Mapping the economic value of landslide regulation by forests

The role of forests in regulating landslide risks is well established but estimates of the economic value of this ecosystem service are limited. In order to incorporate the role of forests for landslide risk mitigation in spatial planning and other decision-making contexts, there is a need for spatially explicit information regarding the value of this service. We develop a methodological framework to combine bio-physical modelling of natural hazard risk and socio-economic exposure in a predictive model to estimate and map of the economic value of forest regulation of landslides. This method is applied in a case study of Adjara Autonomous Republic of Georgia to examine alternative scenarios for forest management and associated land cover change. The approach produces credible spatially explicit results to inform policy decisions regarding investment in forest management; and has the potential for replication in other data scarce regions

Recognizing and interpreting vegetational belts: New wine in the old bottles of a von Humboldt's legacy

Since von Humboldt, recognizing and using elevational subdivisions is at the core of biogeographical and ecological studies in mountain ecosystems. However, despite the large use of vegetational belts, their conceptual definition and practical identification appear to be surprisingly loose and inconsistent. Many authors use variations in climatic conditions to identify elevational belts. These belts are useful to set a framework for ecological studies but cannot be considered a surrogate of vegetational belts, because factors different from climate play a major role in determining the distribution of plant assemblages. Vegetation physiognomy can be used to identify \u2018biome-type\u2019 belts that are useful for comparisons across geographical areas with different floras. However, to properly reflect ecological conditions at local scale, vegetational belts should be based on species composition. One of the most effective statistical approaches for this purpose is the use spatially constrained cluster analysis. The use of indicator species analysis may be also recommended to identify the species that most characterize vegetational belts. This can help researchers to identify belts in the field. Since species identification can be difficult, some authors use plant functional types for belt delimitation. Plant functional types can be helpful to trace the adaptative responses of vegetation along elevational gradients, but cannot be recommended as a standard way to identify belts. In general, criteria to identify vegetational belts can be based on both vegetation structure (namely physiognomy and structural parameters) and/or species composition, depending on the scale and the aim of the analyses, and they should be clearly stated

Numerical classification and ordination of Esenli (Giresun) forest vegetation

KARAKOSE, MUSTAFA/0000-0003-0534-3996WOS: 000503432200003The forest vegetation of Esenli Forest Planning Unit was investigated between 2015 and 2018 from the phytosociological point of view. The study area is situated in the Euxine province of Euro-Siberian Region. Phytosociological studies were carried out in accordance with the classical Braun-Blanquet methodology, and 131 releves were collected during the field survey. The releves were classified using the Modified TWINSPAN classification, and general distribution patterns of vegetation were analysed using indirect ordination analysis (Principal Component Analysis) with the R-Project available in the JUICE program. In addition to topographic factors, ecological factors were assessed using the mean Ellenberg Indicator Values to observe the ecological relationships among communities. Four new plant associations (Cirsio trachylepidis-Pinetum sylvestris, Angelico sylvestri-Alnetum barbatae, Circaeo lutetianae-Fagetum orientalis, and Veronico chamaedryo-Piceetum orientalis) were described as belonging to humid montane coniferous and thermophilous deciduous forests within four classes. Distribution pattern of plant communities was strictly influenced by altitude, inclination, moisture, nutrient content, and light