Modelling Phytophthora disease risk in Austrocedrus chilensis forests of Patagonia

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: Mahalanobi...

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Autores principales: la Manna, L., Matteucci, S.D., Kitzberger, T.
Formato: JOUR
Materias:
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
Acceso en línea:http://hdl.handle.net/20.500.12110/paper_16124669_v131_n2_p323_laManna
Aporte de:
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires
id todo:paper_16124669_v131_n2_p323_laManna
record_format dspace
spelling todo:paper_16124669_v131_n2_p323_laManna2023-10-03T16:28:04Z Modelling Phytophthora disease risk in Austrocedrus chilensis forests of Patagonia la Manna, L. Matteucci, S.D. Kitzberger, T. 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 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. JOUR info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar http://hdl.handle.net/20.500.12110/paper_16124669_v131_n2_p323_laManna
institution Universidad de Buenos Aires
institution_str I-28
repository_str R-134
collection Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA)
topic 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
spellingShingle 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
la Manna, L.
Matteucci, S.D.
Kitzberger, T.
Modelling Phytophthora disease risk in Austrocedrus chilensis forests of Patagonia
topic_facet 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
description 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.
format JOUR
author la Manna, L.
Matteucci, S.D.
Kitzberger, T.
author_facet la Manna, L.
Matteucci, S.D.
Kitzberger, T.
author_sort la Manna, L.
title Modelling Phytophthora disease risk in Austrocedrus chilensis forests of Patagonia
title_short Modelling Phytophthora disease risk in Austrocedrus chilensis forests of Patagonia
title_full Modelling Phytophthora disease risk in Austrocedrus chilensis forests of Patagonia
title_fullStr Modelling Phytophthora disease risk in Austrocedrus chilensis forests of Patagonia
title_full_unstemmed Modelling Phytophthora disease risk in Austrocedrus chilensis forests of Patagonia
title_sort modelling phytophthora disease risk in austrocedrus chilensis forests of patagonia
url http://hdl.handle.net/20.500.12110/paper_16124669_v131_n2_p323_laManna
work_keys_str_mv AT lamannal modellingphytophthoradiseaseriskinaustrocedruschilensisforestsofpatagonia
AT matteuccisd modellingphytophthoradiseaseriskinaustrocedruschilensisforestsofpatagonia
AT kitzbergert modellingphytophthoradiseaseriskinaustrocedruschilensisforestsofpatagonia
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