Galia Modabbernia , Behnam Meshgi , Ahmad Ali Hanafi-Bojd
{"title":"Anticipating the potential distribution of Fasciola spp. in Gilan province of Iran: Insights from MaxEnt and climate change scenarios","authors":"Galia Modabbernia , Behnam Meshgi , Ahmad Ali Hanafi-Bojd","doi":"10.1016/j.smallrumres.2024.107370","DOIUrl":null,"url":null,"abstract":"<div><div>Fasciolosis, a parasitic disease affecting humans and animals, is uniquely influenced by climatic and environmental factors. Gilan province in northern Iran is recognized as a high-endemic area for this parasite. This study aims to assess the prevalence of fasciolosis in Gilan province during the current period and forecast the distribution pattern of the parasite in future periods by analyzing climatic variables and identifying the most critical factors impacting <em>Fasciola</em>. To evaluate the present status of fasciolosis in Gilan, we collected 189 sheep fecal samples from different parts of the province and quantified eggs per gram of feces in each sample. Meteorological and environmental data were obtained and clipped to the study area. A total of 19 presence points were used to model the habitat suitability of <em>Fasciola</em> spp. through the maximum entropy (MaxEnt) algorithm, with jackknife analysis to determine variable importance. To project the potential distribution of <em>Fasciola</em> spp. in Gilan province under future scenarios, we employed MaxEnt using current (1970–2000) and projected climatic data based on three representative concentration pathway scenarios (RCP 2.6, RCP 4.5, and RCP 8.5) to predict habitat suitability in 2030, 2050, and 2070.</div><div>The results of this study indicate the proportion of <em>Fasciola</em> spp. infection was highest in Talesh (46.37 %) and Langarud (45.7 %), while Rudsar (0 %) and Shaft (16.25 %) exhibited the lowest infection rates in Gilan province. MaxEnt modeling highlighted the significance of bioclimatic variables, particularly those associated with vegetation and temperature, such as temperature seasonality (Bio4) and normalized difference vegetation index (NDVI). The ecological niche modeling illustrated that the highest potential distribution for <em>Fasciola</em> in Gilan province is concentrated in the north-western and central regions, exhibiting an 80–100 % potential. However, projections for the future indicate a decrease to less than 20 % suitability for most of the province under all three scenarios until 2070. This study provides valuable insights into the dynamic relationship between climatic variables and <em>Fasciola</em> distribution, enabling better preparedness and control strategies for this trematode in Gilan province and other regions with similar climates.</div></div>","PeriodicalId":21758,"journal":{"name":"Small Ruminant Research","volume":"240 ","pages":"Article 107370"},"PeriodicalIF":1.6000,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Small Ruminant Research","FirstCategoryId":"97","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0921448824001767","RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"AGRICULTURE, DAIRY & ANIMAL SCIENCE","Score":null,"Total":0}
引用次数: 0
Abstract
Fasciolosis, a parasitic disease affecting humans and animals, is uniquely influenced by climatic and environmental factors. Gilan province in northern Iran is recognized as a high-endemic area for this parasite. This study aims to assess the prevalence of fasciolosis in Gilan province during the current period and forecast the distribution pattern of the parasite in future periods by analyzing climatic variables and identifying the most critical factors impacting Fasciola. To evaluate the present status of fasciolosis in Gilan, we collected 189 sheep fecal samples from different parts of the province and quantified eggs per gram of feces in each sample. Meteorological and environmental data were obtained and clipped to the study area. A total of 19 presence points were used to model the habitat suitability of Fasciola spp. through the maximum entropy (MaxEnt) algorithm, with jackknife analysis to determine variable importance. To project the potential distribution of Fasciola spp. in Gilan province under future scenarios, we employed MaxEnt using current (1970–2000) and projected climatic data based on three representative concentration pathway scenarios (RCP 2.6, RCP 4.5, and RCP 8.5) to predict habitat suitability in 2030, 2050, and 2070.
The results of this study indicate the proportion of Fasciola spp. infection was highest in Talesh (46.37 %) and Langarud (45.7 %), while Rudsar (0 %) and Shaft (16.25 %) exhibited the lowest infection rates in Gilan province. MaxEnt modeling highlighted the significance of bioclimatic variables, particularly those associated with vegetation and temperature, such as temperature seasonality (Bio4) and normalized difference vegetation index (NDVI). The ecological niche modeling illustrated that the highest potential distribution for Fasciola in Gilan province is concentrated in the north-western and central regions, exhibiting an 80–100 % potential. However, projections for the future indicate a decrease to less than 20 % suitability for most of the province under all three scenarios until 2070. This study provides valuable insights into the dynamic relationship between climatic variables and Fasciola distribution, enabling better preparedness and control strategies for this trematode in Gilan province and other regions with similar climates.
期刊介绍:
Small Ruminant Research publishes original, basic and applied research articles, technical notes, and review articles on research relating to goats, sheep, deer, the New World camelids llama, alpaca, vicuna and guanaco, and the Old World camels.
Topics covered include nutrition, physiology, anatomy, genetics, microbiology, ethology, product technology, socio-economics, management, sustainability and environment, veterinary medicine and husbandry engineering.