Spatial micro-distribution of shoots in Posidonia oceanica (L.) Delile meadows

10.7287/peerj.preprints.1053 ◽

2015 ◽

Author(s):

Tiziano Bacci ◽

Sante Francesco Rende ◽

Domenico Rocca ◽

Michele Scardi

Keyword(s):

Spatial Distribution ◽

Posidonia Oceanica ◽

Small Scale ◽

Nearest Neighbour ◽

Data Set ◽

Spatial Indices ◽

Neighbour Distance ◽

Point Patterns ◽

Spatial Point ◽

Spatial Point Patterns

Posidonia oceanica meadows contain huge numbers of shoots and their dynamics are strictly related to spatial distribution patterns of those shoots. In order to investigate the structure of P. oceanica meadows at very small spatial scale (i.e. in the 1 cm 2 -1 m 2 range), patterns in shoot distribution were analyzed. Spatial distribution of shoots was recorded by cutting all the leaves and by digitizing shoot location from images of 10 square frames (1 m 2 ), sampled in seemingly uniformly dense meadows at two sites in Southern Italy. Spatial point patterns have been explored testing the sensitivity and robustness through different spatial indices, based on i) nearest neighbour analysis, ii) quadrat counts analysis, iii) fractal dimension. Clark & Evans nearest neighbour distance index has been proved to be the most suitable for aim of the work and it has been selected for the further analysis. Data analysis of the 10 square frames (1 m 2 ) highlighted regular spatial point patterns (R>1; p<0.0001) in most cases (8 frames), while aggregated (R<1; p<0.01) and random (R=1) spatial point patterns were rare. In addition, mean value of nearest neighbour distance of shoots in each square frame analyzed has been shown to be always close to 2 cm (min: 1.73 cm; max: 2.21 cm). The potential implications of this type of data set were highlighted. Both nearest neighbour distance of shoots and spatial point pattern typology (aggregated, random or regular) could provide useful and integrative information for the study of P. oceanica macrostructure (e.g. implementation of shoot growth models, development of new descriptors). The raw data, provided by the authors as supplementary material, are currently the first and the only information available about shoot spatial micro-distribution. In this regard, although our data set cannot represent the whole spectrum of variability in P. oceanica meadows, it certainly shed some light on the small scale patterns of P. oceanica meadows and it prompts us many questions, some of which are still unanswered.

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NEARFST NEIGHBOUR DISTANCE APPROACH FOR THE ANALYSIS OF SPATIAL POINT PATTERNS

Japanese Journal of Biometrics ◽

10.5691/jjb.8.1_79 ◽

1987 ◽

Vol 8 (1) ◽

pp. 1_79-88

Author(s):

Masaharu Tanemura

Keyword(s):

Neighbour Distance ◽

Point Patterns ◽

Spatial Point ◽

Spatial Point Patterns ◽

Distance Approach

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Can the Spatial Point Patterns of Animal Distributions Be Detected Using Sparse Samples? A Case Study of Four Soricomorpha (Mammalia) Species in Poland / Czy Przestrzenny Wzorzec Rozkładu Punktów W Dystrybucji Zwierząt Może Zostać Określony Na Podstawie Rzadkiego Próbkowania? Studium Przypadku Na Czterech Gatunkach Soricomorpha (Mammalia) Występujących W Polsce.

Zoologica Poloniae ◽

10.2478/zoop-2014-0003 ◽

2014 ◽

Vol 59 (1-4) ◽

pp. 11-24

Author(s):

Youhua Chen

Keyword(s):

Poisson Process ◽

Point Process ◽

Process Models ◽

Spatial Point Process ◽

Data Set ◽

Point Patterns ◽

Spatial Point ◽

Spatial Point Patterns ◽

Point Process Models ◽

K Function

Abstract In the present study, Riley's K function and alternative spatial point process models are calculated and compared for the hybrid distributional records of four Soricomorpha species (Talpa europaea, Sorex araneus, Sorex minutus, and Neomys fodiens) in Poland over different sampling sizes. The following spatial point process models are fitted and compared: homogeneous Poisson process (HPP) and inhomogeneous Poisson process (IPP) models. For IPP models, the covariates explaining the trend are latitude and longitude. Spatial process models and true distributional aggregation status (using K function) of the four species are also calculated based on the full observed data set for the purpose to check how many grids are required to sample so as to reflect the true spatial distributional point patterns. When performind tha sampling, the sanpling size 5, 10, 30, 60 and 100 are considered. For each sampling size, 500 replicates are performed to keep consistence and reduce uncertainty. The results showed that, for the full observed data set over the whole territory of Poland, IPP models were much better than the null HPP model for explaining the distribution of Soricomorpha species. For every sample size, the true aggregation status and the associated spatial point process models of each species over the studied area can be perfectly identified when using the information derived from limiting samples only. Based on the results, it is found that around 20% of grid cells should be used as the minimum threshold for accurately detecting the true spatial point patterns

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Does fluvial channel-belt clustering predict net sand to gross rock volume? Architectural metrics and point-pattern analysis of a digital outcrop model

Journal of Sedimentary Research ◽

10.2110/jsr.2019.60 ◽

2019 ◽

Vol 89 (11) ◽

pp. 1109-1126

Author(s):

Alexander R. Koch ◽

Cari L. Johnson ◽

Lisa Stright

Keyword(s):

Large Scale ◽

Small Scale ◽

Point Pattern ◽

Point Patterns ◽

Spatial Point ◽

Channel Belt ◽

John Henry Member ◽

Scale Point ◽

Belt Width ◽

Digital Outcrop

ABSTRACT Spatial point-pattern analyses (PPAs) are used to quantify clustering, randomness, and uniformity of the distribution of channel belts in fluvial strata. Point patterns may reflect end-member fluvial architecture, e.g., uniform compensational stacking and avulsion-generated clustering, which may change laterally, especially at greater scales. To investigate spatial and temporal changes in fluvial systems, we performed PPA and architectural analyses on extensive outcrops of the Cretaceous John Henry Member of the Straight Cliffs Formation in southern Utah, USA. Digital outcrop models (DOMs) produced using unmanned aircraft system-based stereophotogrammetry form the basis of detailed interpretations of a 250-m-thick fluvial succession over a total outcrop length of 4.5 km. The outcrops are oriented roughly perpendicular to fluvial transport direction. This transverse cross-sectional exposure of the fluvial system allows a study of the system's variation along depositional strike. We developed a workflow that examines spatial point patterns using the quadrat method, and architectural metrics such as net sand to gross rock volume (NTG), amalgamation index, and channel-belt width and thickness within moving windows. Quadrat cell sizes that are ∼ 50% of the average channel-belt width-to-thickness ratio (16:1 aspect ratio) provide an optimized scale to investigate laterally elongate distributions of fluvial-channel-belt centroids. Large-scale quadrat point patterns were recognized using an array of four quadrat cells, each with 237× greater area than the median channel belt. Large-scale point patterns and NTG correlate negatively, which is a result of using centroid-based PPA on a dataset with disparately sized channel belts. Small-scale quadrat point patterns were recognized using an array of 16 quadrat cells, each with 21× greater area than the median channel belt. Small-scale point patterns and NTG correlate positively, and match previously observed stratigraphic trends in the fluvial John Henry Member, suggesting that these are regional trends. There are deviations from these trends in architectural statistics over small distances (hundreds of meters) which are interpreted to reflect autogenic avulsion processes. Small-scale autogenic processes result in architecture that is difficult to correlate between 1D datasets, for example when characterizing a reservoir using well logs. We show that 1D NTG provides the most accurate prediction for surrounding 2D architecture.

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