Contributors: William W. Macfarlane, Joseph M. Wheaton, Nicolaas Bouwes, Martha L. Jensen, Jordan T. Gilbert, Nate Hough-Snee, John A. Shivik
... The construction of beaver dams facilitates a suite of hydrologic, hydraulic, geomorphic, and ecological feedbacks that increase stream complexity and channel–floodplain connectivity that benefit aquatic and terrestrial biota. Depending on where beaver build dams within a drainage network, they impact lateral and longitudinal connectivity by introducing roughness elements that fundamentally change the timing, delivery, and storage of water, sediment, nutrients, and organic matter. While the local effects of beaver dams on streams are well understood, broader coverage network models that predict where beaver dams can be built and highlight their impacts on connectivity across diverse drainage networks are lacking. Here we present a capacity model to assess the limits of riverscapes to support dam-building activities by beaver across physiographically diverse landscapes. We estimated dam capacity with freely and nationally-available inputs to evaluate seven lines of evidence: (1) reliable water source, (2) riparian vegetation conducive to foraging and dam building, (3) vegetation within 100m of edge of stream to support expansion of dam complexes and maintain large colonies, (4) likelihood that channel-spanning dams could be built during low flows, (5) the likelihood that a beaver dam is likely to withstand typical floods, (6) a suitable stream gradient that is neither too low to limit dam density nor too high to preclude the building or persistence of dams, and (7) a suitable river that is not too large to restrict dam building or persistence. Fuzzy inference systems were used to combine these controlling factors in a framework that explicitly also accounts for model uncertainty. The model was run for 40,561km of streams in Utah, USA, and portions of surrounding states, predicting an overall network capacity of 356,294 dams at an average capacity of 8.8dams/km. We validated model performance using 2852 observed dams across 1947km of streams. The model showed excellent agreement with observed dam densities where beaver dams were present. Model performance was spatially coherent and logical, with electivity indices that effectively segregated capacity categories. That is, beaver dams were not found where the model predicted no dams could be supported, beaver avoided segments that were predicted to support rare or occasional densities, and beaver preferentially occupied and built dams in areas predicted to have pervasive dam densities. The resulting spatially explicit reach-scale (250m long reaches) data identifies where dam-building activity is sustainable, and at what densities dams can occur across a landscape. As such, model outputs can be used to determine where channel–floodplain and wetland connectivity are likely to persist or expand by promoting increases in beaver dam densities.
Contributors: Samuel M. Gross, Robert Tibshirani
... A model is presented for the supervised learning problem where the observations come from a fixed number of pre-specified groups, and the regression coefficients may vary sparsely between groups. The model spans the continuum between individual models for each group and one model for all groups. The resulting algorithm is designed with a high dimensional framework in mind. The approach is applied to a sentiment analysis dataset to show its efficacy and interpretability. One particularly useful application is for finding sub-populations in a randomized trial for which an intervention (treatment) is beneficial, often called the uplift problem. Some new concepts are introduced that are useful for uplift analysis. The value is demonstrated in an application to a real world credit card promotion dataset. In this example, although sending the promotion has a very small average effect, by targeting a particular subgroup with the promotion one can obtain a 15% increase in the proportion of people who purchase the new credit card.
Evaluating long-term monitoring of temperate reef fishes: A simulation testing framework to compare methods
Contributors: Denham Parker, Henning Winker, Anthony Bernard, Albrecht Götz
... A simulation testing framework was developed to evaluate the efficacy of detecting population trends of two sampling methods used to monitor inshore fish populations: angling and baited remote underwater stereo-video systems (stereo-BRUVs). The study is based on data collected as part of a long-term monitoring program in the Tsitsikamma National Park marine protected area, South Africa. As a test scenario, declining population trajectories of the most abundant species, Chrysoblephus laticeps, were simulated by introducing consecutive years of reduced recruitment over periods of 10 and 20 years applying an age-structured operating model. The operating model was designed to generate method-specific relative abundance indices and length–frequency data, using parameters derived from existing data collected in the long-term monitoring program. These were then fitted with an age-structured estimation model. Estimated spawner-biomass depletion was compared to the ‘true’ simulated population to quantify method-specific accuracy and bias using root-mean-squared error. Due to higher data variability and inherent size selectivity of angling, stereo-BRUVs provided more accurate spawner-biomass trends when describing a distinct population decline over 10 and 20 years. Additionally, spawner-biomass was found to be a more accurate population estimate than relative abundance indices due to the inclusion of population size structure information. The study demonstrates the potential of using simulation testing to evaluate sampling methods, given that the process generates the ‘true’ population with a known abundance and size structure.
Contributors: Li Li, Xiqun (Micheal) Chen, Lei Zhang
... How to calibrate the parameters of car-following models based on observed traffic data is a vital problem in traffic simulation. Usually, the core of calibration is cast into an optimization problem, in which the decision variables are car-following model parameters and the objective function usually characterizes the difference between empirical vehicle movements and their simulated correspondences. Since the objective function is usually nonlinear and non-convex, various greedy or stochastic algorithms had been proposed during the last two decades. However, the performance of these algorithms remains to be further examined. In this paper, we revisit this important problem with a special focus on the geometric feature of the objective function. First, we prove that, from a global perspective, most existing objective functions are Lipschitz continuous. Second, we show that, from a local perspective, many of these objective functions are relatively flat around the global optimal solution. Based on these two features, we propose a new global optimization algorithm that integrates global direct search and local gradient search to find the optimal solution in an efficient manner. We compare this new algorithm with several existing algorithms, including Nelder–Mead (NM) algorithm, sequential quadratic programming (SQP) algorithm, genetic algorithm (GA), and simultaneous perturbation stochastic approximation (SPSA) algorithm, on NGSIM trajectory datasets. Results demonstrate that the proposed algorithm has a fast convergence speed and a high probability of finding the global optimal solution. Moreover, it has only two major configuration parameters that can be easily determined in practice.
Short Communication - Dietary preferences of Hawaiian tree snails to inform culture for conservation
Contributors: Richard O'Rorke, Brenden S. Holland, Gerry M. Cobian, Kapono Gaughen, Anthony S. Amend
... One strategy to safeguard endangered species against extinction is raising subpopulations in ex situ facilities. Feeding animals ex situ is difficult when their diet is cryptic. We present a combined molecular and behavioral approach to assess the diet of Achatinella, a critically endangered genus of tree snail, to determine how diet of captive snails differs from wild snails. Cultured snails are currently fed biofilms growing on leaf surfaces, as well as a Cladosporium fungus isolated from this same habitat. Amplicon sequencing of DNA extracted from feces of wild and cultured snails confirms that this Cladosporium is abundant in the wild (~1.5% of sequences), but it dominates the ex situ snails' diet (~38%) and the diet of captive snails is still significantly less diverse than wild snails. To test the hypothesis that snails have diet preferences, we conducted feeding trials. These used a surrogate snail species, Auriculella diaphana, which is a confamilial Oahu endemic, though non-federally listed. Contrary to our expectations we found that snails do have feeding preferences. Furthermore, our feeding preference trials show that over all other feeding options snails most preferred the “no-microbe” control, which consisted only of potato dextrose agar (PDA). PDA is rich in simple carbohydrates, in contrast to the oligotrophic environment of wild tree-snails. These results suggest further research should focus on calorie budgets of snails, devising new approaches to supplementing their ex situ diet and determining whether a wild diet is an optimum diet.
Using hierarchical centering to facilitate a reversible jump MCMC algorithm for random effects models
Contributors: C.S. Oedekoven, R. King, S.T. Buckland, M.L. Mackenzie, K.O. Evans, L.W. Burger Jr.
... Hierarchical centering has been described as a reparameterization method applicable to random effects models. It has been shown to improve mixing of models in the context of Markov chain Monte Carlo (MCMC) methods. A hierarchical centering approach is proposed for reversible jump MCMC (RJMCMC) chains which builds upon the hierarchical centering methods for MCMC chains and uses them to reparameterize models in an RJMCMC algorithm. Although these methods may be applicable to models with other error distributions, the case is described for a log-linear Poisson model where the expected value λ includes fixed effect covariates and a random effect for which normality is assumed with a zero-mean and unknown standard deviation. For the proposed RJMCMC algorithm including hierarchical centering, the models are reparameterized by modeling the mean of the random effect coefficients as a function of the intercept of the λ model and one or more of the available fixed effect covariates depending on the model. The method is appropriate when fixed-effect covariates are constant within random effect groups. This has an effect on the dynamics of the RJMCMC algorithm and improves model mixing. The methods are applied to a case study of point transects of indigo buntings where, without hierarchical centering, the RJMCMC algorithm had poor mixing and the estimated posterior distribution depended on the starting model. With hierarchical centering on the other hand, the chain moved freely over model and parameter space. These results are confirmed with a simulation study. Hence, the proposed methods should be considered as a regular strategy for implementing models with random effects in RJMCMC algorithms; they facilitate convergence of these algorithms and help avoid false inference on model parameters.
Contributors: Panos Bravakos, Georgios Kotoulas, Katerina Skaraki, Adriani Pantazidou, Athena Economou-Amilli
... Strains of Cyanobacteria isolated from mats of 9 thermal springs of Greece have been studied for their taxonomic evaluation. A polyphasic taxonomic approach was employed which included: morphological observations by light microscopy and scanning electron microscopy, maximum parsimony, maximum likelihood and Bayesian analysis of 16S rDNA sequences, secondary structural comparisons of 16S–23S rRNA Internal Transcribed Spacer sequences, and finally environmental data. The 17 cyanobacterial isolates formed a diverse group that contained filamentous, coccoid and heterocytous strains. These included representatives of the polyphyletic genera of Synechococcus and Phormidium, and the orders Oscillatoriales, Spirulinales, Chroococcales and Nostocales. After analysis, at least 6 new taxa at the genus level provide new evidence in the taxonomy of Cyanobacteria and highlight the abundant diversity of thermal spring environments with many potential endemic species or ecotypes.
Contributors: ChangHwan Lee, Samantha E. Roberts, Amy S. Gladfelter
... mRNA positioning in the cell is important for diverse cellular functions and proper development of multicellular organisms. Single-molecule RNA FISH (smFISH) enables quantitative investigation of mRNA localization and abundance at the level of individual molecules in the context of cellular features. Details about spatial mRNA patterning at various times, in different genetic backgrounds, at different developmental stages, and under varied environmental conditions provide invaluable insights into the mechanisms and functions of spatial regulation. Here, we describe detailed methods for performing smFISH along with immunofluorescence for two large, multinucleate cell types: the fungus Ashbya gossypii and cultured mouse myotubes. We also put forward a semi-automated image processing tool that systematically detects mRNAs from smFISH data and statistically analyzes the spatial pattern of mRNAs using a customized MATLAB code. These protocols and image analysis tools can be adapted to a wide variety of transcripts and cell types for systematically and quantitatively analyzing mRNA distribution in three-dimensional space.
A new digital background calibration for redundant radix-4 pipelined ADCs by modeling of adaptive filter for linear and nonlinear errors
Contributors: Esmaeil Fatemi-Behbahani, Ebrahim Farshidi, Karim Ansari-Asl
... In this paper a new digital background correction and calibration technique for redundant multi-bit pipeline stages is presented. In this method output voltage of each stage in converter is defined as sum of the ideal product and error signal, which error voltage include of linear non-ideal section or first order error and nonlinearity undesired signal or third order error. Linear error is formed by capacitor mismatch, op-amp offset, comparator offset and finite op-amp gain effects. Nonlinear error is deformed the output voltage depend on the nonlinear results of open loop residue amplifier. Correction begins with separately calculation and cancelation of the nonlinear and linear errors respectively. For calibration of each stage at first step, the nonlinear effects in digital output of backend ADC is eliminated and then by digital modeling of first order analog error the influence of this unfavorable signal is diminished from digital equivalent of input voltage. Therefore for cancelation of non-ideal impairment in each stage a digital filter consist of linear and nonlinear channel in digital domain is designed. The first order and third order coefficients of designed digital function are unknown and should by a pertinent method be estimated simultaneously. Adaptive filter are best choose for this method. Simulation results show that INL/DNL parameters of 14-bit radix-4 pipelined converter are improved from 17LSB/3LSB to 0.45LSB/0.41LSB after calibration. The SNDR/SFDR parameters are increased from 30dB/36dB to 83dB/90dB.
Genome-wide analysis of WRKY family of transcription factors in common bean, Phaseolus vulgaris: Chromosomal localization, structure, evolution and expression divergence
Contributors: Ning Wang, En-Hua Xia, Li-Zhi Gao
... WRKY transcription factors play critical roles in plant growth and development, as well as the response to biotic and abiotic stresses. Despite the fact that WRKY gene family has been characterized in a number of plant species, very little is known in the common bean, Phaseolus vulgaris. The recently released genome sequences provide us a good opportunity for genome-wide analysis of WRKY genes in this legume crop. In this study, a total of 90 WRKY genes (PvWRKYs) were identified and classified into three groups, of which the second group was further separated into five subgroups based on the structure of the conserved domains. All the WRKY genes were located on chromosomes 1 to 11 with a non-uniformed distribution. The phylogenetic analysis revealed that the majority of the PvWRKY genes were clustered with those from soybean, indicating that most of the WRKY genes may be originated from the same ancestor with Glycine max; both tandem and segmental duplications have played an important role in the evolution and diversification of the WRKY gene family in P. vulgaris. The variety and complexity of protein structure indicate that WRKY genes may be of significance in plant growth regulation and stress responses. The analysis of expression profiles revealed that the majority of WRKY genes showed tissue-specific expression, which is indicative of diverged expression during the development of common bean.