Evidence map›Paper›PMID 34668609›Full record

ArticleConservation biology : the journal of the Society for Conservation Biology2022

Translating habitat class to land cover to map area of habitat of terrestrial vertebrates.

Maria Lumbierres, Prabhat Raj Dahal, Moreno Di Marco, Stuart H M Butchart, Paul F Donald, Carlo Rondinini

Abstract read
In one paragraph

Article in Conservation biology : the journal of the Society for Conservation Biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed.

  1. Article
  2. Mapping area of habitat for inland wetland species.Conservation biology : the journal of the Society for Conservation Biology · 2026
    Article
  3. Article
  4. Article
  5. Effectiveness of Key Biodiversity Areas in representing global avian diversity.Conservation biology : the journal of the Society for Conservation Biology · 2025
    Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Translating habitat class to land cover to map area of habitat of terrestrial vertebrates.Conservation biology : the journal of the Society for Conservation Biology · 2022
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Maria LumbierresGlobal Mammal Assessment Program, Department of Biology and Biotechnologies, Sapienza University of Rome, Rome, Italy.ORCID 0000-0001-8442-1264
Prabhat Raj DahalGlobal Mammal Assessment Program, Department of Biology and Biotechnologies, Sapienza University of Rome, Rome, Italy.ORCID 0000-0003-3490-4078
Moreno Di MarcoDepartment of Biology and Biotechnologies, Sapienza University of Rome, Rome, Italy.
Stuart H M ButchartDepartment of Biology and Biotechnologies, Sapienza University of Rome, Rome, Italy.ORCID 0000-0002-1140-4049
Paul F DonaldDepartment of Biology and Biotechnologies, Sapienza University of Rome, Rome, Italy.ORCID 0000-0003-0023-6200
Carlo RondininiGlobal Mammal Assessment Program, Department of Biology and Biotechnologies, Sapienza University of Rome, Rome, Italy.ORCID 0000-0002-6617-018X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Area of habitat (AOH) is defined as the "habitat available to a species, that is, habitat within its range" and is calculated by subtracting areas of unsuitable land cover and elevation from the range. The International Union for the Conservation of Nature (IUCN) Habitats Classification Scheme provides information on species habitat associations, and typically unvalidated expert opinion is used to match habitat to land-cover classes, which generates a source of uncertainty in AOH maps. We developed a data-driven method to translate IUCN habitat classes to land cover based on point locality data for 6986 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 or classes assigned to each species occurring there. Then, we modeled each land-cover class as a function of IUCN habitat with (SSG, using) logistic regression models. The resulting odds ratios were used to assess the strength of the association between each habitat and land-cover class. We then compared the performance of our data-driven model with those from a published translation table based on expert knowledge. We calculated the association between habitat classes and land-cover classes as a continuous variable, but to map AOH as binary presence or absence, it was necessary to apply a threshold of association. This threshold can be chosen by the user according to the required balance between omission and commission errors. Some habitats (e.g., forest and desert) were assigned to land-cover classes with more confidence than others (e.g., wetlands and artificial). The data-driven translation model and expert knowledge performed equally well, but the model provided greater standardization, objectivity, and repeatability. Furthermore, our approach allowed greater flexibility in the use of the results and uncertainty to be quantified. Our model can be modified for regional examinations and different taxonomic groups.

Indexed as

Conservation of Natural ResourcesEcosystemAnimalsBirdsForestsMammalsVertebratescommission and omission errorsCopernicus Global Land Service Land Cover (CGLS-LC100)errores de comisión y omisiónESA Climate Change Initiative (ESA-CCI)Esquema de Clasificación de Hábitats de la UICNhabitat suitability modelsIniciativa de Cambio Climático ESA (ESA-CCI)IUCN Habitat Classification SchemeIUCN Red List《 IUCN 栖息地分类方案》《 IUCN 红色名录》Lista Roja de la UICNmodelos de idoneidad de hábitat哥白尼全球土地服务土地覆盖 (CGLS-LC100)栖息地适宜性模型欧洲航天局气候变化倡议 (ESA-CCI)错分与漏分误差

Identifiers

PMID34668609
PMCPMC9299587

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.