Watershed‐level brook trout genetic structuring: Evaluation and application of riverscape genetics models

Abstract

Stream connectivity promotes resilience and population viability of aquatic organisms. Landscape genetic approaches, traditionally applied to terrestrial systems, may reveal important watershed‐level dynamics that influence connectivity. Additional validation would improve understanding of how the models perform when gene flow is constrained to dendritic networks. The objectives of this study were to use simulations to assess the utility of landscape genetics analyses in dendritic stream networks and investigate riverscape variables influencing gene flow among brook trout Salvelinus fontinalis populations in headwater streams. We used an individual‐based simulation program to simulate different dispersal scenarios and used combinations of landscape genetic models, model selection methods, and genetic metrics to determine the best combination for riverscape systems. We also sampled brook trout from 76 headwater streams in two watersheds (c. 1,000 km2) in Connecticut, U.S.A. to assess connectivity and riverscape influences on genetic structuring. Gravity models with Bayesian information criterion (BIC) selection were the most accurate (>85%) landscape genetic models that consistently identified the correct simulated gene flow barrier. However, all models performed poorly when unidirectional barriers were simulated without a distance‐based dispersal limitation. Excluding this scenario, model accuracy for the gravity models using BIC selection was >90% across multiple genetic metrics, validating the application of landscape genetic models to riverscape systems. We found highly variable levels of brook trout genetic connectivity (FST range 0.01–0.19) at the watershed level (5–15 river km). Gravity models identified increases in upstream impervious surfaces and decreases in riparian tree canopy cover as riverscape variables associated with increases in genetic differentiation in one watershed, while the other watershed was consistent with an isolation by distance pattern. In this study we used demogenetic (i.e. combined demographic and genetic) simulations to demonstrate the utility of landscape genetics techniques in dendritic river networks. Our empirical genetic study documented gene flow among headwater populations of brook trout at the watershed level and also suggested connectivity can be limited by watershed development. Incorporating the heterogeneity of riverscapes into connectivity‐focused conservation planning is essential to the development of effective restoration actions, and landscape genetics approaches can be useful tools to identify watershed‐level connectivity in stream systems.

Biological Diversity in Headwater Streams

“…Dams downstream, whether large or small, will further diminish the rates of colonising other headwater streams for species limited to the stream network. Moreover, urban development can also result in the loss of connectivity and genetic differentiation of headwater populations, such as Brook Trout [108]. As discussed above, this isolation of populations coupled with small population sizes provide the conditions for genetic divergence and even diversification [90].…”

Evaluation of Genetic Structuring within GIS‐Derived Brook Trout Management Units

“…Based on the scale of the underlying NHD catchments, patches could span main‐stem habitat that does not support Brook Trout occupancy throughout the year (Figure 1A). Although such habitat is often considered unsuitable, discrete populations of Brook Trout in headwater streams can be genetically connected due to gene flow at the watershed scale (Aunins et al 2015; Kelson et al 2015; Nathan et al 2019). Mean patch size across the region (Maine to Georgia, latitude range: 34.4–47.6) was 19 km 2 (range = 0–4,114 km 2 ) and followed a general pattern of increasing size at the northern extent of the range.…”

“…To genotype individuals, we used a suite of eight microsatellites: SfoC‐24 , SfoD‐75 , SfoC‐88 , SfoD‐100 , SfoC‐113 , SfoC‐115 , SfoC‐129 (King et al 2012), and SsaD‐237 (King et al 2005). We followed the amplification protocols described by Nathan et al (2019). We manually scored genotypes using a Beckman Coulter GenomeLab GeXP Genetic Analysis System and a Beckman Coulter Genetic Analysis System (Beckman Coulter, Indianapolis, Indiana).…”

“…All analyses in this study were conducted in R version 3.4.1 (R Core Team 2017). We used results of previous analyses with these data to identify and remove putative hatchery hybrids or admixed individuals (Nathan et al 2019; D. Greene, L. Nathan, and J. Vokoun, University of Connecticut, unpublished data).…”

Utilizing conservation genetics as a strategy for recovering the endangered Candy Darter (Etheostoma osburni) in West Virginia

“…The higher levels of agriculture and development are a potential threat to the reintroduced populations because even at moderate levels, agricultural land cover and impervious surfaces and land cover can impact fish distribution and genetics (Hudy et al 2008;Nathan et al 2019 0.007 0.001 0.031 0.009 0.029 0.040 0.062 - Table 52 5.…”