Biodiversity is in decline due to human‐induced pressures on ecosystems around the world. To be able to counteract this alarming trend, it is paramount to closely monitor biodiversity at global scales. Because this is practically impossible with traditional methods, the last decade has seen a strong push for new solutions. In aquatic ecosystems, the monitoring of species from environmental DNA (eDNA) has emerged as one of the most powerful tools at our disposal, but in terrestrial ecosystems, the power of eDNA for monitoring has so far been hampered by the local scale of the samples. In this study, we report the successful detection of insects from airborne eDNA from samples taken in the field. We compare our results to two traditional insect monitoring methods (1) light traps for moth monitoring and (2) transect walks for the monitoring of butterflies and wild bees. Airborne eDNA metabarcoding revealed DNA from six classes of arthropods, and twelve order of insects—including representatives from the four largest orders: Diptera (flies), Lepidoptera (butterflies and moths), Coleoptera (beetles), and Hymenoptera (bees, wasps, and ants). We did not detect all species observed using traditional methods and suggest further directions for the development of airborne eDNA metabarcoding. We also recovered DNA from nine species of vertebrates, including frogs, birds, and mammals as well as from 12 other phyla. Airborne eDNA has the potential to become a powerful tool for terrestrial biodiversity monitoring, with many impactful applications including the monitoring of pests, invasive, or endangered species and disease vectors.

For its fifth assessment report, the Intergovernmental Panel on Climate Change divided future scenario projections (2005–2100) into two groups: Socio-Economic Pathways (SSPs) and Representative Concentration Pathways (RCPs). Each SSP has country-level urban and rural population projections, while the RCPs are based on radiative forcing caused by greenhouse gases, aerosols and associated land-use change. In order for these projections to be applicable in earth system models, SSP and RCP population projections must be at the same spatial scale. Thus, a gridded population dataset that takes into account both RCP-based urban fractions and SSP-based population projection is needed. To support this need, an annual (2000–2100) high resolution (approximately 1km at the equator) gridded population dataset conforming to both RCPs (urban land use) and SSPs (population) country level scenario data were created.

We present a novel, global 30 arc seconds (∼1 km at the equator) population projection dataset covering each year from 2010 to 2100 that is consistent with both country level population and gridded urban fractions from the Coupled Model Intercomparison Project 6 (CMIP6). While IPCC population projections until 2100 are available at country level for Socio-Economic Pathways (SSPs), land cover (including the urban fraction) is only available for Representative Concentration Pathways (RCPs). To perform simulations of e.g., future supply and demand for agricultural products, fine scale projections of population density are needed for combinations of SSPs and RCPs. Therefore, we generated a 30 arc seconds dataset consistent with both SSPs and RCPs within the framework of the IPCC. This data set is useful in applications where spatially explicit projections of aspects of global change are investigated at a fine spatial scale. For example, if a link function between night-time lights and population density is found based on current satellite images and recent population density data, a projection of night-time light lights can be generated by using this link function with our projected population density. Such a projection can for example be used to evaluate the potential for future light pollution.

Knowledge about the past, current and future distribution of the human population is fundamental for tackling many global challenges. Censuses are used to collect information about population within a specified spatial unit. The spatial units are usually arbitrarily defined and their numbers, size and shape tend to change over time. These issues make comparisons between areas and countries difficult. We have in related work proposed that the shape of the lit area derived from nighttime lights, weighted by its intensity can be used to analyse characteristics of the population distribution, such as the mean centre of population. We have processed global nighttime lights data for the period 1992–2013 and derived centroids for administrative levels 0–2 of the Database of Global Administrative Areas, corresponding to nations and two levels of sub-divisions, that can be used to analyse patterns of global or local population changes. The consistency of the produced dataset was investigated and distance between true population centres and derived centres are compared using Swedish census data as a benchmark.