Application of the Maryblyt Model for the Infection of Fire Blight on Apple Trees at Chungju,Jecheon, and Eumsung during 2015–2020

To preventively control fire blight in apple trees and determine policies regarding field monitoring, the Maryblyt ver. 7.1 model (MARYBLYT) was evaluated in the cities of Chungju, Jecheon, and Eumseong in Korea from 2015 to 2020. The number of blossom infection alerts was the highest in 2020 and the lowest in 2017 and 2018. And the common feature of MARYBLYT blossom infection risks during the flowering period was that the time of BIR-High or BIR-Infection alerts was the same regardless of location. The flowering periods of the trees required to operate the model varied according to the year and geographic location. The model predicts the risk of “Infection” during the flowering periods, and recommends the appropriate times to control blossom infection. In 2020, when flower blight was severe, the difference between the expected date of blossom blight symptoms presented by MARYBLYT and the date of actual symptom detection was only 1–3 days, implying that MARYBLYT is highly accurate. As the model was originally developed based on data obtained from the eastern region of the United States, which has a climate similar to that of Korea, this model can be used in Korea. To improve field utilization, however, the entire flowering period of multiple apple varieties needs to be considered when the model is applied. MARYBLYT is believed to be a useful tool for determining when to control and monitor apple cultivation areas that suffer from serious fire blight problems.     

A practical method for predicting the short-time trend of bivoltine populations of Ips typographus (L.) (Col., Scolytidae)

Abstract: Ips typographus is the main spruce pest of European forests. In most areas of the Italian Alps there are two generations per year; overwintering adults fly in May looking for trees suitable for breeding, their offspring emerge in summer, 7–8 weeks after tree colonization, and the adults of the second generation emerge in spring of the following year after overwintering under the bark or in the litter. A long‐term population monitoring was carried out in north‐east Italy with the aim at developing a prediction model able to estimate the population density of the following year. Between 1996 and 2004, pheromone traps monitored populations of I. typographus annually. Monitoring lasted 4 months (May–August), with replacement of pheromone dispensers after 8 weeks. Insects trapped before dispenser change were called ‘spring captures’ (May–June), and included both overwintering and re‐emerging adults. Beetles caught after dispenser change were called ‘summer captures’ (July–August), and included the adults of the first generation. The results show a high positive correlation between the ratio of summer and spring captures of one year (Summer_x/Spring_x), and the ratio of total captures of the following year (Y_x+1) and those of the current year (Y_x) (Y_x+1/Y_x). Summer_x/Spring_x lower than 0.62 indicate decreasing populations in the following year (Y_x+1/Y_x <1), whereas Summer_x/Spring_x higher than 0.62 indicate increasing populations (Y_x+1/Y_x >1). The applicability of the model in the study of I. typographus risk of outbreak and in the forest management is discussed. The prediction of the short‐time trend of the population allows assessing its density in the following year, and therefore the risk of outbreak.     

荔枝蝽生物学及防治技术的研究进展

荔枝蝽Tessaratoma papillasa是荔枝和龙眼生产中的重要害虫,在中国南方及东南亚地区危害严重.该虫以若虫及成虫吸食寄主植物的嫩芽、嫩梢、花穗和幼果,成虫和3龄以上若虫是传播龙眼鬼帚病病原体的传病介体昆虫.本文综述了近60年来国内荔枝蝽的研究情况,旨在为广大科研工作者提供更详尽的荔枝蝽研究现状,以期为该虫...     

Assessment of a large number of empirical plant species niche models by elicitation of knowledge from two national experts

Quantitative models play an increasing role in exploring the impact of global change on biodiversity. To win credibility and trust, they need validating. We show how expert knowledge can be used to assess a large number of empirical species niche models constructed for the British vascular plant and bryophyte flora. Key outcomes were (a) scored assessments of each modeled species and niche axis combination, (b) guidance on models needing further development, (c) exploration of the trade‐off between presenting more complex model summaries, which could lead to more thorough validation, versus the longer time these take to evaluate, (d) quantification of the internal consistency of expert opinion based on comparison of assessment scores made on a random subset of models evaluated by both experts. Overall, the experts assessed 39% of species and niche axis combinations to be “poor” and 61% to show a degree of reliability split between “moderate” (30%), “good” (25%), and “excellent” (6%). The two experts agreed in only 43% of cases, reaching greater consensus about poorer models and disagreeing most about models rated as better by either expert. This low agreement rate suggests that a greater number of experts is required to produce reliable assessments and to more fully understand the reasons underlying lack of consensus. While area under curve (AUC) statistics showed generally very good ability of the models to predict random hold‐out samples of the data, there was no correspondence between these and the scores given by the experts and no apparent correlation between AUC and species prevalence. Crowd‐sourcing further assessments by allowing web‐based access to model fits is an obvious next step. To this end, we developed an online application for inspecting and evaluating the fit of each niche surface to its training data.     

山东省亚洲玉米螟越冬特性及越冬代成虫发生期预测

【目的】亚洲玉米螟Ostrinia furnacalis(Guenée)是我国玉米生产上的重要害虫。亚洲玉米螟越冬特性及预测预报的研究,对于提高防治效果具有重要意义。【方法】本文通过对山东省东昌府、曲阜、商河、滨城区、即墨和栖霞地区玉米秸秆和穗轴进行剖查,统计越冬幼虫虫量和位置分布。并将幼虫带回济南放于室外待其化蛹,统计化蛹时间及存活率。【结果】山东省6个地区2007年春季玉米秸秆和穗轴中亚洲玉米螟平均越冬虫量分别为41.80头/百秆和19.91头/百穗,其中在玉米秸秆中越冬虫量占总量的67.30%。越冬幼虫在玉米秸秆上、中和下部比例分别为21.18%、38.80%和40.02%。越冬代亚洲玉米螟化蛹始盛期、高峰期、盛末期分别为5月27日、6月5日和6月21日,预测越冬代羽化始盛期、高峰期和盛末期分别为6月8日、6月17日和7月1日。【结论】应重视对亚洲玉米螟越冬虫源的控制,及时处理玉米秸秆和穗轴,第一代幼虫应该在6月中下旬防治,第二代应该在7月中旬防治。     

闽北地区四代三化螟发生动态Fuzzy预测模型研究

根据Fuzzy数学原理和三化螟生物学特性,组建了四代三化螟发生动态综合预测模型经对福建省将乐县16年四代三化螟发生期、发生量回报及1995年的预报,拟合率及正确率均达100％。     

Model-free model elimination: A new step in the model-free dynamic analysis of NMR relaxation data

Model-free analysis is a technique commonly used within the field of NMR spectroscopy to extract atomic resolution, interpretable dynamic information on multiple timescales from the R ₁, R ₂, and steady state NOE. Model-free approaches employ two disparate areas of data analysis, the discipline of mathematical optimisation, specifically the minimisation of a χ² function, and the statistical field of model selection. By searching through a large number of model-free minimisations, which were setup using synthetic relaxation data whereby the true underlying dynamics is known, certain model-free models have been identified to, at times, fail. This has been characterised as either the internal correlation times, τ _e , τ _f , or τ _s , or the global correlation time parameter, local τ _m , heading towards infinity, the result being that the final parameter values are far from the true values. In a number of cases the minimised χ² value of the failed model is significantly lower than that of all other models and, hence, will be the model which is chosen by model selection techniques. If these models are not removed prior to model selection the final model-free results could be far from the truth. By implementing a series of empirical rules involving inequalities these models can be specifically isolated and removed. Model-free analysis should therefore consist of three distinct steps: model-free minimisation, model-free model elimination, and finally model-free model selection. Failure has also been identified to affect the individual Monte Carlo simulations used within error analysis. Each simulation involves an independent randomised relaxation data set and model-free minimisation, thus simulations suffer from exactly the same types of failure as model-free models. Therefore, to prevent these outliers from causing a significant overestimation of the errors the failed Monte Carlo simulations need to be culled prior to calculating the parameter standard deviations.     

Disentangling effects of multiple stressors on matter flow in a lake food web

Understanding the relative importance of multiple stressors is valuable to prioritize conservation and restoration measures. Yet, the effects of multiple stressors on ecosystem functioning remain largely unknown in many fresh waters. Here, we provided a methodology combining ecosystem modeling with linear regression to disentangle the effects of multiple stressors on matter flow, an important ecosystem function. Treating a shallow lake as the model ecosystem, we simulated matter flow dynamics during 1950s–2010s with different combinations of stressors by Ecopath with Ecosim (EwE) modeling and determined the relative importance of each stressor by generalized linear mixed models. We found that matter flow of the lake food web was highly dynamic, attributing to effects of multiple anthropogenic stressors. Biological invasion played the strongest role in driving the matter flow dynamics, followed by eutrophication, while biomanipulation (i.e., phytoplankton control by planktivorous fish stocking) was of little importance. Eutrophication had a stronger role on primary producers, pelagic food chain, and top predators, while biological invasion on consumers in the middle of food chains. The former was more important in driving the quantity of matter flow, while the latter on trophic transfer efficiencies. Scenario forecasting showed that reducing nutrients contents could largely shape the matter flow pattern, while biomanipulation had little effect. Our findings provided new insights into understanding the mechanistic links between anthropogenic stressors and ecosystem functioning by combining ecosystem modeling with linear regression.     

Estimation of climatic factors relating to occurrence of the maize orange leafhopper, Cicadulina bipunctata

The maize orange leafhopper, Cicadulina bipunctata is a serious pest of forage maize in East and Southeast Asia. In temperate Japan, the occurrence of this leafhopper fluctuates widely among years. Here, we examined effects of climatic factors (temperature, precipitation and sunlight) on the occurrence of C. bipunctata. Seasonal occurrence of adult C. bipunctata in a census field from July to August, when forage maize was most susceptible to the pest, could be described by a simple exponential function with two parameter: estimated density of C. bipunctata on 1 July (N ₀) and intrinsic rate of natural increase (r) for each year. Forward stepwise multiple regression analysis using seasonal occurrence data from 2004 to 2009 detected positive contributions of average temperatures in the previous December and February and a negative contribution of total precipitation during the previous winter to N ₀. The analysis also indicated that average temperature in July of the current year and N ₀ contributed positively and negatively to r, respectively. These results indicated that high temperature and little precipitation during winter and high temperature in early summer induced high occurrence of C. bipunctata in summer. A prediction model based on these factors supported the actual seasonal occurrence in 2010, suggesting that this prediction model is applicable to C. bipunctata forecasting. The prediction model indicated that further global warming in the future is likely to cause further outbreaks of C. bipunctata.     

An ensemble model for predicting Rhopalosiphum padi abundance

A novel modeling method is proposed to predict the abundance of the main vector of barley yellow dwarf virus in autumn sown cereal crops. An ensemble model based on artificial neural networks (ANN) was developed to predict the number of Rhopalosiphum padi (L.) (Homoptera: Aphididae) caught in traps during the autumn flight period at Lincoln, Canterbury, New Zealand, over the period 1982–2003. Artificial neural networks were trained using historical weather data and aphid data collected from a suction trap. Model results were compared with those obtained using multiple regression (MR) models using the same independent variables. Both ANN and MR models were validated by leave‐one‐out validation, in other words, by sequentially jackknifing each year out of the data set, fitting a model to the remaining data, then using that model to predict the number of aphids for each jackknifed year. A linear ensemble of ANN models further improved the predictions and represented the trends in the number of aphids over the 22‐year period very well. The r² between the predicted and observed numbers of aphids for the ANN models changed from 0.68 to 0.83 using the linear ensemble model, but the ensemble approach did not change the prediction for the MR models. The absolute mean error (ABSME) of prediction was much lower for the ANN ensemble predictions compared to that for the MR models. The ABMSE for the ANN models dropped from 109 to 86 aphids compared to an ABMSE reduction from 245 to 220 aphids for the MR models. We discuss the potential for ensemble models for predicting insect abundance when long‐term historical data are available.