<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Gavilán, Rosario G</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The use of climatic parameters and indices in vegetation distribution. A case study in the Spanish Sistema Central.</style></title><secondary-title><style face="normal" font="default" size="100%">International journal of biometeorology</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">climate</style></keyword><keyword><style  face="normal" font="default" size="100%">climatic indices</style></keyword><keyword><style  face="normal" font="default" size="100%">Mediterranean vegetation</style></keyword><keyword><style  face="normal" font="default" size="100%">multivariate analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">phytoclimatology</style></keyword><keyword><style  face="normal" font="default" size="100%">Plants</style></keyword><keyword><style  face="normal" font="default" size="100%">Plants: classification</style></keyword><keyword><style  face="normal" font="default" size="100%">Principal component analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Seasons</style></keyword><keyword><style  face="normal" font="default" size="100%">Spain</style></keyword><keyword><style  face="normal" font="default" size="100%">Vegetation distribution</style></keyword><keyword><style  face="normal" font="default" size="100%">Weather</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2005</style></year></dates><volume><style face="normal" font="default" size="100%">50</style></volume><pages><style face="normal" font="default" size="100%">111-120</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">In this study, over 100 phytoclimatic indices and other climatic parameters were calculated using the climatic data from 260 meteorological stations in a Mediterranean territory located in the centre of the Iberian Peninsula. The nature of these indices was very different; some of them expressed general climatic features (e.g. continentality), while others were formulated for different Mediterranean territories and included particular limits of those indices that expressed differences in vegetation distribution. We wanted to know whether all of these indices were able to explain changes in vegetation on a spatial scale, and whether their boundaries worked similarly to the original territory. As they were so numerous, we investigated whether any of them were redundant. To relate vegetation to climate parameters we preferred to use its hierarchical nature, in discrete units (characterized by one or more dominant or co-dominant species), although it is known to vary continuously. These units give clearer results in this kind of phytoclimatic study. We have therefore used the main communities that represent natural potential vegetation. Multivariate and estimative analyses were used as statistical methods. The classification showed different levels of correlation among climatic parameters, but all of them were over 0.5. One hundred and eleven parameters were grouped into five larger groups: temperature (T), annual pluviothermic indices (PTY), summer pluviothermic indices (SPT), winter potential evapotranspiration (WPET) and thermal continentality indices (K). The remaining parameters showed low correlations with these five groups; some of them revealed obvious spatial changes in vegetation, such as summer hydric parameters that were zero in most vegetation types but not in high mountain vegetation. Others showed no clear results. For example, the Kerner index, an index of thermal continentality, showed lower values than expected for certain particular types of vegetation. Parameters relating to the water balance turned out to be very discriminative for separating vegetation types according to the season or the month when water begins to be scarce. Thus, water availability in soils is a limiting factor for the development of vegetation in spring or autumn as well as in summer. As expected, precipitation and temperature discriminated the altitudinal levels of vegetation. Finally, these index limits only worked in the territories where they were formulated, or in nearby areas.</style></abstract><accession-num><style face="normal" font="default" size="100%">15997399</style></accession-num></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Gavilán, Rosario G.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The use of climatic parameters and indices in vegetation distribution. A case study in the Spanish Sistema Central.</style></title><secondary-title><style face="normal" font="default" size="100%">International journal of biometeorology</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">climate</style></keyword><keyword><style  face="normal" font="default" size="100%">climatic indices</style></keyword><keyword><style  face="normal" font="default" size="100%">Mediterranean vegetation</style></keyword><keyword><style  face="normal" font="default" size="100%">multivariate analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">phytoclimatology</style></keyword><keyword><style  face="normal" font="default" size="100%">Plants</style></keyword><keyword><style  face="normal" font="default" size="100%">Plants: classification</style></keyword><keyword><style  face="normal" font="default" size="100%">Principal component analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Seasons</style></keyword><keyword><style  face="normal" font="default" size="100%">Spain</style></keyword><keyword><style  face="normal" font="default" size="100%">Vegetation distribution</style></keyword><keyword><style  face="normal" font="default" size="100%">Weather</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2005</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2005///</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/pubmed/15997399</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">50</style></volume><pages><style face="normal" font="default" size="100%">111 - 120</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">In this study, over 100 phytoclimatic indices and other climatic parameters were calculated using the climatic data from 260 meteorological stations in a Mediterranean territory located in the centre of the Iberian Peninsula. The nature of these indices was very different; some of them expressed general climatic features (e.g. continentality), while others were formulated for different Mediterranean territories and included particular limits of those indices that expressed differences in vegetation distribution. We wanted to know whether all of these indices were able to explain changes in vegetation on a spatial scale, and whether their boundaries worked similarly to the original territory. As they were so numerous, we investigated whether any of them were redundant. To relate vegetation to climate parameters we preferred to use its hierarchical nature, in discrete units (characterized by one or more dominant or co-dominant species), although it is known to vary continuously. These units give clearer results in this kind of phytoclimatic study. We have therefore used the main communities that represent natural potential vegetation. Multivariate and estimative analyses were used as statistical methods. The classification showed different levels of correlation among climatic parameters, but all of them were over 0.5. One hundred and eleven parameters were grouped into five larger groups: temperature (T), annual pluviothermic indices (PTY), summer pluviothermic indices (SPT), winter potential evapotranspiration (WPET) and thermal continentality indices (K). The remaining parameters showed low correlations with these five groups; some of them revealed obvious spatial changes in vegetation, such as summer hydric parameters that were zero in most vegetation types but not in high mountain vegetation. Others showed no clear results. For example, the Kerner index, an index of thermal continentality, showed lower values than expected for certain particular types of vegetation. Parameters relating to the water balance turned out to be very discriminative for separating vegetation types according to the season or the month when water begins to be scarce. Thus, water availability in soils is a limiting factor for the development of vegetation in spring or autumn as well as in summer. As expected, precipitation and temperature discriminated the altitudinal levels of vegetation. Finally, these index limits only worked in the territories where they were formulated, or in nearby areas.</style></abstract><issue><style face="normal" font="default" size="100%">2</style></issue><notes><style face="normal" font="default" size="100%">The following values have no corresponding Zotero field:&lt;br/&gt;accession-num: 15997399</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Laurent, J.-M.</style></author><author><style face="normal" font="default" size="100%">Bar-Hen, A</style></author><author><style face="normal" font="default" size="100%">François, L</style></author><author><style face="normal" font="default" size="100%">Ghislain, M</style></author><author><style face="normal" font="default" size="100%">Cheddadi, R</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Refining vegetation simulation models: From plant functional types to bioclimatic affinity groups of plants</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Vegetation Science</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">CARAIB</style></keyword><keyword><style  face="normal" font="default" size="100%">Discriminant analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Hierarchical clusteranalysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Moisture</style></keyword><keyword><style  face="normal" font="default" size="100%">pollen</style></keyword><keyword><style  face="normal" font="default" size="100%">Seasonality</style></keyword><keyword><style  face="normal" font="default" size="100%">Temperature</style></keyword><keyword><style  face="normal" font="default" size="100%">Vegetation distribution</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2004</style></year></dates><publisher><style face="normal" font="default" size="100%">Blackwell Publishing Ltd</style></publisher><volume><style face="normal" font="default" size="100%">15</style></volume><pages><style face="normal" font="default" size="100%">739-746</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Question: How to refine simulations based on a global vegetation model in order to apply it to regional scale? Location: Europe from 35° N to 71° N and 25° W to 70° E. Methods: Geographical ranges of European plants were georeferenced and used with monthly mean climatic data (diurnal temperature ranges, ground frost frequencies, precipitation, relative humidity, rain frequencies, amount of sunshine hours and temperature) and growing degree days to infer climatic boundaries for 320 taxa. We performed a discriminant analysis to define their potential geographic ranges. Hierarchical clustering was computed on potential ranges. Results: Clustering provided 25 Bioclimatic Affinity Groups (BAG) of plants consisting of 13 tree, seven shrub and five herb groups. These B AGs are characterized by different geographical ranges and climatic tolerances and requirements. Conclusion: The use of monthly data instead of annual values improved the prediction of potential distribution ranges and highlighted the importance of climate seasonality for defining the plant groups with accuracy. The B AGs are detailed enough to provide finer reconstructions and simulations of the vegetation at the regional scale.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Laurent, J.-M.</style></author><author><style face="normal" font="default" size="100%">Bar-Hen, A.</style></author><author><style face="normal" font="default" size="100%">François, L.</style></author><author><style face="normal" font="default" size="100%">Ghislain, M.</style></author><author><style face="normal" font="default" size="100%">Cheddadi, R.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Refining vegetation simulation models: From plant functional types to bioclimatic affinity groups of plants</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Vegetation Science</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">CARAIB</style></keyword><keyword><style  face="normal" font="default" size="100%">Discriminant analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Hierarchical clusteranalysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Moisture</style></keyword><keyword><style  face="normal" font="default" size="100%">pollen</style></keyword><keyword><style  face="normal" font="default" size="100%">Seasonality</style></keyword><keyword><style  face="normal" font="default" size="100%">Temperature</style></keyword><keyword><style  face="normal" font="default" size="100%">Vegetation distribution</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2004</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2004///</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1111/j.1654-1103.2004.tb02316.x</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">15</style></volume><pages><style face="normal" font="default" size="100%">739 - 746</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Question: How to refine simulations based on a global vegetation model in order to apply it to regional scale? Location: Europe from 35° N to 71° N and 25° W to 70° E. Methods: Geographical ranges of European plants were georeferenced and used with monthly mean climatic data (diurnal temperature ranges, ground frost frequencies, precipitation, relative humidity, rain frequencies, amount of sunshine hours and temperature) and growing degree days to infer climatic boundaries for 320 taxa. We performed a discriminant analysis to define their potential geographic ranges. Hierarchical clustering was computed on potential ranges. Results: Clustering provided 25 Bioclimatic Affinity Groups (BAG) of plants consisting of 13 tree, seven shrub and five herb groups. These B AGs are characterized by different geographical ranges and climatic tolerances and requirements. Conclusion: The use of monthly data instead of annual values improved the prediction of potential distribution ranges and highlighted the importance of climate seasonality for defining the plant groups with accuracy. The B AGs are detailed enough to provide finer reconstructions and simulations of the vegetation at the regional scale.</style></abstract><issue><style face="normal" font="default" size="100%">6</style></issue><notes><style face="normal" font="default" size="100%">The following values have no corresponding Zotero field:&lt;br/&gt;publisher: Blackwell Publishing Ltd</style></notes></record></records></xml>