جستجوی مقالات مرتبط با کلیدواژه « متغیرهای زیست اقلیمی » در نشریات گروه « اکولوژی »

Species distribution models (SDMs) are the most important ecological models used to model species distribution, identify new habitats, and protect endangered species. In this study, habitat potential modeling and determine the most important factors affecting on distribution of Persian oak (Quercus brantii Lindl.) was investigated using MaxEnt model in Lorestan province. To map the potential distribution of Persian oak, 22 environmental layers were prepared in ArcGIS 10.4.1 and converted to ASCII format. Then 5064 species presence points were extracted using 1.5 × 1.5 km grid from the map of geographical distribution of the Persian oak species (1:250000) and converted to CSV format. After preparing the layers, MaxEnt 3.3.3 software was used to model distribution of the species. The results showed that 7 variables were more important among of the 22 environmental variables affecting on distribution of Persian oak. Annual mean temperature, Precipitation of wettest quarter and Min temperature of coldest month had the highest impact on distribution of the species with 38, 24.2 and 13.3 %, respectively. The area under the curve (AUC) of the model was 0.669 and the threshold value of 0.33 was used to convert the probability map to the presence and absence maps. The potential vegetation map of Persian oak shows that approximately 62.5% of the area of Lorestan province has the potential for growing Persian oak. The main new habitat is located in Khorramabad, Aligudarz, Kuhdasht and Poldokhtar, which recommended for restoration and rehabilitation.

Climate change may make challenges to the conservation of plant species such as the Fritillaria imperialis that have narrow geographical distribution. In this study, the modeling suitable habitats of F. imperialis in Iran was done in the current condition and under climate change (2050). In so doing, 78 species presence data along with 12 environmental variables including bioclimatic, physiographic and land cover/land use variables were used. The ensemble modelling consisting of seven Species Distribution Models (SDMs) was coupled with three general circulation models and four Representative Concentration Pathways (RCPs) scenario. Our findings showed that estimated potential habitats of the species cover about 7.69% of the study area while approximately 9.08% of these suitable areas were covered by conservation areas. Due to climate change, the least and most decline of suitable habitats 46.1% to 77.37% might occur in 2050 respectively. Minimum temperature of the coldest month, elevation, and annual precipitation, had the greatest effects on the species’ distribution in the study area. In keeping with the results, F. imperialis was predicted to shift toward higher elevation under climate change. The accuracy of the maps was assessed and functioning of all models was acceptable (AUC> 0.87 and TSS> 0.75). The suitable habitat identified could be considered for the adoption of management and conservation approaches, including re-introduction and the establishment of new protected areas in order to protect F. imperialis against the expected climate change impacts.

Climatic change can impose physiological constraints on species and can therefore affect species distribution. Bioclimatic predictors، including annual trends، regimes، thresholds and bio-limiting factors are the most important independent variables in species distribution models. Water and temperature are the most limiting factors in arid ecosystem in central Iran. Therefore، mapping of climatic factors in species distribution models seems necessary. In this study، we describe the extraction of 20 important bioclimatic variables from climatic data and compare different interpolation methods including inverse distance weighting، ordinary kriging، kriging with external trend، cokriging، and five radial basis functions. Normal climatic data (1950-2010) in 26 synoptic stations in central Iran were used to extract bioclimatic data. Spatial correlation، heterogeneity and trend in data were evaluated using three models of semivariogram (spherical، exponential and Gaussian) and the best model was selected using cross validation. The optimum model for bioclimatic variables was assessed based on the root mean square error and mean bias error. Exponential model was considered to be the best fit mathematical model to empirical semivariogram. IDW and cokriging were recognised as the best interpolating methods for average annual temperature and annual precipitation، respectively. Use of elevation as an auxiliary variable appeared to be necessary for optimizing interpolation methods of climatic and bioclimatic variables.