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Abstract:

Austrocedrus chilensis forests suffer from a disease caused by Phytophthora austrocedrae, 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. © 2011 Springer-Verlag.

Registro:

Documento: Artículo
Título:Modelling Phytophthora disease risk in Austrocedrus chilensis forests of Patagonia
Autor:la Manna, L.; Matteucci, S.D.; Kitzberger, T.
Filiación:Laboratorio de Suelos, Centro de Investigación y Extensión Forestal Andino Patagónico, Universidad Nacional de la Patagonia San Juan Bosco, Ruta 259 km 4, C.C. 14, 9200 Esquel, Chubut, Argentina
Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina
Grupo de Ecología del Paisaje y Medio Ambiente, Universidad de Buenos Aires, Ciudad Universitaria, Pabellón 3, Piso 4, 1428 Buenos Aires, Argentina
Laboratorio Ecotono, INBIOMA, CONICET, Universidad Nacional del Comahue, Quintral 1250, 8400 Bariloche, Argentina
Palabras clave:Abiotic factors; Logistic regression; Mahalanobis distance; Mal del ciprés; Maxent; Risk model; disease; environmental conditions; eukaryote; landscape; management; numerical model; regression analysis; risk factor; Patagonia; Austrocedrus chilensis; Phytophthora
Año:2012
Volumen:131
Número:2
Página de inicio:323
Página de fin:337
DOI: http://dx.doi.org/10.1007/s10342-011-0503-7
Título revista:European Journal of Forest Research
Título revista abreviado:Eur. J. For. Res.
ISSN:16124669
Registro:https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_16124669_v131_n2_p323_laManna

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Citas:

---------- APA ----------
la Manna, L., Matteucci, S.D. & Kitzberger, T. (2012) . Modelling Phytophthora disease risk in Austrocedrus chilensis forests of Patagonia. European Journal of Forest Research, 131(2), 323-337.
http://dx.doi.org/10.1007/s10342-011-0503-7
---------- CHICAGO ----------
la Manna, L., Matteucci, S.D., Kitzberger, T. "Modelling Phytophthora disease risk in Austrocedrus chilensis forests of Patagonia" . European Journal of Forest Research 131, no. 2 (2012) : 323-337.
http://dx.doi.org/10.1007/s10342-011-0503-7
---------- MLA ----------
la Manna, L., Matteucci, S.D., Kitzberger, T. "Modelling Phytophthora disease risk in Austrocedrus chilensis forests of Patagonia" . European Journal of Forest Research, vol. 131, no. 2, 2012, pp. 323-337.
http://dx.doi.org/10.1007/s10342-011-0503-7
---------- VANCOUVER ----------
la Manna, L., Matteucci, S.D., Kitzberger, T. Modelling Phytophthora disease risk in Austrocedrus chilensis forests of Patagonia. Eur. J. For. Res. 2012;131(2):323-337.
http://dx.doi.org/10.1007/s10342-011-0503-7