The predictive performance of process-explicit range change models remains largely untested

Simple item page
dc.article.numbere06048
dc.catalogadorgjm
dc.contributor.authorUribe Rivera, David E.
dc.contributor.authorGuillera Arroita, Gurutzeta
dc.contributor.authorWindecker, Saras M.
dc.contributor.authorPliscoff, Patricio
dc.contributor.authorWintle, Brendan A.
dc.date.accessioned2023-05-30T14:19:10Z
dc.date.available2023-05-30T14:19:10Z
dc.date.issued2022
dc.description.abstractEcological models used to forecast range change (range change models; RCM) have recently diversified to account for a greater number of ecological and observational processes in pursuit of more accurate and realistic predictions. Theory suggests that process-explicit RCMs should generate more robust forecasts, particularly under novel environmental conditions. RCMs accounting for processes are generally more complex and data hungry, and so, require extra effort to build. Thus, it is necessary to understand when the effort of building a more realistic model is likely to generate more reliable forecasts. Here, we review the literature to explore whether process-explicit models have been tested through benchmarking their temporal predictive performance (i.e. their predictive performance when transferred in time) and model transferability (i.e. their ability to keep their predictive performance when transferred to generate predictions into a different time) against simpler models, and highlight the gaps between the rapid development of process-explicit RCMs and the testing of their potential improvements. We found that, out of five ecological processes (dispersal, demography, physiology, evolution, species interactions) and two observational processes (sampling bias, imperfect detection) that may influence reliability of forecasts, only the effects of dispersal, demography and imperfect detection have been benchmarked using temporally-independent datasets. Only nine out of twenty-nine process-explicit model types have been tested to assess whether accounting for processes improves temporal predictive performance. We found no benchmarks assessing model transferability. We discuss potential reasons for the lack of empirical validation of process-explicit models. Considering these findings, we propose an expanded research agenda to properly test the performance of process-explicit RCMs, and highlight some opportunities to fill the gaps by suggesting models to be benchmarked using existing historical datasets.
dc.format.extent14 páginas
dc.fuente.origenWOS
dc.identifier.doi10.1111/ecog.06048
dc.identifier.eissn1600-0587
dc.identifier.issn0906-7590
dc.identifier.urihttps://onlinelibrary.wiley.com/doi/10.1111/ecog.06048
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/70499
dc.identifier.wosidWOS:000842765600001
dc.information.autorucInstituto de Geografía; Pliscoff, Patricio; 0000-0002-5971-8880; 1435
dc.issue.numero4
dc.language.isoen
dc.nota.accesoContenido completo
dc.revistaEcography
dc.rightsacceso abierto
dc.subjectEcological forecast
dc.subjectModel transferability
dc.subjectPredictive performance
dc.subjectProcess-explicit models
dc.subjectRange shift
dc.subjectSpecies distribution models
dc.subject.ods13 Climate Action
dc.subject.ods15 Life on Land
dc.subject.odspa13 Acción por el clima
dc.subject.odspa15 Vida de ecosistemas terrestres
dc.titleThe predictive performance of process-explicit range change models remains largely untested
dc.typeartículo
dc.volumen2023
sipa.codpersvinculados1435
sipa.indexWOS
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Ecography - 2022 - Uribe‐Rivera - The predictive performance of process‐explicit range change models remains largely.pdf
Size:
1.5 MB
Format:
Adobe Portable Document Format
Description:
Download
Collections
Artículos de revistas