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The Himalaya and Hengduan Mountains (HDM) are recognized as two global biodiversity hotspots, harboring the world's richest alpine flora. However, to what extent the distribution of alpine plants here is affected by climate change remains largely unknown. Bergenia (Saxifragaceae) are perennial medicinal herbs mainly distributed in the Himalaya-HDM region. In this study, we used bioclimatic data for current and future climate scenarios to assess the impact of climate change on the potential distribution of three Bergenia (Saxifragaceae) species. Our results revealed that the geographical distribution of the studied Bergenia species is primarily influenced by precipitation and elevation. By 2090, the three Bergenia species are expected to show contrasting range changes. The western Himalayan alpine species Bergenia stracheyi is expected to expand its range with 21.93 and 17.36% under the optimistic (SSP1-2.6) and moderate (SSP2-4.5) climate change scenario, respectively, while its distribution will shrink by 5.26% under the pessimistic scenario (SSP5-8.5). The Himalayan mid-elevation species B. ciliata is expected to expand its range from 142.42 to 157.14%. In contrast, the distribution range of the east Himalaya-HDM alpine species B. purpurascens is expected to shrink with 34.88 to 47.24%, with most of the habitats in the southeast chains of the HDM at lower elevation summits being lost. In addition, all three Bergenia species are projected to shift their ranges to higher elevations in response to temperature increases. Overall, we conclude that alpine plants may be more vulnerable to climate change than their congeners at lower elevations, supporting the "nowhere to go" hypothesis. [ABSTRACT FROM AUTHOR]

The relationship between plant species and climatic factors has always been a fundamental issue in plant ecology, and the use of multivariate statistical methods can be effective in revealing the relationship between climatic factors and plant species distribution. Therefore, in this study, climatic factors affecting the distribution of Artemisia sieberi and Artemisia aucheri, widely distributed in Iran, were investigated. For this purpose, 117 climatic factors were used, and to reduce the number of factors and determine the most important effective ones, a factor analysis was used by principal component analysis. The results showed that six factors including heating temperature, spring and summer precipitation, wind, autumn–winter precipitation, and dusty and cloudiness days explained 37.32%, 22.54%, 7.18%, 6.6%, 4.22%, and 4.15% of data variation, respectively. Together these seven factors account for 82% of data variation. The autumn–winter precipitation and heating temperature had the greatest impact on the presence of Artemisia sieberi and Artemisia aucheri, respectively, so that the autumn–winter precipitation was negative in areas where Ar.sieberi is observed. The heating temperature factor is negative in areas where Ar.aucheri is present, while it is positive in areas lacking Ar.aucheri. The study of the effect of environmental factors on Artemisia species distribution is very important in the planning and management of natural resources, and Artemisia is one of the most important plants in the country's rangelands; therefore, the results of this research can be used for practical planning, management, and reclamation of these rangelands. [ABSTRACT FROM AUTHOR]

The aim of this study was to have a comparative investigation and evaluation of the capabilities of correlative and mechanistic modeling processes, applied to the projection of future distributions of date palm in novel environments and to establish a method of minimizing uncertainty in the projections of differing techniques. The location of this study on a global scale is in Middle Eastern Countries. We compared the mechanistic model CLIMEX (CL) with the correlative models MaxEnt (MX), Boosted Regression Trees (BRT), and Random Forests (RF) to project current and future distributions of date palm ( Phoenix dactylifera L.). The Global Climate Model (GCM), the CSIRO-Mk3.0 (CS) using the A2 emissions scenario, was selected for making projections. Both indigenous and alien distribution data of the species were utilized in the modeling process. The common areas predicted by MX, BRT, RF, and CL from the CS GCM were extracted and compared to ascertain projection uncertainty levels of each individual technique. The common areas identified by all four modeling techniques were used to produce a map indicating suitable and unsuitable areas for date palm cultivation for Middle Eastern countries, for the present and the year 2100. The four different modeling approaches predict fairly different distributions. Projections from CL were more conservative than from MX. The BRT and RF were the most conservative methods in terms of projections for the current time. The combination of the final CL and MX projections for the present and 2100 provide higher certainty concerning those areas that will become highly suitable for future date palm cultivation. According to the four models, cold, hot, and wet stress, with differences on a regional basis, appears to be the major restrictions on future date palm distribution. The results demonstrate variances in the projections, resulting from different techniques. The assessment and interpretation of model projections requires reservations, especially in correlative models such as MX, BRT, and RF. Intersections between different techniques may decrease uncertainty in future distribution projections. However, readers should not miss the fact that the uncertainties are mostly because the future GHG emission scenarios are unknowable with sufficient precision. Suggestions towards methodology and processing for improving projections are included. [ABSTRACT FROM AUTHOR]