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.

scholarly journals Redundancy in metrics describing the composition, structure, and functioning of the North Sea demersal fish community

2012 ◽  
Vol 69 (1) ◽  
pp. 8-22 ◽  
Author(s):  
Simon P. R. Greenstreet ◽  
Helen M. Fraser ◽  
Stuart I. Rogers ◽  
Verena M. Trenkel ◽  
Stephen D. Simpson ◽  
...  
Keyword(s):  
North Sea ◽  
Fish Community ◽  
Size Structure ◽  
Demersal Fish ◽  
Community Size ◽  
The North ◽  
Management Objectives ◽  
The North Sea ◽  
Composition Structure ◽  
And Function

Abstract Greenstreet, S. P. R., Fraser, H. M., Rogers, S. I., Trenkel, V. M., Simpson, S. D., and Pinnegar, J. K. 2012. Redundancy in metrics describing the composition, structure, and functioning of the North Sea demersal fish community. – ICES Journal of Marine Science, 69: 8–22. Broader ecosystem management objectives for North Sea demersal fish currently focus on restoring community size structure. However, most policy drivers explicitly concentrate on restoring and conserving biodiversity, and it has not yet been established that simply restoring demersal fish size composition will be sufficient to reverse declines in biodiversity and ensure a generally healthy community. If different aspects of community composition, structure, and function vary independently, then to monitor all aspects of community general health will require application of a suite of metrics. This assumes low redundancy among the metrics used in any such suite and implies that addressing biodiversity issues specifically will require explicit management objectives for particular biodiversity metrics. This issue of metric redundancy is addressed, and 15 metrics covering five main attributes of community composition, structure, and function are applied to groundfish survey data. Factor analysis suggested a new interpretation of the metric information and indicated that a minimum suite of seven metrics was necessary to ensure that all changes in the general health of the North Sea demersal fish community were monitored properly. Covariance among size-based and species-diversity metrics was low, implying that restoration of community size structure would not necessarily reverse declines in species diversity.

Dynamic factor analysis to estimate common trends in fisheries time series

2003 ◽  
Vol 60 (5) ◽  
pp. 542-552 ◽  
Author(s):  
A F Zuur ◽  
I D Tuck ◽  
N Bailey
Keyword(s):  
Time Series ◽  
Factor Analysis ◽  
Dynamic Factor Analysis ◽  
Dynamic Factor ◽  
Common Trends ◽  
Explanatory Variables ◽  
The North ◽  
The North Atlantic Oscillation ◽  
Aquatic Sciences

Dynamic factor analysis (DFA) is a technique used to detect common patterns in a set of time series and relationships between these series and explanatory variables. Although DFA is used widely in econometric and psychological fields, it has not been used in fisheries and aquatic sciences to the best of our knowledge. To make the technique more widely accessible, an introductory guide for DFA, at an intermediate level, is presented in this paper. A case study is presented. The analysis of 13 landings-per-unit-effort series for Nephrops around northern Europe identified three common trends for 12 of the series, with one series being poorly fitted, but no relationships with the North Atlantic Oscillation (NAO) or sea surface temperature were found. The 12 series could be divided into six groups based on factor loadings from the three trends.

scholarly journals Demersal Fish Assemblages in NE Atlantic Seagrass and Kelp

Diversity ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 366
Author(s):  
Evie Furness ◽  
Richard K.F. Unsworth
Keyword(s):  
Species Richness ◽  
Fish Assemblages ◽  
Demersal Fish ◽  
Kelp Forests ◽  
Evidence Based ◽  
Ecological Value ◽  
Commercial Fish ◽  
Seagrass Meadows ◽  
The North ◽  
Commercial Species

Global fisheries are in decline, calling for urgent evidence-based action. One such action is the identification and protection of fishery-associated habitats such as seagrass meadows and kelp forests, both of which have suffered long-term loss and degradation in the North Atlantic region. Direct comparisons of the value of seagrass and kelp in supporting demersal fish assemblages are largely absent from the literature. Here, we address this knowledge gap. Demersal fish were sampled using a baited camera to test for differences between habitats in (1) the species composition of the fish assemblages, (2) the total abundance and species richness of fishes, and (3) the abundances of major commercial species. Seagrass and kelp-associated fish assemblages formed two significantly distinct groupings, which were driven by increased whiting (Merlangius merlangus) and dogfish (Scyliorhinus canicula) presence in seagrass and higher abundances of pollock (Pollachius pollachius) and goby (Gobiusculus flavescens) in kelp. The abundance, diversity, and species richness did not change significantly between the two habitats. We conclude that seagrass and kelp do support unique demersal fish assemblages, providing evidence that they have different ecological value through their differing support of commercial fish species. Thus, this study improves the foundation for evidence-based policy changes.

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.

Dissolved oxygen and temperature best predict deep-sea fish community structure in the Gulf of California with climate change implications

2020 ◽  
Vol 637 ◽  
pp. 159-180
Author(s):  
ND Gallo ◽  
M Beckwith ◽  
CL Wei ◽  
LA Levin ◽  
L Kuhnz ◽  
...  
Keyword(s):  
Climate Change ◽  
Community Structure ◽  
Community Composition ◽  
Environmental Conditions ◽  
Deep Sea ◽  
Gulf Of California ◽  
Fish Community ◽  
Demersal Fish ◽  
Fish Community Structure ◽  
Explained Variance

Natural gradient systems can be used to examine the vulnerability of deep-sea communities to climate change. The Gulf of California presents an ideal system for examining relationships between faunal patterns and environmental conditions of deep-sea communities because deep-sea conditions change from warm and oxygen-rich in the north to cold and severely hypoxic in the south. The Monterey Bay Aquarium Research Institute (MBARI) remotely operated vehicle (ROV) ‘Doc Ricketts’ was used to conduct seafloor video transects at depths of ~200-1400 m in the northern, central, and southern Gulf. The community composition, density, and diversity of demersal fish assemblages were compared to environmental conditions. We tested the hypothesis that climate-relevant variables (temperature, oxygen, and primary production) have more explanatory power than static variables (latitude, depth, and benthic substrate) in explaining variation in fish community structure. Temperature best explained variance in density, while oxygen best explained variance in diversity and community composition. Both density and diversity declined with decreasing oxygen, but diversity declined at a higher oxygen threshold (~7 µmol kg-1). Remarkably, high-density fish communities were observed living under suboxic conditions (<5 µmol kg-1). Using an Earth systems global climate model forced under an RCP8.5 scenario, we found that by 2081-2100, the entire Gulf of California seafloor is expected to experience a mean temperature increase of 1.08 ± 1.07°C and modest deoxygenation. The projected changes in temperature and oxygen are expected to be accompanied by reduced diversity and related changes in deep-sea demersal fish communities.

scholarly journals Fish community responses and the temporal dynamics of recovery following river habitat restorations in Europe

2015 ◽  
Vol 34 (3) ◽  
pp. 975-990 ◽  
Author(s):  
Gregor Thomas ◽  
Armin W. Lorenz ◽  
Andrea Sundermann ◽  
Peter Haase ◽  
Armin Peter ◽  
...  
Keyword(s):  
Fish Community ◽  
Temporal Dynamics ◽  
Community Responses ◽  
River Habitat

Multivariate Markov-switching score-driven models: an application to the global crude oil market

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Szabolcs Blazsek ◽  
Alvaro Escribano ◽  
Adrian Licht
Keyword(s):  
Crude Oil ◽  
Moving Average ◽  
Local Level ◽  
Markov Switching ◽  
Impulse Response Functions ◽  
Common Trends ◽  
Vector Autoregressive ◽  
Oil Market ◽  
Long Run ◽  
Special Cases

Abstract A new class of multivariate nonlinear quasi-vector autoregressive (QVAR) models is introduced. It is a Markov switching score-driven model with stochastic seasonality for the multivariate t-distribution (MS-Seasonal-t-QVAR). As an extension, we allow for the possibility of having common-trends and nonlinear co-integration. Score-driven nonlinear updates of local level and seasonality are used, which are robust to outliers within each regime. We show that VAR integrated moving average (VARIMA) type filters are special cases of QVAR filters. Using exclusion, sign, and elasticity identification restrictions in MS-Seasonal-t-QVAR with common-trends, we provide short-run and long-run impulse response functions for the global crude oil market.

scholarly journals Container Throughput Forecasting Using Dynamic Factor Analysis and ARIMAX Model

2017 ◽  
Vol 29 (5) ◽  
pp. 529-542 ◽  
Author(s):  
Marko Intihar ◽  
Tomaž Kramberger ◽  
Dejan Dragan
Keyword(s):  
Factor Analysis ◽  
Goodness Of Fit ◽  
Moving Average ◽  
Real Data ◽  
Information Criteria ◽  
Dynamic Factor Analysis ◽  
Forecasting Model ◽  
Dynamic Factor ◽  
Macroeconomic Indicators ◽  
The Impact

The paper examines the impact of integration of macroeconomic indicators on the accuracy of container throughput time series forecasting model. For this purpose, a Dynamic factor analysis and AutoRegressive Integrated Moving-Average model with eXogenous inputs (ARIMAX) are used. Both methodologies are integrated into a novel four-stage heuristic procedure. Firstly, dynamic factors are extracted from external macroeconomic indicators influencing the observed throughput. Secondly, the family of ARIMAX models of different orders is generated based on the derived factors. In the third stage, the diagnostic and goodness-of-fit testing is applied, which includes statistical criteria such as fit performance, information criteria, and parsimony. Finally, the best model is heuristically selected and tested on the real data of the Port of Koper. The results show that by applying macroeconomic indicators into the forecasting model, more accurate future throughput forecasts can be achieved. The model is also used to produce future forecasts for the next four years indicating a more oscillatory behaviour in (2018-2020). Hence, care must be taken concerning any bigger investment decisions initiated from the management side. It is believed that the proposed model might be a useful reinforcement of the existing forecasting module in the observed port.

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