scholarly journals Modeling the Effect of Environmental Factors on the Ricker Stock-Recruitment Relationship for North Pacific Albacore Using Generalized Additive Models

2014 ◽  
Vol 25 (4) ◽  
pp. 581
Author(s):  
Chia-Lung Shih ◽  
Yi-Hsiu Chen ◽  
Chien-Chung Hsu
Keyword(s):  
Environmental Factors ◽  
North Pacific ◽  
Generalized Additive Models ◽  
Additive Models ◽  
Stock Recruitment

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>

Spatial distribution analysis of the North Pacific spiny dogfish,Squalus suckleyi, in the North Pacific using generalized additive models

2017 ◽  
Vol 26 (6) ◽  
pp. 668-679 ◽  
Author(s):  
Toshikazu Yano ◽  
Seiji Ohshimo ◽  
Minoru Kanaiwa ◽  
Tsutomu Hattori ◽  
Masa-aki Fukuwaka ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
North Pacific ◽  
Generalized Additive Models ◽  
Additive Models ◽  
Spiny Dogfish ◽  
Distribution Analysis ◽  
The North ◽  
The North Pacific

scholarly journals Performance of harvest control rules in a variable environment

2010 ◽  
Vol 67 (5) ◽  
pp. 1051-1062 ◽  
Author(s):  
Thomas Brunel ◽  
Gerjan J. Piet ◽  
Ralf van Hal ◽  
Christine Röckmann
Keyword(s):  
Dynamic Models ◽  
Generalized Additive Models ◽  
Maximum Sustainable Yield ◽  
Additive Models ◽  
Precautionary Approach ◽  
Variable Environment ◽  
Control Rules ◽  
Stock Biomass ◽  
Stock Recruitment ◽  
Harvest Control Rules

AbstractBrunel, T., Piet, G. J., van Hal, R., and Röckmann, C. 2010. Performance of harvest control rules in a variable environment. – ICES Journal of Marine Science, 67: 1051–1062. Population dynamic models used for fisheries management assume that stocks are isolated entities, ignoring the influence of environmental factors on stock productivity. An operating model parameterized for North Sea cod, plaice, and herring is developed, in which the link between recruitment and environment is assumed to be known and described by generalized additive models. This tool is used to compare the performance of harvest control rules (HCRs) when recruitment is independent of the environment or when recruitment is affected by an environment varying according to different scenarios. The first HCR exploited the stock with a fixed fishing mortality (F) corresponding to maximum sustainable yield, and in the second HCR, F was set equal to the precautionary approach F (i.e. Fpa), but reduced from Fpa when stock biomass fell below Bpa. The performance of the HCRs altered only slightly in a randomly varying environment compared with a constant one. For a detrimental change in the environment, however, no HCR could prevent a massive decrease in stock size. The performance of the HCRs was also influenced by the stock characteristics, such as recruitment variability or the shape of the stock–recruitment relationship. The performance of “environmental” HCRs (eHCRs), in which F varies depending on environmental conditions, was compared with that of conventional HCRs. The gain in using the eHCR was small, except for a detrimental change in the environment, where the eHCR performed markedly better than a conventional HCR. The benefits of using the eHCR were the greatest for the stock with the strongest environment–recruitment relationship.

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.

scholarly journals Relationship of Neotropical otter vestiges with environmental and anthropogenic factors

2019 ◽  
Vol 49 (3) ◽  
pp. 183-192 ◽  
Author(s):  
Ana Marta ANDRADE ◽  
Danilo Leal ARCOVERDE ◽  
Ana Luisa ALBERNAZ
Keyword(s):  
Environmental Factors ◽  
Habitat Use ◽  
Generalized Additive Models ◽  
Additive Models ◽  
Anthropogenic Factors ◽  
Fishing Gear ◽  
Factors Affecting ◽  
Water Characteristics ◽  
Rock Formations ◽  
Main River Channel

ABSTRACT The Neotropical otter, Lontra longicaudis (Mustelidae) is a semi-aquatic mustelid that exploits a variety of freshwater habitats. To understand the relative influence of human activities and environmental factors affecting its distribution and habitat use, we conducted systematic, seasonal surveys of otter signs along the middle Guamá River, in Pará state in the eastern Brazilian Amazon. We applied generalized additive models to compare distribution of otters along the river with data collected on environmental factors (landcover type derived from satellite imagery, and in situ measurements of physicochemical water characteristics) and anthropogenic factors (fishing gear in the river and human habitation along the river). Most otter signs (indicators of otter habitat use) occurred along the shoreline of the main river channel during the dry season; we observed fewer signs during peak flow, probably because the shoreline and floodplain are flooded, which hid signs and made access to the floodplain difficult. The best-fit model included variables for proportion of forest, presence of fishing gear and boats, bank steepness, and presence of rock formations and sand banks. Otter occurrence was negatively related to forested area and positively related to the presence of fishing gear and boats. Otters are likely attracted to fish trapped in fishing gear because they can easily predate on the trapped fish.

scholarly journals Predicting Malaria Transmission Dynamics in Dangassa, Mali: A Novel Approach Using Functional Generalized Additive Models

2020 ◽  
Vol 17 (17) ◽  
pp. 6339
Author(s):  
François Freddy Ateba ◽  
Manuel Febrero-Bande ◽  
Issaka Sagara ◽  
Nafomon Sogoba ◽  
Mahamoudou Touré ◽  
...  
Keyword(s):  
Wind Speed ◽  
Malaria Incidence ◽  
Generalized Additive Models ◽  
Community Health Center ◽  
Additive Model ◽  
Additive Models ◽  
Functional Regression ◽  
Novel Approach ◽  
Malaria Incidence Rate ◽  
Mean Wind Speed

Mali aims to reach the pre-elimination stage of malaria by the next decade. This study used functional regression models to predict the incidence of malaria as a function of past meteorological patterns to better prevent and to act proactively against impending malaria outbreaks. All data were collected over a five-year period (2012–2017) from 1400 persons who sought treatment at Dangassa’s community health center. Rainfall, temperature, humidity, and wind speed variables were collected. Functional Generalized Spectral Additive Model (FGSAM), Functional Generalized Linear Model (FGLM), and Functional Generalized Kernel Additive Model (FGKAM) were used to predict malaria incidence as a function of the pattern of meteorological indicators over a continuum of the 18 weeks preceding the week of interest. Their respective outcomes were compared in terms of predictive abilities. The results showed that (1) the highest malaria incidence rate occurred in the village 10 to 12 weeks after we observed a pattern of air humidity levels >65%, combined with two or more consecutive rain episodes and a mean wind speed <1.8 m/s; (2) among the three models, the FGLM obtained the best results in terms of prediction; and (3) FGSAM was shown to be a good compromise between FGLM and FGKAM in terms of flexibility and simplicity. The models showed that some meteorological conditions may provide a basis for detection of future outbreaks of malaria. The models developed in this paper are useful for implementing preventive strategies using past meteorological and past malaria incidence.

The Relationship between Mobility and COVID-19 in Germany: Modeling Case Occurrence using Apple's Mobility Trends Data

2021 ◽  
Author(s):  
Mark David Walker ◽  
Mihály Sulyok
Keyword(s):  
Generalized Additive Models ◽  
Information Criterion ◽  
Additive Models ◽  
Online Data ◽  
German Government ◽  
Explanatory Variables ◽  
Mobility Data ◽  
Community Mobility ◽  
The Relationship ◽  
Potential Use

Abstract Background Restrictions on social interaction and movement were implemented by the German government in March 2020 to reduce the transmission of coronavirus disease 2019 (COVID-19). Apple's “Mobility Trends” (AMT) data details levels of community mobility; it is a novel resource of potential use to epidemiologists. Objective The aim of the study is to use AMT data to examine the relationship between mobility and COVID-19 case occurrence for Germany. Is a change in mobility apparent following COVID-19 and the implementation of social restrictions? Is there a relationship between mobility and COVID-19 occurrence in Germany? Methods AMT data illustrates mobility levels throughout the epidemic, allowing the relationship between mobility and disease to be examined. Generalized additive models (GAMs) were established for Germany, with mobility categories, and date, as explanatory variables, and case numbers as response. Results Clear reductions in mobility occurred following the implementation of movement restrictions. There was a negative correlation between mobility and confirmed case numbers. GAM using all three categories of mobility data accounted for case occurrence as well and was favorable (AIC or Akaike Information Criterion: 2504) to models using categories separately (AIC with “driving,” 2511. “transit,” 2513. “walking,” 2508). Conclusion These results suggest an association between mobility and case occurrence. Further examination of the relationship between movement restrictions and COVID-19 transmission may be pertinent. The study shows how new sources of online data can be used to investigate problems in epidemiology.

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