No abstract

The increase in aridity, mainly by decreases in precipitation but also by higher temperatures, is likely the main threat to the diversity and survival of Mediterranean forests. Changes in land use, including the abandonment of extensive crop activities, mainly in mountains and remote areas, and the increases in human settlements and demand for more resources with the resulting fragmentation of the landscape, hinder the establishment of appropriate management tools to protect Mediterranean forests and their provision of services and biodiversity. Experiments and observations indicate that if changes in climate, land use and other components of global change, such as pollution and overexploitation of resources, continue, the resilience of many forests will likely be exceeded, altering their structure and function and changing, mostly decreasing, their capacity to continue to provide their current services. A consistent assessment of the impacts of the changes, however, remains elusive due to the difficulty of obtaining simultaneous and complete data for all scales of the impacts in the same forests, areas and regions. We review the impacts of climate change and other components of global change and their interactions on the terrestrial forests of Mediterranean regions, with special attention to their impacts on ecosystem services. Management tools for counteracting the negative effects of global change on Mediterranean ecosystem- services are finally discussed.

Leaf unfolding in temperate forests is driven by spring temperature, but little is known about the spatial variance of that temperature dependency. Here we use in situ leaf unfolding observations for eight deciduous tree species to show that the two factors that control chilling (number of cold days) and heat requirement (growing degree days at leaf unfolding, GDDreq) only explain 30% of the spatial variance of leaf unfolding. Radiation and aridity differences among sites together explain 10% of the spatial variance of leaf unfolding date, and 40% of the variation in GDDreq. Radiation intensity is positively correlated with GDDreq and aridity is negatively correlated with GDDreq spatial variance. These results suggest that leaf unfolding of temperate deciduous trees is adapted to local mean climate, including water and light availability, through altered sensitivity to spring temperature. Such adaptation of heat requirement to background climate would imply that models using constant temperature response are inherently inaccurate at local scale.

Abstract. Oil seed crops, especially oil palm, are among the most rapidly expanding agricultural land uses, and their expansion is known to cause significant environmental damage. Accordingly, these crops often feature in public and policy debates, which are hampered or biased by a lack of accurate information on environmental impacts. In particular, the lack of accurate global crop maps remains a concern. Recent advances in machine learning and remotely-sensed data access make it possible to address this gap. We present an up-to-date map of closed-canopy oil palm (Elaeis guineensis) plantations by typology (industrial vs. smallholder plantations) at the global scale and with an unprecedented detail (10-meter resolution). Sentinel-1 and Sentinel-2 data were used to train a DeepLabv3+ model, a convolutional neural network (CNN) for semantic segmentation. The characteristic backscatter response of closed-canopy oil palm stands in Sentinel-1 and the ability of CNN to learn the spatial patterns, such as the harvest road networks, allowed the distinction between industrial and smallholder plantations globally (overall accuracy = 97.5 % and kappa = 84.9 %). The user's accuracy in industrial and smallholders was 73.8 % and 89.4 %, and the producer's accuracy was 85.6 % and 78.8 % respectively. The global oil palm layer reveals that oil palm plantations are found in 47 tropical countries. Southeast Asia ranks as the main producing region with 17.47 × 106 ha, or 90 % of global plantations. Our analysis confirms significant regional variation in the ratio of industrial versus smallholder growers, but also that, from a typical land development perspective, large areas of legally defined smallholder oil palm resemble industrial-scale plantings. The overall oil palm surface per country is similar to the harvested area reported by FAO, except for countries in Western Africa, where our estimates are lower due to the omission of feral oil palm plantations. In Indonesia, the world's largest producer, our planted area estimate is higher because FAO does not report unregistered landholdings. Our model identifies primarily closed-canopy oil palm stands and misses young or sparsely planted oil palm stands. An accurate global map of planted oil palm can help to shape the ongoing debate about the environmental impacts of oil seed crop expansion, especially if other crops can be mapped to the same level of accuracy. As our model can be regularly rerun as new imagery is published, it can be used to reliably to monitor the expansion of a crop. The global oil palm layer for the second half of the year 2019 at a spatial resolution of 10 meters can be found at https://doi.org/10.5281/zenodo.3884602 (Descals et al., 2020).