scholarly journals Age-specific differences in the seasonal spatial distribution of butterfish (Peprilus triacanthus)

2016 ◽  
Vol 74 (1) ◽  
pp. 170-179 ◽  
Author(s):  
Charles F. Adams
Keyword(s):  
Time Series ◽  
Spatial Distribution ◽  
Reference Points ◽  
Trawl Survey ◽  
Spatial Equilibrium ◽  
Science Center ◽  
Fisheries Science ◽  
Specific Analysis ◽  
Stock Assessments ◽  
Seasonal Analysis

The spatial distribution of butterfish (Peprilus triacanthus) in the Northwest Atlantic Ocean was investigated using a suite of spatial indicators based on Northeast Fisheries Science Center spring and fall bottom trawl survey data, 1982–2013. In the spring, ages 2 and 3 were found farther northeast and deeper than age 1 butterfish, while in the fall, age 3 butterfish were found farther northeast and deeper than ages 0 and 1. There was no significant northward movement of butterfish in spring or fall over the course of either time-series. However, there was a significant increase in the area occupied by ages 1–3 in the spring that was correlated with surface temperature. This illustrates that responses to climate change may be manifested as range expansions, rather than poleward movement of the centre of gravity (i.e. bivariate weighted mean location of the population). Two changes were observed over the course of the fall time series, both for ages 1 and 2: increased spatial dispersion; and a decrease in depth. The former result would have been masked, while the latter would have been erroneously generalized to all age classes, if an age-specific analysis had not been done. This study demonstrates the importance of an age-based and seasonal analysis. It is also shown how a spatial distribution analyses can inform stock assessments by providing insights into diverging survey indices and availability to surveys in general. Similarly, spatial distribution analyses can be used to verify the spatial equilibrium assumption for the calculation of biological reference points.

scholarly journals Time-series metabarcoding analysis of zooplankton diversity of the NW Atlantic continental shelf

2019 ◽  
Vol 76 (4) ◽  
pp. 1162-1176 ◽  
Author(s):  
Ann Bucklin ◽  
Heidi D Yeh ◽  
Jennifer M Questel ◽  
David E Richardson ◽  
Bo Reese ◽  
...  
Keyword(s):  
Time Series ◽  
Gulf Of Maine ◽  
Diversity Index ◽  
Hypervariable Region ◽  
Detailed Examination ◽  
Science Center ◽  
Ecosystem Monitoring ◽  
Fisheries Science ◽  
Ocean Ecosystem ◽  
Taxonomic Groups

Abstract Biodiversity of zooplankton is central to the functioning of ocean ecosystems, yet morphological taxonomic analysis requires teams of experts and detailed examination of many samples. Metabarcoding (DNA sequencing of short amplified regions of one or a few genes from environmental samples) is a powerful tool for analysis of the composition and diversity of natural communities. The 18S rRNA V9 hypervariable region was sequenced for 26 zooplankton samples collected from the Gulf of Maine, Georges Bank, and Mid-Atlantic Bight during ecosystem monitoring surveys by the U.S. Northeast Fisheries Science Center during 2002–2012. A total of 7 648 033 sequences and 22 072 operational taxonomic units (OTUs) were identified and classified into 28 taxonomic groups of plankton. Comparative analysis of molecular (V9 sequence numbers) and morphological (abundance counts) focused on seven taxonomic groups and revealed similar patterns of variation among years and regions. Sequence numbers and abundance counts showed positive correlation for all groups, with significant correlations (p < 0.05) for Calanoida, Gastropoda, and Chaetognatha. Shannon diversity index values calculated using sequence numbers and abundance counts showed highly significant correlation (r = 0.625; p < 0.001) across all regions during 2002–2012. This study demonstrates the potential of metabarcoding for time-series analysis of zooplankton biodiversity, ocean ecosystem assessment, and fisheries management.

scholarly journals Extracting groundfish survey indices from the Ocean Biogeographic Information System (OBIS): an example from Fisheries and Oceans Canada

2009 ◽  
Vol 67 (4) ◽  
pp. 638-645 ◽  
Author(s):  
Daniel Ricard ◽  
Robert M. Branton ◽  
Donald W. Clark ◽  
Peter Hurley
Keyword(s):  
Time Series ◽  
Information System ◽  
Trawl Survey ◽  
Independent Information ◽  
Stock Assessments ◽  
The World ◽  
Abundance Trends ◽  
Selected Subset ◽  
Temporal Abundance ◽  
Trawl Surveys

Abstract Ricard, D., Branton, R. M., Clark, D. W., and Hurley, P. 2010. Extracting groundfish survey indices from the Ocean Biogeographic Information System (OBIS): an example from Fisheries and Oceans Canada. – ICES Journal of Marine Science, 67: 638–645. Scientific trawl surveys have been conducted in different regions of the world and by a variety of countries and agencies since the mid-1900s. Although the data are collected in a scientifically and statistically appropriate context and represent an important source of fishery-independent information for agency-specific stock assessments, their use and dissemination has often been limited to the agencies conducting the surveys. In recent years, Internet data portals such as the Ocean Biogeographic Information System have provided an arena for the wider distribution and use of marine fish data. Despite the increased accessibility of such data, their scientific acceptability has been limited by a lack of reproducibility in data analyses. We present a methodology for the computation of time-series of groundfish stock indices using publicly available trawl survey data derived from the Canadian Department of Fisheries and Oceans Maritimes region. Potential pitfalls associated with the computation of time-series are discussed and proper stratified random estimates of temporal abundance trends are compared with other methods for a selected subset of species. Also, the broader applicability of the methods for datasets collected under similar sampling designs is discussed, along with the reproducibility of the analyses and results.

scholarly journals Stock assessment and projections incorporating discard estimates in some years: an application to the hake stock in ICES Divisions VIIIc and IXa

2010 ◽  
Vol 67 (6) ◽  
pp. 1185-1197 ◽  
Author(s):  
C. Fernández ◽  
S. Cerviño ◽  
N. Pérez ◽  
E. Jardim
Keyword(s):  
Time Series ◽  
Stock Assessment ◽  
Reference Points ◽  
Assessment Model ◽  
Marine Science ◽  
Fishing Mortality ◽  
Change Over Time ◽  
Age Structured ◽  
Available Information ◽  
Over Time

Abstract Fernández, C., Cerviño, S., Pérez, N., and Jardim, E. 2010. Stock assessment and projections incorporating discard estimates in some years: an application to the hake stock in ICES Divisions VIIIc and IXa. – ICES Journal of Marine Science, 67: 1185–1197. A Bayesian age-structured stock assessment model is developed to take into account available information on discards and to handle gaps in the time-series of discard estimates. The model incorporates mortality attributable to discarding, and appropriate assumptions about how this mortality may change over time are made. The result is a stock assessment that accounts for information on discards while, at the same time, producing a complete time-series of discard estimates. The method is applied to the hake stock in ICES Divisions VIIIc and IXa, for which the available data indicate that some 60% of the individuals caught are discarded. The stock is fished by Spain and Portugal, and for each country, there are discard estimates for recent years only. Moreover, the years for which Portuguese estimates are available are only a subset of those with Spanish estimates. Two runs of the model are performed; one assuming zero discards and another incorporating discards. When discards are incorporated, estimated recruitment and fishing mortality for young (discarded) ages increase, resulting in lower values of the biological reference points Fmax and F0.1 and, generally, more optimistic future stock trajectories under F-reduction scenarios.

scholarly journals Quirky patterns in time-series of estimates of recruitment could be artefacts

2014 ◽  
Vol 72 (1) ◽  
pp. 111-116 ◽  
Author(s):  
M. Dickey-Collas ◽  
N. T. Hintzen ◽  
R. D. M. Nash ◽  
P-J. Schön ◽  
M. R. Payne
Keyword(s):  
Time Series ◽  
Stock Assessment ◽  
Meta Analysis ◽  
Reference Points ◽  
Fish Stocks ◽  
Stock Recruitment ◽  
Modelling Assumptions ◽  
Meta Analyses ◽  
Recruitment Model ◽  
Recruitment Time

Abstract The accessibility of databases of global or regional stock assessment outputs is leading to an increase in meta-analysis of the dynamics of fish stocks. In most of these analyses, each of the time-series is generally assumed to be directly comparable. However, the approach to stock assessment employed, and the associated modelling assumptions, can have an important influence on the characteristics of each time-series. We explore this idea by investigating recruitment time-series with three different recruitment parameterizations: a stock–recruitment model, a random-walk time-series model, and non-parametric “free” estimation of recruitment. We show that the recruitment time-series is sensitive to model assumptions and this can impact reference points in management, the perception of variability in recruitment and thus undermine meta-analyses. The assumption of the direct comparability of recruitment time-series in databases is therefore not consistent across or within species and stocks. Caution is therefore required as perhaps the characteristics of the time-series of stock dynamics may be determined by the model used to generate them, rather than underlying ecological phenomena. This is especially true when information about cohort abundance is noisy or lacking.

scholarly journals Road Short-Term Travel Time Prediction Method Based on Flow Spatial Distribution and the Relations

2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
Mingjun Deng ◽  
Shiru Qu
Keyword(s):  
Time Series ◽  
Spatial Distribution ◽  
Travel Time ◽  
Nonparametric Regression ◽  
Nearest Neighbor ◽  
Nearest Neighbor Search ◽  
Short Term ◽  
Combination Model ◽  
The Road ◽  
Neighbor Search

There are many short-term road travel time forecasting studies based on time series, but indeed, road travel time not only relies on the historical travel time series, but also depends on the road and its adjacent sections history flow. However, few studies have considered that. This paper is based on the correlation of flow spatial distribution and the road travel time series, applying nearest neighbor and nonparametric regression method to build a forecasting model. In aspect of spatial nearest neighbor search, three different space distances are defined. In addition, two forecasting functions are introduced: one combines the forecasting value by mean weight and the other uses the reciprocal of nearest neighbors distance as combined weight. Three different distances are applied in nearest neighbor search, which apply to the two forecasting functions. For travel time series, the nearest neighbor and nonparametric regression are applied too. Then minimizing forecast error variance is utilized as an objective to establish the combination model. The empirical results show that the combination model can improve the forecast performance obviously. Besides, the experimental results of the evaluation for the computational complexity show that the proposed method can satisfy the real-time requirement.

Satellite Detection of Ghost Nets and Plastic Debris in Pacific Atolls

2021 ◽  
Author(s):  
Yangrong Ling ◽  
Lauren Biermann ◽  
Mark Manuel ◽  
Ellen Ramirez ◽  
Austin Coates ◽  
...  
Keyword(s):  
Satellite Imagery ◽  
Pacific Islands ◽  
Science Center ◽  
Spectral Angle Mapper ◽  
Plastic Debris ◽  
Fisheries Science ◽  
The Pacific ◽  
The Pacific Ocean ◽  
Derelict Fishing Gear ◽  
Satellite Analysis

<p><span>Since 2014, the NOAA Satellite Analysis Branch has used high resolution optical satellite imagery in an effort to detect ghost nets (derelict fishing gear) and other large plastic debris in the Pacific Ocean and its atolls in support of clean-up efforts (by the NOAA Pacific Islands Fisheries Science Center, Ocean Voyages Institute, etc.). Until recently, reliable detection has proven challenging. With the application of Worldview imagery matched to <em>in situ</em> information on known net locations, we have been able to extract spectral signatures of floating plastics and use these to detect and identify other instances of plastic debris. Using ENVI’s Spectral Angle Mapper (SAM) target detection method, a number of likely locations of nets/plastics in the Pearl and Hermes atoll in the Northwestern Hawaiian Islands (NWHI) were highlighted. The resulting locations of the 41 debris detections were strikingly similar to the distributions along the coast reported in surveys, and are consistent with those that would be expected due to the seasonal ocean currents. This satellite imagery analysis procedure will be repeated shortly before the next NWHI clean-up effort, which will better enable us to support the removal of ghost nets and other marine plastics, and also assess the accuracy and rapid reproducibility of the technique.</span></p>

scholarly journals Risk assessment of cartilaginous fish populations

2014 ◽  
Vol 72 (3) ◽  
pp. 1057-1068 ◽  
Author(s):  
Enric Cortés ◽  
Elizabeth N. Brooks ◽  
Kyle W. Shertzer
Keyword(s):  
United States ◽  
Risk Assessment ◽  
The United States ◽  
Cartilaginous Fish ◽  
Assessment Methods ◽  
Reference Points ◽  
Assessment Process ◽  
Model Complexity ◽  
Stock Assessments ◽  
Demographic Methods

Abstract We review three broad categories of risk assessment methodology used for cartilaginous fish: productivity-susceptibility analysis (PSA), demographic methods, and quantitative stock assessments. PSA is generally a semi-quantitative approach useful as an exploratory or triage tool that can be used to prioritize research, group species with similar vulnerability or risk, and provide qualitative management advice. Demographic methods are typically used in the conservation arena and provide quantitative population metrics that are used to quantify extinction risk and identify vulnerable life stages. Stock assessments provide quantitative estimates of population status and the associated risk of exceeding biological reference points, such as maximum sustainable yield. We then describe six types of uncertainty (process, observation, model, estimation, implementation, and institutional) that affect the risk assessment process, identify which of the three risk assessment methods can accommodate each type of uncertainty, and provide examples mostly for sharks drawn from our experience in the United States. We also review the spectrum of stock assessment methods used mainly for sharks in the United States, and present a case study where multiple methods were applied to the same species (dusky shark, Carcharinus obscurus) to illustrate differing degrees of model complexity and type of uncertainty considered. Finally, we address the common and problematic case of data-poor bycatch species. Our main recommendation for future work is to use Management Strategy Evaluation or similar simulation approaches to explore the effect of different sources of uncertainty, identify the most critical data to satisfy predetermined management objectives, and develop harvest control rules for cartilaginous fish. We also propose to assess the performance of data-poor and -rich methods through stepwise model construction.

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