h. bashari

Spatiotemporal changes of net primary production (NPP) is one of the essential indicators in determining rangeland ecosystem condition. Therefore, the aim of current research was to map and monitor the actual and potential NPP of rangeland ecosystems in Kohgiluyeh and Boyer-Ahmad province, using CASA and Miami models, MODIS and field data from 2009 to 2018. The accuracy of the produced NPP maps was assessed in 253 sampling sites located in different vegetation types with good, fair and poor rangeland conditions, using linear regression. Results showed that the difference between actual and potential NPP was greater than 56 gr C/m2/month, which can be a sign of human impacts and interferences in the rangeland ecosystems of the region. The highest and lowest relationships between modeled and field productions were observed in the Astragalus spp. - Bromus tomentellus vegetation type with good rangeland condition (R2=0.84) and Astragalus sieberi- Stipa capensis vegetation type with poor rangeland condition (R2=28), respectively. The present research findings indicated the importance of drought conditions, vegetation type and rangeland condition in estimating the actual and potential NPP and the difference of these productions can be used as an index to determine and monitor the condition of rangeland ecosystems.

Climate change has far-reaching consequences on many ecosystems around the world and induced large-scale shifts in species distribution. Astragalus verus Olivier is a plant with medicinal, industrial, and soil protection value that its geographical distribution has declined considerably in recent decades. This study aimed to predict the distribution of A. verus under climate change scenarios in Isfahan and Chaharmahal va Bakhtiari provinces. The stratifiedrandom sampling method was used to collect the presence points of the species in different altitude levels in 83 rangeland places. The random forest model was used to model the species distribution in the study area. The Biomod2 package in R software was used to run CCSM4 general circulation model and assess the effects of climate change on the species distribution in the current condition and 2050 under two scenarios of RCP2.6 and RCP8.5. Bio1, Bio5, Bio19, and altitude were identified as the main predictive variables after performing the Pearson correlation test and removal of high correlated layers. The results showed that the random forest model had excellent performance, with AUC and TSS values of 0.9. The highly suitable habitat area of A. verus in the current condition 6917 km2, which occupies about 5.6% of the total study area. According to the results increasing temperature due to climate change will lead to a decrease in the highly suitable area of the species by about 56.5% under the optimistic scenarios and 88.3% under the pessimistic scenarios, respectively. The results indicated that the species habitat will move to higher altitudes with lower temperatures. The results of this study can be used for identifying threatened habitats and reintroduce this species to degraded habitats in the Zagros and Central Iran.

Southern Oscillation Index (SOI) and Sea Surface Temperature (SST) patterns affect rainfall in many parts of the world. This study aimed to investigate the relationship between monthly and seasonal rainfall of Iran versus SOI and Pacific and Indian sea surface temperature. Monthly rainfall data، from 50 synoptic stations with at least 30 years of records up to the end of 2007، were used. Monthly and seasonal time series of each station were divided to several groups by four methods (Average SOI، SOI Phases، Indian SST Phases and Pacific SST Phases) using Rainman software and with regard to 0-3 months lead-time. Significant differences among rainfall groups in each method were assessed by the non-parametric Kruskal-Wallis and Kolmogorov-Smirnov tests، and the significant relationship was validated using Linear Error in Probability Space (LEPS) test. The results showed that SOI during summer (July-September) was related to autumn (October-December) and October rainfall in the west and northwest of Iran and the west Caspian Sea coast. The El Niño (negative) phase was associated with an increase in rainfall and the La Niña (positive) phase was associated with a decrease in rainfall in these regions. Average SOI is a useful index for rainfall forecasting in the above-mentioned areas. However، Indian and Pacific SST phases are not suggested for rainfall forecasting in Iran، duo to weak or non-persistence relationships. In conclusion، Iran rainfall is not limited to SOI، Pacific and Indian SST; therefore، Rainman could not be used as an aid to water resources management over a year in Iran. It is suggested that we study the teleconnection between Iran rainfall and other ocean-atmospheric oscillations developing a model similar to Rainman in order to that we investigating the variation in Iran rainfall with aid of other effective ocean-atmospheric indicators