The Centre for Mountain Studies (CMS) is located at Perth College, University of the Highlands and Islands, Scotland. Since its establishment in 2000, staff and students at the CMS have been active in research and knowledge exchange activities at all scales, from the local, in Scotland, to the global. Projects in Scotland have focused mainly on estates, wild land (see below), and forests. Work across part, or all, of Europe has addressed mountain foods (see below), adaptation to climate change, large-scale regional planning, and the characterization of Europe's mountains. At the global scale, the CMS has been involved in activities relating to sustainable mountain development, global change, interdisciplinary research, and biodiversity conservation. The CMS also runs a part-time online MSc in Sustainable Mountain Development. This article summarizes some recent and ongoing activities. More information, including additional published references, is available on the CMS website.

Rangelands are considered critical ecosystems in the Nepal Himalayas and provide multiple ecosystem services that support local livelihoods. However, these rangelands are under threat from various anthropogenic stresses. This study analyzes an example of conflict over the use of rangeland, involving two villages in the Mustang district of Nepal. This prolonged conflict over the use of rangeland rests on how use rights are defined by the parties, that is, whether they are based on traditional use or property ownership. Traditionally, such conflicts in remote areas were managed under the Mukhiya (village chief) system, but this became dysfunctional after the political change of 1990. The continuing conflict suggests that excessive demand for limited rangelands motivates local villagers to gain absolute control of the resources. In such contexts, external support should focus on enhancing the management and production of forage resources locally, which requires the establishment of local common property institutions to facilitate sustainable rangeland management.

Sustainable management of rangeland ecosystems has direct implications for conservation of biological diversity and for the livelihoods of local communities in the Himalayan region in general and the Sagarmatha National Park and Buffer Zone (SNPBZ) in particular. This study aims to analyze the status of rangeland management in the SNPBZ from an ecological perspective. We used multivariate and bivariate analysis and geographic information system techniques to analyze ecological data and land use trends. A significant annual change with a 3.38% decrease in glacier area was observed between 1978 and 1996. We observed 168 plant species in the SNPBZ with a range of 3–17 species per sample plot, where about 67% of plants were found to be palatable for livestock. Our study shows that total available fodder biomass on rangeland in the SNPBZ has not been fully utilized yet, because the total available supply exceeds the present demand under some assumptions: reduction of biomass through grazing causes higher productivity, resulting in a higher number of species, according to the intermediate disturbance hypothesis. The results of this study could help improve decision-making related to sustainable rangeland management.

Reforestation programs have been proposed as a remedial measure to tackle deforestation and forest ecosystems degradation. Because one of the main constraints to the implementation of restoration practices is lack of funding, these programs need to be carefully planned to efficiently use the economic and human resources invested. In this study we present a geospatial decision-making tool to identify suitable areas for restoration. The overall approach entails (1) the use of the simple multiattribute rating technique (SMART) to identify and rank the attributes according to their importance for prioritizing areas for restoration and (2) the implementation of 0–1 integer programming to select the areas that maximize the environmental benefit. The approach is exemplified through a case study in central Mexico's mountainous state of Estado de México, encompassing an area just above 2 million ha. Specialists in different aspects of reforestation selected the following attributes to identify priority areas for reforestation: erosion, land use/land cover, position in the watershed, soil type, terrain slope, and precipitation. In total, 644,642 ha were classified under very high priority for reforestation. Of these, 17,059 ha were selected to maximize the environmental benefit without exceeding the available budget. The selected sites were mainly in the forested zones of steeply sloped mountains. Although the multiattribute decision analysis, the optimization model, and the spatial analysis were only loosely coupled, their combination proved to be an innovative and practical approach to systematically identify priority areas for reforestation on a yearly basis.

Recent studies of future food production in South Asia generally agree that the conditions for production will radically change in the years to come, in particular due to climate change and market variations. However, because we do not know how conditions will be modified and what adaptations will be required by farmers, the article assumes that innovative farming systems will cope best with changes, whatever those changes turn out to be. The challenge, then, is to identify circumstances that either promote or hamper innovation. A comparative analysis of 2 farming communities in the Himalayas concludes that no single parameter can explain the observed variation of agricultural innovation. Rather than restricting analyses to “innovation systems” that consist of social institutions only, the article proposes an approach that includes social actors, as well as natural resources, in processes that produce “innovative places.” In this study, water availability, farm size, and an active national nongovernmental organization are parameters that encourage innovation.

This paper presents the results of research on local air pollution (nitrogen and sulfur concentrations) and on the changes in the health status of trees observed in spruce stands in the period 1980–2011 in the Krkonoše Mountains National Park. Data on precipitation and sulfur and nitrogen deposition were collected in regular 2-week intervals from 1994 to 2010. Precipitation was measured at 5 monitoring stations; the health status of forest stands was evaluated on 6 research plots located in stands dominated by Norway spruce (Picea abies [L.] Karst) in zones where there has been a significant threat from air pollution since 1980. The health status of spruce stands was assessed on the basis of the degree of defoliation, classified into 6 levels. In all localities since 1994, the total deposition of sulfates decreased significantly, from 50–80 kg ha⁻¹ year⁻¹ to 8–13 kg ha⁻¹ year⁻¹; however, no clear trend in the development of nitrogen deposition could be stated. The mean defoliation of living and all trees was 32% (± 0.5 SE) and 63% (± 0.8 SE), respectively, on plots with autochthonous stands, and 91.5% (±5.8 SE) and 97.6% (±1.7 SE), respectively, on a plot with an allochthonous stand. The defoliation of living and all trees differed across research plots. Despite a negative relationship between defoliation of all trees on plots and atmospheric deposition of sulfur (P  =  0.012; r  =  −0.25), air pollution in the Sudeten still represents a serious hazard for the forest ecosystem. This relationship differed across research plots (F[5, 96]  =  110, P < 0.001). Close-to-nature management techniques aimed at the enhancement of forest complexity and preferential use of autochthonous populations of trees in timely regenerated forest stands may be of crucial importance for the restoration and preservation of these mountain forest ecosystems.

The present article focuses on the distribution of Pinus mugo under conditions in the central Tatra Mountains, the main mountain range of the Western Carpathians. We analyze the response of P. mugo distribution to selected abiotic habitat conditions in the eastern Tatra Mountains. The study also compares data on the distribution of P. mugo in the higher central Tatras and in the hills of the western Tatras published in previous studies. The source data for this study were aerial photographs from 3 periods (1955, 1986, and 2002). Mountain areas covered by mountain pine were identified and analyzed by ArcGIS 10, and pine fields were classified with the help of the gray scale mode. A strip of mountain pine above the upper limit of the forest represents an easily identifiable boundary on the aerial photographs: 25 well-recognized localities were selected to examine the changes in the tree line in the eastern Tatras. The distribution of mountain pine increased in the central granite and eastern limestone Tatra Mountains from 1955 to 2002 at all monitored sites. The percentage of total surface area covered in P. mugo increased from 28.11% in 1955 to 34.74% in 1986 and to 39.01% in 2002. The study also analyzes the dispersal of mountain pine over 40 years in relation to elevation, slope, radiation aspect, flow accumulation, and vertical and horizontal curvature. The results of this study explain ongoing vegetation changes and are of importance as a contribution to monitoring of climate change in the mid-European mountain areas.

In recent decades, mountainous areas that contain some of the best-preserved habitats worldwide are experiencing significant, rapid changes. Efficient monitoring of these areas is crucial for impact assessments, understanding the key processes underlying the changes, and development of measures that mitigate degradation. Remote sensing is an efficient, cost-effective means of monitoring landscapes. One of the main challenges in the development of remote sensing techniques is improving classification accuracy, which is complicated in mountainous areas because of the rugged topography. This study evaluated the 3 main steps in the supervised vegetation classification of a mountainous area in the Spanish Pyrenees using Landsat-5 Thematic Mapper imagery. The steps were (1) choosing the training data sampling type (expert supervised or random selection), (2) deciding whether to include ancillary data, and (3) selecting a classification algorithm. The combination (in order of importance) of randomly selected training data, ancillary data (topographic and vegetation index), and a random forest classifier improved classification accuracy significantly (4–11%) in the study area in the Spanish Pyrenees. The classification procedure includes important steps that improve classification accuracies; these are often ignored in standard vegetation classification protocols. Improved accuracy is vital to the study of landscape changes in highly sensitive mountain ecosystems.

Mountain river basins provide the majority of western North America with snowmelt runoff water resources throughout the late spring–early summer snowmelt season. However, this snowmelt water resource is extremely vulnerable to any changes in air temperature and precipitation. Studies of larger mountain river basins have projected potentially warmer and drier climates in the future, but the resolution of these studies is often incompatible with smaller basins and subsequent water resources planning. The purpose of this study was to test the potential of increasing the resolution of future climate projections by combining a series of surface and upper-level atmospheric datasets using a statistical downscaling technique and to then project how the future climate could change for a typical small snowmelt fed mountain basin in western North America, the Animas River Basin, Colorado, over the course of the 21st century. Results indicated that, in general, a warmer and drier climate may occur, with this technique more effectively capturing changes in air temperature over precipitation. With this kind of data at hand, increasing levels of sustainable water resource planning for a range of future climate scenarios may be achieved for mountain river basins of a similar scale.

Land surface temperature (LST) is an essential parameter in the physics of land surface processes. The spatiotemporal variations of LST on the Tibetan Plateau were studied using AQUA Moderate Resolution Imaging Spectroradiometer LST data. Considering the data gaps in remotely sensed LST products caused by cloud contamination, the harmonic analysis of time series (HANTS) algorithm was used to eliminate the influence of cloud cover and to describe the periodical signals of LST. Observed air temperature data from 79 weather stations were employed to evaluate the fitting performance of the HANTS algorithm. Results indicate that HANTS can effectively fit the LST time series and remove the influence of cloud cover. Based on the HANTS-derived mean term and annual harmonics, annual mean LST, seasonal fluctuation, and peak time of the LST annual cycle are discussed. The spatial distribution of annual mean LST generally exhibits consistency with altitude in the study area, and the spatial distribution of seasonal oscillation is closely related to precipitation. However, the timing of the peak LST does not exhibit an obvious regular pattern. The LST characteristics of different land cover types were also studied. Bare land has the highest mean LST and exhibits remarkable seasonal fluctuation. Snow, ice, or both show the lowest mean temperature, and forest shows the weakest seasonality. Different land cover types also reflect different peak occurrences of the LST annual cycle, with grassland showing the lowest annual phase value. This paper provides detailed information on the LST variations on the Tibetan Plateau, with the cloud contamination removed. The HANTS algorithm is demonstrated to be effective for understanding spatiotemporal variations of remotely sensed LST, especially for regions over which dense clouds cause large gaps in the LST data.

Destructive debris flows occur frequently on glacierized Mount Nyenchen Tanglha, Bomi, Tibet. Since 1953, hundreds of such flows have occurred in the Guxiang valley during periods of atypically hot or rainy weather in summer or early autumn. From 1964 to 1965, 95 debris flows were documented at a temporary debris flow observation station; 25 of these debris flows with a peak discharge Q_max above 50 m³/s were considered in the present study. Supported by meteorological data from the nearby Bomi station, statistical analysis showed that outburst debris flows from the Guxiang glacier are highly correlated with atypical weather. Finally, the conditional probability of a debris flow from the Guxiang glacier as a function of daily rainfall R and maximum temperature T_max for rainy days (R ≥ 5 mm) and dry days (R < 5 mm) is suggested as a plausible link between weather and outbursts of debris flow.