Nutrient criteria for lakes,ponds, and reservoirs: A Bayesian TREED model approach

We develop regional-scale eutrophication models for lakes, ponds, and reservoirs to investigate the link between nutrients and chlorophyll-a. The Bayesian TREED (BTREED) model approach allows association of multiple environmental stressors with biological responses, and quantification of uncertainty sources in the empirical water quality model. Nutrient data for lakes, ponds, and reservoirs across the United States were obtained from the Environmental Protection Agency (EPA) National Nutrient Criteria Database. The nutrient data consist of measurements for both stressor variables (such as total nitrogen and total phosphorus), and response variables (such as chlorophyll-a), used in the BTREED model. Markov chain Monte Carlo (McMC) posterior exploration guides a stochastic search through a rich suite of candidate trees toward models that better fit the data. The Bayes factor provides a goodness of fit criterion for comparison of resultant models. We randomly split the data into training and test sets; the training data were used in model estimation, and the test data were used to evaluate out-of-sample predictive performance of the model. An average relative efficiency of 1.02 between the training and test data for the four highest log-likelihood models suggests good out-of-sample predictive performance. Reduced model uncertainty relative to over-parameterized alternative models makes the BTREED models useful for nutrient criteria development, providing the link between nutrient stressors and meaningful eutrophication response.     

Poecilogony in Polydora hoplura (Polychaeta: Spionidae) from commercially important molluscs in South Africa

Females of the polychaete Polydora hoplura (Claparède 1869) that produced planktotrophic and adelphophagic larvae were compared genetically to determine whether the different reproductive morphs represented sibling species or poecilogony. Worms were collected from Saldanha Bay, South Africa (33°0′37.71S, 17°56′59.74E), and cultured in a laboratory setting from November 2012 to April 2013. The results based on mitochondrial and nuclear DNA (Cyt b and ATPSα, respectively) showed shared haplotypes between reproductive morphs for both markers. Additionally, variation in mtDNA sequences was significantly higher within morphs than between morphs (95.8 and 4.2 %, respectively). A comparison of developmental modes found that broods of planktotrophic larvae were larger than broods of adelphophagic larvae, while the latter had larger eggs and larvae at hatching. Developmental time from oviposition to settlement for planktotrophic and adelphophagic larvae was 40.2 and 16.6 days, respectively. Polydora hoplura represents the fifth confirmed case of poecilogony in the Spionidae.     

Approaches to Evaluate Water Quality Model Parameter Uncertainty for Adaptive TMDL Implementation1

Abstract: The National Research Council recommended Adaptive Total Maximum Daily Load implementation with the recognition that the predictive uncertainty of water quality models can be high. Quantifying predictive uncertainty provides important information for model selection and decision‐making. We review five methods that have been used with water quality models to evaluate model parameter and predictive uncertainty. These methods (1) Regionalized Sensitivity Analysis, (2) Generalized Likelihood Uncertainty Estimation, (3) Bayesian Monte Carlo, (4) Importance Sampling, and (5) Markov Chain Monte Carlo (MCMC) are based on similar concepts; their development over time was facilitated by the increasing availability of fast, cheap computers. Using a Streeter‐Phelps model as an example we show that, applied consistently, these methods give compatible results. Thus, all of these methods can, in principle, provide useful sets of parameter values that can be used to evaluate model predictive uncertainty, though, in practice, some are quickly limited by the “curse of dimensionality” or may have difficulty evaluating irregularly shaped parameter spaces. Adaptive implementation invites model updating, as new data become available reflecting water‐body responses to pollutant load reductions, and a Bayesian approach using MCMC is particularly handy for that task.     

Enzymatic pattern processing

 A table-top prototype has been constructed that uses the enzyme malate dehydrogenase to recognize input signal patterns. The device is controlled by the enzyme in response to injection of Mg²⁺ used as a signaling substance. Output is monitored spectroscopically. If Mg²⁺ is injected along either of two signal lines (i.e., if the input signal pattern is 10 or 01) the device emits an output of 1. Injection along neither or both lines results in an output of 0. The enzyme in effect is used as a transform that converts the linearly inseparable exclusive-or problem into a linearly separable problem. Received: 3 April 2000 / Accepted in revised form: 16 June 2000     

Interpolating SRTM Elevation Data to Higher Resolution to Improve Hydrologic Analysis

The Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) has been a valuable resource for hydrological analysis, providing elevation data at a consistent resolution on a near‐global scale. However, its resolution (three arc‐second or 90 m) is sometimes too low to obtain the desired level of accuracy and precision for hydrologic analysis. We evaluated the performance of several methods for interpolating SRTM three arc‐second data to a 30‐m resolution grid to better represent topography and derive terrain characteristics of the landscape. STRM data were interpolated to 30‐m DEMs on a common grid using spline, inverse distance weighting (IDW), kriging (KR), natural neighbor methods, and cubic convolution (CC) resampling. Accuracy of the methods was assessed by comparing interpolated and resampled 30‐m grids with the reference data. Slope, aspect, sinks, and stream networks were derived for the 30‐m grids and compared on a cell‐by‐cell basis to evaluate their performance in reproducing the derivatives. The comparisons identify spline and KR as the most accurate interpolation methods, of which spline is preferred because of its relative simplicity. IDW provided the greatest bias in all methods with artifacts evident in slope and aspect maps. The performance of CC projection directly to a 30‐m resolution was comparable to spline interpolation, thus is recommended as the most convenient method for interpolating SRTM to a higher resolution.     

A comparison of three methods for estimating call densities of migrating bowhead whales using passive acoustic monitoring

Environmental and Ecological Statistics - Various methods for estimating animal density from visual data, including distance sampling (DS) and spatially explicit capture-recapture (SECR), have...