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Predictive models for integrated pest management of the leaf beetle Chrysophtharta bimaculata in Eucalyptus nitens plantations in Tasmania

机译:塔斯马尼亚桉树人工林叶甲虫Chrysophtharta bimaculata害虫综合防治的预测模型

摘要

The Tasmanian Eucalyptus leaf beetle, Chrysophtharta bimaculata (Coleoptera: Chrysomelidae) is the most important phytophagous insect pest of eucalypts in Tasmania regularly defoliating new season’s growth over large areas of forest. An integrated pest management (IPM) system is currently in operation to minimise economic losses from defoliation of Eucalyptus nitens plantations due to browsing by larvae. However, the utility of the IPM system has been limited by the lack of : (a) an efficient sampling scheme for population monitoring, (b) the ability to predict an ‘action threshold’ (i.e. the population density for which it is profitable to apply insecticide at a standard rate), and (c) the ability to predict C. bimaculata population phenology. This study attempted to rectify this situation by developing predictive models that were calibrated using data from a number of sources including that obtained from a caged-shoot, larval feeding trial and from stage-frequency sampling carried out by the author.ududSampling schemes for monitoring egg populations were investigated using (i) counts of the number of leaves occupied by one or more egg batches for a sample of shoots and trees where the total number of leaves on each shoot was unknown [‘occupied leaf count’ (OLC) sampling], (ii) counts of the number of occupied trees for a known total sample size (binomial sampling), and (iii) double sampling using a combination of both (i) and (ii). Novel statistical methods were employed in developing these sampling schemes. For OLC sampling, the current multi-stage technique for modelling the variance of the counts using either Taylor’s power law (TPL) or Iwao’s regression was compared to single-stage modelling using generalised linear mixed models (GLMMs). To do this, GLMMs were extended to incorporate a negative binomial variance function. Efficiency of estimation of TPL was improved by using a more accurate approximation to the variance of the marginal mean. For binomial sampling, William’s method III of handling over-dispersion in a binomial generalised linear model is shown to have theoretical and practical advantages over current methods that use simple linear regression.ududModels of the growth of E. nitens, with and without browsing by larvae, were developed at the leaf, shoot, tree, and stand level. The models included (i) a leaf expansion model, (ii) a process model of the impact of larval browsing on total shoot leaf area incorporating (i) above and calibrated using data from the caged-shoot feeding trial, and (iii) a model of the impact of defoliation level on the growth of tree diameter and height calibrated using data from artificial defoliation trials. A method of predicting action thresholds for insecticide application from inputs of initial stand conditions, silvicultural regime, cost of control, stumpage value, and cash discount rate was developed. This involved the comparison of growth predicted from existing stand and tree growth models for E. nitens with that obtained by combining those models with the browsing and defoliation impact models (ii) and (iii).ududModels of (i) egg and larval development rate as a function of ambient field temperature and (ii) the progression of the population through key life stages (egg to final instar larvae) were constructed. Maximum likelihood estimation was used for (i) based on interval-censored development times. Continuation ratio models for (ii), incorporating physiological time based on the models for (i) and calibrated using the stage-frequency data, were used to estimate time of peak occurrence of each of first, second, and third instar larvae. The models and estimation methods used are shown to have advantages over those currently used in population studies.ududFinally, the practical application of this suite of predictive models for decision support within the leaf beetle IPM system was demonstrated. For a typical 15-year rotation pulpwood regime on a site of average quality with a discount rate of 8%, a cost of control of $35/ha, and a stumpage of $30/m3, a value of 0.3 occupied leaves per shoot was the recommended action threshold. In addition, since approximately 90% of defoliation is caused by the last two (i.e. third and fourth) larval instars any application of insecticide should not be delayed past the time of the peak in the number of second instar larvae. The time from the start of mass oviposition to this peak was predicted to be approximately 175 DD[5] where DD[5] is the day-degrees calculated with a 5 lower threshold.
机译:塔斯马尼亚桉树的叶甲虫Chrysophtharta bimaculata(鞘翅目:菊科)是塔斯马尼亚州桉树中最重要的植物性食虫性害虫,经常使大面积森林的新季节生长脱落。目前正在运行一个综合的有害生物管理(IPM)系统,以最大程度减少因幼虫浏览而造成的桉树人工林落叶而造成的经济损失。但是,由于缺乏以下方面,IPM系统的实用性受到了限制:(a)一种有效的人口监测抽样方案,(b)预测“行动阈值”的能力(即可以从中受益的人口密度)以标准剂量施用杀虫剂),以及(c)预测双毛衣藻种群物候的能力。这项研究试图通过开发预测模型来纠正这种情况,该预测模型使用来自许多来源的数据进行了校准,这些数据包括从网箱拍摄,幼体喂养试验以及作者进行的阶段频率采样中获得的数据。 ud ud用于监视卵种群的方法是使用以下方法研究的:(i)一个或多个卵批次所占据的枝条和树木样品的叶子数计数,其中每个枝条上的叶子总数未知['占用叶数'(OLC)采样],(ii)已知总样本大小的占用树木数量计数(二项式采样),以及(iii)结合使用(i)和(ii)进行两次采样。在开发这些抽样方案时采用了新颖的统​​计方法。对于OLC采样,将当前使用泰勒幂定律(TPL)或Iwao回归建模计数方差的多阶段技术与使用广义线性混合模型(GLMM)的单阶段建模进行了比较。为此,将GLMM扩展为合并负二项式方差函数。通过使用更精确的边际均值方差近似,可以提高TPL的估算效率。对于二项式采样,与使用简单线性回归的当前方法相比,在二项式广义线性模型中处理过度分散的William方法III已显示出理论上和实践上的优势。 ud ud通过幼虫浏览,分别在叶子,枝条,树和林分等级发育。这些模型包括(i)叶片扩张模型,(ii)幼虫浏览对总枝叶面积的影响的过程模型,其中包括(i)以上内容并使用笼养饲喂试验的数据进行了校准,以及(iii)a人工脱叶试验数据校准的脱叶水平对树木直径和高度生长影响的模型。开发了一种根据初始林分条件,营林制度,控制成本,立杆价值和现金贴现率的输入来预测杀虫剂作用阈值的方法。这包括将现有的林分和林木生长模型预测的生长与通过将这些模型与浏览和脱叶影响模型(ii)和(iii)组合而获得的生长模型进行比较。 ud ud(i)卵和构造了幼虫的发育速度与周围环境温度的关系,以及(ii)种群在关键生命阶段(例如到最终的幼龄幼虫)的进程。基于区间删节的展开时间,将最大似然估计用于(i)。 (ii)的延续比率模型,基于(i)的模型并结合生理时间,并使用阶段频率数据进行校准,用于估计第一,第二和第三龄幼虫的峰值出现时间。结果表明,所使用的模型和估计方法具有优于目前人口研究中使用的模型和方法的优势。 ud ud最后,证明了这套预测模型在叶甲IPM系统中为决策支持的实际应用。对于一个平均质量为15%,折扣率为8%,控制成本为35美元/公顷,立杆成本为30美元/立方米的典型15年轮浆制而言,每枝芽占0.3片叶子的价值是建议的动作阈值。另外,由于大约90%的落叶是由后两个(即第三和第四)幼虫引起的,所以不应将任何杀虫剂的施用延迟到第二个幼虫的数量达到峰值的时间。从质量排卵开始到该峰值的时间预计约为175 DD [5],其中DD [5]是使用5个下限阈值计算的日度。

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