Multifaceted framework for defining conservation units: An example from Atlantic salmon (Salmo salar) in Canada

Abstract

Abstract Conservation units represent important components of intraspecific diversity that can aid in prioritizing and protecting at‐risk populations, while also safeguarding unique diversity that can contribute to species resilience. In Canada, identification and assessments of conservation units is done by the Committee on the Status of Endangered Wildlife in Canada (COSEWIC). COSEWIC can recognize conservation units below the species level (termed “designatable units”; DUs) if the unit has attributes that make it both discrete and evolutionarily significant. There are various ways in which a DU can meet criteria of discreteness and significance, and increasing access to “big data” is providing unprecedented information that can directly inform both criteria. Specifically, the incorporation of genomic data for an increasing number of non‐model species is informing more COSEWIC assessments; thus, a repeatable, robust framework is needed for integrating these data into DU characterization. Here, we develop a framework that uses a multifaceted, weight of evidence approach to incorporate multiple data types, including genetic and genomic data, to inform COSEWIC DUs. We apply this framework to delineate DUs of Atlantic salmon (Salmo salar, L.), an economically, culturally, and ecologically significant species, that is also characterized by complex hierarchical population structure. Specifically, we focus on an in‐depth example of how our approach was applied to a previously data limited region of northern Canada that was defined by a single large DU. Application of our framework with newly available genetic and genomic data led to subdividing this DU into three new DUs. Although our approach was developed to meet criteria of COSEWIC, it is widely applicable given similarities in the definitions of a conservation unit.

Keywords

• conservation units
• COSEWIC
• endangered species
• evolutionary significance
• genetic discreteness
• genomics