocates who have decided outcomes before evaluation • Disregarding peer reviews without explanation • Lack of information in a determination regarding uncertainty, data variability, or estimation error	For example, the National Marine Fisheries Service listed the Arctic subspecies of ringed seal—while acknowledging the inference of experts that its population numbers in the millions—based on projections of sea ice loss, but absent data that links projected sea ice loss to a decline in the population (Alaska Oil & Gas Assn v. NMFS, Case No. 14-29 (D. Ak. March 11, 2016))