A novel quantitative framework for riverscape genetics

Abstract

Riverscape genetics, which applies concepts in landscape genetics to riverine ecosystems, lack appropriate quantitative methods that address the spatial autocorrelation structure of linear stream networks and account for bidirectional geneflow. To address these challenges, we present a general framework for the design and analysis of riverscape genetic studies. Our framework starts with the estimation of pairwise genetic distance at sample sites and the development of a spatially structured ecological network (SSEN) on which riverscape covariates are measured. We then introduce the novel bidirectional geneflow in riverscapes (BGR) model that uses principles of isolation-by-resistance to quantify the effects of environmental covariates on genetic connectivity, with spatial covariance defined using simultaneous autoregressive models on the SSEN and the generalized Wishart distribution to model pairwise distance matrices arising through a random walk model of geneflow. We highlight the utility of this framework in an analysis of riverscape genetics for brook trout (Salvelinus fontinalis) in north central Pennsylvania, USA. Using the fixation index (F ST) as the measure of genetic distance, we estimated the effects of 12 riverscape covariates on geneflow by evaluating the relative support of eight competing BGR models. We then compared the performance of the top-ranked BGR model to results obtained from comparable analyses using multiple regression on distance matrices (MRM) and the program STRUCTURE. We found that the BGR model had more power to detect covariate effects, particularly for variables that were only partial barriers to geneflow and/or uncommon in the riverscape, making it more informative for assessing patterns of population connectivity and identifying threats to species conservation. This case study highlights the utility of our modeling framework over other quantitative methods in riverscape genetics, particularly the ability to rigorously test hypotheses about factors that influence geneflow and probabilistically estimate the effect of riverscape covariates, including stream flow direction. This framework is flexible across taxa and riverine networks, is easily executable, and provides intuitive results that can be used to investigate the likely outcomes of current and future management scenarios.

Inferring future changes in gene flow under climate change in riverscapes

“…Our results showed that gene flow in the cold- [17,20,21] and therefore migration may also occur frequently in these streams. Strahler's stream order of the edge Even in cold-water fish, the mainstem may function as a corridor that facilitates connectivity among populations [9].…”

“…To understand the real gene flow, its determinants should be identified directly from genetic data. Fortunately, alternative methods for modeling gene flow from genetic data within a spatially explicit graph-theoretic framework have been developed rapidly in recent years [6,9]. Although not yet practically applied to predictions under environmental changes, we thought that these “riverscape genetics”-dedicated methods are the key to determining landscape resistance and modeling current and future gene flow.…”

Optimization of a suite of flathead catfish (Pylodictis olivaris) microsatellite markers for understanding the population genetics of introduced populations in the northeast United States

“…Though connectivity was apparent, genetic structure within the Susquehanna River suggests that different reaches may be demographically independent, suggesting regional management strategies may be effective in mitigating population growth and spread. Additionally, it may be possible to use genetic tools to model invasion rate in a riverscape genetics framework [ 28 ] and monitor human-mitigated spread of flathead catfish.…”

Riverscape community genomics: A comparative analytical approach to identify common drivers of spatial structure

“…For aquatic biodiversity, patterns of genetic divergence will also be governed by the structure and architecture of the riverine network (in contemporary and past representations). Organisms within such dendritic networks are demonstrably impacted by the physical structure of the habitat, which constrains their movement (Peterson et al, 2013; White et al, 2020; Paz-Vinas et al, 2015) and leads to correspondence between genetic relatedness and the underlying structural hierarchy (Hughes et al, 2009). While this is most apparent within the contemporary structure of river networks, their historic structure, i.e., paleohydrology, also serves to bookmark genetic diversity, and the effects of past connectivity or isolation can still be observed within contemporary spatial patterns (Mayden, 1988; Strange & Burr, 1997).…”

“…For aquatic biodiversity, patterns of genetic divergence will also be governed by the structure and architecture of the riverine network (in contemporary and past representations). Organisms within such dendritic networks are demonstrably impacted by the physical structure of the habitat, which constrains their movement (Peterson et al, 2013;White et al, 2020; and leads to correspondence between genetic relatedness and the underlying structural hierarchy (Hughes et al, 2009).…”