Biology subjects

Lengyel, A.

Publications and source records attributed to Lengyel, A..

8 recordsLinked to original sources

bioRxiv2025

Small grassland patches are hotspots for medicinal plants and associated phytochemical diversity in European agricultural landscapes

O_LIMedicinal plants are threatened by overcollection and habitat loss due to human activities. In this study, we aimed to reveal how ancient earthen burial mounds covered with successional grasslands of different ages can contribute to conservation of medicinal plants and related phytochemical diversity in European agricultural landscapes. C_LIO_LIUsing vegetation data collected from 166 mounds in Hungary, we identified the medicinal plant species present at these sites, and based on multiple literature sources, provided the secondary metabolite profiles of the medicinal plants and plant communities. By using generalized linear mixed models and redundancy analysis we investigated how the age of the vegetation, percentage of croplands around the sites, site area, and soil properties influence the diversity of medicinal plants and associated phytochemicals. C_LIO_LIWe found that mounds provide safe havens for a wide range of medicinal plants and secondary metabolites. Our results showed that grassland-related medicinal plants producing iridoids and diterpenes were typical on mounds with older grasslands. A large proportion of croplands in the neighboring landscape and high soil pH supported generalist medicinal plants producing nitrogen-containing and sesquiterpene related secondary metabolites. Higher soil nitrogen, phosphorus, and potassium content favored medicinal plants producing lignans, glycosides, and organic acids. C_LIO_LIOur results suggest that the high phytochemical diversity is supported by the occurrence of various compounds facilitating the adaptation of medicinal plants to the various environmental stressors and biotic factors, such as competition, herbivory and pathogens, present in sites embedded in agricultural landscapes. C_LI Societal Impact StatementBesides being important components of landscape-level biodiversity, medicinal plants are essential resources for traditional and modern healthcare. However, in parallel with biodiversity loss driven by human activities, populations of medicinal plants have also declined. By maintaining connections between nature, culture, and people, sacred natural sites can help counteract this trend. We studied the potential of ancient burial mounds to maintain populations of medicinal plants and related phytochemical diversity in European agricultural landscapes. Our findings highlight the importance of conserving grassland vegetation and associated biodiversity at these sites, which is crucial for preserving a key ecosystem service in intensively managed landscapes.

ecology↗

bioRxiv2024

A high resolution model of the grapevine leaf morphospace predicts synthetic leaves

O_LIGrapevine leaves are a model morphometric system. Sampling over ten thousand leaves using dozens of landmarks, the genetic, developmental, and environmental basis of leaf shape has been studied and a morphospace for the genus Vitis predicted. Yet, these representations of leaf shape fail to capture the exquisite features of leaves at high resolution. C_LIO_LIWe measure the shapes of 139 grapevine leaves using 1672 pseudo-landmarks derived from 90 homologous landmarks with Procrustean approaches. From hand traces of the vasculature and blade, we have derived a method to automatically detect landmarks and place pseudo-landmarks that results in a high-resolution representation of grapevine leaf shape. Using polynomial models, we create continuous representations of leaf development in 10 Vitis spp. C_LIO_LIWe visualize a high-resolution morphospace in which genetic and developmental sources of leaf shape variance are orthogonal to each other. Using classifiers, V. vinifera, Vitis spp., rootstock and dissected leaf varieties as well as developmental stages are accurately predicted. Theoretical eigenleaf representations sampled from across the morphospace that we call synthetic leaves can be classified using models. C_LIO_LIBy predicting a high-resolution morphospace and delimiting the boundaries of leaf shapes that can plausibly be produced within the genus Vitis, we can sample synthetic leaves with realistic qualities. From an ampelographic perspective, larger numbers of leaves sampled at lower resolution can be projected onto this high-resolution space; or, synthetic leaves can be used to increase the robustness and accuracy of machine learning classifiers. C_LI Societal Impact StatementGrapevine leaves are emblematic of the strong visual associations people make with plants. At a glance, leaf shape is immediately recognizable, and it is because of this reason it is used to distinguish grape varieties. In an era of computationally-enabled, machine learning-derived representations of reality, we can revisit how we view and use the shapes and forms that plants display to understand our relationship with them. Using computational approaches combined with time-honored methods, we can predict theoretical leaves that are possible to understand the genetics, development, and environmental responses of plants in new ways.

plant biology↗

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↗

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↗