欧洲森林昆虫爆发的概率:根据六种森林昆虫概况校准的通用模型

Dorian COLLOT, Christelle ROBINET
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引用次数: 0

摘要

虫害是森林面临的主要威胁之一。虽然入侵物种造成的影响越来越大,但本地物种也会造成暂时性的严重破坏。昆虫的种群动态取决于从天气到林地条件等多个因素。由于全球变化,昆虫可能会面临从未遇到过的条件,导致不寻常的种群爆发。森林管理者需要考虑这些可能出现的害虫,但预测昆虫爆发仍然非常具有挑战性。在这种情况下,我们在统计模型和机理模型的交叉点上开发了一个数学模型,用于描述一组 6 种昆虫的爆发可能性:树皮甲虫、长角甲虫、龟纹夜蛾、其他蛾类、蚜虫和膜翅目昆虫。该模型描述了在特定时间和特定地区,基于若干条件的疫情爆发概率。该模型是根据记录最多的欧洲森林害虫种类建立的,并对其进行了参数化。这些害虫种类的参数化可以作为一个基线,用于探索密切相关害虫种类的爆发风险。我们举例说明了该模型在欧洲西北部达到流行水平的橡树游蛾(Thaumetopoea processionea)中的应用。这种通用的疫情爆发模型在指出某些年份或地区不可能爆发疫情方面表现尤为突出,因此可以正确地针对抑制疫情爆发的因素。该模型仍处于探索阶段,应进一步改进,以用于林分监测和管理。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Probability of outbreaks of forest insects in Europe: a generic model calibrated on six forest insect profiles
Insect pests are one of the major threats to forests. Although invasive species cause more and more impacts, native species could also generate temporary very high damages. The population dynamics of insects relies on several factors, going from weather to stand conditions. Due to global change, insects could face conditions they have never encountered before, leading to unusual population outbreaks. Forest managers need to consider these possible emerging pests but predicting insect outbreaks is still very challenging. In this context, we have developed a mathematical model at the crossroad of statistical and mechanistic models to describe the likelihood of outbreaks for a set of 6 insect profiles: bark beetles, longhorn beetles, tortrix moth, other moths, aphids, and Hymenoptera. This model describes the probability of occurrence of an outbreak at a given time and at a given area, based on several conditions. It has been built and parametrized on the most documented orders of European forest pests. Parametrization for these species' profiles can be used as a baseline to explore the risk of outbreaks for closely related pest species. We provide an illustration of the model application for the oak processionary moth, Thaumetopoea processionea, which reach epidemic levels in north-western Europe. This generic outbreak model is particularly performant to point out some years or areas as unlikely for an outbreak, and thus targets correctly factors that inhibit outbreaks. It is still at an exploratory level and should be further improved for an operational use in forest stand surveillance and management.
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