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dc.contributor.authorVega, José Antonio
dc.contributor.authorArellano Pérez, Stéfano
dc.contributor.authorÁlvarez González, Juan Gabriel
dc.contributor.authorFernández Filgueira, Cristina
dc.contributor.authorJiménez, Enrique
dc.contributor.authorFernández Alonso, José María
dc.contributor.authorVega Nieva, Daniel José
dc.contributor.authorBriones Herrera, Carlos Iván
dc.contributor.authorAlonso Rego, Cecilia
dc.contributor.authorFontúrbel, María Teresa
dc.contributor.authorRuiz González, Ana Daría
dc.date.accessioned2022-03-23T09:52:47Z
dc.date.available2022-03-23T09:52:47Z
dc.date.issued2022
dc.identifier.citationForest Ecology and Management 505 (2022) 119926. https://doi.org/10.1016/j.foreco.2021.119926
dc.identifier.urihttp://hdl.handle.net/10347/27718
dc.description.abstractShrub-dominated ecosystems cover large areas globally and play essential roles in ecological processes. Aboveground biomass expressed on an area basis (AGB) is central to many of the ecological processes and services provided by shrublands and is important as the main fuel source for wildfires. Hence, its accurate estimation in shrublands is crucial for ecologists and land managers. This is especially relevant in fire-prone regions such as NW Spain, where shrublands are an important part of the landscape, providing multiple services, but are severely impacted by wildfires. Although biomass models are available for numerous shrub species at the individual plant level, operational models based directly on easily measured shrub stand attributes are scarce. In this study, equations for estimating AGB and loads of different fuel components by size and condition (live and dead) from stand biometric variables were developed for the nine most prevalent shrub communities in NW Spain. Non-linear iterative seemingly unrelated regression was used to fit compatible systems of equations for estimating fuel loads, with shrub stand height and cover and litter depth as predictors for individual shrub communities and all data combined. In general, the goodness-of-fit statistics indicated that the estimates were reasonably accurate for all communities (grouped and ungrouped). The best results were obtained for AGB and total fuel load, including litter, whereas the poorest results were obtained for standing live and dead fine fuel load. Model performance was reduced when height was the only independent variable, although the reduction was small for most fuel categories, except litter load for which the variability was adequately explained by the litter depth. These results illustrate the feasibility of the stand level approach for constructing operational models of shrub fuel load that are accurate for most of fuel components, while also highlighting the ongoing challenges in live and dead fine fuel modelling. The equations developed represent an appreciable advance in shrubland biomass assessment in the region and areas with similar characteristics and may be instrumental in generating fuel maps, fire management improvement and better C storage assessment by vegetation, among other many uses
dc.language.isoeng
dc.publisherElsevier
dc.rights© 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectShrubland biomass
dc.subjectDominant shrub species
dc.subjectCompatible equations systems
dc.subjectBiometric variables
dc.subjectLitter fuel load
dc.subjectFuel fractions load
dc.subjectFuel components
dc.subjectLive and dead fine fuel
dc.subjectFuel management
dc.titleModelling aboveground biomass and fuel load components at stand level in shrub communities in NW Spain
dc.typejournal article
dc.identifier.doi10.1016/j.foreco.2021.119926
dc.relation.publisherversionhttps://doi.org/10.1016/j.foreco.2021.119926
dc.type.hasVersionVoR
dc.identifier.essn0378-1127
dc.rights.accessRightsopen access
dc.contributor.affiliationUniversidade de Santiago de Compostela. Departamento de Enxeñaría Agroforestal
dc.description.peerreviewedSI


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© 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
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 © 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)





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