Farag Peter F, Alkhalifah Dalal Hussien M, Ali Shimaa K, Tagyan Aya I, Hozzein Wael N
Department of Microbiology, Faculty of Science, Ain Shams University, Abbasia, Egypt.
Department of Biology, College of Science, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.
Front Plant Sci. 2025 Apr 16;16:1512294. doi: 10.3389/fpls.2025.1512294. eCollection 2025.
Climate change forms one of the most dangerous problems that disturb the earth today. It not only devastates the environment but also affects the biodiversity of living organisms, including fungi. (Tassi) Goid. is one of the most pervasive and destructive soil-borne fungus that threatens food security, so predicting its current and future distribution will aid in following its emergence in new regions and taking precautionary measures to control it.
Throughout this work, there are about 324 records of were used to model its global prevalence using 19 environmental covariates under several climate change scenarios for analysis. Maximum Entropy (MaxEnt) model was used to predict the spatial distribution of this fungus throughout the world while algorithms of DIVA-GIS were chosen to confirm the predicted model.
Based on the Jackknife test, minimum temperature of coldest month (bio_6) represented the most effective bioclimatological parameter to fungus distribution with a 52.5% contribution. Two representative concentration pathways (RCPs) 2.6 and 8.5 of global climate model (GCM) code MG, were used to forecast the global spreading of the fungus in 2050 and 2070. The area under curve (AUC) and true skill statistics (TSS) were assigned to evaluate the resulted models with values equal to 0.902 ± 0.009 and 0.8, respectively. These values indicated a satisfactory significant correlation between the models and the ecology of the fungus. Two-dimensional niche analysis illustrated that the fungus could adapt to a wide range of temperatures (9 °C to 28 °C), and its annual rainfall ranges from 0 mm to 2000 mm. In the future, Africa will become the low habitat suitability for the fungus while Europe will become a good place for its distribution.
The MaxEnt model is potentially useful for predicting the future distribution of under changing climate, but the results need further intensive evaluation including more ecological parameters other than bioclimatological data.
气候变化是当今困扰地球的最危险问题之一。它不仅破坏环境，还影响包括真菌在内的生物多样性。（塔西）戈德氏菌是最普遍且具破坏性的土传真菌之一，威胁着粮食安全，因此预测其当前及未来分布将有助于追踪其在新区域的出现，并采取预防措施加以控制。
在整个研究过程中，利用约324条记录，结合19个环境协变量，在多种气候变化情景下对其全球流行情况进行建模分析。采用最大熵（MaxEnt）模型预测该真菌在全球的空间分布，同时选用DIVA - GIS算法对预测模型进行验证。
基于刀切法检验，最冷月最低温度（生物气候变量6）是对真菌分布最有效的生物气候参数，贡献率为52.5%。采用全球气候模型（GCM）代码MG的两种代表性浓度路径（RCPs）2.6和8.5，预测该真菌在2050年和2070年的全球扩散情况。用曲线下面积（AUC）和真实技能统计量（TSS）评估所得模型，其值分别为0.902±0.009和0.8。这些值表明模型与真菌生态之间存在令人满意的显著相关性。二维生态位分析表明，该真菌能适应较宽的温度范围（9℃至28℃），年降雨量范围为0毫米至2000毫米。未来，非洲将成为该真菌栖息地适宜性较低的地区，而欧洲将成为其分布的适宜地区。
MaxEnt模型在预测气候变化下戈德氏菌的未来分布方面可能有用，但结果需要进一步深入评估，包括纳入除生物气候数据之外的更多生态参数。
