A global map of terrestrial habitat types

Jung, Martin; Dahal, Prabhat Raj; Butchart, Stuart H. M.; Donald, Paul F.; Lamo, Xavier de; Lesiv, Myroslava; Kapos, Valerie; Rondinini, Carlo; Visconti, Piero

doi:10.1038/s41597-020-00599-8

Cited by 102 publications

(102 citation statements)

References 61 publications

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“…We assessed the intactness of coastal regions in proximity to tidal flats (Murray et al 2019), saltmarshes (Mcowen et al 2017), mangroves (Bunting et al 2018), seagrasses (UNEP-WCMC 2003), estuaries (Alder 2003), kelp forests (Mora-Soto et al 2020), coral reefs (UNEP-WCMC et al 2018), savannah (Jung et al 2020), deserts (Jung et al 2020), rocky areas (Jung et al 2020) and forests (Jung et al 2020). This was achieved by buffering the polygons representing each habitat type by 50 km (a radius equal to the radius used to quantify coastal intactness) and summarising the distribution of coastal intactness values within the buffer.…”

Section: Methodsmentioning

confidence: 99%

The global rarity of intact coastal regions

Watson

Beyer

et al. 2021

Preprint

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Management of the land-sea interface is considered essential for global conservation and sustainability objectives, as coastal regions maintain natural processes that support biodiversity and the livelihood of billions of people. However, assessments of coastal regions have focused on either strictly the terrestrial or marine realm, and as a consequence, we still have a poor understanding of the overall state of Earths coastal regions. Here, by integrating the terrestrial human footprint and marine cumulative human impact maps, we provide a global assessment of the anthropogenic pressures affecting coastal areas. Just 15.5% of coastal areas globally can be considered having low anthropogenic pressure, mostly found in Canada, Russia, and Greenland. Conversely, 47.9% of coastal regions are heavily impacted by humanity with most countries (84.1%) having >50% of their coastal regions degraded. Nearly half (43.3%) of protected areas across coastal regions are exposed to high human pressures. In order to meet global sustainability objectives, we identify those nations that must undertake greater actions to preserve and restore coastal regions so as to ensure global sustainable development objectives can be met.

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Section: Methodsmentioning

confidence: 99%

The global rarity of intact coastal regions

Watson

Beyer

et al. 2021

Preprint

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“…Major occurrences of croplands were taken from the map of Habitat type 14.1 by Jung et al (2020) based on the IUCN Habitats Classification Scheme (version 3.1) (IUCN, 2012). We compared this to cropping areas in consensus land-cover maps (Tuanmu & Jetz, 2014) and found that maps of Jung et al (2020) more closely matched the concept of T7.1. Occurrences were extracted from fractional aggregated 1 km resolution base data (Jung et al 2020), approximating 30 arc seconds spatial resolution.…”

Section: T71mentioning

confidence: 99%

“…We compared this to cropping areas in consensus land-cover maps (Tuanmu & Jetz, 2014) and found that maps of Jung et al (2020) more closely matched the concept of T7.3. Occurrences were extracted from fractional aggregated 1-km resolution base data (Jung et al, 2020), approximating 30 arc seconds spatial resolution.…”

Section: T73mentioning

confidence: 99%

“…Major occurrences of tropical swamp forest and flooded forest were taken from the map of Habitat type 1.8 by Jung et al (2020) based on the IUCN Habitats Classification Scheme v3.1 (IUCN, 2012). We compared this to areas of tropical swamp forest and flooded forest mapped Global Lakes and Wetlands Database (Lehner & Döll, 2004) as well as ecoregions with such forests mentioned in their description (Dinerstein et al, 2017), and found that maps of Jung et al (2020) more closely matched the concept of TF1.1. Occurrences were extracted from fractional aggregated 1-km resolution base data (Jung et al 2020), approximating 30 arc seconds spatial resolution.…”

Section: Tf11mentioning

confidence: 99%

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IUCN Global Ecosystem Typology 2.0: descriptive profiles for biomes and ecosystem functional groups

Kingsford¹,

Catford²,

Rains³

et al. 2020

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Ecosystems are critically important components of Earth’s biological diversity and as the natural capital that sustains human life and well-being. Yet all of the world’s ecosystems show hallmarks of human influence, and many are under acute risks of collapse, with consequences for habitats of species, genetic diversity, ecosystem services, sustainable development and human well-being. The IUCN Global Ecosystem Typology is a hierarchical classification system that, in its upper levels, defines ecosystems by their convergent ecological functions and, in its lower levels, distinguishes ecosystems with contrasting assemblages of species engaged in those functions. This report describes the three upper levels of the hierarchy, which provide a framework for understanding and comparing the key ecological traits of functionally different ecosystems and their drivers. An understanding of these traits and drivers is essential to support ecosystem management.

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“…IUCN standardized habitat definitions independently of taxonomy or geography. However, IUCN habitat classes are not spatially explicit, although recent efforts have attempted to delimit them (Jung et al, 2020). Land-cover classes derived from remote sensing have been widely used as a surrogate of habitat (e.g.…”

Section: Introductionmentioning

confidence: 99%

A habitat class to land cover translation model for mapping Area of Habitat of terrestrial vertebrates

Lumbierres

Marco

et al. 2021

Preprint

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Area of Habitat (AOH) is defined as the ‘habitat available to a species, that is, habitat within its range’ and is produced by subtracting areas of unsuitable land cover and elevation from the range. Habitat associations are documented using the IUCN Habitats Classification Scheme, and unvalidated expert opinion has been used so far to match habitat to land-cover classes generating a source of uncertainty in AOH maps. We develop a data-driven method to translate IUCN habitat classes to land-cover based on point locality data for 6,986 species of terrestrial mammals, birds, amphibians and reptiles. We extracted the land-cover class at each point locality and matched it to the IUCN habitat class(es) assigned to each species occurring there. Then we modelled each land cover class as a function of IUCN habitat using logistic regression models. The resulting odds ratios were used to assess the strength of the association of each habitat land-cover class. We then compared the performance of our data-driven model with those from a published expert knowledge translation table. The results show that some habitats (e.g. forest and desert) could be more confidently assigned to land-cover classes than others (e.g. wetlands and artificial). We calculated the association between habitat classes and land-cover classes as a continuous variable, but to map AOH, which is in the form of a binary presence/absence, it is necessary to apply a threshold of association. This can be chosen by the user according to the required balance between omission and commission errors. We demonstrate that a data-driven translation model and expert knowledge perform equally well, but the model provides greater standardization, objectivity and repeatability. Furthermore, this approach allows greater flexibility in the use of the results and allows uncertainty to be quantified. Our model can be developed regionally or for different taxonomic groups.

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A global map of terrestrial habitat types

Cited by 102 publications

References 61 publications

The global rarity of intact coastal regions

The global rarity of intact coastal regions

IUCN Global Ecosystem Typology 2.0: descriptive profiles for biomes and ecosystem functional groups

A habitat class to land cover translation model for mapping Area of Habitat of terrestrial vertebrates

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