No abstract

Biodiversity encompasses the complex variety of life at all scales, ranging from genes to species to ecosystems. It encapsulates the structure, function, distribution, traits and composition of all living things. Crisis-level losses of biodiversity are stimulating action from local to global scales, as evidenced by establishment of the United Nations Sustainable Development Goals (SDGs) and Aichi targets and the current post-2020 negotiation of the Convention on Biological Diversity (CBD), as well as the first round of risk assessments by the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) 1 . In response to these losses of biodiversity, the Group on Earth Observations Biodiversity Observation Network (GEO BON) 2,3 proposes a common framework of essential biodiversity variables (EBVs) 4 for monitoring biodiversity. These EBVs form a core set of complementary biological measurements for capturing considerable biodiversity change and are produced by

No abstract

No abstract

No abstract

Aim: To propose a species distribution modelling framework and its companion "iSDM" R package for predicting the potential and realized distributions of invasive species within the invaded range.Location: Northern France. Methods:The non-equilibrium distribution of invasive species with the environment within the invaded range affects the environmental representativeness of species presenceabsence data collected from the field and introduces uncertainty in observed absences as these may either reflect unsuitable sites or be incidental. To address these issues, we here propose an environmental systematic sampling design to collect presence-absence data from the field and a probability index to sort and subsequently separate environmental absences (EAs: reflecting environmentally unsuitable sites) from dispersal-limited absences (DLAs: reflecting sites out of dispersal reach). We first conducted a comprehensive test based on a virtual species to evaluate the performance of our framework. Then, we applied it on different life stages of a non-native tree species (Prunus serotina Ehrh.) invasive in Europe. Results:Regarding the potential distribution, we found higher model performances for both the virtual species (true skill statistics (TSS) > 0.75) and P. serotina (TSS ≥ 0.68) after carefully selecting absences with a low probability to be DLAs compared with classical models that incorporate both EAs and DLAs (e.g. TSS = 0.11 for P. serotina with 80% of DLAs). On the contrary, both EAs and DLAs as well as dispersal-related covariates were needed to capture the realized distribution of both the virtual species and P. serotina. Main Conclusions:Our framework helps overcoming the conceptual and methodological limitations of the disequilibrium in species' distribution models inherent to invasive species and enables managers to robustly estimate both the realized and potential distributions of invasive species. Although more relevant for modelling the distribution of non-native species, this framework can also be applied to native species. K E Y W O R D Salien species, biological invasions, dispersal limitations, potential niche, realized niche, species distribution modelling, virtual species

51Changes varied also per land cover type: changes in forests were more profound and 52 persistent than those in shrub land. The characteristic phenology of land covers (e.g. 53coniferous vs. deciduous forest) also resulted in a clearly different post-fire behavior. This 54 research provides insights in the understanding of short-term fire effects on regional climate. 55

Highlights• Th y w ba d f W dVi w-2 image significantly improves spectral discrimination of savannah tree species.• Th t a siti t s sc c ph gica p i d is th m st id a f disc imi ati g t sp ci s in African savannah.• Mu ti-phenology data improves tree species classification using multispectral data alone.