National Centers for Coastal Ocean Science (NCCOS) research highlights in the Chesapeake Bay

The Chesapeake Bay is the largest estuary in the United States. It is a unique and valuable national treasure because of its ecological, recreational, economic and cultural benefits. The problems facing the Bay are well known and extensively documented, and are largely related to human uses of the watershed and resources within the Bay. Over the past several decades as the origins of the Chesapeake’s problems became clear, citizens groups and Federal, State, and local governments have entered into agreements and worked together to restore the Bay’s productivity and ecological health. In May 2010, President Barack Obama signed Executive Order number 13508 that tasked a team of Federal agencies to develop a way forward in the protection and restoration of the Chesapeake watershed. Success of both State and Federal efforts will depend on having relevant, sound information regarding the ecology and function of the system as the basis of management and decision making. In response to the executive order, the National Oceanic and Atmospheric Administration’s National Centers for Coastal Ocean Science (NCCOS) has compiled an overview of its research in Chesapeake Bay watershed. NCCOS has a long history of Chesapeake Bay research, investigating the causes and consequences of changes throughout the watershed’s ecosystems. This document presents a cross section of research results that have advanced the understanding of the structure and function of the Chesapeake and enabled the accurate and timely prediction of events with the potential to impact both human communities and ecosystems. There are three main focus areas: changes in land use patterns in the watershed and the related impacts on contaminant and pathogen distribution and concentrations; nutrient inputs and algal bloom events; and habitat use and life history patterns of species in the watershed. Land use changes in the Chesapeake Bay watershed have dramatically changed how the system functions. A comparison of several subsystems within the Bay drainages has shown that water quality is directly related to land use and how the land use affects ecosystem health of the rivers and streams that enter the Chesapeake Bay. Across the Chesapeake as a whole, the rivers that drain developed areas, such as the Potomac and James rivers, tend to have much more highly contaminated sediments than does the mainstem of the Bay itself. In addition to what might be considered traditional contaminants, such as hydrocarbons, new contaminants are appearing in measurable amounts. At fourteen sites studied in the Bay, thirteen different pharmaceuticals were detected. The impact of pharmaceuticals on organisms and the people who eat them is still unknown. The effects of water borne infections on people and marine life are known, however, and the exposure to certain bacteria is a significant health risk. A model is now available that predicts the likelihood of occurrence of a strain of bacteria known as Vibrio vulnificus throughout Bay waters.

Predicting the distribution of Vibrio vulnificus in Chesapeake Bay

Vibrio vulnificus is a gram-negative pathogenic bacterium endemic to coastal waters worldwide, and a leading cause of seafood related mortality. Because of human health concerns, understanding the ecology of the species and potentially predicting its distribution is of great importance. We evaluated and applied a previously published qPCR assay to water samples (n = 235) collected from the main-stem of the Chesapeake Bay (2007 – 2008) by Maryland and Virginia State water quality monitoring programs. Results confirmed strong relationships between the likelihood of Vibrio vulnificus presence and both temperature and salinity that were used to develop a logistic regression model. The habitat model demonstrated a high degree of concordance (93%), and robustness as subsequent bootstrapping (n=1000) did not change model output (P > 0.05). We forced this empirical habitat model with temperature and salinity predictions generated by a regional hydrodynamic modeling system to demonstrate its utility in future pathogen forecasting efforts in the Chesapeake Bay.

Maturity, ovarian cycle, fecundity, and age-specific parturition of black rockfish (Sebastes melanops)

From 1995 to 1998, we collected female black rockfish (Sebastes melanops) off Oregon in order to describe their basic reproductive life history and determine age-specific fecundity and temporal patterns in parturition. Female black rockfish had a 50% probability of being mature at 394 mm fork length and 7.5 years-of-age. The proportion of mature fish age 10 or older significantly decreased each year of this study, from 0.511 in 1996 to 0.145 in 1998. Parturition occurred between mid-January and mid-March, and peaked in February. We observed a trend of older females extruding larvae earlier in the spawning season and of younger fish primarily responsible for larval production during the later part of the season. There were differences in absolute fecundity at age between female black rockfish with prefertilization oocytes and female black rockfish with fertilized eggs; fertilized-egg fecundity estimates were considered superior. The likelihood of yolked oocytes reaching the developing embryo stage increased with maternal age. Absolute fecundity estimates (based on fertilized eggs) ranged from 299,302 embryos for a 6-year-old female to 948,152 embryos for a 16-year-old female. Relative fecundity (based on fertilized eggs) increased with age from 374 eggs/g for fish age 6 to 549 eggs/g for fish age 16.

Estimating the emigration rate of fish stocks from marine sanctuaries using tag-recovery data

A critical process in assessing the impact of marine sanctuaries on fish stocks is the movement of fish out into surrounding fished areas. A method is presented for estimating the yearly rate of emigration of animals from a protected (“no-take”) zone. Movement rates for exploited populations are usually inferred from tag-recovery studies, where tagged individuals are released into the sea at known locations and their location of recapture is reported by fishermen. There are three drawbacks, however, with this method of estimating movement rates: 1) if animals are tagged and released into both protected and fished areas, movement rates will be overestimated if the prohibition on recapturing tagged fish later from within the protected area is not made explicit; 2) the times of recapture are random; and 3) an unknown proportion of tagged animals are recaptured but not reported back to researchers. An estimation method is proposed which addresses these three drawbacks of tag-recovery data. An analytic formula and an associated double-hypergeometric likelihood method were derived. These two estimators of emigration rate were applied to tag recoveries from southern rock lobsters (Jasus edwardsii) released into a sanctuary and into its surrounding fished area in South Australia.

Estimating long-term growth-rate changes of southern bluefin tuna (Thunnus maccoyii) from two periods of tag-return data

Southern bluefin tuna (SBT) (Thunnus maccoyii) growth rates are estimated from tag-return data associated with two time periods, the 1960s and 1980s. The traditional von Bertalanffy growth model (VBG) and a two-phase VBG model were fitted to the data by maximum likelihood. The traditional VBG model did not provide an adequate representation of growth in SBT, and the two-phase VBG yielded a significantly better fit. The results indicated that significant change occurs in the pattern of growth in relation to a VBG curve during the juvenile stages of the SBT life cycle, which may be related to the transition from a tightly schooling fish that spends substantial time in near and surface shore waters to one that is found primarily in more offshore and deeper waters. The results suggest that more complex growth models should be considered for other tunas and for other species that show a marked change in habitat use with age. The likelihood surface for the two-phase VBG model was found to be bimodal and some implications of this are investigated. Significant and substantial differences were found in the growth for fish spawned in the 1960s and in the 1980s, such that after age four there is a difference of about one year in the expected age of a fish of similar length which persists over the size range for which meaningful recapture data are available. This difference may be a density-dependent response as a consequence of the marked reduction in the SBT population. Given the key role that estimates of growth have in most stock assessments, the results indicate that there is a need both for the regular monitoring of growth rates and for provisions for changes in growth over time (possibly related to changes in abundance) in the stock assessment models used for SBT and other species.

A general framework for integrating environmental time series into stock assessment models: model description, simulation testing, and example

We present a method to integrate environmental time series into stock assessment models and to test the significance of correlations between population processes and the environmental time series. Parameters that relate the environmental time series to population processes are included in the stock assessment model, and likelihood ratio tests are used to determine if the parameters improve the fit to the data significantly. Two approaches are considered to integrate the environmental relationship. In the environmental model, the population dynamics process (e.g. recruitment) is proportional to the environmental variable, whereas in the environmental model with process error it is proportional to the environmental variable, but the model allows an additional temporal variation (process error) constrained by a log-normal distribution. The methods are tested by using simulation analysis and compared to the traditional method of correlating model estimates with environmental variables outside the estimation procedure. In the traditional method, the estimates of recruitment were provided by a model that allowed the recruitment only to have a temporal variation constrained by a log-normal distribution. We illustrate the methods by applying them to test the statistical significance of the correlation between sea-surface temperature (SST) and recruitment to the snapper (Pagrus auratus) stock in the Hauraki Gulf–Bay of Plenty, New Zealand. Simulation analyses indicated that the integrated approach with additional process error is superior to the traditional method of correlating model estimates with environmental variables outside the estimation procedure. The results suggest that, for the snapper stock, recruitment is positively correlated with SST at the time of spawning.

Seasonal growth of King George whiting (Sillaginodes punctata) estimated from length-at-age samples of the legal-size harvest

Samples of 11,000 King George whiting (Sillaginodes punctata) from the South Australian commercial and recreational catch, supplemented by research samples, were aged from otoliths. Samples were analyzed from three coastal regions and by sex. Most sampling was undertaken at fish processing plants, from which only fish longer than the legal minimum length were obtained. A left-truncated normal distribution of lengths at monthly age was therefore employed as model likelihood. Mean length-at-monthly-age was described by a generalized von Bertalanffy formula with sinusoidal seasonality. Likelihood standard deviation was modeled to vary allometrically with mean length. A range of related formulas (with 6 to 8 parameters) for seasonal mean length at age were compared. In addition to likelihood ratio tests of relative fit, model selection criteria were a minimum occurrence of high uncertainties (>20% SE), of high correlations (>0.9, >0.95, and >0.99) and of parameter estimates at their biological limits, and we sought a model with a minimum number of parameters. A generalized von Bertalanffy formula with t0 fixed at 0 was chosen. The truncated likelihood alleviated the overestimation bias of mean length at age that would otherwise accrue from catch samples being restricted to legal sizes.

Dynamic condition monitoring of buried gas pipes

This paper describes a device which can be used to detect variation in the integrity of the surrounding medium which supports buried gas pipes. The method is also applicable to other kinds of pipe. Variation in pipe support condition leads to increased likelihood of pipe damage under vehicle loading. A vibrating 'pig' has been developed and tested on buried pipelines in Britain and the measured data obtained is compared with theoretical models. Certain features, such as voids, hard spots and joints, display characteristic responses to vibration and a library of such characteristics has been constructed both experimentally and from the theoretical models.

Identification of support conditions of buried pipes using a vibrating pig

This paper describes a method for monitoring the variation in support condition of pipelines using a vibration technique. The method is useful for detecting poor support of buried pipelines and for detecting spanning and depth of cover in sub-sea lines. Variation in the pipe support condition leads to increased likelihood of pipe damage. Under roadways, poorly supported pipe may be damaged by vehicle loading. At sea, spanned sections of pipe are vulnerable to ocean current loading and also to snagging by stray anchors in shallow waters. A vibrating `pig' has been developed and tested on buried pipelines. Certain features of pipe support, such as voids and hard spots, display characteristic responses to vibration, and these are measured by the vibrating pig. Post-processing of the measured vibration data is used to produce a graphical representation of the pipeline support and certain `feature characteristics' are identified. In field tests on a pipeline with deliberately constructed support faults, features detected by the vibrating pig are in good agreement with the known construction.

Phone-level pronunciation scoring and assessment for interactive language learning

This paper investigates a method of automatic pronunciation scoring for use in computer-assisted language learning (CALL) systems. The method utilizes a likelihood-based `Goodness of Pronunciation' (GOP) measure which is extended to include individual thresholds for each phone based on both averaged native confidence scores and on rejection statistics provided by human judges. Further improvements are obtained by incorporating models of the subject's native language and by augmenting the recognition networks to include expected pronunciation errors. The various GOP measures are assessed using a specially recorded database of non-native speakers which has been annotated to mark phone-level pronunciation errors. Since pronunciation assessment is highly subjective, a set of four performance measures has been designed, each of them measuring different aspects of how well computer-derived phone-level scores agree with human scores. These performance measures are used to cross-validate the reference annotations and to assess the basic GOP algorithm and its refinements. The experimental results suggest that a likelihood-based pronunciation scoring metric can achieve usable performance, especially after applying the various enhancements.

Face recognition from video

In spite of over two decades of intense research, illumination and pose invariance remain prohibitively challenging aspects of face recognition for most practical applications. The objective of this work is to recognize faces using video sequences both for training and recognition input, in a realistic, unconstrained setup in which lighting, pose and user motion pattern have a wide variability and face images are of low resolution. In particular there are three areas of novelty: (i) we show how a photometric model of image formation can be combined with a statistical model of generic face appearance variation, learnt offline, to generalize in the presence of extreme illumination changes; (ii) we use the smoothness of geodesically local appearance manifold structure and a robust same-identity likelihood to achieve invariance to unseen head poses; and (iii) we introduce an accurate video sequence "reillumination" algorithm to achieve robustness to face motion patterns in video. We describe a fully automatic recognition system based on the proposed method and an extensive evaluation on 171 individuals and over 1300 video sequences with extreme illumination, pose and head motion variation. On this challenging data set our system consistently demonstrated a nearly perfect recognition rate (over 99.7%), significantly outperforming state-of-the-art commercial software and methods from the literature. © Springer-Verlag Berlin Heidelberg 2006.

Phylogenetic study of complete cytochrome b genes in musk deer (genus Moschus) using museum samples

As an endangered animal group, musk deer (genus Moschus) are not only a great concern of wildlife conservation, but also of special interest to evolutionary studies due to long-standing arguments on the taxonomic and phylogenetic associations in this group. Using museum samples, we sequenced complete mitochondrial cytochrome b genes (1140 bp) of all suggested species of musk deer in order to reconstruct their phylogenetic history through molecular information. Our results showed that the cytochrome b gene tree is rather robust and concurred for all the algorithms employed (parsimony, maximum likelihood, and distance methods). Further, the relative rate test indicated a constant sequence substitution rate among all the species, permitting the dating of divergence events by molecular clock. According to the molecular topology, M. moschiferus branched off the earliest from a common ancestor of musk deer (about 700,000 years ago); then followed the bifurcation forming the M. berezouskii lineage and the lineage clustering M. fuscus, M. chrysogaster, and M. leucogaster (around 370,000 years before present), interestingly the most recent speciation event in musk deer happened rather recently (140,000 years ago), which might have resulted from the diversified habitats and geographic barriers in southwest China caused by gigantic movements of the Qinghai-Tibetan Plateau in history. Combining the data of current distributions, fossil records, and molecular data of this study, we suggest that the historical dispersion of musk deer might be from north to south in China. Additionally, in our further analyses involving other pecora species, musk deer was strongly supported as a monophyletic group and a valid family in Artiodactyla, closely related to Cervidae. (C) 1999 Academic Press.

Speaker and noise factorisation on the AURORA4 task

For many realistic scenarios, there are multiple factors that affect the clean speech signal. In this work approaches to handling two such factors, speaker and background noise differences, simultaneously are described. A new adaptation scheme is proposed. Here the acoustic models are first adapted to the target speaker via an MLLR transform. This is followed by adaptation to the target noise environment via model-based vector Taylor series (VTS) compensation. These speaker and noise transforms are jointly estimated, using maximum likelihood. Experiments on the AURORA4 task demonstrate that this adaptation scheme provides improved performance over VTS-based noise adaptation. In addition, this framework enables the speech and noise to be factorised, allowing the speaker transform estimated in one noise condition to be successfully used in a different noise condition. © 2011 IEEE.

Structured discriminative models for noise robust continuous speech recognition

Recently there has been interest in structured discriminative models for speech recognition. In these models sentence posteriors are directly modelled, given a set of features extracted from the observation sequence, and hypothesised word sequence. In previous work these discriminative models have been combined with features derived from generative models for noise-robust speech recognition for continuous digits. This paper extends this work to medium to large vocabulary tasks. The form of the score-space extracted using the generative models, and parameter tying of the discriminative model, are both discussed. Update formulae for both conditional maximum likelihood and minimum Bayes' risk training are described. Experimental results are presented on small and medium to large vocabulary noise-corrupted speech recognition tasks: AURORA 2 and 4. © 2011 IEEE.

Multiple pitch estimation using non-homogeneous poisson processes

Novel statistical models are proposed and developed in this paper for automated multiple-pitch estimation problems. Point estimates of the parameters of partial frequencies of a musical note are modeled as realizations from a non-homogeneous Poisson process defined on the frequency axis. When several notes are combined, the processes for the individual notes combine to give a new Poisson process whose likelihood is easy to compute. This model avoids the data-association step of linking the harmonics of each note with the corresponding partials and is ideal for efficient Bayesian inference of unknown multiple fundamental frequencies in a signal. © 2011 IEEE.