Human systems shape not only ecological data collection, but also perspectives and assumptions made during model building, generating uncertainty in how dominant value systems and cultural bias limit ecological inference. Understanding how this uncertainty propagates in conservation decision making requires translation beyond abstract statistical measures and toward performance on real-world objectives. Here we develop a quantitative framework combining uncertainty quantification and decision theory to understand the value of reducing uncertainty generated by cultural bias for a conservation decision maker. We demonstrate this framework in the context of decision making in the Columbia River Basin (CRB), where the Pacific lamprey – an ancient, foundational species in the CRB – modulates salmonid ocean survival both as a parasite of salmonids and a predation buffer against marine mammals. The Pacific lamprey, however, has faced a precipitous decline in the last century, largely stemming from Euro-American cultural bias, which has led to asymmetric measurement error and observation of local, rather than global, properties of the system. We show that multiple functional forms of this three-species dynamical system are statistically indistinguishable yet result in very different optimal management actions because of differences that lie outside the range of observed data. We use methods from decision theory to quantify the cost of lamprey uncertainty in units relevant for a decision maker, demonstrating that the value of information around overlooked species can be considerable. These results highlight how Situated Modeling in ecological management can be used to interrogate how modeling as a process and practice is contextualized.

Keywords: decision theory; value of information; Bayesian statistics; cultural bias