Cost analysis of composite sampling for classification

When an environmental sampling objective is to classify all the sample units as contaminated or not, composite sampling with selective retesting can substantially reduce costs by reducing the number of units that require direct analysis. The tradeoff, however, is increased complexity that has its own hidden costs. For this reason, we propose a model for assessing the relative cost, expressed as the ratio of total expected cost with compositing to total expected cost without compositing (initial exhaustive testing). Expressions are derived for the following retesting protocols: (i) exhaustive, (ii) sequential and (iii) binary split. The effects of both false positive and false negative rates are also derived and incorporated. The derived expressions of relative cost are illustrated for a range of values for various cost components that reflect typical costs incurred with hazardous waste site monitoring. Results allow those who are designing sampling plans to evaluate if any of these compositing/retesting protocols will be cost effective for particular applications.     

Use of post-stratification in composite sampling for estimating mean

We show with the results of a study conducted in the Hamadan Province, Iran as to how the use of composite sampling for estimating mean zinc concentration in the soil can help reduce analytical costs by reducing the number of analysis required. We also introduce post-stratification methodology in the composition step to take advantage of possible spatial dispersion. We speculate that the zinc concentration value depends on the sample location, we first stratified the sample set and then composite units from different strata randomly. The results of a simulation study show that the use of this approach not only reduces the total costs but also increases the precision of the estimator.     

Sample support and related scale issues in composite sampling

Composite sampling offers the great promise of efficiency and economy in environmental decision making. However, if careful attention is not paid to matching the support of the sample to that required for making the desired decision, the promise is unfulfilled. Obviously this attention must be applied in the design phase of a composite sampling strategy. Less obvious is the potential for alteration of the design sample support during sample collection and assay. The consequences of not paying attention to these aspects of sample design and assay are discussed in this issue paper illustrated with a series of examples taken from the authors consulting experience.     

Analysis of composite sampling data using the principle of maximum entropy

A new tool is presented based on the principle of maximum entropy for enhanced hot spot detection based on composite sampling with no (or minimal) requirement for composite breakdown. The methodology presented here is easy to implement and facilitates the use of multiple criteria for evaluating attainment of site remediation objectives. The new methodology provides very simple decision rules that can easily be used by non-statisticians and complements the use of composites for control of residual mean concentrations.     

An assessment of sampling,preservation, and analytical procedures for arsenic speciation in potentially contaminated waters

This study was undertaken to ascertain optimal methods of sampling, preserving, separating, and analyzing arsenic species in potentially contaminated waters. Arsenic species are readily transformed in nature by slight changes in conditions. Each species has a different toxicity and mobility. The conventional field sampling method using filters of 0.45 μm in size could overestimate the dissolved arsenic concentrations, as passing suspended particles that can act as a sink or source of arsenic depending on the site condition. For arsenic species in neutral pH and iron-poor waters, the precipitation can be stable for up to 3 days without any treatment, but for longer periods, a preservative, such as phosphoric acid, is required. Also, the analytical procedure must be selected carefully because the levels and hydride generation efficiencies of arsenic in different species can vary, even for the same amount of arsenic. For arsenic speciation in samples that also include organic species, a hybrid high-performance liquid chromatography (HPLC) column and inductively coupled plasma mass spectrometry (ICP-MS) gave the best resolution and lowest detection limits. However, the procedure using a solid phase extraction (SPE) cartridge can be used economically and conveniently for analyzing samples containing only inorganic arsenic species, such as groundwater, especially that related to mine activity.     

Species-specific tuning increases robustness to sampling bias in models of species distributions: An implementation with Maxent

Various methods exist to model a species’ niche and geographic distribution using environmental data for the study region and occurrence localities documenting the species’ presence (typically from museums and herbaria). In presence-only modelling, geographic sampling bias and small sample sizes represent challenges for many species. Overfitting to the bias and/or noise characteristic of such datasets can seriously compromise model generality and transferability, which are critical to many current applications - including studies of invasive species, the effects of climatic change, and niche evolution. Even when transferability is not necessary, applications to many areas, including conservation biology, macroecology, and zoonotic diseases, require models that are not overfit. We evaluated these issues using a maximum entropy approach (Maxent) for the shrew Cryptotis meridensis, which is endemic to the Cordillera de Mérida in Venezuela. To simulate strong sampling bias, we divided localities into two datasets: those from a portion of the species’ range that has seen high sampling effort (for model calibration) and those from other areas of the species’ range, where less sampling has occurred (for model evaluation). Before modelling, we assessed the climatic values of localities in the two datasets to determine whether any environmental bias accompanies the geographic bias. Then, to identify optimal levels of model complexity (and minimize overfitting), we made models and tuned model settings, comparing performance with that achieved using default settings. We randomly selected localities for model calibration (sets of 5, 10, 15, and 20 localities) and varied the level of model complexity considered (linear versus both linear and quadratic features) and two aspects of the strength of protection against overfitting (regularization). Environmental bias indeed corresponded to the geographic bias between datasets, with differences in median and observed range (minima and/or maxima) for some variables. Model performance varied greatly according to the level of regularization. Intermediate regularization consistently led to the best models, with decreased performance at low and generally at high regularization. Optimal levels of regularization differed between sample-size-dependent and sample-size-independent approaches, but both reached similar levels of maximal performance. In several cases, the optimal regularization value was different from (usually higher than) the default one. Models calibrated with both linear and quadratic features outperformed those made with just linear features. Results were remarkably consistent across the examined sample sizes. Models made with few and biased localities achieved high predictive ability when appropriate regularization was employed and optimal model complexity was identified. Species-specific tuning of model settings can have great benefits over the use of default settings.     

Efficiency of composite sampling for estimating a lognormal distribution

In many environmental studies measuring the amount of a contaminant in a sampling unit is expensive. In such cases, composite sampling is often used to reduce data collection cost. However, composite sampling is known to be beneficial for estimating the mean of a population, but not necessarily for estimating the variance or other parameters. As some applications, for example, Monte Carlo risk assessment, require an estimate of the entire distribution, and as the lognormal model is commonly used in environmental risk assessment, in this paper we investigate efficiency of composite sampling for estimating a lognormal distribution. In particular, we examine the magnitude of savings in the number of measurements over simple random sampling, and the nature of its dependence on composite size and the parameters of the distribution utilizing simulation and asymptotic calculations.     

Identifying the largest individual sample value from a two-way composite sampling design

Composite sampling techniques for identifying the largest individual sample value seem to be cost effective when the composite samples are internally homogeneous. However, since it is not always possible to form homogeneous composite samples, these methods can lead to higher costs than expected. In this paper we propose a two-way composite sampling design as a way to improve on the cost effectiveness of the methods available to identify the largest individual sample value.     

Growth increments in the shell of the living brachiopod Terebratalia transversa

Scanning electron micrographs revealed growth increments in the primary shell layer of the extant terebratulid brachiopod Terebratalia transversa collected from Anacortes, Washington, USA, during the summers of 1982–1984. The increments extend into the secondary shell layer, but only as poorly-defined continuations. Growth increments narrow and widen cyclically, producing patterns similar to tidally-induced, fortnightly growth patterns in bivalve molluscs. Asymmetric increments or doublets that consist of paired wide and narrow bands present additional evidence that the growth pattern is produced by the interference of tidal (24 h 50 min) and solar (24 h) daily environmental influences on growth, as is the case in bivalves. Growth increments are relatively well-defined in specimens from tidal habitats and are poorly-defined in subtidal specimens. Specimens grown in laboratory aquaria and subjected to simulated tidal emersions and submersions had well-defined increments, whereas those that were continuously submerged in aquaria had poorly-defined increments, paralleling the distinctness in growth increments produced at various depths in the natural habitat. Production of growth increments in T. transversa may be related to valve movement and changes in mantle-cavity pH. When the valves are closed, pH drops and, when they are open, pH rises. Highperformance liquid chromatography (HPLC) of mantle tissue and fluid failed to detect any single organic acid that could have been responsible for the pH changes, confirming that intermediate metabolism in T. transversa is very low compared to that of bivalve molluscs. Calculations indicate that cycles in intermediate metabolism may nevertheless be responsible for cycles in growth-increment production and shell dissolution.Please address all correspondence to Dr. G.D. Rosenberg in Indianapolis     

A proposed standard method for composite sampling of water chemistry and plankton in small lakes

In this paper a method for collection of vertically and horizontally integrated volume-weighted composite samples for analysis of water chemistry and plankton is presented. The method, which requires a proper knowledge of lake morphometry parameters, includes proposed standard procedures for determination of sampling interval thickness, maximum depth of sampling, selection of sampling stations, and distribution of discrete samples. An example of the outcome of the method in a lake with uncomplicated basin morphometry is given and the results are discussed against background of general lake basin morphometry data. The aim of the paper is to start a debate about optimization (statistical as well as ecological) of volume weighted composite sampling.     

Comparison of a two-stage sampling design and its composite sample alternative: An application to soil studies

The objective of a long-term soil survey is to determine the mean concentrations of several chemical parameters for the pre-defined soil layers and to compare them with the corresponding values in the past. A two-stage random sampling procedure is used to achieve this goal. In the first step, n subplots are selected from N subplots by simple random sampling without replacement; in the second step, m sampling sites are chosen within each of the n selected subplots. Thus n · m soil samples are collected for each soil layer. The idea of the composite sample design comes from the challenge of reducing very expensive laboratory analyses: m laboratory samples from one subplot and one soil layer are physically mixed to form a composite sample. From each of the n selected subplots, one composite sample per soil layer is analyzed in the laboratory, thus n per soil layer in total. In this paper we show that the cost is reduced by the factor m — 1 when instead of the two-stage sampling its composite sample alternative is used; however, the variance of the composite sample mean is increased. In the case of positive intraclass correlation the increase is less than 12.5%; in the case of negative intraclass correlation the increase depends on the properties of the variable as well. For the univariate case we derive the optimal number of subplots and sampling sites. A case study is discussed at the end.     

Estimation of the mean in line intercept sampling

Ratio estimation of the parametric mean for a characteristic measured on plants sampled by a line intercept method is presented and evaluated via simulation using different plant dispersion patterns (Poisson, regular cluster, and Poisson cluster), plant width variances, and numbers of lines. The results indicate that on average the estimates are close to the parametric mean under all three dispersion patterns. Given a fixed number of lines, variability of the estimates is similar across dispersion patterns with variability under the Poisson pattern slightly smaller than varia-bility under the cluster patterns. No variance estimates were negative under the Poisson pattern, but some estimates were negative under the cluster patterns for smaller numbers of lines. Variance estimates become closer to zero similarly for all spatial patterns as the number of lines increases. Ratio estimation of the parametric mean in line intercept sampling works better, from the viewpoint of approximate unbiasedness and variability of estimates, under the Poisson pattern with larger numbers of lines than other combinations of spatial patterns, plant width variances and numbers of lines.     

Determining the role of eudaimonic values in conservation behavior

Values are the fundamental reasons why people engage in conservation behaviors. Recent research has called for a more refined approach to studying values in a way that accounts for the concept of eudaimonia. However, the empirical properties for a eudaimonic value scale have not been tested given that previous investigations have remained at the theoretical level. Drawing from an on-site survey of visitors to Denali National Park and Preserve, Alaska, we used a latent profile analysis to better understand the expression of multiple values of nature. Specifically, we segmented respondents by their value orientations with a particular focus on evaluating eudaimonic and hedonic values, alongside the established dimensions of altruistic, biospheric, and egoistic values. We identified 4 distinct subgroups defined by value orientations and validated these subgroups based on measures of conservation behavior and sociodemographic characteristics (e.g., age). These results indicated campaign messaging should harness a combination of eudaimonic, biospheric, and altruistic values to propel individual behavior. We also observed that hedonic and egoistic values defined how people related to nature and played a role, albeit less pronounced, in motivating them to take action. Our study is one of the first efforts to operationalize eudaimonia in a conservation context; thus, we have opened a new avenue for protected-area managers to align their strategies with the underlying values of stakeholders.     

Quantitative relationships among phytoplankton body size classes and production processes in the North Adriatic frontal region

The variation patterns of phytoplankton standing crop and productivity in the North Adriatic frontal region and the relative importance of pico-, nano-, micro-phytoplankton are shown. Data of standing crop (chlorophyll a - Chl a ) and productivity ( 14 C assimilation) and PAR radiation ( w Em m 2 r s m 1 ) were collected during the four oceanographic cruises of the PRISMA II project. Average standing crop and productivity in the study area were 1.41 - 0.42 r mg r m m 3 Chl a and 1.23 - 0.37 r mg r C r m m 3 r h m 1 ; average assimilation number (P/B) was 0.872 - 0.589 r mg r C (mg-Chl a ) m 1 h m 1 and average photosynthetic efficiency (PE) was 0.020 - 0.054 (mg r C(mg-Chl a ) m 1 r h m 1 ) [ w Em m 2 r s m 1 ] m 1 . Phytoplankton biomass and productivity showed significant patterns of variation with the distance from the coast, with increasing depth and decreasing light intensity. The same patterns were shown by the three phytoplankton size classes. The spatio-temporal variations were significantly larger within the microplankton than within the pico- and nano-plankton size classes. Planktonic guilds were dominated by picoplankton, both as standing crop and productivity, in the northern stations (0.539 - 0.21 r mg r m m 3 Chl a and 0.572 - 0.25 r mg r C r m m 3 r h m 1 ) and in those more offshore, while microplankton was more important in the coastal and southern stations (0.727 - 0.58 r mg r m m 3 Chl a and 0.63 - 0.28 r mg r C r m m 3 r h m 1 ). In relative terms, picoplankton accounts for the 53% and 46% of biomass and primary production, while the microplankton account for the 43.6% and 48%. Assimilation number and photosynthetic efficiency did not show spatio-temporal variations but PE was inversely related with PAR radiation for all the size classes. Data suggest that the spatio-temporal patterns observed in this study are affected by the competitive relationships among body size classes in the phytoplankton guilds.     

Effects of experience and weather on foraging rate and pollen versus nectar collection in the bumblebee, Bombus terrestris

This study examines factors that affect foraging rate of free-flying bumblebees, Bombus terrestris, when collecting nectar, and also what factors determine whether they collect pollen or nectar. We show that nectar foraging rate (mass gathered per unit time) is positively correlated with worker size, in accordance with previous studies. It has been suggested that the greater foraging rate of large bees is due to their higher thermoregulatory capacity in cool conditions, but our data suggest that this is not so. Workers differing in size were not differentially affected by the weather. Regardless of size, naïve bees were poor foragers, often using more resources than they gathered. Foraging rate was not maximised until at least 30 trips had been made from the nest. Foraging rates were positively correlated with humidity, perhaps because nectar secretion rates were higher or evaporation of nectar lower at high humidity. Temperature, wind speed and cloud cover did not significantly influence foraging rate, within the summertime range that occurred during the study. Weather greatly influenced whether bees collected pollen or nectar. Pollen was preferably collected when it was warm, windy, and particularly when humidity was low; and preferably during the middle of the day. We suggest that bees collect pollen in dry conditions, and avoid collecting pollen when there is dew or rain-water droplets on the vegetation, which would make grooming pollen into the corbiculae difficult. Availability of sufficient dry days for pollen collection may be an important factor determining the success of bumblebee colonies.Communicated by M. Giurfa     

Characteristics and mixing state of S-rich particles in haze episodes in Beijing

Direct individual analysis using Scanning Electron Microscopy combined with online observation was conducted to examine the S-rich particles in PM_2.5 of two typical polluted haze episodes in summer and winter from 2014 to 2015 in Beijing. Four major types of S-rich particles, including secondary CaSO₄ particles (mainly observed in summer), S-rich mineral particles (SRM), S-rich water droplets (SRW) and (C, O, S)-rich particles (COS) were identified.We found the different typical morphologies and element distributions of S-rich particles and considered that (C, O, S)-rich particles had two major mixing states in different seasons. On the basis of the S-rich particles’ relative abundances, S concentrations and their relationships with PM_2.5 as well as the seasonal comparison, we revealed that the S-participated formation degrees of SRM and SRW would enhance with increasing PM_2.5 concentration. Moreover, C-rich matter and sulfate had seasonally different but significant impacts on the formation of COS.

Open image in new window

     

The effects of littoral zone vegetation on turbulent mixing in lakes

Micro-scale thermal profile data were acquired in four lakes in northwest England and southeast Australia that ranged from a small, sheltered pond with a surface area of about 1 ha to more open lakes with surface areas of several square kilometres. These lakes provided a range of topographic and climatic contexts, basin morphologies and dominant macrophyte species. The data were acquired using two SCAMP profilers, one deployed in the open water and the other mounted on a field traverse deployed within the vegetated littoral zone. From these profile data, turbulence parameters were calculated. The results show the variation in the influence of vegetation on turbulence in the four lakes, which depends on the combination of wind stress, solar radiative forcing and macrophyte mechanical properties. In the sheltered pond, the vegetation alters the light climate within the water, thus reducing stratification and allowing weak, thermally-driven mixing. In the larger lakes, however, the primary action of the vegetation is to prevent surface-generated TKE from penetrating the water column, although this effect becomes less important as the plant separation increases. A simple mechanistic model, calibrated against the field data, suggests that the macrophyte mechanical properties are most important in determining the turbulent kinetic energy (TKE) profile. Increasing the number of turbulence-generating plants reduces the transport of surface-generated TKE into the deeper water, consistent with the field observations. The model suggests that solar forcing, as measured by the temperature gradient between the surface and bottom waters, is of less importance since the TKE profile is similar in runs with different gradients. Perhaps most surprisingly, the value of the surface-wind stress used in the model is not important, within the limitations of the model, as it does not change the TKE profile, except in a thin surface layer.     

Making sense of predator scents: investigating the sophistication of predator assessment abilities of fathead minnows

According to the threat-sensitive predator avoidance hypothesis, selection favors prey that accurately assess the degree of threat posed by a predator and adjust their anti-predator response to match the level of risk. Many species of animals rely on chemical cues to estimate predation risk; however, the information content conveyed in these chemical signatures is not well understood. We tested the threat-sensitive predator avoidance hypothesis by determining the specificity of the information conveyed to prey in the chemical signature of their predator. We found that fathead minnows (Pimephales promelas) could determine the degree of threat posed by northern pike (Esox lucius) based on the concentration of chemical cues used. The proportion of minnows that exhibited an anti-predator response when exposed to a predator cue increased as the concentration of the pike cue used increased. More surprisingly, the prey could also distinguish large pike from small pike based on their odor alone. The minnows responded more intensely to cues of small pike than to cues of large pike. In this predator–prey system small pike likely represent a greater threat than large pike.Communicated by A. Mathis     

Factors influencing cannibalism in the wolf spider Pardosa agrestis (Araneae, Lycosidae)

Cannibalistic tendencies are well known in spiders and may be a significant factor influencing population size. The wolf spider, Pardosa agrestis, is the dominant non-web-building spider in a wide range of central European agricultural habitats. Preliminary field observations indicated an extended reproductive period, which results in a very wide size distribution of juvenile instars. We hypothesised that if cannibalism is enhanced by differences in size, especially during periods when prey is scarce, these populations might be susceptible to cannibalism in an ecologically significant way. Laboratory studies were conducted on juvenile P. agrestis in arenas. We analysed the following specific aspects of cannibalism: (1) the effect of the weight ratio between the opponents; (2) the effect of weight per se, and (3) the role of hunger level in determining cannibalistic tendencies of spiders. The role of weight and hunger were analysed in separate experiments, in both cases by controlling for the other variable. The results showed that cannibalism was strongly positively correlated with both weight ratio and hunger, but absolute size/age of an individual could not predict the occurrence of a cannibalistic event. These experiments generated the plausible hypothesis that cannibalism might be an important phenomenon in the regulation of real populations, which should be tested specifically in future field experiments. Received: 15 December 1997 / Accepted after revision: 10 October 1998