Forecasting model of bacterial leaf spot disease of mulberry caused by Xanthomonas campestris pv. mori

In order to develop weather-based forecasting model of bacterial leaf spot (BLS) disease of mulberry caused by Xanthomonas campestris pv. mori, weekly disease severity data were recorded for three years on the ruling cultivar S-1. Daily meteorological data viz. maximum temperature, minimum temperature, maximum relative humidity, minimum relative humidity, rainfall and number of rainy days were also recorded. It was observed that BLS appeared in April/May and continued up to November with maximum severity in July. The correlation coefficient between disease severity and meteorological parameters revealed that the BLS disease severity has significant positive correlation with minimum temperatures, maximum and minimum relative humidity, rainfall and number of rainy days and negative correlation with maximum temperature. Multiple regressions analysis revealed that average of maximum temperature, minimum temperature and rainfall of preceding seven days and maximum relative humidity, minimum relative humidity of previous 9–15 days was found to maximally influence BLS disease severity. The contribution of the meteorological factors was found to be highest of minimum temperature (40.65%) followed by maximum temperature (24.20%), maximum relative humidity (16.41%), minimum relative humidity (8.07%), rainfall (5.29%) and number of rainy days (5.38%).     

Early warning signal reliability varies with COVID-19 waves

Early warning signals (EWSs) aim to predict changes in complex systems from phenomenological signals in time series data. These signals have recently been shown to precede the emergence of disease outbreaks, offering hope that policymakers can make predictive rather than reactive management decisions. Here, using a novel, sequential analysis in combination with daily COVID-19 case data across 24 countries, we suggest that composite EWSs consisting of variance, autocorrelation and skewness can predict nonlinear case increases, but that the predictive ability of these tools varies between waves based upon the degree of critical slowing down present. Our work suggests that in highly monitored disease time series such as COVID-19, EWSs offer the opportunity for policymakers to improve the accuracy of urgent intervention decisions but best characterize hypothesized critical transitions.     

Climate‐driven geographic distribution of the desert locust during recession periods: Subspecies’ niche differentiation and relative risks under scenarios of climate change

The desert locust is an agricultural pest that is able to switch from a harmless solitarious stage, during recession periods, to swarms of gregarious individuals that disperse long distances and affect areas from western Africa to India during outbreak periods. Large outbreaks have been recorded through centuries, and the Food and Agriculture Organization keeps a long‐term, large‐scale monitoring survey database in the area. However, there is also a much less known subspecies that occupies a limited area in Southern Africa. We used large‐scale climatic and occurrence data of the solitarious phase of each subspecies during recession periods to understand whether both subspecies climatic niches differ from each other, what is the current potential geographical distribution of each subspecies, and how climate change is likely to shift their potential distribution with respect to current conditions. We evaluated whether subspecies are significantly specialized along available climate gradients by using null models of background climatic differences within and between southern and northern ranges and applying niche similarity and niche equivalency tests. The results point to climatic niche conservatism between the two clades. We complemented this analysis with species distribution modeling to characterize current solitarious distributions and forecast potential recession range shifts under two extreme climate change scenarios at the 2050 and 2090 time horizon. Projections suggest that, at a global scale, the northern clade could contract its solitarious recession range, while the southern clade is likely to expand its recession range. However, local expansions were also predicted in the northern clade, in particular in southern and northern margins of the current geographical distribution. In conclusion, monitoring and management practices should remain in place in northern Africa, while in Southern Africa the potential for the subspecies to pose a threat in the future should be investigated more closely.     

江苏棉区第5代棉铃虫种群动态的模拟及预测

用澳大利亚热带害虫研究中心开发的通用制模软件Dymex为外壳,以棉铃虫Helicoverpa armigera种群生命表为基础,构建了棉铃虫种群动态模型,以逐日历史气温统计值为驱动变量进行了第4、5代棉铃虫种群动态的模拟。结果表明,除早发高温的年份外,其它年份的绝大多数第5代棉铃虫在江苏棉区11月底前不能发育至蛹而无法安全越冬,故不能成为翌年第1代的有效虫源。根据模拟结果可预测第4代越冬蛹量及第5代卵的发生期、发生量及其越冬蛹数量并制成了简便易用的预测检索表。     

基于栖息地指数的东太平洋黄鳍金枪鱼渔场预报

黄鳍金枪鱼是东太平洋海域重要的金枪鱼种类之一,也是我国金枪鱼延绳钓的主要捕捞对象之一。根据2011年东太平洋海域(20°N—35°S、85°W—155°W)延绳钓生产统计数据,结合表温(SST)和海面高度(SSH)的遥感数据,采用频次分析法获得黄鳍金枪鱼分布的SST和SSH适宜范围;运用一元非线性回归方法,以渔获量为适应性指数,按季度分别建立了基于SST和SSH的长鳍金枪鱼栖息地适应性指数,采用算术平均法获得基于SST和SSH环境因子的栖息地指数综合模型,并用2012年各月实际作业渔场进行验证。结果显示,黄鳍金枪鱼渔场多分布在SST为24—29℃、SSH为0.3—0.7 m的海域。采用一元非线性回归建立的各因子适应性指数模型在统计上均为显著(P0.05)。经与2012年实际生产情况比较,作业渔场预报准确性达66%以上。研究获得栖息地指数模型可为金枪鱼延绳钓渔船寻找中心渔场提供参考。     

中国森林生物多样性动态的灰色预测

我国森林生物多样性动态分析具有少数据和贫信息带来的灰色不确定性, 灰色系统理论是进行相关研究的重要工具。在前人工作的基础上, 作者根据PSR(Pressure-State-Response)模型计算得到1973–1998年间我国5次森林资源连续清查期内的森林生物多样性指数序列, 包括压力指数、森林生态系统多样性指数和森林物种多样性指数, 以及由这3个指数建立的森林生物多样性总指数; 并建立了各个指数的GM(1,1)灰色模型, 预测我国森林生物多样性的动态。结果表明, 在未来2个森林资源连续清查期(大约10年), (1) 我国森林生物多样性指数将继续增加, 且与过去5个森林资源连续清查期相比其增加速度将有所提高; (2) 压力指数将维持继续增大的趋势不变; (3) 森林生态系统多样性指数将维持在当前水平, 有轻微波动; (4) 森林物种多样性指数将继续增加, 但与过去5个森林资源连续清查期相比其增加速度将渐趋平缓。研究表明, 根据PSR模型建立我国森林生物多样性动态的灰色预测模型, 适合我国森林资源管理的实际需要。     

Matching the forecast horizon with the relevant spatial and temporal processes and data sources

Most phenomenological, statistical models used to generate ecological forecasts take either a time-series approach, based on long-term data from one location, or a space-for-time approach, based on data describing spatial patterns across environmental gradients. However, the magnitude and even the sign of environment–response relationships detected using these two approaches often differs, leading to contrasting predictions about responses to future environmental change. Here we consider how the forecast horizon determines whether more accurate predictions come from the time-series approach, the space-for-time approach or a combination of the two. As proof of concept, we use simulated case studies to show that forecasts for short and long forecast horizons need to focus on different ecological processes, which are reflected in different kinds of data. First, we simulated population or community dynamics under stationary temperature using two simple, mechanistic models. Second, we fit statistical models to the simulated data using a time-series approach, a space-for-time approach or a weighted average. We then forecast the response to a temperature increase using the statistical models, and compared these forecasts to temperature effects simulated by the mechanistic models. We found that the time-series approach made accurate short-term predictions because it captured initial conditions and effects of fast processes such as birth and death. The space-for-time approach made more accurate long-term predictions because it better captured the influence of slower processes such as evolutionary and ecological selection. The weighted average made accurate predictions at all time scales, including intermediate time-scales where the other two approaches performed poorly. A weighted average of time-series and space-for-time approaches shows promise, but making this weighted model operational will require new research to predict the rate at which slow processes begin to influence dynamics.     

混合效应模型在林火发生预测中的适用性

林火是森林生态系统的重要影响因子,建立科学准确的林火预测预报模型对林火管理工作至关重要。本研究以不同气象因子为主要预测变量,基于Logistic回归和广义线性混合效应模型建立福建省林火发生预测模型,通过对比Logistic基础模型和广义线性混合效应模型的拟合度和预测精度,研究混合效应模型在林火预报中的适用性。结果表明: Logistic基础模型的受试者工作特征曲线下面积(AUC)值为0.664,验证准确率为60.4%。添加随机效应后,模型的拟合和检验精度均获得了提升。其中,考虑行政区划和海拔差异效应的两水平混合效应模型的表现最优,其AUC值和验证准确率分别比基础模型提升0.057和6.0%。用此混合效应模型对福建省各地区的林火发生概率进行预测的结果表明,福建省西北部和南部为林火中高发区域,西南部和东部为林火低发区域,与实际观测的火点分布一致。混合效应模型在数据拟合和林火发生预测方面均优于Logistic基础模型,可作为林火预测和管理的重要工具。     

A simple Bayesian network to interpret the accuracy of armyworm outbreak forecasts

Forecasting of outbreaks of armyworm (larvae of the moth Spodoptera exempta) employs information from rain gauges and moth traps. Rainfall is an independent variable, but moth catch is affected by rainfall, and outbreak risk is affected by both moth catch and rainfall. A simple Bayesian network was used to describe these relationships and so derive conditional probabilities. The data were from a new initiative, community‐based forecasting of armyworm in Tanzania, in which outbreak risk for a village is determined locally from a single moth trap and rain gauge located within the village. It was found that, following a positive forecast, an armyworm outbreak was approximately twice as likely to occur as would be expected by chance. If the forecast was negative because of insufficient moths, outbreaks were half as likely as would be expected by chance. If the forecast was negative because of insufficient rain, however, the outbreak probability remained similar to chance: an aspect of the forecast that requires improvement. Overall, a high forecasting accuracy can be achieved by village communities using simple rules to predict armyworm outbreaks.