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Wyświetlanie 1-4 z 4
Tytuł:
New 14C data of megafaunal remains from Lithuania : implications for the palaeoenvironmental interpretation of the Middle Weichselian
Autorzy:
Satkūnas, Jonas
Girininkas, Algirdas
Rimkus, Tomas
Daugnora, Linas
Grigienė, Alma
Stančikaitė, Miglė
Slah, Gvidas
Skuratovič, Žana
Uogintas, Domas
Žulkus, Vladas
Tematy:
Mammuthus primigenius Blumenbach
Rangifer tarandus Linnaeus
1758
14C
environmental dynamics
Middle Weichselian
Lithuania
Pokaż więcej
Wydawca:
Państwowy Instytut Geologiczny – Państwowy Instytut Badawczy
Powiązania:
https://bibliotekanauki.pl/articles/58907121.pdf  Link otwiera się w nowym oknie
Opis:
Palaeobiological data, supplemented by new 14C dates in conjunction with palaeobotanical and lithological information, have allowed reconstruction of Middle Weichselian (MIS 3) environmental fluctuations in the southern Eastern Baltic region. Palaeoenvironmental reconstructions implying non-glacial conditions during the Middle Weichselian (MIS 3) are supported by the spatial and temporal context of recently discovered remains of Mammuthus primigenius Blumenbach and Rangifer tarandus Linnaeus, 1758. Recording both cold and warm climatic reversals of MIS 3, representatives of the megafauna thrived in an environment characterized by a heterogeneity of vegetation and climate. 14C dating shows that the majority of the megafaunal remains analysed represent the 38–45 cal kyr BP time-interval, which correlates with the Nemunas 2c cold interval (cryomer), and the 31–34 cal kyr BP or Mickñnai 3 thermomer. From pollen data, the palaeovegetation pattern varied from tree-less tundra to birch-predominating forest with an admixture of temporal tree species providing additional information about the diet and habitat preferences of these herbivores in the context of the MIS 3 climatic events.
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Learning the syntax of plant assemblages
Autorzy:
Garbolino, Emmanuel
Campos, Juan Antonio
Bonnet, Pierre
Čarni, Andraž
Servajean, Maximilien
De Sanctis, Michele
Uogintas, Domas
Jandt, Ute
Argagnon, Olivier
Moeslund, Jesper Erenskjold
Chytrý, Milan
Dziuba, Tetiana
Sibik, Jozef
Joly, Alexis
Jansen, Florian
Stančić, Zvjezdana
Ćušterevska, Renata
Thuiller, Wilfried
Bruelheide, Helge
Aćić, Svetlana
Biurrun, Idoia
Pielech, Remigiusz
Dengler, Jürgen
Leblanc, César
Bonari, Gianmaria
Pérez-Haase, Aaron
Wohlgemuth, Thomas
Lenoir, Jonathan
Opis:
To address the urgent biodiversity crisis, it is crucial to understand the nature of plant assemblages. The distribution of plant species is shaped not only by their broad environmental requirements but also by micro-environmental conditions, dispersal limitations, and direct and indirect species interactions. While predicting species composition and habitat type is essential for conservation and restoration purposes, it remains challenging. In this study, we propose an approach inspired by advances in large language models to learn the ‘syntax’ of abundance-ordered plant species sequences in communities. Our method, which captures latent associations between species across diverse ecosystems, can be fine-tuned for diverse tasks. In particular, we show that our methodology is able to outperform other approaches to (1) predict species that might occur in an assemblage given the other listed species, despite being originally missing in the species list (16.53% higher accuracy in retrieving a plant species removed from an assemblage than co-occurrence matrices and 6.56% higher than neural networks), and (2) classify habitat types from species assemblages (5.54% higher accuracy in assigning a habitat type to an assemblage than expert system classifiers and 1.14% higher than tabular deep learning). The proposed application has a vocabulary that covers over 10,000 plant species from Europe and adjacent countries and provides a powerful methodology for improving biodiversity mapping, restoration and conservation biology. As ecologists begin to explore the use of artificial intelligence, such approaches open opportunities for rethinking how we model, monitor and understand nature.
Dostawca treści:
Repozytorium Uniwersytetu Jagiellońskiego
Artykuł
Tytuł:
A deep-learning framework for enhancing habitat identification based on species composition
Autorzy:
Servajean, Maximilien
Leblanc, César
Šibík, Jozef
Stančić, Zvjezdana
Garbolino, Emmanuel
Uogintas, Domas
Lenoir, Jonathan
Moeslund, Jesper Erenskjold
Bergamini, Ariel
Jandt, Ute
Campos, Juan A.
Dengler, Jürgen
Golub, Valentin
Bonnet, Pierre
Čarni, Andraž
De Sanctis, Michele
Swacha, Grzegorz
Joly, Alexis
Pielech, Remigiusz
Argagnon, Olivier
Wohlgemuth, Thomas
Bonari, Gianmaria
Stanisci, Angela
Vassilev, Kiril
Biurrun, Idoia
Chytrý, Milan
Pérez-Haase, Aaron
Jansen, Florian
Lebedeva, Maria
Aćić, Svetlana
Ćušterevska, Renata
Opis:
Aims: The accurate classification of habitats is essential for effective biodiversity conservation. The goal of this study was to harness the potential of deep learning to advance habitat identification in Europe. We aimed to develop and evaluate models capable of assigning vegetation-plot records to the habitats of the European Nature Information System (EUNIS), a widely used reference framework for European habitat types. Location: The framework was designed for use in Europe and adjacent areas (e.g., Anatolia, Caucasus). Methods: We leveraged deep-learning techniques, such as transformers (i.e., models with attention components able to learn contextual relations between categorical and numerical features) that we trained using spatial k-fold cross-validation (CV) on vegetation plots sourced from the European Vegetation Archive (EVA), to show that they have great potential for classifying vegetation-plot records. We tested different network architectures, feature encodings, hyperparameter tuning and noise addition strategies to identify the optimal model. We used an independent test set from the National Plant Monitoring Scheme (NPMS) to evaluate its performance and compare its results against the traditional expert systems. Results: Exploration of the use of deep learning applied to species composition and plot-location criteria for habitat classification led to the development of a framework containing a wide range of models. Our selected algorithm, applied to European habitat types, significantly improved habitat classification accuracy, achieving a more than twofold improvement compared to the previous state-of-the-art (SOTA) method on an external data set, clearly outperforming expert systems. The framework is shared and maintained through a GitHub repository. Conclusions: Our results demonstrate the potential benefits of the adoption of deep learning for improving the accuracy of vegetation classification. They highlight the importance of incorporating advanced technologies into habitat monitoring. These algorithms have shown to be better suited for habitat type prediction than expert systems. They push the accuracy score on a database containing hundreds of thousands of standardized presence/absence European surveys to 88.74%, as assessed by expert judgment. Finally, our results showcase that species dominance is a strong marker of ecosystems and that the exact cover abundance of the flora is not required to train neural networks with predictive performances. The framework we developed can be used by researchers and practitioners to accurately classify habitats.
Dostawca treści:
Repozytorium Uniwersytetu Jagiellońskiego
Artykuł
Tytuł:
sPlot : a new tool for global vegetation analyses
Autorzy:
Ejrnæs, Rasmus
Kącki, Zygmunt
Curran, Michael
Landucci, Flavia
Zizka, Georg
Macanović, Armin
Stančić, Zvjezdana
Uogintas, Domas
Lenoir, Jonathan
Jiménez-Alfaro, Borja
Moeslund, Jesper Erenskjold
Vanselow, Kim André
Noroozi, Jalil
Sop, Tenekwetche
Peyre, Gwendolyn
Schaminée, Joop H. J.
Winter, Marten
Küzmič, Filip
Brisse, Henry
Dimopoulos, Panayotis
Mencuccini, Maurizio
Sandel, Brody
Revermann, Rasmus
Walker, Donald A.
Rodwell, John
Wohlgemuth, Thomas
Holubová, Dana
Šilc, Urban
Berg, Christian
Fotiadis, Georgios
El-Sheikh, Mohamed Abd El-Rouf Mousa
Weiher, Evan
Smyth, Anita
Li, Ching‐Feng
Kolomiychuk, Vitaliy
Jansen, Florian
Purschke, Oliver
Alvarez, Miguel
de Ruffray, Patrice
Violle, Cyrille
Casella, Laura
Wagner, Viktoria
Lopez-Gonzalez, Gabriela
Kavgacı, Ali
Cayuela, Luis
Apostolova, Iva
Rūsiņa, Solvita
Arnst, Elise
He, Tianhua
Jandt, Ute
Işık Gürsoy, Deniz
De Bie, Els
Agrillo, Emiliano
Jiroušek, Martin
Schmiedel, Ute
Wana, Desalegn
Sabatini, Francesco Maria
Swacha, Grzegorz
Hölzel, Norbert
Fagúndez, Jaime
Bergeron, Yves
Rašomavičius, Valerijus
Svenning, Jens-Christian
Samimi, Cyrus
Overbeck, Gerhard E.
Schrodt, Franziska
Sparrow, Ben
Kuzemko, Anna
Pedashenko, Hristo
Škvorc, Željko
Virtanen, Risto
Kozhevnikov, Maria
Munzinger, Jérôme
Niinemets, Ülo
García-Mijangos, Itziar
Nobis, Marcin
Bjorkman, Anne D.
Beckmann, Michael
Tsiripidis, Ioannis
Uğurlu, Emin
de Gasper, André Luis
Field, Richard
Tang, Zhiyao
Angelini, Pierangela
Boyle, Brad
Bruelheide, Helge
Finckh, Manfred
Jürgens, Norbert
Yamalov, Sergey
Lee, Michael T.
Borchardt, Peter
Turtureanu, Pavel Dan
Dengler, Jürgen
Černý, Tomáš
Kearsley, Elizabeth
Hennekens, Stephan M.
Manning, Peter
Valachovič, Milan
Kattge, Jens
von Wehrden, Henrik
Nowak, Arkadiusz
Peterka, Tomáš
Bergmeier, Erwin
Korolyuk, Andrey
Whitfeld, Timothy
Minden, Vanessa
Csiky, János
Wesche, Karsten
Byun, Chaeho
Mahdavi, Parastoo
Peñuelas, Josep
Krstonošić, Daniel
Chepinoga, Victor
Liu, Hongyan
Martynenko, Vassiliy
Peet, Robert K.
Pauchard, Anibal
Vélez-Martin, Eduardo
Chytrý, Milan
Venanzoni, Roberto
Gutierrez, Alvaro G.
Attorre, Fabio
Indreica, Adrian
Higuchi, Pedro
Aćić, Svetlana
Ćušterevska, Renata
Hatim, Mohamed Z.
Bondareva, Viktoria
Vibrans, Alexander Christian
Vashenyak, Yulia
Willner, Wolfgang
Homeier, Jürgen
Petřík, Petr
Onyshchenko, Viktor
Phillips, Oliver L.
Arfin Khan, Mohammed A. S.
Müller, Jonas V.
Kühl, Hjalmar
Prokhorov, Vadim
Wiser, Susan
Ambarlı, Didem
Baraloto, Christopher
Kühn, Ingolf
Enquist, Brian
Forey, Estelle
Pillar, Valério D.
Golub, Valentin
Schmidt, Marco
Breen, Amy
De Sanctis, Michele
Sopotlieva, Desislava
Font, Xavier
Botta-Dukát, Zoltán
Ewald, Jörg
Janssen, John
Jedrzejek, Birgit
Marcenò, Corrado
Moretti, Marco
Aubin, Isabelle
Haider, Sylvia
Vassilev, Kiril
Knollová, Ilona
Zverev, Andrei
Biurrun, Idoia
Dressler, Stefan
Levesley, Aurora
Pérez-Haase, Aaron
Ruprecht, Eszter
Dajić Stevanović, Zora
Kessler, Michael
Lysenko, Tatiana
Ozinga, Wim A.
Cabido, Marcelo R.
Šibík, Jozef
Jansen, Steven
Kozub, Łukasz
Dostawca treści:
Repozytorium Uniwersytetu Jagiellońskiego
Artykuł
    Wyświetlanie 1-4 z 4

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