Fig 1.
Study area and spatial distribution of Anopheles stephensi occurrence points in Hormozgan Province, Iran.
Map created using ArcGIS Desktop (version 10.5; Environmental Systems Research Institute, Redlands, CA, USA) (www.esri.com).
Table 1.
List of environmental variables used in Anopheles stephensi distribution modeling, after multicollinearity testing.
Fig 2.
Environmental suitability of the Anopheles stephensi species under current climatic conditions in Hormozgan province, Iran.
Map created using ArcGIS Desktop (version 10.5; Environmental Systems Research Institute, Redlands, CA, USA) (www.esri.com).
Fig 3.
Gain, loss, and stable maps for Anopheles stephensi under three climate change scenarios in the 2030s, 2050s, and 2070s in Hormozgan Province, Iran.
The Map was generated using R-4.4.2v, with raster processing performed using the terra package and visualization using the ggplot2 package.
Fig 4.
Percentage scaling of gain, loss, and stable areas based on different scenarios in the 2030s, 2050s, and 2070s.
The Y-axis shows the climate scenarios (SSP1-2.6, SSP2-4.5, SSP5-8.5) in different periods, and the X-axis shows the percentage of habitat categorized as gain, loss, or stable. Colors indicate habitat change: green = stable, yellow = gain, and red = loss. The Map was generated using R-4.4.2v, with visualization using the ggplot2 package.
Fig 5.
Area of projected environmental suitability, gain, loss, and stable habitat based on various climate scenarios and time periods, shown in square kilometers.
Projected environmental suitability: suitable area (km²) under current conditions and three climate scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5) for 2030, 2050, and 2070; Gain, Loss, and Stable: areas of habitat expected to increase, decrease, and areas expected to remain stable, respectively, under the same scenarios, in comparison with environmental suitability. The Map was generated using R-4.4.2v, with visualization using the ggplot2 package.
Fig 6.
Relative importance of environmental variables to the MaxEnt model.
Bio3 = Isothermality ((Mean Diurnal Range/ Temperature Annual Range) ×100), Bio4 = Temperature seasonality (SD × 100), Bio8 = Mean Temperature of Wettest Quarter, Bio9 = Mean Temperature of Driest Quarter, Bio10 = Mean Temperature of Warmest Quarter, Bio14 = Precipitation of Driest Month, Bio15 = Precipitation seasonality (CV), Bio18 = Precipitation of Warmest Quarter, Bio19 = Precipitation of Coldest Quarter, Alt = Altitude (m).
Fig 7.
Jackknife analysis of environmental variable importance in the MaxEnt model.
The Y-axis shows environmental variables, and the X-axis represents model gain during testing. Colors indicate the contribution of each variable: green = variable excluded from the model, blue = model with only that variable, red = model with all variables included. Variables with taller blue bars demonstrate strong predictive power when used alone, while shorter green bars indicate that excluding the variable substantially reduces the model’s predictive accuracy, highlighting its critical importance.
Fig 8.
Response curves of the environmental variables that contributed to the MaxEnt model.
The curves (blue) show the mean response of the 10 replicate MaxEnt runs [8] and the mean + /- one standard deviation. The x-axis shows environmental variables (see Table 1), and the y-axis denotes the predicted probability of species presence on the cloglog scale, where cloglog transforms the model output into an occurrence probability between 0 and 1.
Fig 9.
TSS and AUC metrics for model performance.
The Map was generated using R-4.4.2v, with visualization using the ggplot2 package.