A large wild salmon stock shows genetic and life history differentiation within, but not between, rivers

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

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.

Gene flow in a pioneer plant metapopulation (Myricaria germanica) at the catchment scale in a fragmented alpine river system

River alterations for natural hazard mitigation and land reclamation result in habitat decline and fragmentation for riparian plant species. Extreme events such as floods are responsible for additional local species loss or population decline. Tributaries might provide refugia and subsequent source populations for the colonization of downstream sites in connected riverine networks with metapopulations of plant species. In this study, we analyzed the metapopulation structure of the endangered riparian shrub species Myricaria germanica along the river Isel, Austria, which is part of the Natura 2000 network, and its tributaries. The use of 22 microsatellite markers allowed us to assess the role of tributaries and single populations as well as gene flow up- and downstream. The analysis of 1307 individuals from 45 sites shows the influence of tributaries to the genetic diversity at Isel and no overall isolation by distance pattern. Ongoing bidirectional gene flow is revealed by the detection of first-generation migrants in populations of all tributaries as well as the river Isel, supporting upstream dispersal by wind (seeds) or animals (seeds and pollen). However, some populations display significant population declines and high inbreeding, and recent migration rates are non-significant or low. The genetic pattern at the mouth of river Schwarzach into Isel and shortly thereafter river Kalserbach supports the finding that geographically close populations remain connected and that tributaries can form important refugia for M. germanica in the dynamic riverine network. Conservation and mitigation measures should therefore focus on providing sufficient habitat along tributaries of various size allowing pioneer plants to cope with extreme events in the main channel, especially as they are expected to be more frequent under changing climate.

Using Food Webs and Metabolic Theory to Monitor, Model, and Manage Atlantic Salmon—A Keystone Species Under Threat

Populations of Atlantic salmon are crashing across most of its natural range: understanding the underlying causes and predicting these collapses in time to intervene effectively are urgent ecological and socioeconomic priorities. Current management techniques rely on phenomenological analyses of demographic population time-series and thus lack a mechanistic understanding of how and why populations may be declining. New multidisciplinary approaches are thus needed to capitalize on the long-term, large-scale population data that are currently scattered across various repositories in multiple countries, as well as marshaling additional data to understand the constraints on the life cycle and how salmon operate within the wider food web. Here, we explore how we might combine data and theory to develop the mechanistic models that we need to predict and manage responses to future change. Although we focus on Atlantic salmon—given the huge data resources that already exist for this species—the general principles developed here could be applied and extended to many other species and ecosystems.