Browsing by Author "Freeman, Mary C."

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    Simple statistical models can be sufficient for testing hypotheses with population time-series data
    Wenger, 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.
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    Stream fish colonization but not persistence varies regionally across a large North American river basin
    Wheeler, Kit; Wenger, Seth J.; Walsh, Stephen J.; Martin, Zachary P.; Jelks, Howard L.; Freeman, Mary C. (2018-07)
    Many species have distributions that span distinctly different physiographic regions, and effective conservation of such taxa will require a full accounting of all factors that potentially influence populations. Ecologists recognize effects of physiographic differences in topography, geology and climate on local habitat configurations, and thus the relevance of landscape heterogeneity to species distributions and abundances. However, research is lacking that examines how physiography affects the processes underlying metapopulation dynamics. We used data describing occupancy dynamics of stream fishes to evaluate evidence that physiography influences rates at which individual taxa persist in or colonize stream reaches under different flow conditions. Using periodic survey data from a stream fish assemblage in a large river basin that encompasses multiple physiographic regions, we fit multi-species dynamic occupancy models. Our modeling results suggested that stream fish colonization but not persistence was strongly governed by physiography, with estimated colonization rates considerably higher in Coastal Plain streams than in Piedmont and Blue Ridge systems. Like colonization, persistence was positively related to an index of stream flow magnitude, but the relationship between flow and persistence did not depend on physiography. Understanding the relative importance of colonization and persistence, and how one or both processes may change across the landscape, is critical information for the conservation of broadly distributed taxa, and conservation strategies explicitly accounting for spatial variation in these processes are likely to be more successful for such taxa.
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    Toward Improved Understanding of Streamflow Effects on Freshwater Fishes
    Freeman, 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.