Significant Positive Autocorrelation Research Articles

A heteroskedastic error covariance matrix estimator using a first-order conditional autoregressive Markov simulation for deriving asympotical efficient estimates from ecological sampled Anopheles arabiensis aquatic habitat covariates

BackgroundAutoregressive regression coefficients for Anopheles arabiensis aquatic habitat models are usually assessed using global error techniques and are reported as error covariance matrices. A global statistic, however, will summarize error estimates from multiple habitat locations. This makes it difficult to identify where there are clusters of An. arabiensis aquatic habitats of acceptable prediction. It is therefore useful to conduct some form of spatial error analysis to detect clusters of An. arabiensis aquatic habitats based on uncertainty residuals from individual sampled habitats. In this research, a method of error estimation for spatial simulation models was demonstrated using autocorrelation indices and eigenfunction spatial filters to distinguish among the effects of parameter uncertainty on a stochastic simulation of ecological sampled Anopheles aquatic habitat covariates. A test for diagnostic checking error residuals in an An. arabiensis aquatic habitat model may enable intervention efforts targeting productive habitats clusters, based on larval/pupal productivity, by using the asymptotic distribution of parameter estimates from a residual autocovariance matrix. The models considered in this research extends a normal regression analysis previously considered in the literature.MethodsField and remote-sampled data were collected during July 2006 to December 2007 in Karima rice-village complex in Mwea, Kenya. SAS 9.1.4® was used to explore univariate statistics, correlations, distributions, and to generate global autocorrelation statistics from the ecological sampled datasets. A local autocorrelation index was also generated using spatial covariance parameters (i.e., Moran's Indices) in a SAS/GIS® database. The Moran's statistic was decomposed into orthogonal and uncorrelated synthetic map pattern components using a Poisson model with a gamma-distributed mean (i.e. negative binomial regression). The eigenfunction values from the spatial configuration matrices were then used to define expectations for prior distributions using a Markov chain Monte Carlo (MCMC) algorithm. A set of posterior means were defined in WinBUGS 1.4.3®. After the model had converged, samples from the conditional distributions were used to summarize the posterior distribution of the parameters. Thereafter, a spatial residual trend analyses was used to evaluate variance uncertainty propagation in the model using an autocovariance error matrix.ResultsBy specifying coefficient estimates in a Bayesian framework, the covariate number of tillers was found to be a significant predictor, positively associated with An. arabiensis aquatic habitats. The spatial filter models accounted for approximately 19% redundant locational information in the ecological sampled An. arabiensis aquatic habitat data. In the residual error estimation model there was significant positive autocorrelation (i.e., clustering of habitats in geographic space) based on log-transformed larval/pupal data and the sampled covariate depth of habitat.ConclusionAn autocorrelation error covariance matrix and a spatial filter analyses can prioritize mosquito control strategies by providing a computationally attractive and feasible description of variance uncertainty estimates for correctly identifying clusters of prolific An. arabiensis aquatic habitats based on larval/pupal productivity.

Market Microstructure and Private Information: A Study on National Stock Exchange of India

This paper is an attempt to examine the impact of changes in market microstructure on stock return variances during exchange trading and nontrading hours for a sample of stocks listed in National Stock Exchange of India. The study has found that daytime return variance is higher than overnight return variance and opening return variance is higher than closing return variance. First order autocorrelations in opening returns have shown small but significant negative autocorrelations for more companies whereas closing returns have shown significant positive autocorrelation for few companies. Speed of adjustment estimated from opening returns has shown a tendency towards overreaction whereas closing returns have shown a tendency towards under reaction.

Price trends and patterns in technical analysis: A theoretical and empirical examination

While many technical trading rules are based upon patterns in asset prices, we lack convincing explanations of how and why these patterns arise, and why trading rules based on technical analysis are profitable. This paper provides a model that explains the success of certain trading rules that are based on patterns in past prices. We point to the importance of confirmation bias, which has been shown to play a key role in other types of decision making. Traders who acquire information and trade on the basis of that information tend to bias their interpretation of subsequent information in the direction of their original view. This produces autocorrelations and patterns of price movement that can predict future prices, such as the “head-and-shoulders” and “double-top” patterns. The model also predicts that sequential price jumps for a particular stock will be positively autocorrelated. We test this prediction and find that jumps exhibit statistically and economically significant positive autocorrelations.

Detecting dispersal of Nuphar lutea in river corridors using microsatellite markers

Summary1. River corridors are well‐known for their role in plant dispersal. The buoyancy of seeds, the possibility of dispersal by vegetative fragments, and the frequency and efficiency of dispersal among different river catchments determine linear distribution patterns. Little is known about the relative importance of these factors to observed patterns of genetic variation.2. One hundred and fifty‐six Nuphar lutea individuals from forty‐four sampling sites in the river catchments of the Cidlina River, the Mrlina River and the Labe River (Czech Republic) were studied using ten microsatellite markers. Interpretation of patterns in genetic variation allowed several conclusions about dispersal mechanisms.3. Vegetative long‐distance dispersal is probably very limited in this species. Only one multilocus genotype was found in more than one sampling site. The distance between the sites was about 75 km.4. To explain the distribution of Bayesian based clusters of related multilocus genotypes, both along‐river and inter‐river long‐distance dispersals have to be invoked.5. A marginally significant tendency for higher genetic diversity in the lower part of the river Cidlina was detected. Continuous downstream dispersal of seeds by water currents could be a valid explanation.6. Significant positive autocorrelation was found among individuals at within‐river distances of up to 25 km. Repeated dispersal of seeds over distances in the range of tens of kilometers is common.

Genetic succession and spatial genetic structure in a natural old growth Cryptomeria japonica forest revealed by nuclear and chloroplast microsatellite markers

Spatial genetic structure and diversity of Cryptomeria japonica trees in old growth forest but selectively logged in approximately 300 years ago were analyzed using seven nuclear microsatellite markers and one chloroplast microsatellite marker. The individuals were sampled from a 4-ha natural forest plot (200 m × 200 m), which are consisted of three size classes including newly regenerated individuals such as regenerated individuals (REG class; stem diameter at breast height (DBH), 5–110 cm), remaining individuals (REM class; DBH ≥ 110 cm), and old stumps. Genetic diversity was similar between the three groups. These results suggest that genes from the old growth forest may have been passed on to the next generation (i.e., the current forest). DNA extraction and analysis from old stumps was partly successful and we could compare the genetic diversity between old stumps and living trees using four microsatellite loci. Patterns of spatial genetic structure detected by analyses of the nuclear and chloroplast markers differed substantially; all nuclear markers showed significant positive autocorrelation ( P < 0.05) over short distances, but the chloroplast marker showed no significant autocorrelation over any distance. We concluded that Moran's I spatial autocorrelation pattern for the chloroplast microsatellite may have been influenced by the extensive pollen flow characteristic of this species, because chloroplast DNA of C. japonica is paternally inherited through pollen and is a wind-pollinated species that produces abundant pollen every few years. In contrast, Moran's I spatial autocorrelation pattern for nuclear microsatellites may reflect limited seed dispersal in gaps where the forest has regenerated and low levels of seed shadow overlap due to past logging. The average seed dispersal distance was 86.0 m at the study site and about 34.0% of the seeds were from the outside of the site, which revealed by parentage analysis. The individuals remaining after logging, and the extensive pollen flow from outside our study plot, may play important roles in maintaining the forests on this island. Gaps created by the selective logging activities appear to have been important for the regeneration of this species in the past but in the present time, natural disturbances such as typhoon and landslide are also important for gap creation.

Spatiotemporal Relationships Between Disease Development and Airborne Inoculum in Unmanaged and Managed Botrytis Leaf Blight Epidemics

Comparatively little quantitative information is available on both the spatial and temporal relationships that develop between airborne inoculum and disease intensity during the course of aerially spread epidemics. Botrytis leaf blight and Botrytis squamosa airborne inoculum were analyzed over space and time during 2 years (2002 and 2004) in a nonprotected experimental field, using a 6 x 8 lattice of quadrats of 10 x 10 m each. A similar experiment was conducted in 2004 and 2006 in a commercial field managed for Botrytis leaf blight using a 5 x 5 lattice of quadrats of 25 x 25 m each. Each quadrat was monitored weekly for lesion density (LD) and aerial conidium concentration (ACC). The adjustment of the Taylor's power law showed that heterogeneity in both LD and ACC generally increased with increasing mean. Unmanaged epidemics were characterized in either year, with aggregation indices derived from SADIE (Spatial Analysis by Distance Indices). For LD, the aggregation indices suggested a random pattern of disease early in the season, followed by an aggregated pattern in the second part of the epidemic. The index of aggregation for ACC in 2002 was significantly greater than 1 at only one date, while it was significantly greater than 1 at most sampling dates in 2004. In both years and for both variables, positive trends in partial autocorrelation were observed mainly for a spatial lag of 1. In 2002, the overall pattern of partial autocorrelations over sampling dates was similar for LD and ACC with no significant partial autocorrelation during the first part of the epidemic, followed by a period with significant positive autocorrelation, and again no autocorrelation on the last three sampling dates. In 2004, there was no significant positive autocorrelation for LD at most sampling dates while for ACC, there was a fluctuation between significant and non-significant positive correlation over sampling dates. There was a significant spatial correlation between ACC at given date (t(i)) and LD 1 week later (t(i + 1)) on most sampling dates in both 2002 and 2004 for the unmanaged and managed sites. It was concluded that LD and ACC were not aggregated in the early stage of epidemics, when both disease intensity and airborne conidia concentration were low. This was supported by the analysis of LD and ACC from a commercial field, where managed levels of disease were low, and where no aggregation of both variables was detected. It was further concluded that a reliable monitoring of airborne inoculum for management of Botrytis leaf blight is achievable in managed fields using few spore samplers per field.

Time series patterns in credit ratings

This article offers a substitute setting to simulate credit rating migrations. The internal correlations model tracks time-series movements within credit rating entries, rather than cross-ratings correlations. The proposed nonhomogeneous process is authenticated through the likelihood ratio Dickey–Fuller test, and is found to be statistically and economically significant, by better fitting observed cumulative default rates. Several nonlinear regression models assist to better identify these time-related patterns. The economic structure underlying the time dependency often corresponds to changes in GDP, business cycles, and market risk. Furthermore, significant positive autocorrelation is detected mostly among downgrade probabilities.

The rhythm of savanna patch dynamics

1. Patch dynamics is a new, potentially unifying mechanism for the explanation of tree-grass coexistence in savannas. In this scale-explicit paradigm, savannas consist of patches in which a cyclical succession between woody and grassy dominance proceeds spatially asynchronously. The growing ecological and economic problem of shrub encroachment is a natural transient phase in this cycle. 2. An important step towards understanding patterns at the landscape scale is achieved by investigating mechanisms at a smaller scale. We developed the spatially explicit individual-based simulation model SATCHMO to test the null hypothesis that cyclical succession cannot emerge from a realistic patch scale simulation model of the population dynamics of savanna woody species. 3. We calculated the partial temporal autocorrelation coefficient for 100 simulated time series of shrub cover over 500 years for time lags of up to 200 years to establish the existence and duration of successional cycles. We found a significant positive autocorrelation indicating the existence of cycles with a typical duration of about 33 years. 4. The shrub size frequency distributions over the course of a cycle showed shifts from dominance of small shrub sizes towards larger sizes during the increasing phase of a cycle and the reverse in the declining phase. This supports the three phase explanation as follows: (i) an initial phase when spatially and temporally overlapping favourable conditions lead to mass recruitment of shrubs; (ii) a build-up phase when the shrub cohort grows; and (iii) a break-down phase when increased competition due to crowding and unfavourable conditions lead to the break-down of the shrub cohort. The frequency distribution of shrub age at death over 10 simulations was also in agreement with this explanation. 5. We investigated the relationship between shrub cover, annual precipitation and time-lagged shrub cover to identify the driver of the cyclical successions. More than 90% of the variation in shrub cover was explained by shrub cover of the previous year, precipitation, and their interaction. 6. With the demonstration of precipitation-driven cyclical succession at the patch scale, we show that the mechanistic, temporal component of patch dynamics can be used to explain tree-grass coexistence in semi-arid savannas.

Ant-Mediated Seed Dispersal Contributes to the Local Spatial Pattern and Genetic Structure of Globba lancangensis (Zingiberaceae)

Globba lancangensis is a typical myrmecochorous perennial herb found in southwest China. In 2002 and 2003, seed dispersal by ants was examined. Twelve ant species were recorded and found to move seeds 0.01-3.35 m. This removal promoted seed dispersal. In 2003, ant exclusion was made in 4 of 8 study plots for 1 year to practically detect the effects of ant dispersal. As a result, nearest neighbor analyses indicated that although ant dispersal could not significantly change the mean nearest neighbor distance, it obviously reduced the clustering degree of seedlings. Spatial genetic structure (SGS) of mature plants in 2003 and sequent seedlings in 2004 were investigated with intersimple sequence repeats. The whole correlagram of all individuals from 2003 showed significant positive autocorrelations between genetic and geographical distances within 4 m, suggesting a patchy structure at such short distances in the studied population. This pattern is likely associated with limited seed dispersal. The comparative analysis of fine-scale correlograms (<4 m) for ant-excluded treatment indicated lacking ants as the primary dispersers could have a similar pattern to ant dispersal presence, which is likely due to the restriction of ant dispersal distance. In conclusion, ant-mediated dispersal contributes to reduce seedling clustering degree and plays a minor role in developing and maintaining the local SGS in G. lancangensis.

Clonal and spatial genetic structure in natural populations of Luohanguo ( Siraitia grosvenorii), an economic species endemic to South China, as revealed by RAPD markers

Clonal growth is generally expected to have significant effects on the spatial genetic structure within populations. In this study, random amplified polymorphic DNA (RAPD) markers were used to reveal clonal and spatial genetic structure of four natural populations of Luohanguo ( Siraitia grosvenorii), an economic vine species endemic to South China. A total of 351 ramets were assigned to 76 distinct multi-locus genotypes (i.e. genets), with the G/ N varying from 0.121 to 0.350. No widespread genet was found across different populations. The clonal diversity ( D) and evenness ( E) ranged from 0.333 to 0.828 and from 0 to 0.741, respectively. While most genets consisted of fewer than five ramets, we observed some dominant genets that had much more (up to 69) ramets and spread over large areas. Spatial autocorrelation analyses revealed a spatial genetic structure (i.e. significant positive autocorrelation within 20 m and negative autocorrelation beyond 40 m) in one population, but not in other three populations with smaller population size. This study highlights the importance of clonal growth in shaping the spatial genetic structure in Luohanguo, which may have complex effects on the dynamics and evolution of its declining populations.

Price Trends and Patterns in Technical Analysis: A Theoretical and Empirical Examination

We develop a formal model of asset prices in which investors are subject to confirmation bias, which describes the tendency of individuals to search for and interpret information selectively to conform to a given set of beliefs. The model produces three notable results. First, the model generates price patterns which validate certain well-documented trading strategies, in particular the head-and-shoulders pattern. Second, asset prices exhibit negative autocorrelations over very short horizons, positive autocorrelations over intermediate horizons, and negative autocorrelations over long horizons, which matches the observed stylized properties of U.S. equity prices. Third, the model predicts that sequential price jumps for a particular stock will be positively autocorrelated. Several recent econometric papers have shown that one can identify significant jumps by comparing realized volatility and bi-power return variation. Using this methodology, together with tick-by-tick data on all stocks in the S&P 100 index from 1999-2005, we identify and calculate significant jumps in stock prices. Consistent with the predictions of the model, we find that jumps exhibit statistically and economically significant positive autocorrelations.

Analysis of drawdowns and drawups in the US$ interest-rate market

We investigate the statistical properties of drawdowns and drawups in interest rates (US$) using over 10 years' worth of daily data. We analyse the nature of the drawdowns in terms of length of runs, magnitude of the individual price moves and coincidence of their occurrence across the maturity spectrum. We document significant positive autocorrelation for several holding periods, pronounced term structure effects and an unexpectedly low degree of coincidence in the occurrence of drawdowns across the maturity spectrum (despite high correlation in daily moves). By drawing on previous work by Rebonato et al. (2005) we try to provide a coherent explanation for a complex set of empirical observations. An essential ingredient of this explanation appears to be the existence of at least two distinct types (normal and excited) of price dynamics, with different serial correlation properties. We concur with the results by Sornette and Johansen (Significance of log-periodic precursors to financial crashes. Quant. Finance, 2001, 1, 452–471) for different asset classes that very large drawdowns belong to the `undemocratic' case, and may therefore result from an amplification mechanism.

Pilot study of using GIS to visualize health status distribution: case study of Songjiang District, Shanghai.

The aim of this study was to investigate the geographic variation in mortality indicators in Songjiang District of Shanghai under rapid change with urbanization. Geographic Information System (GIS) was used to visualize the distributions of health status and to test the extent of spatial auto-correlation in the indictors. In 2001, total crude mortality was 687/100,000 for the district; the rates for the townships (N=15) in the district ranged from 444/100,000 to 805/100,000. GIS maps indicated that the mortality was higher in the marginal areas of the district. Significant positive auto-correlations were found in total crude mortality rates, death rate for infectious diseases, and death rates for digestive diseases. The measures that consider intra-region inequality in health needs will be required in the regions under urbanization.

Spatial autocorrelation study of population genetic structure of two orchid species

The spatial pattern or structure of genetic variation within populations is an important part of evolutionary and ecological genetic processes in natural populations of plants, and may provide deep insight into the conservation of endangered species. In this paper, we investigated the spatial distribution patterns of population genetic variation of two orchid species, Paphiopedilum micranthum and Changnienia amoena , using RAPD markers. A total of 89 and 42 individuals in two populations of each of the species, respectively, were involved in our study. Based on the polymorphic bands generated by 12 ( P. micranthum ) and 16 ( C. amoena ) RAPD primers, Moran′s I spatial autocorrelation coefficient was calculated, which revealed significant spatial structure of genetic variation within populations of P. micranthum (with significant positive autocorrelation over short distance class: 3～4 m). In contrast, the genetic variation in C. amoena populations was found to be randomly distributed. The different spatial distribution patterns may be attributed to the different reproductive systems of these two species. P. micranthum undergoes significant clonal growth while no significant clonal reproduction was found in C. amoena . These results have important implications for the conservation and management of these species, especially for the sampling strategies for ex situ conservation.

Spatial Autocorrelation of Genetic Variation in Three Stands of Ophiopogon xylorrhizus(Liliaceaes.l. )

Using spatial autocorrelation analysis, the spatial distribution of genotypes and gene frequencies at three allozyme loci, as well as the spatial distribution of family outcrossing rates were investigated in three stands of the tropical rainforest endangered perennial Ophiopogon xylorrhizus in Southwest China. Similar spatial patterns of the different allozymes were found both in individual stands and among stands, particularly at the whole distance classes. There were slightly different associations between like-homozygous and like-heterozygous plants. Most like-homozygous plant pairs exhibited a significant positive autocorrelation in shorter distance classes, and a random distribution or slightly negative autocorrelation in the following one or two distance classes. In contrast, a significant positive autocorrelation was found in the last one or two distance classes, indicating that like-homozygous plants were clustered in patches with a diameter of 5–10m. Most like-heterozygous plant pairs exhibited a random distribution or a slightly negative autocorrelation in shorter distance classes, a significantly positive autocorrelation in the next one or two distance classes, and tended to a random distribution in the last one or two distance classes. Fifty-five per cent of unlike-homozygous plant pairs existed in different patches with similar patch sizes and these patches did not overlap. Highly consistent patterns were found in the spatial distribution of gene frequencies. Different genes existed in different patches, with a highly consistent average patch size in all three stands. Family outcrossing rates were randomly distributed in space. These results imply that it is not possible to sample the genetic variation of a population by merely conserving one part of it because the population is unlikely to be homogenous.

Caribou movement as a correlated random walk.

Movement is a primary mechanism coupling animals to their environment, yet there exists little empirical analysis to test our theoretical knowledge of this basic process. We used correlated random walk (CRW) models and satellite telemetry to investigate long-distance movements of caribou, the most vagile, non-volant terrestrial vertebrate in the world. Individual paths of migratory and sedentary female caribou were quantified using measures of mean move length and angle, and net squared displacements at each successive move were compared to predictions from the models. Movements were modelled at two temporal scales. For paths recorded through one annual cycle, the CRW model overpredicted net displacement of caribou through time. For paths recorded over shorter intervals delineated by seasonal behavioural changes of caribou, there was excellent correspondence between model predictions and observations for most periods for both migratory and sedentary caribou. On the smallest temporal scale, a CRW model significantly overpredicted displacements of migratory caribou during 3 months following calving; this was also the case for sedentary caribou in late summer, and in late winter. In all cases of overprediction there was significant positive autocorrelation in turn direction, indicating that movements were more tortuous than expected. In one case of underprediction, significant negative autocorrelation of sequential turn direction was evident, indicating that migratory caribou moved in straightened paths during spring migration to calving grounds. Results are discussed in light of known migration patterns and possible limiting factors for caribou, and indicate the applicability of CRW models to animal movement at vast spatial and temporal scales, thus assisting in future development of more sophisticated models of population spread and redistribution for vertebrates.

Trade Liberalization and the Behavior of Emerging Stock Market Prices

The subject of this paper is to examine the effect of trade liberalization on the behavior of real emerging stock market prices. The paper first explores the theoretical link between trade liberalization and real stock price behavior by developing an open economy asset pricing model with production. Using this model we develop three regimes of trade liberalization: (I) no liberalization, where there is a fixed quota on the level of imported inputs; (ii) partial liberalization, where the home country's government allows the level of imports to grow up to a threshold; (iii) full liberalization where the quantity constraint on the imported intermediate inputs is removed. The predictions of the model are that real equity returns show negative serial correlation in the first regime, positive autocorrelation in the second regime and zero serial correlation or no predictability in the third regime. In the paper, we then test to determine if the stock price behavior predictions from the theory are confirmed by actual real stock price data from 25 emerging market countries. Employing return autocorrelation and variance ratio tests we find that there is a distinct change in the serial correlation property of the returns as these countries have liberalized the trade sector. Emerging market real stock prices tend to obey the random walk property after these countries liberalized, while real returns show significant positive autocorrelation during the period when these countries were relatively closed.

Non-linear analysis of chlorophyll a transects as a method of quantifying spatial structure

A method of analysis, initially developed as a method of distinguishing between chaotic dynamics and observational noise in time series, was applied to transects of chlorophyll a from the Southern Ocean. The algorithm works by predicting the behaviour of a data set based on patterns present within the data. This differs from previous analyses in that it permits classification of the dynamics governing the system. In a chaotic system, predictions become exponentially poor as one attempts to predict further ahead, due to the sensitivity of the system to initial conditions. However, in a system governed by stochastic noise, no such deterioration in predictions is observed. This work represents the first application of the algorithm to oceanic transect data, and our results show a less than exponential decline in predictive ability. The behaviour of the prediction curves is closely related to that of the corresponding autocorrelation functions, indicative of a stochastic, but spatially coherent data set with significant positive autocorrelations. Furthermore, successful prediction is correlated with the sum of the chlorophyll power spectrum. We conclude that the predictive ability of the algorithm is greatest when spatial variation in the chlorophyll transects is high.

Spatial data analysis in the quantitative assessment of cerebral white matter pathology on MRI in HIV infection

This study was carried out using MRI (proton density--and T2-weighted) on 16 HIV-negative controls, 9 symptom-free HIV-positive patients and 25 with CDC IV HIV disease. The studies from this last group had previously been allocated by a radiologist to the following categories: 8 with focal mass lesions and normal-appearing white matter; 9 with diffuse encephalopathy (high signal on T2-weighted images, affecting most or all of the white matter) and 8 with patchy encephalopathy (high signal affecting only one or two areas within the white matter). Moran's I, a statistic of spatial autocorrelation, was calculated for the grey-scale values of a sampled pixel array from a central white matter region of each of the images. All values of Moran's I calculated in this study showed a large positive excess over the expected value under randomisation, indicating highly significant positive auto-correlation in the spatial arrangement of the grey-scale values. On T2-weighted images a statistically significant increase in the mean value of Moran's I, compared with controls, was found in the diffuse encephalopathy group, indicating that quantifiable changes in the spatial autocorrelation of pixel data can be related to recognised qualitative changes in the appearance of white matter in subjects with HIV disease. A lesser, but significant, rise in the mean value of Moran's I was also found in the focal mass lesion group, suggesting that changes in spatial autocorrelation may indicate pathological change in advance of qualitative MRI changes.

The effect of environment on puerulus settlement of the western rock lobster (PanuUrus cygnus) in Western Australia

ABSTRACTThe level of puerulus settlement has proven to be a reliable predictor of the recruitment to the western rock lobster (Panulirus cygnus) fishery three and four years later. It is generally accepted that the early larval stages are moved offshore by wind‐driven surface currents. Previous studies have shown that coastal sea level, used as an indicator of the strength of the Leeuwin Current flowing down the west coast of Australia, was positively related to the level of puerulus settling on the inshore reefs after a 9‐ to 11‐month larval life. In this paper a significant relationship is also demonstrated between winter/spring storms, which are usually associated with onshore westerly winds, and the level of puerulus settlement (multiple correlation of 0.83, R1= 0.68, n = 22). Rainfall from coastal localities was used as an index of the storms. The residuals from this relationship showed a significant positive autocorrelation. The autocorrelation was incorporated into the relationship by using time series analysis with a transfer function model and a first‐order autocorrelation (R2= 0.76).The possible relationship between storms and the Leeuwin Current and their effect on the puerulus settlement are considered, and the potential for the environmental variables to bias or obscure the spawning stock recruitment relationship is discussed.