Effects of climate change on the distribution of Iberian tree species - Benito Garzón, Marta; Sánchez de Dios, Rut; Sainz Ollero, Helios (Applied Vegetation Science, 2008)
Question: Will the predicted climate changes affect species distribution in the Iberian Peninsula? Location: Iberian Peninsula (Spain and Portugal). Methods: We modelled current and future tree distributions as a function of climate, using a computational framework that made use of one machine learning technique, the random forest (RF) algorithm. This algorithm provided good predictions of the current distribution of each species, as shown by the area under the corresponding receiver operating characteristics (ROC) curves. Species turnover, richness and the change in distributions over time to 2080 under four Intergovernmental panel on climate change (IPCC) scenarios were calculated using the species map outputs. Results and Conclusions: The results show a notable reduction in the potential distribution of the studied species under all the IPCC scenarios, particularly so for mountain conifer species such as Pinus sylvestris, P. uncinata and Abies alba. Temperate species, especially Fagus sylvatica and Quercus petraea, were also predicted to suffer a reduction in their range; also sub-mediterranean species, especially Q. pyrenaica, were predicted to undergo notable decline. In contrast, typically Mediterranean species appeared to be generally more capable of migration, and are therefore likely to be less affected.
Keywords: 1990; 2002; amaral franco; avishai; for quercus pubescens; Forest distributions; govaerts; iberian peninsula; learning; machine; Machine learning; nomenclature; random forest

Effects of climate change on the distribution of Iberian tree species - Benito Garzón, Marta; Sánchez de Dios, Rut; Sainz Ollero, Helios (Applied Vegetation Science, 2008)
Question: Will the predicted climate changes affect species distribution in the Iberian Peninsula? Location: Iberian Peninsula (Spain and Portugal). Methods: We modelled current and future tree distributions as a function of climate, using a computational framework that made use of one machine learning technique, the random forest (RF) algorithm. This algorithm provided good predictions of the current distribution of each species, as shown by the area under the corresponding receiver operating characteristics (ROC) curves. Species turnover, richness and the change in distributions over time to 2080 under four Intergovernmental panel on climate change (IPCC) scenarios were calculated using the species map outputs. Results and Conclusions: The results show a notable reduction in the potential distribution of the studied species under all the IPCC scenarios, particularly so for mountain conifer species such as Pinus sylvestris, P. uncinata and Abies alba. Temperate species, especially Fagus sylvatica and Quercus petraea, were also predicted to suffer a reduction in their range; also sub-mediterranean species, especially Q. pyrenaica, were predicted to undergo notable decline. In contrast, typically Mediterranean species appeared to be generally more capable of migration, and are therefore likely to be less affected.
The following values have no corresponding Zotero field: publisher: Blackwell Publishing Ltd
Keywords: 1990; 2002; amaral franco; avishai; for quercus pubescens; Forest distributions; govaerts; iberian peninsula; learning; machine; Machine learning; nomenclature; random forest