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Habitat suitability and distribution patterns of Rouget's rail ( Rougetius rougetii Guérin-méneville, 1843) in Ethiopia.

Hailu Tilahun ArgawAfework BekeleAnagaw AtickemNils Chr StensethDiress TsegayeBezawork Afework Bogale
Published in: Ecology and evolution (2024)
Geographical distribution and diversity patterns of bird species are influenced by climate change. The Rouget's rail ( Rougetius rougetii ) is a ground-dwelling endemic bird species distributed in Ethiopia and Eritrea. It is a near-threatened species menaced by habitat loss, one of the main causes of population declines for bird species. The increasing effects of climate change may further threaten the species' survival. So far, the spatial distribution of this species is not fully documented. With this study, we develop current potential suitable habitat and predict the future habitat shift of R . rougetii based on environmental data such as bioclimatic variables, population density, vegetation cover, and elevation using 10 algorithms. We evaluated the importance of environmental factors in shaping the bird's distribution and how it shifts under climate change scenarios. We used 182 records of R. rougetii from Ethiopia and nine bioclimatic, population density, vegetation cover, and elevation variables to run the 10 model algorithms. Among 10 algorithms, eight were selected for ensembling models according to their predictive abilities. The current suitable habitats for R. rougetii were predicted to cover an area of about 82,000 km 2 despite being highly fragmented. The model suggested that temperature seasonality (bio4), elevation, and mean daily air temperatures of the driest quarter (bio9) contributed the most to delimiting suitable areas for this species. R. rougetii is sensitive to climate change associated with elevation, which leads shrinking distribution of suitable areas. The projected spatial and temporal pattern of habitat loss of R. rougetii suggests the importance of climate change mitigation and implementing long-term conservation and management strategies for this threatened endemic bird species.
Keyphrases
  • climate change
  • human health
  • machine learning
  • deep learning
  • genetic diversity
  • risk assessment
  • artificial intelligence
  • quality improvement
  • current status
  • big data