ECOPERSIA
Volume:10 Issue: 1, Winter 2022

In this paper, the semi–circular bund treatments were contrasted to address the following questions: how does semi-circular bund affect the vascular plants richness and diversity  in Naroon and Neron rangelands of Sistan and Baloochestan? and do the soil properties responses to the semi–circular bund?

Next to every site, a site was selected as controlled ones. Vegetation (production, vegetation cover, density, richness, diversity) and soil data (texture, pH, EC, organic carbon, total nitrogen, available phosphorus, available potassium, CaCO3) were analyzed in completely randomized design.

In total, 11 species from 6 families and 8 genera were observed. Control treatment of Neron exhibited the highest number of plant species. Semi-circular bunds exhibited the highest vegetation cover, production and density. The highest and lowest species richness and diversity were measured in Naroon and Neron semi-circular bunds. Results showed that the amounts of organic carbon, nitrogen, potassium and phosphorus increased significantly in semi-circular bunds compared with the control areas. The highest organic carbon, nitrogen and potassium related to Neron semi-circular bund. Naroon and Neron semi-circular bunds had the highest amounts of clay and the least value was measured in the control treatments.  

In total, the results of study showed that semi-circular bund had positive effect on vegetation cover and soil properties in the rangelands of study area.

The purpose of this study was to evaluate the competency of logistic regression (LR) and maximum entropy (MaxEnt) models to predict the distribution of Dorema ammoniacum D. Don. in rangeland habitats in the central region of Iran, Yazd province.

The potential distribution map of Dorema ammoniacum D. Don. was prepared. The homogenous habitats were identified, and vegetation sampling was conducted using a systematic random method. The data including: soil (physical and chemical properties), physiographic (slope, aspect and altitude), and vegetation data (presence and absence) were used. Soil sampling was performed at two depths of 0-30, and 30-60 cm. The required maps were prepared using interpolation method. Statistics were taken from 90 plots along 9 transect both in the presence and absence area. Response curve and Jackknife test (for MaxEnt method) were employed to identify the most important environmental predictive factors. The kappa index was used to determine the agreement between the actual and predicted maps.

The accuracy of predicted map was weak in LR Model (AUC= 0.65), but it was considerably high in the MaxEnt model (AUC=0.87). The agreement between the predicted map of MaxEnt model, and ground truths was very good (kappa=0.74), and the agreement between predicted map generated by LR with the ground-truths was medium (kappa=0.5).

This plant has a limited ecological niche; therefore, the MaxEnt model could take precedence over the LR model because the only data it employs is the presence of the species.

Fire is one of the most important ecological disturbances in rangelands which change the characteristics of vegetation and soil. Up to now few studies have been conducted to study the short-term effects of fire on soil and vegetation in semi-arid shrubland. The positive or negative effects of fire on soil and vegetation of rangeland have been investigated in this research.

Soil pH, electrical conductivity (EC), total potassium (K), calcium (Ca), sodium (Na), and magnesium (Mg) content were selected as soil attributes and vegetation richness, diversity and density (percent ground cover) as vegetation properties. Vegetation and soil sampling was performed based on systematic randomized method along 3 transects (The length of transects in the control and burnt area was 100 m and the distance between each plots was 10 m). Information about vegetation traits (production, density, diversity, richness and evenness) were recorded in 1 m2 plots. Soil samples were taken at two depths of 0-15 and 15-30 cm by auger.

Compared to unburnt rangeland, the percentage of vegetation (16.30 %), production (20.47 Kg ha-1) and density of vegetation (6.74 %) in burnt region have been decreased significantly (P<0.05).

The results of the present study had shown a significant decrease in species diversity and richness in burnt rangeland. There were not any positive effects of fire on soil or vegetation cover in studied area.

Molecular phylogenetic relationship and morphological characteristics of the reef margin blenny, Entomacrodus striatus was undertaken in order to clarify its taxonomic status in the Iranian coasts of the Gulf of Oman. Entomacrodus striatus is distributed in the Indo-Pacific realm.

This study is based on four samples taken from Chabahar in the northern Gulf of Oman, during February 2019. The mt-DNA marker (COI) of one E. striatus from the present study and 15 Entomacrodus species was used for reverification over traditional morpho-taxonomy.

Bayesian Inference (BI) and Maximum Likelihood (ML) approaches result in five clades including, clade A (Entomacrodus corneliae, E. chiostictus, E. nigricans, E. sealei, E. cymatobiotus, E. caudofasciatus), clade B (E. striatus, E. niuafoouensis, E. epalzeocheilos, E. randalli), clade C (E. macrospilus, E. thalassinus), clade D (E. vermiculatus, E. decussatus), and clade E (E. stellifer). The morphological characters of the examined materials were in the range of its original description, except in having a pair of pores in one position of the preopercular series of sensory pores, which may be hypothesized a population variation associated with geographical distribution. The sexual dimorphism is distinguishable especially in color pattern of specimens which the blotches and bands on the male bodies are more bolded than the female ones. 

The inferred ML and BI trees resulted a homological clustering of taxa that are grouped according to their morphological characteristics and were agree with molecular reconstructions based on nuclear markers.

This study aims to evaluate the Soil Hydrological Response (SHR) under LU/LC using a field-oriented and remote sensing database in the Jiroft watershed, Iran.

Land use maps were extracted from Landsat images using the supervised classification method for 1987-2017. The results were validated against field data from 100 points, where we found the Kapp index to be greater than 80%, indicating an acceptable land-use classification. The LU/LC map was then projected for 2047 using the CA-Markov model. The Curve Number (CN) for each land use was determined from superimposing LU/LC and the soil hydrological group map. The Soil Conservation Services-Curve Number method (SCS-CN) was employed to estimate runoff.  

Good (densely vegetated) and moderate rangelands had a decreasing trend (i.e., -2.94% and -3.64% in 1987- 2017), while croplands, orchards, residential, and saline areas expanded (by 1.46%, 0.88%, 0.33%, and 7.21%). We found that agricultural lands, saline lands, and residential areas would increase by 0.75, 5.5, and 0.13%, by 2047, respectively.

We found a considerable increase (up to 6 mm increase) in runoff depth in some land-uses and more than 3.4% increase in the area of the high runoff producing class (IV).  We believe that higher runoff production potential and more intense and short rain showers should be considered seriously in terms of possible flash floods in the future.

This investigation aims to study the effect of biochar on gas exchange, water relations and photosynthetic pigments of European yew (Taxus baccata) seedling in different light intensities (LI).

Two-year potted seedlings of European yew are placed in forest stands with canopies of closed (15% LI), semi-closed (45% LI) and open (75% LI). In late June 2018, biochar was added to potted soils at the levels of zero, 10, 20 and 30 g.kg-1 and until the end of November, every 3 (or 4) days 100 mL water was given to each pot.

Seedling survival at different levels of treatments was 100%. Regardless of biochar, the highest photosynthesis, stomatal conductance, intercellular CO2 concentration, relative water content, chlorophyll (chl) contents in closed canopy, and the highest leaf temperature in open canopy was observed. With increasing biochar concentration, chl b and total chl increased (28-86%, respectively) in open canopy and decreased (28 and 28%, respectively) in closed canopy. Neither biochar nor LI affected significantly electrolyte leakage and carotenoid content.

Although yew seedlings were able to maintain their vegetative quality and health in different LI, and the measured variables responded differently to light-biochar combination, it may be best to grow yew in shade together with biochar. Since, the effect of biochar becomes more tangible over time, further research in next years can lead to more accurate findings affected by the combined light-biochar treatment.

Due to increase of demand for industrial and agricultural products, many tropical regions of Iran have experienced landscape changes. The present study aims to detect the land use/land cover (LULC) using some pixel/object-based approaches.  

This research was conducted in Jiroft area using some pixel-based and object-based image analysing methods (PBIA and OBIA respectively). To this end, at the first phase, the LULC maps were extracted using PBIA for September, 2020. The PBIA are including as Mahalanobis distance (MD), maximum likelihood (ML), neural network (NN), support vector machine (SVM). At the second phase, the LULC was produced using OBIA approach, encompassing the multi-resolution method and decision tree (DT) technique, for segmentation and classification respectively. Using a hybrid methodology, the high-resolution images of Worldview-2 were segmented. The segmented objects were later combined with the 7-month time series of NDVI, to find the necessary thresholds for DT.  

Results of the LULC maps demonstrated that the kappa coefficient and overall accuracy for ISODATA, MD, ML, NN, and SVM methods were calculated to be (51%, 66%), (81%, 86%), (88%, 91%), (90%, 93%) and (88% and 92%), respectively. The outcomes of the second phase for mapping the LULC showed the OBIA achieved a high overall accuracy of about 96%.

among the PBIA techniques and regarding both accuracy and execution time, the ML was the best. Although both PBIA and OBIA approaches are applicable in mapping LULC, the OBIA significantly outperformed the PBIA classifiers by higher overall accuracy and Kappa statistics