Multi-stage sampling for large scale natural resources surveys: a case study of rice and waterfowl

Large-scale sample surveys to estimate abundance and distribution of organisms and their habitats are increasingly important in ecological studies. Multi-stage sampling (MSS) is especially suited to large-scale surveys because of the natural clustering of resources. To illustrate an application, we: (1) designed a stratified MSS to estimate late autumn abundance (kg/ha) of rice seeds in harvested fields as food for waterfowl wintering in the Mississippi Alluvial Valley (MAV); (2) investigated options for improving the MSS design; and (3) compared statistical and cost efficiency of MSS to simulated simple random sampling (SRS). During 2000-2002, we sampled 25-35 landowners per year, 1 or 2 fields per landowner per year, and measured seed mass in 10 soil cores collected within each field. Analysis of variance components and costs for each stage of the survey design indicated that collecting 10 soil cores per field was near the optimum of 11-15, whereas sampling >1 field per landowner provided few benefits because data from fields within landowners were highly correlated. Coefficients of variation (CV) of annual estimates of rice abundance ranged from 0.23 to 0.31 and were limited by variation among landowners and the number of landowners sampled. Design effects representing the statistical efficiency of MSS relative to SRS ranged from 3.2 to 9.0, and simulations indicated SRS would cost, on average, 1.4 times more than MSS because clustering of sample units in MSS decreased travel costs. We recommend MSS as a potential sampling strategy for large-scale natural resource surveys and specifically for future surveys of the availability of rice as food for waterfowl in the MAV and similar areas.     

Statistical inference on patch-specific survival and movement rates from marked animals

Empirical estimates of patch-specific survival and movement rates are needed to parametrize spatially explicit population models, and for inference on the effects of habitat quality and fragmentation on populations. Data from radio-marked animals, in which both the fates and habitat locations of animals are known over time, can be used in conjunction with continuous-time proportional hazards models to obtain inferences on survival rates. Discrete-time conditional logistic models may provide inference on both survival and movement rates. We use Monte Carlo simulation to investigate accuracy of estimates of survival from both approaches, and movement rates from conditional logistic regression, for two habitats. Bias was low (relative bias < 0.04) and interval coverage accurate (close to the nominal 0.95) for estimates of habitat effect on survival based on proportional hazards. Bias was high ( relative bias 0.60) and interval coverage poor ( = 0.26 vs. nominal 0.95) for estimates of habitat effect based on conditional logistic regression; bias was especially influenced by heterogeneity in survival and the shape of the hazard function, whereas both bias and coverage were affected by ‘memory’ effects in movement patterns. Bias estimates of movement rate was low ( relative bias < 0.05), but interval coverage was poor ( = 0.48–0.80), possibly as a result of poor performance of a Taylor series estimate of variance. An example is provided from a radio-telemetry study of 47 wintering American woodcock (Scolopax minor), illustrating practical difficulties in field studies to parametrize these models. We also discuss extensions of continuous-time models to explicitly include a movement process, and further examine tradeoffs between continuous and discrete models.     

Rough Set Rule Induction for Suitability Assessment

The data that characterize an environmental system are a fundamental part of an environmental decision-support system. However, obtaining complete and consistent data sets for regional studies can be difficult. Data sets are often available only for small study areas within the region, whereas the data themselves contain uncertainty because of system complexity, differences in methodology, or data collection errors. This paper presents rough-set rule induction as one way to deal with data uncertainty while creating predictive if–then rules that generalize data values to the entire region. The approach is illustrated by determining the crop suitability of 14 crops for the agricultural soils of the Willamette River Basin, Oregon, USA. To implement this method, environmental and crop yield data were spatially related to individual soil units, forming the examples needed for the rule induction process. Next, four learning algorithms were defined by using different subsets of environmental attributes. ROSETTA, a software system for rough set analysis, was then used to generate rules using each algorithm. Cross-validation analysis showed that all crops had at least one algorithm with an accuracy rate greater than 68%. After selecting a preferred algorithm, the induced classifier was used to predict the crop suitability of each crop for the unclassified soils. The results suggest that rough set rule induction is a useful method for data generalization and suitability analysis.     

产聚乙烯醇降解酶的培养条件

放线菌StreptomycesvenezuelaeGY1产生的聚乙烯醇(PVA)降解酶是一种诱导酶.以4种不同类型的PVA为唯一碳源时,该菌株单位质量细胞产酶能力比以糖类物质为唯一碳源时提高10倍以上.聚合度和醇解度最高的PVA1799是该菌株产生PVA降解酶的适宜底物,其浓度为1gL-1时,PVA降解酶的产量为120u/g(细胞).培养基中PVA1799浓度由1gL-1上升到5gL-1时,该菌株单位质量细胞产酶能力下降73%,表明PVA1799浓度过高会抑制产酶.GY1菌株产酶的最适温度和pH分别为30℃和7.0.在GY1菌株生长过程中控制以下条件有利于产生PVA降解酶:(1)保持培养体系中较高的溶氧水平;(2)在氮源中补充NO-3;(3)在一定浓度范围内添加MgSO4·7H2O、CaCl2、MnSO4、BaCl2、ZnSO4、FeSO4·7H2O和CuSO4等金属盐.Pseudomonassp.产生的PVA降解酶能够作用伯醇或仲醇类化合物,以这些伯醇或仲醇类化合物代替培养基中的PVA,不能诱导GY1菌株产生PVA降解酶;而在培养基中有PVA存在时,再添加0.5gL-1的3戊醇和环己醇能够明显促进PVA降解酶的产生(单位质量细胞产酶能力分别提高了21%和32%).图8表1参10     

Biological Connectivity of Seasonally Ponded Wetlands across Spatial and Temporal Scales

Many species that inhabit seasonally ponded wetlands also rely on surrounding upland habitats and nearby aquatic ecosystems for resources to support life stages and to maintain viable populations. Understanding biological connectivity among these habitats is critical to ensure that landscapes are protected at appropriate scales to conserve species and ecosystem function. Biological connectivity occurs across a range of spatial and temporal scales. For example, at annual time scales many organisms move between seasonal wetlands and adjacent terrestrial habitats as they undergo life‐stage transitions; at generational time scales, individuals may disperse among nearby wetlands; and at multigenerational scales, there can be gene flow across large portions of a species’ range. The scale of biological connectivity may also vary among species. Larger bodied or more vagile species can connect a matrix of seasonally ponded wetlands, streams, lakes, and surrounding terrestrial habitats on a seasonal or annual basis. Measuring biological connectivity at different spatial and temporal scales remains a challenge. Here we review environmental and biological factors that drive biological connectivity, discuss implications of biological connectivity for animal populations and ecosystem processes, and provide examples illustrating the range of spatial and temporal scales across which biological connectivity occurs in seasonal wetlands.     

Near-term forecasts of stream temperature using deep learning and data assimilation in support of management decisions

Deep learning (DL) models are increasingly used to make accurate hindcasts of management-relevant variables, but they are less commonly used in forecasting applications. Data assimilation (DA) can be used for forecasts to leverage real-time observations, where the difference between model predictions and observations today is used to adjust the model to make better predictions tomorrow. In this use case, we developed a process-guided DL and DA approach to make 7-day probabilistic forecasts of daily maximum water temperature in the Delaware River Basin in support of water management decisions. Our modeling system produced forecasts of daily maximum water temperature with an average root mean squared error (RMSE) from 1.1 to 1.4°C for 1-day-ahead and 1.4 to 1.9°C for 7-day-ahead forecasts across all sites. The DA algorithm marginally improved forecast performance when compared with forecasts produced using the process-guided DL model alone (0%–14% lower RMSE with the DA algorithm). Across all sites and lead times, 65%–82% of observations were within 90% forecast confidence intervals, which allowed managers to anticipate probability of exceedances of ecologically relevant thresholds and aid in decisions about releasing reservoir water downstream. The flexibility of DL models shows promise for forecasting other important environmental variables and aid in decision-making.     

乌梁素海湿地鸟类栖息地生态服务功能评估探讨

本文通过收集近30年鸟类种类及种群数据变化的资料,分析乌粱素海不同鸟类栖息环境的栖息地服务功能,从而评估鸟梁素海湿地鸟类栖息地的综合生态服务功能。评估结果表明：乌梁素海湿地鸟类栖息地综合生态服务功能为不好。     

Where are the Sunday babies? III. Caesarean sections,decreased weekend births,and midwife involvement in Germany

A previous study has shown a marked and continuing decline in weekend births in Germany between 1988 and 2003 (Lerchl, Naturwissenschaften 92:592-594, 2005). The present study was performed to investigate the possible influence of caesarean sections (CS) on weekend birth number and on the involvement of midwives in births for all 16 German states for the year 2003. In total, data from 706,721 births were sorted according to weekday of births and state, respectively, and the weekend births avoidance rates were calculated. Weekend births were consistently less frequent than births during the week, with an average of -15.3% for all states and due to fewer births on Saturdays (-13.6%) and Sundays (-16.7%). Between the states, weekend births avoidance rates ranged from -11.6% (Bremen) to -24.2% (Saarland). The proportion of CS was 25.5% for all states, ranging from 19.2% (Sachsen and Sachsen-Anhalt) to 30.5% (Saarland). CS and weekend births avoidance rates were significantly correlated, consistent with the hypothesis that primary (planned) CS are regularly scheduled on weekdays. The number of births per midwife (BPM) was calculated according to the number of active members in the states' professional midwives' organizations. The mean number of BPM was 59.5, ranging from 45.2 (Bremen) to 82.4 (Sachsen-Anhalt). CS and BPM were significantly correlated, consistent with the hypothesis that higher CS ratios are associated with lower midwife involvement in births. It is concluded that the decline in weekend births and lower involvement of midwives are caused, at least in part, by an increased number of caesarean sections.     

车载物资振动环境谱的数据处理与归纳

车载物资或设备振动环境谱的数据处理与归纳是车载物资或设备环境可靠性试验的重要组成部分,对保障车载物资或设备的完整性和可靠性具有十分重要的意义。以货车为例,依据GB10593．3～90进行数据合并,然后采用统计容差法进行振动环境条件随机振动规范谱的估计。所得到的规范谱为车栽物资或设备振动环境的可靠性评定奠定了基础。