Page 11 - Volume 4, Issue 1, January 2011

© Benaki Phytopathological Institute
Uncertainty in pest risk analysis 9
tween W and the parameters Tmin and Wmin
was always close to zero for all tempera-
tures. This result showed that the model
output is not sensitive to the values of these
two parameters. The parameter Topt had a
strong and positive effect on W for temper-
ature in the range 15-20°C, and a strong and
negative effect for temperature in the range
27-32°C. Its effect was negligible for ex-
treme temperatures i.e. when T was close to
5°C or to 35°C and when T was close to 25°C.
The parameter Tmax had a negative effect on
W, but its effect was negligible for extreme
temperatures. When T was close to 5°C or to
35°C, the model output was sensitive to only
one parameter: Wmax. This sensitivity analy-
sis thus reveals that the model output is sen-
sitive to three parameters Topt, Tmax and Wmax
and that the effect of these parameters is
strongly dependent on the temperature.
This work was partly funded by the European
Project PRATIQUE (7 th Framework Programme
for Research and Technological Development).
The author thanks the Panel on Plant Health of
the European Food Safety Authority (EFSA) for
useful discussions.
Literature cited
Araujo, M.B. and New, M. 2006. Ensemble forecast-
ing of species distributions. Trend in Ecology and
Evolution, 22: 42-47.
Buckland, S.T., Burnham, K.P. and Augustin, N.H.
1997. Model selection: an integral part of infer-
ence. Biometrics, 53: 603-618.
Chatfield, C. 2005. Model uncertainty, data mining,
and statistical inference. Journal of the Royal Sta-
tistical Society: Series A, 158: 419-466.
Draper, D. 2005. Assessment and propagation of
model uncertainty. Journal of the Royal Statisti-
cal Society: Series A, 57: 45-97.
EFSA. 2008a. Pest Risk Assessment. Science in sup-
port of phytosanitary decision-making in the
European Community. EFSA Scientific Collo-
quium Summary Report 10, Parma.
EFSA. 2008b. Scientific opinion of the Panel on Plant
Health on a request from the European Com-
mission on Guignardia citricarpa Kiely. The EFSA
Journal, 925: 1-108.
Gelman, A., Carlin, J.B. and Stern, H.S. 2004. Bayesian
data analysis . 2nd edition. Chapman & Hall/CRC.
Giovanelli, J.G.R., Ferreira de Siqueira, M., Haddad,
C.F.B. and Alexandrino, J. 2010. Modeling a spa-
tially restricted distribution in the Neotropics:
how the size of calibration area affects the per-
formance of five presence-only methods. Eco-
logical modeling, 221: 215-224.
Hernandez, P.A., Graham, C.H., Master, L.L. and Al-
bert, D.L. 2006. The effect of sample size and
species characteristics on performance of dif-
ferent species distribution modelling methods.
Ecography, 29: 773-785.
Hoeting, J.A., Madigan, D., Raftery, A.E. and Volinsky,
C.T. 1999. Bayesian model averaging: a tutorial.
Statistical Science, 14: 382-417.
Holt, J. 2006. Score averaging for alien species risk
assessment: a probabilistic alternative. Journal
of Environmental Management , 81: 58-62.
Koch, F.H., Yemshanov, D., McKenney, D.W. and
Smith, W.D. 2009. Evaluating critical uncertainty
thresholds in a spatial model of forest pest inva-
sion risk. Risk Analysis, 29: 1227-1241.
Lobo, J.M., Jiménez-Valverde, A. and Real, R. 2008.
AUC: a misleading measure of the performance
of predictive distribution models. Global Ecolo-
gy and Biogeography, 17: 145-151.
Magarey, R.D., Borchert, D.M., Fowler, G.L, Sutton,
T.G., Colunga-Garcia, M., and Simpson, J.A. 2007.
NAPPFAST, an Internet System for the Weather-
based Mapping of Plant Pathogens. Plant Dis-
ease, 91: 336-445.
Magarey, R.D., Sutton, T.B. and Thayer, C.L. 2005. A
simple generic infection model for foliar fungal
plant pathogens. Phytopathology, 95: 92-100.
Makowski, D. and Mittinty, M.N. 2010. Comparison
of scoring systems for invasive pests using ROC
analysis and Monte Carlo simulations. Risk anal-
ysis , 30: 906-915.
Marmion, M., Parviainen, M., Luoto, M., Heikkinen,
R.K. and Thuiller, W. 2009. Evaluation of consen-
sus methods in predictive species distribution
modelling. Diversity and Distribution , 15: 59-69.
Monod, H., Naud, C. and Makowski, D. 2006. Uncer-
tainty and sensitivity analysis for crop models. In
Wallach, D., Makowski, D. and Jones, J. (eds).
Working with dynamic crop models. Elsevier, p.
55-100.
O’Leary, R. A., Low Choy, S., Kynn, M., Denham, R.,
Martin, T., Murray, J., and Mengersen, K. 2008.
Comparison of three expert elicitation methods
for logistic regression on predicting the pres-
ence of the threatened brush-tailed rock-walla-
by Petrogale penicillata. Environmetrics, 20: 379-
398.
Peterson, G.L., Whitaker, T.B., Stefanski, R.J., Podleck-
is, E.V., Phillips, J.G., Wu, J.S. and Martinez, W.H.
2009. A risk assessment model for importation