Browsing by Author "Kanno, Yoichiro"
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- Population Genetics of Brook Trout in the Southern Appalachian MountainsKazyak, David C.; Lubinski, Barbara A.; Kulp, Matt A.; Pregler, Kasey C.; Whiteley, Andrew R.; Hallerman, Eric M.; Coombs, Jason A.; Kanno, Yoichiro; Rash, Jacob M.; Morgan, Raymond P. II; Habera, Jim; Henegar, Jason; Weathers, T. Casey; Sell, Matthew T.; Rabern, Anthony; Rankin, Dan; King, Tim L. (Wiley, 2022-01-07)Broad-scale patterns of genetic diversity for Brook Trout Salvelinus fontinalis remain poorly understood across their endemic range in the eastern United States. We characterized variation at 12 microsatellite loci in 22,020 Brook Trout among 836 populations from Georgia, USA, to Quebec, Canada, to the western Great Lakes region. Within-population diversity was typically lower in the southern Appalachian Mountains relative to the mid-Atlantic and northeastern regions. Effective population sizes in the southern Appalachians were often very small, with many estimates less than 30 individuals. The population genetics of Brook Trout in the southern Appalachians are far more complex than a conventionally held simple “northern” versus “southern” dichotomy would suggest. Contemporary population genetic variation was consistent with geographic expansion of Brook Trout from Mississippian, mid-Atlantic, and Acadian glacial refugia as well as differentiation among drainages within these broader clades. Genetic variation was pronounced among drainages (57.4% of overall variation occurred among 10-digit hydrologic unit code [HUC10] units or larger units) but was considerable even at fine spatial scales (13% of variation occurred among collections within HUC12 drainage units). Remarkably, 87.2% of individuals were correctly assigned to their collection of origin. While comparisons with fish from existing major hatcheries showed impacts of stocking in some populations, genetic introgression did not overwhelm the signal of broad-scale patterns of population genetic structure. Although our results reveal deep genetic structure in Brook Trout over broad spatial extents, fine-scale population structuring is prevalent across the southern Appalachians. Our findings highlight the distinctiveness and vulnerability of many Brook Trout populations in the southern Appalachians and have important implications for wild Brook Trout management. To facilitate application of our findings by conservation practitioners, we provide an interactive online visualization tool to allow our results to be explored at management-relevant scales.
- Simple statistical models can be sufficient for testing hypotheses with population time-series dataWenger, Seth J.; Stowe, Edward S.; Gido, Keith B.; Freeman, Mary C.; Kanno, Yoichiro; Franssen, Nathan R.; Olden, Julian D.; Poff, N. LeRoy; Walters, Annika W.; Bumpers, Phillip M.; Mims, Meryl C.; Hooten, Mevin B.; Lu, Xinyi (Wiley, 2022-09)Time-series data offer wide-ranging opportunities to test hypotheses about the physical and biological factors that influence species abundances. Although sophisticated models have been developed and applied to analyze abundance time series, they require information about species detectability that is often unavailable. We propose that in many cases, simpler models are adequate for testing hypotheses. We consider three relatively simple regression models for time series, using simulated and empirical (fish and mammal) datasets. Model A is a conventional generalized linear model of abundance, model B adds a temporal autoregressive term, and model C uses an estimate of population growth rate as a response variable, with the option of including a term for density dependence. All models can be fit using Bayesian and non-Bayesian methods. Simulation results demonstrated that model C tended to have greater support for long-lived, lower-fecundity organisms (K life-history strategists), while model A, the simplest, tended to be supported for shorter-lived, high-fecundity organisms (r life-history strategists). Analysis of real-world fish and mammal datasets found that models A, B, and C each enjoyed support for at least some species, but sometimes yielded different insights. In particular, model C indicated effects of predictor variables that were not evident in analyses with models A and B. Bayesian and frequentist models yielded similar parameter estimates and performance. We conclude that relatively simple models are useful for testing hypotheses about the factors that influence abundance in time-series data, and can be appropriate choices for datasets that lack the information needed to fit more complicated models. When feasible, we advise fitting datasets with multiple models because they can provide complementary information.
- Toward Improved Understanding of Streamflow Effects on Freshwater FishesFreeman, Mary C.; Bestgen, Kevin R.; Carlisle, Daren; Frimpong, Emmanuel A.; Franssen, Nathan R.; Gido, Keith B.; Irwin, Elise; Kanno, Yoichiro; Luce, Charles; Kyle McKay, S.; Mims, Meryl C.; Olden, Julian D.; LeRoy Poff, N.; Propst, David L.; Rack, Laura; Roy, Alliso H.; Stowe, Edward S.; Walters, Annika; Wenger, Seth J. (Wiley, 2022-07)Understanding the effects of hydrology on fish populations is essential to managing for native fish conservation. However, despite decades of research illustrating streamflow influences on fish habitat, reproduction, and survival, biologists remain challenged when tasked with predicting how fish populations will respond to changes in flow regimes. This uncertainty stems from insufficient understanding of the context-dependent mechanisms underlying fish responses to, for example, periods of reduced flow or altered frequency of high-flow events. We aim to address this gap by drawing on previous research to hypothesize mechanisms by which low and high flows influence fish populations and communities, identifying challenges that stem from data limitations and ecological complexity, and outlining research directions that can advance an empirical basis for prediction. Focusing flow ecology research on testing and refining mechanistic hypotheses can help narrow management uncertainties and better support species conservation in changing flow regimes.