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El Nino effects in the Palmyra Atoll region:: oceanographic changes and bigeye tuna (Thunnus obesus) catch rate variability

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FISHERIES OCEANOGRAPHY 2006年第6期15卷 477-489页

作者： Howell, Evan A. Kobayashi, Donald R. NOAA Ecosyst & Oceanog Div Pacific Isl Fisheries Sci Ctr NMFS Honolulu HI 96822 USA Univ Technol Sydney Dept Environm Sci Broadway NSW Australia

A generalized additive model (GAM) was constructed to separate and quantify the effects of fishery-based (operational) and oceanographic parameters on the bigeye tuna (Thunnus obesus) catch rates at Palmyra Atoll in the central Tropical Pacific. Bigeye catch, the number of hooks per set, and set location from 4884 longline sets spanning January 1994 to December 2003 were used with a temporally corresponding El Nino-Southern Oscillation (ENSO) indicator built from sea surface height (SSH) data. Observations of environmental data combined with the results from the GAM indicated that there is an increase in bigeye catch rates corresponding to an increase in eastward advection during the winter months of El Nino events. A seasonal pattern with higher bigeye catch rates from December to April and a spatial pattern with higher rates to the northeast and northwest of the atoll were observed during this study period. It is hypothesized that the combination of the eastward advection of the warm pool coupled with vertical changes in temperature during the winter months of El Nino events increases the availability of bigeye tuna in this region. This increase in availability may be due to a change in exploitable population size, location, or both.

关键词： bigeye tuna El Nino empirical orthogonal function fishery oceanography generalized additive model Palmyra Atoll

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Multi-scale effect of landscape processes and habitat quality on newt abundance:: Implications for conservation

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BIOLOGICAL CONSERVATION 2006年第4期130卷 495-504页

作者： Denoel, Mathieu Lehmann, Anthony Univ Liege Dept Environm Sci Behav Biol Unit B-4020 Liege Belgium Swiss Ctr Faunal Cartog CH-2000 Neuchatel Switzerland

Recent studies in population dynamics suggest that landscape processes and habitat quality. act at different scales on population abundances, but very few have modelled their simultaneous effects. However, at a time of large declines in natural populations, it is essential to understand such multivariate components. We tested the hypothesis that natural populations of palmate newts (Triturus helveticus) are affected on three scales: breeding patch (pond), habitat complementation (terrestrial cover), and metapopulation. structure (density of ponds, surrounding populations). We conducted our survey in 130 ponds from southern France (Larzac) and analysed data with generalized additive models (GAM). Two main novel results emerge from these models: (1) the three landscape scales have significant effects on newt abundance, with more newts in deep, vegetated ponds, devoid of fish and surrounded by wooded areas and inhabited ponds;(2) the quality of the surrounding breeding patches is of primary importance in determining the abundance at core sites in a complex way: high abundances are associated positively with high densities of inhabited ponds, but negatively with the number of surrounding ponds. Deforestation, invasive species and abandonment of ponds all have negative impacts on the persistence of palmate newt populations. Future studies should encompass landscapes at different scales and incorporate the habitat quality in surrounding sites to better understand population dynamics and. provide adequate conservation measures. (c) 2006 Elsevier Ltd. All rights reserved.

关键词： amphibian decline fish introduction generalized additive model habitat complementation metapopulation structure

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A comparative study of the use of GAM and GLM in air pollution research

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ENVIRONMETRICS 2006年第1期17卷 81-93页

作者： He, S Mazumdar, S Arena, VC Univ Pittsburgh Grad Sch Publ Hlth Dept Biostat Pittsburgh PA 15260 USA Sanofi Aventis Malvern PA 19355 USA

generalized additive models (GAMs) have been used as a standard analytic tool in time-series Studies of air pollution and health during the last decade. A major statistical concern came into view recently about the appropriateness of the use of GAMs in the presence of concurvity, which is likely to be present in the data of all air pollution studies. It has been shown that the standard statistical software, such as S-plus with its gam function, can seriously overestimate the GAM model parameters and underestimate their variances in the presence of concurvity. A recently developed S-plus package, ***, allows a robust assessment of parameter uncertainties for only symmetric smoothers. To date, the impact of concurvity on the parameter estimates has not been investigated fully and is limited to only high values. In this article, we have extended the scope of this investigation by encompassing a wide range of the degrees of concurvity and an alternative class of models. We have performed a simulation study where generalized linear models with natural cubic splines as the smoother function (GLM+NS) are compared systematically with GAMs with smoothing splines as the smoother function (GAM+S) in the presence of varying degrees of concurvity. We believe that, as GLM+NS provides a straightforward parametric modeling approach, its comparison with the flexible non-parametric approach, GAM+S, is well warranted. Our results indicate that GLM+NS performs better than GAM+S in regard to bias and variance estimates when medium-to-high concurvity exists in the data. Results from our illustrative example With NMMAPS Pittsburgh data are consistent with our findings from the simulations. We conclude that non-parametric smoother based models should be used for exploratory analysis for their flexibility and easy use and for Suggesting parametric smoother based models when concurvity exists in the time series data. Copyright (c) 2005 John Wiley & Sons, Ltd.

关键词： concurvity generalized additive model generalized linear model air pollution time series

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Associations between fine particles and respiratory mortality in Seoul, South Korea

Associations between fine particles and respiratory mortalit...

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Air and Waste Management Association's - 99th Annual Conference and Exhibition 2006

作者： Kang, Choong-Min Lee, Hak Sung Park, Sung Kyun Koutrakis, Petros Exposure Epidemiology and Risk Program Harvard School of Public Health 401 Park Dr. Boston MA 02215 United States Department of Environmental Civil and Information System Seowon University Chongju 361-742 Korea Republic of

Fine particles (PM2.5, particles 2.5 concentration estimated from a prediction regression model in Seoul, South Korea, during the period of 1996-2002 was studied. The association with PM2.5 concentration was also stra... 详细信息

ISBN: (纸本)0923204806

Fine particles (PM_2.5, particles < 2.5-μm aerodynamic diameter) are typically generated from anthropogenic sources, e.g., vehicle exhausts, manufacturing, power generation, etc., and usually consist of large fractions of (frequently acidic and toxic) sulfate and nitrate. The association of total respiratory and cause-specific mortality with the PM_2.5 concentration estimated from a prediction regression model in Seoul, South Korea, during the period of 1996-2002 was studied. The association with PM_2.5 concentration was also stratified by age, sex, and season. A stronger association in the male population was observed, relative to that in the female population. Total respiratory mortality increased by 6.60% in men, while the mortality in women increased by 4.91%. The season with highest increase in total respiratory mortality for a 10-μg/cu m increase in 5-day moving average of PM_2.5 was winter, followed by spring, summer, and fall. This is an abstract of a paper presented at the AWMA's 99th Annual Conference and Exhibition (New Orleans, LA 6/20-23/2006).

关键词： Fine particles generalized additive model Prediction model Respiratory mortality Season

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generalized additive modelling of sample extremes

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JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES C-APPLIED STATISTICS 2005年第1期54卷 207-222页

作者： Chavez-Demoulin, V Davison, AC Swiss Fed Inst Technol Sch Basic Sci Inst Math CH-1015 Lausanne Switzerland ETH Zurich Switzerland

We describe smooth non-stationary generalized additive modelling for sample extremes, in which spline smoothers are incorporated into models for exceedances over high thresholds. Fitting is by maximum penalized likelihood estimation, with uncertainty assessed by using differences of deviances and bootstrap simulation. The approach is illustrated by using data on extreme winter temperatures in the Swiss Alps, analysis of which shows strong influence of the north Atlantic oscillation. Benefits of the new approach are flexible and appropriate modelling of extremes, more realistic assessment of estimation uncertainty and the accommodation of complex dependence patterns.

关键词： bootstrap generalized additive model generalized pareto distribution natural cubic spline North Atlantic oscillation parameter orthogonality peaks over threshold penalized likelihood statistics of extremes temperature data

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Efficient estimation of the risk of a disease by quantile-categories of a key predictor variable using generalized additive models

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STATISTICS IN MEDICINE 2005年第4期24卷 623-645页

作者： Borkowf, CB Albert, PS Ctr Dis Control & Prevent Natl Ctr Infect Dis Div Viral & Rickettsial Dis Influenza BranchEpidemiol Sect Atlanta GA 30333 USA NCI Biomet Res Branch Div Canc Treatment & Diag Bethesda MD 20892 USA

Suppose that one wishes to make inference to the risk of a disease by the population quartile-categories of a key continuous predictor variable. When one collects data on a prospective cohort, the standard method is simply to categorize the key predictor variable by the empirical quartiles. One may then include indicator variables for these empirical quartile-categories as predictors, along with other covariates, in a generalized linear model (GLM), with the observed health status of each subject as the response. The standard GLM method, however, is relatively inefficient, because it treats all observations that fall in the same quartile-category of the predictor variable identically, regardless of whether they lie in the centre or near the boundaries of that category. Alternatively, one may include the key predictor variable, along with other covariates, in a generalized additive model (GAM), again with the observed health status of each subject as the response. The alternative GAM method non-parametrically estimates the functional relationship between the key predictor variable and the response. One may then compute statistics of interest, such as proportions and odds ratios. from the fitted GAM equation using the empirical quartile-categories. Simulations show that both the GLM and GAM methods are nearly unbiased, but the latter method produces smaller variances and narrower bootstrap confidence intervals. An example from nutritional epidemiology illustrates the use of these methods. Published in 2005 by John Wiley Sons, Ltd.

关键词： cancer bootstrap epidemiology generalized additive model permutation test quantile-category

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Non-parametric estimation of the odds ratios for continuous exposures using generalized additive models with an unknown link function

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STATISTICS IN MEDICINE 2005年第8期24卷 1169-1184页

作者： Cadarso-Suárez, C Roca-Pardiñas, J Figueiras, A González-Manteiga, W Univ Santiago de Compostela Biostat Unit Dept Stat & Operat Res Santiago De Compostela Spain Univ Vigo Dept Stat & Operat Res Vigo Spain Univ Santiago de Compostela Dept Prevent Med & Publ Hlth Santiago De Compostela Spain

The generalized additive, model (GAM) is a powerful and widely used tool that allows researchers to fit, non-parametrically, the effect of continuous predictors on a transformation of the mean response variable. Such a transformation is given by a so-called link function, and in GAMs this link function is assumed to be known. Nevertheless, if an incorrect choice is made for the link, the resulting GAM is misspecified and the results obtained may be misleading. In this paper, we propose a modified version of the local scoring algorithm that allows for the non-parametric estimation of the link function, by using local linear kernel smoothers. To better understand the effect that each covariate produces on the outcome results are expressed in terms of the non-parametric odds ratio (OR) curves. Bootstrap techniques were used to correct the bias in the OR estimation and to construct pointwise confidence intervals. A simulation study was carried out to assess the behaviour of the resulting estimates. The proposed methodology was illustrated using data from the AIDS Register of Galicia (NW Spain), with a view to assessing the effect of the CD4 lymphocyte count on the probability of being AIDS-diagnosed via Tuberculosis (TB). This application shows how the link's flexibility makes it possible to obtain OR curve estimates that are less sensitive to the presence of outliers and unusual values that are often present in the extremes of the covariate distributions. Copyright (c) 2004 John Wiley & Sons, Ltd.

关键词： generalized additive model local scoring link function local linear kernel smoothers bootstrap AIDS

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Comparison of logbook reports of incidental blue shark catch rates by Hawaii-based longline vessels to fishery observer data by application of a generalized additive model

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FISHERIES RESEARCH 2002年第1期58卷 79-94页

作者： Walsh, WA Kleiber, P McCracken, M Univ Hawaii Joint Inst Marine & Atmospher Res Honolulu HI 96822 USA

A generalized additive model (GAM) of blue shark, Prionace glauca, catch rates (catch per set) was fitted to data gathered by National Marine Fisheries Service (NMFS) observers stationed aboard Hawaii-based commercial longline vessels (N = 2010 longline sets) from March 1994 to December 1997. Its coefficients were then applied to the values of predictor variables, which were also contained in logbook records that described the remainder of fishery-wide effort during the study period (N = 41319 longline sets). The objective was to determine whether predictions generated by such a GAM could serve in lieu of observers on the large fraction of longline trips that do not carry an observer (approximately 95%). After deleting data considered false or inaccurate, much of which was associated with a small number of vessels, the relationship between catch rates as reported in logbooks and GAM predictions was expressed by log(e) (Y + 1) = 0.7952 log(e) (X + 1) - 0.0586 where Y is the catch rate (i.e., the number of blue shark caught per set) and X the GAM predictions (R-2 = 0.307, N = 40 243). Patterns of correspondence between logbook trends and GAM predictions were further refined by plotting the trends according to the type of fishing effort (e.g., tuna- or swordfish-directed). The highest mean catch rates reported in logbooks, the highest mean GAM predictions, and the greatest differences between the two occurred consistently in mid-year on swordfish trips. In contrast, mean values from logbooks and mean GAM predictions were closest for tuna-directed effort, but this reflected an order of magnitude reduction in the scale of catch rates rather than closely similar trends. A bootstrapping algorithm developed for the GAM yielded an estimate of 23.9% under-reporting for the study period, with approximate 95% prediction limits of 15.4-28.9%. We conclude that prediction with a GAM fitted to fishery observer data is a useful monitoring technique for the Hawaii-based commercial lo

关键词： blue shark generalized additive model incidental catch rates fishery observer data logbook reliability

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Evaluation of spatial predictions of site index obtained by parametric and nonparametric methods - A case study of lodgepole pine productivity

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FOREST ECOLOGY AND MANAGEMENT 2005年第1-3期214卷 201-211页

作者： Wang, YH Raulier, F Ung, CH Canadian Forest Serv No Forestry Ctr Edmonton AB T6H 3S5 Canada Univ Laval Fac Foresterie & Geomat Ste Foy PQ G1K 7P4 Canada Canadian Forest Serv Laurentian Forestry Ctr Ste Foy PQ G1V 4C7 Canada

We demonstrate the potential of using least-squares regression, generalized additive model, tree-based model, and neural network model on layers of environmental data grids for mapping site index in a case study. Grids of numerical environmental variables represented layered data, and a sparse site index plot network was located in the grids. Site index data were based on stem analysis (observed height at the index age of 50 years) of 431 lodgepole pine trees in 88 sample plots. The plots were established in a 17,460 km(2) boreal mixedwood forest of Alberta, Canada dominated by mature and over-mature stands. The generalized additive model presented a better fit and better adaptability to extreme data (i.e., mature stands) than the least squares nonlinear and other nonparametric techniques, such as the tree-based model and neural network model. Among the four models tested, nonlinear regression is of the data modeling culture, which assumes a stochastic data to relate productivity to environmental variables, and such models are optimized for estimation. Other three models belong to the algorithm modeling culture, which treat the relationship between productivity and independent variables as an unknown black box and try to find a function between them;therefore, these models are more suitable for prediction purpose. Implications for biophysical site index modelling with extreme data are discussed. Crown Copyright (c) 2005 Published by Elsevier B.V. All rights reserved.

关键词： generalized additive model tree-based model neural network model lodgepole pine nonlinear regression mature stands over-mature stand

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Spatial assessment of model errors from four regression techniques

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FOREST SCIENCE 2005年第4期51卷 334-346页

作者： Zhang, LJ Gove, JH SUNY Coll Environm Sci & Forestry Fac Forest & Nat Resources Management Syracuse NY 13210 USA USDA Forest Serv NE Res Stn Durham NH 03824 USA

Forest modelers have attempted to account for the spatial autocorrelations among trees in growth and yield models by applying alternative regression techniques such as linear mixed models (LMM), generalized additive models (GAM), and geographically weighted regression (GWR). However, the model errors are commonly assessed using average errors across the entire study area and across tree size classes. Little attention has been paid to the spatial heterogeneity of model performance. In this study, we used local Moran coefficients to investigate the spatial distributions of the model errors from the four regression models. The results indicated that GAM improved model-fitting to the data and provided better predictions for the response variable. However, it is nonspatial in nature and, consequently, generated spatial distributions for the model errors similar to the ones from ordinary least-squares (OLS). Furthermore, OLS and GAM yielded more clusters of similar (either positive or negative) model errors, indicating that trees in some subareas were either all underestimated or all overestimated for the response variable. In contrast, LMM was able to model the spatial covariance structures in the data and obtain more accurate predictions by accounting for the effects of spatial autocorrelations through the empirical best linear unbiased predictors. GWR is a varying-coefficient modeling technique. It estimated the model coefficients locally at each tree in the example plot and resulted in more accurate predictions for the response variable. Moreover, the spatial distributions of the model errors from LMM and GWR were more desirable, with fewer clusters of dissimilar model errors than the ones derived from OLS and GAM.

关键词： spatial autocorrelation and heterogeneity local indicator of spatial autocorrelation ordinary least-squares linear mixed model generalized additive model geographically weighted regression

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