toggle visibility Search & Display Options

Select All    Deselect All
 |   | 
Details
   print
  Records Links
Author (up) Hirn, J.; Garcia, J.E.; Montesinos-Navarro, A.; Sanchez-Martin, R.; Sanz, V.; Verdu, M. url  doi
openurl 
  Title A deep Generative Artificial Intelligence system to predict species coexistence patterns Type Journal Article
  Year 2022 Publication Methods in Ecology and Evolution Abbreviated Journal Methods Ecol. Evol.  
  Volume 13 Issue Pages 1052-1061  
  Keywords artificial intelligence; direct interactions; generative adversarial networks; indirect interactions; species coexistence; variational AutoEncoders  
  Abstract Predicting coexistence patterns is a current challenge to understand diversity maintenance, especially in rich communities where these patterns' complexity is magnified through indirect interactions that prevent their approximation with classical experimental approaches. We explore cutting-edge Machine Learning techniques called Generative Artificial Intelligence (GenAI) to predict species coexistence patterns in vegetation patches, training generative adversarial networks (GAN) and variational AutoEncoders (VAE) that are then used to unravel some of the mechanisms behind community assemblage. The GAN accurately reproduces real patches' species composition and plant species' affinity to different soil types, and the VAE also reaches a high level of accuracy, above 99%. Using the artificially generated patches, we found that high-order interactions tend to suppress the positive effects of low-order interactions. Finally, by reconstructing successional trajectories, we could identify the pioneer species with larger potential to generate a high diversity of distinct patches in terms of species composition. Understanding the complexity of species coexistence patterns in diverse ecological communities requires new approaches beyond heuristic rules. Generative Artificial Intelligence can be a powerful tool to this end as it allows to overcome the inherent dimensionality of this challenge.  
  Address [Hirn, Johannes; Enrique Garcia, Jose; Sanz, Veronica] Univ Valencia, CSIC, Inst Fis Corpuscular IFIC, Valencia, Spain, Email: miguel.verdu@ext.uv.es  
  Corporate Author Thesis  
  Publisher Wiley Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 2041-210x ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000765239700001 Approved no  
  Is ISI yes International Collaboration yes  
  Call Number IFIC @ pastor @ Serial 5155  
Permanent link to this record
 

 
Author (up) Hirn, J.; Sanz, V.; Garcia Navarro, J.E.; Goberna, M.; Montesinos-Navarro, A.; Navarro-Cano, J.A.; Sanchez-Martin, R.; Valiente-Banuet, A.; Verdu, M. doi  openurl
  Title Transfer learning of species co-occurrence patterns between plant communities Type Journal Article
  Year 2024 Publication Ecological Informatics Abbreviated Journal Ecol. Inform.  
  Volume 83 Issue Pages 102826 - 8pp  
  Keywords Generative artificial intelligence; Patchy vegetation; Plant communities; Restoration ecology; Species co-occurrence; Variational autoencoders  
  Abstract Aim: The use of neural networks (NNs) is spreading to all areas of life, and Ecology is no exception. However, the data-hungry nature of NNs can leave out many small, valuable datasets. Here we show how to apply transfer learning to rescue small datasets that can be invaluable in understanding patterns of species co-occurrence. Location: Semiarid plant communities in Spain and Me<acute accent>xico. Time period: 2016-2022. Major taxa studied: Angiosperms. Methods: Based on a large sample of plant species co-occurrence in vegetation patches in a semi-arid area of eastern Spain, we fit a generative artificial intelligence (AI) model that correctly reproduces which species live with which in these patches. Subsequently, we train the same type of model on two communities for which we only have smaller datasets (another semi-arid community in eastern Spain, and a tropical community in Mexico). Results: When we transfer the knowledge learnt from the large dataset directly to the other two, the predictions improve for the community more similar to our reference one. As for the more dissimilar community, improving the accuracy of the transfer requires a further tuning of the model to the local data. In particular, the knowledge transferred relates primarily to species frequency and, to a lesser extent, to their phylogenetic relationships, which are known to be determinants of species interaction patterns. Main conclusions: This AI-based approach can be performed for communities similar or not so similar to the reference community, opening the door to systematic transfer learning for accurate predictions on small datasets. Interestingly, this transfer operates by matching unrelated species between the origin and target datasets, implying that arbitrary datasets can then be transferred to, or even combined in order to augment each other, irrespective of the species involved, potentially allowing such models to be applied to a wide range of plant communities in different climates.  
  Address [Hirn, Johannes; Montesinos-Navarro, Alicia; Sanchez-Martin, Ricardo; Verdu, Miguel] Univ Valencia Generalitat Valenciana, Ctr Invest Desertificac CIDE, CSIC, Valencia, Spain  
  Corporate Author Thesis  
  Publisher Elsevier Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1574-9541 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:001327519900001 Approved no  
  Is ISI yes International Collaboration yes  
  Call Number IFIC @ pastor @ Serial 6278  
Permanent link to this record
Select All    Deselect All
 |   | 
Details
   print

Save Citations:
Export Records:
ific federMinisterio de Ciencia e InnovaciĆ³nAgencia Estatal de Investigaciongva