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In the light of unprecedented change in global biodiversity, real-time and accurate ecosystem and biodiversity assessments are becoming increasingly essential. Nevertheless, estimation of biodiversity using ecological field data can be difficult for several reasons. For instance, for very large areas, it is challenging to collect data that provide reliable information. Some of these restrictions in Earth observation can be avoided through the use ofremote sensingapproaches. Various studies have estimated biodiversity on the basis of the Spectral Variation Hypothesis (SVH). According to this hypothesis, spectral heterogeneity over the different pixel units of a spatial grid reflects a higher niche heterogeneity, allowing more organisms to coexist. Recently, the spectral species concept has been derived, following the consideration that spectral heterogeneity at a landscape scale corresponds to a combination of subspaces sharing a similarspectral signature. With the use of high resolution remote sensing data, on a local scale, these subspaces can be identified as separate spectral entities, the so called “spectral species”. Our approach extends this concept over wide spatial extents and to a higher level of biological organization. We applied this method toMODISimagery data across Europe. Obviously, in this case, a spectral species identified by MODIS is not associated to a singleplant speciesin the field but rather to a species assemblage, habitat, or ecosystem. Based on such spectral information, we propose a straightforward method to deriveα- (local relative abundance and richness of spectral species) andβ-diversity (turnover of spectral species) maps over wide geographical areas.

International audience; In the face of the growing challenges brought about by human activities, effective planning and decision-making in biodiversity and ecosystem conservation, restoration, and sustainable development are urgently needed. Ecological models can play a key role in supporting this need and helping to safeguard the natural assets that underpin human wellbeing and support life on land and below water (United Nations Sustainable Development Goals; SDG 15 & 14). The urgency and complexity of safeguarding forest (SDG 15.2) and mountain ecosystems (SDG 15.4), for example, and halting decline in biodiversity (SDG 15.5) in the Anthropocene requires a re-envisioning of how ecological models can best support the comprehensive assessments of biodiversity and its change that are required for successful action. A key opportunity to advance ecological modeling for both predictive and explanatory purposes arises through a collaboration between ecologists and the Earth observation community, and a close integration of remote sensing and species distribution models. Remote sensing products have the capacity to provide continuous spatiotemporal information about key factors driving the distribution of organisms, therefore improving both the use and accuracy of these models for management and planning. Here we first survey the literature on remote sensing data products available to ecological modelers interested in improving predictions of species range dynamics under global change. We specifically explore the key biophysical processes underlying the distribution of species in the Anthropocene including climate variability, changes in land cover, and disturbances. We then discuss potential synergies between the ecological modeling and remote sensing communities, and highlight opportunities to close the data and conceptual gaps that currently impede a more effective application of remote sensing for the monitoring and modeling of ecological systems. Specific attention is given to how potential collaborations between the two communities could lead to new opportunities to report on progress towards global agendas - such as the Agenda 2030 for sustainable development of the United Nations or the Post-2020 Global Biodiversity Framework of the Convention for Biological Diversity, and help guide conservation and management strategies towards sustainability.

AIM: Mountains and islands are both well known for their high endemism. To explain this similarity, parallels have been drawn between the insularity of "true islands" (land surrounded by water) and the isolation of habitats within mountains (so-called "mountain islands"). However, parallels rarely go much beyond the observation that mountaintops are isolated from one another, as are true islands. Here, we challenge the analogy between mountains and true islands by re-evaluating the literature, focusing on isolation (the prime mechanism underlying species endemism by restricting gene flow) from a dynamic perspective over space and time. FRAMEWORK: We base our conceptualization of "isolation" on the arguments that no biological system is completely isolated; instead, isolation has multiple spatial and temporal dimensions relating to biological and environmental processes. We distinguish four key dimensions of isolation: (a) environmental difference from surroundings; (b) geographical distance to equivalent environment [points (a) and (b) are combined as "snapshot isolation"]; (c) continuity of isolation in space and time; and (d) total time over which isolation has been present [points (c) and (d) are combined as "isolation history"]. We evaluate the importance of each dimension in different types of mountains and true islands, demonstrating that substantial differences exist in the nature of isolation between and within each type. In particular, different types differ in their initial isolation and in the dynamic trajectories they follow, with distinct phases of varying isolation that interact with species traits over time to form present-day patterns of endemism. CONCLUSIONS: Our spatio-temporal definition of isolation suggests that the analogy between true islands and mountain islands masks important variation of isolation over long time-scales. Our understanding of endemism in isolated systems can be greatly enriched if the dynamic spatio-temporal dimensions of isolation enter models as explanatory variables and if these models account for the trajectories of the history of a system. Department of Biodiversity, Macroecology & Biogeography at University of Gottingen; The Netherlands Organization for Scientific Research (NWO, no. 2012/13248/ALW to H. Hooghiemstra); European Research Council (ERC) Advanced Grant (no. 741413, Humans on Planet Earth (HOPE); Marie Sklodowska-Curie grant agreement (no. 707968); Danish National Research Foundation for support of the Center for Macroecology, Evolution and Climate (no. DNRF96); Austrian Science Foundation (Fonds zur Forderung der wissenschaftlichen Forschung, FWF, no. I 3757-B29); Portuguese National Funds, through Fundação para a Ciência e a Tecnologia (FCT), within the project UID/BIA/00329/2013 and the research Fellowship PD/BD/114380/2016; Horizon 2020 research and innovation programme (no. 641762). info:eu-repo/semantics/publishedVersion