Applying landscape genetics to evaluate threats affecting endangered Atlantic salmon populations

Bowlby, Heather D.; Fleming, Ian A.; Gibson, Arthur

doi:10.1007/s10592-016-0824-7

Cited by 6 publications

(8 citation statements)

References 97 publications

(113 reference statements)

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“…For example, migration distance (Dionne et al., ) and conditions along migratory routes are important factors influencing salmon population structure (Hecht, Matala, Hess, & Narum, ; Micheletti, Matala, Matala, & Narum, ) and may be confounded with temperature due to the relative geographic isolation of Lake Melville. The inclusion of additional environmental parameters, such as land use (Micheletti et al., ), water quality measures (Bowlby, Fleming, & Gibson, ; Bradbury et al., ), salinity and pathogen or parasite diversity (Dionne, Miller, Dodson, & Bernatchez, ), may provide further insight into the mechanisms influencing spatial structure in the region. Admittedly, the use of air temperature as a proxy for temperature in fresh water lacks consideration of water depth and may be erroneous, particularly across small spatial scales (Hansen, Read, Hansen, & Winslow, ).…”

Section: Discussionmentioning

confidence: 99%

Environmental extremes drive population structure at the northern range limit of Atlantic salmon in North America

et al. 2018

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Conservation of exploited species requires an understanding of both genetic diversity and the dominant structuring forces, particularly near range limits, where climatic variation can drive rapid expansions or contractions of geographic range. Here, we examine population structure and landscape associations in Atlantic salmon (Salmo salar) across a heterogeneous landscape near the northern range limit in Labrador, Canada. Analysis of two amplicon-based data sets containing 101 microsatellites and 376 single nucleotide polymorphisms (SNPs) from 35 locations revealed clear differentiation between populations spawning in rivers flowing into a large marine embayment (Lake Melville) compared to coastal populations. The mechanisms influencing the differentiation of embayment populations were investigated using both multivariate and machine-learning landscape genetic approaches. We identified temperature as the strongest correlate with genetic structure, particularly warm temperature extremes and wider annual temperature ranges. The genomic basis of this divergence was further explored using a subset of locations (n = 17) and a 220K SNP array. SNPs associated with spatial structuring and temperature mapped to a diverse set of genes and molecular pathways, including regulation of gene expression, immune response, and cell development and differentiation. The results spanning molecular marker types and both novel and established methods clearly show climate-associated, fine-scale population structure across an environmental gradient in Atlantic salmon near its range limit in North America, highlighting valuable approaches for predicting population responses to climate change and managing species sustainability.

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Section: Discussionmentioning

confidence: 99%

Environmental extremes drive population structure at the northern range limit of Atlantic salmon in North America

et al. 2018

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“…To determine whether there was evidence of isolation by distance (IBD), the test provided by Mantel (Mantel, 1967; Sokal, 1979) was performed in the VEGAN package of the R software, version 3.4.2 (Oksanen et al, 2019; R Core Team, 2019) using Spearman's correlation, Euclidean dissimilarity indices for geographic distances, and the Bray–Curtis index for genetic distances. These values were used in the interindividual genetic analyses due to their consistent performance in this context; they effectively replace the genetic distances commonly used in population analyses (Bowlby et al., 2016; Shirk et al., 2017).…”

Section: Methodsmentioning

confidence: 99%

“…Cluster analysis was also performed in PAST (Hammer et al, 2001) using the same subdivision and Bray-Curtis index (Bowlby et al, 2016;Shirk et al, 2017), according to the Neighbor-Joining (NJ) method (Saitou and Nei, 1987). The nodes were supported by bootstrap analysis with 10,000 replicates.…”

Section: Statistical Analysesmentioning

confidence: 99%

Identifying Priority Giant Anteater (Myrmecophaga tridactyla) Populations for Conservation in São Paulo State, Brazil

Sartori

Lopes

Aires

et al. 2020

Ecology and Evolution

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Habitat loss is the main threat to biodiversity conservation worldwide. Some species may be particularly susceptible to the effects of fragmentation and the isolation of populations. The impacts of human activity on wild animal populations may be understood through relationships between individual genetic data and spatial landscape variables, particularly when considering local population dynamics influenced by fragmented habitats. Thus, the objective of this study was to analyze the population structure and genetic diversity of the giant anteater (Myrmecophaga tridactyla) using an individual sampling scheme (ISS) on a regional geographic scale. Data were collected from 41 specimens from twenty different locations in São Paulo State, Brazil, and six polymorphic microsatellite loci were genotyped. Our results indicate that barriers to gene flow exist and have segregated individuals of the farther away areas into two spatially structured clusters. The populations were also found to have high genetic diversity. The experimental sampling approach used herein enabled an analysis of the population dynamics of the giant anteater on a regional scale, as well as the identification of priority populations for genetic resource conservation for this species. The results reflect the need for adequate management plans. The efficacy of the sampling scheme may vary based on the study model used, but we argue that the use of an ISS combined with suitable molecular markers and statistical methods may serve as an important tool for initial analyses of threatened or vulnerable species, particularly in anthropized regions where populations are small or hard to characterize.

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“…We refer readers to Legendre and Fortin () and Legendre, Fortin, Borcard, and Peres‐Neto () for reviews on the discussion. Typical methods include multiple‐regression on distance matrices (MRDM; Bowlby et al, ; Diniz‐Filho, Nabout, Telles, Soares, & Rangel, 2009b; Kanno et al, ), causal modeling (Cushman et al, ; Cushman, Wasserman, Landguth, & Shirk, ), ordination techniques (Kierepka & Latch, ), and regression modeling (Hand et al, ), but the most common tools are the Mantel test and its derivatives (Mantel, ; Smouse, Lone, & Sokal, ; Storfer et al, ; Zeller et al, ).…”

Section: Applying the Mechanics Of Lg To Riverscapesmentioning

confidence: 99%

Refining and defining riverscape genetics: How rivers influence population genetic structure

Davis

Epps

Flitcroft³

et al. 2018

WIREs Water

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Traditional analysis in population genetics evaluates differences among groups of individuals and, in some cases, considers the effects of distance or potential barriers to gene flow. Genetic variation of organisms in complex landscapes, seascapes, or riverine systems, however, may be shaped by many forces. Recent research has linked habitat heterogeneity and landscape or seascape configuration to genetic structure by integrating methods from landscape ecology, population genetics, and spatial statistics in approaches known as landscape or seascape genetics. However, functional differences between terrestrial or seascapes systems in comparison to riverscape topography (i.e., movement pathways for aquatic obligate species are constrained to river channels) make translating these approaches into freshwater analyses problematic. Studies that may be described as riverscape genetics (RG) have linked temperature, stream gradient, and confluences to genetic variability. Lack of consistency in methodology, however, has made comparisons across species and scales difficult. We provide a perspective on how RG could be used to provide a more comprehensive conceptual and applied understanding of connectivity and dispersal in freshwater systems. We describe four thematic areas of study representing current and future research opportunities and describe a basic workflow for conducting RG analysis. Although numerous methodological challenges remain, a RG approach can enhance our understanding of habitat heterogeneity in shaping gene flow and spatial genetic structure. These characteristics of populations are critical components for interpreting demographic and evolutionary consequences of habitat loss and fragmentation.

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Applying landscape genetics to evaluate threats affecting endangered Atlantic salmon populations

Cited by 6 publications

References 97 publications

Environmental extremes drive population structure at the northern range limit of Atlantic salmon in North America

Environmental extremes drive population structure at the northern range limit of Atlantic salmon in North America

Identifying Priority Giant Anteater (Myrmecophaga tridactyla) Populations for Conservation in São Paulo State, Brazil

Refining and defining riverscape genetics: How rivers influence population genetic structure

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