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Abstract The purpose of this study was to evaluate the predictive accuracy of habitat suitability models, identifying the potential distribution range of Dorema ammoniacum, and its habitat requirements in the rangelands of Yazd province, central Iran. Bafgh, Mehriz and Nadoushan, were three habitats that were identified, and sampling was conducted in each habitat using a random-systematic method. A set of 10 plots were established (at equal distances) along 350 m long 18 transects. Soil samples (two depths: 0–30 and 30–60 cm from 36 profiles) were collected and measured in the laboratory. Elevation, slope, and aspect maps were derived, and climate information was collected from nearby meteorological stations. The habitat prediction of the species was modeled using Logistic Regression (LR), Maximum Entropy (MaxEnt), and Artificial Neural Network (ANN). The Kappa coefficient and the area under the curve (AUC) were calculated to assess the accuracy of the forecasted maps. The LR model for habitat prediction of the studied species in Mehriz (K = 0.67) and Nadoushan (K = 0.56) habitats were identified as good. The MaxEnt model predicted the habitat distribution for the selected species in Bafgh and Mehriz habitats as excellent (K = 0.89, AUC = 0.76, K = 0.89, AUC = 0.98), and in the Nadoushan habitat as very good (K = 0.78, AUC = 0.85). However, the ANN model predicted Bafgh and Nadoushan habitats as excellent and Mehriz habitat as very good (K = 0.87, K = 0.90, and K = 0.63, respectively). In general, in order to protect species D. ammoniacum, the development of its habitats in other areas of Yazd province and the habitats under study in conservation programs should be given priority.

Abstract Currently, a combination of natural and human factors has led to the lack of cost-effective production, resulting in the downturn of traditional animal husbandry (DTLH) in rural regions. This research aims to investigate the factors contributing to the DTLH in indigenous communities in northwestern Iran. The statistical population consisted of pastoralists residing in the villages of Namin-Ardabil, Iran. The research method employed was descriptive-analytical, and a questionnaire was used as the data collection tool. The sample size was determined by selecting approximately 5–10% of households in each village, preferably the household head. Friedman's test was utilized to prioritize the most significant factors influencing DTLH. The findings reveals that the most crucial economic criteria contributing to the DTLH were “the high cost of providing inputs and fodder required for livestock (mean rank = 11.12),” “more economic opportunities and access to well-paying employment opportunities in the city (mean rank = 8.99)”, and “low profit from livestock farming in the rangeland (mean rank = 10.60)”. Moreover, the most important social criteria were “uncertainty regarding the future well-being of children and the social and economic situation in the village (mean rank = 14.12)”, “motivation for academic and career development in urban areas (mean rank = 14.10)”, and “access to specialized and professional training opportunities in cities (mean rank = 13.80)”. Furthermore, the most crucial ecological criteria impacting the DTLH were “drought” (mean ran = 7.96)” and “non-implementation of range management plans and grazing permits” (mean rank = 6.07)”. In general, the results indicated that the ecological criteria had the highest impact on the DTLH, followed by social and economic criteria, with values of 41.1%, 34.6%, and 24.29%, respectively. Overall, understanding the challenges faced by traditional livestock husbandry and identifying the factors contributing to its decline is crucial for developing effective strategies to support and sustain this industry in rural areas.

Abstract Festuca ovina L. (sheep fescue), a perennial grass plant found in mountainous regions, is important from both an ecological and economic viewpoint. However, the variability of biological yield of sheep fescue due to its reliance on different characteristics makes it difficult to accurately prediction using classic modeling techniques. In this study, machine learning methods and multiple regression models (linear and non-linear) are used to investigate the interdependence of various morphological and physiological characteristics on accurate prediction of the biological yield (BY) of sheep fescue. Principal components analysis and stepwise regression were used to select six agronomic parameters i.e. thousand seed weight (TSW), relative water content (RWC), canopy cover (CC), leaf area index, number of florescence, and viability (VA), while the output variable was BY. To optimized the artificial neural network (ANN) structure, different transfer functions and training algorithms, different number of neurons in each layer, different number of hidden layers and training iteration were tested. The accuracy of the models and algorithms is analyzed by root mean square error (RMSE), mean absolute error (MAE), and determination coefficient (R2). According to the findings, ANN models were more accurate than regression models. The ANN model with two hidden layers (i.e. structure of 6–4–8–1) which had RMSE, MAE and R2 scores of 0.087, 0.065 and 0.96, respectively, was discovered as the best model for predicting the BY. In addition, result of the sensitivity analysis showed TSW, RWC and CC, in that order, were the variables most important for high-quality BY estimation in both models regardless of input combination. Finally, the paper concludes that early flowering sheep fescue genotypes with long maturation and great TSW must be regarded as the most suitable model for increasing BY in breeding projects.

Leaf area index (LAI), one of the most crucial vegetation biophysical variables, is required to evaluate the structural characteristic of plant communities. This study, therefore, aimed to evaluate the LAI of ecoregions in Iran obtained using Sentinel-2B, Landsat 8 (OLI), MODIS, and AVHRR data in June and July 2020. A field survey was performed in different ecoregions throughout Ardabil Province during June and July 2020 under the satellite image dates. A Laipen LP 100 (LP 100) field-portable device was used to measure the LAI in 822 samples with different plant functional types (PFTs) of shrubs, bushes, and trees. The LAI was estimated using the SNAPv7.0.4 (Sentinel Application Platform) software for Sentinel-2B data and Google Earth Engine (GEE) system–based EVI for Landsat 8. At the same time, for MODIS and AVHRR, the LAI products of GEE were considered. The results of all satellite-based methods verified the LAI variations in space and time for every PFT. Based on Sentinel-2B, Landsat 8, MODIS, and AVHRR application, the minimum and maximum LAIs were respectively obtained at 0.14–1.78, 0.09–3.74, 0.82–4.69, and 0.35–2.73 for shrubs; 0.17–5.17, 0.3–2.3, 0.59–3.84, and 0.63–3.47 for bushes; and 0.3–4.4, 0.3–4.5, 0.7–4.3, and 0.5–3.3 for trees. These estimated values were lower than the LAI values of LP 100 (i.e., 0.4–4.10 for shrubs, 1.6–7.7 for bushes, and 3.1–6.8 for trees). A significant correlation (p < 0.05) for almost all studied PFTs between LP 100-LAI and estimated LAI from sensors was also observed in Sentinel-2B (|r| > 0.63 and R2 > 0.89), Landsat 8 (|r| > 0.50 and R2 > 0.72), MODIS (|r| > 0.65 and R2 > 0.88), and AVHRR (|r| > 0.59 and R2 > 0.68). Due to its high spatial resolution and relatively significant correlation with terrestrial data, Sentinel-2B was more suitable for calculating the LAI. The results obtained from this study can be used in future studies on sustainable rangeland management and conservation.

This study evaluated the distribution pattern, species diversity index (richness, diversity, and evenness), importance value index (IVI), and family importance value (FIV) of all vascular plant species and various plant community life-form spectra along the Moghan Plain–Sabalan Mountain rangelands, Ardabil province, Iran. Sampling was conducted in 11 elevation classes at 300-m elevation intervals (100–3300 masl). In each elevation interval, 30 quadrats (1 × 1 m) were laid to collect vegetation data. Different vegetation attributes (density, frequency, and canopy cover) were measured for each quadrat. Regression analyses were employed to explore the interrelation of elevation with diversity, species richness, evenness, and IVI. In total, 251 species (143 genera, 38 families) were recorded across the study area. Poaceae, Fabaceae, and Asteraceae were most dominant families according to the FIV. According to the IVI, Poa bulbosa, Festuca ovina, Medicago minima, and Artemisia austriaca were the dominant species along the elevation gradient. Total diversity and species richness showed a normal distribution along the elevation gradient. Overall, hemicryptophytes and therophytes were the dominant life forms, while chamaephytes and geophytes were less frequent forms. The life-form patterns changed along elevation gradients. While the diversity and species richness values of therophytes and chamaephytes showed a decreasing trend, hemicryptophytes increased with increasing elevation. Diversity, species richness, and evenness of geophytes were not significantly different across the elevation gradient. Class I (highly palatable) and class II (mostly palatable) species declined as the elevation increased and then increased, while class III (hardly or unpalatable) species showed a reverse trend. The difference in the abundance and distribution of species in elevation classes could be related to resource availability, overlap of habitats, habitat patchiness, land area, degree of human influence, or biotic disturbances.

The leaf area index (LAI) is an important vegetation biophysical index that provides broad information on the dynamic behavior of an ecosystem’s productivity and related climate, topography, and edaphic impacts. The spatiotemporal changes of LAI were assessed throughout Ardabil Province—a host of relevant plant communities within the critical ecoregion of a semi-arid climate. In a comparative study, novel data from Google Earth Engine (GEE) was tested against traditional ENVI measures to provide LAI estimations. Moreover, it is of important practical significance for institutional networks to quantitatively and accurately estimate LAI, at large areas in a short time, and using appropriate baseline vegetation indices. Therefore, LAI was characterized for ecoregions of Ardabil Province using remote sensing indices extracted from Landsat 8 Operational Land Imager (OLI), including the Enhanced Vegetation Index calculated in GEE (EVIG) and ENVI5.3 software (EVIE), as well as the Normalized Difference Vegetation Index estimated in ENVI5.3 software (NDVIE). Moreover, a new field measurement method, i.e., the LaiPen LP 100 portable device (LP 100), was used to evaluate the accuracy of the derived indices. Accordingly, the LAI was measured in June and July 2020, in 822 ground points distributed in 16 different ecoregions-sub ecoregions having various plant functional types (PFTs) of the shrub, bush, and tree. The analyses revealed heterogeneous spatial and temporal variability in vegetation indices and LAIs within and between ecoregions. The mean (standard deviation) value of EVIG, EVIE, and NDVIE at a province scale yielded 1.1 (0.41), 2.20 (0.78), and 3.00 (1.01), respectively in June, and 0.67 (0.37), 0.80 (0.63), and 1.88 (1.23), respectively, in July. The highest mean values of EVIG-LAI, EVIE-LAI, and NDVIE-LAI in June are found in Meshginshahr (1.40), Meshginshahr (2.80), and Hir (4.33) ecoregions and in July are found in Andabil ecoregion respectively with values of 1.23, 1.5, and 3.64. The lowest mean values of EVIG-LAI, EVIE-LAI, and NDVIE-LAI in June were observed for Kowsar (0.67), Meshginshahr (1.8), and Neur (2.70) ecoregions, and in July, the Bilesavar ecoregion, respectively, with values of 0.31, 0.31, and 0.81. High correlation and determination coefficients (r > 0.83 and R2 > 0.68) between LP 100 and remote sensing derived LAI were observed in all three PFTs (except for NDVIE-LAI in June with r = 0.56 and R2 = 0.31). On average, all three examined LAI measures tended to underestimate compared to LP 100-LAI (r > 0.42). The findings of the present study could be promising for effective monitoring and proper management of vegetation and land use in the Ardabil Province and other similar areas.

This study was conducted to evaluate Pb and Zn uptake, mobility, and accumulation in Stipa hohenackeriana using ï¬eld soil in pot and ï¬eld experiments. Moreover, the effects of Municipal Solid Waste Compost (MSWC) (0, 1, and 2%) and Nano-Silica (NS) (0, 250, and 500 mg/kg) on Stipa biomass, Pb and Zn availability in the soil, and Pb and Zn uptake and accumulation were studied using pot experiments. Samples of soil, root, and shoots of Stipa were collected from field and greenhouse and after drying, extraction of Pb and Zn was done by acid digestion. Bio-Concentration Factor (BCF) and Translocation Factor (TF) were calculated to determine Pb and Zn phytoremediation efï¬ciency. The amount of Zn and Pb remediation by Stipa from soil was determined by remediation factor (RF). The results of ï¬eld experiments showed the Pb and Zn level decreased in the order of: soil >shoot>root. Results of the pot experiments also showed that plants grown in NS500-amended pots had 33% and 32% higher Pb in roots and shoots compared to control pots, respectively. In comparison, roots Pb concentration in pots amended with MSWC1% and MSWC2% decreased 22.4% and 1.7%, respectively. Roots and shoots Zn concentration in NS500-amended pots was 5.6% and 6.5% higher, respectively. However, root Zn concentration in treatments of MSWC1% and MSWC2% decreased 52.3% and 39.4%, respectively. Shoots Zn concentration decreased 52.5% and 40.0%, respectively. Although MSWC decreased the uptake and accumulation of Pb and Zn in Stipa roots and shoots, it improved the plant growth and consequently increased RF and soil remediation compared to the NS. Thus, it seems that applying MSWC and NS simultaneously can be a suitable strategy for the purpose of improving phytoremediation capability of Stipa in the Pb and Zn contaminated soils. In general, Stipa can be a suitable candidate for the accumulation of heavy metals, especially for Pb and Zn contaminated soils.