Distributions of Earth's species are changing at accelerating rates, increasingly driven by human-mediated climate change. Such changes are already altering the composition of ecological communities, but beyond conservation of natural systems, how and why does this matter? We review evidence that climate-driven species redistribution at regional to global scales affects ecosystem functioning, human well-being, and the dynamics of climate change itself. Production of natural resources required for food security, patterns of disease transmission, and processes of carbon sequestration are all altered by changes in species distribution. Consideration of these effects of biodiversity redistribution is critical yet lacking in most mitigation and adaptation strategies, including the United Nation's Sustainable Development Goals.

The global extent and distribution of forest trees is central to our understanding of the terrestrial biosphere. We provide the first spatially continuous map of forest tree density at a global scale. This map reveals that the global number of trees is approximately 3.04 trillion, an order of magnitude higher than the previous estimate. Of these trees, approximately 1.39 trillion exist in tropical and subtropical forests, with 0.74 trillion in boreal regions and 0.61 trillion in temperate regions. Biome-level trends in tree density demonstrate the importance of climate and topography in controlling local tree densities at finer scales, as well as the overwhelming effect of humans across most of the world. Based on our projected tree densities, we estimate that over 15 billion trees are cut down each year, and the global number of trees has fallen by approximately 46% since the start of human civilization.

Topographic variation underpins a myriad of patterns and processes in hydrology, climatology, geography and ecology and is key to understanding the variation of life on the planet. A fully standardized and global multivariate product of different terrain features has the potential to support many large-scale research applications, however to date, such datasets are unavailable. Here we used the digital elevation model products of global 250 m GMTED2010 and near-global 90 m SRTM4.1dev to derive a suite of topographic variables: elevation, slope, aspect, eastness, northness, roughness, terrain roughness index, topographic position index, vector ruggedness measure, profile/tangential curvature, first/second order partial derivative, and 10 geomorphological landform classes. We aggregated each variable to 1, 5, 10, 50 and 100 km spatial grains using several aggregation approaches. While a cross-correlation underlines the high similarity of many variables, a more detailed view in four mountain regions reveals local differences, as well as scale variations in the aggregated variables at different spatial grains. All newly-developed variables are available for download at Data Citation 1 and for download and visualization at http://www.earthenv.org/topography.

Aim For many applications in biodiversity and ecology, existing remote sensing‐derived land‐cover products have limitations due to among‐product inconsistency and their typically non‐continuous nature. Here we aim to help address these shortcomings by generating a 1‐km resolution global product that provides scale‐integrated and accuracy‐weighted consensus land‐cover information on an approximately continuous scale. Location Global. Methods Using a generalized classification scheme and an accuracy‐based integration approach, we integrated four global land‐cover products. We evaluated the performance of this product compared with inputs for estimating subpixel 30‐m resolution land cover. We also compared the accuracy of deductive and inductive species distribution models built with the different products for modelling the continental distributions of six avian habitat specialists. Results Our product offers accuracy‐weighted consensus information on the prevalence of 12 land‐cover classes within every nominal 1‐km pixel across the globe (except for Antarctica). Compared with the four base products, it better captures the land‐cover information contained in the fine‐grain validation data for all classes combined and for most individual classes. It also has the highest sensitivity and overall accuracy for detecting the presence of every fine‐grain land‐cover class. Both deductive and inductive models built with the consensus dataset have the highest or second highest accuracy for modelling bird species distributions. Main conclusions Our consensus product integrates the four base products and successfully maximizes accuracy and reduces errors of omission. Specifically, the consensus product reduces limitations caused by misclassifications, false absence rates and the categorical format of existing land‐cover products. Consequently, it surpasses single base products in the ability to capture subpixel land‐cover information and the utility for modelling species distributions. Both the presented methodology and the consensus product have multiple applications in biodiversity research and for understanding and modelling of global terrestrial ecosystems.