Using generalized additive models for prediction of chlorophyll a in Lake Okeechobee, Florida

1996 ◽  
Vol 2 (1-2) ◽  
pp. 37-46 ◽  
Author(s):  
E. Conrad Lamon ◽  
Kenneth H. Reckhow ◽  
Karl E. Havens
Keyword(s):  
Chlorophyll A ◽  
Generalized Additive Models ◽  
Additive Models ◽  
Lake Okeechobee

scholarly journals Sardine potential habitat and environmental forcing off western Portugal

2010 ◽  
Vol 67 (8) ◽  
pp. 1553-1564 ◽  
Author(s):  
Juan P. Zwolinski ◽  
Paulo B. Oliveira ◽  
Victor Quintino ◽  
Yorgos Stratoudakis
Keyword(s):  
Chlorophyll A ◽  
Seasonal Pattern ◽  
Generalized Additive Models ◽  
Additive Models ◽  
Seasonal Effect ◽  
Oceanic Circulation ◽  
Environmental Forcing ◽  
Potential Habitat ◽  
The North ◽  
Spatio Temporal

Abstract Zwolinski, J. P., Oliveira, P. B., Quintino, V., and Stratoudakis, Y. 2010. Sardine potential habitat and environmental forcing off western Portugal. – ICES Journal of Marine Science, 67: 1553–1564. Relationships between sardine (Sardina pilchardus) distribution and the environment off western Portugal were explored using data from seven acoustic surveys (spring and autumn of 2000, 2001, 2005, and spring 2006). Four environmental variables (salinity, temperature, chlorophyll a, and acoustic epipelagic backscatter other than fish) were related to the acoustic presence and density of sardine. Univariate quotient analysis revealed sardine preferences for waters with high chlorophyll a content, low temperature and salinity, and low acoustic epipelagic backscatter. Generalized additive models depicted significant relationships between the environment and sardine presence but not with sardine density. Maps of sardine potential habitat (SPH) built upon the presence/absence models revealed a clear seasonal effect in the across-bathymetry and alongshelf extension of SPH off western Portugal. During autumn, SPH covered a large part of the northern Portuguese continental shelf but was almost absent from the southern region, whereas in spring SPH extended farther south but was reduced to a narrow band of shallow coastal waters in the north. This seasonal pattern agrees with the spatio-temporal variation of primary production and oceanic circulation described for the western Iberian shelf.

scholarly journals Model of Habitat Characteristics of Sardinella lemuru in the Bali Strait, Indonesia

2021 ◽  
Vol 56 (3) ◽  
pp. 229-240
Author(s):  
Adi Wijaya ◽  
Abu Bakar Sambah ◽  
Daduk Setyohadi ◽  
Umi Zakiyah
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Chlorophyll A ◽  
Environmental Conditions ◽  
Generalized Additive Models ◽  
Additive Models ◽  
Sea Surface ◽  
Model Parameters ◽  
Photosynthetically Available Radiation ◽  
Chlorophyll A Concentration

This article describes a new approach to the study of the environmental conditions that relate to the Sardinella lemuru habitat in the Bali Strait, through remote sensing data and fish catch data using the generalized additive model. Data that are acquired daily and then compiled into monthly data for sea surface temperature, sea surface chlorophyll-a concentration, photosynthetically available radiation, and sea surface depth (SSD) were used for the years 2008–2010. The objectives of the study are to describe the variability of the environmental conditions in the Bali Strait, to analyze a combination model of environmental factors in estimating the Sardinella lemuru habitat, and to map potential Sardinella lemuru fishing areas. We illustrate the proposed method by constructing seven generalized additive models with catches of Sardinella lemuru as a variable response and use sea surface temperature, sea surface chlorophyll-a concentration, photosynthetically available radiation, and SSD as covariant models for assessing the environmental characteristics of the abundance of Sardinella lemuru. Predicted values were validated using a linear model. Based on the three model parameters, habitat selection for Sardinella lemuru was significantly (P < 0.0001) influenced by photosynthetically available radiation (50–55 Einstein m-2 d-1), sea surface chlorophyll-a concentration (0.2–2.0 mgm-3), sea surface temperature (28.95–29.64 °C), and SSD (60–150 m). Catch predictions show a consistent trend toward environmental conditions and water depth. Our method allows for improvement with the validation of catch predictions that were observed and collected monthly, and the result was significant (P < 0.001, r2 = 0.816). Photosynthetically available radiation explains the highest deviation in continued generalized additive models; therefore, it was considered to be the best predictor of habitat, followed by sea surface chlorophyll-a concentration, sea surface temperature, and then SSD. New research results supplement several previous studies that relate to the analysis of environmental parameters in estimating the fish habitat and can be used in mapping the distribution of potential Sardinella lemuru fishing areas in spatial and temporal scales.

scholarly journals Increases in Picocyanobacteria Abundance in Agriculturally Eutrophic Pampean Lakes Inferred from Historical Records of Secchi Depth and Chlorophyll-a

Water ◽  
2022 ◽  
Vol 14 (2) ◽  
pp. 159
Author(s):  
Horacio Ernesto Zagarese ◽  
Nadia R. Diovisalvi ◽  
María de los Ángeles González Sagrario ◽  
Irina Izaguirre ◽  
Paulina Fermani ◽  
...  
Keyword(s):  
Chlorophyll A ◽  
Size Structure ◽  
Generalized Additive Models ◽  
Secchi Depth ◽  
Additive Models ◽  
Validation Dataset ◽  
Secchi Disc ◽  
Secchi Disc Depth ◽  
Before And After ◽  
Pampa Region

Phytoplankton size structure has profound consequences on food-web organization and energy transfer. Presently, picocyanobacteria (size < 2 µm) represent a major fraction of the autotrophic plankton of Pampean lakes. Glyphosate is known to stimulate the development of picocyanobacteria capable of degrading the herbicide. Due to the worldwide adoption of glyphosate-resistant crops, herbicide usage has increased sharply since the mid-1990s. Unfortunately, there are very few studies (none for the Pampa region) reporting picocyanobacteria abundance before 2000. The proliferation of µm sized particles should decrease Secchi disc depth (ZSD). Therefore ZSD, conditional to chlorophyll-a, may serve as an indicator of picocyanobacteria abundance. We use generalized additive models (GAMs) to analyze a “validation” dataset consisting of 82 records of ZSD, chlorophyll-a, and picocyanobacteria abundance from two Pampean lakes surveys (2009 and 2015). In support of the hypothesis, ZSD was negatively related to picocyanobacteria after accounting for the effect of chlorophyll-a. We then fitted a “historical” dataset using hierarchical GAMs to compare ZSD conditional to chlorophyll-a, before and after 2000. We estimated that ZSD levels during 2000–2021 were, on average, only about half as deep as those during 1980–1999. We conclude that the adoption of glyphosate-resistant crops has stimulated outbreaks of picocyanobacteria populations, resulting in lower water transparency.

Oceanographic Conditions on Small Pelagic Fishery in the Gulf of Bone Waters

2018 ◽  
Vol 1 (2) ◽  
Author(s):  
Safruddin Safruddin ◽  
Rachmat Hidayat ◽  
Mukti Zainuddin
Keyword(s):  
Chlorophyll A ◽  
Generalized Additive Models ◽  
Pelagic Fish ◽  
Additive Models ◽  
Fish Distribution ◽  
Fishing Ground ◽  
Small Pelagic Fish ◽  
The Relationship ◽  
Pelagic Fishery ◽  
Oceanographic Conditions

This study aimed to investigate the relationship between the dinamic oceanographic condition and fluctuation in the catch of small pelagic fish. Study on the dinamic oceanographic conditions were focused on the sea surface temperature (SST), chlorophyll-a concentration (SSC) and water depth. The study took place in the area of Gulf of Bone located, data collection was started from April to September 2017. The data were collected using experimental fishing metode (large liftnet) and applications of remote sensing in satellite oceanography, which then analysed using Geographic Information System (GIS) dan Generalized Additive Models (GAMs). The result showed that the distribution of small pelagic fish tends to be within the area of temperature ranging from 29.5 to 30.0oC, the chlorophyll-a from 0.7 to 0.9 mg.m-3 and concentrated within the coastal area with in waters depth maximum of 100 m.Keywords: Oceanography, small pelagic fish, distribution, fishing ground, Gulf of Bone

scholarly journals Prediction of Potential Fishing Zones for Skipjack Tuna During the Northwest Monsoon Using Remotely Sensed Satellite Data

2017 ◽  
Vol 22 (2) ◽  
pp. 59-66
Author(s):  
Mukti Zainuddin ◽  
Safruddin Safruddin ◽  
Muhammad Banda Selamat ◽  
Aisjah Farhum ◽  
Sarip Hidayat
Keyword(s):  
Chlorophyll A ◽  
Generalized Additive Models ◽  
Additive Models ◽  
Skipjack Tuna ◽  
Catch Per Unit Effort ◽  
Catch Data ◽  
Mean Sea Surface ◽  
Fishery Data ◽  
And Migration ◽  
The Relationship

One of economically important fish in the Bay of Bone is Skipjack tuna which their distribution and migration are influenced by surrounding environment.  This study aims to investigate the relationship between skipjack tuna and their environments, and to predict potential fishing zones (PFZs) for the fish in the Bone Bay-Flores Sea using satellite-based oceanography and catch data. Generalized additive models (GAMs) were used to assess the relationship. A generalized linear model(GLM) constructed from GAMs was used for prediction. Monthly mean sea surface temperature (SST) and chlorophyll-a during the northwest monsoon (December-January) together with catch data were used for the year 2012-2013. We used the GAMs to assess the effect of the environment variables on skipjack tuna CPUE (catch per unit effort). The best GLM was selected to predict skipjack tuna abundance.  Results indicated that the highest CPUEs (fish/trip) occurred in areas where SST and chlorophyll-a ranged from 29.5°-31.5°C and 0.15 - 0.25 mg m-3, respectively. The PFZs for skipjack were closely related to the spatial distribution of the optimum oceanographic conditions and these mainly developed in three locations, northern area of Bone Bay in December, in the middle area of the bay (4°-5.5°S and 120.5°-121.5°E) during January and moved to the Flores Sea in February. The movement of skipjack concentration was consistent with the fishery data.  This suggests that the dynamics of the optimum oceanographic signatures provided a good indicator for predicting feeding grounds as hotspot areas for skipjack tuna in Bone Bay-Flores Sea during northwest monsoon. Keywords:  skipjack tuna, potential fishing zones, satellite based-oceanographic data, Northwest monsoon

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.

scholarly journals Comparing Charlson and Elixhauser comorbidity indices with different weightings to predict in-hospital mortality: an analysis of national inpatient data

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Narayan Sharma ◽  
René Schwendimann ◽  
Olga Endrich ◽  
Dietmar Ausserhofer ◽  
Michael Simon
Keyword(s):  
Hospital Mortality ◽  
Patient Population ◽  
Generalized Additive Models ◽  
Routine Data ◽  
Predictive Performance ◽  
Population Based ◽  
Mortality Prediction ◽  
Additive Models ◽  
General Hospitals ◽  
Net Reclassification Improvement

Abstract Background Understanding how comorbidity measures contribute to patient mortality is essential both to describe patient health status and to adjust for risks and potential confounding. The Charlson and Elixhauser comorbidity indices are well-established for risk adjustment and mortality prediction. Still, a different set of comorbidity weights might improve the prediction of in-hospital mortality. The present study, therefore, aimed to derive a set of new Swiss Elixhauser comorbidity weightings, to validate and compare them against those of the Charlson and Elixhauser-based van Walraven weights in an adult in-patient population-based cohort of general hospitals. Methods Retrospective analysis was conducted with routine data of 102 Swiss general hospitals (2012–2017) for 6.09 million inpatient cases. To derive the Swiss weightings for the Elixhauser comorbidity index, we randomly halved the inpatient data and validated the results of part 1 alongside the established weighting systems in part 2, to predict in-hospital mortality. Charlson and van Walraven weights were applied to Charlson and Elixhauser comorbidity indices. Derivation and validation of weightings were conducted with generalized additive models adjusted for age, gender and hospital types. Results Overall, the Elixhauser indices, c-statistic with Swiss weights (0.867, 95% CI, 0.865–0.868) and van Walraven’s weights (0.863, 95% CI, 0.862–0.864) had substantial advantage over Charlson’s weights (0.850, 95% CI, 0.849–0.851) and in the derivation and validation groups. The net reclassification improvement of new Swiss weights improved the predictive performance by 1.6% on the Elixhauser-van Walraven and 4.9% on the Charlson weights. Conclusions All weightings confirmed previous results with the national dataset. The new Swiss weightings model improved slightly the prediction of in-hospital mortality in Swiss hospitals. The newly derive weights support patient population-based analysis of in-hospital mortality and seek country or specific cohort-based weightings.

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