Biodiversity hotspots

This data includes both the terrestrial, marine and marine+terrestrial key biodiversity areas in of the Philippines as of 2020. Key biodiversity areas are landscapes that showcases high species endemicity and sometimes facing threats due to various human related factors. These places are mostly identified by experts and are not necessarily included in the declared protected areas.

Due to the increasing size and complexity of global forest monitoring data sources, analysis and interpretation tools for this data are ever more important for intervention efforts, allowing for the quick identification and interpretation of significant forest loss. The emerging hot spots data set identifies the most significant clusters of primary forest loss between 2002-2025 at a country level basis, on a tropical scale. The term ‘hot spot’ is defined as an area that exhibits statistically significant clustering in the spatial patterns of loss. In this analysis, observed patterns of primary forest loss are likely to be attributable to underlying, as opposed to random, spatial processes. The different categories of hot spots are described below:  New: A location that is a statistically significant hot spot only for the year 2025 and has never been a hot spot before.  Sporadic: A location that is an on-again then off-again hot spot. Less than 22 of the 24 years have been statistically significant hot spots.  Intensifying: A location that has been a statistically significant hot spot for more than 21 of the 24 years (>90%), including the most recent year (2025). In addition, the intensity of clustering of high counts in each year is increasing.  Persistent: A location that has been a statistically significant hot spot for more than 21 of the 24 years (>90%), with no discernible trend indicating an increase or decrease in the intensity of clustering over time.  Diminishing: A location that has been a statistically significant hot spot for more than 21 of the 24 years (>90%). In addition, the intensity of clustering of high counts in each year is decreasing, or the most recent year (2025) is not hot.  The emerging hot spots analysis uses the annual Hansen et al 2013 tree cover loss data set between the years 2002 – 2025, the Turubanova et al. 2018 primary forest extent data set for the year 2001, and the ESRI ArcGIS Emerging Hot Spot Analysis geoprocessing tool. In this analysis, primary forest is defined as mature natural humid tropical forest cover that has not been completely cleared and regrown in recent history. Forest loss is defined as ‘stand replacement disturbance,’ or the complete removal of tree cover canopy at the Landsat pixel scale. The emerging hot spots analysis tool uses a combination two statistical measures, the Getis-Ord Gi\* statistic to identify the location and degree of spatial clustering of forest loss, and the Mann-Kendall trend test to evaluate the temporal trend over time.  The forest loss data used in this analysis has a user’s accuracy of 87% and a producer’s accuracy of 83.1% across the tropical biome. Additionally, because this analysis was run for individual countries, results are relative to the patterns and amount of loss in each country. Results should not be directly compared between countries - please use caution when viewing layer at a global scale.  To identify statistically significant clusters of primary forest loss on a country level basis

The RESOLVE Ecoregions 2017 dataset provides a global depiction of 846 terrestrial ecoregions representing the living planet. Ecoregions are ecosystems of regional extent and represent distinct assemblages of biodiversity, including taxa beyond vegetation. The dataset groups terrestrial ecoregions into 14 biomes and 8 realms. It provides a useful basemap for conservation planning because ecoregions are based on natural boundaries, distinct biogeographic assemblages, and ecological habitats rather than political boundaries. Each ecoregion includes attributes such as ecoregion name, ecoregion ID, biome name, realm, color codes, area, and Nature Needs Half (NNH) protection **Data creation:** 2026-06-26 **Supplemental Information:** The dataset contains 846 terrestrial ecoregions grouped into 14 biomes and 8 realms. It includes four Nature Needs Half categories: 1) Half Protected: More than 50% of the total ecoregion area is already protected. 2) Nature Could Reach Half: Less than 50% of the total ecoregion area is protected but the amount of remaining unprotected natural habitat could bring protection to over 50% if new conservation areas are added to the system. 3) Nature Could Recover: The amount of protected and unprotected natural habitat remaining is less than 50% but more than 20%. Ecoregions in this category would require restoration to reach Half Protected. 4)Nature Imperiled: The amount of protected and unprotected natural habitat remaining is less than or equal to 20%. Achieving half protected is not possible in the short term and efforts should focus on conserving remaining, native habitat fragments. The dataset includes fields for ecoregion name, biome name, realm, ecoregion ID, NNH category, NNH category name, ecoregion color, biome color, and NNH color. **Citation:** Bioscience, An Ecoregions-Based Approach to Protecting Half the Terrestrial Realm doi:10.1093/biosci/bix014 **Contact points:** Metadata Contact: [Carolyn Burns](mailto:sdesposito@resolve.ngo) **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International licence (CC-BY-4.0) **Online resources:**

The Intact Forest Landscapes (IFL) data set identifies unbroken expanses of natural ecosystems within the zone of forest extent that show no signs of significant human activity and are large enough that all native biodiversity, including viable populations of wide-ranging species, could be maintained. To map IFL areas, a set of criteria was developed and designed to be globally applicable and easily replicable, the latter to allow for repeated assessments over time as well as verification. IFL areas were defined as unfragmented landscapes, at least 50,000 hectares in size, and with a minimum width of 10 kilometers. These were then mapped from Landsat satellite imagery for the year 2000.  Changes in the extent of IFLs were identified within year 2000 IFL boundary using  Landsat Analysis Ready Data (ARD), the global forest cover loss dataset (Hansen et al., 2013), Sentinel-2 imagery, and high resolution data from Google Earth and Planet. Areas identified as “reduction in extent” met the IFL criteria in 2000, but no longer met the criteria in 2025. The main causes of change were clearing for agriculture and tree plantations, industrial activity such as logging and mining, fragmentation due to infrastructure and new roads, and fires assumed to be caused by humans.  This data can be used to assess forest intactness, alteration, and degradation at global and regional scales. Identifies the world’s last remaining unfragmented forest landscapes, large enough to retain all native biodiversity and showing no signs of human alteration as of the year 2020. This layer also shows the reduction in the extent of Intact Forest Landscapes from 2000 to 2025.

Generated by Impact Observatory, in collaboration with Vizzuality, these datasets estimate terrestrial Biodiversity Intactness as 100-meter gridded maps for the years 2017-2020. Maps depicting the intactness of global biodiversity have become a critical tool for spatial planning and management, monitoring the extent of biodiversity across Earth, and identifying critical remaining intact habitat. Yet, these maps are often years out of date by the time they are available to scientists and policy-makers. The datasets in this STAC Collection build on past studies that map Biodiversity Intactness using the PREDICTS database of spatially referenced observations of biodiversity across 32,000 sites from over 750 studies. The approach differs from previous work by modeling the relationship between observed biodiversity metrics and contemporary, global, geospatial layers of human pressures, with the intention of providing a high resolution monitoring product into the future. Biodiversity intactness is estimated as a combination of two metrics: Abundance, the quantity of individuals, and Compositional Similarity, how similar the composition of species is to an intact baseline. Linear mixed effects models are fit to estimate the predictive capacity of spatial datasets of human pressures on each of these metrics and project results spatially across the globe. These methods, as well as comparisons to other leading datasets and guidance on interpreting results, are further explained in a methods white paper entitled “Global 100m Projections of Biodiversity Intactness for the years 2017-2020.” All years are available under a Creative Commons BY-4.0 license.

Terrestrial protected areas are totally or partially protected areas of at least 1,000 hectares that are designated by national authorities as scientific reserves with limited public access, national parks, natural monuments, nature reserves or wildlife sanctuaries, protected landscapes, and areas managed mainly for sustainable use. Marine areas, unclassified areas, littoral (intertidal) areas, and sites protected under local or provincial law are excluded.