Local ecological niche modelling to provide suitability maps for 27 forest tree species in edge conditions

dc.contributor.authorStephan, Jean
dc.contributor.authorBercachy, Christelle
dc.contributor.authorBechara, Joseph
dc.contributor.authorCharbel, Eliane
dc.contributor.authorLópez Tirado, Javier
dc.date.accessioned2024-06-14T11:50:31Z
dc.date.available2024-06-14T11:50:31Z
dc.date.issued2020
dc.description.abstractEcological Niche Modelling (ENM) portrays the relationship between the actual geographical distribution of a species and the environmental factors that in duced this distribution. Yet most models study species over the wider range of their distribution; thus, they are rarely appropriate for forest management and forest restoration on the local scale. This study aims to understand the major environmental factors affecting the distribution of 27 species, through limiting ENM at national level (Lebanon). MaxENT software was used for mod elling. Area under the curve (AUC) values showed a very good robustness of the models. Minimal biogeographic and climatic parameters such as elevation, distance from the sea, annual mean precipitation, the average minimum tem perature of the coldest month, the average maximum temperature of the warmest month, and Emberger Quotient were sufficient to obtain robust mod elling results. Cloud coverage during summer was identified as a novelty factor explaining species distribution at the edge of their range. Composite soil and topography predictors such as Potential Direct Incident Radiation (PDIR) and the Integrated Moisture Index (IMI) were reduced to simple factors such as as pect, slope and available water content, whose contribution was conditioned to higher data resolution. The high number of presence points enabled us to study the range of species distribution gathering them according to their eco logical characteristics. The generated reforestation suitability maps and the likelihood of occurrence of each species were achieved to define priority species for conservation and forest management. This information could be useful for decision-makers and foresterses_ES
dc.description.departmentCiencias Integradas
dc.description.sponsorshipJean Stephan designed and conceived this research; Christel Bercachy conducted field data collection, model running and mapping; Joseph Bechara provided assis tance in model development and data stocktaking; Eliane Charbel and Javier Lopez-Tirado reviewed the paper, namely the material and method and discussion. The Lebanese Reforestation Initiative pro vided the technical assistance and the fi nancial support of this work. We are thank ful for the National Center for Remote Sensing of Lebanon for providing precipita tion and cloud cover data and for Maya Nehme and Samar Haddad for editing the manuscriptes_ES
dc.identifier.citationStephan, J., Bercachy, C., Bechara, J., Charbel, E., & López-Tirado, J. (2020). Local ecological niche modelling to provide suitability maps for 27 forest tree species in edge conditions. In iForest - Biogeosciences and Forestry (Vol. 13, Issue 1, pp. 230–237). Italian Society of Sivilculture and Forest Ecology (SISEF). https://doi.org/10.3832/ifor3331-013es_ES
dc.identifier.doi10.3832/ifor3331-013
dc.identifier.issn1971-7458
dc.identifier.urihttps://hdl.handle.net/10272/23930
dc.language.isoenges_ES
dc.publisherSISEFes_ES
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.accessRightsopen accesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subject.otherEcological Niche Modellinges_ES
dc.subject.otherSuitability Mapses_ES
dc.subject.otherCloud Coveragees_ES
dc.subject.otherRange Of Distributiones_ES
dc.subject.otherMaxEntes_ES
dc.subject.unesco2417 Biología Vegetal (Botánica)es_ES
dc.subject.unesco3106 Ciencia Forestales_ES
dc.titleLocal ecological niche modelling to provide suitability maps for 27 forest tree species in edge conditionses_ES
dc.typejournal articlees_ES
dc.type.hasVersionVoRes_ES
dspace.entity.typePublication
relation.isAuthorOfPublication874421fc-1057-4ffc-8e27-d789f76c1fd3
relation.isAuthorOfPublication.latestForDiscovery874421fc-1057-4ffc-8e27-d789f76c1fd3

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