Home About us Contact
|
Home About us Contact | ||
|
|
||
Autocorrelation
Kinds of Autocorrelation Terms modified by Autocorrelation Selected AbstractsSTOCK PRICE VOLATILITY, NEGATIVE AUTOCORRELATION AND THE CONSUMPTION,WEALTH RATIO: THE CASE OF CONSTANT FUNDAMENTALSPACIFIC ECONOMIC REVIEW, Issue 2 2010Charles Ka Yui Leung Based on infinite horizon models, previous theoretical works show that the empirical stock price movement is not justified by the changes in dividends. The present paper provides a simple overlapping generations model with constant fundamentals in which the stock price displays volatility and negative autocorrelation even without changes in dividend. The horizon of the agents matters. In addition, as in recent empirical works, the aggregate consumption,wealth ratio ,predicts' the asset return. Thus, this framework may be useful in understanding different stylized facts in asset pricing. Directions for future research are also discussed. [source] Loglinear Residual Tests of Moran's I Autocorrelation and their Applications to Kentucky Breast Cancer DataGEOGRAPHICAL ANALYSIS, Issue 3 2007Ge Lin This article bridges the permutation test of Moran's I to the residuals of a loglinear model under the asymptotic normality assumption. It provides the versions of Moran's I based on Pearson residuals (IPR) and deviance residuals (IDR) so that they can be used to test for spatial clustering while at the same time account for potential covariates and heterogeneous population sizes. Our simulations showed that both IPR and IDR are effective to account for heterogeneous population sizes. The tests based on IPR and IDR are applied to a set of log-rate models for early-stage and late-stage breast cancer with socioeconomic and access-to-care data in Kentucky. The results showed that socioeconomic and access-to-care variables can sufficiently explain spatial clustering of early-stage breast carcinomas, but these factors cannot explain that for the late stage. For this reason, we used local spatial association terms and located four late-stage breast cancer clusters that could not be explained. The results also confirmed our expectation that a high screening level would be associated with a high incidence rate of early-stage disease, which in turn would reduce late-stage incidence rates. [source] Exploring Relationships Between the Global and Regional Measures of Spatial AutocorrelationJOURNAL OF REGIONAL SCIENCE, Issue 4 2003Daniel A. Griffith We calculate 1990 population density by census block group, county, and state for the 48 coterminous states and the District of Columbia of the United States, calculations of interest to a wide variety of spatial scientists. We explore relations between these levels and their variation across the nation. The empirical findings generated by this work furnish implications concerning the Modifiable Areal Unit Problem (MAUP), spatial autocorrelation statistics, scale effects, and resolution. [source] Testing for Local Spatial Autocorrelation in the Presence of Global AutocorrelationJOURNAL OF REGIONAL SCIENCE, Issue 3 2001J. Keith Ord A fundamental concern of spatial analysts is to find patterns in spatial data that lead to the identification of spatial autocorrelation or association. Further, they seek to identify peculiarities in the data set that signify that something out of the ordinary has occurred in one or more regions. In this paper we provide a statistic that tests for local spatial autocorrelation in the presence of the global autocorrelation that is characteristic of heterogeneous spatial data. After identifying the structure of global autocorrelation, we introduce a new measure that may be used to test for local structure. This new statistic Oi is asymptotically normally distributed and allows for straightforward tests of hypotheses. We provide several numerical examples that illustrate the performance of this statistic and compare it with another measure that does not account for global structure. [source] Capacity expansion with lead times and autocorrelated random demandNAVAL RESEARCH LOGISTICS: AN INTERNATIONAL JOURNAL, Issue 2 2003Sarah M. Ryan Abstract The combination of uncertain demand and lead times for installing capacity creates the risk of shortage during the lead time, which may have serious consequences for a service provider. This paper analyzes a model of capacity expansion with autocorrelated random demand and a fixed lead time for adding capacity. To provide a specified level of service, a discrete time expansion timing policy uses a forecast error-adjusted minimum threshold level of excess capacity position to trigger an expansion. Under this timing policy, the expansion cost can be minimized by solving a deterministic dynamic program. We study the effects of demand characteristics and the lead time length on the capacity threshold. Autocorrelation acts similarly to randomness in hastening expansions but has a smaller impact, especially when lead times are short. However, the failure either to recognize autocorrelation or to accurately estimate its extent can cause substantial policy errors. © 2003 Wiley Periodicals, Inc. Naval Research Logistics, 2003 [source] Optimal Valuation of Noisy Real AssetsREAL ESTATE ECONOMICS, Issue 3 2002Paul D. Childs We study the optimal valuation of real assets when true asset values are unobservable. In our model, the observed value cointegrates with the unobserved true asset value to cause serial correlation in the time series of observed values. Autocorrelation as well as total variance in the observed value are used to calculate an efficient unbiased estimate of the true asset value (the time,filtered value). The optimal value estimate is shown to have three time,weighted terms: a deterministic forward value, a comparison of observed values with previously determined time,filtered values, and a convexity correction for incomplete information. The residual variance measures the precision of the value estimate, which can increase or decrease monotonically over time as well as display a linear or nonlinear time trend. We also show how to revise time,filtered estimates based on the arrival of new information. Our results relate to work on illiquid asset markets, including appraisal smoothing, tests of market efficiency, and the valuation of options on real assets. [source] The Interplay between Climate Variability and Density Dependence in the Population Viability of Chinook SalmonCONSERVATION BIOLOGY, Issue 1 2006RICHARD W. ZABEL análisis de viabilidad poblacional; especies en peligro; Oncorhynchus tshawytscha Abstract:,The viability of populations is influenced by driving forces such as density dependence and climate variability, but most population viability analyses (PVAs) ignore these factors because of data limitations. Additionally, simplified PVAs produce limited measures of population viability such as annual population growth rate (,) or extinction risk. Here we developed a "mechanistic" PVA of threatened Chinook salmon (Oncorhynchus tshawytscha) in which, based on 40 years of detailed data, we related freshwater recruitment of juveniles to density of spawners, and third-year survival in the ocean to monthly indices of broad-scale ocean and climate conditions. Including climate variability in the model produced important effects: estimated population viability was very sensitive to assumptions of future climate conditions and the autocorrelation contained in the climate signal increased mean population abundance while increasing probability of quasi extinction. Because of the presence of density dependence in the model, however, we could not distinguish among alternative climate scenarios through mean , values, emphasizing the importance of considering multiple measures to elucidate population viability. Our sensitivity analyses demonstrated that the importance of particular parameters varied across models and depended on which viability measure was the response variable. The density-dependent parameter associated with freshwater recruitment was consistently the most important, regardless of viability measure, suggesting that increasing juvenile carrying capacity is important for recovery. Resumen:,La viabilidad de poblaciones esta influida por fuerzas conductoras como la denso dependencia y la variabilidad climática, pero la mayoría de los análisis de viabilidad poblacional (AVP) ignoran estos factores debido a limitaciones en la disponibilidad de datos. Adicionalmente, los AVP simplificados producen medidas limitadas de la viabilidad poblacional tales como la tasa anual de crecimiento poblacional (,) o el riesgo de extinción. Aquí desarrollamos un AVP "mecanicista" de Oncorhynchus tshawytscha en el que, con base en datos detallados de 40 años, relacionamos el reclutamiento de juveniles en agua dulce con la densidad de reproductores, y la supervivencia en el océano al tercer año con índices mensuales de condiciones oceánicas y climáticas a amplia escala. La inclusión de la variabilidad climática en el modelo produjo efectos importantes: la viabilidad poblacional estimada fue muy sensible a las suposiciones de condiciones climáticas futuras y la autocorrelación contenida en la señal climática aumentó la abundancia poblacional promedio al mismo tiempo que incrementó la probabilidad de cuasi extinción. Sin embargo, debido a la presencia de denso densidad en el modelo no pudimos distinguir entre escenarios climáticos alternativos a través de los valores promedio de ,, lo que enfatiza la importancia de considerar medidas múltiples para dilucidar la viabilidad poblacional. Nuestros análisis de sensibilidad demostraron que la importancia de parámetros particulares varió en los modelos y dependió de la medida de viabilidad utilizada como variable de respuesta. El parámetro de denso dependencia asociada con el reclutamiento en agua dulce consistentemente fue el más importante, independientemente de la medida de viabilidad, lo que sugiere que el incremento en la capacidad de carga de juveniles es importante para la recuperación. [source] Marketing Category Forecasting: An Alternative of BVAR-Artificial Neural Networks¶DECISION SCIENCES, Issue 4 2000James J. Jiang ABSTRACT Analyzing scanner data in brand management activities presents unique difficulties due to the vast quantity of the data. Time series methods that are able to handle the volume effectively often are inappropriate due to the violation of many statistical assumptions in the data characteristics. We examine scanner data sets for three brand categories and examine properties associated with many time series forecasting methods. Many violations are found with respect to linearity, normality, autocorrelation, and heteroscedasticity. With this in mind we compare the forecasting ability of neural networks that require no assumptions to two of the more robust time series techniques. Neural networks provide similar forecasts to Bayesian vector autoregression (BVAR), and both outperform generalized autoregressive conditional herteroscedasticty (GARCH) models. [source] Effects of species and habitat positional errors on the performance and interpretation of species distribution modelsDIVERSITY AND DISTRIBUTIONS, Issue 4 2009Patrick E. Osborne Abstract Aim, A key assumption in species distribution modelling is that both species and environmental data layers contain no positional errors, yet this will rarely be true. This study assesses the effect of introduced positional errors on the performance and interpretation of species distribution models. Location, Baixo Alentejo region of Portugal. Methods, Data on steppe bird occurrence were collected using a random stratified sampling design on a 1-km2 pixel grid. Environmental data were sourced from satellite imagery and digital maps. Error was deliberately introduced into the species data as shifts in a random direction of 0,1, 2,3, 4,5 and 0,5 pixels. Whole habitat layers were shifted by 1 pixel to cause mis-registration, and the cumulative effect of one to three shifted layers investigated. Distribution models were built for three species using three algorithms with three replicates. Test models were compared with controls without errors. Results, Positional errors in the species data led to a drop in model performance (larger errors having larger effects , typically up to 10% drop in area under the curve on average), although not enough for models to be rejected. Model interpretation was more severely affected with inconsistencies in the contributing variables. Errors in the habitat layers had similar although lesser effects. Main conclusions, Models with species positional errors are hard to detect, often statistically good, ecologically plausible and useful for prediction, but interpreting them is dangerous. Mis-registered habitat layers produce smaller effects probably because shifting entire layers does not break down the correlation structure to the same extent as random shifts in individual species observations. Spatial autocorrelation in the habitat layers may protect against species positional errors to some extent but the relationship is complex and requires further work. The key recommendation must be that positional errors should be minimised through careful field design and data processing. [source] Patterns of spatial autocorrelation of assemblages of birds, floristics, physiognomy, and primary productivity in the central Great Basin, USADIVERSITY AND DISTRIBUTIONS, Issue 3 2006Erica Fleishman ABSTRACT We fitted spatial autocorrelation functions to distance-based data for assemblages of birds and for three attributes of birds' habitats at 140 locations, separated by up to 65 km, in the Great Basin (Nevada, USA). The three habitat characteristics were taxonomic composition of the vegetation, physical structure of the vegetation, and a measure of primary productivity, the normalized difference vegetation index, estimated from satellite imagery. We found that a spherical model was the best fit to data for avifaunal composition, vegetation composition, and primary productivity, but the distance at which spatial correlation effectively was zero differed substantially among data sets (c. 30 km for birds, 20 km for vegetation composition, and 60 km for primary productivity). A power-law function was the best fit to data for vegetation structure, indicating that the structure of vegetation differed by similar amounts irrespective of distance between locations (up to the maximum distance measured). Our results suggested that the spatial structure of bird assemblages is more similar to vegetation composition than to either vegetation structure or primary productivity, but is autocorrelated over larger distances. We believe that the greater mobility of birds compared with plants may be responsible for this difference. [source] Spatial autocorrelation of assemblages of benthic invertebrates and its relationship to environmental factors in two upland rivers in southeastern AustraliaDIVERSITY AND DISTRIBUTIONS, Issue 5 2005Natalie J. Lloyd ABSTRACT The nature of spatial autocorrelation of biota may reveal much about underlying ecological and biological factors responsible for producing those patterns, especially dispersal processes (drift, adult flight, etc.). We report here on assemblage-level autocorrelation in the benthic-invertebrate assemblages (retained in sieves of 300 µm mesh) of riffles in two adjacent, relatively pristine rivers in southeastern Victoria, Australia (40-km reaches of the Wellington and Wonnangatta Rivers). These are related to patterns of autocorrelation in physical and catchment conditions (,environmental variables') in the vicinity of the sampling points. Both the invertebrate assemblages and environmental variables were autocorrelated at small scales (= 8 km) in the Wellington River in one of the sampling years (1996). Dissimilarities of invertebrate assemblages were correlated with dissimilarities of environmental variables in both sampling years (1996 and 1997) in that river. Environmental variables were autocorrelated in the Wonnangatta River, but this was not expressed as autocorrelation in the assemblages of invertebrates, which were not autocorrelated at any scale studied. Individual environmental variables showed different spatial patterns between the two rivers. These results suggest that individual rivers have their own idiosyncratic patterns and one cannot assume that even similar, geographically adjacent rivers will have the same patterns, which is a difficulty for ecological assessment and restoration. [source] Morphometric analysis and tectonic interpretation of digital terrain data: a case studyEARTH SURFACE PROCESSES AND LANDFORMS, Issue 8 2003Gyozo Jordan Abstract Tectonic movement along faults is often re,ected by characteristic geomorphological features such as linear valleys, ridgelines and slope-breaks, steep slopes of uniform aspect, regional anisotropy and tilt of terrain. Analysis of digital elevation models, by means of numerical geomorphology, provides a means of recognizing fractures and characterizing the tectonics of an area in a quantitative way. The objective of this study is to investigate the use of numerical geomorphometric methods for tectonic geomorphology through a case study. The methodology is based on general geomorphometry. In this study, the basic geometric attributes (elevation, slope, aspect and curvatures) are complemented with the automatic extraction of ridge and valley lines and surface speci,c points. Evans' univariate and bivariate methodology of general geomorphometry is extended with texture (spatial) analysis methods, such as trend, autocorrelation, spectral, and network analysis. Terrain modelling is implemented with the integrated use of: (1) numerical differential geometry; (2) digital drainage network analysis; (3) digital image processing; and (4) statistical and geostatistical analysis. Application of digital drainage network analysis is emphasized. A simple shear model with principal displacement zone with an NE,SW orientation can account for most of the the morphotectonic features found in the basin by geological and digital tectonic geomorphology analyses. Copyright © 2003 John Wiley & Sons, Ltd. [source] Geographic body size gradients in tropical regions: water deficit and anuran body size in the Brazilian CerradoECOGRAPHY, Issue 4 2009Miguel Á. Olalla-Tárraga A recent interspecific study found Bergmann's size clines for Holarctic anurans and proposed an explanation based on heat balance to account for the pattern. However, this analysis was limited to cold temperate regions, and exploring the patterns in warmer tropical climates may reveal other factors that also influence anuran body size variation. We address this using a Cerrado anuran database. We examine the relationship between mean body size in a grid of 1° cells and environmental predictors and test the relative support for four hypotheses using an AIC-based model selection approach. Also, we considered three different amphibian phylogenies to partition the phylogenetic and specific components of the interspecific variation in body size using a method analogous to phylogenetic eigen vector regression (PVR). To consider the potential effects of spatial autocorrelation we use eigenvector-based spatial filters. We found the largest species inhabiting high water deficit areas in the northeast and the smallest in the wet southwest. Our results are consistent with the water availability hypothesis which, coupled with previous findings, suggests that the major determinant of interspecific body size variation in anurans switches from energy to water towards the equator. We propose that anuran body size gradients reflect effects of reduced surface to volume ratios in larger species to control both heat and water balance. [source] Comment on "Methods to account for spatial autocorrelation in the analysis of species distributional data: a review"ECOGRAPHY, Issue 3 2009Matthew G. Betts First page of article [source] Coefficient shifts in geographical ecology: an empirical evaluation of spatial and non-spatial regressionECOGRAPHY, Issue 2 2009L. Mauricio Bini A major focus of geographical ecology and macroecology is to understand the causes of spatially structured ecological patterns. However, achieving this understanding can be complicated when using multiple regression, because the relative importance of explanatory variables, as measured by regression coefficients, can shift depending on whether spatially explicit or non-spatial modeling is used. However, the extent to which coefficients may shift and why shifts occur are unclear. Here, we analyze the relationship between environmental predictors and the geographical distribution of species richness, body size, range size and abundance in 97 multi-factorial data sets. Our goal was to compare standardized partial regression coefficients of non-spatial ordinary least squares regressions (i.e. models fitted using ordinary least squares without taking autocorrelation into account; "OLS models" hereafter) and eight spatial methods to evaluate the frequency of coefficient shifts and identify characteristics of data that might predict when shifts are likely. We generated three metrics of coefficient shifts and eight characteristics of the data sets as predictors of shifts. Typical of ecological data, spatial autocorrelation in the residuals of OLS models was found in most data sets. The spatial models varied in the extent to which they minimized residual spatial autocorrelation. Patterns of coefficient shifts also varied among methods and datasets, although the magnitudes of shifts tended to be small in all cases. We were unable to identify strong predictors of shifts, including the levels of autocorrelation in either explanatory variables or model residuals. Thus, changes in coefficients between spatial and non-spatial methods depend on the method used and are largely idiosyncratic, making it difficult to predict when or why shifts occur. We conclude that the ecological importance of regression coefficients cannot be evaluated with confidence irrespective of whether spatially explicit modelling is used or not. Researchers may have little choice but to be more explicit about the uncertainty of models and more cautious in their interpretation. [source] A spatial model of bird abundance as adjusted for detection probabilityECOGRAPHY, Issue 2 2009P. Marcos Gorresen Modeling the spatial distribution of animals can be complicated by spatial and temporal effects (i.e. spatial autocorrelation and trends in abundance over time) and other factors such as imperfect detection probabilities and observation-related nuisance variables. Recent advances in modeling have demonstrated various approaches that handle most of these factors but which require a degree of sampling effort (e.g. replication) not available to many field studies. We present a two-step approach that addresses these challenges to spatially model species abundance. Habitat, spatial and temporal variables were handled with a Bayesian approach which facilitated modeling hierarchically structured data. Predicted abundance was subsequently adjusted to account for imperfect detection and the area effectively sampled for each species. We provide examples of our modeling approach for two endemic Hawaiian nectarivorous honeycreepers: ,i,iwi Vestiaria coccinea and ,apapane Himatione sanguinea. [source] Representing genetic variation as continuous surfaces: an approach for identifying spatial dependency in landscape genetic studiesECOGRAPHY, Issue 6 2008Melanie A. Murphy Landscape genetics, an emerging field integrating landscape ecology and population genetics, has great potential to influence our understanding of habitat connectivity and distribution of organisms. Whereas typical population genetics studies summarize gene flow as pairwise measures between sampling localities, landscape characteristics that influence population genetic connectivity are often continuously distributed in space. Thus, there are currently gaps in both the ability to analyze genotypic data in a continuous spatial context and our knowledge of expected of landscape genetic structure under varying conditions. We present a framework for generating continuous "genetic surfaces", evaluate their statistical properties, and quantify statistical behavior of landscape genetic structure in a simple landscape. We simulated microsatellite genotypes under varying parameters (time since vicariance, migration, effective population size) and used ancestry (q) values from STRUCTURE to interpolate a genetic surface. Using a spatially adjusted Pearson's correlation coefficient to test the significance of landscape variable(s) on genetic structure we were able to detect landscape genetic structure on a contemporary time scale (,5 generations post vicariance, migration probability ,0.10) even when population differentiation was minimal (FST,0.00015). We show that genetic variation can be significantly correlated with geographic distance even when genetic structure is due to landscape variable(s), demonstrating the importance of testing landscape influence on genetic structure. Finally, we apply genetic surfacing to analyze an empirical dataset of black bears from northern Idaho USA. We find black bear genetic variation is a function of distance (autocorrelation) and habitat patch (spatial dependency), consistent with previous results indicating genetic variation was influenced by landscape by resistance. These results suggest genetic surfaces can be used to test competing hypotheses of the influence of landscape characteristics on genetic structure without delineation of categorical groups. [source] Detecting spatial hot spots in landscape ecologyECOGRAPHY, Issue 5 2008Trisalyn A. Nelson Hot spots are typically locations of abundant phenomena. In ecology, hot spots are often detected with a spatially global threshold, where a value for a given observation is compared with all values in a data set. When spatial relationships are important, spatially local definitions , those that compare the value for a given observation with locations in the vicinity, or the neighbourhood of the observation , provide a more explicit consideration of space. Here we outline spatial methods for hot spot detection: kernel estimation and local measures of spatial autocorrelation. To demonstrate these approaches, hot spots are detected in landscape level data on the magnitude of mountain pine beetle infestations. Using kernel estimators, we explore how selection of the neighbourhood size (,) and hot spot threshold impact hot spot detection. We found that as , increases, hot spots are larger and fewer; as the hot spot threshold increases, hot spots become larger and more plentiful and hot spots will reflect coarser scale spatial processes. The impact of spatial neighbourhood definitions on the delineation of hot spots identified with local measures of spatial autocorrelation was also investigated. In general, the larger the spatial neighbourhood used for analysis, the larger the area, or greater the number of areas, identified as hot spots. [source] Methods to account for spatial autocorrelation in the analysis of species distributional data: a reviewECOGRAPHY, Issue 5 2007Carsten F. Dormann Species distributional or trait data based on range map (extent-of-occurrence) or atlas survey data often display spatial autocorrelation, i.e. locations close to each other exhibit more similar values than those further apart. If this pattern remains present in the residuals of a statistical model based on such data, one of the key assumptions of standard statistical analyses, that residuals are independent and identically distributed (i.i.d), is violated. The violation of the assumption of i.i.d. residuals may bias parameter estimates and can increase type I error rates (falsely rejecting the null hypothesis of no effect). While this is increasingly recognised by researchers analysing species distribution data, there is, to our knowledge, no comprehensive overview of the many available spatial statistical methods to take spatial autocorrelation into account in tests of statistical significance. Here, we describe six different statistical approaches to infer correlates of species' distributions, for both presence/absence (binary response) and species abundance data (poisson or normally distributed response), while accounting for spatial autocorrelation in model residuals: autocovariate regression; spatial eigenvector mapping; generalised least squares; (conditional and simultaneous) autoregressive models and generalised estimating equations. A comprehensive comparison of the relative merits of these methods is beyond the scope of this paper. To demonstrate each method's implementation, however, we undertook preliminary tests based on simulated data. These preliminary tests verified that most of the spatial modeling techniques we examined showed good type I error control and precise parameter estimates, at least when confronted with simplistic simulated data containing spatial autocorrelation in the errors. However, we found that for presence/absence data the results and conclusions were very variable between the different methods. This is likely due to the low information content of binary maps. Also, in contrast with previous studies, we found that autocovariate methods consistently underestimated the effects of environmental controls of species distributions. Given their widespread use, in particular for the modelling of species presence/absence data (e.g. climate envelope models), we argue that this warrants further study and caution in their use. To aid other ecologists in making use of the methods described, code to implement them in freely available software is provided in an electronic appendix. [source] Spatial variation in population density across the geographical range in helminth parasites of yellow perch Perca flavescensECOGRAPHY, Issue 5 2007Robert Poulin The abundance of a species is not constant across its geographical range; it has often been assumed to decrease from the centre of a species' range toward its margins. The central assumption of this "favourable centre" model is tested for the first time with parasites, using different species of helminth parasites exploiting fish as definitive hosts. Data on prevalence (percentage of hosts that are infected) and abundance (mean no. parasites per host) were compiled for 8 helminth species occurring in 23 populations of yellow perch Perca flavescens, from continental North America. For each parasite species, correlations were computed between latitude and both local prevalence and abundance values. In addition, the relationships between the relative prevalence or abundance in one locality and the distance between that locality and the one where the maximum value was reported, were assessed separately for each species to determine whether abundance tends to decrease away from the presumed centre of the range, where it peaks. For both the cestode Proteocephalus pearsei and the acanthocephalan Leptorhynchoides thecatus, there was a positive relationship between prevalence or abundance and the latitude of the sampled population. There was also a significant negative relationship between relative prevalence and the distance from the locality showing the maximum value in P. pearsei, but no such pattern was observed for the other 7 parasite species. Since this single significant decrease in prevalence with increasing distance from the peak value may be confounded by a latitudinal gradient, it appears that the distribution of abundance in parasites of perch does not follow the favourable centre model. This means that the environmental variables affecting the density of parasites (host availability, abiotic conditions) do not show pronounced spatial autocorrelation, with nearby sites not necessarily providing more similar conditions for the growth of parasite populations than distant sites. [source] Spatial distribution and environmental correlates of Australian snubfin and Indo-Pacific humpback dolphinsECOGRAPHY, Issue 3 2006Guido J. Parra We present data on the spatial distribution of Australian snubfin and humpback dolphins using boat-based line transect surveys in three adjacent bays located in the Far Northern Section of the Great Barrier Reef Marine Park, northeast Queensland. We used Geographic Information Systems (GIS), and both randomization and Mantel tests to examine the relationship between the spatial distribution of the dolphins and three simple, readily quantified, environmental variables: distance to land, distance to river mouth, and water depth. Mantel tests allowed us to make clear inferences about the correlation of the species' distributions with environmental variables, while taking into account spatial autocorrelation and intercorrelation among variables. Randomization tests indicated snubfin and humpback dolphins occur closer to land than would be expected at random. Two-sample randomization tests indicated snubfin dolphins were found closer to river mouths than were humpback dolphins. Taking spatial autocorrelation into account, Mantel tests indicated all environmental variables were correlated with the spatial distribution of snubfin and humpback dolphins. Interspecific differences in spatial distribution appeared to be related to proximity to river mouths. Preference by snubfin and humpback dolphins for nearshore, estuarine waters is likely related to the productivity of these tropical coastal areas. This spatial analysis suggests that existing protected areas in this region may not include the most critical habitats for snubfin and humpback dolphins. The techniques used here shown relationships between the spatial distribution of the dolphins and environmental features that should facilitate their management and conservation. [source] Analysis of determinants of mammalian species richness in South America using spatial autoregressive modelsECOGRAPHY, Issue 4 2004Marcelo F. Tognelli Classically, hypotheses concerning the distribution of species have been explored by evaluating the relationship between species richness and environmental variables using ordinary least squares (OLS) regression. However, environmental and ecological data generally show spatial autocorrelation, thus violating the assumption of independently distributed errors. When spatial autocorrelation exists, an alternative is to use autoregressive models that assume spatially autocorrelated errors. We examined the relationship between mammalian species richness in South America and environmental variables, thereby evaluating the relative importance of four competing hypotheses to explain mammalian species richness. Additionally, we compared the results of ordinary least squares (OLS) regression and spatial autoregressive models using Conditional and Simultaneous Autoregressive (CAR and SAR, respectively) models. Variables associated with productivity were the most important at determining mammalian species richness at the scale analyzed. Whereas OLS residuals between species richness and environmental variables were strongly autocorrelated, those from autoregressive models showed less spatial autocorrelation, particularly the SAR model, indicating its suitability for these data. Autoregressive models also fit the data better than the OLS model (increasing R2 by 5,14%), and the relative importance of the explanatory variables shifted under CAR and SAR models. These analyses underscore the importance of controlling for spatial autocorrelation in biogeographical studies. [source] Macroecology of a host-parasite relationshipECOGRAPHY, Issue 1 2000Caryn C. Vaughn The larvae of freshwater mussels are obligate ectoparasites on fishes while adults are sedentary and benthic. Dispersal of mussels is dependent on the movement of fish hosts, a regional process, but growth and reproduction should be governed by local processes. Thus, mussel assemblage attributes should be predictable from the regional distribution and abundance of fishes. At a broad spatial scale in the Red River drainage, USA, mussel species richness and fish species richness were positively associated; maximum mussel richness was limited by fish richness, but was variable beneath that constraint. Measured environmental variables and the associated local fish assemblages each significantly accounted for the regional variation in mussel assemblages. Furthermore, mussel assemblages showed strong spatial autocorrelation. Variation partitioning revealed that pure fish effects accounted for 15.4% of the variation in mussel assemblages; pure spatial and environmental effects accounted for 16.1% and 7.8%, respectively. Shared variation among fish, space and environmental variables totaled 40%. Of this shared variation, 36.8% was associated with the fish matrix. Thus, the variation in mussel assemblages that was associated with the distribution and abundance of fishes was substantial (> 50%), indicating that fish community structure is an important determinant of mussel community structure. Although animals commonly disperse plants and, thus, influence the structure of plant communities, our results show a strong macroecological association between two disparate animal groups with one strongly affecting the assemblage structure of the other. [source] Estimation of dispersal distances of the obligately plant-associated ant Crematogaster decameraECOLOGICAL ENTOMOLOGY, Issue 5 2010MANFRED TÜRKE 1. In obligate symbioses with horizontal transmission, the population dynamics of the partner organisms are highly interdependent. Host population size limits symbiont number, and distribution of partners is restricted by the presence and thus dispersal abilities of their respective partner. The Crematogaster decamera,Macaranga hypoleuca ant,plant symbiosis is obligate for both partners. Host survival depends on colonisation by its ant partner while foundress queens require hosts for colony establishment. 2. An experimental approach and population genetic analyses were combined to estimate dispersal distances of foundresses in their natural habitat in a Bornean primary rainforest. 3. Colonisation frequency was significantly negatively correlated with distance to potential reproductive colonies. Results were similar for seedlings at natural densities as well as for seedlings brought out in the area experimentally. Population genetic analysis revealed significant population differentiation with an FST of 0.041 among foundresses (n = 157) located at maximum 2280 m apart. In genetic spatial autocorrelation, genotypes of foundresses were significantly more similar than expected at random below 550 m and less similar above 620 m. Direct estimation of dispersal distances by pedigree analysis yielded an average dispersal distance of 468 m (maximum 1103 m). 4. For ants that disperse on the wing, genetic differentiation at such small spatial scales is unusual. The specific nesting requirements of the queens and the necessity for queens to find a host quickly could lead to colonisation of the first suitable seedling encountered, promoting short dispersal distances. Nonetheless, dispersal distances of C. decamera queens may vary with habitat or host spatial distribution. [source] Patterns and causes of species richness: a general simulation model for macroecologyECOLOGY LETTERS, Issue 9 2009Nicholas J. Gotelli Abstract Understanding the causes of spatial variation in species richness is a major research focus of biogeography and macroecology. Gridded environmental data and species richness maps have been used in increasingly sophisticated curve-fitting analyses, but these methods have not brought us much closer to a mechanistic understanding of the patterns. During the past two decades, macroecologists have successfully addressed technical problems posed by spatial autocorrelation, intercorrelation of predictor variables and non-linearity. However, curve-fitting approaches are problematic because most theoretical models in macroecology do not make quantitative predictions, and they do not incorporate interactions among multiple forces. As an alternative, we propose a mechanistic modelling approach. We describe computer simulation models of the stochastic origin, spread, and extinction of species' geographical ranges in an environmentally heterogeneous, gridded domain and describe progress to date regarding their implementation. The output from such a general simulation model (GSM) would, at a minimum, consist of the simulated distribution of species ranges on a map, yielding the predicted number of species in each grid cell of the domain. In contrast to curve-fitting analysis, simulation modelling explicitly incorporates the processes believed to be affecting the geographical ranges of species and generates a number of quantitative predictions that can be compared to empirical patterns. We describe three of the ,control knobs' for a GSM that specify simple rules for dispersal, evolutionary origins and environmental gradients. Binary combinations of different knob settings correspond to eight distinct simulation models, five of which are already represented in the literature of macroecology. The output from such a GSM will include the predicted species richness per grid cell, the range size frequency distribution, the simulated phylogeny and simulated geographical ranges of the component species, all of which can be compared to empirical patterns. Challenges to the development of the GSM include the measurement of goodness of fit (GOF) between observed data and model predictions, as well as the estimation, optimization and interpretation of the model parameters. The simulation approach offers new insights into the origin and maintenance of species richness patterns, and may provide a common framework for investigating the effects of contemporary climate, evolutionary history and geometric constraints on global biodiversity gradients. With further development, the GSM has the potential to provide a conceptual bridge between macroecology and historical biogeography. [source] Temporal autocorrelation and stochastic population growthECOLOGY LETTERS, Issue 3 2006Shripad Tuljapurkar Abstract How much does environmental autocorrelation matter to the growth of structured populations in real life contexts? Interannual variances in vital rates certainly do, but it has been suggested that between-year correlations may not. We present an analytical approximation to stochastic growth rate for multistate Markovian environments and show that it is accurate by testing it in two empirically based examples. We find that temporal autocorrelation has sizeable effect on growth rates of structured populations, larger in many cases than the effect of interannual variability. Our approximation defines a sensitivity to autocorrelated variability, showing how demographic damping and environmental pattern interact to determine a population's stochastic growth rate. [source] Consistent High-precision Volatility from High-frequency DataECONOMIC NOTES, Issue 2 2001Fulvio Corsi Estimates of daily volatility are investigated. Realized volatility can be computed from returns observed over time intervals of different sizes. For simple statistical reasons, volatility estimators based on high-frequency returns have been proposed, but such estimators are found to be strongly biased as compared to volatilities of daily returns. This bias originates from microstructure effects in the price formation. For foreign exchange, the relevant microstructure effect is the incoherent price formation, which leads to a strong negative first-order autocorrelation ,(1),40 per cent for tick-by-tick returns and to the volatility bias. On the basis of a simple theoretical model for foreign exchange data, the incoherent term can be filtered away from the tick-by-tick price series. With filtered prices, the daily volatility can be estimated using the information contained in high-frequency data, providing a high-precision measure of volatility at any time interval. (J.E.L.: C13, C22, C81). [source] Changes in per capita alcohol sales during the partial privatization of British Columbia's retail alcohol monopoly 2003,2008: a multi-level local area analysisADDICTION, Issue 11 2009Tim Stockwell ABSTRACT Aim To investigate the independent effects on liquor sales of an increase in (a) the density of liquor outlets and (b) the proportion of liquor stores in private rather than government ownership in British Columbia between 2003/4 and 2007/8. Design The British Columbia Liquor Distribution Branch provided data on litres of ethanol sold through different types of outlets in 89 local health areas of the province by beverage type. Multi-level regression models were used to examine the relationship between per capita alcohol sales and outlet densities for different types of liquor outlet after adjusting for potential confounding social, economic and demographic factors as well as spatial and temporal autocorrelation. Setting Liquor outlets in 89 local health areas of British Columbia, Canada. Findings The number of private stores per 10 000 residents was associated significantly and positively with per capita sales of ethanol in beer, coolers, spirits and wine, while the reverse held for government liquor stores. Significant positive effects were also identified for the number of bars and restaurants per head of population. The percentage of liquor stores in private versus government ownership was also associated significantly with per capita alcohol sales when controlling for density of liquor stores and of on-premise outlets (P < 0.01). Conclusion The trend towards privatisation of liquor outlets between 2003/04 and 2007/08 in British Columbia has contributed to increased per capita sales of alcohol and hence possibly also to increased alcohol-related harm. [source] Combining data from multiple years or areas to improve variogram estimationENVIRONMETRICS, Issue 6 2007John F. Walter III Abstract A requirement for geostatistical prediction is estimation of the variogram from the data. Often low sample size is a major impediment to elucidating a variogram even for a highly autocorrelated spatial process. This paper presents a methodology for improving variogram estimation when samples exist from multiple years or regions sharing a similar process for generating spatial autocorrelation. Such samples may come from annual monitoring programs for natural resources or from multiple geologic regions. As each set of samples contains some information on the spatial autocorrelation, combining information through the construction of a combined variogram cloud and binning to obtain a common variogram improves the estimation of the variogram. In both simulations and in real datasets of oyster abundance the method proposed here reduces the likelihood of failing to obtain a variogram from a set of samples and improves the efficiency of variogram estimation. In practice, the benefits obtained by estimating an otherwise elusive variogram generally outweigh the costs of using a slightly incorrect variogram model if different sampling stanzas are combined when they do not share the same spatial autocorrelation. Copyright © 2007 John Wiley & Sons, Ltd. [source] Using spatial models and kriging techniques to optimize long-term ground-water monitoring networks: a case studyENVIRONMETRICS, Issue 5-6 2002Kirk Cameron Abstract In a pilot project, a spatial and temporal algorithm (geostatistical temporal,spatial or GTS) was developed for optimizing long-term monitoring (LTM) networks. Data from two monitored ground-water plumes were used to test the algorithm. The primary objective was to determine the degree to which sampling, laboratory analysis, and/or well construction resources could be pared without losing key statistical information concerning the plumes. Optimization of an LTM network requires an accurate assessment of both ground-water quality over time and trends or other changes in individual monitoring wells. Changes in interpolated concentration maps over time indicate whether ground-water quality has improved or declined. GTS separately identifies temporal and spatial redundancies. Temporal redundancy may be reduced by lengthening the time between sample collection. Spatial redundancy may be reduced by removing wells from the network which do not significantly impact assessment of ground-water quality. Part of the temporal algorithm in GTS involves computation of a composite temporal variogram to determine the least redundant overall sampling interval. Under this measure of autocorrelation between sampling events, the lag time at which the variogram reaches a sill is the sampling interval at which same-well measurements lack correlation and are therefore non-redundant. The spatial algorithm assumes that well locations are redundant if nearby wells offer nearly the same statistical information about the underlying plume. A well was considered redundant if its removal did not significantly change: (i) an interpolated map of the plume; (ii) the local kriging variances in that section of the plume; and (iii) the average global kriging variance. To identify well redundancy, local kriging weights were accumulated into global weights and used to gauge each well's relative contribution to the interpolated plume map. By temporarily removing that subset of wells with the lowest global kriging weights and re-mapping the plume, it was possible to determine how many wells could be removed without losing critical information. Test results from the Massachusetts Military Reserve (MMR) indicated that substantial savings in sampling, analysis and operational costs could be realized by utilizing GTS. Annual budgetary savings that would accrue were estimated at between 35 per cent and 5 per cent for both LTM networks under study.Copyright © 2002 John Wiley & Sons, Ltd. [source] | |||||||||||||||||||||||||||||||||||||||||||||||||