Changes in the above-ground biomass stock in forest indicate the balance between gains in biomass stock due to forest growth and losses due to wood removals, natural losses, fire, wind, pests and diseases. At country level and over a longer period, sustainable forest management would imply a stable or increasing biomass stock per hectare, while a long-term reduction of biomass stock per hectare would imply either unsustainable management of the forests and degradation or unexpected major losses due to fire, wind, pests or diseases.

Extract from the UN Statistics Division SDG metadata:

This indicator Proportion of important sites for terrestrial and freshwater biodiversity that are covered by protected areas shows temporal trends in the mean percentage of each important site for terrestrial and freshwater biodiversity (i.e., those that contribute significantly to the global persistence of biodiversity) that is covered by designated protected areas.

The full metadata record is available here.

The indicator Coverage of protected areas in relation to marine areas shows temporal trends in the mean percentage of each important site for marine biodiversity (i.e., those that contribute significantly to the global persistence of biodiversity) that is covered by designated protected areas.

Extract from the UN Statistics Division SDG metadata:

This indicator Coverage by protected areas of important sites for mountain biodiversity shows temporal
trends in the mean percentage of each important site for mountain biodiversity (i.e., those that
contribute significantly to the global persistence of biodiversity) that is covered by designated protected
areas

The full metadata record is available here.

Extract from UN Statistics Division SDG metadata:

This indicator Proportion of important sites for terrestrial and freshwater biodiversity that are covered by protected areas shows temporal trends in the mean percentage of each important site for terrestrial and freshwater biodiversity (i.e., those that contribute significantly to the global persistence of biodiversity) that is covered by designated protected areas.

The full metadata record is available here.

Index of biodiversity importance based on Key Biodiversity Areas coverage and species richness from IUCN Red List. It can be used as proxy of biodiversity exposure in risk and vulnerability analysis.

It is a dimension of the composite indicator on vulnerability to global travel and tourism crises.

References

BirdLife International ([2021]). The World Database of Key Biodiversity Areas. Developed by the KBA Partnership: BirdLife International, International Union for the Conservation of Nature, Amphibian Survival Alliance, Conservation International, Critical Ecosystem Partnership Fund, Global Environment Facility, Global Wildlife Conservation, NatureServe, Rainforest Trust, Royal Society for the Protection of Birds, Wildlife Conservation Society and World Wildlife Fund. Available at www.keybiodiversityareas.org. [Accessed (please insert date of download 01/01/2021)]

IUCN Red List of Threatened Species TM country summaries (IUCN, 2020c) available from: http://www.iucnredlist.org/about/summary-statistics, accessed through DOPA 4.0 rest services on 09/2020.

Terrestrial Ecosystems in Country and WDPA protected sites. Analysis performed by L. Battistella in February 2021. Original dataset provided by "USGS-TNC" (World Terrestrial Ecosystems, Sayre et al. 2020).

Diversity of terrestrial ecosystems in protected areas and countries expressed by Shannon index.

Shannon or Shannon--Weaver (or Shannon--Wiener) index is defined as $H = -sum p_i log(b) p_i$, where $p_i$ is the proportional abundance of species $i$ and $b$ is the base of the logarithm. It is most popular to use natural logarithms, but some argue for base $b = 2$ (which makes sense, but no real difference). 

Range: SHDI ≥ 0, without limit. SHDI = 0 when the landscape contains only 1 patch (i.e., no diversity). SHDI increases as the number of different patch types (i.e., patch richness) increases and/or the proportional distribution of area among patch types becomes more equitable.

Trends in forest area are crucial for monitoring SFM. The first sub-indicator focuses on both the direction of change (whether there is a loss or gain in forest area) and how the change rate is changing over time; the latter is important in order to capture progress among countries that are losing forest area, but have managed to reduce the rate of annual forest area loss.

Forests fulfil a number of functions that are vital for humanity, including the provision of goods (wood
and non-wood forest products) and services such as habitat for biodiversity, carbon sequestration,
coastal protection and soil and water conservation.
The indicator provides a measure of the relative extent of forest in a country. The availability of accurate
data on a country's forest area is a key element for forest policy and planning within the context of
sustainable development.
Changes in forest area reflect the demand for land for other uses and may help identify unsustainable
practices in the forestry and agricultural sector.
Forest area as percentage of total land area may be used as a rough proxy for the extent to which the
forests in a country are being conserved or restored, but it is only partly a measure for the extent to
which they are sustainably managed.
The indicator was included among the indicators for the Millennium Development Goals (MDG) (indicator
7.1 “Proportion of land covered by forest”).

This sub-indicator is the forest area that is certified by an independently verified forest management certification scheme. Such certification schemes apply standards that generally are higher than those established by the countries’ own normative frameworks, and compliance is verified by an independent and accredited certifier. An increase in certified forest area therefore provides an additional indication of progress towards sustainable forest management. It should however be noted that there are significant areas of sustainably managed forest which are not certified, either because their owners have chosen not to seek certification (which is voluntary and market-based) or because no credible or affordable certification scheme is in place for that area.

The Forest Landscape Integrity Index (FLII) is a composite index   created to show the degree of forest integrity for 2019.

The authors identified three Forest Integrity categories: “high ”“medium”, and “low”.

Here it is presented the % of forested area with High Integrity over the total forested area by country. For Hight Forest Integrity, the authors mean “Interiors and natural edges of more or less unmodified naturally regenerated (i.e., non-planted) forest ecosystems, comprised entirely or almost entirely of native species, occurring over large areas either as continuous blocks or natural mosaics with non-forest vegetation; typically little human use other than low-intensity recreation or spiritual uses and/or low-intensity extraction of plant and animal products and/or very sparse presence of infrastructure; key ecosystem functions such as carbon storage, biodiversity, and watershed protection and resilience expected to be very close to natural levels (excluding any effects from climate change) although some declines possible in the most sensitive elements (e.g., some high value hunted species).”

Source: Grantham, H.S., Duncan, A., Evans, T.D. et al. Anthropogenic modification of forests means only 40% of remaining forests have high ecosystem integrity. Nat Commun 11, 5978 (2020).

Indicator of potential human pressure in and around protected areas derived from indices of forest loss, population and agriculture pressures (DOPA 4.0).

This indicator is a proxi for potential human activities pressure inside and in a 10km-buffer around protected areas.  Therefore it may indicate potential vulnerability in conditions whereby protected area financial resources, local economy and supply chains are disrupted by national and/or international crises, and so coping and adaptation strategies may consist in an increased pressure from human activities on natural resources.

It is a component of the vulnerablity indicator to gloabl tourism and travel crisis.

This dataset shows the Mangrove Forests Protection for the year 2016 and the country mangrove extent  for years 1996 and 2016. The dataset is a subset of the global mangrove extent dataset generated by the Global Mangrove Watch (GMW) – a collaboration between Aberystwyth University (U.K.), solo Earth Observation (soloEO), Wetlands International, the World Conservation Monitoring Centre (UNEP-WCMC) and the Japan Aerospace Exploration Agency (JAXA). 

Bunting P., Rosenqvist A., Lucas R., Rebelo L-M., Hilarides L., Thomas N., Hardy A., Itoh T., Shimada M. and Finlayson C.M. (2018). The Global Mangrove Watch – a New 2010 Global Baseline of Mangrove Extent. Remote Sensing 10(10): 1669. doi: 10.3390/rs1010669

More reference: Thomas N, Lucas R, Bunting P, Hardy A, Rosenqvist A, Simard M. (2017). Distribution and drivers of global mangrove forest change, 1996-2010. PLOS ONE 12: e0179302. doi: 10.1371/journal.pone.0179302

Analysis performed by L. Battistella in March 2021

                                   
•                                        
  Mangroves Loss
                                   
•                                        
  Mangroves Gain
                                   
•                                        
  Mangroves Stable
                               

This data comes from a BIOPAMA analysis on the WDPA March 2018 and follows the methodology set out in this document on the UNEP-WCMC website: here The change in coverage over time is based on the information in the STATUS_YEAR field in the WDPA. Year 0 refers to those protected areas where there is no information on the year in which they were designated. The total coverage statistics may not always match the official figures on coverage statistics.

The Mountain Green Cover Index (MGCI) is designed to measure the extent and the changes of green vegetation in mountain areas - i.e. forest, shrubs, trees, pasture land, crop land, etc. – in order to monitor progress towards the mountain target. MGCI is defined as the percentage of green cover over the total surface of the mountain region of a given country and for given reporting year. The aim of the index is to monitor the evolution of the green cover and thus assess the status of conservation of mountain ecosystems.

Nature Needs Half is one of the approaches proposed to identifying transformational actions supporting the post-2020 global biodiversity framework, focusing on protecting, conserving, restoring and retaining natural areas of land and sea. Nature Needs Half call for protection of half of any region.

A  group of conservation scientist (Dinerstein et al. 2017) assessed the progresses toward the protection of 50% of the terrestrial biosphere to address the species-extinction crisis and conserve a global ecological heritage for future generations. Using a map of Earth’s 846 terrestrial ecoregions, they defined ecoregions: (1) Half Protected, when more than 50% of the total ecoregion area is protected; (2) Nature Could Reach Half: less than 50% of the total ecoregion area is protected but the sum of total ecoregion protected and unprotected natural habitat remaining is more than 50%. (3) Nature Could Recover: the sum of the amount of natural habitat remaining and the amount of the total ecoregion that is protected is less than 50% but more than 20%; (4) Nature Imperiled: the sum of the amount of natural habitat remaining and the amount of the total ecoregion that is protected is less than or equal to 20%.

they found that 98 ecoregions (12%) exceed Half Protected; 313 ecoregions (37%) fall short of Half Protected but have sufficient unaltered habitat remaining to reach the target; and 207 ecoregions (24%) are in peril, where an average of only 4% of natural habitat remains.

Protected areas including imperiled ecoregions are essential for ensuring the viability of these ecosystems, as well as their functions and their services.

This indicator shows how protected areas and countries conserve imperiled ecoregions.

Extract from the UN Statistics Division SDG metadata:

The indicator is composed of five sub-indicators that measure progress towards all dimensions of
sustainable forest management. The environmental values of forests are covered by three sub-indicators
focused on the extension of forest area, biomass within the forest area and protection and maintenance
of biological diversity, and of natural and associated cultural resources. Social and economic values of
forests are reconciled with environmental values through sustainable management plans. The subindicator
provides further qualification to management of forest areas, by assessing areas which are
independently verified for compliance with a set of national or international standards.

The full metadata record is available here.

Extract from the UN Statistics Division SDG metadata:

The Red List Index measures change in aggregate extinction risk across groups of species. It is based on
genuine changes in the number of species in each category of extinction risk on The IUCN Red List of
Threatened Species (IUCN 2015) is expressed as changes in an index ranging from 0 to 1.

The full metadata record is available here.

Number of terrestrial semi-natural ecosystems protected compared to those existing in each country.

Terrestrial Ecosystems in Country and WDPA protected sites. Analysis performed by L. Battistella in February 2021. Original dataset provided by "USGS-TNC" (World Terrestrial Ecosystems, Sayre et al. 2020).

Number of terrestrial ecosystems protected compared to those existing in each country.

Terrestrial Ecosystems in Country and WDPA protected sites. Analysis performed by L. Battistella in February 2021. Original dataset provided by "USGS-TNC" (World Terrestrial Ecosystems, Sayre et al. 2020).

This data comes from a BIOPAMA analysis on the WDPA March 2018 and follows the methodology set out in this document on the UNEP-WCMC website:here. The change in coverage over time is based on the information in the STATUS_YEAR field in the WDPA. Year 0 refers to those protected areas where there is no information on the year in which they were designated. The total coverage statistics may not always match the official figures on coverage statistics.

The trend analysis for surface water extent is based on the Global Surface Water dataset and shows the change in the areal extent of permanent water from 1983 to 2016. The chart shows only the actual observed areas and does not include the estimate for the un-observed area (i.e. a measure of the confidence level). Sudden increases or decreases in the data are normally a sign that there was no available imagery for the country for those years. Generally image acquisition was more systematic from 1999 onwards.

The European Commission's Joint Research Centre developed this water dataset in the framework of the Copernicus Programme. This maps the location and temporal distribution of water surfaces at the global scale over the past 32 years and provides statistics on the extent and change of those water surfaces. The dataset, produced from Landsat imagery (courtesy USGS and NASA), will support applications including water resource management, climate modelling, biodiversity conservation and food security.

Jean-Francois Pekel, Andrew Cottam, Noel Gorelick, Alan S. Belward, High-resolution mapping of global surface water and its long-term changes. Nature 540, 418-422 (2016). (doi:10.1038/nature20584)

Which protected areas are visited most frequently?

Average annual number of visitors to a protected area. Data are currently available for a limited set of sitesand from different sources, published in the study: Balmford A, Green JMH, Anderson M, Beresford J, Huang C, Naidoo R, et al. (2015) Walk on the Wild Side: Estimating the Global Magnitude of Visits to Protected Areas. PLoS Biol 13(2): e1002074. https://doi.org/10.1371/journal.pbio.1002074.

This indicator can be used as a proxy of economic sensitivity to tourism and travel crises and it is one of the dimensions of the vulnerability to global travel and tourism crises indicator for protected sites.

The dataset shows the country's protection percentage of Warm-Water Corals for African, Caribbean and Pacific countries. The original dataset and statistics are provided by UNEP World Conservation Monitoring Centre (UNEP-WCMC). 

Source:UNEP-WCMC (2021). Ocean+ Habitats [On-line], [July 2021]. Available at: habitats.oceanplus.org

The landscape features indicator refers to the presence of lakes, coral reefs and mountain ranges in the countries. The landscape feature indicator is simple presence-absence of big lakes (GLWD_P(0/1), Lehner & Döll, 2004), mountain ranges (GMBA_P(0/1), Global Mountain Biodiversity Assessment, Mountain Inventory v1.2) and warm-water coral reefs (CR_P(0/1), UNEP-WCMC) combined at country level without weighting factors. The indicator varies between 0 and 3. LFR = GLWD_P(0/1) + GMBA_P(0/1) + CR_P(0/1) Despite its simplistic and coarse approach, we included this indicator because it covers some dimensions of wildlife potential underestimated by the other two components. The presence of lakes is strongly correlated with birdwatching, which is quite an important ecotourism niche. Coral reef diving is generally driven by wildlife attraction (coloured corals and fishes), sometimes targeting specific species such as sea turtles and whale sharks. In our indicator, this feature accounts for marine wildlife tourism potential, without overlapping with generic beach tourism. Mountain ranges are prominent landscapes hosting some iconic species – though less known than the Big Five – such as gelada baboon and Ethiopian wolves, mountain forest birds and primates (e.g., mangabeys). These landscape features represent desirable add-ons to usual safaris, since they provide opportunities for tourists to walk or swim, and so obtain relief from offroad driving and rough conditions. The wildlife economy potential composite indicator was calculated in two steps: 1. Species Richness and Biomes Richness indicators were reclassified on a scale from 0 to 3 based on value distribution natural clusters. 2. The reclassified indicators were summed up with the Landscape Features indicator. The final indicator ranges between 1 and 9 and was rescaled to low potential (1-3), medium potential (4-6), and high potential (7-9).