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Additive Models (additive + models)
Kinds of Additive Models Selected AbstractsSpatial utilisation of fast-ice by Weddell seals Leptonychotes weddelli during winterECOGRAPHY, Issue 3 2005Samantha Lake This study describes the distribution of Weddell seals Leptonychotes weddelli in winter (May,September 1999) at the Vestfold Hills, in Prydz Bay, East Antarctica. Specifically, we describe the spatial extent of haul-out sites in shore,fast sea-ice, commonly referred to as fast-ice. As winter progressed, and the fast-ice grew thick (ca 2 m), most of the inshore holes closed over, and the seals' distribution became restricted to ocean areas beyond land and islands. Using observations from the end of winter only, we fitted Generalised Additive Models (GAMs) to generate resource selection functions, which are models that yield values proportional to the probability of use. The models showed that seal distribution was defined mainly by distance to ice-edge and distance to land. Distance to ice-bergs was also selected for models of some regions. We present the results as maps of the fitted probability of seal presence, predicted by the binomial GAM for offshore regions, both with and without autocorrelation terms. The maps illustrate the expected distribution encompassing most of the observed distribution. On this basis, we hypothesise that propensity for the fast-ice to crack is the major determinant of Weddell seal distribution in winter. Proximity to open water and pack-ice habitats could also influence the distribution of haul-out sites in fast-ice areas. This is the first quantitative study of Weddell seal distribution in winter. Potential for regional variation is discussed. [source] Extracting long-term patterns of population changes from sporadic counts of migrant birdsENVIRONMETRICS, Issue 5 2010Joanna Mills Flemming Abstract Declines of many North American birds are of conservation concern. For almost 40 years, experienced birders have kept daily counts of migrant landbirds during visits to Seal and Brier Islands, both of which are off Nova Scotia's southern tip. Here we assess the utility of Generalized Additive Models (GAMs) to extract patterns of population change of a common migrant to Seal Island, the Ruby-crowned Kinglet, while controlling for other influences including season, weather and effort. We also demonstrate, using counts of the Kinglet from Brier Island as well as counts of another common migrant, the Yellow-rumped Warbler, how our GAM methods can combine data from different geographic areas or distinct species. Most existing analyses of similar long-term data sets have used linear models to estimate trends. Our results and comparisons suggest that GAMs are a powerful way of extracting more information from such data. Copyright © 2009 John Wiley & Sons, Ltd. [source] BIOMOD , optimizing predictions of species distributions and projecting potential future shifts under global changeGLOBAL CHANGE BIOLOGY, Issue 10 2003Wilfried ThuillerArticle first published online: 9 OCT 200 Abstract A new computation framework (BIOMOD: BIOdiversity MODelling) is presented, which aims to maximize the predictive accuracy of current species distributions and the reliability of future potential distributions using different types of statistical modelling methods. BIOMOD capitalizes on the different techniques used in static modelling to provide spatial predictions. It computes, for each species and in the same package, the four most widely used modelling techniques in species predictions, namely Generalized Linear Models (GLM), Generalized Additive Models (GAM), Classification and Regression Tree analysis (CART) and Artificial Neural Networks (ANN). BIOMOD was applied to 61 species of trees in Europe using climatic quantities as explanatory variables of current distributions. On average, all the different modelling methods yielded very good agreement between observed and predicted distributions. However, the relative performance of different techniques was idiosyncratic across species, suggesting that the most accurate model varies between species. The results of this evaluation also highlight that slight differences between current predictions from different modelling techniques are exacerbated in future projections. Therefore, it is difficult to assess the reliability of alternative projections without validation techniques or expert opinion. It is concluded that rather than using a single modelling technique to predict the distribution of several species, it would be more reliable to use a framework assessing different models for each species and selecting the most accurate one using both evaluation methods and expert knowledge. [source] Descriptive biogeography of Tomicus (Coleoptera: Scolytidae) species in SpainJOURNAL OF BIOGEOGRAPHY, Issue 12 2004D. Gallego Abstract Aim, location,Tomicus (Coleoptera, Scolytidae) species are some of the principal pests of Eurasian forest and are represented by three coexisting species in Spain, Tomicus piniperda (Linnaeus, 1758), Tomicus destruens (Wollaston, 1865) and Tomicus minor (Harting, 1834). The distribution of two taxa are unknown as they have until recently been considered separate species. Therefore, we model the potential distribution centres and establish the potential distribution limits of Tomicus species in Iberia. We also assess the effectiveness of different models by comparing predicted results with observed data. These results will have application in forest pest management. Methods, Molecular and morphological techniques were used to identify species from 254 specimens of 81 plots. For each plot, a Geographical Information System was used to extract a set of 14 environmental (one topographic, six climatic) and biotic variables (seven host tree distributions). General Additive Models and Ecological Niche Factor Analysis models are applied for modelling and predicting the potential distribution of the three especies of Tomicus. Results, The results of both modelling methodologies are in agreement. Tomicus destruens is the predominant species in Spain, living in low and hot areas. Tomicus piniperda occurs in lower frequency and prefers wet and cold areas of north-central Spain. We detected sympatric populations of T. destruens and T. piniperda in Northern coast of Spain, infesting mainly P. pinaster. Tomicus minor is the rarest species, and it occupies a fragmented distribution located in high and wet areas. The remarkable biotic variable is the distribution of P. sylvestris, incorporated into the models of T. destruens and T. piniperda. Main conclusions, These results indicate that in wet areas of north-central Spain where T. piniperda occurs (and possibly the high altitudes of the southern mountains), T. destruens has a climatic distribution limit. In the northern border of this area, both species overlap their distributions and some co-occurrences were detected. Tomicus minor potentially occurs in high and wet fragmented areas. [source] Generalized Additive Models and Lucilia sericata Growth: Assessing Confidence Intervals and Error Rates in Forensic Entomology,JOURNAL OF FORENSIC SCIENCES, Issue 4 2008Aaron M. Tarone Ph.D. Abstract:, Forensic entomologists use blow fly development to estimate a postmortem interval. Although accurate, fly age estimates can be imprecise for older developmental stages and no standard means of assigning confidence intervals exists. Presented here is a method for modeling growth of the forensically important blow fly Lucilia sericata, using generalized additive models (GAMs). Eighteen GAMs were created to predict the extent of juvenile fly development, encompassing developmental stage, length, weight, strain, and temperature data, collected from 2559 individuals. All measures were informative, explaining up to 92.6% of the deviance in the data, though strain and temperature exerted negligible influences. Predictions made with an independent data set allowed for a subsequent examination of error. Estimates using length and developmental stage were within 5% of true development percent during the feeding portion of the larval life cycle, while predictions for postfeeding third instars were less precise, but within expected error. [source] Generalized Additive Models: an Introduction with R by S. N. WoodJOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES A (STATISTICS IN SOCIETY), Issue 1 2007T. Verbeke No abstract is available for this article. [source] Handgrip strength in English schoolchildrenACTA PAEDIATRICA, Issue 7 2010DD Cohen Abstract Aims:, The aims of this study were to evaluate patterns of handgrip (HG) strength in relation to gender and age in English schoolchildren and to compare this with existing data and produce reference data for this population. Methods:, The HG of 7147 English schoolchildren (3773 boys and 3374 girls) aged 10,15.9 years was measured using a portable Takei handgrip dynamometer (Takei Scientific Instruments Co. Ltd, Tokyo, Japan). Centile data were produced using the Generalized Additive Models for Location, Scale and Shape. Z -scores were generated using existing data for European children. Age and gender interactions were analysed using analysis of covariance. Results:, In boys and girls, significant increases in HG were found between every age-group (p < 0.001). Boys were significantly stronger than girls at every age (p < 0.001) and the boys' age-related increase was significantly greater than the girls' (p < 0.001). Conclusion:, This study provides reference data for handgrip strength in English schoolchildren. Handgrip strength in English children is broadly similar to existing European data, after adjusting for mass and stature. These data could be used for clinical or athletic screening of low and high strength in this population. [source] The detailed forms of the LMC Cepheid PL and PLC relationsMONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY, Issue 4 2007C. Koen ABSTRACT Possible deviations from linearity of the Large Magellanic Cloud Cepheid period,luminosity (PL) and period,luminosity,colour (PLC) relations are investigated. Two data sets are studied, respectively from the Optical Gravitational Lensing Experiment (OGLE) and MACHO projects. A non-parametric test, based on linear regression residuals, suggests that neither PL relation is linear. If colour dependence is allowed for, then the MACHO PL relation is found to deviate more significantly from the linear, while the OGLE PL relation is consistent with linearity. These findings are confirmed by fitting ,Generalized Additive Models' (non-parametric regression functions) to the two data sets. Colour dependence is shown to be non-linear in both data sets, distinctly so in the case of the MACHO Cepheids. It is also shown that there is interaction between the period and the colour functions in the MACHO data. [source] Reference values for anaerobic performance and agility in ambulatory children and adolescents with cerebral palsyDEVELOPMENTAL MEDICINE & CHILD NEUROLOGY, Issue 10 2010OLAF VERSCHUREN Aim, The aim of this study was to provide reference values of anaerobic performance and agility in a group of children and adolescents with spastic cerebral palsy (CP). Method, A total of 300 children (184 males, 116 females) with spastic CP were recruited from 26 rehabilitation centres in six different countries. Of these, 215 were classified at GMFCS level I (mean age 11y 2mo, SD 3y, range 6,18y) and 85 were classified at GMFCS level II (mean age 11y; SD 3y 1mo, range 6,18y). The children performed the Muscle Power Sprint Test (MPST) and the 10×5m sprint test in a standardized manner. To establish reference values, reference curves were created using generalized additive models for location, scale, and shape. Results, Height-related reference curves were created based on performance on the two tests. Interpretation, This study provides height-related reference values for anaerobic performance and agility for children and adolescents with CP classified at GMFCS levels I and II. These curves are clinically relevant and provide a user-friendly method in the interpretation of anaerobic performance and agility for children with spastic CP. [source] Disentangling the relative effects of environmental versus human factors on the abundance of native and alien plant species in Mediterranean sandy shoresDIVERSITY AND DISTRIBUTIONS, Issue 4 2010Marta Carboni Abstract Aim, Mediterranean coastal sand dunes are characterized by both very stressful environmental conditions and intense human pressure. This work aims to separate the relative contributions of environmental and human factors in determining the presence/abundance of native and alien plant species in such an extreme environment at a regional scale. Location, 250 km of the Italian Tyrrhenian coast (Region Lazio). Methods, We analysed alien and native plant richness and fitted generalized additive models in a multimodel-inference framework with comprehensive randomizations to evaluate the relative contribution of environmental and human correlates in explaining the observed patterns. Results, Native and alien richness are positively correlated, but different variables influence their spatial patterns. For natives, human population density is the most important factor and is negatively related to richness. Numbers of natives are unexpectedly lower in areas with a high proportion of natural land cover (probably attributable to local farming practices) and, to a lesser degree, affected by the movement of the coastline. On the other hand, alien species richness is strongly related to climatic factors, and more aliens are found in sectors with high rainfall. Secondarily, alien introductions appear to be related to recent urban sprawl and associated gardening. Main conclusions, Well-adapted native species in a fragile equilibrium with their natural environment are extremely sensitive to human-driven modifications. On the contrary, for more generalist alien species, the availability of limited resources plays a predominant role. [source] Can distribution models help refine inventory-based estimates of conservation priority?DIVERSITY AND DISTRIBUTIONS, Issue 4 2010A case study in the Eastern Arc forests of Tanzania, Kenya Abstract Aim, Data shortages mean that conservation priorities can be highly sensitive to historical patterns of exploration. Here, we investigate the potential of regionally focussed species distribution models to elucidate fine-scale patterns of richness, rarity and endemism. Location, Eastern Arc Mountains, Tanzania and Kenya. Methods, Generalized additive models and land cover data are used to estimate the distributions of 452 forest plant taxa (trees, lianas, shrubs and herbs). Presence records from a newly compiled database are regressed against environmental variables in a stepwise multimodel. Estimates of occurrence in forest patches are collated across target groups and analysed alongside inventory-based estimates of conservation priority. Results, Predicted richness is higher than observed richness, with the biggest disparities in regions that have had the least research. North Pare and Nguu in particular are predicted to be more important than the inventory data suggest. Environmental conditions in parts of Nguru could support as many range-restricted and endemic taxa as Uluguru, although realized niches are subject to unknown colonization histories. Concentrations of rare plants are especially high in the Usambaras, a pattern mediated in models by moisture indices, whilst overall richness is better explained by temperature gradients. Tree data dominate the botanical inventory; we find that priorities based on other growth forms might favour the mountains in a different order. Main conclusions, Distribution models can provide conservation planning with high-resolution estimates of richness in well-researched areas, and predictive estimates of conservation importance elsewhere. Spatial and taxonomic biases in the data are essential considerations, as is the spatial scale used for models. We caution that predictive estimates are most uncertain for the species of highest conservation concern, and advocate using models and targeted field assessments iteratively to refine our understanding of which areas should be prioritised for conservation. [source] Where do Swainson's hawks winter?DIVERSITY AND DISTRIBUTIONS, Issue 5 2008Satellite images used to identify potential habitat ABSTRACT During recent years, predictive modelling techniques have been increasingly used to identify regional patterns of species spatial occurrence, to explore species,habitat relationships and to aid in biodiversity conservation. In the case of birds, predictive modelling has been mainly applied to the study of species with little variable interannual patterns of spatial occurrence (e.g. year-round resident species or migratory species in their breeding grounds showing territorial behaviour). We used predictive models to analyse the factors that determine broad-scale patterns of occurrence and abundance of wintering Swainson's hawks (Buteo swainsoni). This species has been the focus of field monitoring in its wintering ground in Argentina due to massive pesticide poisoning of thousands of individuals during the 1990s, but its unpredictable pattern of spatial distribution and the uncertainty about the current wintering area occupied by hawks led to discontinuing such field monitoring. Data on the presence and abundance of hawks were recorded in 30 × 30 km squares (n = 115) surveyed during three austral summers (2001,03). Sixteen land-use/land-cover, topography, and Normalized Difference Vegetation Index (NDVI) variables were used as predictors to build generalized additive models (GAMs). Both occurrence and abundance models showed a good predictive ability. Land use, altitude, and NDVI during spring previous to the arrival of hawks to wintering areas were good predictors of the distribution of Swainson's hawks in the Argentine pampas, but only land use and NDVI were entered into the model of abundance of the species in the region. The predictive cartography developed from the models allowed us to identify the current wintering area of Swainson's hawks in the Argentine pampas. The highest occurrence probability and relative abundances for the species were predicted for a broad area of south-eastern pampas that has been overlooked so far and where neither field research nor conservation efforts aiming to prevent massive mortalities has been established. [source] Predicting species distributions from museum and herbarium records using multiresponse models fitted with multivariate adaptive regression splinesDIVERSITY AND DISTRIBUTIONS, Issue 3 2007Jane Elith ABSTRACT Current circumstances , that the majority of species distribution records exist as presence-only data (e.g. from museums and herbaria), and that there is an established need for predictions of species distributions , mean that scientists and conservation managers seek to develop robust methods for using these data. Such methods must, in particular, accommodate the difficulties caused by lack of reliable information about sites where species are absent. Here we test two approaches for overcoming these difficulties, analysing a range of data sets using the technique of multivariate adaptive regression splines (MARS). MARS is closely related to regression techniques such as generalized additive models (GAMs) that are commonly and successfully used in modelling species distributions, but has particular advantages in its analytical speed and the ease of transfer of analysis results to other computational environments such as a Geographic Information System. MARS also has the advantage that it can model multiple responses, meaning that it can combine information from a set of species to determine the dominant environmental drivers of variation in species composition. We use data from 226 species from six regions of the world, and demonstrate the use of MARS for distribution modelling using presence-only data. We test whether (1) the type of data used to represent absence or background and (2) the signal from multiple species affect predictive performance, by evaluating predictions at completely independent sites where genuine presence,absence data were recorded. Models developed with absences inferred from the total set of presence-only sites for a biological group, and using simultaneous analysis of multiple species to inform the choice of predictor variables, performed better than models in which species were analysed singly, or in which pseudo-absences were drawn randomly from the study area. The methods are fast, relatively simple to understand, and useful for situations where data are limited. A tutorial is included. [source] Differences in spatial predictions among species distribution modeling methods vary with species traits and environmental predictorsECOGRAPHY, Issue 6 2009Alexandra D. Syphard Prediction maps produced by species distribution models (SDMs) influence decision-making in resource management or designation of land in conservation planning. Many studies have compared the prediction accuracy of different SDM modeling methods, but few have quantified the similarity among prediction maps. There has also been little systematic exploration of how the relative importance of different predictor variables varies among model types and affects map similarity. Our objective was to expand the evaluation of SDM performance for 45 plant species in southern California to better understand how map predictions vary among model types, and to explain what factors may affect spatial correspondence, including the selection and relative importance of different environmental variables. Four types of models were tested. Correlation among maps was highest between generalized linear models (GLMs) and generalized additive models (GAMs) and lowest between classification trees and GAMs or GLMs. Correlation between Random Forests (RFs) and GAMs was the same as between RFs and classification trees. Spatial correspondence among maps was influenced the most by model prediction accuracy (AUC) and species prevalence; map correspondence was highest when accuracy was high and prevalence was intermediate (average prevalence for all species was 0.124). Species functional type and the selection of climate variables also influenced map correspondence. For most (but not all) species, climate variables were more important than terrain or soil in predicting their distributions. Environmental variable selection varied according to modeling method, but the largest differences were between RFs and GLMs or GAMs. Although prediction accuracy was equal for GLMs, GAMs, and RFs, the differences in spatial predictions suggest that it may be important to evaluate the results of more than one model to estimate the range of spatial uncertainty before making planning decisions based on map outputs. This may be particularly important if models have low accuracy or if species prevalence is not intermediate. [source] Novel methods improve prediction of species' distributions from occurrence dataECOGRAPHY, Issue 2 2006Jane Elith Prediction of species' distributions is central to diverse applications in ecology, evolution and conservation science. There is increasing electronic access to vast sets of occurrence records in museums and herbaria, yet little effective guidance on how best to use this information in the context of numerous approaches for modelling distributions. To meet this need, we compared 16 modelling methods over 226 species from 6 regions of the world, creating the most comprehensive set of model comparisons to date. We used presence-only data to fit models, and independent presence-absence data to evaluate the predictions. Along with well-established modelling methods such as generalised additive models and GARP and BIOCLIM, we explored methods that either have been developed recently or have rarely been applied to modelling species' distributions. These include machine-learning methods and community models, both of which have features that may make them particularly well suited to noisy or sparse information, as is typical of species' occurrence data. Presence-only data were effective for modelling species' distributions for many species and regions. The novel methods consistently outperformed more established methods. The results of our analysis are promising for the use of data from museums and herbaria, especially as methods suited to the noise inherent in such data improve. [source] Ambient temperature and risk of death from accidental drug overdose in New York City, 1990,2006ADDICTION, Issue 6 2010Amy S. B. Bohnert ABSTRACT Background Mortality increases as ambient temperature increases. Because cocaine affects core body temperature, ambient temperature may play a role in cocaine-related mortality in particular. The present study examined the association between ambient temperature and fatal overdoses over time in New York City. Methods Mortality data were obtained from the Office of the Chief Medical Examiner for 1990 to 2006, and temperature data from the National Oceanic and Atmospheric Association. We used generalized additive models to test the relationship between weekly average temperatures and counts of accidental overdose deaths in New York City, controlling for year and average length of daylight hours. Results We found a significant relation between ambient temperature and accidental overdose fatality for all models where the overdoses were due in whole or in part to cocaine (all P < 0.05), but not for non-cocaine overdoses. Risk of accidental overdose deaths increased for weeks when the average temperature was above 24°Celsius. Conclusions These results suggest a strong relation between temperature and accidental overdose mortality that is driven by cocaine-related overdoses rising at temperatures above 24°Celsius; this is a substantially lower temperature than prior estimates. To put this into perspective, approximately 7 weeks a year between 1990 and 2006 had an average weekly temperature of 24 or above in New York City. Heat-related mortality presents a considerable public health concern, and cocaine users constitute a high-risk group. [source] Distribution and richness of diadromous fish assemblages in Western Europe: large-scale explanatory factorsECOLOGY OF FRESHWATER FISH, Issue 2 2007M. Béguer Abstract,,, The aim of this study was to analyse the distribution of 14 diadromous fish at the beginning of the 20th century in western Europe. This study was conducted on a set of 41 water basins. Five environmental variables were selected and we used generalised additive models for explaining the presence,absence of species. The richest basins were located in the centre of the study area. Six main assemblage types were identified along a latitudinal gradient; they were constituted of a common species basis but differed by the absence or presence of other species. The 10 single species models produced have moderate to very good discrimination level and they can correctly predict both absence and presence. Temperature is included in all but one model, response curves vary according to the species; surface area is included in six models. [source] Nonparametric harmonic regression for estuarine water quality dataENVIRONMETRICS, Issue 6 2010Melanie A. Autin Abstract Periodicity is omnipresent in environmental time series data. For modeling estuarine water quality variables, harmonic regression analysis has long been the standard for dealing with periodicity. Generalized additive models (GAMs) allow more flexibility in the response function. They permit parametric, semiparametric, and nonparametric regression functions of the predictor variables. We compare harmonic regression, GAMs with cubic regression splines, and GAMs with cyclic regression splines in simulations and using water quality data collected from the National Estuarine Reasearch Reserve System (NERRS). While the classical harmonic regression model works well for clean, near-sinusoidal data, the GAMs are competitive and are very promising for more complex data. The generalized additive models are also more adaptive and require less-intervention. Copyright © 2009 John Wiley & Sons, Ltd. [source] Smoothing splines for trend estimation and prediction in time seriesENVIRONMETRICS, Issue 3 2009Richard Morton Abstract We consider the use of generalized additive models with correlated errors for analysing trends in time series. The trend is represented as a smoothing spline so that it can be extrapolated. A method is proposed for choosing the smoothing parameter. It is based on the ability to predict a short term into the future. The choice not only addresses the purpose in hand, but also performs very well, and avoids the tendency to under-smooth or to interpolate the data that can occur with other data-driven methods used to choose the smoothing parameter. The method is applied to data from a chemical process and to stream salinity measurements. Copyright © 2008 John Wiley & Sons, Ltd. [source] Modelling the effects of air pollution on health using Bayesian dynamic generalised linear modelsENVIRONMETRICS, Issue 8 2008Duncan Lee Abstract The relationship between short-term exposure to air pollution and mortality or morbidity has been the subject of much recent research, in which the standard method of analysis uses Poisson linear or additive models. In this paper, we use a Bayesian dynamic generalised linear model (DGLM) to estimate this relationship, which allows the standard linear or additive model to be extended in two ways: (i) the long-term trend and temporal correlation present in the health data can be modelled by an autoregressive process rather than a smooth function of calendar time; (ii) the effects of air pollution are allowed to evolve over time. The efficacy of these two extensions are investigated by applying a series of dynamic and non-dynamic models to air pollution and mortality data from Greater London. A Bayesian approach is taken throughout, and a Markov chain monte carlo simulation algorithm is presented for inference. An alternative likelihood based analysis is also presented, in order to allow a direct comparison with the only previous analysis of air pollution and health data using a DGLM. Copyright © 2008 John Wiley & Sons, Ltd. [source] Partial regression method to fit a generalized additive modelENVIRONMETRICS, Issue 6 2007Shui He Abstract Generalized additive models (GAMs) have been used as a standard analytic tool in studies of air pollution and health during the last decade. The air pollution measure is usually assumed to be linearly related to the health indicator and the effects of other covariates are modeled through smooth functions. A major statistical concern is the appropriateness of fitting GAMs in the presence of concurvity. Generalized linear models (GLM) with natural cubic splines as smoothers (GLM,+,NS) have been shown to perform better than GAM with smoothing splines (GAM,+,S), in regard to the bias and variance estimates using standard model fitting methods. As nonparametric smoothers are attractive for their flexibility and easy implementation, search for alternative methods to fit GAM,+,S is warranted. In this article, we propose a method using partial residuals to fit GAM,+,S and call it the "partial regression" method. Simulation results indicate better performance of the proposed method compared to gam.exact function in S-plus, the standard tool in air pollution studies, in regard to bias and variance estimates. In addition, the proposed method is less sensitive to the degree of smoothing and accommodates asymmetric smoothers. Copyright © 2007 John Wiley & Sons, Ltd. [source] Statistical analysis of temperature impact on daily hospital admissions: analysis of data from Udine, ItalyENVIRONMETRICS, Issue 1 2006Francesco Pauli Abstract This article is devoted to the analysis of the relationship between the health status of an urban population and meteorological variables. The analysis considers daily number of hospital admissions, not due to surgery, regarding the population resident in the Municipality of Udine, aged 75 and over. Hourly records on temperature, humidity, rain, atmospheric pressure, solar radiation, wind velocity and direction recorded at an observation site located near the center of Udine are considered. The study also considers hourly measures of pollutant concentrations collected by six monitoring stations. All data are relative to the summer periods of years 1995,2003. Generalized additive models (GAM) are used in which the response variable is the number of hospital admissions and is assumed to be distributed as a Poisson whose rate varies as a possibly non-linear function of the meteorological variables and variables allowing for calendar effects and pollutant concentrations. The subsequent part of the analysis explores the distribution of temperature conditional on the number of daily admissions through quantile regression. A non-linear (N-shaped) relationship between hospital admissions and temperature is estimated; temperature at 07:00 is selected as a covariate, revealing that nighttime temperature is more relevant than daytime. The quantile regression analysis points out, as expected, that the distribution of temperature on days with more admissions has higher q -quantiles with q near unity, while a clear-cut conclusion is not reached for q quantiles with q near 0. Copyright © 2005 John Wiley & Sons, Ltd. [source] INTERGENOMIC EPISTASIS AND COEVOLUTIONARY CONSTRAINT IN PLANTS AND RHIZOBIAEVOLUTION, Issue 5 2010Katy D. Heath Studying how the fitness benefits of mutualism differ among a wide range of partner genotypes, and at multiple spatial scales, can shed light on the processes that maintain mutualism and structure coevolutionary interactions. Using legumes and rhizobia from three natural populations, I studied the symbiotic fitness benefits for both partners in 108 plant maternal family by rhizobium strain combinations. Genotype-by-genotype (G × G) interactions among local genotypes and among partner populations determined, in part, the benefits of mutualism for both partners; for example, the fitness effects of particular rhizobium strains ranged from uncooperative to mutualistic depending on the plant family. Correlations between plant and rhizobium fitness benefits suggest a trade off, and therefore a potential conflict, between the interests of the two partners. These results suggest that legume,rhizobium mutualisms are dynamic at multiple spatial scales, and that strictly additive models of mutualism benefits may ignore dynamics potentially important to both the maintenance of genetic variation and the generation of geographic patterns in coevolutionary interactions. [source] Spatial and temporal patterns of walleye pollock (Theragra chalcogramma) spawning in the eastern Bering Sea inferred from egg and larval distributionsFISHERIES OCEANOGRAPHY, Issue 2 2010NATHAN M. BACHELER Abstract Walleye pollock Theragra chalcogramma (pollock hereafter) is a key ecological and economic species in the eastern Bering Sea, yet detailed synthesis of the spatial and temporal patterns of pollock ichthyoplankton in this important region is lacking. This knowledge gap is particularly severe considering that egg and larval distribution are essential to reconstructing spawning locations and early life stages drift pathways. We used 19 yr of ichthyoplankton collections to determine the spatial and temporal patterns of egg and larval distribution. Generalized additive models (GAMs) identified two primary temporal pulses of pollock eggs, the first occurring from 20 February to 31 March and the second from 20 April to 20 May; larvae showed similar, but slightly lagged, pulses. Based on generalized cross-validation and information theory, a GAM model that allowed for different seasonal patterns in egg density within three unique areas outperformed a GAM that assumed a single fixed seasonal pattern across the entire eastern Bering Sea. This ,area-dependent' GAM predicted the highest densities of eggs (i.e., potential spawning locations) in three major areas of the eastern Bering Sea: near Bogoslof Island (February,April), north of Unimak Island and the Alaska Peninsula (March,April), and around the Pribilof Islands (April,August). Unique temporal patterns of egg density were observed for each area, suggesting that pollock spawning may be more spatially and temporally complex than previously assumed. Moreover, this work provides a valuable baseline of pollock spawning to which future changes, such as those resulting from climate variability, may be compared. [source] Spatio-temporal distribution of albacore (Thunnus alalunga) catches in the northeastern Atlantic: relationship with the thermal environmentFISHERIES OCEANOGRAPHY, Issue 2 2010Y. SAGARMINAGA Abstract When the spring seasonal warming starts, North Atlantic albacore (Thunnus alalunga) juveniles and pre-adults perform a trophic migration to the northeastern Atlantic, to the Bay of Biscay and to the southeast of Ireland. During this migration, they are exploited by Spanish trolling and baitboat fleets. The present study analyzes the relationship between the albacore spatio-temporal distribution and the thermal environment. For this approach, several analyses have been performed on a database including fishing logbooks and sea surface temperature (SST) images, covering the period between 1987 and 2003. SST values and the SST gradients at the catch locations have been statistically compared to broader surrounding areas to test whether the thermal environment determines the spatial distribution of albacore. General additive models (GAM) have been used also to evaluate the relative importance of environmental variables and fleet behaviour. The results obtained show that, although juvenile albacore catch locations are affected by fleet dynamics, there is a close spatial and temporal relationship with the seasonal evolution of a statistically significant preferential SST window (16,18°C). However, differences have been identified between the relationship of albacore with SST within the Bay of Biscay in July and August (higher temperature). Such differences are found also in the spatial distribution of the catch locations; these reflect clearly the presence of two groups, differentiated after the third week of the fishing campaign at the end of June. The analysis undertaken relating the distribution of North Atlantic albacore juveniles with thermal gradients did not provide any evidence of a relationship between these catch locations and the nearby occurrence of thermal gradients. [source] Environmental and spatial effects on the distribution of blue marlin (Makaira nigricans) as inferred from data for longline fisheries in the Pacific OceanFISHERIES OCEANOGRAPHY, Issue 6 2008NAN-JAY SU Abstract Blue marlin is distributed throughout tropical and temperate waters in the Pacific Ocean. However, the preference of this species for particular habitats may impact its vulnerability to being caught. The relationship between spatio-temporal patterns of blue marlin abundance and environmental factors is examined using generalized additive models fitted to catch and effort data from longline fisheries. The presence of blue marlin, and the catch rate given presence, are modeled separately. Latitude, longitude, and sea-surface temperature explain the greatest proportion of the deviance. Spatial distributions of relative density of blue marlin, based on combining the probability of presence and relative density given presence, indicate that there is seasonal variation in the distribution of blue marlin, and that the highest densities occur in the tropics. Seasonal patterns in the relative density of blue marlin appear to be related to shifts in SST. The distribution and relative abundance of blue marlin are sufficiently heterogeneous in space and time that the results of analyses of catch and effort data to identify ,hotspots' could be used as the basis for time-area management to reduce the amount of blue marlin bycaught in longline fisheries. [source] Bigeye tuna (Thunnus obesus) vertical movements in the Azores Islands determined with pop-up satellite archival tagsFISHERIES OCEANOGRAPHY, Issue 2 2008H. ARRIZABALAGA Abstract Movement patterns of 17 bigeye tuna (Thunnus obesus) near the Azores Islands were analyzed between April and May 2001 and 2002 using pop-up satellite archival tags. Despite short attachment durations (1 to 21 days, 8.2 days on average), their vertical movements revealed much shallower distribution of bigeye tuna in comparison with previous studies in the tropical Pacific and tropical Atlantic. Depth and temperature histograms were unimodal, although overall depth distribution during the day was deeper than during the night due to daily incursions in deeper waters. Although generalized additive models showed significant non-linear relationships with weight of the fish and sea level anomaly (as a proxy for variability of thermocline depth), the effect of these variables on bigeye depth appeared minor, suggesting that vertical movements of bigeye in the Azores during the spring migration may be influenced by food availability in upper water layers. [source] Habitat associations of Atlantic herring in the Shetland area: influence of spatial scale and geographic segmentationFISHERIES OCEANOGRAPHY, Issue 3 2001CHRISTOS D. Maravelias This study considers the habitat associations of a pelagic species with a range of biotic and abiotic factors at three different spatial scales. Generalized additive models (GAM) are used to analyse trends in the distributional abundance of Atlantic herring (Clupea harengus) in relation to thermocline and water depth, seabed roughness and hardness, sea surface salinity and temperature, zooplankton abundance and spatial location. Two geographical segments of the population, those east and west of the Shetland Islands (northern North Sea, ICES Div IVa), are examined. The differences in the ecological preferences of the species in these two distinct geographical areas are elucidated and the degree that these environmental relationships might be modulated by the change of support of the data is also considered. Part of the observed variability of the pre-spawning distribution of herring was explained by different parameters in these two regions. Notwithstanding this, key determinants of the species' spatial aggregation in both areas were zooplankton abundance and the nature of the seabed substrate. The relative importance of the variables examined did not change significantly at different spatial scales of the observation window. The diverse significance of various environmental factors on herring distribution was attributed mainly to the interaction of species' dynamics with the different characteristics of the ecosystem, east and west of the Shetland Islands. Results suggest that the current 2.5 nautical miles as elementary sampling distance unit (ESDU) is a reasonable sampling scheme that combines the need to reduce the data volume while maintaining spatial resolution to distinguish the species/environment relationships. [source] Patterns in the spawning of cod (Gadus morhua L.), sole (Solea solea L.) and plaice (Pleuronectes platessa L.) in the Irish Sea as determined by generalized additive modellingFISHERIES OCEANOGRAPHY, Issue 1 2000Eleven ichthyoplankton cruises were undertaken covering most of the Irish Sea during the period February to June, 1995. To identify spawning localities and investigate temporal trends in egg production, the data on stage 1 A egg distributions of cod (Gadus morhua), plaice (Pleuronectes platessa) and sole (Solea solea) have been modelled using generalized additive models (GAMs). A two-stage approach was adopted where presence/absence was firstly modelled as a binary process and a GAM surface subsequently fitted to egg production (conditional on presence). We demonstrate that this approach can be used to model egg production both in space and in time. The spawning sites for cod, plaice and sole in the Irish Sea were defined in terms of the probability of egg occurrence. For cod, we demonstrate that by integrating under predicted egg production surfaces, a cumulative production curve can be generated and used to define percentiles of production and thus delimit the extent of the spawning season. However, for plaice and sole, the surveys did not fully cover the spawning season and the limitations that this imposes on GAM modelling of these data are discussed. Comparison of the spawning sites in 1995 with historical data suggests that the locations of cod, plaice and sole egg production in the Irish Sea have probably remained relatively constant over the last 30 years. [source] Assigning macroinvertebrate tolerance classifications using generalised additive modelsFRESHWATER BIOLOGY, Issue 5 2004Lester L. Yuan Summary 1. Macroinvertebrates are frequently classified in terms of their tolerance to human disturbance and pollution. These tolerance values have been used effectively to assess the biological condition of running waters. 2. Generalised additive models were used to associate the presence and absence of different macroinvertebrate genera with different environmental gradients. The model results were then used to classify each genera as sensitive, intermediately tolerant or tolerant to different stressor gradients as quantified by total phosphorus concentration, sulphate ion concentration, qualitative habitat score and stream pH. The analytical approach provided a means of estimating stressor-specific tolerance classifications while controlling for covarying, natural environmental gradients. 3. Computed tolerance classification generally conformed with expectations and provided some capacity for distinguishing between different stressors in test data. [source] | |||||||||||||||||||||||||||||||||||||