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Song S. Qian

Publications and source records attributed to Song S. Qian.

25 records · Page 2Linked to original sources
USGS2018

“Asian carp” is societally and scientifically problematic. Let's replace it

Bighead Carp Hypophthalmichthys nobilis , Black Carp Mylopharyngodon piceus , Grass Carp Ctenopharyngodon idella , and Silver Carp H. molitrix are considered invasive species in North America and Europe. In North America, they are typically referred to collectively as “Asian carp”, a reference to their native range. The category “Asian carp” fails to acknowledge the cultural value and the ecological differences of these fishes, causes confusion when translated into Chinese, and frequently causes problems of communication with the public and occasionally among professionals when some species are intentionally or inadvertently in‐ or excluded when referred to collectively. Herein we review the long history of aquaculture of these species in China, their human cultural significance, the origin of the category “Asian carp”, and the problems the term “Asian carp” might cause when used in cross‐cultural communication. We recommend discontinuing use of the term Asian carp and replacing it with individual species names. When a group term is required, there are several more favorable alternatives, including existing Chinese terms that have been used for centuries.

Fisheries↗
USGS2018

The multiple-comparison trap and the Raven’s paradox—perils of using null hypothesis testing in environmental assessment

Detecting and quantifying environmental thresholds is frequently an important step in understanding ecological responses to environmental stressors. We discuss two statistical issues often encountered in threshold detection and quantification when statistical null hypothesis testing is used as a main analytical tool.The hidden multiple-comparison trap (leading to a much higher risk of a false detection) and Raven’s paradox(rendering a "detection" meaningless) are often obscured when statistical hypothesis testing is used as part of a more elaborate model, especially models based on computer-intensive methods. Using two examples, we show that the hidden multiple-comparison trap can be exposed using computer simulation to estimate the probability of making a false detection; Raven’s paradox can be avoided by clearly stating the null and alternative hypotheses using scientific terms to substantiate that the rejection of the null is equivalent to proving that the alternative of interest is true. The hidden multiple-comparison trap implies that a null hypothesis testing based on a computer-intensive method should be used with caution. The implication of Raven’s paradox re-quires that we focus on providing evidence supporting the proposed hypothesis or model, rather than seeking evidence against the frequently irrelevant null hypothesis. These two problems, and many others related to null hypothesis testing, suggest that statistical hypothesis testing should be used only as a component of the body of evidence, perhaps, as the devil’s advocate.

Environmental Monitoring and Assessment↗
USGS2018

Inferred fish behavior its implications for hydroacoustic surveys in nearshore habitats

Population availability and vessel avoidance effects on hydroacoustic abundance estimates may be scale dependent; therefore, it is important to evaluate these biases across systems. We performed an inter-ship comparison survey to determine the effect of vessel size, day-night period, depth, and environmental gradients on walleye ( Sander vitreus ) density estimates in Lake Erie, an intermediate-scaled system. Consistent near-bottom depth distributions coupled with horizontal fish movements relative to vessel paths indicated avoidance behavior contributed to higher walleye densities from smaller vessels in shallow water (i.e., <15 m), although the difference decreased with increasing depth. Diel bank migrations in response to seasonally varying onshore-to-offshore environmental gradients likely contributed to day-night differences in densities between sampling locations and seasons. Spatial and unexplained variation accounted for a high proportion of total variation; however, increasing sampling intensity can mitigate effects on precision. Therefore, researchers should minimize systematic avoidance and availability related biases (i.e., vessel and day-night period) to improve population abundance estimates. Quantifying availability and avoidance behavior effects and partitioning sources of variation provides informed flexibility for designing future hydroacoustic surveys in shallow-water nearshore environments.

Lake Erie↗
USGS2013

A critique of the use of indicator-species scores for identifying thresholds in species responses

Identification of ecological thresholds is important both for theoretical and applied ecology. Recently, Baker and King (2010, King and Baker 2010) proposed a method, threshold indicator analysis (TITAN), to calculate species and community thresholds based on indicator species scores adapted from Dufrêne and Legendre (1997). We tested the ability of TITAN to detect thresholds using models with (broken-stick, disjointed broken-stick, dose-response, step-function, Gaussian) and without (linear) definitive thresholds. TITAN accurately and consistently detected thresholds in step-function models, but not in models characterized by abrupt changes in response slopes or response direction. Threshold detection in TITAN was very sensitive to the distribution of 0 values, which caused TITAN to identify thresholds associated with relatively small differences in the distribution of 0 values while ignoring thresholds associated with large changes in abundance. Threshold identification and tests of statistical significance were based on the same data permutations resulting in inflated estimates of statistical significance. Application of bootstrapping to the split-point problem that underlies TITAN led to underestimates of the confidence intervals of thresholds. Bias in the derivation of the z-scores used to identify TITAN thresholds and skewedness in the distribution of data along the gradient produced TITAN thresholds that were much more similar than the actual thresholds. This tendency may account for the synchronicity of thresholds reported in TITAN analyses. The thresholds identified by TITAN represented disparate characteristics of species responses that, when coupled with the inability of TITAN to identify thresholds accurately and consistently, does not support the aggregation of individual species thresholds into a community threshold.

Freshwater Science↗
USGS2010

On the application of multilevel modeling in environmental and ecological studies

This paper illustrates the advantages of a multilevel/hierarchical approach for predictive modeling, including flexibility of model formulation, explicitly accounting for hierarchical structure in the data, and the ability to predict the outcome of new cases. As a generalization of the classical approach, the multilevel modeling approach explicitly models the hierarchical structure in the data by considering both the within- and between-group variances leading to a partial pooling of data across all levels in the hierarchy. The modeling framework provides means for incorporating variables at different spatiotemporal scales. The examples used in this paper illustrate the iterative process of model fitting and evaluation, a process that can lead to improved understanding of the system being studied.

Ecology↗
USGS2005

Nonlinear regression modeling of nutrient loads in streams: A Bayesian approach

A Bayesian nonlinear regression modeling method is introduced and compared with the least squares method for modeling nutrient loads in stream networks. The objective of the study is to better model spatial correlation in river basin hydrology and land use for improving the model as a forecasting tool. The Bayesian modeling approach is introduced in three steps, each with a more complicated model and data error structure. The approach is illustrated using a data set from three large river basins in eastern North Carolina. Results indicate that the Bayesian model better accounts for model and data uncertainties than does the conventional least squares approach. Applications of the Bayesian models for ambient water quality standards compliance and TMDL assessment are discussed.

Water Resources Research↗