This updated layer of The Gridded Livestock of the World (GLW)database provided modelled livestock densities of the world, adjusted to match official (FAOSTAT)national estimates for the reference year 2005, at a spatial resolution of 3 minutes of arc (about 565 km at the equator).Recent methodological improvements have significantly enhanced these distributions: more up-to date and detailed sub-national livestock statistics have been collected; a new, higher resolution set of predictor variables is used; and the analyticalprocedure has been revised and extended to include a more systematic assessment of model accuracy and therepresentation of uncertainties associated with the predictions.<br><br>For further details on mapping methods see: Robinson, T.P., Wint, G.R.W., Conchedda, G., Van Boeckel, T.P., Ercoli, V., Palamara, E., Cinardi, G., D’Aietti, L., Hay, S.I., Gilbert, M., 2014. Mapping the Global Distribution of Livestock. PLoS ONE 9, e96084. <a href=\"https://doi.org/10.1371/journal.pone.0096084\"target=_blank>https://doi.org/10.1371/journal.pone.0096084</a><br/><br>These digital layers are made publically available via the Livestock Geo-Wiki (<a href=\"http://www.livestock.geo-wiki.org\"target=_blank>livestock.geo-wiki.org</a><br/>

The forest structural condition index is derived from the University of Maryland canopy cover, canopy height, and time since forest loss data sets. The index spans from short, open-canopy, recently disturbed forests to tall, closed canopy forests that have not been disturbed with the last 14 years. Forest stature and canopy cover are products of both the biophysical potential of a local site and of disturbance history. The tallest, most dense forests are found in settings with favorable climate and soils but with low levels if natural or human disturbance. Such forests have been shown to support high levels of biodiversity, store high levels of carbon, and be more resilient to climate variability. Our maps of forest structural condition are the first to identify locations in the humid tropics of tall, dense forests resulting from high biophysical potential and low disturbance rates.<br><br>Data are provided by the Montana State University for South America, Africa and Asia separately, and have been merged into a single dataset here.<br><br>License information: <a href "https://creativecommons.org/licenses/by/4.0/"> CC-4.0 Attribution</a>.<br/>

The distribution of forest biomass vertically and horizontally is an important predictor of biodiversity, disturbance risk, carbon storage, and hydrological flows. Human activities may alter the influence of forest structure on biodiversity through hunting, introducing non-native species, and altering disturbance regimes. The authors introduce two new remotely sensed indices describing forest structure and human pressure in tropical forests. The Forest Structural Condition Index (SCI) uses best existing global forest data sets to represent a gradient from low to high forest structure development. Remotely sensed estimates of canopy height, tree cover, and time since disturbance comprise inputs of the index. The index distinguishes short, open-canopy, or recently disturbed stands such as those recently deforested from tall, closed-canopy, older stands typical of primary of late secondary forest. The SCI was validated against estimates of foliage height diversity derived from airborne lidar and estimates of aboveground biomass derived from forest inventory plots. The Forest Integrity Index overlays an index of human pressure, the Human Footprint, on SCI to identify structurally complex forests with low human pressure that are likely to be most valuable for biodiversity and ecosystem services. The SCI and Forest Integrity Index are being used to assess progress for countries in reaching the 2020 forest fragmentation and connectivity targets under the Convention on Biodiversity. Broader potential applications include using the SCI and Forest Integrity as predictors of habitat quality, community richness, carbon storage, hydrological yield, and restoration of secondary forest.<br><br>This dataset is provided from the University of Montana through a partnerhsip with the NASA Biodiversity and Ecological Forecasting Program.<br/><br>License information: <a href "https://creativecommons.org/licenses/by/4.0/" target="_blank">CC-4.0 Attribution</a>.<br/>

The fragmentation dataset classifies forested areas into several fragmentation classes using spatial pattern analysis, and includes effects of both deforestation and regrowth on forest fragmentation. Multiple variations of this dataset are available for the years 2000 and 2012, each making different assumptions about: the distance that fragmentation effects extend into forests (500 metres or 1000 metres); the canopy cover (%) that defines forest area (25 or 50%); and the minimum area (in hectares) of a fragment to be be considered a forest fragment (0, 1 or 5 ha). The map presented here shows effects extending 500 metres into forest interiors. This dataset has a 30-metre spatial resolution. Data derived from: Hansen, M.C., et al. 2013. High-Resolution Global Maps of 21st-Century Forest Cover Change. Science 342, 850–853. 10.1126/science.1244693 Created as part of the GEF-funded Global Support to Sixth National Report Project in collaboration with the NASA-funded Forest Integrity Project.

Forest connectivity identifies key areas between Intact Forest Landscapes (IFLs) in 2013. IFLs are large areas (greater than or equal to 500 square kilometres) of forest and other natural vegetation that show no remotely detected signs of human disturbance. Data Sources: Hansen, M.C., et al. 2013. High-Resolution Global Maps of 21st-Century Forest Cover Change. Science 342, 850–853. DOI: <a href="https://doi.org/10.1126/science.1244693" target="_blank">10.1126/science.1244693</a> Potapov, P., et al., 2017. The last frontiers of wilderness: Tracking loss of intact forest landscapes from 2000 to 2013. Science Advances 3, e1600821. <a href="https://doi.org/10.1126/sciadv.1600821" target="_blank">10.1126/sciadv.1600821</a>

This dataset show general agricultural suitability at a spatial resolution of 30 arc-second (~1km), considering rainfed conditions and irrigation on currently irrigated areas. The agricultural suitability represents for each pixel the maximum suitability value of considered 16 plants, including: We show a subset of the data that covers three time periods (1981-2010, 2011-2040, 2071-2100), as well as changes in agricultural suitability over the same periods.<br><br>For futher details see: <a href="http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0107522">Zabel F., Putzenlechner B., Mauser W. (2014): Global agricultural land resources – a high resolution suitability evaluation and its perspectives until 2100 under climate change conditions</a><br/><br>Data can also be downloaded from <a href="http://geoportal-glues.ufz.de/stories/globalsuitability.html">here</a>.<br/>

Aboveground live woody carbon density change (2003-2014): The data provided here are the result of a time-series analysis of carbon density change between 2003-2014 spanning tropical America, Africa, and Asia (23.45 N lat.-23.45 S lat.). For further information about these results please see the associated journal article (Baccini et al. 2017, Science). Spatial (raster) and tabular data described in the journal article are available for download from the links below. Data can be visualized at www.thecarbonsource.org. The visualization includes the ability to select a given change pixel (loss or gain) and display the trajectory of carbon density during the 2003-2014 study period. Raster Data Information: The carbon density change data are divided into three regions: America, Africa, and Asia. For each region there are two raster (.tif) files representing: 1) carbon density net gain, and 2) carbon density net loss. The value of each pixel (463 x 463 m) represents the total net carbon density change (Mg/ha) over the period 2003-2014. Only pixels exhibiting statistical significance at the 95% level are reported. All raster files are in the original MODIS sinusoidal projection.Baccini, A., W. Walker, L. Carvalho, M. Farina, D. Sulla-Menashe, R.A. Houghton. 2017. Tropical forests are a net carbon source based on aboveground measurements of gain and loss. Science 2017 Vol. 358, Issue 6360, pp. 230-234 DOI:10.1126/science.aam5962. Data available online from www.thecarbonsource.org.

The forest integrrity index is derived by overlaying the human footprint (Venter et al. 2016) on the forest structural condition. The name is consistent with the concept of ecological integrity. Ecological integrity has been defined as, “the system’s capacity to maintain structure and ecosystem functions using processes and elements characteristic for its ecoregion.” (Parks Canada 2008).  This capacity is a result of the climate, soil, topography, biota and other biophysical properties of the ecoregion and the extent to which these properties are not altered by modern human pressures. Consistent with this definition, the forest integrity index is based on on the structural complexity of a stand relative to the natural potential of the ecoregion and level of human pressure. Thus, forest of high integrity are relatively tall, high in canopy cover, older, and with relatively low human pressure.  An increasing number of studies have shown that human pressure in various forms can have negative effects on native species.  Thus, high integrity forests may be uniquely important for conservation because they support species and processes that are require well-developed forests and are sensitive to human activities.  Such forests often also have high economic value and have likely been preferentially converted to more intense human land uses.  Thus, identifying remaining areas of high forest integrity is important for conservation planning.<br><br>Data is provided by Montana State University.<br/><br>License information: <a href "https://creativecommons.org/licenses/by/4.0/">CC-4.0 Attribution</a>.<br/>

This dataset show general agricultural suitability at a spatial resolution of 30 arc-second (~1km), considering rainfed conditions and irrigation on currently irrigated areas. The agricultural suitability represents for each pixel the maximum suitability value of considered 16 plants, including: We show a subset of the data that covers three time periods (1981-2010, 2011-2040, 2071-2100), as well as changes in agricultural suitability over the same periods.<br><br>For futher details see: <a href="http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0107522">Zabel F., Putzenlechner B., Mauser W. (2014): Global agricultural land resources – a high resolution suitability evaluation and its perspectives until 2100 under climate change conditions</a><br/><br>Data can also be downloaded from <a href="http://geoportal-glues.ufz.de/stories/globalsuitability.html">here</a>.<br/>