## 4 Discussion and Conclusions  
In a particular biogeographic context, subject to a Mediterranean bioclimate under the intermittent but increasingly pronounced Saharan influence [44], our work highlights the potential provision of ES in an Algerian mountain forest. The limited availability of georeferenced environmental maps in this region of the world, especially at the spatial scale of the Ouled Hanneche forest, led us to create qualitative and quantitative geospatial indicators that can be assimilated to the spatial units of ES provision [28]. Given the multiplicity of ES that can be estimated by remote sensing, it appears that a generic approach, as proposed in our study, is often necessary upstream to better identify the overall capacity of a territory in terms of productivity and diversity. This methodological approach is relatively simple but remains entirely relevant in a logic of complementarity and dependence between the datasets used (see Figure 1). Each indicator thus provides biotic or abiotic information related to the ES, which can be broken down according to different spatial scales and different types of SPU [32; 33]. From a qualitative point of view, the land cover classes derived from the SVM classification provide information at the smallest scale, followed by the geomorphon classes describing the landscape in a more detailed way. Locally, the vegetation formations indicate the ES supply in the geographical space where they are present. This same logic of spatial nesting is found in the quantitative indicators. The size of the mobile window defines the geographical scale of assessment of the supply of ES and conditions the local estimate. The abiotic environmental heterogeneity [82] represented by the Shannon entropy of geomorphon classes according to a 9 \(\times\) 9 window (72,900 m\({}^{2}\) for LANDSAT) reveals the largest spatial landscape scale. The Rao entropy of the 3 \(\times\) 3 focal window (8100 m\({}^{2}\) for LANDSAT) is at the intermediate scale, whereas the NDVI incorporates information on the minimum area that is the ground resolution of the sensor (900 m\({}^{2}\)). The neighborhood relationship of varying sizes reflects variations in the abiotic and biotic components of ecosystems along gradients. For Shannon heterogeneity, it synthesizes the topographic complexity of a mountain range, which is a determining and explanatory factor of biogeographic speciation at the origin of taxonomic and functional diversity [82; 83]. Similarly, the measure of dissimilarity between NDVI values described by [PERSON]'s quadratic entropy quantifies the variations in the biotic signal according to different portions of the considered analysis space, which is synonymous with the estimation of biodiversity \(\beta\)[84; 85; 64]. This biodiversity \(\beta\) reflects the fluctuation of the response of functional traits according to the adaptation of ecosystems to environmental factors and exerts significant control on different ecosystem services through the modulation of traits [39]. Figure 6 illustrates this principle by the variability of NDVI values according to the type of plant formations. Indeed, the spatial adequacy between the survey area (400 m\({}^{2}\)) and the pixel size means that the local NDVI signal reflects the expression of the functional traits of the dominant species in the survey area. A difference in the NDVI value per formation can therefore be equated to a difference in the integrated signature of functional traits [35; 40; 86] and thus to a disparity in the level of ES provision. In summary, the variation in NDVI due to plant formation is a surrogate for expressing functional diversity in the Ouled Hanneche forest. Despite using a simple index restricted to the red and near-infrared spectral bands and tested on a relatively limited sample of surveys, these results are interesting for this type of Mediterranean forest. Indicators other than NDVI could have been used to more effectively estimate ecosystem functions.