scholarly journals Comparative analysis of statistical tools to identify recruitment–environment relationships and forecast recruitment strength

2005 ◽  
Vol 62 (7) ◽  
pp. 1256-1269 ◽  
Author(s):  
Bernard A. Megrey ◽  
Yong-Woo Lee ◽  
S. Allen Macklin
Keyword(s):  
Linear Regression ◽  
Generalized Additive Models ◽  
Simulated Data ◽  
Additive Models ◽  
Clupea Harengus ◽  
Influential Factor ◽  
Data Simulation ◽  
Sea Temperature ◽  
The North ◽  
The North Atlantic Oscillation

Abstract Many of the factors affecting recruitment in marine populations are still poorly understood, complicating the prediction of strong year classes. Despite numerous attempts, the complexity of the problem often seems beyond the capabilities of traditional statistical analysis paradigms. This study examines the utility of four statistical procedures to identify relationships between recruitment and the environment. Because we can never really know the parameters or underlying relationships of actual data, we chose to use simulated data with known properties and different levels of measurement error to test and compare the methods, especially their ability to forecast future recruitment states. Methods examined include traditional linear regression, non-linear regression, Generalized Additive Models (GAM), and Artificial Neural Networks (ANN). Each is compared according to its ability to recover known patterns and parameters from simulated data, as well as to accurately forecast future recruitment states. We also apply the methods to published Norwegian spring-spawning herring (Clupea harengus L.) spawner–recruit–environment data. Results were not consistently conclusive, but in general, flexible non-parametric methods such as GAMs and ANNs performed better than parametric approaches in both parameter estimation and forecasting. Even under controlled data simulation procedures, we saw evidence of spurious correlations. Models fit to the Norwegian spring-spawning herring data show the importance of sea temperature and spawning biomass. The North Atlantic Oscillation (NAO) did not appear to be an influential factor affecting herring recruitment.

scholarly journals Non-Parametric Generalized Additive Models as a Tool for Evaluating Policy Interventions

Mathematics ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 299
Author(s):  
Jaime Pinilla ◽  
Miguel Negrín
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Linear Trend ◽  
Generalized Additive Models ◽  
Additive Models ◽  
Linear Regression Models ◽  
Non Linear ◽  
Nonlinear Trends ◽  
The Impact ◽  
Non Parametric

The interrupted time series analysis is a quasi-experimental design used to evaluate the effectiveness of an intervention. Segmented linear regression models have been the most used models to carry out this analysis. However, they assume a linear trend that may not be appropriate in many situations. In this paper, we show how generalized additive models (GAMs), a non-parametric regression-based method, can be useful to accommodate nonlinear trends. An analysis with simulated data is carried out to assess the performance of both models. Data were simulated from linear and non-linear (quadratic and cubic) functions. The results of this analysis show how GAMs improve on segmented linear regression models when the trend is non-linear, but they also show a good performance when the trend is linear. A real-life application where the impact of the 2012 Spanish cost-sharing reforms on pharmaceutical prescription is also analyzed. Seasonality and an indicator variable for the stockpiling effect are included as explanatory variables. The segmented linear regression model shows good fit of the data. However, the GAM concludes that the hypothesis of linear trend is rejected. The estimated level shift is similar for both models but the cumulative absolute effect on the number of prescriptions is lower in GAM.

Accounting for responsive movement in line transect estimates of abundance

2001 ◽  
Vol 58 (4) ◽  
pp. 777-787 ◽  
Author(s):  
D L Palka ◽  
P S Hammond
Keyword(s):  
Animal Movement ◽  
Generalized Additive Models ◽  
Radial Distance ◽  
Additive Models ◽  
Line Transect ◽  
The North ◽  
Minke Whales ◽  
Orientation Density ◽  
Using Data ◽  
Animal Orientation

A method is developed to account for effects of animal movement in response to sighting platforms in line transect density estimates using data on animal orientation. Models of expected distributions of animal orientation show that presence of responsive movement is determined by the ratio of animal sightings with angles of orientation in the third quadrant relative to the first quadrant. The distance at which response began is estimated using logistic generalized additive models of the relationship between radial distance and orientation. Density corrected for responsive movement is estimated by applying the Buckland and Turnock two-team analysis method to data poststratified into regions "close" to and "far" from (beyond the distance that responsive movement began) the observation platform instead of the original stratification by observation team. For data collected in the North Atlantic, white-sided dolphins, harbor porpoises, and minke whales responded by avoiding the survey ship, and white-beaked dolphins were attracted to the ship. For these populations, our method to correct for responsive movement gave significantly higher estimates, from 1.4 to 2.7 times the uncorrected estimates.

Using habitat association models to predict Alewife and Blueback Herring marine distributions and overlap with Atlantic Herring and Atlantic Mackerel: can incidental catches be reduced?

2015 ◽  
Vol 73 (7) ◽  
pp. 1912-1924 ◽  
Author(s):  
Sara M. Turner ◽  
John P. Manderson ◽  
David E. Richardson ◽  
John J. Hoey ◽  
Jonathan A. Hare
Keyword(s):  
Environmental Gradients ◽  
Generalized Additive Models ◽  
Habitat Associations ◽  
Additive Models ◽  
Clupea Harengus ◽  
Atlantic Herring ◽  
Alosa Pseudoharengus ◽  
Atlantic Mackerel ◽  
Blueback Herring ◽  
River Herring

Abstract Concern over the impacts of incidental catches of Alewife, Alosa pseudoharengus and Blueback Herring, A. aestivalis (collectively managed as ‘river herring’) in the commercial Atlantic Herring (Clupea harengus) and Atlantic Mackerel (Scomber scombrus) fisheries has resulted in the recent implementation of river herring incidental catch limits. These incidental catches are highly variable in frequency and magnitude, and the environmental conditions associated with these catches are poorly understood. We used generalized additive models (GAMs) to describe habitat associations of Alewife, Blueback Herring, Atlantic Herring, and Atlantic Mackerel. Bottom temperature, bottom depth, bottom salinity, solar azimuth and elevation, and region of the Northeast U.S. continental shelf were all significant in the habitat models; GAMs explained 25.2, 16.9, 18.9, and 20.6% of the deviance observed for the presence/absence of Alewife, Blueback Herring, Atlantic Herring, and Atlantic Mackerel. A subset of the data was omitted from the model and the probability of presence was compared with observations; 66–77% of observations were correctly predicted. The individual probabilities of presence were used to quantify and evaluate the accuracy of modelled overlap of Alewife and Blueback Herring with Atlantic Herring (68–72% correct predictions) and Alewife and Blueback Herring with Atlantic Mackerel (57–69% correct predictions). Our findings indicate that environmental gradients influence the distributions and overlap of Alewife, Blueback Herring, Atlantic Herring, and Atlantic Mackerel, and with further testing and refinement these models could be developed into a tool to aid industry in reducing incidental catches of river herring.

Diel variation of stomach contents of North Sea cod (Gadus morhua) during a 24-h fishing survey: an analysis using generalized additive models

2000 ◽  
Vol 57 (12) ◽  
pp. 2363-2367 ◽  
Author(s):  
Sara A Adlerstein ◽  
Henny C Welleman
Keyword(s):  
North Sea ◽  
Stomach Content ◽  
Gadus Morhua ◽  
Generalized Additive Models ◽  
Stomach Contents ◽  
Additive Models ◽  
Large Prey ◽  
Diel Variation ◽  
The North ◽  
The North Sea

Results show that the weight of cod (Gadus morhua) stomach contents sampled in the North Sea varies significantly within 24 h. To determine whether feeding varied with time, over 1100 cod stomachs were collected around the clock between 7 and 18 May 1984 in two areas in the central North Sea thought to be representative for feeding studies. Here we investigate temporal feeding patterns based on the analysis of stomach-content data, using generalized additive models (GAMs). Results show significant variation of content weight and indicate morning and evening peaks. The relative peak importance differed between and within areas. We propose that differences are due to diet composition, namely, prey size and diel availability. Cod fed primarily on molluscs, mainly ocean quahog (Cyprina islandica), crustaceans, sandeels (Ammodytes spp.), haddock (Melanogrammus aeglefinus), herring (Clupea harrengus), and several flatfish species. In one area, the diet was dominated by fish, relatively large prey that perform diel vertical migration, and in the other by invertebrates, smaller prey that are digested faster. The diel pattern was more pronounced where invertebrate prey were dominant. Generalisation of results and implications for predation-mortality estimates based on data from the North Sea Stomach Content Database, used to implement multispecies models in the region, are discussed.

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 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 Using generalized additive models to analyze biomedical non-linear longitudinal data

2021 ◽  
Author(s):  
Ariel I. Mundo ◽  
John R. Tipton ◽  
Timothy J. Muldoon
Keyword(s):  
Longitudinal Data ◽  
Biomedical Research ◽  
Repeated Measures ◽  
Generalized Additive Models ◽  
Simulated Data ◽  
Additive Models ◽  
Biomedical Literature ◽  
Missing Observations ◽  
Non Linear ◽  
Biased Estimates

In biomedical research, the outcome of longitudinal studies has been traditionally analyzed using the repeated measures analysis of variance (rm-ANOVA) or more recently, linear mixed models (LMEMs). Although LMEMs are less restrictive than rm-ANOVA in terms of correlation and missing observations, both methodologies share an assumption of linearity in the measured response, which results in biased estimates and unreliable inference when they are used to analyze data where the trends are non-linear, which is a common occurrence in biomedical research. In contrast, generalized additive models (GAMs) relax the linearity assumption, and allow the data to determine the fit of the model while permitting missing observations and different correlation structures. Therefore, GAMs present an excellent choice to analyze non-linear longitudinal data in the context of biomedical research. This paper summarizes the limitations of rm-ANOVA and LMEMs and uses simulated data to visually show how both methods produce biased estimates when used on non-linear data. We also present the basic theory of GAMs, and using trends of oxygen saturation in tumors reported in the biomedical literature, we simulate example longitudinal data (2 treatment groups, 10 subjects per group, 6 repeated measures for each group) to demonstrate how these models can be computationally implemented. We show that GAMs are able to produce estimates that are consistent with the trends of biomedical non-linear data even in the case when missing observations exist (with 40% of the observations missing), allowing reliable inference from the data. To make this work reproducible, the code and data used in this paper are available at: https://github.com/aimundo/GAMs-biomedical-research.

scholarly journals Factors associated with the spatial heterogeneity of COVID-19 in France: a nationwide ecological study

2020 ◽  
Author(s):  
Jean Gaudart ◽  
Jordi Landier ◽  
laetitia huiart ◽  
Eva Legendre ◽  
Laurent Lehot ◽  
...  
Keyword(s):  
Spatial Heterogeneity ◽  
Ecological Study ◽  
Generalized Additive Models ◽  
Principal Component ◽  
Additive Models ◽  
Factors Associated ◽  
North East ◽  
The North ◽  
Incidence And Mortality ◽  
The Impact

Like in many countries and regions, spread of the COVID 19 pandemic has exhibited important spatial heterogeneity across France, one of the most affected countries so far. To better understand factors associated with incidence, mortality and lethality heterogeneity across the 96 administrative departments of metropolitan France, we thus conducted a geoepidemiological analysis based on publicly available data, using hierarchical ascendant classification (HAC) on principal component analysis (PCA) of multidimensional variables, and multivariate analyses with generalized additive models (GAM). Our results confirm a marked spatial heterogeneity of in-hospital COVID 19 incidence and mortality, following the North East / South West diffusion of the epidemic. The delay elapsed between the first COVID-19 associated death and the onset of the national lockdown on March 17th, 2020, appeared positively associated with in-hospital incidence, mortality and lethality. Mortality was also strongly associated with incidence. Mortality and lethality rates were significantly higher in departments with older population, but they were not significantly associated with the number of intensive-care beds available in 2018. We did not find any significant association between incidence, mortality or lethality rates and incidence of new chloroquine and hydroxychloroquine dispensations in pharmacies either, nor between COVID 19 incidence and climate, nor between economic indicators and in-hospital COVID 19 incidence or mortality. This ecological study highlights the impact of population age structure, epidemic spread and transmission mitigation policies in COVID-19 morbidity or mortality heterogeneity.

scholarly journals The impact of air pollution on COVID-19 pandemic varied within different cities in South America using different models

2021 ◽  
Author(s):  
Haining Huang ◽  
Congtian Lin ◽  
Xiaobo Liu ◽  
Liting Zhu ◽  
Ricardo David Avellán-Llaguno ◽  
...  
Keyword(s):  
Air Pollution ◽  
Linear Regression ◽  
South America ◽  
Multiple Linear Regression ◽  
Generalized Additive Models ◽  
Environmental Pollutants ◽  
Additive Models ◽  
Local Conditions ◽  
Multiple Regions ◽  
The Impact

Abstract There is a rising concern that air pollution plays an important role in the COVID-19 pandemic. However, the results weren’t consistent on the association between air pollution and the spread of COVID-19. In the study, air pollution data and the confirmed cases of COVID-19 were both gathered from five severe cities across three countries in South America. Daily real-time population regeneration (Rt) were calculated to assess the spread of COVID-19. Two frequently used model, generalized additive models (GAM) and multiple linear regression, were both used to explore the impact of environmental pollutants on the epidemic. Wide ranges of all the six air pollutants were detected across the five cities. Spearman's correlation analysis confirmed the positive correlation within six pollutants. Rt value showed a gradual decline in all the five cities. Further analysis showed that the association between air pollution and COVID-19 varied across five cities. Multiple linear regression and GAM did not give the same trend in a specific city. For example, in Sao Paulo, the GAM model shows that PM10 has a nonlinear negative correlation with Rt, while PM10 has no significant correlation in the multiple linear model. According to our research results, even for the same region, varied models gave inconsistent results. Moreover, in the case of multiple regions, current used models should be selected according to local conditions.

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