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<p>Coffee leaf rust (CLR) is a major plant disease found in coffee crops worldwide and is caused by the obligate hemicyclic fungus, <i>Hemileia vastatrix</i>. Despite the detrimental effects it has caused on the Philippine coffee industry over the last decades, the pathogen still remains massively understudied, and records of its occurrences are gravely lacking. Thus, this multipart study was conducted to construct models on the distributions of <i>H. vastatri</i>x nationwide to determine the possible reemergence of the pathogenic disease. Local occurrence records of the pathogen were collated to subject on different machine-learning algorithms (CTA, GAM, GBM, GLM, MaxEnt, MARS, RF, and SRE) under the <i>biomod2</i> framework. Future distributions were projected using the EC-Earth3-Veg general circulation model (GCM) under two future scenarios (optimistic and pessimistic) to assess their range shift and estimate resurgence possibilities. Risk maps revealed that the highest risk for disease re-emergence looms over the year 2100, viewed optimistically where efforts are exerted to mitigate the effects of climate change, whilst the threat of an epidemic in the near future is as close as the year 2060 should the worst anthropogenic scenario take place. This paper presents the first ensemble species distribution models of coffee leaf rust in the Philippines and comprehensive modeling of host-pathogen spatial distribution under changing climate scenarios.</p>

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Spatiotemporal modeling of Hemileia vastatrix using multiple machine learning algorithms: implications for disease surveillance of the coffee leaf rust disease in the Philippines

  • 期刊论文

  • 发表日期：

    2025年7月14日

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  • Francisco Geronimo-Isidro,
  • Jazpher John Figueroa-Jimenez,
  • Nicole Andrea Gabayno-Laguatan,
  • Teresa Elika Joy Lacuesta-Jalotjot,
  • Jeffer Troy Cabangbang-Jaranilla,
  • Sittie Aisha B. Macabago,
  • Christian Elmarc Ocenar-Bautista,
  • James Eduard Limbo-Dizon,
  • Don Enrico Buebos-Esteve,
  • Nikki Heherson A. Dagamac

摘要

Coffee leaf rust (CLR) is a major plant disease found in coffee crops worldwide and is caused by the obligate hemicyclic fungus, Hemileia vastatrix. Despite the detrimental effects it has caused on the Philippine coffee industry over the last decades, the pathogen still remains massively understudied, and records of its occurrences are gravely lacking. Thus, this multipart study was conducted to construct models on the distributions of H. vastatrix nationwide to determine the possible reemergence of the pathogenic disease. Local occurrence records of the pathogen were collated to subject on different machine-learning algorithms (CTA, GAM, GBM, GLM, MaxEnt, MARS, RF, and SRE) under the biomod2 framework. Future distributions were projected using the EC-Earth3-Veg general circulation model (GCM) under two future scenarios (optimistic and pessimistic) to assess their range shift and estimate resurgence possibilities. Risk maps revealed that the highest risk for disease re-emergence looms over the year 2100, viewed optimistically where efforts are exerted to mitigate the effects of climate change, whilst the threat of an epidemic in the near future is as close as the year 2060 should the worst anthropogenic scenario take place. This paper presents the first ensemble species distribution models of coffee leaf rust in the Philippines and comprehensive modeling of host-pathogen spatial distribution under changing climate scenarios.