<?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%">La Manna, L.</style></author><author><style face="normal" font="default" size="100%">Matteucci, S.</style></author><author><style face="normal" font="default" size="100%">T. Kitzberger</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Modelling Phytophthora disease risk in Austrocedrus chilensis forests of Patagonia</style></title><secondary-title><style face="normal" font="default" size="100%">European Journal of Forest Research</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1007/s10342-011-0503-7</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer Berlin / Heidelberg</style></publisher><pages><style face="normal" font="default" size="100%">1-15</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;em&gt;Austrocedrus chilensis&lt;/em&gt; forests suffer from a disease caused by &lt;em&gt;Phytophthora austrocedrae&lt;/em&gt;, which is found often in wet soils. We applied three widely used modelling techniques, with different data requirements, to model disease potential distribution under current environmental conditions: Mahalanobis distance, Maxent and Logistic regression. Each model was built using field data of health condition and landscape layers of environmental conditions (distance to streams, slope, aspect, elevation, mean annual precipitation and soil pH NaF). We compared model predictions by area under the receiver operating characteristic curve and Kappa statistics. A reasonable ability to predict observed disease distribution was found for each of the three modelling techniques. However, Maxent and Logistic regression presented the best predictive performance, with significant differences with respect to the Mahalanobis distance model. Our results suggested that if good absence data are available, Logistic regression should be used in order to better discriminate sites with high risk of disease. On the other hand, if absence data are not available or doubtful, Maxent could be a very good option. The three models predicted that around 50% (49–56%) of the currently asymptomatic forests are located on sites at risk of disease according to abiotic factors. Most of these asymptomatic forests surround the current diseased patches, at distances lower than 100 m from diseased patches. Management considerations and the scope of future studies were discussed in this article.&lt;/p&gt;</style></abstract><notes><style face="normal" font="default" size="100%">10.1007/s10342-011-0503-7</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%">La Manna, L.</style></author><author><style face="normal" font="default" size="100%">Collantes, M.</style></author><author><style face="normal" font="default" size="100%">Bava, J.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Seedling recruitment of Austrocedrus chilensis in relation to cattle use, microsite environment and forest disease</style></title><secondary-title><style face="normal" font="default" size="100%">Ecologia Austral.(Abr</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><number><style face="normal" font="default" size="100%">1</style></number><volume><style face="normal" font="default" size="100%">18</style></volume><pages><style face="normal" font="default" size="100%">27–41</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></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%">La Manna, L.</style></author><author><style face="normal" font="default" size="100%">S.D. Matteucci</style></author><author><style face="normal" font="default" size="100%">T. Kitzberger</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Abiotic factors related to the incidence of the Austrocedrus chilensis disease syndrome at a landscape scale</style></title><secondary-title><style face="normal" font="default" size="100%">Forest Ecology and Management</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Patagonia</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.sciencedirect.com/science/article/B6T6X-4T4JDN7-1/2/6ec5c810e304a848ea7deb23b39e8977</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">5</style></number><volume><style face="normal" font="default" size="100%">256</style></volume><pages><style face="normal" font="default" size="100%">1087 - 1095</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">In this paper, the incidence of the A. chilensis disease syndrome in the &quot;16 de Octubre&quot; Valley (Chubut, Argentinean Patagonia) was related to landscape climatic, topographic and edaphic attributes, using remote sensing, geographic information systems and statistical methods. A strong relationship between the occurrence and incidence of the A. chilensis disease syndrome and site variables related to poor soil drainage was found. Non-allophanized soils with fine textures on flat and wavy soil phases, geomorphologies associated to alluvial processes, and low elevations and gentle slopes were positively related to the incidence of the disease. These relationships at a landscape scale agree with previous studies carried out at the stand level. A logistic predictive model of diseased occurrence was developed for the study area considering aspect, elevation, slope, mean annual precipitation and soil phase (classified according to predominant slopes).</style></abstract></record></records></xml>