GEO BON with its scientic partners introduces a new generation of global indicators integrating biodiversity observations, remote sensing data, and models for assessing progress towards the CBD Strategic Plan 2011-2020 and Aichi Targets 5, 11, 12, 14, 15 and 19.

A GEO BON consortium involving researchers and organizations around the world has developed a novel set of global indicators to address important gaps in our understanding of biodiversity change across scales, from national to global. These indicators are embedded in open online analysis platforms following GEO data sharing principles and have the long-term commitment of established research institutions. The new set of indicators is characterized by the rigorous use of open access large global datasets, state of the art remote-sensing based information, model-based integration of multiple data sources and types, including in situ (ground based) observations, and online infrastructure enabling inexpensive and dynamic updates, with full transparency. This has become possible through direct collaboration with technical and research support partners such as Google and NASA, the development of a dedicated infrastructure such as Map of Life, and the engagement of the larger GEO BON community. The following pages describe five new indicators for assessing and reporting progress against Aichi Targets 5, 11, 12, 14, 15 and 19 and are derived by integrating data from three Essential Biodiversity Variables: species distributions, taxonomic diversity and ecosystem extent. By integrating the complementary strengths of different types of data, the resulting indicators oer some important benefits. For example, they help to ll geographical and taxonomic gaps in the coverage of measures based purely on in situ biological data and are able to translate measures based purely on remote sensing, for example of habitat loss and degradation, into biologically-scaled indicators of likely impacts on biodiversity.

One key advantage of these new indicators is that they cover the entire terrestrial surface of the planet at 1km grid resolution. By operating at this spatial resolution the indicators can effectively account for important relationships between species distributions and patterns of habitat loss and protection that play out at scales much finer than those typically addressed by previous global indicators. This fine resolution of analysis then underpins reporting of the indicators at any desired level of spatial aggregation, including the national level. Such automated national reporting is being integrated into the BON-in-a-Box toolkit of GEO BON. Developing robust global indicators is a component of a larger GEO BON effort to improve our understanding of the biotic response to global change, by integrating previously disconnected dimensions of biodiversity and also by connecting local trends to regional and global trends, offering tests of the predictive capacity of models in response to global change, a critical step in making ecological forecast more rigorous.

Species Habitat Indices

The Species Habitat Indices (SHIs) quantify changes in the suitable habitats of single species to provide aggregate estimates of potential population losses and extinction risk increases in a region or worldwide.

EBV class Species Populations
Species distribution
EBV class Ecosystem Structure
Ecosystem extent and fragmentation
Purpose

To provide annually updated biodiversity change metrics that transparently build on single species data and that can be reported regionally and globally. The indices address trends in the sizes of species potential distributions and populations for habitat –dependent and threatened species. The Species Habitat Indices use remote sensing data, local observations, and models in a web-based informatics infrastructure. They are designed to measure and report on progress in relation to CBD Aichi Targets 5 and 12.

CBD Aichi Target 5
Habitat loss halved or reduced
CBD Aichi Target 12
Reducing risk of extinction
Coverage

The indices use environmental and species data addressing all terrestrial areas of the world at 1 km spatial resolution. They can be aggregated at spatial levels ranging from 1 km to small regions, countries, biomes, and the whole planet. The indices build on land cover information available annually from Landsat and MODIS satellites since 2001 onwards. With continuation of these remote sensing products, this enables annual update of indices, including reporting Aichi Target 5 and 12 achievements, for ten data points from 2011 to 2020.

Methods

Indicators addressing Aichi Targets 5 and 12 are typically constrained in their adequate geographic representation, the level of disaggregation they allow, their temporal resolution, and their scientific underpinning and transparency. The Species Habitat Indices are part of a new generation of indicators that address these limitations by utilizing ongoing, spatially and temporally highly resolved remote sensing at near global-extent, together with biodiversity observations, and adequate and transparent modeling frameworks. The indices build on detailed, remote-sensing informed maps of suitable habitat for single species. Maps are modeled using literature- and expert-based data on habitat restrictions and published land-cover products from MODIS and Landsat satellites available annually at 30 m and 1 km resolution. These detailed maps of habitat suitable for a species are validated with eld data on species locations from surveys and citizen science.

Modications in the area and fragmentation of individual species’ remaining habitat are quantified annually and changes in extinction risk are estimated. The species-level metrics are then aggregated and reported over user-dened regions, such as countries. Separate indices can be calculated for species dependent on certain habitats types (e.g. natural forests), and for threatened species. The indices can also be subset to species with particularly rapid recent habitat changes, and they can account for countries’ stewardship of species (their portion of a species’ global range). All underlying data and metrics are available through a dedicated dashboard in the Map of Life web interface that has been developed with Google Earth Engine as technology partner. Currently, the Species Habitat Indices are based on > 20,000 species of terrestrial vertebrate and invertebrate, and plant species, and validated with > 300 million location records, a growing number.

Developed by

MOLNASANSFGBIFGoogle Earth Engine

Biodiversity Habitat Index

The Biodiversity Habitat Index uses biologically-scaled environmental mapping and modelling to estimate impacts of habitat loss, degradation and fragmentation on retention of terrestrial biodiversity globally, from remotely-sensed forest change and land-cover change datasets.

EBV class Community Composition
Species distribution
EBV class Ecosystem Structure
Ecosystem extent and fragmentation
Purpose

To provide a rigorous, yet cost-effective, approach to estimating impacts of habitat loss, degradation and fragmentation on biodiversity globally, by linking remotely-sensed forest change and land-cover change datasets to recent advances in biodiversity informatics, ecological meta-analysis, and macro-ecological modelling. The Biodiversity Habitat Index is designed specifically as an indicator for measuring and reporting progress in relation to the Convention on Biological Diversity’s Aichi Target 5.

CBD Aichi Target 5
Habitat loss halved or reduced
Coverage

The approach uses data covering the entire terrestrial area of all countries of the world, at 1km grid resolution. This allows the Biodiversity Habitat Index to be calculated and reported at any desired level of spatial aggregation, ranging from individual 1km grid-cells up to whole ecoregions, countries, biomes and realms, or the entire planet. The approach utilises the full temporal coverage of Hansen et al.’s (2013, Science 342: 850-853) Global Forest Change dataset, i.e. 2000 onwards; and NASA’s (Friedl et al 2010, Remote Sensing of Environment 114: 168-182) MODIS Land Cover Change dataset, i.e. 2001 onwards. Changes in the Biodiversity Habitat Index can therefore be reported annually, including reporting Aichi Target 5 achievement for ten annual data points from 2011 to 2020 (assuming ongoing annual updating of the above two remote-sensing products).

Methods

Changes in habitat degradation and fragmentation are estimated across all terrestrial biomes by translating remotely-sensed land-cover change (NASA’s MCD12Q1 dataset) into land-use change through statistical downscaling of coarse-scale land-use mapping to 1 km resolution, and using the PREDICTS meta-analysis (Newbold et al 2015, Nature 520: 45-50) to assign habitat-condition scores to resulting land-use classes. Mapping of habitat change in forest biomes is further rened by incorporating Hansen et al.’s 30m-resolution Global Forest Change dataset. These habitat-change layers are then integrated with global modelling of fine-scaled spatial variation in biodiversity composition (beta diversity), derived by scaling environmental and geographical gradients using >300 million location records for >400,000 plant, invertebrate and vertebrate species. The Biodiversity Habitat Index resulting from this integration estimates change in the proportion of collective biological (gamma) diversity retained within any specied spatial unit (e.g. an ecoregion, a country, or an entire biome) as a function of habitat loss, degradation and fragmentation across that unit.

Developed by

CSIROGBIFMOLPREDICTS

Species Protection Index

The Species Protection Index (SPI) measures how much suitable habitat for single species is under protection and estimates the regional or global biodiversity representativeness of terrestrial protected areas.

EBV class Species Populations
Species distribution
Purpose

To provide an annually updated, remote-sensing informated, spatially explicit, and global metric of how well terrestrial species are represented in terrestrial protected areas. The Species Protection Index capitalizes on detailed remote sensing data, a global biodiversity informatics infrastructure and integrative models. It is designed to measure and report progress in relation to CBD Aichi Target 11.

CBD Aichi Target 11
Protected Areas
Coverage

The index uses environmental and species data addressing all terrestrial areas of the world at 1km spatial resolution. It can be aggregated at spatial levels ranging from 1km to small regions, countries, biomes, and the whole planet. The index uses land cover information available annually from Landsat and MODIS satellites since 2001 onwards. With continuation of these remote sensing products, this enables annual index updates, including reporting Aichi Target 11 achievements, for ten data points from 2011 to 2020.

Methods

Indicators addressing Aichi Target 11 are typically constrained in their adequate geographic representation, the level of disaggregation they allow, their temporal resolution, and their scientific underpinning and transparency. The Species Protection Index is part of a new generation of indicators that utilize ongoing, spatially and temporally highly resolved remote sensing at near global-extent, together with biodiversity observations and adequate modeling frameworks, to help address these limitations.

The Species Protection Index builds on detailed, remote-sensing informed species distributions and their overlap with protected areas. These species maps are modeled using literature- and expert-based data on habitat restrictions and published land-cover products from MODIS and Landsat satellites available annually at 30m and 1km resolution and validated with eld data on species locations from surveys and citizen science. Modications in the area of individual species’ overall distribution and the proportion under protection are quantfied and updated annually based on changes in protected areas and available
suitable habitat. The index represents the aggregate of species-level metrics over any specified spatial unit such as countries or biomes. It can be calculated for different minimum sizes or categories of protected areas and be separated by biological group. A version of the index can also account for countries’ stewardship of species (their portion of a species’ global range, according to the best-available estimate).

All underlying data and metrics are available through a dedicated dashboard in the Map of Life web interface that has been developed with Google Earth Engine as technology partner. Currently, the Species Protection Index is addressing all protected areas of the World Database on Protected Areas and is calculated for >30,000 species of terrestrial vertebrates and invertebrates, and plant species, and validated with > 350 million location records. This list is growing rapidly as more species and data are entering the database.

Developed by

MOLNASANSFGBIFGoogle Earth Engine

Protected Area Representativeness & Connectedness Indices

The Protected Area Representativeness & Connectedness Indices (PARC) use biologically-scaled environmental mapping and modelling globally to assess the extent to which terrestrial protected areas are ecologically representative and well connected.

EBV class Community Composition
Taxonomic diversity
EBV class Ecosystem Structure
Ecosystem extent and fragmentation
Purpose

To provide a rigorous, yet cost-eective, approach to assessing global terrestrial protected-area representativeness and connectedness at an unprecedentedly fine spatial resolution. This is achieved by harnessing the power of recent advances in remote environmental mapping, biodiversity informatics, and macroecological modelling. The PARC Indices are designed specifically as indicators for measuring and reporting progress in relation to the Convention on Biological Diversity’s Aichi Target 11.

CBD Aichi Target 11
Protected Areas
Coverage

The approach uses data covering the entire terrestrial area of all countries of the world, at 1km grid resolution. This allows the PARC Indices to be calculated and reported at any desired level of spatial aggregation, ranging from individual 1km grid-cells up to whole ecoregions, countries, biomes and realms, or the entire planet. The approach utilises the full temporal coverage of the World Database on Protected Areas (WDPA) and of NASA’s (Friedl et al 2010, Remote Sensing of Environment 114: 168-182) MODIS Land Cover Change dataset (2001 onwards). The PARC Indices can therefore report changes in the representativeness and connectedness of protected areas annually, including reporting Aichi Target 11 achievement for ten annual data points from 2011 to 2020.

Methods

The PARC Indices are underpinned by global modelling of fine-scaled spatial variation in biodiversity composition (beta diversity) derived by scaling environmental and geographical gradients using >300 million location records for >400,000 plant, invertebrate and vertebrate species. This modelling is then integrated with data on protected-area boundaries (from the WDPA) and land use in surrounding landscapes, derived by translating remotely-sensed land-cover change (NASA’s MCD12Q1 dataset) into land-use change through statistical downscaling of coarse-scale land-use mapping to 1 km resolution.

Separate indices can be calculated and reported for ecological representativeness (the proportion of biologically-scaled environmental diversity included in protected areas) and for connectedness (a relative index between 0 and 1), or these can be combined into a single composite measure of representativeness and connectedness of protected areas within any specied spatial unit (e.g. an ecoregion, a country, or an entire biome). Likewise, separate indices can be reported for the three major biological groups (plants, invertebrates and vertebrates) or these can be combined into a single measure across all groups.

Developed by

CSIROGBIFMOLPREDICTS

Local Biodiversity Intactness Index

The Local Biodiversity Intactness Index (LBII) is based on a purpose-built global database of local biodiversity surveys combined with highresolution global land-use data. The index provides estimates of human impacts on the intactness of local biodiversity worldwide, and how this may change over time.

EBV class Species Populations
Species distribution, Population abundance
EBV class Community Composition
Taxonomic diversity
Purpose

The Local Biodiversity Intactness Index (LBII) estimates how much of a terrestrial site’s original biodiversity remains in the face of human land use and related pressures. Because LBII relates to site-level biodiversity, it can be averaged and reported for any larger spatial scale (e.g., countries, biodiversity hotspots or biomes as well as globally) without additional assumptions. Building on research published recently in Nature, and repurposing existing biodiversity survey data, it combines scientific rigour with affordability. The LBII is particularly relevant for Aichi Targets 12 (Preventing Extinctions) and 14 (Essential Ecosystem Services). Existing indicators for these targets lack a broad biodiversity perspective; in particular, they are heavily biased towards vertebrates, which make up only 0.5% of the world’s species and relate to only simple biodiversity measures. The LBII can report on both species-richness and mean abundance, and is being developed further to also report on geographic range rarity (endemism) and phylogenetic diversity. LBII is strongly complementary to the proposed Biodiversity Habitat Index (BHI). LBII’s focus is on average local biotic intactness, which reects species’ persistence within the landscape and the local ecosystem’s ability to provide many ecosystem services; BHI, by contrast, focuses on how the overall diversity of a larger region is hit by habitat loss and degradation (Target 5). LBII was first proposed in 2005 but the data needed to make it operational have only now been brought together.

CBD Aichi Target 12
Reducing risk of extinction
CBD Aichi Target 14
Essential ecosystems services
Coverage

The LBII covers the world’s entire terrestrial realm, and can report both globally and at any smaller scale relevant for global policy. Although published analyses have so far had coarse spatial and temporal grain1, CSIRO’s development of annual, global, fine-scale land-use maps allows LBII to report annually at 1km resolution from 2001 to 2020.

Methods

The LBII is based on rigorously peer-reviewed and transparent science. The global statistical models were published recently in Nature, along with global maps of net changes in local biodiversity by 2005, a hindcast of change from 1500-2005, and global and national projections of future changes under the Representative Concentration Pathway scenarios.

Models of how land use aects the similarity of an ecological community to that of intact sites are now in review; a paper combining these with our earlier models to map LBII is in preparation. These models all use the PREDICTS database, which has collated data from studies that compared terrestrial biodiversity at sites facing dierent intensities of human pressures; it currently holds over 3 million records for over 26,000 sites (in 94 countries) and a taxonomically representative set of over 45,000 plant, invertebrate and vertebrate species. These data, contributed by a network of over 500 researchers worldwide, will be made freely available in the coming months (some metadata are already available). The database will continue to grow, enabling more precise modeling. Annual land-use data since 2001 are produced by using remotely-sensed land cover change data to statistically downscale global land-use maps to 1km resolution; a paper is in preparation.

Developed by

PREDICTSNatural History MuseumUNEP-WCMCCSIRO

Global Ecosystem Restoration Index

The Global ecosystem restoration index (GERI) is a composite index that integrates structural and functional aspects of the ecosystem restoration process. These elements are evaluated through a window that looks into a baseline for degraded ecosystems with the objective to assess restoration improvements or declines in a more integrated manner.

EBV class Ecosystem Function
Net primary productivity
EBV class Ecosystem Structure
Ecosystem extent and fragmentation
Purpose

There is still a lack of global indicators to assess Target 15 of the Convention on Biological Diversity. One of the main problems rests in the actual denition of restoration. Many ecosystem changes may lead to restoration of ecosystem functioning; however, not all these changes are caused by the same process. Land restoration is a composite term that describes different processes acting at different times, scales, and extents. We build upon recent advances on biodiversity science, remote sensing, and ecosystem mapping to design a composite index tailored to three key and complementary elements of ecosystem restoration: (1) change in ecosystem productivity (2) change in the ecosystem energy balance and (3) changes in land cover. Degraded areas dened in a baseline are the window into the world upon which we monitor these three aspects relative to the goals of Target 15.

CBD Aichi Target 15
Restoration of 15 percent of degraded ecosystems
Coverage

This composite index is produced for near the entire terrestrial surface of the planet at a spatial resolution of 1 km². This allows for the GERI index to be aggregated to small regions, states, countries, continents and the planet. This index uses well vetted products derived from MODIS sensor, in orbit since 2001. These products are continuously being outputted and the expected data availability and planned mission continuity is an assurance of the temporal sustainability of the proposed index.

Methods

The Global Ecosystem Restoration Index (GERI) addresses Target 15 goals by integrating three datasets derived from remote sensing and produced at global extent. These datasets were carefully selected because they address three different but related aspects of the land restoration process. The first functional aspect is based on changes in land productivity, the second aspect is also functional and it is related to changes in the energy balance of the ecosystem, the third and final aspect is related to the structural changes and it is measured using identity transitions in land cover. These datasets are evaluated together though a window (i.e., mask) dened by a baseline of land degradation that aims to capture and discriminate very highly degraded ecosystems from degraded ecosystems.

Developed by

iDiv

Species Status Information Index

The Species Status Information Index (SSII) measures the adequacy of data on the distribution of single species and on the make-up of species assemblages in a location or region.

EBV class Species Populations
Species distribution
EBV class Community Composition
Taxonomic diversity
Purpose

To provide an annually updated metric of how growth in the amount and detail of digitally accessible information on species occurrences in space and time is addressing regional and global information gaps. The Species Status Information Index benefits from a large stack of species distribution data, a continuously updated informatics infrastructure, and interactive reporting tools. It is designed to measure and report progress in relation to CBD Aichi Target 19.

CBD Aichi Target 19
Sharing information and knowledge
Coverage

The index uses species data addressing all terrestrial areas of the world from 100km down to, for some groups, 1km spatial resolution. It can be aggregated at spatial levels ranging from small regions, countries, biomes, to the whole planet. It will be updated continuously with latest mobilized records from GBIF and many other location data sources. This enables at least twice-annual formal index updates and a reporting on Aichi Target 19 achievements for twenty time points between 2011 and 2020.

Methods

Indicators addressing Aichi Target 19 have limits in their ability to relate knowledge improvements and sharing to the knowledge needs. The data needs to adequately represent biodiversity status and trends increase with the number of species and the spatial extent of their populations. The Species Status Information Index combines data availability and data needs into a single metric that enables a standardized, transparent, and quantitative tracking of how well information gaps are getting filled. The index builds on model- and expert-based information about the geographic distribution of species, available through Map of Life. It then assesses how well currently accessible digital point occurrence locations for each species are able to spatially represent and ultimately track this distribution over time. In doing this, the index draws on a variety of sources, including GBIF, and takes into account the varying spatial and temporal accuracy of species location records. The index represents the aggregate of species-level metrics over any specied spatial unit such as countries or biomes. It can be calculated for different cut-os of spatial or temporal detail and be reported separately by biological group. A version of the index can also account for countries’ stewardship of species (the proportion of the range that, according to the best estimate, is restricted to them). All underlying data and metrics are available through a dedicated dashboard in the Map of Life web interface (see example below). Currently, the Species Status Information Index is available for > 35,000 terrestrial species and validated with > 350 million location records. Extensions to increase species coverage and include freshwater and marine groups are underway.

Developed by

MOLNASANSFGBIFGoogle Earth Engine