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The concept of ecosystem services (ES) is increasingly used to analyze the relationships and interactions between humans and nature. Understanding the ecosystem services’ flow and the ecosystems’ capacity to generate these services is an essential element in considering the sustainability of ecosystem uses and the development of ecosystem accounts. For such purpose, we conduct spatially explicit analyses of nine ecosystem services in the Maamora forest, Morocco. The ecosystem services included are timber and industry wood harvest, firewood harvest, cork gathering, forage production, acorn gathering, forest carbon storage, and recreational hiking. Results make it possible to distinguish between the forest capacity to provide ecosystem services from their current use (demand) and assess them quantitatively. It came out that both capacity and flow differ in spatial extent as well as in quantity. Distinguishing capacity and flow of ES also provided an estimate of over-or under-utilization of services, and offer the possibility to map the ecosystem service provision hotspots (SPA) and degraded SPHs. The respective assessment of capacity and flux in a space-explicit manner can therefore support the monitoring of the forest ecosystem use sustainability.

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Climate change, which is expected to continue in the future, is increasingly becoming a major concern affecting many components of the biodiversity and human society. Understanding its impacts on forest ecosystems is essential for undertaking long-term management and conservation strategies. This study was focused on modeling the potential distribution of Quercus suber in the Maamora Forest, the world’s largest lowland cork oak forest, under actual and future climate conditions and identifying the environmental factors associated with this distribution. Maximum Entropy approach was used to train a Species Distribution Model and future predictions were based on different greenhouse gas emission scenarios (Representative Concentration Pathway RCPs). The results showed that the trained model was highly reliable and reflected the actual and future distributions of Maamora’s cork oak. It showed that the precipitation of the coldest and wettest quarter and the annual temperature range are the environmental factors that provide the most useful information for Q. suber distribution in the study area. The computed results of cork oak’s habitat suitability showed that predicted suitable areas are site-specific and seem to be highly dependent on climate change. The predicted changes are significant and expected to vary (decline of habitat suitability) in the future under the different emissions pathways. It indicates that climate change may reduce the suitable area for Q. suber under all the climate scenarios and the severity of projected impacts is closely linked to the magnitude of the climate change. The percent variation in habitat suitability indicates negative values for all the scenarios, ranging –23% to –100%. These regressions are projected to be more important under pessimist scenario RCP8.5. Given these results, we recommend including the future climate scenarios in the existing management strategies and highlight the usefulness of the produced predictive suitability maps under actual and future climate for the protection of this sensitive forest and its key species – cork oak, as well as for other forest species.

Aim of study:The aim of the study is to present a diagnosis for the state of Argan forest degradation in Morocco through GIS and remote sensing utilizing Sentinel 2 satellite images of the year 2019 (dated 28/08/2019).Area of study: The study was carried out in a forest commune in Idmine, South West Morocco, which is located in semi-arid bioclimatic region.Material and methods: In the study, two methods were tested. These are; (i) the vegetation indices (VIs) [Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Soil-Adjusted Vegetation Index (SAVI), Brilliance Index (IB)] and their combination and (ii) the supervised classification and spectral analysis.Main results: Two methods have given the same results (Kappa coefficient=90%) to describe the state of forest degradation. Consequently, three classes pertaining to forest degradation within the study area were; low (34%), medium (44%) and critical degradation (22%).Highlights: This monitoring might help managers to create forest management plans and to evaluate the speed of deforestation and degradation.