Morisita-based space-clustering analysis of Swiss seismicity

Telesca, Luciano; Golay, Jean; Kanevski, Mikhaïl

doi:10.1016/j.physa.2014.10.004

Cited by 8 publications

(4 citation statements)

References 46 publications

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“…The Morisita index of aggregation and modifications of the index have been applied to examine spatial patterns and processes of seed dispersal, seed bank, and tree establishment (Houle 1994;Mosandl and Kleinert 1998;Shaukat and Sadiqi 2004;de Almeida and Galetti 2007); to compare spatial patterns of recruitment and adults (Hubbell 1979; Barros Henriques and Girnos de Sousa 1989); and to compare and rank (aggregated to uniform) the spatial distributions of species within a forest (Amaral et al 2015). Distributions of sampling locations in environmental monitoring networks (Tuia and Kanevski 2008;Golay et al 2014) and the spatial clustering of earthquake locations (Ouchi and Uekawa 1986;Telesca et al 2015) . Adult stems (circles) can be seen to be widely distributed throughout various topographic positions at the three sites.…”

Section: Methodsmentioning

confidence: 99%

Spatial Clustering and Ecological Crowding of Valley Oak (Quercus lobata, Née) Associated with Shifts in Recruitment Establishment Sites in Southern California

Hayes¹,

Donnelly

2017

International Journal of Plant Sciences

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Premise of research. Valley oak (Quercus lobata, Née) has received much attention due to concerns about regeneration failure and, more recently, compositional and structural community changes associated with this foundation species. Changes in the structure and spatial distribution of valley oak stands, as examined in this article, are likely affecting ecological processes and interactions throughout communities where it is found. Methodology. We used quadrat-based methods of spatial analysis, Morisita's index of aggregation (I M), and a derived index (I Mr) to examine patterns of clustering, intensity of crowding, and changes in the probability of crowding as the definition of crowding (stems/area) is changed. Saplings, defined as stems with a diameter at breast height (dbh) greater than 1 cm and less than 10 cm (1 cm ≤ dbh ! 10 cm) and adults (dbh ≥ 10 cm) were analyzed using four quadrat sizes. We mapped clusters of stems on the basis of spatial autocorrelation of dbh using Anselin's local Moran's I to identify the location of sapling and adult stem clusters. Pivotal results. Adults and saplings were clustered for all quadrat sizes, although the intensity of clustering and crowding differed among sites. Two sites had much higher intensity of clustering and crowding, and saplings were spatially segregated from adults away from open savannah habitat. At a third site, stems were less intensely clustered, and saplings did not show clustering away from adults. The intensity of sapling crowding differed among sites as the definition of crowding changed, revealing that saplings experience greater levels of crowding at two of the sites than at the third. Conclusions. Valley oak regeneration is spatially aggregated and ecologically crowded, indicating a shift to higher-density riparian woodland and increasingly open grassland seen at other sites and in other studies. Land change is an important driver of spatial regeneration patterns and may be playing a role here, although additional work is needed to examine the effects of possible drivers of crowding and the habitat shift. Future work should consider the ecological implications of structural and spatial changes in valley oak stands for associated species and processes.

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Section: Methodsmentioning

confidence: 99%

Spatial Clustering and Ecological Crowding of Valley Oak (Quercus lobata, Née) Associated with Shifts in Recruitment Establishment Sites in Southern California

Hayes¹,

Donnelly

2017

International Journal of Plant Sciences

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“…This index has the advantage of not being affected by the population density and sample size, which usually limits comparisons and the use of many indexes for spatial correlation and distribution (Morisita 1962, Hurlbert 1990, Hayes and Castillo 2017). The only limitation of the Morisita Index is the scale of the study and the quadrat size, which can affect some distribution indexes and limit comparisons between different populations and different study areas (Golay et al 2014, Telesca et al 2015). Addressing the use of different quadrat sizes for the same area and verifying the impact that the change of the quadrat size has on the index can be helpful to avoid this problem and better determine quadrat sizes in future studies (Fortin 1999, Hayes and Castillo 2017).…”

Section: Discussionmentioning

confidence: 99%

Assessing spatial attributes of wild bird populations using the Morisita Index and Ripley's K function: Demonstration with Southern Lapwing (Vanellus chilensis) data

Delfino

2023

The Wilson Journal of Ornithology

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“…The theory stated by Morisita [19] and further studies [18,54,55] do not specify how to define the range of the quadrat size or how to choose a quadrat size for I Mr . Golay et al [16] tackled the scale problem by linking I Mr index and the multifractality concept through quadrat-based methods, i.e., Rényi's generalized dimensions and the lacunarity index [16,17,56]. It appears that we are approaching the problem of scale by measuring the degree of crowding for different quadrat sizes.…”

Section: Discussionmentioning

confidence: 99%

A New Approach for Interpreting the Morisita Index of Aggregation through Quadrat Size

Hayes

Castillo

2017

IJGI

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Abstract:Spatial point pattern analysis is commonly used in ecology to examine the spatial distribution of individual organisms or events, which may shed light on the operation of underlying ecological processes driving the development of a spatial pattern. Commonly used quadrat-based methods of measuring spatial clustering or dispersion tend to be strongly influenced by the choice of quadrat size and population density. Using valley oak (Quercus lobata) stands at multiple sites, we show that values of the Morisita Index are sensitive to the choice of quadrat size, and that the comparative interpretation of the index for multiple sites or populations is problematic due to differences in scale and clustering intensity from site to site, which may call for different quadrat sizes for each site. We present a new method for analyzing the Morisita Index to estimate the appropriate quadrat size for a given site and to aid interpretation of the clustering index across multiple sites with local differences. By plotting the maximum clustering intensity (Imr) found across a range of quadrat sizes, we were able to describe how a spatial pattern changes when quadrat size varies and to identify scales of clustering and quadrat sizes for analysis of spatial patterns under different local conditions. Computing and plotting the instantaneous rate of change (first derivative of rMax), we were able to evaluate clustering across multiple sites on a standardized scale. The magnitude of the rMax first derivative is a useful tool to quantify the degree of crowding, dispersion, or random spatial distribution as a function of quadrat size.

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Morisita-based space-clustering analysis of Swiss seismicity

Cited by 8 publications

References 46 publications

Spatial Clustering and Ecological Crowding of Valley Oak (Quercus lobata, Née) Associated with Shifts in Recruitment Establishment Sites in Southern California

Spatial Clustering and Ecological Crowding of Valley Oak (Quercus lobata, Née) Associated with Shifts in Recruitment Establishment Sites in Southern California

Assessing spatial attributes of wild bird populations using the Morisita Index and Ripley's K function: Demonstration with Southern Lapwing (Vanellus chilensis) data

A New Approach for Interpreting the Morisita Index of Aggregation through Quadrat Size

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