Assessing the Global Risk of Establishment ofCydia pomonella(Lepidoptera: Tortricidae) using CLIMEX and MaxEnt Niche Models

Abstract

Accurate assessment of insect pest establishment risk is needed by national plant protection organizations to negotiate international trade of horticultural commodities that can potentially carry the pests and result in inadvertent introductions in the importing countries. We used mechanistic and correlative niche models to quantify and map the global patterns of the potential for establishment of codling moth (Cydia pomonella L.), a major pest of apples, peaches, pears, and other pome and stone fruits, and a quarantine pest in countries where it currently does not occur. The mechanistic model CLIMEX was calibrated using species-specific physiological tolerance thresholds, whereas the correlative model MaxEnt used species occurrences and climatic spatial data. Projected potential distribution from both models conformed well to the current known distribution of codling moth. None of the models predicted suitable environmental conditions in countries located between 20°N and 20°S potentially because of shorter photoperiod, and lack of chilling requirement (<60 d at ≤10°C) in these areas for codling moth to break diapause. Models predicted suitable conditions in South Korea and Japan where codling moth currently does not occur but where its preferred host species (i.e., apple) is present. Average annual temperature and latitude were the main environmental variables associated with codling moth distribution at global level. The predictive models developed in this study present the global risk of establishment of codling moth, and can be used for monitoring potential introductions of codling moth in different countries and by policy makers and trade negotiators in making science-based decisions.

Global Potential Distribution of Bactrocera carambolae and the Risks for Fruit Production in Brazil

“…The models ran with the default settings had comparatively poor performance. This indicates the importance of testing different MaxEnt configurations to obtain high-performance models, corroborating the findings of studies conducted with other species [27,37,38,42,43]. Also, by changing the default settings of MaxEnt one can build models with an appropriate level of complexity, which is a desirable attribute for improving model’s transferability to other regions [43].…”

“…A feature is a function of an environmental variable, and in MaxEnt it can be a combination of six classes: linear (L), quadratic (Q), product (P), hinge (H) and threshold (T). Because parsimonious models can be generated using different combinations of feature classes [36–38], five of these combinations were tested in this study: L; H; LQ; LQH and LQHPT.…”

“…The regularization coefficient can be tuned by multiplying it by a user-specified constant (Regularization multiplier), altering the overall level of regularization rather than changing the β parameter individually [26,27]. Studies have demonstrated that less complex and transferable models can be built by tuning the regularization multiplier to values higher than the default of MaxEnt [26, 27,38]. Therefore, in addition to MaxEnt default, regularization multiplier values of 3 and 5 were also tested in the development of the models.…”

Fytosanitaire na-oogst behandel- en detectietechnieken voor plaagorganismen in tuinbouw

“…Nu komt de fruitmot in de gematigde klimaatzone in de meeste teeltgebieden van appel en peer voor (Barnes 1991), maar niet in Zuid Korea en Japan (Kumar et al 2015). In ZuidBrazilië werd fruitmot voor het eerst aangetroffen in 1991.…”

“…In de tropen, tussen de 20 ste breedtegraad ten noorden en zuiden van de evenaar, komt de soort niet voor. Dit hangt samen met de korte daglengte, waardoor de volgroeide rupsen in winterrust gaan, in combinatie met een gebrek aan lage temperaturen die nodig zijn om die winterrust te doorbreken (Kumar et al 2015).…”