scholarly journals Do environmental factors affect recruits per spawner anomalies of New England groundfish?

2005 ◽  
Vol 62 (7) ◽  
pp. 1394-1407 ◽  
Author(s):  
Jon Brodziak ◽  
Loretta O'Brien
Keyword(s):  
Environmental Factors ◽  
New England ◽  
Gulf Of Maine ◽  
Generalized Additive Models ◽  
Additive Models ◽  
Winter Flounder ◽  
Water Volume ◽  
Melanogrammus Aeglefinus ◽  
Georges Bank ◽  
Yellowtail Flounder

Abstract We evaluated the influence of environmental factors on recruits per spawner (RS) anomalies of 12 New England groundfish stocks. Nonparametric methods were used to analyse time-series of RS anomalies derived from stock-recruitment data in recent assessments. The 12 stocks occur in three geographic regions: the Gulf of Maine (cod Gadus morhua, redfish Sebastes fasciatus, winter flounder Pseudopleuronectes americanus, American plaice Hippoglossoides platessoides, witch flounder Glyptocephalus cynoglossus, and yellowtail flounder Limanda ferruginea), Georges Bank (cod, haddock Melanogrammus aeglefinus, and yellowtail flounder), and Southern New England (summer flounder Paralichthys dentatus, yellowtail flounder, and winter flounder). Randomization tests were applied to detect years when RS anomalies were unusually high or low for comparison with oceanographic conditions such as the 1998 intrusion of Labrador Subarctic Slope water into the Gulf of Maine region. Randomization methods were also used to evaluate the central tendency and dispersion of all RS anomalies across stocks. Average RS anomalies were significantly positive in 1987 across stocks and regions, indicating that environmental forcing was coherent and exceptional in that year. Responses of RS values of individual stocks to lagged and contemporaneous environmental variables such as the North Atlantic Oscillation (NAO) index, water temperature, windstress, and shelf water volume anomalies were evaluated using generalized additive models. Overall, the NAO forward-lagged by 2 years had the largest impact on RS anomalies. This apparent effect is notable because it could provide a leading indicator of RS anomalies for some commercially exploited stocks. In particular, the three primary groundfish stocks on Georges Bank (cod, haddock, and yellowtail flounder) all exhibited positive RS anomalies when the NAO2 variable was positive.

Environmental and geographical effects on fog occurrence

2021 ◽  
Author(s):  
Iva Hunova ◽  
Marek Brabec ◽  
Marek Malý ◽  
Alexandru Dumitrescu ◽  
Jan Geletič
Keyword(s):  
Air Pollution ◽  
Environmental Factors ◽  
Tensor Product ◽  
Generalized Additive Models ◽  
Additive Models ◽  
Low Rank ◽  
Seasonal Component ◽  
Explanatory Variables ◽  
The Individual

<p>Fog is a very complex phenomenon (Gultepe et al., 2007). In some areas it can contribute substantially to hydrological and chemical inputs and is therefore of high environmental relevance (Blas et al., 2010). Fog formation is affected by numerous factors, such as meteorology, air pollution, terrain (geomorphology), and land-use.</p><p>In our earlier studies we addressed the role of meteorology and air pollution on fog occurrence (Hůnová et al., 2018) and long-term trends in fog occurrence in Central Europe (Hůnová et al., 2020). This study builds on earlier model identification of year-to-year and seasonal components in fog occurrence and brings an analysis of the deformation of the above components due to the individual explanatory variables. The aim of this study was to indicate the geographical and environmental factors affecting the fog occurrence.</p><p>       We have examined the data on fog occurrence from 56 meteorological stations of various types from Romania reflecting different environments and geographical areas. We used long-term records from the 1981–2017 period. </p><p>       We considered both the individual explanatory variables and their interactions. With respect to geographical factors, we accounted for the altitude and landform. With respect to environmental factors,   we accounted for proximity of large water bodies, and proximity of forests. Geographical data from Copernicus pan-European (e.g. CORINE land cover, high resolution layers) and local (e.g. Urban Atlas) projects were used. Elevation data from EU-DEM v1.1 were source for morphometric analysis (Copernicus, 2020).</p><p>        We applied a generalized additive model, GAM (Wood, 2017; Hastie & Tibshirani, 1990) to address nonlinear trend shapes in a formalized and unified way. In particular, we employed penalized spline approach with cross-validated penalty coefficient estimation. To explore possible deformations of annual and seasonal components with various covariates of interest, we used (penalized) tensor product splines to model (two-way) interactions parsimoniously, Wood (2006).</p><p>       The fog occurrence showed significant decrease over the period under review. In general the selected explanatory variables significantly affected the fog occurrence and their effect was non-linear. Our results indicated that, the geographical and environmental variables affected primarily the seasonal component of the model. Of the factors which were accounted for, it was mainly the altitude showing the clear effect on seasonal component deformation (Hůnová et al., in press).</p><p>      </p><p> </p><p>References:</p><p>Blas, M, Polkowska, Z., Sobik, M., et al. (2010). Atmos. Res. 95, 455–469.</p><p>Copernicus Land Monitoring Service (2020). Accessed online at: https://land.copernicus.eu/.</p><p>Gultepe, I., Tardif, R., Michaelidis, S.C., Cermak, J., Bott, A. et al. (2007). Pure Appl Geophys, 164, 1121-1159.</p><p>Hastie, T.J., Tibshirani, R.J. (1990). Generalized Additive Models. Boca Raton, Chapman & Hall/CRC.</p><p>Hůnová, I., Brabec, M., Malý, M., Dumitrescu, A., Geletič, J. (in press) Sci. Total Environ. 144359.</p><p>Hůnová, I., Brabec, M., Malý, M., Valeriánová, A. (2018) Sci. Total Environ. 636, 1490–1499.</p><p>Hůnová, I., Brabec, M., Malý, M., Valeriánová, A. (2020) Sci. Total Environ. 711, 135018.</p><p>Wood, S.N. (2006) Low rank scale invariant tensor product smooths for generalized additive mixed models. Biometrics 62(4):1025-1036</p><p>Wood, S.N. (2017). Generalized Additive Models: An Introduction with R (2nd ed). Boca Raton, Chapman & Hall/CRC.</p><p> </p>

Investigating spatial variation and temperature effects on maturity of female winter flounder (Pseudopleuronectes americanus) using generalized additive models

2014 ◽  
Vol 71 (9) ◽  
pp. 1279-1290 ◽  
Author(s):  
Megan V. Winton ◽  
Mark J. Wuenschel ◽  
Richard S. McBride
Keyword(s):  
Spatial Structure ◽  
Spatial Variation ◽  
Clustering Algorithm ◽  
Generalized Additive Models ◽  
Additive Models ◽  
Winter Flounder ◽  
Effect Of Temperature ◽  
Spatial Effects ◽  
Age At Maturity ◽  
Pseudopleuronectes Americanus

Generalized additive models were used to investigate fine-scale spatial variation in female maturity across the three United States’ winter flounder (Pseudopleuronectes americanus) stocks. The effect of temperature on maturity was also investigated. Maturity models explicitly incorporating spatial structure performed better than “traditional” methods incorporating spatial effects by aggregating data according to predefined stock boundaries. Models including temperature explained more of the variability in maturity than those based only on fish size or age but did not improve fit over models incorporating spatial structure. Based on the size- and age-at-maturity estimates from the spatially explicit models, distinct subareas were objectively identified using a spatially constrained clustering algorithm. The results suggested greater variation in size- and age-at-maturity within than between existing stock areas. The approach outlined here provides a method for identifying areas with different vital rates without the need to presume subjective boundaries.

Estimating Population Size from Relative Abundance Data Measured with Error

1983 ◽  
Vol 40 (11) ◽  
pp. 1871-1879 ◽  
Author(s):  
Jeremy S. Collie ◽  
Michael P. Sissenwine
Keyword(s):  
New England ◽  
Population Size ◽  
Survey Data ◽  
Relative Abundance ◽  
Population Analysis ◽  
Melanogrammus Aeglefinus ◽  
Trawl Survey ◽  
Georges Bank ◽  
Abundance Data ◽  
Population Size Estimates

A modified DeLury method to estimate fish population size from relative abundance data was developed. The method may be applied either with or without knowledge of the age composition of catch. In addition to estimating catchability coefficients, the technique accounts for error in the measurement of relative abundance. A general-purpose nonlinear regression subroutine was used to fit the model. The technique is demonstrated using Northeast Fisheries Center bottom trawl survey data as a measure of relative abundance. Fitting was carried out for four fish populations: Georges Bank and Southern New England yellowtail flounder (Limanda ferruginea); Georges Bank and NAFO SA 4X haddock (Melanogrammus aeglefinus). Catchability coefficients calculated in this manner are consistent with prior estimates. In addition, the technique smooths the survey data by filtering measurement error from true fluctuations in population size. Population size estimates for haddock derived by this method agree closely with virtual population analysis (VPA) estimates.

scholarly journals Potential growth of pelagic juvenile cod in relation to the 1978–2006 winter–spring zooplankton on the Northeast US continental shelf

2015 ◽  
Vol 72 (9) ◽  
pp. 2549-2568 ◽  
Author(s):  
R. G. Lough ◽  
T. Kristiansen
Keyword(s):  
Time Series ◽  
New England ◽  
Gulf Of Maine ◽  
Climatic Conditions ◽  
Calanus Finmarchicus ◽  
Georges Bank ◽  
Potential Growth ◽  
Lower Growth ◽  
Centropages Typicus ◽  
Pelagic Juvenile

Abstract Environmental conditions during the pelagic juvenile cod period determine their fitness to survive settlement as demersal juveniles (0-group) and recruitment. This study examines the potential growth of pelagic juvenile cod in five areas of the New England Shelf based on time series of zooplankton and ocean temperature from surveys. An individual-based model was used to estimate the temporal variation in growth of juvenile cod at each survey station based on available prey of appropriate sized copepods of Calanus finmarchicus, Pseudocalanus spp., Centropages typicus, and Centropages hamatus. Mean juvenile cod growth was low (1–7% d−1) during January–February and March–April time series across all areas, Gulf of Maine (GOM), Eastern Georges Bank, Western Georges Bank, southern New England to Middle Atlantic Bight (MAB). Growth increased significantly in May–June with the seasonal increase in copepod density and temperature generally from South to North. The 1990–1999 warm years had the highest growth of 12–14% d−1 compared with the cooler 2000–2006 years and colder 1978–1989 years of similarly lower growth of 8–11% d−1. Growth in the MAB stayed the same 13% d−1 as in 1990–1999, whereas GOM growth decreased significantly to ∼6% d−1. High prey densities during May–June 1990–1999 for Georges Bank and GOM, followed by a strong decrease in 2000–2006 may explain the decrease in growth during the same periods. While all four copepod species contributed to potential growth, C. typicus, a more southern species, could be the more important prey for juveniles in the coastal areas during all months in contrast to Pseudocalanus spp. for the larvae. Centropages typicus also is the most adaptable and likely species able to expand and thrive under warmer climatic conditions, which could be of significance to future recruitment. Age-1 recruitment for Georges Bank cod was found to be related to juvenile growth.

A bioeconomic model of marine sanctuaries on Georges Bank

2000 ◽  
Vol 57 (6) ◽  
pp. 1307-1319 ◽  
Author(s):  
Daniel S Holland
Keyword(s):  
New England ◽  
Gulf Of Maine ◽  
Georges Bank ◽  
Bioeconomic Model ◽  
Seasonal Movement ◽  
Southern New England ◽  
Modeling Methodology ◽  
Spawning Stock ◽  
Age Structured ◽  
Groundfish Fishery

An empirically estimated fleet dynamics model for New England trawlers is integrated with spatial, age-structured models of primary groundfish species on Georges Banks, southern New England, and the Gulf of Maine. This bioeconomic model is used to explore how permanent marine sanctuaries on Georges Bank might affect catches, revenues, and spawning stock of principal groundfish species in New England. The simulations explore how the location of sanctuaries relative to major ports and their orientation relative to seasonal movement patterns of fish stocks impact their effectiveness and the distribution of benefits across groups of fishers from different ports. The simulation results also demonstrate that the impacts of sanctuaries can vary greatly across species, sometimes increasing yields for some while decreasing yields for others. While the specific results from the simulations reflect the characteristics of the New England groundfish fishery, the modeling methodology and some general conclusions are applicable to other fisheries.

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