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Autocorrelation Structure (autocorrelation + structure)
Selected AbstractsType and spatial structure of distribution data and the perceived determinants of geographical gradients in ecology: the species richness of African birdsGLOBAL ECOLOGY, Issue 5 2007Jana M. McPherson ABSTRACT Aim, Studies exploring the determinants of geographical gradients in the occurrence of species or their traits obtain data by: (1) overlaying species range maps; (2) mapping survey-based species counts; or (3) superimposing models of individual species' distributions. These data types have different spatial characteristics. We investigated whether these differences influence conclusions regarding postulated determinants of species richness patterns. Location, Our study examined terrestrial bird diversity patterns in 13 nations of southern and eastern Africa, spanning temperate to tropical climates. Methods, Four species richness maps were compiled based on range maps, field-derived bird atlas data, logistic and autologistic distribution models. Ordinary and spatial regression models served to examine how well each of five hypotheses predicted patterns in each map. These hypotheses propose productivity, temperature, the heat,water balance, habitat heterogeneity and climatic stability as the predominant determinants of species richness. Results, The four richness maps portrayed broadly similar geographical patterns but, due to the nature of underlying data types, exhibited marked differences in spatial autocorrelation structure. These differences in spatial structure emerged as important in determining which hypothesis appeared most capable of explaining each map's patterns. This was true even when regressions accounted for spurious effects of spatial autocorrelation. Each richness map, therefore, identified a different hypothesis as the most likely cause of broad-scale gradients in species diversity. Main conclusions, Because the ,true' spatial structure of species richness patterns remains elusive, firm conclusions regarding their underlying environmental drivers remain difficult. More broadly, our findings suggest that care should be taken to interpret putative determinants of large-scale ecological gradients in light of the type and spatial characteristics of the underlying data. Indeed, closer scrutiny of these underlying data , here the distributions of individual species , and their environmental associations may offer important insights into the ultimate causes of observed broad-scale patterns. [source] Spatial patterns of kangaroo density across the South Australian pastoral zone over 26 years: aggregation during drought and suggestions of long distance movementJOURNAL OF APPLIED ECOLOGY, Issue 5 2007ANTHONY R. POPLE Summary 1Wildlife surveys usually focus on estimating population size, and management actions such as commercial harvesting, culling and poison baiting are referenced commonly to population size alone, without taking into account the way in which those animals are distributed. This paper outlines how point-based aerial survey data can be converted to continuous density surfaces using spatial analysis techniques. Using this approach, we describe and explore the spatial patterns of density of two species of kangaroos in an area exceeding 200 000 km2 in South Australia over a 26-year period. 2Densities of red and western grey kangaroos were estimated in 2 km2 segments along aerial survey transect lines, yielding point density estimates. Universal kriging provided an unbiased interpolation of these data using the spatial autocorrelation structure described by the semi-variogram. The Getis statistic identified clusters of high and low kangaroo density. 3Considerable year-to-year variation in the spatial patterns of kangaroo density was observed. In many cases, annual rates of increase over large areas were too high to be explained by vital rates alone, implying immigration from surrounding areas. These large shifts in distribution were occasionally to areas that had received better rainfall than the surrounding areas. For both species, there was no obvious local spatial autocorrelation pattern or clustering of kangaroo density beyond that described by average density and the present set of management regions, suggesting the latter are appropriate divisions for harvest management. 4Data for both species fitted the power law relationship extremely well. During dry times, red kangaroos, but not western grey kangaroos, were more aggregated, supporting past ground observations at a fine spatial scale. 5Synthesis and applications. Kriged density surfaces enable estimation of kangaroo density on individual properties, which are the management units at which harvest quotas or culling approvals are allocated. These estimates will be marked improvements over systematic sampling estimates when sampling intensity is low. Predictions of shifts in kangaroo distribution using rainfall or satellite imagery will allow more accurate allocation of harvest quotas. Similarly, predictions of more even kangaroo dispersion following high rainfall will allow managers to anticipate downturns in harvest rate. [source] Statistical simulation of flood variables: incorporating short-term sequencingJOURNAL OF FLOOD RISK MANAGEMENT, Issue 1 2008Y. Cai Abstract The pluvial and fluvial flooding in the United Kingdom over the summer of 2007 arose as a result of anomalous climatic conditions that persisted for over a month. Gaining an understanding of the sequencing of storm events and representing their characteristics within flood risk analysis is therefore of importance. This paper provides a general method for simulating univariate time series data, with a given marginal extreme value distribution and required autocorrelation structure, together with a demonstration of the method with synthetic data. The method is then extended to the multivariate case, where cross-variable correlations are also represented. The multivariate method is shown to work well for a two-variable simulation of wave heights and sea surges at Lerwick. This work was prompted by an engineering need for long time series data for use in continuous simulation studies where gradual deterioration is a contributory factor to flood risk and potential structural failure. [source] Identifying the time of polynomial drift in the mean of autocorrelated processesQUALITY AND RELIABILITY ENGINEERING INTERNATIONAL, Issue 5 2010Marcus B. Perry Abstract Control charts are used to detect changes in a process. Once a change is detected, knowledge of the change point would simplify the search for and identification of the special ause. Consequently, having an estimate of the process change point following a control chart signal would be useful to process engineers. This paper addresses change point estimation for covariance-stationary autocorrelated processes where the mean drifts deterministically with time. For example, the mean of a chemical process might drift linearly over time as a result of a constant pressure leak. The goal of this paper is to derive and evaluate an MLE for the time of polynomial drift in the mean of autocorrelated processes. It is assumed that the behavior in the process mean over time is adequately modeled by the kth-order polynomial trend model. Further, it is assumed that the autocorrelation structure is adequately modeled by the general (stationary and invertible) mixed autoregressive-moving-average model. The estimator is intended to be applied to data obtained following a genuine control chart signal in efforts to help pinpoint the root cause of process change. Application of the estimator is demonstrated using a simulated data set. The performance of the estimator is evaluated through Monte Carlo simulation studies for the k=1 case and across several processes yielding various levels of positive autocorrelation. Results suggest that the proposed estimator provides process engineers with an accurate and useful estimate for the last sample obtained from the unchanged process. Copyright © 2009 John Wiley & Sons, Ltd. [source] | ||||||||||