Variability in Population Abundance and the Classification of Extinction Risk

Abstract: Classifying species according to their risk of extinction is a common practice and underpins much conservation activity. The reliability of such classifications rests on the accuracy of threat categorizations, but very little is known about the magnitude and types of errors that might be expected. The process of risk classification involves combining information from many sources, and understanding the quality of each source is critical to evaluating the overall status of the species. One common criterion used to classify extinction risk is a decline in abundance. Because abundance is a direct measure of conservation status, counts of individuals are generally the preferred method of evaluating whether populations are declining. Using the thresholds from criterion A of the International Union for Conservation of Nature (IUCN) Red List (critically endangered, decline in abundance of >80% over 10 years or 3 generations; endangered, decline in abundance of 50–80%; vulnerable, decline in abundance of 30–50%; least concern or near threatened, decline in abundance of 0–30%), we assessed 3 methods used to detect declines solely from estimates of abundance: use of just 2 estimates of abundance; use of linear regression on a time series of abundance; and use of state‐space models on a time series of abundance. We generated simulation data from empirical estimates of the typical variability in abundance and assessed the 3 methods for classification errors. The estimates of the proportion of falsely detected declines for linear regression and the state‐space models were low (maximum 3–14%), but 33–75% of small declines (30–50% over 15 years) were not detected. Ignoring uncertainty in estimates of abundance (with just 2 estimates of abundance) allowed more power to detect small declines (95%), but there was a high percentage (50%) of false detections. For all 3 methods, the proportion of declines estimated to be >80% was higher than the true proportion. Use of abundance data to detect species at risk of extinction may either fail to detect initial declines in abundance or have a high error rate.     

Foraging in a variable environment: weather patterns and the behavioral ecology of baboons

We investigated the long-standing premise in behavioral ecology that the environment affects behavior and demography. We did this by evaluating the extent to which year-to-year variability in the behavioral ecology of a nonhuman primate population could be modeled from meteorological patterns. Data on activity profiles and home range use for baboons (Papio cynocephalus) in Amboseli, Kenya, were obtained over a 10-year period for three social groups: two completely wild-foraging ones, and a third that supplemented its diet with refuse from a nearby tourist lodge. The relationships across years among activity budgeting, travel distance, group size, and measures of temperature and rainfall patterns differed among the social groups. Although meteorological variation generally correlated with behavioral variation in the completely wild-foraging groups, different weather variables and direction of relationships resulted for each group. In addition, different relationships among variables were found before and after home-range shifts. The food-enhanced group spent half as much time foraging as did the other groups and therefore could be used to evaluate the relative extent to which foraging time was a limiting factor for resting and social time. Under their relaxed ecological conditions, the food-enhanced animals increased resting time much more than social time. These findings, combined with supplementary information on the population, lead us to suggest that baboons use a suite of interrelated responses to ecological variability that includes not only changes in activity budgets, but also home-range shifts, changes in the length of the active period, and changes in group size through fissions. Moreover, our results imply that group differences as well as interpopulational and interspecific differences in behavioral ecology provide significant sources of variability. Therefore, social groups rather than populations may be the appropriate unit of analysis for understanding the behavioral ecology of baboons and other highly social primates. The different patterns we observed among groups may have fitness consequences for the individuals in those groups and thereby affect population structure over time. Received: 18 February 1995/Accepted after revision: 6 January 1996     

A Framework for Analyzing Longitudinal and Temporal Variation in River Flow and Developing Flow-Ecology Relationships1

Larned, Scott T., David B. Arscott, Jochen Schmidt, and Jan C. Diettrich, 2010. A Framework for Analyzing Longitudinal and Temporal Variation in River Flow and Developing Flow-Ecology Relationships. Journal of the American Water Resources Association (JAWRA) 46(3):541-553. DOI: 10.1111/j.1752-1688.2010.00433.x Abstract: We propose a framework for analyzing longitudinal flow variation and exploring its ecological consequences in four steps: (1) generating longitudinally continuous flow estimates; (2) computing indices that describe site-specific and longitudinal flow variation, including intermittence; (3) quantifying and visualizing longitudinal dynamics; (4) developing quantitative relationships between hydrological indices and ecological variables (flow-ecology relationships). We give examples of each step, using data from a New Zealand river and an empirical longitudinal flow model, ELFMOD. ELFMOD uses spot-gauging data and flow or proxy variable time series to estimate flow magnitude and state (flowing or dry) at user-defined intervals along river sections. Analyses of flow-ecology relationships for the New Zealand river indicated that fish and benthic and hyporheic invertebrate communities responded strongly to variation in mean annual flow permanence, flow duration, dry duration, drying frequency, inter-flood duration, and distances to flowing reaches. To put longitudinal flow variation into a broader context and guide future research, we propose a conceptual model that combines elements of two contrasting perspectives: rivers as longitudinal continua, and rivers as patch mosaics. In this conceptual model, hydrologically complex rivers are composed of linear sequences of nested hydrological gradients, which are bordered by hydrogeomorphic discontinuities, and which collectively generate hydrological dynamics at river-section scales.     

城市污水处理厂运行稳定性评估方法初探

城市污水处理厂的运行稳定性，直接关系到其运行的成本和对环境的影响。然而，如何判断并改进污水厂的运行稳定性，尚缺乏深入的探讨。通过建立计算机辅助决策支持系统，引入统计学方法，探讨了城市污水处理厂运行稳定性的评估方法。污水处理厂运行稳定性的一个重要表征就是污水厂各工艺单元的各项指标和监测数值处于相对平稳的范围，因此，可以借鉴统计学的原理和方法，构建评估的指标和标准体系，对各常规监测指标长时段的监测数据进行统计分析，寻找时间序列的散布特征，判别工艺单元运行的稳定性，从而评估污水处理厂长期运行状况和趋势，为污水处理厂的管理与维护提供指导意见，改进其运行的稳定性。     

成都平原西部土壤氮素空间变异特征及影响因素

为探究成都平原西部土壤氮素的空间异质性及其影响因素,基于134个耕层土壤采样点,运用经典统计学和地统计学方法揭示w（TN）和w（AN）（AN为碱解氮）的空间变异特征,并利用方差分析和回归分析,研究不同因素对其空间变异的影响程度.结果表明:研究区土壤w（TN）为0.81~3.50 g/kg,平均值为1.94 g/kg;w（AN）为44.42~263.99 mg/kg,平均值为138.70 mg/kg.半方差分析显示,土壤w（TN）和w（AN）的块金效应分别为52.41%和63.92%,具有中等程度的空间自相关,表明其空间分布受结构性和随机性因素共同影响.土壤氮素空间分布特征均呈现由东北向西南逐渐递增趋势.回归分析结果表明,成土母质能独立解释14.8%和9.4%的w（TN）和w（AN）空间变异;土壤类型（土类、亚类和土属）对研究区w（TN）和w（AN）空间变异的独立解释能力分别在3.6%~17.2%和5.7%~17.2%之间.各土地利用方式下,土壤w（TN）、w（AN）平均值均表现为耕地>农林用地>园地,土地利用方式对土壤w（TN）空间变异的独立解释能力仅5.8%,对w（AN）无显著影响.研究显示,成都平原西部土壤氮素含量总体处于丰富水平,其中温江-郫县一带含量相对较低,高值区在金马河以南区域.成土母质和土壤类型对土壤氮素空间变异的影响总体高于土地利用方式.      

Seasonal evaluation and spatial variability of suspended particulate matter in the vicinity of a large coal-fired power station in India—A case study

Coal combustion in the power sector gives rise to the emission of primary and secondary particulate pollutants. Since the emission of pollutants depends on coal quality and combustion technology, and given that transport, transformation and deposition of contaminants depend on regional climatic conditions, specific studies for the power stations is needed to evaluate their environmental impacts. Monitoring of ambient respirable suspended particulate matter (RSPM) and suspended particulate matter (SPM) levels around a large coal-fired power station in India was carried out. The specific objectives were the determination of spatial and seasonal variability in RSPM and SPM levels, and their relationship with meteorological parameters such as wind velocity and relative humidity. The results have shown a marked seasonal trend and spatial variability in RSPM and SPM levels in the study area. Higher concentrations of ambient RSPM and SPM were found in downwind monitoring stations compared to upwind direction. Ratios of RSPM to SPM and correlation coefficient values between RSPM and SPM along with meteorological parameters were also worked out. Relative humidity and wind velocity have shown an inverse relation with particulate deposition pattern.     

An evaluation of grid size uncertainty in empirical soil loss modeling with digital elevation models

This paper presents a study on the effect of topographic variability on grid-based empirical estimation of soil erosion and sediment transport with raster geographic information systems (GIS). An original digital elevation model (DEM) of 10 m resolution for a case watershed is resampled to six realizations of greater grid sizes for a comparative examination. The Universal Soil Loss Equation (USLE) and a distance-based sediment delivery equation are applied to the watershed to calculate soil loss from each cell and total sediment transport to streams, respectively. The results suggest that the selection of the DEM gird size has considerable influence on the soil loss estimation with the empirical models. The estimate of total soil loss from the watershed decreases significantly with the increasing DEM cell size as the spatial variability is reduced by the cell aggregation. The empirical modeling approach is a useful tool for qualitative assessment of soil erosion, provided that spatial variability can be adequately represented by applied DEMs. However, discretion is suggested for its applications to quantitative estimation of soil loss concerning the sensitivity to the grid size selection.     

IMPACTS OF CALIFORNIA'S CLIMATIC REGIMES AND COASTAL LAND USE CHANGE ON STREAMFLOW CHARACTERISTICS1

ABSTRACT: To investigate the impacts of urbanization and climatic fluctuations on stream flow magnitude and variability in a Mediterranean climate, the HEC‐HMS rainfall/runoff model is used to simulate stream flow for a 14‐year period (October 1, 1988, to September 30, 2002) in the Atascadero Creek watershed located along the southern coast of California for 1929, 1998, and 2050 (estimated) land use conditions (8, 38 and 52 percent urban, respectively). The 14‐year period experienced a range of climatic conditions caused mainly by El Nino‐Southern Oscillation variations. A geographic information system is used to delineate the watershed and parameterize the model, which is calibrated using data from two stream flow and eight rainfall gauges. Urbanization is shown to increase peak discharges and runoff volume while decreasing stream flow variability. In all cases, the annual and 14‐year distributions of stream flow are shown to be highly skewed, with the annual maximum 24 hours of discharge accounting for 22 to 52 percent of the annual runoff and the maximum ten days of discharge from an average El Nino year producing 10 to 15 percent of the total 14‐year discharge. For the entire period of urbanization (1929 to 2050), the average increase in annual maximum discharges and runoff was 45 m³/s (300 percent) and 15 cm (350 percent), respectively. Additionally, the projected increase in urbanization from 1998 to 2050 is half the increase from 1929 to 1998; however, increases in runoff (22 m³/s and 7 cm) are similar for both scenarios because of the region's spatial development pattern.     

EFFECTS OF CLIMATE VARIABILITY ON RIVERS: CONSEQUENCES FOR LONG TERM WATER QUALITY ANALYSIS1

ABSTRACT: Associations between the El Nino Southern Oscillation (ENSO) climate pattern and temporal variability in flow and 12 water quality variables were assessed at 77 river sites throughout New Zealand over a 13‐year period (1989 through 2001). Trends in water quality were determined for the same period. All 13 variables showed statistically significant linear regression relationships with values of the Southern Oscillation Index (SOI). The strongest relationships were for water temperature (mean R²= 0.20), dissolved reactive phosphorus (0.18), and oxidized nitrogen (0.17). The association with SOI varied by climate region. The observed patterns were generally consistent with known ENSO effects on New Zealand rainfall and air temperature. Trends in water quality variables for the periods 1989 through 1993, 1994 through 1998, and 1989 through 1998 were reasonably consistent with trends in SOI, even when the influence of river flow was removed from the data. This suggests that SOI effects on water quality are not necessarily a direct consequence of changes in flow associated with rainfall variation. In addition, both Baseline (32 upstream) and Impact (45 downstream) sites showed similar trends, indicating that changes in management were not directly responsible. We conclude that interpretation of long term water quality datasets in rivers requires that climate variability be fully acknowledged and dealt with explicitly in trend analyses.     

INFLUENCE OF WELL WATER QUALITY VARIABILITY ON SAMPLING DECISIONS AND MONITORING1

ABSTRACT: Local governmental agencies responsible for decisions in ground water quality management need not only data on ground water quality but they also must understand the relationship of accuracies and risks associated with this data as related to the number of wells to sample. In this report we address this problem by using the philosophical doctrines of probabilism and relativism with simple statistical procedures. This requires a reasonable estimate of the population variance in a quality parameter for a given management-unit area, and requires that the decisionmaker formulate constraints with an acceptable standard error of the sample mean and be willing to accept some level of probability of being Wrong. This technique is illustrated using a 21-year data base of well water chemical data in a 653 km² ground water quality study area in the San Joaquin Valley of California.