基于自适应调整蚁群-RBF神经网络模型的中长期径流预测

径流预测历来是水利部门的一项重要工作,针对水库和河流中长期径流预测精度不高,提出了自适应调节人工蚁群算法(ARACS),对RBF神经网络参数进行优化,建立了自适应调节人工蚁群-RBF神经网络组合算法(ARACS-RBF)预测模型,综合考虑影响径流预变化因素,对安康水库进行中长期径流预测。对预测效果进行检验,结果证实该模型可真实地反映河川径流变化的总体趋势, 并为判断时间序列数据的非线性提供了一种新方法。与RBF神经网络模型、人工蚁群-RBF神经网络模型预测结果进行对比,结果表明,应用ARACS-RBF模型对中长期径流量进行预测,预测精度更高、效果更好。该方法克服了RBF神经网络和人工蚁群算法易陷于局部极值、搜索质量差和精度不高的缺点,改善了RBF神经网络的泛化能力,收敛速度快,输出稳定性好,提高了径流预测的精度,置信度为98%时的预测相对误差小于6.5%。可有效用于水库和河川中长期径流预测。     

兰州市南北两山不同生境红砂种群数量动态研究

对兰州市南北两山不同生境天然分布的红砂种群进行数量动态研究,通过径级结构回归年龄结构,编制静态生命表,绘制生存函数曲线,计算动态指数及谱分析,结果表明:不同生境红砂种群均表现为幼龄个体多,中老龄个体少,种群在Ⅲ龄级时死亡率最高,随着年龄增加,死亡率有所降低,Ⅶ、Ⅷ龄级由于生理衰老死亡率上升,种群存活曲线均接近于Deevey-Ⅲ型,年龄结构均接近增长型,增长潜力阴坡山下(5.90%)>阳坡山下(5.62%)>阴坡山上(4.77%)>阳坡山上(3.79%),并存在明显的周期性,红砂种群的数量动态变化整体上受其生命周期中生物学特性的控制;由于坡向及海拔差异带来的环境异质性,阴坡山下红砂种群整体生长最佳,阴坡山上和阳坡山下次之,而阳坡山上的红砂种群危险系数最大,受环境和种内压力共同作用显著,应适当抚育,从而促进种群持续发育。     

动态浸滤及几种静态预测法对云浮硫铁矿酸化特征及产酸潜力的比较

用几种静态预测方法研究了来自云浮硫铁矿矿石的产酸潜力,并结合动态浸滤试验方法对比了新旧矿石的氧化过程及产酸的动力学特征.静态预测结果表明:该矿矿石的净中和酸潜力NNP<-20、中和酸能力与产酸潜力比NP/AP<1:1、静产酸量NAG>10、NAG-pH<4,是强产酸矿.连续25周的动态浸滤试验研究表明:受碳酸盐控制,新矿浸滤液的pH值在7.46—6.45呈缓慢下降的趋势,25周内减小了近1个pH单位;旧矿已经产酸,浸滤液初期pH<4,10周内迅速下降到pH<3,体系氧化性及硫化物的氧化速率远远高于新矿,金属的释放大大增加.     

Modelling environmental stochasticity in adult survival for a long-lived species

Stochastic matrix population models are often used to help guide the management of animal populations. For a long-lived species, environmental stochasticity in adult survival will play an important role in determining outcomes from the model. One of the most common methods for modelling such stochasticity is to randomly select the value of adult survival for each year from a distribution with a specified mean and standard deviation. We consider four distributions that can provide realistic models for stochasticity in adult survival. For values of the mean and standard deviation that cover the range we would expect for long-lived species, all four distributions have similar shapes, with small differences in their skewness and kurtosis. This suggests that many of the outcomes from a population model will be insensitive to the choice of distribution, assuming that distribution provides a realistic model for environmental stochasticity in adult survival. For a generic age-structured model, the estimate of the long-run stochastic growth rate is almost identical for the four distributions, across this range of values for the mean and standard deviation. Model outcomes based on short-term projections, such as the probability of a decline over a 20-year period, are more sensitive to the choice of distribution.     

Generation time and the maximum growth rate for populations with age-specific fecundities and unknown juvenile survival

In age-classified population models where all parameters are known, the generation time and growth rate are calculated in a straightforward manner. For many populations, some parameters, such as juvenile survival, are difficult to estimate accurately. In a simplified population model where fecundity and survival are constant from the onset of breeding, it is known that generation time may be calculated given only adult survival, age at first reproduction, and the population growth rate. However, the assumption of constant fecundity from the onset of breeding does not hold for many populations. An extended population model allows calculation of generation time with the additional knowledge of the ratio of age-specific fecundities compared to a maximum fecundity rate. When these relative fecundities are unknown, an ad hoc adjustment to the simplified model performs well.When the study population is in an ideal environment, the optimal generation time and maximum growth rate are linked, and both may be approximated knowing only adult survival, age at first reproduction, and the relative fecundities. The maximum growth rate has important conservation implications, and calculating it correctly is therefore important. Improper use of the simplified population model to calculate the maximum growth rate, combined with a simple decision rule, leads to an average overharvest of 36%, and >60% for three of six bird species studied, compared to the full population model. By comparison, using the approximation from the extended or adjusted models results in average overharvests of only 8% (extended model) and 5% (adjusted model), and <50% for all six species (either model).     

Updating coarse-scale species distribution models using small fine-scale samples

Large, fine-grained samples are ideal for predictive species distribution models used for management purposes, but such datasets are not available for most species and conducting such surveys is costly. We attempted to overcome this obstacle by updating previously available coarse-grained logistic regression models with small fine-grained samples using a recalibration approach. Recalibration involves re-estimation of the intercept or slope of the linear predictor and may improve calibration (level of agreement between predicted and actual probabilities). If reliable estimates of occurrence likelihood are required (e.g., for species selection in ecological restoration) calibration should be preferred to other model performance measures. This updating approach is not expected to improve discrimination (the ability of the model to rank sites according to species suitability), because the rank order of predictions is not altered. We tested different updating methods and sample sizes with tree distribution data from Spain. Updated models were compared to models fitted using only fine-grained data (refitted models). Updated models performed reasonably well at fine scales and outperformed refitted models with small samples (10-100 occurrences). If a coarse-grained model is available (or could be easily developed) and fine-grained predictions are to be generated from a limited sample size, updating previous models may be a more accurate option than fitting a new model. Our results encourage further studies on model updating in other situations where species distribution models are used under different conditions from their training (e.g., different time periods, different regions).     

Choosing classes for size projection matrix models

Projection matrix models are intensely used in ecology to model the dynamics of structured populations. When dealing with size-structured populations, there is no satisfactory algorithm to partition size into discrete classes. We show that the Vandermeer-Moloney algorithm for choosing classes is inconsistent with the Usher model, and systematically selects the finest classes. Considering that the matrix model is a discrete approximation of a continuous model, we define an approximation error as the sum of a distribution error (the difference between the discrete distribution and its continuous counterpart), and a sample error. The optimal partition of size into classes is the one that minimizes the approximation error. This method for choosing classes also shows that the choice of the class width cannot be disconnected from the choice of the time step. When applied to 520 trees of Dicorynia guianensis in French Guiana, this algorithm identified 8 classes of 11.4 cm in width, which is in agreement with the empirical choice of foresters.     

Roads,Interrupted Dispersal,and Genetic Diversity in Timber Rattlesnakes

Abstract: Anthropogenic habitat modification often creates barriers to animal movement, transforming formerly contiguous habitat into a patchwork of habitat islands with low connectivity. Roadways are a feature of most landscapes that can act as barriers or filters to migration among local populations. Even small and recently constructed roads can have a significant impact on population genetic structure of some species, but not others. We developed a research approach that combines fine‐scale molecular genetics with behavioral and ecological data to understand the impacts of roads on population structure and connectivity. We used microsatellite markers to characterize genetic variation within and among populations of timber rattlesnakes (Crotalus horridus) occupying communal hibernacula (dens) in regions bisected by roadways. We examined the impact of roads on seasonal migration, genetic diversity, and gene flow among populations. Snakes in hibernacula isolated by roads had significantly lower genetic diversity and higher genetic differentiation than snakes in hibernacula in contiguous habitat. Genetic‐assignment analyses revealed that interruption to seasonal migration was the mechanism underlying these patterns. Our results underscore the sizeable impact of roads on this species, despite their relatively recent construction at our study sites (7 to 10 generations of rattlesnakes), the utility of population genetics for studies of road ecology, and the need for mitigating effects of roads.     

关中-天水经济区人口-经济-生态协调发展研究

在经济迅速发展的背景下,我国人口、经济和生态的矛盾日益突出。选择关中-天水经济区为研究对象,基于变异系数的协调函数模型对其人口-经济-生态协调度进行了测算。结果表明：1）1999—2008年,关中-天水经济区各地市人口-经济-生态协调度整体呈现上升的趋势,总体由失调状态转向勉强协调,其中天水市呈直线上升趋势,咸阳、宝鸡、铜川3市的协调度在波动中有所增加,西安市变化幅度不大;2）人口、经济、生态3个子系统的变化都会影响到整个系统的变化,西安市生态子系统协调度的下降,制约了整个系统的协调发展;3）协调度在空间上表现为关中-天水经济区中部地市的协调度较高,渭南和天水市的协调度始终处于该地区的较低水平。