Cystic echinococcosis (CE) is caused by the larval form of Echinococcus granulosus that can cause serious health and economic problems in the endemic foci. CE is globally distributed in various climatic conditions from circumpolar to tropical latitudes. Iran is an important endemic area with a spectrum of weather conditions. The aim of this study was to determine the effects of geo-climatic factors on the distribution of livestock CE in south-western Iran (SWI) in 2016 to 2018. Data of livestock CE were retrieved from veterinary organizations of four provinces of SWI. The geo-climatic factors, including mean annual temperature (MAT), minimum MAT (MinMAT), maximum MAT (MaxMAT), mean annual rainfall (MAR), elevation, mean annual evaporation (MAE), sunny hours, wind speed, mean annual humidity (MAH), slope, frost days and land cover, were analysed using geographical information systems (GIS) approaches. The statistical analysis showed that MAR, frost days, elevation, slope and semi-condensed forest land cover were positively and MAE, MAT, MaxMAT, MinMAT and salt and salinity land cover were negatively correlated with CE occurrence. MAE was shown to be a predictive factor in the stepwise linear logistic regression model. In short, the current GIS-based study found that areas with lower evaporation were the main CE risk zones, though those with lower temperature and higher rainfall, altitude and slope, especially where covered with or in close proximity of semi-condensed forest, should be prioritized for consideration by health professionals and veterinarians for conducting control programmes in SWI.
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http://dx.doi.org/10.1017/S0022149X20000553 | DOI Listing |
New Phytol
January 2025
Climate & Ecosystem Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA.
Grass-dominated ecosystems cover wide areas of the land surface yet have received far less attention from the Earth System Model (ESM) community. This limits model projections of ecosystem dynamics in response to global change and coupled vegetation-climate dynamics. We used the Functionally Assembled Terrestrial Ecosystem Simulator (FATES), a dynamic vegetation demography model, to determine ecosystem sensitivity to alternate, observed grass allometries and biophysical traits, and evaluated model performance in capturing California C annual grasslands structure and fire regimes.
View Article and Find Full Text PDFBMC Plant Biol
January 2025
Institute of Biology, Fundamental and Applied Research in Chemical Ecology, University of Neuchâtel, Neuchâtel, Switzerland.
Background: Upland cotton (Gossypium hirsutum) plants constitutively store volatile terpenes in their leaves, which are steadily emitted at low levels. Herbivory leads to a greater release of these stored volatiles. Additionally, damaged plants increase the accumulation of volatile terpenes in their leaves and begin to synthesize and emit other terpenes and additional compounds.
View Article and Find Full Text PDFEnviron Monit Assess
January 2025
LMFE, Faculty of Sciences Semlalia, Cadi Ayyad University, 40000, Marrakesh, Morocco.
In the last decades, natural and anthropogenic pressures have caused observable changes in the argan landscape despite its significance in Morocco. Remote sensing data can be used to monitor these changes over time and provide information on vegetation health and land cover changes. This study assesses the performance of supervised methods (support vector machine, maximum likelihood, and minimum distance) and unsupervised classification method (Isodata) for mapping the argan forest in the Smimou area of Essaouira province using remote sensing data from Landsat-5 and Landsat-8 (1985 and 2019).
View Article and Find Full Text PDFEnviron Sci Pollut Res Int
January 2025
Department of Geography, Fakir Mohan University, Vyasa Vihar, Nuapadhi, Balasore, 756089, Odisha, India.
Forests play a vital role in environmental balance, supporting biodiversity and contributing to atmospheric purification. However, forest fires threaten this balance, making the identification of forest fire probability (FFP) areas crucial for effective mitigation. This study assesses forest fire trends and susceptibility in the Similipal Biosphere Reserve (SBR) from 2012 to 2023 using four machine learning models-extreme gradient boosting tree (XGBTree), AdaBag, random forest (RF), and gradient boosting machine (GBM).
View Article and Find Full Text PDFEnviron Monit Assess
January 2025
Department of Environmental Management, Institute of Environmental Engineering, RUDN University, Miklukho-Maklaya Street, 117198, Moscow, Russia.
Globally, agricultural lands are among the top emitters of greenhouse gases (GHGs), responsible for over 20% of total greenhouse gas (GHG) emissions. Climatic conditions, an acute challenge in sub-Saharan Africa (SSA), where access to mitigation technologies remains limited, have heavily influenced these lands. This study explores GHG contributions from crop production and their devastating and deteriorating impacts on the economy and environment and proposes a sustainable solution.
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