dc.creator | Henry, E. G. | es |
dc.creator | Santini, L. | es |
dc.creator | Butchart, S. H. M. | es |
dc.creator | González Suárez, Manuela | es |
dc.creator | Lucas Ibáñez, Pablo Miguel | es |
dc.creator | Benítez López, Ana | es |
dc.creator | Di Marco, M. | es |
dc.date.accessioned | 2024-05-31T15:50:27Z | |
dc.date.available | 2024-05-31T15:50:27Z | |
dc.date.issued | 2023-12-02 | |
dc.identifier.citation | Henry, E.G., Santini, L., Butchart, S.H.M., González Suárez, M., Lucas Ibáñez, P.M., Benítez López, A. y Di Marco, M. (2023). Modelling the probability of meeting IUCN Red List criteria to support reassessments. Global Change Biology, 30 (1), e17119. https://doi.org/10.1111/gcb.17119. | |
dc.identifier.issn | 1365-2486 | es |
dc.identifier.issn | 1354-1013 | es |
dc.identifier.uri | https://hdl.handle.net/11441/159568 | |
dc.description.abstract | Comparative extinction risk analysis—which predicts species extinction risk from
correlation with traits or geographical characteristics—has gained research attention
as a promising tool to support extinction risk assessment in the IUCN Red List of
Threatened Species. However, its uptake has been very limited so far, possibly because
existing models only predict a species' Red List category, without indicating which
Red List criteria may be triggered. This prevents such approaches to be integrated
into Red List assessments. We overcome this implementation gap by developing models
that predict the probability of species meeting individual Red List criteria. Using
data on the world's birds, we evaluated the predictive performance of our criterion-specific
models and compared it with the typical criterion-blind
modelling approach.
We compiled data on biological traits (e.g. range size, clutch size) and external drivers
(e.g. change in canopy cover) often associated with extinction risk. For each specific
criterion, we modelled the relationship between extinction risk predictors and species'
Red List category under that criterion using ordinal regression models. We found
criterion-specific
models were better at identifying threatened species compared to
a criterion-blind
model (higher sensitivity), but less good at identifying not threatened
species (lower specificity). As expected, different covariates were important for
predicting extinction risk under different criteria. Change in annual temperature was
important for criteria related to population trends, while high forest dependency was
important for criteria related to restricted area of occupancy or small population size.
Our criteria-specific
method can support Red List assessors by producing outputs
that identify species likely to meet specific criteria, and which are the most important
predictors. These species can then be prioritised for re-evaluation.
We expect this new approach to increase the uptake of extinction risk models in Red List assessments,
bridging a long-standing
research-implementation
gap. | es |
dc.description.sponsorship | German Research Foundation FZT 118, 202548816 | es |
dc.description.sponsorship | Volkswagen Foundation Freigeist Fellowship A118199 | es |
dc.description.sponsorship | Ramón y Cajal RYC2021-031737- I | es |
dc.description.sponsorship | Microsoft Corporation 5313.006-AI4E | es |
dc.description.sponsorship | Sapienza University of Rome AR22117A859F7D58 | es |
dc.description.sponsorship | Junta de Andalucía EMERGIA20_00135 | es |
dc.format | application/pdf | es |
dc.language.iso | eng | es |
dc.publisher | Wiley | es |
dc.relation.ispartof | Global Change Biology, 30 (1), e17119. | |
dc.rights | Atribución-NoComercial 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc/4.0/ | * |
dc.subject | assessment | es |
dc.subject | Aves | es |
dc.subject | biodiversity conservation | es |
dc.subject | birds | es |
dc.subject | comparative analysis | es |
dc.subject | extinction risk | es |
dc.subject | functional traits | es |
dc.title | Modelling the probability of meeting IUCN Red List criteria to support reassessments | es |
dc.type | info:eu-repo/semantics/article | es |
dc.type.version | info:eu-repo/semantics/publishedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Biología Vegetal y Ecología | es |
dc.relation.projectID | 202548816 | es |
dc.relation.projectID | A118199 | es |
dc.relation.projectID | RYC2021-031737- I | es |
dc.relation.projectID | 5313.006-AI4E | es |
dc.relation.projectID | AR22117A859F7D58 | es |
dc.relation.projectID | EMERGIA20_00135 | es |
dc.relation.publisherversion | https://doi.org/10.1111/gcb.17119 | es |
dc.identifier.doi | 10.1111/gcb.17119 | es |
dc.journaltitle | Global Change Biology | es |
dc.publication.volumen | 30 | es |
dc.publication.issue | 1 | es |
dc.publication.initialPage | e17119 | es |
dc.contributor.funder | Deutsche Forschungsgemeinschaft / German Research Foundation (DFG) | es |
dc.contributor.funder | Volkswagen Fundation | es |
dc.contributor.funder | Ramón y Cajal | es |
dc.contributor.funder | Microsoft Corporation | es |
dc.contributor.funder | Universidad Sapienza de Roma | es |
dc.contributor.funder | Junta de Andalucía | es |