Density-dependent population regulation detected in short time series of saproxylic beetles

Understanding the regulation of natural populations has been a long-standing problem in ecology. Here we analyze the population dynamics of 17 species of saproxylic beetles in Shizuoka Prefecture, Japan collected over 11–12 years using autoregressive integrated moving average (ARIMA) models. We first examined the dynamics for indications of the order of the ARIMA models and evaluated the time series to determine that it was not simply a random, white noise sequence. All species dynamics were not mere random noise, and ARIMA models up to lag 3 were considered. The best model was selected from the possible ones using several criteria: model convergence, weak residual autocorrelation, the small sample AIC must be among the smallest that were not significantly different, and the lag indicated by the cutoff values in the detrended partial autocorrelation function. We found significant and nearly significant direct density-dependence for 14 of the 17 species, varying from −0.709 and stronger. The characteristic return rates were strong and only one species had a weak return rate (>0.9), implying that these species were strongly regulated by density-dependent factors. We found that populations with higher order ARIMA models (lag 2 and 3) had weaker return rates than populations with ARIMA models with only one lag, suggesting that species with more complex dynamics were more weakly regulated. These results contrast with previous suggestions that 20+ years are needed to detect density dependence from population time series and that most populations are weakly regulated.     

我国水资源生态足迹分析与预测

生态足迹模型的提出为水资源可持续利用的定量评价提供了新思路.通过构建水资源生态足迹和水资源生态承载力的计算模型,对我国1949～2007年水资源的可持续利用状况作出了客观的评价,并运用ARIMA模型对我国水资源生态足迹变动趋势作出深入的研究.结果表明,1949～2007年,我国人均水资源生态承载力总体上呈下降态势,而人均水资源生态足迹则逐年上升,从而造成人均水资源生态赤字逐渐增大,我国水资源处于一种不安全状态.运用ARIMA(2,1,3)模型的预测结果表明,2008～2012年,我国人均水资源生态足迹将继续呈上升态势,水危机形势将日益严峻.在此基础上,针对我国水资源的可持续利用问题提出了一组政策建议.     

Online forecasting chlorophyll a concentrations by an auto-regressive integrated moving average model: Feasibilities and potentials

Algal blooms are commonly observed in freshwater and coastal areas, causing significant damage to drinking water and aquaculture production. Predictive models are effective for algal bloom forecasting and management. In this paper, an auto-regressive integrated moving average (ARIMA) model was developed to predict daily chlorophyll a (Chl a) concentrations, using data from Taihu Lake in China. For comparison, a multivariate linear regression (MVLR) model was also established to predict daily Chl a concentrations using the same data. Results showed that the ARIMA model generally performed better than the MVLR model with respect to the absolute error of peak value, root mean square error and index of agreement. Because the ARIMA model needs only one input variable, it shows greater applicability as an algal bloom early warning system using online sensors of Chl a.     

Patterns of growth in the early life history of the round herring Etrumeus teres

Early life history traits of young‐of‐the‐year (YOY) round herring Etrumeus teres, caught in Tosa Bay (south‐western Japan), were studied using otolith microstructure analysis for the 2000–2003 year classes. Hatch dates ranged from October to March, and were restricted to either autumn or winter within each year class. YOY ranged from 50 to 123 mm total length (L_T) and from 57 to 192 days in age. The relationship of L_T to otolith radius was linear. Individual growth rates (G_I) were backcalculated between the 70th and 150th days (the size range of most YOY caught) using the biological intercept method. G_I ranged from 0·3 to 1·4 mm day^?1 and decreased in most cases as season progressed irrespective of year class, although G_I in winter cohorts were significantly higher than in autumn cohorts. Otolith growth rates (G_O) ranged from 2·13 to 12·25 μm day^?1 for autumn spawned YOY and from 3·12 to 12·41 μm day^?1 for YOY spawned in winter. The G_O trajectories followed three consistent patterns: (1) an increase in increment widths after first feeding through the second week of larval life, then (2) a plateau in increment spacing before increment widths increased again until reaching the maximum growth rate, followed by (3) a gradual decrease in increment widths until the end of the fifth month. The three stages occurred irrespective of spawning season, although YOY spawned in October and December had higher G_O during stages (1) and (2) than YOY spawned in February and March, whereas higher G_O was observed for late‐winter cohorts in stage (3). Otolith growth from YOY spawned in December and January showed an intermediate pattern between YOY hatched in the early autumn (October to December) and late winter (February to March). The G_O trajectories were cross‐matched to the calendar date to estimate time series of otolith growth rates (G_OTS) for each year. A parabolic trend was found with maximum G_OTS in autumn and spring and minimum values in winter. This trend was significantly correlated to daily sea surface temperature variations. The differences in otolith growth trajectories suggest that the otolith microstructure of E. teres may be used as a natural tag for identifying autumn and winter spawned cohorts.     

ARIMA模型在杭州市中小学生咳嗽症状监测中的应用

目的：探讨时间序列ARIMA模型在时间序列资料分析中的应用,建立咳嗽症状监测数据的预测模型.方法：采用条件最小二乘方法估计模型参数.通过对数转换及差分方法使原始序列平稳,按照残差不相关原则、简洁原则确定模型结构,依据AIC和SBC准则确定模型阶数,最终建立起ARIMA预测模型.结果：ARIMA（1,1,1）模型拟合效果较好,方差估计值为0.7361,AIC=95.6092,SBC=98.8310,对模型进行白噪声残差检验,提示残差为白噪声.结论：症状监测这种具有时间序列特点的资料可以用ARIMA模型来进行拟合估计.本文中预测结果可信区间比较宽,可能是因为时间序列比较短,还未能考虑到季节趋势.另外,所用监测数据是在中小学生在校发生症状的人数,故在节假日会出现缺失值,样本量和时间长度均有限,可能影响模型估计的准确性,本研究的结论还有待于将来资料积累后进行修正和深化.     

Seawater temperature, Gambierdiscus spp. variability and incidence of ciguatera poisoning in French Polynesia

In the context of global warming and climate change, ciguatera disease is put forward as an indicator of environmental disturbance. However, to validate this indicator, some unknown parameters such as the delay between environmental perturbation and outbreaks of ciguatera need to be investigated. The main goal of this study was to investigate the temporal link between the growth of Gambierdiscus spp., and one of its influencing factors and the declared cases of ciguatera disease in humans. Algal cell density and seawater temperature (SWT) were recorded monthly from February 1993 to December 2001 on the Atimaono barrier reef of Tahiti Island. Reports of ciguatera cases were obtained from three community health clinics near the study sites. The autoregressive integrated moving average model (ARIMA) shows: (1) SWT were positively associated with Gambierdiscus spp. growth at a lagtime of 13 and 17 months (p < 0.001); (2) Gambierdiscus spp. growth measured at a given time is related to a peak number of cases of ciguatera recorded 3 months after peak densities of this dinoflagellate (p < 0.001). These results allow the construction of a predictive model of the temporal link between ciguatera disease in humans and its etiologic agent: Gambierdiscus spp. This model constructed by using 1993–1999 data, then validated by 2000–2001 data, demonstrates an appreciable ability to predict changes in the incidence of ciguatera disease following algae blooms.     

ARIMA与SVM组合模型在害虫预测中的应用

害虫发生是一种复杂、 动态时间序列数据, 单一预测模型都是基于线性或非线性数据, 不能同时捕捉害虫发生的线性和非线性规律, 很难达到理想的预测精度。本研究首先采用差分自回归移动平均模型对昆虫发生时间序列进行线性建模, 然后采用支持向量机对非线性部分进行建模, 最后得到两种模型的组合预测结果。将组合模型应用到松毛虫Dendrolimus punctatus发生面积的预测, 实验结果表明组合模型的预测精度明显优于单一模型, 发挥了两种模型各自的优势。组合模型是一种切实可行的害虫预测预报方法。