A conceptual framework to inform conservation status assessments of non-charismatic species

The conservation of at-risk species is rooted in the ability of natural resource agencies to recognize when a species is imperiled and in need of regulatory action, which can be a difficult task due to incomplete information. Freshwater mussels (Bivalvia: Unionidae), are a highly imperiled group of aquatic organisms and conservation tools such as the NatureServe Conservation Methodology provide a framework to determine whether a species is in decline and in need of potential management. For data deficient species like mussels this method relies heavily on expert opinion, which can lead to biased estimates of conservation status that may not reflect the true nature of their conservation need. To address these concerns, we developed a standardized and repeatable conservation ranking framework that builds upon the established NatureServe methodology. We compiled a data set of 12,018 species occurrence records of 48 freshwater mussel species, 17 geospatial layers representing environmental threats, and life history information to estimate their response to those threats. Estimated ranks were compared to previous status ranking metrics from IUCN, NatureServe, USFWS and Texas Parks and Wildlife Department. Of the 48 species we evaluated, three were classified as critically imperiled, 16 were imperiled, 15 were vulnerable, 13 were apparently secure, and one was secure. We found 48% of species assessed were less imperiled than NatureServe estimates and found 10% of species assessed to have a higher conservation status than previous evaluations. Our approach can be applied to other species in other regions and should be useful for managers and scientists interested in reducing uncertainty and improving reproducibility in assignment of conservation ranks, particularly for those with limited information. •Recognizing imperiled species is critical for conservation but often hindered by incomplete data.•Incorporating geospatial data into species status assessments reduces biases and allows for repeatability.•Organizational differences in conservation rankings underscores the need for more standardization.•Data-driven frameworks offer better transferability for evaluating at-risk species and guiding future efforts.