The impact of climate change on biodiversity has been the subject of numerous research in recent years. The multiple elements of climate change are expected to affect all levels of biodiversity, including microorganisms. The common worldwide fungus Fusarium oxysporum colonizes plant roots as well as soil and several other substrates. It causes predominant vascular wilt disease in different strategic crops such as banana, tomato, palm, and even cotton, thereby leading to severe losses. So, a robust maximum entropy algorithm was implemented in the well-known modeling program Maxent to forecast the current and future global distribution of F. oxysporum under two representative concentration pathways (RCPs 2.6 and 8.5) for 2050 and 2070. The Maxent model was calibrated using 1885 occurrence points. The resulting models were fit with AUC and TSS values equal to 0.9 (±0.001) and 0.7, respectively. Increasing temperatures due to global warming caused differences in habitat suitability between the current and future distributions of F. oxysporum, especially in Europe. The most effective parameter of this fungus distribution was the annual mean temperature (Bio 1); the two-dimensional niche analysis indicated that the fungus has a wide precipitation range because it can live in both dry and rainy habitats as well as a range of temperatures in which it can live to certain limits. The predicted shifts should act as an alarm sign for decision makers, particularly in countries that depend on such staple crops harmed by the fungus.

Climate change impacts represent one of the most important ecological and medical issues during this century. Several fungal species will change their distribution through space and time as a response to climate changes. This will rearrange many fungal diseases throughout the world. One of the most important and very common fungi is the black mold Aspergillus niger. The COVID-19 pandemic reforms the way in which mycologists think about this fungus as an emerging healthy issue. Through this work, about one thousand records of Aspergillus niger were used to model its current and future global distribution using 19 bioclimatic variables under several climate change scenarios. Maximum entropy implemented in Maxent was chosen as the modeling tool, especially with its accuracy and reliability over the other modeling techniques. The annual mean temperature (bio 1) forms the most contributed climatological parameter to black mold distribution. The produced current distribution model came compatible with the real distribution of the species with a cosmopolitan range. The rise of temperature due to global warming will form a limitation to Aspergillus niger through several parts of its range. The generated maps of the future status of this fungus under two different RCPs for 2050 and 2070, indicate several parts that become free from black mold due to temperature limitations. The present results need more intensive future evaluation using data science and GIS, especially on a local scale including more ecological parameters other than climatological data.