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001-es BibID:	BIBFORM100398
035-os BibID:	(cikkazonosító)1298 (WOS)000747291900059 (Scopus)85123499383
Első szerző:	Fekete Réka (biológus)
Cím:	Road traffic and landscape characteristics predict the occurrence of native halophytes on roadside verges / Fekete R., Bak H., Vincze O., Süveges K., Molnár V. A.
Dátum:	2022
ISSN:	2045-2322
Megjegyzések:	Road management practices, such as winter de-icing create ideal habitats and competitive advantage for salt-tolerant species. We aimed to map the occurrences of halophytes along roads in Hungary. Furthermore, we tested factors that might play a role in the roadside occurrences of five chosen native halophytes from rare to common, we encountered during our field surveys. These were Festuca pseudovina, Limonium gmelinii subsp. hungaricum, Podospermum canum, Puccinellia distans and Spergularia media. We found, that at least one halophyte species was documented in 71% of the total sampling points. Germination experiments indicated that substrate salt concentration significantly decreased germination rates in each of the five species, but in case of L. gmelinii subsp. hungaricum, or P. distans germination occurred on extremely high salt concentrations. Traffic intensity, the presence of other halophytes at the sampling point and the presence of a given species in the surrounding landscape had a significant positive effect on the occurrence of four of the five model species. Our results suggest that the studied species are mostly in the early stage of their roadside spread, colonizing roadsides close to their native distribution ranges. The possibility of a future range expansion along roads cannot be excluded.
Tárgyszavak:	Természettudományok Biológiai tudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk distribution roadside verges vascular plants
Megjelenés:	Scientific Reports. - 12 : 1 (2022), p. 1-12. -
További szerzők:	Bak Henrietta (1998-) Vincze Orsolya (1988-) (biológus) Süveges Kristóf (1994-) Molnár V. Attila (1969-) (biológus, botanikus)
Pályázati támogatás:	K132573 OTKA
Internet cím:	Szerző által megadott URL DOI Intézményi repozitóriumban (DEA) tárolt változat
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001-es BibID:	BIBFORM105789
035-os BibID:	(cikkazonosító)20450 (WoS)000889945500067 (Scopus)85142874450
Első szerző:	Łysko, Andrzej
Cím:	Comparison of discriminant methods and deep learning analysis in plant taxonomy: a case study of Elatine / Andrzej Łysko, Agnieszka Popiela, Paweł Forczmaǹski, Attila Molnár V., Balázs András Lukács, Zoltán Barta, Witold Maćków, Grzegorz J. Wolski
Dátum:	2022
ISSN:	2045-2322
Megjegyzések:	Elatine is a genus in which, flower and seed characteristics are the most important diagnostic features; i.e. seed shape and the structure of its cover found to be the most reliable identification character. We used a combination of classic discriminant methods by combining with deep learning techniques to analyze seed morphometric data within 28 populations of six Elatine species from 11 countries throughout the Northern Hemisphere to compare the obtained results and then check their taxonomic classification. Our findings indicate that among the discriminant methods, Quadratic Discriminant Analysis (QDA) had the highest percentage of correct matching (mean fit - 91.23%); only the deep machine learning method based on Convolutional Neural Network (CNN) was characterized by a higher match (mean fit - 93.40%). The QDA method recognized the seeds of E. brochonii and E. orthosperma with 99% accuracy, and the CNN method with 100%. Other taxa, such as E. alsinastrum, E. trianda, E. californica and E. hungarica were matched with an accuracy of at least 95% (CNN). Our results indicate that the CNN obtains remarkably more accurate classifications than classic discriminant methods, and better recognizes the entire taxa pool analyzed. The least recognized species are E. macropoda and E. hexandra (88% and 78% match).
Tárgyszavak:	Természettudományok Biológiai tudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk Elatine Discriminant methods Machine learning Plant taxonomy
Megjelenés:	Scientific Reports. - 12 : 1 (2022), p. 1-9. -
További szerzők:	Popiela, Agnieszka Forczmaǹski, Paweł Molnár V. Attila (1969-) (biológus, botanikus) Lukács Balázs András (1979-) (ökológus) Barta Zoltán (1967-) (biológus, zoológus) Maćków, Witold Wolski, Grzegorz J.
Pályázati támogatás:	NKFIH-K-138503 Egyéb
Internet cím:	Szerző által megadott URL DOI Intézményi repozitóriumban (DEA) tárolt változat
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