The Veleta cirque is located at the foot of the Veleta peak, one of the highest summits of the Sierra Nevada National Park (southern Spain). This cirque was the source of a glacier valley during the Quaternary cold periods. During the Little Ice Age it sheltered a small glacier, the most southerly in Europe, about which we have possessed written records since the 17th century. This glacier still had ice residues until the mid-20th century. This ice is no longer visible, but a residue persists along with discontinuous permafrost trapped under strata of rock blocks that make up an incipient rock glacier. From 2006 to 2013, this rock glacier was monitored by measurement of the temperature of the active layer, the degree of snow cover on the ground, movements of the body of the rock glacier and geophysical prospection inside it. The results show that the relict ice and trapped permafrost have been steadily declining. The processes that explain this degradation occur in chain, starting from the external radiation that affects the ground in summer, which is when the temperatures are higher. In effect, when this radiation steadily melts the snow on the ground, the thermal expansive wave advances into the heart of the active layer, reaching the ceiling of the frozen mass, which it then degrades and melts. In this entire linked process, the circulation of meltwaters fulfils a highly significant function, as they act as heat transmitters. The complementary nature of these processes explains the subsidence and continuous changes in the entire clastic pack and the melting of the frozen ceiling on which it rests. This happens in summer in just a few weeks. All these events, in particular the geomorphological ones, take place on the Sierra Nevada peaks within certain climate conditions that are at present unfavourable to the maintenance of snow on the ground in summer. These conditions could be related to recent variations in the climate, starting in the mid-19th century and most markedly since the second half of the 20th century. The work and results highlight the climate sensitivity of the peaks of the Sierra Nevada to the effect of climate change and its impact on the dynamics of ecosystems, which is a benchmark for evaluating the current evolution of landscapes of Mediterranean high mountains.

Aim: The aim of this study is to model the past, current, and future distribution of J. phoenicea s.s., J. turbinata, and J. canariensis, based on bioclimatic variables using a maximum entropy model (Maxent) in the Mediterranean and Macaronesian regions.Location: Mediterranean and Macaronesian.Taxon: Cupressaceae, Juniperus.Methods: Data on the occurrence of the J. phoenicea complex were obtained from the Global Biodiversity Information Facility (GBIF.org), the literature, herbaria, and the authors’ field notes. Bioclimatic variables were obtained from the WorldClim database and Paleoclim. The climate data related to species localities were used for predictions of niches by implementation of Maxent, and the model was evaluated with ENMeval.Results: The potential niches of Juniperus phoenicea during the Last Interglacial period (LIG), Last Glacial Maximum climate (LGM), and Mid‐Holocene (MH) covered 30%, 10%, and almost 100%, respectively, of the current potential niche. Climate warming may reduce potential niches by 30% in RCP2.6 and by 90% in RCP8.5. The potential niches of Juniperus turbinata had a broad circum‐Mediterranean and Canarian distribution during the LIG and the MH; its distribution extended during the LGM when it was found in more areas than at present. The predicted warming in scenarios RCP2.6 and RCP8.5 could reduce the current potential niche by 30% and 50%, respectively. The model did not find suitable niches for J. canariensis during the LIG and the LGM, but during the MH its potential niche was 30% larger than at present. The climate warming scenario RCP2.6 indicates a reduction in the potential niche by 30%, while RCP8.5 so indicates a reduction of almost 60%.Main conclusions: This research can provide information for increasing the protection of the juniper forest and for counteracting the phenomenon of local extinctions caused by anthropic pressure and climate changes.

Summary: The prevention of tropical forest deforestation is essential for mitigating climate change. We tested the machine learning algorithm Maxent to predict deforestation across the Peruvian Amazon. We used official annual 2001–2019 deforestation data to develop a predictive model and to test the model’s accuracy using near-real-time forest loss data for 2020. Distance from agricultural land and distance from roads were the predictor variables that contributed most to the final model, indicating that a narrower set of variables contribute nearly 80% of the information necessary for prediction at scale. The permutation importance indicating variable information not present in the other variables was also highest for distance from agricultural land and distance from roads, at 40.5% and 14.3%, respectively. The predictive model registered 73.2% of the 2020 early alerts in a high or very high risk category; less than 1% of forest cover in national protected areas were registered as very high risk, but buffer zones were far more vulnerable, with 15% of forest cover being in this category. To our knowledge, this is the first study to use 19 years of annual data for deforestation risk. The open-source machine learning method could be applied to other forest regions, at scale, to improve strategies for reducing future deforestation.