A major objective of plant ecology research is to determine the underlying processes responsible for the observed spatial distribution patterns of plant species. Plants can be approximated as points in space for this purpose, and thus, spatial point pattern analysis has become increasingly popular in ecological research. The basic piece of data for point pattern analysis is a point location of an ecological object in some study region. Therefore, point pattern analysis can only be performed if data can be collected. However, due to the lack of a convenient sampling method, a few previous studies have used point pattern analysis to examine the spatial patterns of grassland species. This is unfortunate because being able to explore point patterns in grassland systems has widespread implications for population dynamics, community-level patterns and ecological processes. In this study, we develop a new method to measure individual coordinates of species in grassland communities. This method records plant growing positions via digital picture samples that have been sub-blocked within a geographical information system (GIS). Here, we tested out the new method by measuring the individual coordinates of Stipa grandis in grazed and ungrazed S. grandis communities in a temperate steppe ecosystem in China. Furthermore, we analyzed the pattern of S. grandis by using the pair correlation function g(r) with both a homogeneous Poisson process and a heterogeneous Poisson process. Our results showed that individuals of S. grandis were overdispersed according to the homogeneous Poisson process at 0-0.16 m in the ungrazed community, while they were clustered at 0.19 m according to the homogeneous and heterogeneous Poisson processes in the grazed community. These results suggest that competitive interactions dominated the ungrazed community, while facilitative interactions dominated the grazed community. In sum, we successfully executed a new sampling method, using digital photography and a Geographical Information System, to collect experimental data on the spatial point patterns for the populations in this grassland community.

Methods 1. Data collection using digital photographs and GIS

A flat 5 m x 5 m sampling block was chosen in a study grassland community and divided with bamboo chopsticks into 100 sub-blocks of 50 cm x 50 cm (Fig. 1). A digital camera was then mounted to a telescoping stake and positioned in the center of each sub-block to photograph vegetation within a 0.25 m2 area. Pictures were taken 1.75 m above the ground at an approximate downward angle of 90° (Fig. 2). Automatic camera settings were used for focus, lighting and shutter speed. After photographing the plot as a whole, photographs were taken of each individual plant in each sub-block. In order to identify each individual plant from the digital images, each plant was uniquely marked before the pictures were taken (Fig. 2 B).

Digital images were imported into a computer as JPEG files, and the position of each plant in the pictures was determined using GIS. This involved four steps: 1) A reference frame (Fig. 3) was established using R2V software to designate control points, or the four vertexes of each sub-block (Appendix S1), so that all plants in each sub-block were within the same reference frame. The parallax and optical distortion in the raster images was then geometrically corrected based on these selected control points; 2) Maps, or layers in GIS terminology, were set up for each species as PROJECT files (Appendix S2), and all individuals in each sub-block were digitized using R2V software (Appendix S3). For accuracy, the digitization of plant individual locations was performed manually; 3) Each plant species layer was exported from a PROJECT file to a SHAPE file in R2V software (Appendix S4); 4) Finally each species layer was opened in Arc GIS software in the SHAPE file format, and attribute data from each species layer was exported into Arc GIS to obtain the precise coordinates for each species. This last phase involved four steps of its own, from adding the data (Appendix S5), to opening the attribute table (Appendix S6), to adding new x and y coordinate fields (Appendix S7) and to obtaining the x and y coordinates and filling in the new fields (Appendix S8).

1. Data reliability assessment

To determine the accuracy of our new method, we measured the individual locations of Leymus chinensis, a perennial rhizome grass, in representative community blocks 5 m x 5 m in size in typical steppe habitat in the Inner Mongolia Autonomous Region of China in July 2010 (Fig. 4 A). As our standard for comparison, we used a ruler to measure the individual coordinates of L. chinensis. We tested for significant differences between (1) the coordinates of L. chinensis, as measured with our new method and with the ruler, and (2) the pair correlation function g of L. chinensis, as measured with our new method and with the ruler (see section 3.2 Data Analysis). If (1) the coordinates of L. chinensis, as measured with our new method and with the ruler, and (2) the pair correlation function g of L. chinensis, as measured with our new method and with the ruler, did not differ significantly, then we could conclude that our new method of measuring the coordinates of L. chinensis was reliable.

We compared the results using a t-test (Table 1). We found no significant differences in either (1) the coordinates of L. chinensis or (2) the pair correlation function g of L. chinensis. Further, we compared the pattern characteristics of L. chinensis when measured by our new method against the ruler measurements using a null model. We found that the two pattern characteristics of L. chinensis did not differ significantly based on the homogenous Poisson process or complete spatial randomness (Fig. 4 B). Thus, we concluded that the data obtained using our new method was reliable enough to perform point pattern analysis with a null model in grassland communities.

The data and codes are supplyments of the paper entitled “Comparison of Moran’s I and Geary’s c in Multivariate Spatial Pattern Analysis” and published in Geographical Analysis.

Unsupervised exploratory data analysis (EDA) is often the first step in understanding complex data sets. While summary statistics are among the most efficient and convenient tools for exploring and describing sets of data, they are often overlooked in EDA. In this paper, we show multiple case studies that compare the performance, including clustering, of a series of summary statistics in EDA. The summary statistics considered here are pattern recognition entropy (PRE), the mean, standard deviation (STD), 1-norm, range, sum of squares (SSQ), and X4, which are compared with principal component analysis (PCA), multivariate curve resolution (MCR), and/or cluster analysis. PRE and the other summary statistics are direct methods for analyzing datathey are not factor-based approaches. To quantify the performance of summary statistics, we use the concept of the “critical pair,” which is employed in chromatography. The data analyzed here come from different analytical methods. Hyperspectral images, including one of a biological material, are also analyzed. In general, PRE outperforms the other summary statistics, especially in image analysis, although a suite of summary statistics is useful in exploring complex data sets. While PRE results were generally comparable to those from PCA and MCR, PRE is easier to apply. For example, there is no need to determine the number of factors that describe a data set. Finally, we introduce the concept of divided spectrum-PRE (DS-PRE) as a new EDA method. DS-PRE increases the discrimination power of PRE. We also show that DS-PRE can be used to provide the inputs for the k-nearest neighbor (kNN) algorithm. We recommend PRE and DS-PRE as rapid new tools for unsupervised EDA.

Efficient comparisons of biological color patterns are critical for understanding the mechanisms by which organisms evolve in ecosystems, including sexual selection, predator-prey interactions, and thermoregulation. However, elongate or spiral-shaped organisms do not conform to the standard orientation and photographic techniques required for automated analysis. Currently, large-scale color analysis of elongate animals requires time-consuming manual landmarking, which reduces their representation in coloration research despite their ecological importance. We present Batch-Mask: an automated and customizable workflow to facilitate the analysis of large photographic data sets of non-standard biological subjects. First, we present a user guide to run an open-source region-based convolutional neural network with fine-tuned weights for identifying and isolating a biological subject from a background (masking). Then, we demonstrate how to combine masking with existing manual visual analysis tools into a single streamlined, automated workflow for comparing color patterns across images. Batch-Mask was 60x faster than manual landmarking, produced masks that correctly identified 96% of all snake pixels, and produced pattern energy results that were not significantly different from the manually landmarked data set. The fine-tuned weights for the masking neural network, user guide, and automated workflow substantially decrease the amount of time and attention required to quantitatively analyze non-standard biological subjects. By using these tools, biologists will be able to compare color, pattern, and shape differences in large data sets that include significant morphological variation in elongate body forms. This advance will be especially valuable for comparative analyses of natural history collections, and through automation can greatly expand the scale of space, time, or taxonomic breadth across which color variation can be quantitatively examined.

1. Plant to plant interactions are probably the most important driver of species coexistence at fine spatial scales, but their detection represents a challenge in Ecology. Spatial point pattern analysis (SPPA) is likely the approach most used to identify them however, it suffers from some limitations related to the over-simplification of individuals to points.
2. Here, we propose a new approach called Overlapping Area Analysis (OAA) to test whether the consideration of the shape and orientation of the individuals reveal signs of interactions between species that would remain undetected with SPPA. We used this approach to analyze a fully-mapped cryophilic grassland in Sierra de Guadarrama National Park (Spain), where the crown of each individual plant (i.e. the canopy) was approximated by a polygon. We then computed and compared the total overlapping area between the canopy of a focal species and that of any other species in the community with the expectations of a null model of random rotation of each plant around its centroid. We complemented the results of our new approach by comparing with that of SPPA of plants’ centroids.
3. Results of OAA showed that up to 41% of species pairs had less canopy overlap than expected, suggesting that many interspecific canopy associations in this plant community were significantly negative at the finest spatial scale. Contrarily, SPPA estimated that 12% of species pairs were positively associated at spatial scales up to 20 cm, confirming the facilitative effect displayed by the main engineer in the community (Festuca curvifolia Lag.) and by some other dominant species.
4. Our new approach quantifying canopy associations provides new insights into the processes guiding community assembly. Whereas the results of SPPA suggested the prevalence of traditional “stress gradient hypothesis” (i.e. prevalence of positive interactions under stressful abiotic conditions), OAA revealed that many interspecific canopy associations were significantly negative. Overall, most facilitated species optimized this positive effect by placing their centroids as close to the benefactor species as their foraging behaviour allowed while avoiding crown overlap. The method proposed is available in a dedicated R-package that will facilitate its application by other ecologists.

IntroductionChildhood stunting is a global public health concern, associated with both short and long-term consequences, including high child morbidity and mortality, poor development and learning capacity, increased vulnerability for infectious and non-infectious disease. The prevalence of stunting varies significantly throughout Ethiopian regions. Therefore, this study aimed to assess the geographical variation in predictors of stunting among children under the age of five in Ethiopia using 2019 Ethiopian Demographic and Health Survey.MethodThe current analysis was based on data from the 2019 mini Ethiopian Demographic and Health Survey (EDHS). A total of 5,490 children under the age of five were included in the weighted sample. Descriptive and inferential analysis was done using STATA 17. For the spatial analysis, ArcGIS 10.7 were used. Spatial regression was used to identify the variables associated with stunting hotspots, and adjusted R2 and Corrected Akaike Information Criteria (AICc) were used to compare the models. As the prevalence of stunting was over 10%, a multilevel robust Poisson regression was conducted. In the bivariable analysis, variables having a p-value < 0.2 were considered for the multivariable analysis. In the multivariable multilevel robust Poisson regression analysis, the adjusted prevalence ratio with the 95% confidence interval is presented to show the statistical significance and strength of the association.ResultThe prevalence of stunting was 33.58% (95%CI: 32.34%, 34.84%) with a clustered geographic pattern (Moran’s I = 0.40, p40 (APR = 0.74, 95%CI: 0.55, 0.99). Children whose mother had secondary (APR = 0.74, 95%CI: 0.60, 0.91) and higher (APR = 0.61, 95%CI: 0.44, 0.84) educational status, household wealth status (APR = 0.87, 95%CI: 0.76, 0.99), child aged 6–23 months (APR = 1.87, 95%CI: 1.53, 2.28) were all significantly associated with stunting.ConclusionIn Ethiopia, under-five children suffering from stunting have been found to exhibit a spatially clustered pattern. Maternal education, wealth index, birth interval and child age were determining factors of spatial variation of stunting. As a result, a detailed map of stunting hotspots and determinants among children under the age of five aid program planners and decision-makers in designing targeted public health measures.

This exploratory study aimed to assess the efficacy of pattern-matching statistical methods within the Meyers Neuropsychological Battery (MNB). It compared neuropsychological test data profiles of Alzheimer’s disease (AD) patients from three independent samples against four MNB dementia groups: MNB-AD, MNB-Vascular Dementia (VaD), MNB-Dementia with Lewy bodies (DLB), and MNB-Parkinson’s disease dementia (PDD). Three AD-independent samples completed either the MNB (referred to as I-MNB-AD), Dementia Rating Scale-2 with additional testing (denoted as DRS-Plus-AD), or the Repeatable Battery for the Assessment of Neuropsychological Status (designated as RBANS-AD). Test data profiles were cross-validated with four MNB dementia comparison group datasets. Statistical methods included Pearson correlation, Kullback-Leibler (KL) divergence, pooled effect size (Cohen’s d), Configuration, and MNB Code. Classification accuracy ranged from 40% (Pearson r) to 88% (Cohen’s d) in the I-MNB-AD sample, 47% (Cohen’s d) to 93% (KL) in the DRS-Plus-AD sample, and 47% (Pearson r) to 78% (Configuration) in the RBANS-AD sample. Some methods showed limited effectiveness depending on the sample and comparison group analyzed, while others demonstrated strong performance. Using a simple majority count of agreement, classification rates for selecting the MNB-AD comparison group were 80% (I-MNB-AD), 85% (DRS-Plus-AD), and 66% (RBANS-AD). This exploratory study demonstrates that specific statistical methods employed in the MNB for pattern-matching analysis effectively differentiated neuropsychological profiles of individuals with AD from other types of dementia, contributing to improved diagnostic precision. The findings underscore the potential advantages of pattern-matching analysis, advocating for further research to validate and refine its application.

Dataset from the article Porta A, Bari V, Cairo B, De Maria B, Vaini E, Barbic F, Furlan R. Comparison of symbolization strategies for complexity assessment of spontaneous variability in individuals with signs of cardiovascular control impairment. Biomedical signal processing and control. 2020; doi: 10.1016/j.bspc.2020.102128

Abstract

Symbolic analysis was frequently utilized in cardiovascular control assessment from spontaneous variability of heart period (HP) and systolic arterial pressure (SAP). However, due to different symbolization approaches comparison among studies present in literature is difficult especially on pathological groups with signs of autonomic dysfunction. We performed HP and SAP symbolic analyses via different symbolization methods over a group of Parkinson’s disease (PD) patients with no sign of orthostatic hypotension and over age- and gender-matched healthy (H) controls at rest in supine position (REST) and during head-up tilt (HUT). The most frequently exploited symbolization methods applied directly to the original values, referred to as amplitude-based (AB) techniques, and to their first variations, labeled as variation-based (VB) methods, were compared. The rates of pattern families featuring different amount of variability among symbols were computed. In agreement with the inclusion criteria PD patients could maintain steady SAP during HUT via a tachycardic response. In spite of similar trends symbolic markers exhibited subtle differences. The level of consistency among different symbolic approaches is more dependent on the type of series and pattern family than on symbolization strategy (i.e. AB or VB). Consistency was higher over SAP than HP symbolic indexes and over highly variable patterns than more stable ones. All the symbolic methods detected an increased complexity of cardiac and vascular controls in PD patients compared to H subjects more evident during HUT than at REST. Symbolic markers of highly variable patterns provide interpretation independent of the symbolization technique exploited for their computation.

Feeding tests 2.1. Tests on entire plants To describe the feeding pattern of the pollen beetle, one individual was placed on the main inflorescence of an entire plant at the green-yellow bud stage as described in Hervé et al. (2014a). After three days, the number of buds damaged by feeding was counted. Buds damaged by feeding are recognizable as they show irregular holes of usually large sizes and located anywhere on the buds, while buds damaged by oviposition show stereotypic small holes at their basis. The length (i.e. maturity) of damaged buds was measured under a binocular microscope (0.1 mm precision). Ten replicates were performed. Data analysis – All statistical analyses were performed with the R software v. 4.0.2 (R Core Team, 2020). The mean number of buds damaged was compared between maturity classes (young, intermediate and old buds) using a Wald test applied on a Generalized Linear Mixed Model (GLMM) including the individual plant as random factor (distribution: Poisson, link function: log) (R package ‘lme4’ (Bates et al., 2015) and ‘car’ (Fox & Weisberg., 2019)). Here and in the following experiments, pairwise comparisons of Estimated Marginal Means (EMMeans) were systematically performed using the 'emmeans' package (Lenth, 2019) and p-values adjusted using the False Discovery Rate correction (Benjamini & Hochberg 1995). 2.2. Tests on plant organs To confirm the preference for flowers over flower buds and to assess the contribution of resource accessibility and resource chemical composition to this preference, dual-choice tests were performed. One individual was placed in a Petri dish (Ø = 55 mm) for 2 h with two different food sources, and it was then recorded whether each food source had been damaged or not. Three choice tests were conducted: one flower vs. one old bud (to confirm preference for flowers), one old bud vs. six anthers just excised from one old bud (to assess influence of resource accessibility), and six anthers just excised from one flower vs. six anthers just excised from one old bud (to assess influence of resource chemical composition). Old buds rather than young buds were chosen to decipher the factors responsible for the preference for flowers for practical aspects and to avoid any bias related to a physical factor, the anthers of old buds and flowers having the same size. Twenty to 27 replicates were performed per choice test. Data analysis – For each choice test, the probability of being damaged was compared between the two food sources using a Cochran’s Q test, which considered the replicate as pairing factor (R package ‘RVAideMemoire’, Hervé 2021). 2.3. Tests on artificial substrates An experimental setup based on agar disks was designed to assess the respective and relative contributions of macronutrients (either their quantity and ratio) and defense metabolites in the preference of pollen beetles for flowers. Artificial substrates consisted of 3 % agar disks (Ø = 5 mm, thickness = 2 mm) supplemented with macronutrients (casein:whey 80:20 as protein source and sucrose as carbohydrate source) and/or pure standards of defense metabolites, depending on the experiment. Macronutrient and defense metabolite concentrations used in experiments varied depending on the hypothesis tested (see Results and Table S1). In all experiments on artificial substrates, two individuals were placed in a Petri dish (Ø = 35 mm) for 3 h, with two to three disks depending on the experiment. Insects were filmed during the experiment and their movements tracked with the Ethovision XT software v. 15 (Noldus, Wageningen, Netherlands). The feeding behavior was estimated as the cumulative duration spent on each disk. Following preliminary observations, it was assumed that individuals were feeding when on disks and that the feeding speed was constant. Thirty replicates were performed per experiment. Data analysis – For each experiment, the total time spent on disks was compared between treatments using a Wald test applied on a Linear Mixed Model (LMM) that included the replicate as random factor (R packages ‘lme4’ and ‘car’). The response was systematically square-root transformed to ensure model fitting. 3. Chemical characterization of plant organs 3.1. Macronutrients Macronutrient quantification was performed on five samples of three different plant organs: anthers from flowers, old buds and young buds. Each sample comprised organs collected from 14-16 plants (four organs per plant), immediately frozen into liquid nitrogen then freeze-dried. Plant organs were sampled from the same plants to ensure unbiased comparisons. Total soluble proteins (P) were extracted from 10 mg of dried powder that was agitated for 15 min at room temperature in 1 mL of acidified phosphate buffer (0.2 M, pH = 6.8), then centrifuged at 12,000 g for 30 min at 4 °C. Quantification was performed using the Bradford’s method (Bradford, 1976) and stand...

RNA tomography computationally reconstructs 3D spatial gene expression patterns genome-widely from 1D tomo-seq data, generated by RNA sequencing of cryosection samples along three orthogonal axes. We developed tomoseqr, an R package designed for RNA tomography analysis of tomo-seq data, to reconstruct and visualize 3D gene expression patterns through user-friendly graphical interfaces. We show the effectiveness of tomoseqr using simulated and real tomo-seq data, validating its utility for researchers. R package tomoseqr is available on Bioconductor (https://doi.org/doi:10.18129/B9.bioc.tomoseqr) and GitHub (https://github.com/bioinfo-tsukuba/tomoseqr).

Comparison of footprint pattern classes and the 2011 census rural-urban classification for output areas in England and Wales.

Network analyses applied to models of complex systems generally contain at least three levels of analyses. Whole-network metrics summarize general organizational features (properties or relationships) of the entire network, while node-level metrics summarize similar organization features but considering individual nodes at the time. The network- and node-level metrics build upon the primary pairwise relationships in the model. As with many analyses, sometimes there are interesting differences at one level that disappear in the summary at another level of analysis. We illustrate this phenomenon with ecosystem network models, where nodes are trophic compartments and pairwise relationships are flows of organic carbon, such as when a predator eats a prey. For this demonstration, we analysed a time-series of 16 models of a lake planktonic food web that describes carbon exchanges within an autumn cyanobacteria bloom and compared the ecological conclusions drawn from the three levels of analysis based on inter-time-step comparisons. A general pattern in our analyses was that the closer the levels are in hierarchy (node versus network, or flow versus node level), the more they tend to align in their conclusions. Our analyses suggest that selecting the appropriate level of analysis, and above all regularly using multiple levels, may be a critical analytical decision.This article is part of the special issue ‘Unifying the essential concepts of biological networks: biological insights and philosophical foundations'.

Bivariate difference test g1,1+2–g2,1+2 testing the density-dependent mortality on F (fire) and F+R (erosion) treatments for each species of Cistaceae family.

Sustainable construction and demolition waste management relies heavily on the attitudes and actions of its constituents; nevertheless, deep analysis for introducing the best estimator is rarely attained. The main objective of this study is to perform a comparison analysis among different approaches of Structural Equation Modeling (SEM) in Construction and Demolition Waste Management (C&DWM) modeling based on an Extended Theory of Planned Behaviour (Extended TPB). The introduced research model includes twelve latent variables, six independent variables, one mediator, three control variables, and one dependent variable. Maximum likelihood (ML), partial least square (PLS), and Bayesian estimators were considered in this study. The output of SEM with the Bayesian estimator was 85.8%, and among effectiveness of six main variables on C&DWM Behavioral (Depenmalaydent variables), five of them have significant relations. Meanwhile, the variation based on SEM with ML estimator was equal to 78.2%, and four correlations with dependent variable have significant relationship. At the conclusion, the R-square of SEM with the PLS estimator was equivalent to 73.4% and three correlations with the dependent variable had significant relationships. At the same time, the values of the three statistical indices include root mean square error (RMSE), mean absolute percentage error (MPE), and mean absolute error (MSE) with involving Bayesian estimator are lower than both ML and PLS estimators. Therefore, compared to both PLS and ML, the predicted values of the Bayesian estimator are closer to the observed values. The lower values of MPE, RMSE, and MSE and the higher values of R-square will generate better goodness of fit for SEM with a Bayesian estimator. Moreover, the SEM with a Bayesian estimator revealed better data fit than both the PLS and ML estimators. The pattern shows that the relationship between research variables can change with different estimators. Hence, researchers using the SEM technique must carefully consider the primary estimator for their data analysis. The precaution is necessary because higher error means different regression coefficients in the research model.

Bivariate difference test g1,1+2–g2,1+2 testing the density-dependent mortality on F (fire) and F+R (erosion) treatments for each species of Fabaceae family.

This archive contains example input maps and R scripts used for examples and comparisons, and instructions to download GraySpatCon source code and binary executable files for 64-bit Linux, macOS, and MS-Windows operating systems.

Variables selected for the analysis.

Anonymous Data and the R script used for data analysis.