scholarly journals Spatial and temporal dynamics of Atlantic menhaden (Brevoortia tyrannus) recruitment in the Northwest Atlantic Ocean

2016 ◽  
Vol 73 (4) ◽  
pp. 1147-1159 ◽  
Author(s):  
Andre Buchheister ◽  
Thomas J. Miller ◽  
Edward D. Houde ◽  
David H. Secor ◽  
Robert J. Latour
Keyword(s):  
Time Series ◽  
Temporal Dynamics ◽  
The United States ◽  
Additive Models ◽  
Atlantic Menhaden ◽  
Dynamic Factor ◽  
Spatial And Temporal Variability ◽  
Brevoortia Tyrannus ◽  
Regional Patterns ◽  
Northwest Atlantic

Abstract Atlantic menhaden, Brevoortia tyrannus, is an abundant, schooling pelagic fish that is widely distributed in the coastal Northwest Atlantic. It supports the largest single-species fishery by volume on the east coast of the United States. However, relatively little is known about factors that control recruitment, and its stock–recruitment relationship is poorly defined. Atlantic menhaden is managed as a single unit stock, but fisheries and environmental variables likely act regionally on recruitments. To better understand spatial and temporal variability in recruitment, fishery-independent time-series (1959–2013) of young-of-year (YOY) abundance indices from the Mid-Atlantic to Southern New England (SNE) were analysed using dynamic factor analysis and generalized additive models. Recruitment time-series demonstrated low-frequency variability and the analyses identified two broad geographical groupings, the Chesapeake Bay (CB) and SNE. Each of these two regions exhibited changes in YOY abundance and different periods of relatively high YOY abundance that were inversely related to each other; CB indices were highest from ca. 1971 to 1991, whereas SNE indices were high from ca. 1995 to 2005. We tested for effects of climatic, environmental, biological, and fishing-related variables that have been documented or hypothesized to influence stock productivity. A broad-scale indicator of climate, the Atlantic Multidecadal Oscillation, was the best single predictor of coast-wide recruitment patterns, and had opposing effects on the CB and SNE regions. Underlying mechanisms of spatial and interannual variability in recruitment likely derive from interactions among climatology, larval transport, adult menhaden distribution, and habitat suitability. The identified regional patterns and climatic effects have implications for the stock assessment of Atlantic menhaden, particularly given the geographically constrained nature of the existing fishery and the climatic oscillations characteristic of the coastal ocean.

scholarly journals Tracking SARS-CoV-2 Spike Protein Mutations in the United States (January 2020–March 2021) Using a Statistical Learning Strategy

Viruses ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 9
Author(s):  
Lue Ping Zhao ◽  
Terry P. Lybrand ◽  
Peter B. Gilbert ◽  
Thomas R. Hawn ◽  
Joshua T. Schiffer ◽  
...  
Keyword(s):  
United States ◽  
Unsupervised Learning ◽  
Statistical Learning ◽  
Learning Strategy ◽  
Temporal Dynamics ◽  
Generalized Additive Models ◽  
The United States ◽  
Additive Models ◽  
Spike Protein ◽  
Protein Mutations

The emergence and establishment of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of interest (VOIs) and variants of concern (VOCs) highlight the importance of genomic surveillance. We propose a statistical learning strategy (SLS) for identifying and spatiotemporally tracking potentially relevant Spike protein mutations. We analyzed 167,893 Spike protein sequences from coronavirus disease 2019 (COVID-19) cases in the United States (excluding 21,391 sequences from VOI/VOC strains) deposited at GISAID from 19 January 2020 to 15 March 2021. Alignment against the reference Spike protein sequence led to the identification of viral residue variants (VRVs), i.e., residues harboring a substitution compared to the reference strain. Next, generalized additive models were applied to model VRV temporal dynamics and to identify VRVs with significant and substantial dynamics (false discovery rate q-value < 0.01; maximum VRV proportion >10% on at least one day). Unsupervised learning was then applied to hierarchically organize VRVs by spatiotemporal patterns and identify VRV-haplotypes. Finally, homology modeling was performed to gain insight into the potential impact of VRVs on Spike protein structure. We identified 90 VRVs, 71 of which had not previously been observed in a VOI/VOC, and 35 of which have emerged recently and are durably present. Our analysis identified 17 VRVs ~91 days earlier than their first corresponding VOI/VOC publication. Unsupervised learning revealed eight VRV-haplotypes of four VRVs or more, suggesting two emerging strains (B1.1.222 and B.1.234). Structural modeling supported a potential functional impact of the D1118H and L452R mutations. The SLS approach equally monitors all Spike residues over time, independently of existing phylogenic classifications, and is complementary to existing genomic surveillance methods.

Temporal Dynamics of Streamflow Using Complex Networks

2020 ◽  
Author(s):  
Bellie Sivakumar
Keyword(s):  
Time Series ◽  
Phase Space ◽  
Complex Networks ◽  
Chaos Theory ◽  
Temporal Dynamics ◽  
The United States ◽  
Phase Space Reconstruction ◽  
Clustering Coefficient ◽  
Network Construction ◽  
Original Time

&lt;p&gt;Modeling the dynamics of streamflow continues to be highly challenging. The present study proposes a new approach to study the temporal dynamics of streamflow. The approach couples the concepts of complex networks and chaos theory. Applications of the concepts of complex networks for studying streamflow dynamics have been gaining momentum in recent years. A key step in such applications is the construction of the network &amp;#8211; a network is a set of points (nodes) connected by lines (links). The present study uses the concept of phase-space reconstruction, an essential first step in chaos theory-based methods, for network construction to study the temporal dynamics of streamflow. The phase-space reconstruction involves representation of a single-variable time series in a multi-dimensional phase space using delay embedding. The reconstructed phase space is treated as a network, with the reconstructed vectors (rather than the original time series) serving as the nodes and the connections between them serving as the links. With this network construction, the clustering coefficient of the individual nodes and the entire network is calculated to assess the node and network strengths. The approach is employed to a large number of streamflow time series observed in the United States. The results indicate the usefulness and effectiveness of the phase-space reconstruction-based approach for network construction. The implications of the outcomes for identification of the appropriate type and complexity of model as well as for classification of catchments are discussed.&lt;/p&gt;

scholarly journals Tracking SARS-CoV-2 Spike Protein Mutations in the United States (2020/01 – 2021/03) Using a Statistical Learning Strategy

2021 ◽  
Author(s):  
Lue Ping Zhao ◽  
Terry P. Lybrand ◽  
Peter B. Gilbert ◽  
Thomas R. Hawn ◽  
Joshua T. Schiffer ◽  
...  
Keyword(s):  
Unsupervised Learning ◽  
Statistical Learning ◽  
Learning Strategy ◽  
Temporal Dynamics ◽  
Homology Modelling ◽  
Generalized Additive Models ◽  
The United States ◽  
Additive Models ◽  
Spike Protein ◽  
Protein Mutations

The emergence and establishment of SARS-CoV-2 variants of interest (VOI) and variants of concern (VOC) highlight the importance of genomic surveillance. We propose a statistical learning strategy (SLS) for identifying and spatiotemporally tracking potentially relevant Spike protein mutations. We analyzed 167,893 Spike protein sequences from US COVID-19 cases (excluding 21,391 sequences from VOI/VOC strains) deposited at GISAID from January 19, 2020 to March 15, 2021. Alignment against the reference Spike protein sequence led to the identification of viral residue variants (VRVs), i.e., residues harboring a substitution compared to the reference strain. Next, generalized additive models were applied to model VRV temporal dynamics, to identify VRVs with significant and substantial dynamics (false discovery rate q-value <0.01; maximum VRV proportion > 10% on at least one day). Unsupervised learning was then applied to hierarchically organize VRVs by spatiotemporal patterns and identify VRV-haplotypes. Finally, homology modelling was performed to gain insight into potential impact of VRVs on Spike protein structure. We identified 90 VRVs, 71 of which have not previously been observed in a VOI/VOC, and 35 of which have emerged recently and are durably present. Our analysis identifies 17 VRVs ~91 days earlier than their first corresponding VOI/VOC publication. Unsupervised learning revealed eight VRV-haplotypes of 4 VRVs or more, suggesting two emerging strains (B1.1.222 and B.1.234). Structural modeling supported potential functional impact of the D1118H and L452R mutations. The SLS approach equally monitors all Spike residues over time, independently of existing phylogenic classifications, and is complementary to existing genomic surveillance methods.

scholarly journals Zooplankton abundance trends and patterns in Shelikof Strait, western Gulf of Alaska, USA, 1990–2017

2020 ◽  
Vol 42 (3) ◽  
pp. 334-354 ◽  
Author(s):  
David G Kimmel ◽  
Janet T Duffy-Anderson
Keyword(s):  
Time Series ◽  
Generalized Additive Models ◽  
Zooplankton Community ◽  
Strong Relationship ◽  
Gulf Of Alaska ◽  
Additive Models ◽  
Environmental Data ◽  
Dynamic Factor ◽  
Zooplankton Abundance ◽  
Underlying Trend

Abstract A multivariate approach was used to analyze spring zooplankton abundance in Shelikof Strait, western Gulf of Alaska. abundance of individual zooplankton taxa was related to environmental variables using generalized additive models. The most important variables that correlated with zooplankton abundance were water temperature, salinity and ordinal day (day of year when sample was collected). A long-term increase in abundance was found for the calanoid copepod Calanus pacificus, copepodite stage 5 (C5). A dynamic factor analysis (DFA) indicated one underlying trend in the multivariate environmental data that related to phases of the Pacific Decadal Oscillation. DFA of zooplankton time series also indicated one underlying trend where the positive phase was characterized by increases in the abundance of C. marshallae C5, C. pacificus C5, Eucalanus bungii C4, Pseudocalanus spp. C5 and Limacina helicina and declines in the abundance of Neocalanus cristatus C4 and Neocalanus spp. C4. The environmental and zooplankton DFA trends were not correlated over the length of the entire time period; however, the two time series were correlated post-2004. The strong relationship between environmental conditions, zooplankton abundance and time of sampling suggests that continued warming in the region may lead to changes in zooplankton community composition and timing of life history events during spring.

scholarly journals Changes and trends in the demersal fish community of the Flemish Cap, Northwest Atlantic, in the period 1988–2008

2012 ◽  
Vol 69 (5) ◽  
pp. 902-912 ◽  
Author(s):  
Alfonso Pérez-Rodríguez ◽  
Mariano Koen-Alonso ◽  
Fran Saborido-Rey
Keyword(s):  
Fish Community ◽  
Temporal Dynamics ◽  
Moving Average ◽  
Demersal Fish ◽  
Dynamic Factor ◽  
Northwest Atlantic ◽  
Common Trends ◽  
Piscivorous Fish ◽  
The North ◽  
Commercial Species

Abstract Pérez-Rodríguez, A., Koen-Alonso, M., and Saborido-Rey, F. 2012. Changes and trends in the demersal fish community of the Flemish Cap, Northwest Atlantic, in the period 1988–2008. – ICES Journal of Marine Science, 69: 902–912. The Flemish Cap fish community (NAFO Division 3M) has been fished since the 1950s, and major changes in the biomass and abundance of its most important commercial species have been reported since the late 1980s. Variations in oceanographic conditions at the Cap, with alternating periods of cold and warm weather, have also been described. This work examines the existence of common trends in the biomass levels of the main demersal species over time using dynamic factor analysis, and the occurrence of “occasional species” was explored in relation to temperature conditions. Overall, there have been significant changes in community structure involving both commercial and non-commercial species. Common trends among species were identified and overall fishing pressure, environmental conditions (represented by a moving average of the North Atlantic Oscillation, NAO), and predation pressure (represented by the abundance of piscivorous fish) emerged as important drivers of the temporal dynamics. The NAO influence in the dynamics of most species was in agreement with their temperature preference. For occasional species, their pattern of occurrence appears also to be linked to changes in temperature regimes.

Faktor-Faktor Yang Mempengaruhi Nilai Tukar Dollar Amerika Serikat Terhadap Rupiah Tahun 2000–2013

2017 ◽  
Vol 1 (2) ◽  
pp. 177-191
Author(s):  
Rizki Rahma Kusumadewi ◽  
Wahyu Widayat
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Money Supply ◽  
Time Series Data ◽  
The United States ◽  
Economic Conditions ◽  
Series Data ◽  
Arch Model ◽  
United States Dollar ◽  
The Exchange Rate

Exchange rate is one tool to measure a country’s economic conditions. The growth of a stable currency value indicates that the country has a relatively good economic conditions or stable. This study has the purpose to analyze the factors that affect the exchange rate of the Indonesian Rupiah against the United States Dollar in the period of 2000-2013. The data used in this study is a secondary data which are time series data, made up of exports, imports, inflation, the BI rate, Gross Domestic Product (GDP), and the money supply (M1) in the quarter base, from first quarter on 2000 to fourth quarter on 2013. Regression model time series data used the ARCH-GARCH with ARCH model selection indicates that the variables that significantly influence the exchange rate are exports, inflation, the central bank rate and the money supply (M1). Whereas import and GDP did not give any influence.

A Note On Consumption Patterns in the Rural Areas of East Pakistan

1963 ◽  
Vol 3 (3) ◽  
pp. 399-413
Author(s):  
Mohammad Irshad Khan
Keyword(s):  
Rural Areas ◽  
The United States ◽  
Development Planning ◽  
Consumption Patterns ◽  
Regional Patterns ◽  
Effective Utilization ◽  
Income Elasticities ◽  
On Demand ◽  
Limited Applicability ◽  
Made In

The main purpose of this paper is to present estimates of income elasticities for various commodity groups in East Pakistan. To date no such studies have been conducted in that province; and estimates made in other areas of the subcontinent have only limited applicability. Analysis of consumption patterns is essential for development planning because priorities and investment targets have to be based on demand forecasts for different commodities. Forecasting demand requires, among other variables, reliable estimates of income elasticities. In addition, knowledge about elasticities can be useful in deciding taxation policies and other controls over consumption. Further, in countries like Pakistan where large quantities of surplus foods are imported under the United States PL 480 programme, knowledge of income elasticities and regional patterns of consumption is important to permit effective utilization of these imports for economic development.

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