Journal of Applied Phycology

, Volume 29, Issue 4, pp 2145–2153 | Cite as

Modeling the species richness and abundance of lotic macroalgae based on habitat characteristics by artificial neural networks: a potentially useful tool for stream biomonitoring programs

  • José Celso Rocha
  • Cleto K. Peres
  • José Leonel L. Buzzo
  • Vinícius de Souza
  • Eric A. Krause
  • Pitágoras C. Bispo
  • Fernando Frei
  • Lucas S. M. Costa
  • Ciro C. Z. BrancoEmail author


One of the major challenges in stream ecology is the development of computational models that can predict aspects of the community structure of organisms from these ecosystems when they are subject to natural or artificial environmental fluctuations. To contribute towards this aim, we conducted a study whose main goal was to evaluate the efficiency and accuracy of different architectures of multilayer artificial neural networks (ANNs) in predicting the species richness and abundance of macroalgae based on environmental variables of tropical streams. We used data from 82 streams located in southern Brazil, where species richness, macroalgal abundance, and environmental parameters were measured. A set of 20 environmental parameters measured directly in the stream was used as explanatory variables. The performance of the ANN architectures was assessed using two different pieces of software (random combinatorial and exhaustive) and the coefficient of determination (R 2) and mean-squared error (MSE). For both species richness and macroalgal abundance, the best ANN architectures were obtained using random combination software and the performance parameters showed a combination of high R 2 and very low MSE. Our results suggest that computational models that are constructed based on ANN frameworks can be efficient and accurate in predicting the species richness and abundance of stream macroalgae from environmental data. Therefore, considering that models based on linear relationships have often failed, we recommend the application of ANNs as a tool to estimate species richness and abundance of lotic macroalgae from environmental data, in the management, conservation, and biomonitoring programs of tropical stream ecosystems.


Stream macroalgae Species richness and abundance Environmental distribution Artificial neural networks Predictive models Stream biomonitoring programs 



This work was supported by funding received by Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP - Grants 2010/17864-0 and 2014/22952-6 to CCZB) and Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq - Grants 306567/2014-8 to CCZB and 305275/2014-3 to PCB). We also thank ICMBio/MMA and the conservation units for granting permission to collect specimens and for providing logistical support during the field works.

Supplementary material

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ESM 1 (DOCX 20 kb)


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Copyright information

© Springer Science+Business Media Dordrecht 2017

Authors and Affiliations

  • José Celso Rocha
    • 1
  • Cleto K. Peres
    • 2
  • José Leonel L. Buzzo
    • 1
  • Vinícius de Souza
    • 1
  • Eric A. Krause
    • 1
  • Pitágoras C. Bispo
    • 1
  • Fernando Frei
    • 1
  • Lucas S. M. Costa
    • 1
  • Ciro C. Z. Branco
    • 1
    Email author
  1. 1.Department of Biological SciencesSão Paulo State University—UNESPAssisBrazil
  2. 2.Federal University for Latin American Integration—UNILAFoz do IguaçuBrazil

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