Human pressures on the ocean are thought to be increasing globally, yet we know little about their patterns of cumulative change, which pressures are most responsible for change, and which places are experiencing the greatest increases. Managers and policymakers require such information to make strategic decisions and monitor progress towards management objectives. Here we calculate and map recent change over 5 years in cumulative impacts to marine ecosystems globally from fishing, climate change, and ocean- and land-based stressors. Nearly 66% of the ocean and 77% of national jurisdictions show increased human impact, driven mostly by climate change pressures. Five percent of the ocean is heavily impacted with increasing pressures, requiring management attention. Ten percent has very low impact with decreasing pressures. Our results provide large-scale guidance about where to prioritize management efforts and affirm the importance of addressing climate change to maintain and improve the condition of marine ecosystems. Halpern, B. S. et al. Spatial and temporal changes in cumulative human impacts on the world’s ocean. Nat. Commun. 6:7615 doi: 10.1038/ncomms8615 (2015).

This dataset provides estimate of the potential increase in soil organic carbon within the top 30 cm of soil in croplands after 20 years, following implementation of better land managment practices under a medium sequestration scenario. The per pixel values here take in to consideration the percent of each pixel which is classified as cropland (from the GLC-Share/GLC-02 dataset), and values have been converted to total tonnes of carbon (x 100) per pixel.<br/><br>See: <a href="https://doi.org/10.1038/s41598-017-15794-8">Zomer, R.J., Bossio, D.A., Sommer, R., Verchot, L.V., 2017. Global Sequestration Potential of Increased Organic Carbon in Cropland Soils. Scientific Reports 7, 15554</a>.<br/>For descriptions of sequrestion scenarions see: <a href="https://doi.org/10.1016/j.jenvman.2014.05.017">Sommer, R., Bossio, D., 2014. Dynamics and climate change mitigation potential of soil organic carbon sequestration. Journal of Environmental Management 144, 83–87</a>.<br/>

This global accessibility map enumerates land-based travel time to the nearest densely-populated area for all areas between 85 degrees north and 60 degrees south for a nominal year 2015. Densely-populated areas are defined as contiguous areas with 1,500 or more inhabitants per square kilometre or a majority of built-up land cover types coincident with a population centre of at least 50,000 inhabitants. This map was produced through a collaboration between MAP (University of Oxford), Google, the European Union Joint Research Centre (JRC), and the University of Twente, Netherlands.The underlying datasets used to produce the map include roads (comprising the first ever global-scale use of Open Street Map and Google roads datasets), railways, rivers, lakes, oceans, topographic conditions (slope and elevation), landcover types, and national borders. These datasets were each allocated a speed or speeds of travel in terms of time to cross each pixel of that type. The datasets were then combined to produce a "friction surface"; a map where every pixel is allocated a nominal overall speed of travel based on the types occurring within that pixel. Least-cost-path algorithms (running in Google Earth Engine and, for high-latitude areas, in R) were used in conjunction with this friction surface to calculate the time of travel from all locations to the nearest (in time) city. The cities dataset used is the high-density-cover product created by the Global Human Settlement Project. Each pixel in the resultant accessibility map thus represents the modelled shortest time from that location to a city. Authors: D.J. Weiss, A. Nelson, H.S. Gibson, W. Temperley, S. Peedell, A. Lieber, M. Hancher, E. Poyart, S. Belchior, N. Fullman, B. Mappin, U. Dalrymple, J. Rozier, T.C.D. Lucas, R.E. Howes, L.S. Tusting, S.Y. Kang, E. Cameron, D. Bisanzio, K.E. Battle, S. Bhatt, and P.W. Gething. A global map of travel time to cities to assess inequalities in accessibility in 2015. (2018). Nature. doi:10.1038/nature25181 Processing notes: Data were processed from numerous sources including OpenStreetMap, Google Maps, Land Cover mapping, and others, to generate a global friction surface of average land-based travel speed. This accessibility surface was then derived from that friction surface via a least-cost-path algorithm finding at each location the closest point from global databases of population centres and densely-populated areas. Please see the associated publication for full details of the processing. Source: https://map.ox.ac.uk/research-project/accessibility_to_cities/

This dataset provides estimate of the potential increase in soil organic carbon within the top 30 cm of soil in croplands after 20 years, following implementation of better land managment practices under a high sequestration scenario. The per pixel values here take in to consideration the percent of each pixel which is classified as cropland (from the GLC-Share/GLC-02 dataset), and values have been converted to total tonnes of carbon (x 100) per pixel.<br/><br>See: <a href="https://doi.org/10.1038/s41598-017-15794-8">Zomer, R.J., Bossio, D.A., Sommer, R., Verchot, L.V., 2017. Global Sequestration Potential of Increased Organic Carbon in Cropland Soils. Scientific Reports 7, 15554</a>.<br/>For descriptions of sequrestion scenarions see: <a href="https://doi.org/10.1016/j.jenvman.2014.05.017">Sommer, R., Bossio, D., 2014. Dynamics and climate change mitigation potential of soil organic carbon sequestration. Journal of Environmental Management 144, 83–87</a>.<br/>

This raster layer represents the number of threatened species of mammals, amphibians and birds potentially occurring in each ~300 m grid cell. <br> Species range data were rasterised at 10 arc-seconds (approximately 300m at the equator) from polygon maps developed for the IUCN Red List (IUCN, 2017; BirdLife international and Handbook of the Birds of the World 2017). Each range was then refined by removing areas of unsuitable land cover (Bontemps et al., 2011) using information on species’ habitat preferences (IUCN, 2017). Areas outside the species’ known altitudinal limits (IUCN, 2017) were also removed using elevation data (Danielson and Gesch, 2011). If species had no habitat preference data available, ranges were refined only by altitude. If altitude limits were missing, then extreme values (for either min or max, or both) outside the global min/max of the elevation dataset were applied. This effectively meant there was no altitude refinement in such cases. This refinement process produced extent of suitable habitat (ESH) maps for each species. These maps were then summed together with equal weighting into a single richness layer. <br> The three taxonomic groups included are those that have been comprehensively assessed for the IUCN Red List, and are used as a proxy to represent threatened terrestrial biodiversity. Thus, users should be aware of the taxonomic bias and that marine areas have been removed from analysis. Similarly, areas of permanent snow and ice have values of zero due to a lack of corresponding IUCN habitat types. <br> <br><b>References:</b> <br> Bontemps, S., Defourny, P., Radoux, J., Van Bogaert, E., Lamarche, C., Achard, F., Mayaux, P., Boettcher, M., Brockmann, C., Kirches, G. and Zülkhe, M. (2013). Consistent global land cover maps for climate modelling communities: current achievements of the ESA’s land cover CCI. In: Proceedings of the ESA Living Planet Symposium, September 2013 (pp. 9-13). <br> Danielson, J.J., and Gesch, D.B. (2011). Global multi-resolution terrain elevation data 2010 (GMTED2010): U.S. Geological Survey Open-File Report 2011–1073, 26 p. <a href="https://pubs.usgs.gov/of/2011/1073/pdf/of2011-1073.pdf" target="_blank">https://pubs.usgs.gov/of/2011/1073/pdf/of2011-1073.pdf</a> IUCN (2017). <a href="http://www.iucnredlist.org" target="_blank">The IUCN Red List of Threatened Species. Version 2017-3.</a> <br><br> <b>Attribution:</b><br>IUCN 2017. <a href="http://www.iucnredlist.org" target="_blank">The IUCN Red List of Threatened Species. Version 2017-1</a>.<br> <a href="http://www.iucnredlist.org/info/terms-of-use" target="_blank">Terms and Conditions</a> <br>Note that commercial use is not permitted.

This raster layer represents the number of species of mammals, amphibians and birds whose distributions overlap each ~300m grid cell. <br> Species range data were rasterised at 10 arc-seconds (approximately 300m at the equator) from polygon maps developed for the IUCN Red List (IUCN, 2017; BirdLife international and Handbook of the Birds of the World 2017). Each range was then refined by removing areas of unsuitable land cover (Bontemps et al., 2011) using information on species’ habitat preferences (IUCN, 2017). Areas outside the species’ known altitudinal limits (IUCN, 2017) were also removed using elevation data (Danielson and Gesch, 2011). If species had no habitat preference data available, ranges were refined only by altitude. If altitude limits were missing, then extreme values (for either min or max, or both) outside the global min/max of the elevation dataset were applied. This effectively meant there was no altitude refinement in such cases. This refinement process produced extent of suitable habitat (ESH) maps for each species. These maps were then summed together into a single layer with equal weighting. <br> The three taxonomic groups included are those that have been comprehensively assessed for the IUCN Red List, and are used as a proxy to represent terrestrial biodiversity. Thus, users should be aware of the taxonomic bias and that marine areas have been removed from analysis. Similarly, areas of permanent snow and ice have values of zero due to a lack of corresponding IUCN habitat types. <br> <br><b>References:</b> <br> Bontemps, S., Defourny, P., Radoux, J., Van Bogaert, E., Lamarche, C., Achard, F., Mayaux, P., Boettcher, M., Brockmann, C., Kirches, G. and Zülkhe, M. (2013). Consistent global land cover maps for climate modelling communities: current achievements of the ESA’s land cover CCI. In: Proceedings of the ESA Living Planet Symposium, September 2013 (pp. 9-13). <br> Danielson, J.J., and Gesch, D.B. (2011). Global multi-resolution terrain elevation data 2010 (GMTED2010): U.S. Geological Survey Open-File Report 2011–1073, 26 p. <a href="https://pubs.usgs.gov/of/2011/1073/pdf/of2011-1073.pdf" target="_blank">https://pubs.usgs.gov/of/2011/1073/pdf/of2011-1073.pdf</a> IUCN (2017). <a href="http://www.iucnredlist.org" target="_blank">The IUCN Red List of Threatened Species. Version 2017-3.</a> <br><br> <b>Attribution:</b><br>IUCN 2017. <a href="http://www.iucnredlist.org" target="_blank">The IUCN Red List of Threatened Species. Version 2017-1</a>.<br> <a href="http://www.iucnredlist.org/info/terms-of-use" target="_blank">Terms and Conditions</a> <br>Note that commercial use is not permitted.

This analysis of 35 years’ worth of satellite data (at approximately 25 square kilometer resolution at the equator) provides a comprehensive record of global land-change dynamics during the period 1982–2016. Contrary to the prevailing view that forest area has declined globally — tree cover has increased by 2.24 million km2 (+7.1% relative to the 1982 level), largely the result of a net loss in the tropics being outweighed by a net gain in the extratropics. Global bare ground cover has decreased by 1.16 million km2 (−3.1%), most notably in agricultural regions in Asia. Of all land changes, 60% are associated with direct human activities and 40% with indirect drivers such as climate change. Land-use change exhibits regional dominance, including tropical deforestation and agricultural expansion, temperate reforestation or afforestation, cropland intensification and urbanization. Consistently across all climate domains, montane systems have gained tree cover and many arid and semi-arid ecosystems have lost vegetation cover.<br><br>For full details see: <a href="https://doi.org/10.1038/s41586-018-0411-9">Song, X.-P., Hansen, M.C., Stehman, S.V., Potapov, P.V., Tyukavina, A., Vermote, E.F., Townshend, J.R., 2018. Global land change from 1982 to 2016. Nature 1</a><br/>.

Modelled fish catch from coral reefs estimates the relative size of coral reef fisheries catch. This catch is determined as a function of estimated reef productivity and fishing effort. A minor modifier to this basic model makes allowance for no-take fishing areas (where catches are zero) with a small buffer of potential enhanced fisheries in adjacent waters (spillover). It does not account for variability in the economic or social value of these fisheries. The estimation of coral-reef associated fisheries involves 4 steps: 1) Estimate the pristine potential maximum sustainable yield on coral reefs – reflecting the potential sustainable production of fish on healthy reefs; 2) Estimate the realistic potential MSY (Maximum Sustainable Yield) on coral reefs, in light of current reef condition (degradation) reducing productivity; 3) Estimate catch based on the potential MSY, adjusted by nearby population and available fishing area; 4) Catch values were reduced to zero (no catch) in the no-take zones while changing the potential catch in a surrounding 1km buffer was raised to the maximum value.<br><br>For more infomration please visit <a href="http://maps.oceanwealth.org/" target="_blank">The Mapping Ocean Wealth Explorer</a>.<br/><br>This data is provided by <a href="www.nature.org" target="_blank">The Nature Conservancy</a><br/>"

The dataset provides the annual estimated value of buillt capital that is protected by coral reefs in flood protection annually.<br><br>For more infomration please visit <a href="http://maps.oceanwealth.org/" target="_blank">The Mapping Ocean Wealth Explorer</a>.<br/><br>This data is provided by <a href="www.nature.org" target="_blank">The Nature Conservancy</a><br/>"

The Human Footprint (HFP) provides a measure of the direct and indirect human pressures on the environment globally in years 1993 and 2009. It is derived from remotely-sensed and bottom-up survey information compiled on eight measured variables. This represents not only the most current information of its type, but also the first temporally-consistent set of Human Footprint maps. Data on human pressures were acquired or developed for: 1) built environments, 2) population density, 3) electric infrastructure, 4) crop lands, 5) pasture lands, 6) roads, 7) railways, and 8) navigable waterways. Pressures were then overlaid to create the standardized Human Footprint maps for all non-Antarctic land areas. The Human Footprint maps find a range of uses as proxies for human disturbance of natural systems and can provide an increased understanding of the human pressures that drive macro-ecological patterns, as well as for tracking environmental change and informing conservation science and application. HFP values range from 0 (no human impact) to 50 (heavily human impacted).<br><br>See: <a href=""https://www.nature.com/articles/ncomms12558"">Venter, O. et al., 2016. Sixteen years of change in the global terrestrial human footprint and implications for biodiversity conservation. Nature Communications, 7, pp.1–11</a>.<br/><br>Data can also be downloaded from <a href=""https://datadryad.org/resource/doi:10.5061/dryad.052q5"">Dryad<a/>.<br/>