Today’s Mediterranean landscapes result from the interaction between ecosystems and anthropogenic activities. This study aims to assess how the land-use changes between the mid-nineteenth and end of the twentieth century influenced the temporal continuity of the ecosystems in central Apennines (central Italy). Information was acquired from Gregorian cadastral maps, orthophotos and aerial photos (1850, 1954, 1980 and 2010), digitised and georeferenced using QGIS 3.10.1 software. Marked changes in land-use types were found. From 1850 to 1954, grasslands were widely transformed into arable lands, but in the next 60 years they changed again into new grasslands and forests. Forests underwent a slow but continuous expansion from 1850 to 2010. Only a small percentage of the forest and grassland patches (14 and 16%, respectively) have seen ecological continuity. These considerations call attention to temporal continuity of ecosystems, together with the historical dynamism of landscapes, in defining land management and nature conservation policies.

The Socotra Archipelago (Yemen) is an interesting biodiversity hotspot, with a significant proportion of endemic species that have evolved to survive in an arid subtropical environment, inscribed as a World Heritage Site by UNESCO. The terrestrial ecosystems of Socotra face several threats, including climate change, overgrazing and soil degradation. Socotra Island has four endemic species of the genus Commiphora (Burseraceae). Little is known about their local distribution and ecology, yet these trees could be useful indicator species. Our study focuses on the distribution and niche characterisation of the four endemic Commiphora species of Socotra and how climate change may affect them. The aim is to improve insights into their habitats and to provide an essential basis for future local management plans and ecological restoration. We compared the current distribution with the forecasted potential distribution under a CMIP6 (Coupled Model Intercomparison Project) climate scenario, allowing us to define target conservation areas and assess potential local extinction risks. To achieve this, we collected distribution data in the field throughout Socotra Island, covering the current distribution ranges of the four species. To assess the potential distribution of these species, we applied three models (GAM, MaxEnt, RandomForest) using bioclimatic, topographic and soil variables. Forecasts under a climate change scenario were made using bioclimatic variables from the CMCC-CESM2 climate model for two different socioeconomic pathways. The distribution of three endemic Socotran Commiphora is mainly correlated to clay content in the soil and winter precipitation, while C. socotrana is affected by seasonal precipitation and temperature. Under different potential future climate scenarios, the distribution of C. ornifolia is predicted to remain stable or increase, while C. parvifolia distribution could increase, yet C. planifrons and C. socotrana are predicted to undergo a strong reduction of suitable areas and an upward shift in the mountains. Our results highlight that it is essential to conserve the unique terrestrial ecosystems in Socotra and to preserve these endemic trees which have a wide range of ecosystem services. Updates on the predicted extinction risk assessment are fundamental to understand conservation priorities and strategize future actions to ensure the persistence of Socotran myrrh trees and other endangered endemic tree taxa on the island.

Socotra Island (Yemen), a global biodiversity hotspot, is characterized by high geomorphological and biological diversity. In this study, we present a high-resolution vegetation map of the island based on combining vegetation analysis and classification with remote sensing. Two different image classification approaches were tested to assess the most accurate one in mapping the vegetation mosaic of Socotra. Spectral signatures of the vegetation classes were obtained through a Gaussian mixture distribution model, and a sequential maximum a posteriori (SMAP) classification was applied to account for the heterogeneity and the complex spatial pattern of the arid vegetation. This approach was compared to the traditional maximum likelihood (ML) classification. Satellite data were represented by a RapidEye image with 5 m pixel resolution and five spectral bands. Classified vegetation relevés were used to obtain the training and evaluation sets for the main plant communities. Postclassification sorting was performed to adjust the classification through various rule-based operations. Twenty-eight classes were mapped, and SMAP, with an accuracy of 87%, proved to be more effective than ML (accuracy: 66%). The resulting map will represent an important instrument for the elaboration of conservation strategies and the sustainable use of natural resources in the island.