Winstanley et les Diggers

Matheron, François

doi:10.3917/mult.009.0069

Cited by 2 publications

(3 citation statements)

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“…Spatial distribution in patches for each direction combination was analyzed with omnidirectional variograms, which are semivariance (inverse of autocorrelation) plots as a function of the separation distance between pairs of observations in each plane. Semivariance was calculated as:

γ (h) = \frac{1}{2 W (h)} \sum_{i = 1}^{W (h)} {[y_{i} ‐ y_{i + h}]}^{2},

following Matheron (1965).…”

Section: Methodsmentioning

confidence: 99%

Spatial relationships between spiders and their host vascular epiphytes within shade trees in a Mexican coffee plantation

et al. 2021

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Within tree canopies, vascular epiphytes create habitats for other taxa, and their heterogeneous spatial distribution could affect the distribution of organisms associated with them, such as spiders. This study was performed in shade trees of a rustic coffee plantation located within a Tropical Cloud Forest region of Mexico. We used a spatially explicit approach to examine (1) if the richness and abundance of epiphytedwelling spiders have a positive association with epiphyte abundance within trees and(2) if spiders (richness and abundance) show the same patterns of spatial distribution as the epiphytic habitat. We found that spiders were distributed on gradients of the same type as their host epiphytes. These gradients were a decrease from the center toward the edges of the tree and a decrease from the base of the trunk toward the canopy. Spiders also had aggregation patches with similar dimensions to those exhibited by their host epiphytes. Those spiders' patches were fully explained when their epiphytic habitat was also aggregated within trees. Spatial models suggested that epiphytes and spiders were also spatially structured at scales larger than a tree and smaller than an epiphyte mat. Our findings demonstrate that the spatial distribution of epiphyte-dwelling spider communities, may partly be explained by the distribution patterns of their host plants. However, other environmental and biotic factors, not associated with epiphyte communities, are likely to be responsible for the remaining spatial patterns of spider distribution.

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γ (h) = \frac{1}{2 W (h)} \sum_{i = 1}^{W (h)} {[y_{i} ‐ y_{i + h}]}^{2},

following Matheron (1965).…”

Section: Methodsmentioning

confidence: 99%

Spatial relationships between spiders and their host vascular epiphytes within shade trees in a Mexican coffee plantation

et al. 2021

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“…Kriging is a family of estimators generally used in geostatistics for the interpolation of spatial data, i.e. to estimate variables at unobserved locations based on observed points at nearby locations. ,, The kriging interpolation method seems to be a promising approach, as based on values measured in points from a certain range, it allows making predictions and the uncertainty of each prediction knowing just the distances to the known instances. The most widely used method is ordinary kriging, which was also selected for this study as it is the simplest model, makes no assumption on the nature or properties of the metric space, and uses only distances between instances and measured values for inference.…”

Section: Methodsmentioning

confidence: 99%

“…One of the most known used methods for quantitative estimation based on topological distances is kriging, a method traditionally used in geostatistics which makes use of Tobler’s first law of geography: ″Everything is related to everything else, but near things are more related than distant things″ , meaning that a spatial dependence in the data is considered contrary to traditional statistical methods which assume that all data are independent. This method involves the estimation of a regionalized variable at a particular unsampled location by the weighted combination of the values of the neighboring locations. − The use of this method has several advantages, namely the following: (1) estimates the estimation error along with the estimate of the property for each compound and this estimation error is minimized, therefore it is expected to be zero at the locations where experiments are performed and to grow with distance from these; (2) easier to comprehend than a black box model; (3) makes use of the distance/similarity between the compounds and it is not dependent on the selection of molecular descriptors; (4) fast enough to apply to a large data set; (5) searches for the relationship among measured properties rather than approximate the modeled system by fitting the parameters of the selected basis functions.…”

Section: Introductionmentioning

confidence: 99%

Structural Similarity Based Kriging for Quantitative Structure Activity and Property Relationship Modeling

Teixeira

Falcão

2014

J. Chem. Inf. Model.

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Structurally similar molecules tend to have similar properties, i.e. closer molecules in the molecular space are more likely to yield similar property values while distant molecules are more likely to yield different values. Based on this principle, we propose the use of a new method that takes into account the high dimensionality of the molecular space, predicting chemical, physical, or biological properties based on the most similar compounds with measured properties. This methodology uses ordinary kriging coupled with three different molecular similarity approaches (based on molecular descriptors, fingerprints, and atom matching) which creates an interpolation map over the molecular space that is capable of predicting properties/activities for diverse chemical data sets. The proposed method was tested in two data sets of diverse chemical compounds collected from the literature and preprocessed. One of the data sets contained dihydrofolate reductase inhibition activity data, and the second molecules for which aqueous solubility was known. The overall predictive results using kriging for both data sets comply with the results obtained in the literature using typical QSPR/QSAR approaches. However, the procedure did not involve any type of descriptor selection or even minimal information about each problem, suggesting that this approach is directly applicable to a large spectrum of problems in QSAR/QSPR. Furthermore, the predictive results improve significantly with the similarity threshold between the training and testing compounds, allowing the definition of a confidence threshold of similarity and error estimation for each case inferred. The use of kriging for interpolation over the molecular metric space is independent of the training data set size, and no reparametrizations are necessary when more compounds are added or removed from the set, and increasing the size of the database will consequentially improve the quality of the estimations. Finally it is shown that this model can be used for checking the consistency of measured data and for guiding an extension of the training set by determining the regions of the molecular space for which new experimental measurements could be used to maximize the model's predictive performance.

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Winstanley et les Diggers

Cited by 2 publications

References 0 publications

Spatial relationships between spiders and their host vascular epiphytes within shade trees in a Mexican coffee plantation

Spatial relationships between spiders and their host vascular epiphytes within shade trees in a Mexican coffee plantation

Structural Similarity Based Kriging for Quantitative Structure Activity and Property Relationship Modeling

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