Ecological Niche Modeling for Halophyte Species with Possible Anthropogenic Use in Agricultural Saline Soils

M. A. Lastiri-Hernández, G. Cruz-Cárdenas, D. Álvarez-Bernal, M. Vázquez-Sánchez, K. Bermúdez-Torres

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

The objective of this study was to generate an ecological niche modeling (ENM) to determine potential sites or areas where the growth and development of halophyte species are the most appropriate. To determine the number of halophyte species in the zone of study, the random quadrant method was used. The material collected per site was separated and identified. One hundred samples were taken from the soil at a depth of 20 cm for the analysis of five chemical properties. Evaporation, precipitation, and minimum and maximum temperature data were obtained from 34 meteorological stations. Geostatistical techniques were applied for the spatial prediction of the climate and soil variables. In addition, five scenes from the Sentinel-2A satellite and the digital elevation model were retrieved. Principal component analysis was performed on the 15 environmental predictors. Ten algorithms were used to conduct the ENM. The ENM results of the halophyte species were reclassified in binary maps. A species richness (PRS) map was created according to the predictions from the sum of binary maps. The PRS map was used to define the potential sites to grow and develop halophyte species of anthropogenic interest. Ten halophyte species were found in the sampled zone, of which 70% were native. The first seven principal components (95% of the cumulative variance) were used for the ENM. The AUC values of the halophyte species models ranged from 0.61 to 0.98. A total of 38% of the study area was considered suitable for potential sites of halophyte species.

Original languageEnglish
Pages (from-to)429-440
Number of pages12
JournalEnvironmental Modeling and Assessment
Volume25
Issue number3
DOIs
StatePublished - 1 Jun 2020

Keywords

  • Biodiversity
  • Maximum entropy
  • Salinity
  • Spatial variability
  • Villamar

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