Spatial Autocorrelation Statistics Research Articles

Optimization of management zone delineation by using spatial principal components

Definition of management zones is the delimitation of sub-areas with similar topographic, soil and crop characteristics within a field. Among the many variables that can be used for this definition, those that are stable and spatially correlated with yield are more often recommended for use. Clustering algorithms such as Fuzzy C-means are also frequently applied to define management zones. Three variable selection techniques that can be applied with Fuzzy C-means are spatial correlation analysis, principal component analysis (PCA), and multivariate spatial analysis based on Moran’s index PCA (MULTISPATI-PCA). In this study, the efficiency of each of these three techniques used in conjunction with the clustering method was assessed. Furthermore, a new variable selection approach, named MPCA-SC, based on the combined use of Moran’s bivariate spatial autocorrelation statistic and MULTISPATI-PCA, was proposed and tested. The evaluation was performed by using data collected from 2010 to 2014 from three agricultural areas in Paraná State, Brazil, with corn and soybean crops, generating two, three, and four classes. The delineated management zones were different according to the method used, and MPCA-SC provided the best performance for the Fuzzy C-means algorithm and the best variance reduction values of the data after the delimitation of the sub-areas. Furthermore, MPCA-SC provided management zones with greater internal homogeneity, making them more viable for implementation from the viewpoint of field operations.

A DATA FIELD METHOD FOR URBAN REMOTELY SENSED IMAGERY CLASSIFICATION CONSIDERING SPATIAL CORRELATION

Spatial correlation between pixels is important information for remotely sensed imagery classification. Data field method and spatial autocorrelation statistics have been utilized to describe and model spatial information of local pixels. The original data field method can represent the spatial interactions of neighbourhood pixels effectively. However, its focus on measuring the grey level change between the central pixel and the neighbourhood pixels results in exaggerating the contribution of the central pixel to the whole local window. Besides, Geary’s C has also been proven to well characterise and qualify the spatial correlation between each pixel and its neighbourhood pixels. But the extracted object is badly delineated with the distracting salt-and-pepper effect of isolated misclassified pixels. To correct this defect, we introduce the data field method for filtering and noise limitation. Moreover, the original data field method is enhanced by considering each pixel in the window as the central pixel to compute statistical characteristics between it and its neighbourhood pixels. The last step employs a support vector machine (SVM) for the classification of multi-features (e.g. the spectral feature and spatial correlation feature). In order to validate the effectiveness of the developed method, experiments are conducted on different remotely sensed images containing multiple complex object classes inside. The results show that the developed method outperforms the traditional method in terms of classification accuracies.

A DATA FIELD METHOD FOR URBAN REMOTELY SENSED IMAGERY CLASSIFICATION CONSIDERING SPATIAL CORRELATION

Abstract. Spatial correlation between pixels is important information for remotely sensed imagery classification. Data field method and spatial autocorrelation statistics have been utilized to describe and model spatial information of local pixels. The original data field method can represent the spatial interactions of neighbourhood pixels effectively. However, its focus on measuring the grey level change between the central pixel and the neighbourhood pixels results in exaggerating the contribution of the central pixel to the whole local window. Besides, Geary’s C has also been proven to well characterise and qualify the spatial correlation between each pixel and its neighbourhood pixels. But the extracted object is badly delineated with the distracting salt-and-pepper effect of isolated misclassified pixels. To correct this defect, we introduce the data field method for filtering and noise limitation. Moreover, the original data field method is enhanced by considering each pixel in the window as the central pixel to compute statistical characteristics between it and its neighbourhood pixels. The last step employs a support vector machine (SVM) for the classification of multi-features (e.g. the spectral feature and spatial correlation feature). In order to validate the effectiveness of the developed method, experiments are conducted on different remotely sensed images containing multiple complex object classes inside. The results show that the developed method outperforms the traditional method in terms of classification accuracies.

Active tectonic deformation along rejuvenated faults in tropical Borneo: Inferences obtained from tectono-geomorphic evaluation

The island of Borneo is enveloped by tropical rainforests and hostile terrain characterized by high denudation rates. Owing to such conditions, studies pertaining to neotectonics and consequent geomorphic expressions with regard to surface processes and landscape evolution are inadequately constrained. Here we demonstrate the first systematic tectono-geomorphic evaluation of north Borneo through quantitative and qualitative morphotectonic analysis at sub-catchment scale, for two large drainage basins located in Sarawak: the Rajang and Baram basins. The extraction of morphometric parameters utilizing digital elevation models arranged within a GIS environment focuses on hypsometric curve analysis, distribution of hypsometric integrals through spatial autocorrelation statistics, relative uplift values, the asymmetry factor and the normalized channel steepness index. Hypsometric analysis suggests a young topography adjusting to changes in tectonic boundary conditions. Autocorrelation statistics show clusters of high values of hypsometric integrals as prominent hotspots that are associated with less eroded, young topography situated in the fold and thrust belts of the Interior Highlands of Borneo. High channel steepness and gradients (>200m0.9) are observed in zones corresponding to the hotspots. Relative uplift values reveal the presence of tectonically uplifted blocks together with relatively subsided or lesser uplifted zones along known faults. Sub-catchments of both basins display asymmetry indicating tectonic tilting. Stream longitudinal profiles demonstrate the presence of anomalies in the form of knickzones without apparent lithological controls along their channel reaches. Surfaces represented by cold spots of low HI values and low channel gradients observed in the high elevation headwaters of both basins are linked to isolated erosional planation surfaces that could be remnants of piracy processes. The implication of our results is that Borneo experiences active folding of the Rajang Group fold–thrust belt to present and these events reactivated old major faults and minor related dislocations. From geomorphic analysis associated with sedimentary record, we posit that the terrain could have undergone high uplift rates since 5Ma or multi-phased uplift with periodic intermittent pulses of high and low uplift rates.

Prevalence of River Epilepsy in the Orientale Province in the Democratic Republic of the Congo.

BackgroundAn increased prevalence of epilepsy has been reported in many onchocerciasis endemic areas.ObjectiveTo determine the prevalence and distribution of epilepsy in an onchocerciasis endemic region in the Democratic Republic of the Congo (DRC).Design/MethodsAn epilepsy prevalence study was carried out in 2014, in two localities of the Bas-Uélé district, an onchocerciasis endemic region in the Orientale Province of the DRC. Risk factors for epilepsy were identified using a random effects logistic regression model and the distribution of epilepsy cases was investigated using the Moran’s I statistic of spatial auto-correlation.ResultsAmong the 12,776 individuals of Dingila, 373 (2.9%) individuals with epilepsy were identified. In a house-to-house survey in Titule, 68 (2.3%) of the 2,908 people who participated in the survey were found to present episodes of epilepsy. Epilepsy showed a marked spatial pattern with clustering of cases occurring within and between adjacent households. Individual risk of epilepsy was found to be associated with living close to the nearest fast flowing river where blackflies (Diptera: Simuliidae)–the vector of Onchocerca volvulus–oviposit and breed.ConclusionsThe prevalence of epilepsy in villages in the Bas-Uélé district in the DRC was higher than in non-onchocerciasis endemic regions in Africa. Living close to a blackflies infested river was found to be a risk factor for epilepsy.

Analysis of spatial pattern of aerosol optical depth and affecting factors using spatial autocorrelation and spatial autoregressive model

Previous studies neglected the implicit information in the spatial pattern of the aerosol optical depth (AOD), such as spatial configuration characteristics, spatial heterogeneity and spatial dependence. The understanding of AOD is helpful in accurately estimating the environmental and climatic effects from aerosols. In this study, a method of spatial autocorrelation statistics from geostatistics was applied to investigate the spatial pattern of AOD in Hubei province in central China during the period 2003–2008. Spatial autoregressive models were used to quantize the correlations between AOD and affecting factors, such as elevation, forest coverage and population density. In addition, the difference between the standard linear regression model and the spatial models was discussed. The results were as follows: the spatial pattern of AOD in Hubei province shows significant spatial autocorrelation, indicating that AODs are clustered such that higher AODs tend to be surrounded by higher AOD neighbors, while lower AODs are surrounded by lower AOD neighbors, and the spatial autocorrelation scale of the AOD over Hubei is approximately 400 km. The high–high zone is mainly distributed in the Wuhan city circle and the Jianghan plain areas, while the low–low zones are mainly located in the middle and high mountain areas of northwest Hubei province. The overall degree and pattern of spatial autocorrelation do not change largely from 2003 to 2008, which indicates a stable spatial configuration of AOD. A significant negative spatial autocorrelation exists between AOD and elevation, which may suggest that AOD and elevation have an inverse spatial distribution, and the same applies for forest coverage. Population density and the AOD show a significant positive spatial autocorrelation, which may imply that they have similar spatial distribution, while industrial production and AOD do not show an obvious positive spatial autocorrelation. Spatial autoregressive models show better predictive ability and stability than the standard linear regression model because the spatial autocorrelation of the AOD is taken into consideration.

Spatial Dimensions of the Unemployment Rate in Jordan 2008

Although many studies examined the existence of spatial pattern of unemployment in some developing and many developed countries in improving the prosperity or social status and reducing the inequalities in unemployment between areas of such country, there is still much work to be done. Some of these studies found spatial pattern for unemployment using different statistical techniques and geographical mapping. The question is raised whether such a spatial pattern exists in Jordan? The objective is to investigate the spatial structure of unemployment rate (UR) across different governoratesto provide implications for policy makers, investigating the hot spots of UR and showing optical picture of UR. The study design is cross-sectional, where the data are collected for 12 governorates based on the census in 2008. A mapping using quartiles is used as a first step to conduct a visual inspection of UR. Two statistics of spatial autocorrelation, based on sharing boundary neighbours, known as global and local Moran’s I, were carried out for examining the global clustering and local clusters, respectively. Out of 12, three governorates (Balqa, Zarqa, and Tafiela) are found as local clusters in UR. In conclusion, the UR varies in the visual inspection based on choropleth mapping across different governorates with black spots in the northwestern, central and southeastern part of the country. Statistically, no significant global clustering can be found, but several significant local clusters are found in the central and western part of the country.

Redundant variables and the quality of management zones

ABSTRACT Precision agriculture (PA) allows farmers to identify and address variations in an agriculture field. Management zones (MZs) make PA more feasible and economical. The most important method for defining MZs is a fuzzy C-means algorithm, but selecting the variable for use as the input layer in the fuzzy process is problematic. BAZZI et al. (2013) used Moran’s bivariate spatial autocorrelation statistic to identify variables that are spatially correlated with yield while employing spatial autocorrelation. BAZZI et al. (2013) proposed that all redundant variables be eliminated and that the remaining variables would be considered appropriate on the MZ generation process. Thus, the objective of this work, a study case, was to test the hypothesis that redundant variables can harm the MZ delineation process. BAZZI This work was conducted in a 19.6-ha commercial field, and 15 MZ designs were generated by a fuzzy C-means algorithm and divided into two to five classes. Each design used a different composition of variables, including copper, silt, clay, and altitude. Some combinations of these variables produced superior MZs. None of the variable combinations produced statistically better performance that the MZ generated with no redundant variables. Thus, the other redundant variables can be discredited. The design with all variables did not provide a greater separation and organization of data among MZ classes and was not recommended.

GIS Analysis of Crime Incidence and Spatial Variation in Thiruvananthapuram City

The rate of crime incidents is increasing in developing countries mainly due to the unequal distribution of wealth and societal status. The present study attempts to identify and explore the rate and spatial variation of crime in Thiruvananthapuram city for a period from 2010 to 2014. The improved computer based technologies like GIS and availability of Geographic data make it possible for law and enforcement agencies to create analytical maps and various analysis to identify the crime hotspot area .The hotspot analysis in Geographic Information System is helpful for the identification of crime hotspot through spatial auto correlation, spatial analysis and interpolation. The Moran's I test statistic of spatial auto correlation has been done prior to GetisOrd Gi* hotspot analysis to find out the clustering pattern as well as the outliers in the data. The crime hotspot analysis uses vectors to identify the locations of statistically significant crime hotspots and cold spots and IDW interpolation method is used for better visualization. These methods are applied on the crime data of Thiruvananthapuram city of Kerala state to find the hotspots for crime incidents like Murder, Robbery, Snatching and Theft.

Identifying Local Spatiotemporal Autocorrelation Patterns of Taxi Pick-ups and Dropoffs

GIScience 2016 Short Paper Proceedings Identifying Local Spatiotemporal Autocorrelation Patterns of Taxi Pick-ups and Drop-offs Song Gao 1 , Rui Zhu 1 , Gengchen Mai 1 STKO Lab, Department of Geography, University of California, Santa Barbara, USA Contact Email: sgao@geog.ucsb.edu Abstract Analyzing spatiotemporal autocorrelation would be helpful to understand the underlying dynamic patterns in space and time simultaneously. In this work, we aim to extend the conventional spatial autocorrelation statistics to a more general framework considering both spatial and temporal dimensions. Specifically, we focus on the spatiotemporal version of Getis-Ord's G * . The proposed indicator STG * can quantify the local association of adjacent features in space and time. As a proof of concept, the proposed method is then applied in a large-scale GPS-enabled taxi dataset to identify local spatiotemporal autocorrelation patterns of taxi pick-ups and drop-offs in New York City. 1. Introduction Nowadays, large-scale spatiotemporal data (e.g., taxi trajectories, phone call records, social media posts) become available, which provide rich information to support research on human behaviors, transportation, urban landscape, and human-environment interactions. However, discovering patterns hidden in large-scale spatiotemporal datasets is challenging and thus attracts a lot of attention from the GIScience community (Hardisty and Klippel 2010; Demsar and Virrantaus 2010; Scholz and Lu 2014; Claramunt and Stewart 2015). Spatial autocorrelation statistics, like Moran's I and Getis-Ord's G are commonly designed for identifying spatial autocorrelation patterns (Fischer and Getis 2009). However, there is a gap in building corresponding measurements for spatiotemporal autocorrelations. For example, although human movements and activities may vary over time across different places, the observed activity hotspots and movement flow might exhibit a pattern of spatial dependence. Also, ignoring the temporal dimension would not be sufficient to discover underlying spatiotemporal dynamics. Therefore, our work aims to contribute to extend the conventional local spatial autocorrelation statistics to include both spatial and temporal dimensions. As a proof of concept, the proposed method is then applied in a large-scale GPS- enabled taxi dataset to identify local autocorrelation patterns of taxi pick-up points (PUPs) and drop-off points (DOPs) in New York City. 2. Methodology Spatial autocorrelation measurements can be divided into two categories: global and local indices. Classic global indices of spatial autocorrelation include Moran’s I, Geary’s C, and Getis-Ord’s General G, while local indices of spatial association (LISA) can be established by transforming the global indices into corresponding local measurements (Anselin 1995). Spatiotemporal autocorrelation concept refers to the relationship between some variable observed in each of space-time settings and the association with its neighbors. In a previous work, Gao (2015) proposed three global spatiotemporal autocorrelation indices but didn’t describe how to decompose them into local versions. As an initial trial, this work focuses on extending Getis-Ord's G * (Equation 1) (Getis and Ord 1992) by adding temporal indexes into

Spatial patterns of recent demographic trends in Serbia (1961–2010)

This study considers the spatial and temporal dimensions of demographic trends in Serbia between 1961 and 2010. Using appropriate spatial autocorrelation statistics, spatial patterns of common demographic indicators including changes in population size, the rate of natural increase and infant mortality rate are investigated across municipalities of Serbia. Also, the impact of differential demographic rates according to ethnic origin on forming spatial clusters is implicitly considered. Different stages of demographic transition across municipalities at the start of the analyzed period determined the spatial pattern of clustered subregions; ethnic origin appeared to be a strong factor of differentiation regarding population dynamics. The two opposed areas regarding the transition of rates of natural increase and infant mortality were clustered; the southern included Kosovo and the least developed subregions of Central Serbia. The City of Belgrade and Kosovo have been the two hubs of population growth in Serbia over the past 50 years, while the strongest depopulation refers to the north and east border regions.

Sensitivity Analysis of Spatial Autocorrelation Using Distinct Geometrical Settings

Inferences based on spatial analysis of areal data depend greatly on the method used to quantify the degree of proximity between spatial units - regions. These proximity measures are normally organized in the form of weights matrices, which are used to obtain statistics that take into account neighbourhood relations between agents. In any scientific field where the focus is on human behaviour, areal datasets are greatly relevant since this is the most common form of data collection (normally as count data). The method or schema used to divide a continuous spatial surface into sets of discrete units influences inferences about geographical and social phenomena, mainly because these units are neither homogeneous nor regular. This article tests the effect of different geometrical data aggregation schemas - administrative regions and hexagonal surface tessellation - on global spatial autocorrelation statistics. Two geographical variables are taken into account: scale (resolution) and form (regularity). This is achieved through the use of different aggregation levels and geometrical schemas. Five different datasets are used, all representing the distribution of resident population aggregated for two study areas, with the objective of consistently test the effect of different spatial aggregation schemas.

Enhanced Autocorrelation-Based Algorithms for Filtering Airborne Lidar Data over Urban Areas

AbstractMany existing algorithms for light detection and ranging (lidar) data classification are known to perform reliably; however, the automation of the classification of complex urban scenes is still a challenging problem. In this paper, two classification algorithms based on spatial autocorrelation statistics, such as the Local Moran’s I and the Getis-Ord Gi*, are proposed. These autocorrelation statistics are computed over sample urban areas, including complex terrain with diverse building characteristics. The proposed autocorrelation-based algorithms are applied to airborne lidar point clouds over the complex urban areas to generate highly accurate digital elevation models (DEMs) and classify the lidar points as ground and nonground points by using the DEMs. It is also demonstrated that the minimum-based rasterization and slope-based filtering can be integrated to effectively remove outliers from the DEMs. The test results showed that the autocorrelation-based algorithms produce high-level assessmen...

Spatial Pattern Detection of Tuberculosis: A Case Study of Si Sa Ket Province, Thailand.

This retrospective population-based study was conducted to analyze spatial patterns of tuberculosis (TB) incidence in Si Sa Ket province, Thailand. TB notification data from 2004 to 2008 collected from TB clinics throughout the province was used along with population data to reveal a descriptive epidemiology of TB incidences. Global clustering patterns of the occurrence were assessed by using global spatial autocorrelation techniques. Additionally, local spatial pattern detection was performed by using local spatial autocorrelation and spatial scan statistic methods. The findings indicated clusters of the disease occurred in the study area. More specifically, significantly high-rate clusters were mostly detected in Mueang Si Sa Ket and Khukhan districts, which are located in the northwestern part of the province, while significantly low-rate clusters were persistent in Kantharalak and Benchalak districts, which are located at the southeastern area.

The potential of 2D Kalman filtering for soil moisture data assimilation

Abstract We examine the potential for parameterizing a two-dimensional (2D) land data assimilation system using spatial error auto-correlation statistics gleaned from a triple collocation analysis and the triplet of: (1) active microwave-, (2) passive microwave- and (3) land surface model-based surface soil moisture products. Results demonstrate that, while considerable spatial error auto-correlation exists in the errors for all three products, the inclusion of this information into a 2D assimilation system does not significantly improve the performance of the system relative to a one-dimensional (1D) baseline. This result is explained via an analytical evaluation of the impact of spatial error auto-correlation on the steady-state Kalman gain, which reveals that 2D filtering requires the existence of large auto-correlation differences (between the assimilation model and the assimilated observations) in order to enhance the analysis relative to a 1D filtering baseline. As a result, large error auto-correlations alone (in both the model or observations) are not sufficient to motivate the application of a 2D land assimilation system. These results have important consequences for the development of land data assimilation systems designed to ingest satellite derived surface soil moisture products for water resource and climate applications.

Applying spatial clustering analysis to a township-level social vulnerability assessment in Taiwan

ABSTRACTThe degree of social vulnerability may vary according to the conditions and backgrounds of different locations, yet spatial clustering phenomena may exist when nearby spatial units exhibit similar characteristics. This study applied spatial autocorrelation statistics to analyze the spatial association of vulnerability among townships in Taiwan. The vulnerability was first assessed on the basis of a social vulnerability index that was constructed using Fuzzy Delphi and analytic hierarchy process methods. Subsequently, the corresponding indicator variables were applied to calculate standardized vulnerability assessment scores by using government data. According to the results of the vulnerability assessment in which T scores were normalized, the distribution of social vulnerabilities varied among the townships. The scores were further analyzed using spatial autocorrelation statistics for spatial clustering of vulnerability distribution. The Local G statistic identified 42 significant spatial association pockets, whereas the Global G statistic indicated no spatial phenomenon of clustering. This phenomenon was verified and explained by applying Moran's I statistics to examine the homogeneity and heterogeneity of spatial associations. Although both statistics were originally designed to identify the existence of spatial clustering, they serve diverse purposes, and the results can be compared to obtain additional insights into the distribution patterns of social vulnerability.

Combining near infrared spectra of feces and geostatistics to generate forage nutritional quality maps across landscapes.

An important asset for the management of wild ungulates is recognizing the spatial distribution of forage quality across heterogeneous landscapes. To do so typically requires knowledge of which plant species are eaten, in what abundance they are eaten, and what their nutritional quality might be. Acquiring such data, however, may be difficult and time consuming. Here, we are proposing a rapid and cost-effective forage quality monitoring tool that combines near infrared (NIR) spectra of fecal samples and easily obtained data on plant community composition. Our approach rests on the premise that NIR spectra of fecal samples collected within low population density exclosures reflect the optimal forage quality of a given landscape. Forage quality can thus be based on the Mahalanobis distance of fecal spectral scans across the landscape relative to fecal spectral scans inside exclosures (referred to as DISTEX). The Gi* spatial autocorrelation statistic can then be applied among neighboring DISTEX values to detect and map "hot spots" and "cold spots" of nutritional quality over the landscape. We tested our approach in a heterogeneous boreal landscape on Anticosti Island (Québec, Canada), where white-tailed deer (Odocoileus virginianus) populations over the landscape have ranged from 20 to 50 individuals/km2 for at least 80 years, resulting in a loss of most palatable and nutritious plant species. Our results suggest that hot spots of forage quality occur when old-growth balsam fir stands comprise >39.8% of 300 ha neighborhoods, whereas cold spots occur in laggs (i.e., transition zones from forest to peatland). In terms of ground-level indicator plant species, the presence of Canada bunchberry (Cornus canadensis) was highly correlated with hot spots, whereas tamarack (Larix laricina) was highly correlated with cold spots. Mean DISTEX values were positively and significantly correlated with the neutral detergent fiber and acid detergent lignin contents of feces. While our approach would need more independent field trials before it is fully validated, its low cost and ease of execution should make it a valuable tool for advancing both the basic and applied ecology of large herbivores.

Spatial pattern of North Atlantic Oscillation impact on rainfall in Serbia

This study examines the spatial pattern of relationships between annual, seasonal and monthly rainfall in Serbia, and the North Atlantic Oscillation (NAO) for the period of 1961–2009. The first correlation analysis between rainfall and the NAO was performed using a Pearson product-moment test. Results suggested negative, mainly statistically significant correlations at annual and winter scales as was expected. However, the highest percentage of stations showed significant result in October suggesting a strong impact of a large scale atmospheric mode throughout a wet season in Serbia. Further spatial analysis that incorporated a spatial autocorrelation statistic of correlation coefficients showed significant clustering at all temporal scales.

Spatio-temporal analysis of the relationship between climate and hand, foot, and mouth disease in Shandong province, China, 2008-2012.

BackgroundHand, foot, and mouth disease (HFMD) is the most common communicable disease in China. Shandong Province is one of the most seriously affected areas. The distribution of HFMD had spatial heterogeneity and seasonal characteristic in this setting. The aim of this study was to explore the associations between climate and HFMD by a Bayesian approach from spatio-temporal interactions perspective.MethodsThe HFMD data of Shandong Province during 2008–2012 were derived from the China National Disease Surveillance Reporting and Management System. And six climatic indicators were obtained from the Meteorological Bureau of Shandong Province. The global spatial autocorrelation statistic (Moran’s I) was used to detect the spatial autocorrelation of HFMD cases in each year. The optimal one among four Bayesian models was further adopted to estimate the relative risk of the occurrence of HFMD via Markov chain Monte Carlo.ResultsThe annual average incidence rate of HFMD was 104.40 per 100,000 in Shandong Province. Positive spatial autocorrelation appeared at county level (Moran’s I ≥0.30, P < 0.001). The best fitting Spatio-temporal interactive model showed that annual average temperature, annual average pressure, annual average relative humidity, annual average wind speed and annual sunshine hours were significantly positive related to the occurrence of HFMD. The estimated relative risk of 36, 87, 91, 79, 65 out of 140 counties for 2008–2012 respectively were significantly more than 1.ConclusionsThere were obvious spatio-temporal heterogeneity of HFMD in Shandong Province, and the climatic indicators were associated with the epidemic of HFMD. Bayesian approach should be recommended to capture the spatial-temporal pattern of HFMD.

Dispersal constraints and fine-scale spatial genetic structure in two earthworm species

The limited dispersal ability of earthworms is expected to result in marked genetic isolation by distance and remarkable spatial patterns of genetic variation. To test this hypothesis, we investigated, using microsatellite loci, the spatial genetic structure of two earthworm species, Allolobophora chlorotica and Aporrectodea icterica, in two plots of less than 1 ha where a total of 282 individuals were collected. We used spatial autocorrelation statistics, partial Mantel tests of isolation-by-distance (IBD) and isolation-by-resistance (IBR), and Bayesian test of clustering to explore recent patterns involved in the observed genetic structure. For A. icterica, a low signal of genetic structure was detected, which may be explained by an important dispersal capacity and/or by the low polymorphism of the microsatellite loci. For A. chlorotica, a weak, but significant, pattern of IBD associated with positive autocorrelation was observed in one of the plots. In the other plot, which had been recently ploughed, two genetically differentiated clusters were identified. These results suggest a spatial neighbourhood structure in A. chlorotica, with neighbour individuals that tend to be more genetically similar to one another, and also highlight that habitat perturbation as a result of human activities may deeply alter the genetic structure of earthworm species, even at a very small scale. © 2015 The Linnean Society of London, Biological Journal of the Linnean Society, 2015, 114, 335–347.