A novel approach for the identification of pointer years

In the context of extreme event ecology, identification of pointer years has become a central aspect of tree-ring research. However, the variety of methods employed for pointer year detection since the introduction of the concept in 1979 impedes a direct comparison among studies. Moreover, most commonly used methods partly rely on arbitrarily selected thresholds, resulting in a potentially inconsistent application of those means. To overcome these discrepancies, we here introduce the “standardized growth change” method SGC, which relies on probability density functions of standardized year-to-year ring width differences and internationally accepted significance levels. To evaluate the performance of SGC, it is applied to 1000 pseudo-populations with known properties as well as to an existing Scots pine tree ring data set and compare the results derived from SGC to the four most frequently applied pointer year detection methods. Our comparative evaluation indicates SGC to supersede the other considered methods. In particular, it identified all artificially introduced pointer years in the pseudo-populations, whereas the other methods missed between 3 and 96 percent of known events. A detailed evaluation of misclassifications by the other approaches points out method-specific weaknesses. Finally, we provide technical aspects and recommendations for the application of SGC in a broader context.     

An improved model for determining degree-day values from daily temperature data

Although using hourly weather data offers the greatest accuracy for estimating growing degree-day values, daily maximum and minimum temperature data are often used to estimate these values by approximating the diurnal temperature trends. This paper presents a new empirical model for estimating the hourly mean temperature. The model describes the diurnal variation using a sine function from the minimum temperature at sunrise until the maximum temperature is reached, another sine function from the maximum temperature until sunset, and a square-root function from then until sunrise the next morning. The model was developed and calibrated using several years of hourly data obtained from five automated weather stations located in California and representing a wide range of climate conditions. The model was tested against an additional data-set at each location. The temperature model gave good results, the root-mean-square error being less than 2.0 °C for most years and locations. The comparison with published models from the literature showed that the model was superior to the other methods. Hourly temperatures from the model were used to calculate degree-day values. A comparison between degree-day estimates determined from the model and those obtained other selected methods is presented. The results showed that the model had the best accuracy in general regardless of the season. Received: 25 October 2000 / Revised: 2 July 2001 / Accepted: 2 July 2001     

Changes in wheat bulb fly (Delia coarctata) populations in East Anglia in relation to crop rotations, climatic data and damage forecasting

Records of wheat bulb fly egg (Delia coarctata) population densities in fields sampled throughout East Anglia in the autumns of 1953–1990 are presented. In descending order of risk, fallow, potato, pea (mainly vining), sugar beet and oilseed rape are the main crops preceding wheat or barley which attract oviposition. A declining trend of egg populations observed over the study period may be associated with climatic changes as well as with the elimination of fallow in the rotation and the dramatic increase in the use of insecticides against the pest. Negative correlations (P < 0.05) in mean annual egg numbers were found with departures from average of July temperature and January air or soil temperature; positive correlations (P<0.05) with departure from average of August raindays. Stepwise regression analysis was used to identify the most important relationships of meteorological variables with mean annual egg numbers, or the proportion of fields sampled with egg numbers in excess of the action threshold of 2.5 million eggs/ha. Up to 59% of the variation in the annual proportion of fields above threshold was accounted for in a regression equation using departures from average of July temperature, August raindays and the percentage of average of October (preceding year) rainfall. Estimated mean annual egg populations and the proportion of fields above threshold showed a good fit with the observed values. The findings are discussed and compared with previous work. The forecasting model may be readily incorporated as a regional risk-prediction component of a knowledge-based system for the management of wheat bulb fly control. Regional forecasts of wheat bulb fly oviposition from this work should be tested and modified as necessary according to experience or changing climatic or agricultural factors.     

Debating the greening vs. browning of the North American boreal forest: differences between satellite datasets

A number of remote sensing studies have evaluated the temporal trends of the normalized difference vegetation index (NDVI or vegetation greenness) in the North American boreal forest during the last two decades, often getting quite different results. To examine the effect that the use of different datasets might be having on the estimated trends, we compared the temporal trends of recently burned and unburned sites of boreal forest in central Canada calculated from two datasets: the Global Inventory, Monitoring, and Modeling Studies (GIMMS), which is the most commonly used 8 km dataset, and a new 1 km dataset developed by the Canadian Centre for Remote Sensing (CCRS). We compared the NDVI trends of both datasets along a fire severity gradient in order to evaluate the variance in regeneration rates. Temporal trends were calculated using the seasonal Mann–Kendall trend test, a rank‐based, nonparametric test, which is robust against seasonality, nonnormality, heteroscedasticity, missing values, and serial dependence. The results showed contrasting NDVI trends between the CCRS and the GIMMS datasets. The CCRS dataset showed NDVI increases in all recently burned sites and in 50% of the unburned sites. Surprisingly, the GIMMS dataset did not capture the NDVI recovery in most burned sites and even showed NDVI declines in some burned sites one decade after fire. Between 50% and 75% of GIMMS pixels showed NDVI decreases in the unburned forest compared with <1% of CCRS pixels. Being the most broadly used dataset for monitoring ecosystem and carbon balance changes, the bias towards negative trends in the GIMMS dataset in the North American boreal forest has broad implications for the evaluation of vegetation and carbon dynamics in this region and globally.     

基于不同光谱指数的植被物候期遥感监测差异

植被物候是陆地生态系统响应气候和环境变化的一项综合性指标.遥感光谱已经被广泛用于提取植被物候期,但遥感提取的物候期与站点观测差别很大,其物理意义尚不明确.本文选取中国东北部的一景MODIS数据(2000—2014年),分析了基于红波段和近红外波段的归一化差值植被指数(NDVI)和简单比植被指数(SR)提取的植被生长季起始期(SOS)和结束期(EOS)的差异.结果表明:两者的物候期存在显著差别,基于NDVI提取的SOS比SR提取的SOS平均早18.9 d,基于NDVI提取的EOS比SR提取的EOS平均晚19.0 d,NDVI得到的生长季长度更长.基于NDVI和SR提取的物候期的年际变化也存在显著差别,超过20%的像元SOS和EOS甚至表现出相反的年际变化趋势.上述差异与两种植被指数自身的季节曲线特征和抗噪性差异有关.NDVI与SR观测数据来源完全一致,仅数学表达形式不同,提取的物候期结果却存在显著差异.说明遥感监测的植被物候期高度依赖于植被指数的数学表达形式,如何建立可靠的植被物候期遥感提取方法仍需进一步研究.     

How will climate novelty influence ecological forecasts? Using the Quaternary to assess future reliability

Future climates are projected to be highly novel relative to recent climates. Climate novelty challenges models that correlate ecological patterns to climate variables and then use these relationships to forecast ecological responses to future climate change. Here, we quantify the magnitude and ecological significance of future climate novelty by comparing it to novel climates over the past 21,000 years in North America. We then use relationships between model performance and climate novelty derived from the fossil pollen record from eastern North America to estimate the expected decrease in predictive skill of ecological forecasting models as future climate novelty increases. We show that, in the high emissions scenario (RCP 8.5) and by late 21st century, future climate novelty is similar to or higher than peak levels of climate novelty over the last 21,000 years. The accuracy of ecological forecasting models is projected to decline steadily over the coming decades in response to increasing climate novelty, although models that incorporate co‐occurrences among species may retain somewhat higher predictive skill. In addition to quantifying future climate novelty in the context of late Quaternary climate change, this work underscores the challenges of making reliable forecasts to an increasingly novel future, while highlighting the need to assess potential avenues for improvement, such as increased reliance on geological analogs for future novel climates and improving existing models by pooling data through time and incorporating assemblage‐level information.     

Prediction of flight of apple sawfly, Hoplocampa testudinea, using temperature sums

A method to predict the beginning of the flight of apple sawfly, Hoplocampa testudinea (Klug) (Hymenoptera, Tenthredinidae) is presented. Adults were monitored during 8 years at the experimental orchard De Schuilenburg, Kesteren, The Netherlands, with visual sticky traps. Temperature sums, accumulated until the day of the first capture of sawfly, were calculated for different starting dates of the summations. Beginning on 1 January, variation of the temperature sums, calculated in each of 8 years, decreased when later starting dates for temperature summation were chosen, reaching a minimum on 1 April for soil temperatures (–5 cm). The soil temperature sum, accumulated from 1 April until the first capture, was on average 134 day-degrees above a threshold of 4 °C. So this was the most suitable starting date to predict the beginning of sawfly flight activity. As use of the summation method based on air temperatures is in practice more appropriate for timing of trap installation by extension services and apple growers, trap deployment is recommended at an adapted value of 157 day-degrees from 15 March.     

The epidemiological characteristics of twinning in Northwest India

The data on twin births from four different places in Northwest India were analyzed to study epidemiological characteristics of twinning. Gujjars manifested the highest incidence of twinning (30.2 per 1000 births). The twinning rate at Amritsar for the year 1987 was 19.20. At Bathinda and Jammu, the average twinning rate over the period 1984–93 were 10.70 and 11.40 respectively. Stillbirth rate among twins was much higher in Gujjars as compared to other three samples. Regional trends of twinning rate showed the highest incidence in the state of Uttar Pradesh, while the lowest in Tamilnadu. There were no significant differences between states for MZ twinning rate, while differences in the DZ twinning rate were significant in some instances. Maternal age and parity specific twinning rate showed the highest incidence at parity 4 and in the maternal age group 30–34 years. There was evidence of both seasonality and secular trends in twin births. The highest incidence of twin births over the period 1984–93 was noted in the summer season followed by rainy season, while the lowest in autumn. The average incidence of twinning in the Jammu region decreased from 13.76 during the years 1984–89 to 9.07 during the year 1990–93.     

用GM(n,h)模型预测森林资源发展趋势

应用灰色系统理论,对森林资源的主要指标进行了预测,建立了灰色动态预测模型GM（1,1）．其预测结果误差小,精度高,为林业宏观决策,森林经营管理提供了科学依据和方法．