Identifying biodiversity hotspots for threatened mammal species in Iran

Ecological assessment of Iran's terrestrial biomes for wildlife conservation

Man-made activities pose the greatest threats to wildlife in Iran's terrestrial biomes, causing significant habitat damage and fragmentation in recent years. To fully understand these threats, the present study was conducted to identify and map the Iran's terrestrial biomes using the IDRISI TerrSet 18.31 Software, the Terrestrial Biomes Ecosystem Service Modeler on the InVEST toolkit (TBESMI), and comprehensive data sources including maps of roads, protected areas, terrestrial biomes, and country-wide land cover maps of 2017. The results showed that the largest terrestrial biome in Iran is deserts and xeric shrublands (DXS), while flooded grasslands and savannas (FGS) is the smallest biome. Roads, along with urban and agricultural developments are among the biggest threats and most destructive stressors in Iran's terrestrial biomes. The results also revealed that there was a growth in destruction of habitats located in the temperate broadleaf and mixed forest (TBMF), temperate coniferous forest (TCF), and FGS, alongside a decrease in the DXS biome. Furthermore, we detected an increase in habitat landscape quality in the DXS, FGS and montane grasslands and shrub lands (MGS), and a decrease in the temperate grasslands, savannas and shrublands (TGSS) and TBMF biomes. Finally, the cumulative risk of habitat degradation increased in the FGS, TCF, TGSS, and TBMF biomes, whereas it decreased in the DXS biome. The FGS biome with the highest consequence cumulative score, and the MGS biome with the highest cumulative risk exposure score were found to be at the highest risk from man-made activities. Stressors associated with agriculture and urbanization had the highest cumulative exposure scores in the MGS, while roads had the highest exposure scores in the TBMF and DXS biomes. Our study underscores the critical importance of conserving Iran's terrestrial biomes and wildlife, especially in high-risk biomes like FGS and MGS, given the substantial threats posed by human activities.

Distribution and conservation of near threatened plants in China

Plants classified as Near Threatened (NT) are at high risk of becoming threatened because of anthropogenic interference and climate change. Especially in conservation efforts, such species have however long been overlooked. Here, we obtained 98,419 precise occurrence points for 2442 NT plants in China, and used species richness, species complementarity, and weighted endemism that consider all, endemic and narrow-ranged species in order to identify the diversity hotspots of NT plants. Then we evaluated the conservation effectiveness of current nature reserves for them. Our results indicate that the diversity hotspots of NT plants were mainly confined to southwestern and southern China, and only 35.87% of hotspots and 71.5% of species were protected by nature reserves. Numerous hotspots in southwestern China (e.g., Sichuan, Yunnan, Guangxi, and Hainan) were identified as conservation gaps. Given that NT plants include large proportions of endemic and narrow-ranged species, they represent an important value in conservation priority. So, more conservation efforts in the future should be tilted towards NT plants. Additionally, when comparing with the recently updated NT list, there are already 87 species raised to threatened categories, while 328 species were lowered to least concern, 56 species were now categorized as data deficient, and 119 species considered as uncertain due to changes of scientific names. It is essential to carry out a continuous assessment of species' threatened categories to realize targeting conservation.

Identifying core habitats and corridors of a near threatened carnivore, striped hyaena (Hyaena hyaena) in southwestern Iran

Conservation of large carnivores requires preservation of extensive core habitats and linkages among them. The goal of this study was to identify core habitats and corridors by predicting habitat suitability (an ensemble approach), and calculating resistant kernel and factorial least-cost path modeling for a relatively unknown carnivore, the striped hyaena in Khuzestan area in southwestern Iran. We used the procedure of spatial randomization test to evaluate the coincidence of striped hyaena road crossing with the predicted corridors. The results revealed that elevation, distance to conservation areas, categorical climate and grasslands density were the most influential variables for predicting the occurrence of the striped hyaena in the study area. In the estimated dispersal distance of 70 km, four core habitats were identified. The largest core habitat was located in the northeast of the study area with the highest connectivity contribution. Only about 12% and 1.5% of core habitats and corridors were protected by conservation areas, respectively. Predicted corridors, crossed by roads represented a high risk for striped hyaenas. Adaptive management plan throughout the landscape (conservation of core habitats and corridors, and reducing species mortality on the roads) must be considered by wildlife managers in Iran.

Predicting range shifts of three endangered endemic plants of the Khorassan-Kopet Dagh floristic province under global change

Endemic plants of the Khorassan-Kopet Dagh (KK) floristic province in northeastern Iran, southern Turkmenistan, and northwestern Afghanistan are often rare and range-restricted. Because of these ranges, plants in the KK are vulnerable to the effects of climate change. Species distribution modelling (SDM) can be used to assess the vulnerability of species under climate change. Here, we evaluated range size changes for three (critically) endangered endemic species that grow at various elevations (Nepeta binaloudensis, Phlomoides binaludensis, and Euphorbia ferdowsiana) using species distribution modelling. Using the HadGEM2-ES general circulation model and two Representative Concentration Pathways Scenarios (RCP 2.6 and RCP 8.5), we predicted potential current and future (2050 and 2070) suitable habitats for each species. The ensemble model of nine algorithms was used to perform this prediction. Our results indicate that while two of species investigated would benefit from range expansion in the future, P. binaludensis will experience range contraction. The range of E. ferdowsiana will remain limited to the Binalood mountains, but the other species will have suitable habitats in mountain ranges across the KK. Using management efforts (such as fencing) with a focus on providing elevational migration routes at local scales in the KK is necessary to conserve these species. Additionally, assisted migration among different mountains in the KK would be beneficial to conserve these plants. For E. ferdowsiana, genetic diversity storage employing seed banks and botanical garden preservation should be considered.

Mapping Land Cover and Tree Canopy Cover in Zagros Forests of Iran: Application of Sentinel-2, Google Earth, and Field Data

The Zagros forests in Western Iran are valuable ecosystems that have been seriously damaged by human interference (harvesting the wood and forest sub-products, converting the forests to the agricultural lands, and grazing) and natural events (drought events and fire). In this study, we generated accurate land cover (LC), and tree canopy cover percentage (TCC%) maps for the forests of Shirvan County, a part of Zagros forests in Western Iran using Sentinel-2, Google Earth, and field data for protective management. First, we assessed the accuracy of Google Earth data using 300 random field plots in 10 different land cover types. For land cover mapping, we evaluated the performance of four supervised classification algorithms (minimum distance (MD), Mahalanobis distance (MaD), neural network (NN), and support vector machine (SVM)). The accuracy of the land cover maps was assessed using a set of 150 stratified random plots in Google Earth. We mapped the forest canopy cover by using the normalized difference vegetation index (NDVI) map, and field plots. We calculated the Pearson correlation between the NDVI values and the TCC% (obtained from field plots). The linear regression between the NDVI values and the TCC% was used to obtain the predictive model of TCC% based on the NDVI. The results showed that Google Earth data yielded an overall accuracy of 94.4%. The SVM algorithm had the highest accuracy for the classification of Sentinel-2 data with an overall accuracy of 81.33% and a kappa index of 0.76. The results of the forest canopy cover analysis showed a Pearson correlation coefficient of 0.93 between the NDVI and TCC%, which is highly significant. The results also showed that the linear regression model is a good predictive model for TCC% estimation based on the NDVI (r2 = 0.864). The results can be used as a baseline for decision-makers to monitor land cover change in the region, whether produced by human activities or natural events and to establish measures for protective management of forests.

Cattle selectivity by leopards suggests ways to mitigate human-leopard conflict

Addressing widespread livestock losses to carnivores requires information on which livestock categories are preferentially selected. We analyzed an individual-based database of cattle grazing in forest (n = 932) and having been killed (n = 70) by leopards (Panthera pardus) in the Hyrcanian forest, Iran. We calculated Jacobs' selectivity index for cattle age, sex, and coloration across four scales: the study area as a whole, three sites, nine villages, and 60 cattle owners. Naturally colored cattle were significantly preferred by leopards at all scales in comparison with black and black-and-white cattle, and there was also a preference for males and juveniles at the study area level. More research is needed to see whether cattle losses would decrease if the share of naturally colored individuals in local holdings was reduced and males and juveniles had limited access to forest. We conclude that phenotypic and biologic characteristics of livestock can affect depredation and appeal for more research in this direction, particularly within the predator-prey framework.