Biology subjects

Botta-Dukat, Z.

Publications and source records attributed to Botta-Dukat, Z..

7 recordsLinked to original sources

bioRxiv2023

Quantitative evaluation of internal clustering validation indices using binary datesets

Different clustering methods often classify the same dataset differently. Selecting the best clustering solution out of a multitude of alternatives is possible with cluster validation indices. The behavior of validity indices changes with the structure of the sample and the properties of the clustering algorithm. Unique properties of each index cause increasing or decreasing performance in some conditions. Due to the large variety of cluster validation indices, choosing the most suitable index concerning the dataset and clustering algorithms is challenging. We aim to assess different internal clustering validation indices. In the present paper, the validity indices consist of geometric and non-geometric methods. For this purpose, we applied simulated datasets with different noise levels. Each dataset was repeated 20 times. Three clustering algorithms with Jaccard dissimilarity are used, and 27 clustering validation indices are evaluated. The results provide a reliability guideline for the selection cluster validity indices.

ecology↗

bioRxiv2022

PADAPT 1.0 - the Pannonian Database of Plant Traits

We present PADAPT 1.0, the Pannonian Database of Plant Traits which relies on regional data sources and integrates existing data and new measurements on a wide range of traits and attributes of the plant species of the Pannonian Biogeographical Region and makes it freely accessible at www.padapt.eu. The current version covers the species of the region occurring in Hungary (cc. 90% of the regions flora) and consists of 126,337 records on 2745 taxa. There are 53 plant attributes in PADAPT 1.0 organised in six major groups: (i) Habitus and strategy, (ii) Reproduction, (iii) Kariology, (iv) Distribution and conservation, (v) Ecological indicator values, and (vi) Leaf traits. By including species of the eastern part of Europe not covered by other databases, PADAPT can facilitate studying the flora and vegetation of the eastern part of the continent. Data collection will continue in the future and the PADAPT team welcomes researchers interested in contributing with data. The main task before an updated version of the database is to include species of the Pannonian region not covered by the current version. In conclusion, although data coverage is far from complete, PADAPT meets the longstanding need for a regional database of the Pannonian flora.

ecology↗

bioRxiv2022

Quartile coefficient of variation is more robust than CV for traits calculated as a ratio

Comparing within-species variations of traits can be used in testing ecological theories. In these comparisons, it is useful to remove the effect of the difference in mean trait values, therefore measures of relative variation, most often the coefficient of variation (CV), are used. The studied traits are often calculated as the ratio of the size or mass of two organs: e.g. SLA is the ratio of leaf size and leaf mass. Often the inverse of these ratios is also meaningful; for example, the inverse of SLA is often referred to as LMA (leaf mass per area). Relative variation of a trait and its inverse should not considerably differ. However, it is illustrated that using the coefficient of variation may result in differences that could influence the interpretation, especially if there are outlier trait values. The alternative way for estimating CV from the standard deviation of log-transformed data assuming log-normal distribution and Kirkwoods geometric coefficient of variation free from this problem, but they proved to be sensitive to outlier values. Quartile coefficient of variation performed best in the tests: it gives the same value for a trait and its inverse and it is not sensitive to outliers.

ecology↗

bioRxiv2021

Using trait data improves correlation between environment and community data only if abundances are considered

A straightforward way to explore variation between communities is to calculate dissimilarity indices and relate them with environmental and spatial variables. Communities are most often represented by the (relative) abundances of taxa they comprise; however, more recently, the distribution of traits of organisms included in the communities has been shown more strongly related to ecosystem properties. In this study, we test whether taxon- or trait-based dissimilarity is correlated more tightly with environmental difference and geographical distance and how the abundance scale influences this correlation. Our study system is grassland vegetation in Hungary, where we sampled vegetation plots spanning a long productivity gradient from open dry grasslands to marshes in three sites. We considered three traits for vascular plants: canopy height, specific leaf area and seed mass. We obtained field estimates of normalized vegetation difference index (NDVI) as proxy of productivity (water availability) for each plot. We calculated between-community dissimilarities using a taxon-based and a trait-based index, using raw and square-root transformed abundances and presence/absence data. We fitted distance-based redundancy analysis models with NDVI difference and geographical distance on the dissimilarity matrices and evaluated them using variance partitioning. Then, using the pooled data, we calculated non-metric multidimensional scaling ordinations (NMDS) from all types of dissimilarity matrices and made pairwise comparisons using Procrustes analysis. Data analysis was done separately for the three sites. We found that taxonomical dissimilarity matches environmental and spatial variables better when presence/absence data is used instead of abundance. This pattern was mainly determined by the increasing variation explained by space at the presence/absence scale. In contrast to this trend, with trait-based dissimilarity, accounting for abundance increased explained variation significantly due to the higher explanatory power of NDVI. With abundance data, considering traits improved environmental matching to a great extent in comparison with taxonomical information. However, with presence/absence data, traits brought no advantage over taxon-based dissimilarity in any respect. Changing the abundance scale caused larger difference between ordinations in the case of trait-based dissimilarity than with taxonomical dissimilarity. We conclude that considering relevant traits improves environmental matching only if abundances are also accounted for. Supporting informationAdditional graphs supporting the results are presented as appendix. Open researchData used in this research are publicly available from Dryad ###link to be supplied upon acceptance###

ecology↗

bioRxiv2021

Review and performance evaluation of trait-based between-community dissimilarity measures

One of the effective tools to study the variation between communities is the use of pairwise dissimilarity indices. Besides species as variables, the involvement of trait information provides valuable insight into the functioning of ecosystems. In recent years, a variety of indices have been proposed to quantify functional dissimilarity between communities. These indices follow different approaches to account for between-species similarities in calculating community dissimilarity, yet they all have been proposed as straightforward tools. In this paper, we review the trait-based dissimilarity indices available in the literature and identify the most important conceptual and technical properties that differentiate among them and that must be considered before their application. We identify two primary aspects that need to be considered before choosing a functional dissimilarity index. The first one is the way communities are represented in the trait space. The three main types of representations are the typical values, the combination of speciesxsites and speciesxtrait matrices, and the hypervolumes. The second decision is the concept of dissimilarity to follow, including two options: distances and (lack of) overlaps. We use the above scheme to discuss the available functional dissimilarity indices and evaluate their relations to each other, their capabilities, and accessibility.

bioinformatics↗

bioRxiv2019

New iterative reallocation algorithms based on the silhouette index for numerical classification

AimsTo introduce REMOS, a new iterative reallocation method (with two variants) for vegetation classification, and to compare its performance with OPTSIL. We test (1) how effectively REMOS and OPTSIL maximize mean silhouette width and minimize the number of negative silhouette widths when run on classifications with different structure; (2) how these three methods differ in runtime with different sample sizes; and (3) if classifications by the three reallocation methods differ in the number of diagnostic species, a surrogate for interpretability. Study areaSimulation; example data sets from grasslands in Hungary and forests in Wyoming and Utah, USA. MethodsWe classified random subsets of simulated data with the flexible-beta algorithm for different values of beta. These classifications were subsequently optimized by REMOS and OPTSIL and compared for mean silhouette widths and proportion of negative silhouette widths. Then, we classified three vegetation data sets of different sizes from two to ten clusters, optimized them with the reallocation methods, and compared their runtimes, mean silhouette widths, numbers of negative silhouette widths, and the number of diagnostic species. ResultsIn terms of mean silhouette width, OPTSIL performed the best when the initial classifications already had high mean silhouette width. REMOS algorithms had slightly lower mean silhouette width than what was maximally achievable with OPTSIL but their efficiency was consistent across different initial classifications; thus REMOS was significantly superior to OPTSIL when the initial classification had low mean silhouette width. REMOS resulted in zero or a negligible number of negative silhouette widths across all classifications. OPTSIL performed similarly when the initial classification was effective but could not reach as low proportion of misclassified objects when the initial classification was inefficient. REMOS algorithms were typically more than an order of magnitude faster to calculate than OPTSIL. There was no clear difference between REMOS and OPTSIL in the number of diagnostic species. ConclusionsREMOS algorithms may be preferable to OPTSIL when (1) the primary objective is to reduce or eliminate negative silhouette widths in a classification, (2) the initial classification has low mean silhouette width, or (3) when the time efficiency of the algorithm is important because of the size of the data set or the high number of clusters.

bioinformatics↗

bioRxiv2018

Silhouette width using generalized mean - a flexible method for assessing clustering efficiency

O_LICluster analysis plays vital role in pattern recognition in several fields of science. Silhouette width is a widely used measure for assessing the fit of individual objects in the classification, as well as the quality of clusters and the entire classification. This index uses two clustering criteria, compactness (average within-cluster distances) and separation (average between-cluster distances), which implies that spherical cluster shapes are preferred over others - a property that can be seen as a disadvantage in the presence 22 of clusters with high internal heterogeneity, which is common in real situations.\nC_LIO_LIWe suggest a generalization of the silhouette width using the generalized mean. By changing the p parameter of the generalized mean between -{infty} and +{infty}, several specific summary statistics, including the minimum, maximum, the arithmetic, harmonic, and geometric means, can be reproduced. Implementing the generalized mean in the calculation of silhouette width allows for changing the sensitivity of the index to compactness vs. connectedness. With higher sensitivity to connectedness instead of compactness the preference of silhouette width towards spherical clusters is expected to reduce. We test the performance of the generalized silhouette width on artificial data sets and on the Iris data set. We examine how classifications with different numbers of clusters prepared by single linkage, group average, and complete linkage algorithms are evaluated, if p is set to different values.\nC_LIO_LIWhen p was negative, well separated clusters achieved high silhouette widths despite their elongated or circular shapes. Positive values of p increased the importance of compactness, hence the preference towards spherical clusters became even more detectable. With low p, single linkage clustering was deemed the most efficient clustering method, while with higher parameter values the performance of group average and complete linkage seemed better.\nC_LIO_LIThe generalized silhouette width is a promising tool for assessing clustering quality. It allows for adjusting the contribution of compactness and connectedness criteria to the index value, thus avoiding underestimation of clustering efficiency in the presence of clusters with high internal heterogeneity.\nC_LI

bioinformatics↗