Sampling The Soil In Long-Term Forest Plots: The Implications Of Spatial Variation

Long-term monitoring of forest soils as part of a pan-European network to detect environmental change depends on an accurate determination of the mean of the soil properties at each monitoring event. Forest soil is known to be very variable spatially, however. A study was undertaken to explore and quantify this variability at three forest monitoring plots in Britain. Detailed soil sampling was carried out, and the data from the chemical analyses were analysed by classical statistics and geostatistics. An analysis of variance showed that there were no consistent effects from the sample sites in relation to the position of the trees. The variogram analysis showed that there was spatial dependence at each site for several variables and some varied in an apparently periodic way. An optimal sampling analysis based on the multivariate variogram for each site suggested that a bulked sample from 36 cores would reduce error to an acceptable level. Future sampling should be designed so that it neither targets nor avoids trees and disturbed ground. This can be achieved best by using a stratified random sampling design.     

Multiscale Spatial and Small-Scale Temporal Variation in the Composition of Riverine Fish Communities

We studied the multiscale (sites, river reaches and rivers) and short-term temporal (monthly) variability in a freshwater fish assemblage. We found that small-scale spatial variation and short-term temporal variability significantly influenced fish community structure in the Macquarie and Namoi Rivers. However, larger scale spatial differences between rivers were the largest source of variation in the data. The interaction between temporal change and spatial variation in fish community structure, whilst statistically significant, was smaller than the variation between rivers. This suggests that although the fish communities within each river changed between sampling occasions, the underlying differences between rivers were maintained. In contrast, the strongest interaction between temporal and spatial effects occurred at the smallest spatial scale, at the level of individual sites. This means whilst the composition of the fish assemblage at a given site may fluctuate, the magnitude of these changes is unlikely to affect larger scale differences between reaches within rivers or between rivers. These results suggest that sampling at any time within a single season will be sufficient to show spatial differences that occur over large spatial scales, such as comparisons between rivers or between biogeographical regions.     

Determining Ecoregions for Environmental and GMO Monitoring Networks

A representative environmental monitoring network at the regional scale cannot use raster-based or random sampling designs, but requires a stratified sampling procedure integrating different information layers, and it has to occur in ecologically differing homogeneous regions (ecoregions). These we have determined using a set of spatial strata with ecological variables which we analysed with classification and regression trees (CART). We present a framework for environmental monitoring, that covers different scales, and we transfer the framework to a potential GMO (genetically modified organisms) monitoring network. We use ecoregion and other environmental strata together with existing environmental monitoring networks to determine GMO monitoring sites more precisely.     

Statistical Analysis of Spatial Pattern: A Comparison of Grid and Hierarchical Sampling Approaches

Previous studies have combined random-site hierarchical sampling designs with analysis of variance techniques, and grid sampling with spatial autocorrelation analysis. We illustrate that analysis techniques and sampling designs are interchangeable using densities of an infaunal bivalve from a study in Poverty Bay, New Zealand. Hierarchical designs allow the estimation of variances associated with each level, but high-level factors are imprecisely estimated, and they are inefficient for describing spatial pattern. Grid designs are efficient for describing spatial pattern, and are amenable to conventional analysis. Our example deals with a continuous spatial habitat, but our conclusions also apply in disjunct or patchy habitats. The influence of errors in positioning is also assessed. The advantages of systematic sampling are reviewed, and more efficient hierarchical approaches are identified. The distinction between biological and statisticalsignificance in all analyses is emphasised.     

Patchy distribution fields: sampling distance unit of a zigzag survey and reconstruction adequacy

A mathematical model was used to examine the effects of choosingvarious units of sampling distance of a zigzag survey on the adequacy of reconstructing patchy distribution fields. The modelsimulates fish or plankton patches (or gaps) of different shapesand spatial orientations, and an acoustic survey by zigzag or parallel transects along which a unit of sampling distance is set. Adequacy of the reconstructed fields to those originally generated is evaluated by calculating their correlations (r). A priori information on the autocorrelation radii for the field in the directions of the survey (R_s) and perpendiculardirection (R_p) allows optimisation of the survey design andthe algorithm of data analysis. A field can be reconstructed properly (r² > 0.70) if the distance between transects D < (1.0–1.5)R_s and the unit of sampling distance d < (1.0–1.5)R_p. A posteriori determination of patchorientation allows reconstruction of the best field attainable onthe basis of the survey data. In cases of field movement, if thedimension of patches in the direction of movement exceeds that ofa surveyed area, a survey in the opposite direction gives bestresults; in contrast, if the dimension of moving patches is smaller than that of a surveyed area, it is reasonable to carryout a survey in the same direction. The criterion remains validwhen a survey is carried out by zigzag transects and a unit ofsampling distance is set along them. The results obtained indicate that, for a fixed transect spacing and a given number of sampling points on each full transect, zigzag pattern allowsless adequate reconstruction of an original distribution field (in cases of both immovable and movable fields) than corresponding parallel pattern.     

Spatial scales of variation in lichens: implications for sampling design in biomonitoring surveys

The variability of biological data is a main constraint affecting the quality and reliability of lichen biomonitoring surveys for estimation of the effects of atmospheric pollution. Although most epiphytic lichen bioindication surveys focus on between-site differences at the landscape level, associated with the large scale effects of atmospheric pollution, current protocols are based on multilevel sampling, thus adding further sources of variation and affecting the error budget. We test the hypothesis that assemblages of lichen communities vary at each spatial scale examined, in order to determine what scales should be included in future monitoring studies. We compared four sites in Italy, along gradients of atmospheric pollution and climate, to test the partitioning of the variance components of lichen diversity across spatial scales (from trunks to landscapes). Despite environmental heterogeneity, we observed comparable spatial variance. However, residuals often overcame between-plot variability, leading to biased estimation of atmospheric pollution effects.     

Effective Long-term Ecological Monitoring Using Spatially and Temporally Nested Sampling

Effective environmental management requires documentation of ecosystem status and changes to that status. Without long-term data, short-term natural variability can mask chronic and/or cumulative impacts, often until critical levels are reached. However, a trade-off generally occurs between sampling in space and time. This study analyses a spatially and temporally nested long-term (12 years) monitoring programme conducted on benthic macrofauna in a large harbour. Sampling was carried out at six sites for 5.5 years, after which only two sites were sampled for the next 5 years. After this period, all six sites were sampled for another 2 years. While ecology is frequently thought of being highly variable, this design was able to detect trends, and cycles, in abundance, with only around 10% of species at each site exhibiting unpredictable temporal variability. Sites exhibiting similar trends in the abundance of a species over the 12.5-year period were generally spatially contiguous, and the spatial scale of change could be assessed. Continuous sampling at two sites identified whether changes in unsampled sites were related to long-term cycles. Moreover, this sampling provided a long-term background of temporal fluctuations against which to assess the ecological significance of observed changes.     

The Variability of Estimates of Variance, and Its Effect on Power Analysis in Monitoring Design

Power analysis can be a valuable aid in the design of monitoringprograms. It requires an estimate of variance, which may come from a pilot study or an existing study in a similar habitat. For marine benthic infauna, natural variation in abundances canbe considerable, raising the question of reliability of varianceestimates. We used two existing monitoring programs to generatemultiple estimates of variance. These estimates were found to differ from nominated best estimates by 50% or more in 43% of cases, in turn leading to under or over-estimation of samplesize in the design of a notional monitoring program. The twostudies, from the same general area, using the same samplingmethods and spanning a similar time scale, gave estimatesvarying by more than an order of magnitude for 25% of taxa.We suggest that pilot studies for ecological monitoring programsof marine infauna should include at least two sampling times.     

Evaluation of a gradient sampling design for environmental impact assessment

Before-After Control Impact (BACI) sampling designsare commonly used in environmental impact assessmentand are considered the most effective for detectingchanges due to anthropogenic disturbances. Thesedesigns handle local spatial variability throughrandomized placement of samples into a treatmentstratum and a control stratum. When a contaminantdisperses with distance from a point source it issuggested that a gradient design will be moresensitive to change than randomized placement ofsamples. This requires allocating samples according todistance, rather than by random placement withinrandomly placed blocks. In this paper gradient versusrandom sampling designs were compared using data froman oil field in the North Sea. The gradient samplingdesign was more powerful than a randomized CI sampling design.     

Sampling state and process variables on coral reefs

Contemporary coral reefs are forced to survive through and recover from disturbances at a variety of spatial and temporal scales. Understanding disturbances in the context of ecological processes may lead to accurate predictive models of population trajectories. Most coral-reef studies and monitoring programs examine state variables, which include the percentage coverage of major benthic organisms, but few studies examine the key ecological processes that drive the state variables. Here we outline a sampling strategy that captures both state and process variables, at a spatial scale of tens of kilometers. Specifically, we are interested in (1) examining spatial and temporal patterns in coral population size-frequency distributions, (2) determining major population processes, including rates of recruitment and mortality, and (3) examining relationships between processes and state variables. Our effective sampling units are randomly selected 75 × 25 m stations, spaced approximately 250?C500 m apart, representing a 10³ m spatial scale. Stations are nested within sites, spaced approximately 2 km apart, representing a 10⁴ m spatial scale. Three randomly selected 16 m² quadrats placed in each station and marked for relocation are used to assess processes across time, while random belt-transects, re-randomized at each sampling event, are used to sample state variables. Both quadrats and belt-transects are effectively sub-samples from which we will derive estimates of means for each station at each sampling event. This nested sampling strategy allows us to determine critical stages in populations, examine population performance, and compare processes through disturbance events and across regions.     

Assessing Spatial, Temporal, and Analytical Variation of Groundwater Chemistry in a Large Nuclear Complex, USA

Statistical analyses were applied at the Hanford Site, USA, to assess groundwater contamination problems that included (1) determining local backgrounds to ascertain whether a facility is affecting the groundwater quality and (2) determining a ‘pre-Hanford' groundwater background to allow formulation of background-based cleanup standards. The primary purpose of this paper is to extend the random effects models for (1) assessing the spatial, temporal, and analytical variability of groundwater background measurements; (2) demonstrating that the usual variance estimate s ², which ignores the variance components, is a biased estimator; (3) providing formulas for calculating the amount of bias; and (4) recommending monitoring strategies to reduce the uncertainty in estimating the average background concentrations. A case study is provided. Results indicate that (1) without considering spatial and temporal variability, there is a high probability of false positives, resulting in unnecessary remediation and/or monitoring expenses; (2) the most effective way to reduce the uncertainty in estimating the average background, and enhance the power of the statistical tests in general, is to increase the number of background wells; and (3) background for a specific constituent should be considered as a statistical distribution, not as a single value or threshold. The methods and the related analysis of variance tables discussed in this paper can be used as diagnostic tools in documenting the extent of inherent spatial and/or temporal variation and to help select an appropriate statistical method for testing purposes.     

Staggered nested designs to assess scales of variability: the advantages of a spatially explicit analysis

A staggered nested sampling design was used to identify spatial scales of variation in the abundance of an intertidal clam Austrovenus stutchburyi. A georeferenced sampling design permitted assessment of abundance at spatial lags between 0.1 and 87 m. An analysis of variance approach produced imprecise estimates of variability, whereas spatially explicit analyses improved the resolution greatly. A geostatistical model identified the spatial scale of residual variance as 13 m and that of the asymptote of spatial dependence as 17 m. It also permitted mapping of bivalve abundance. Staggered nested designs are highly efficient for comparing hierarchies of scale, but in this study analysis of detailed positional information was required to tease out useful spatial information.     

美国EPA地表水质监测与评估的点位设计介绍

介绍了美国EPA地表水质监测与评估的点位设计。首先,指导性文件《基本水质监测方案》中的环境监测计划要求建立1个不少于1 000个点位的国家地表水质环境监测网,并提出了点位设计的标准,包括4点基本要求和针对每一类水域的具体要求。其次,另一指导性文件《准备州综合水质评估(305b报告)和电子升级:报告内容的准则》提出了1种新的对水质的综合性评估技术,要求在传统的判断点位设计的基础上增加概率统计点位设计方法。最后,全国一致的概率统计点位设计是相当有效的获知全国范围的水质情况及变化趋势的方法,EPA完全支持通过这种概率统计点位设计的方法来评估更多的水质状况。概率统计点位设计是EPA地表水质监测与评估的点位设计的发展趋势。     

Sampling of Sparse Species with Probability Proportional to Prediction

In this article, a 3P sampling (Probability Proportional to Prediction) approach is presented for surveying sparse species connected to certain types of substrates. The method uses the surveyors judgement of the probability of finding the species on a substrate as the base for selection of substrates for species inventories. The method is presented together with estimators, variances and variance estimators. The method is first presented for sampling based on all substrates in a study area and then as a subsampling technique in a two-stage design in which plots or strips are selected in a first stage. The presented approach was evaluated as a subsampling technique in a strip survey of calicioid lichen species associated with coarse broadleaf trees. In comparison with a simple random subsampling without replacement, 3P subsampling was in one study area found to be an improvement with 30–55% in terms of standard errors. The improvement was more modest in the other study area, only between 10–16%. The strip survey with 3P subsampling was more cost-efficient than a strip survey without subsampling except in one case. Based on the results in the test, 3P sampling seems to have a potential for sampling sparse species.     

Using statistical power analysis as a tool when designing a monitoring program: experience from a large-scale Swedish landscape monitoring program

The National Inventory of Landscapes in Sweden (NILS) is a large-scale, sample-based monitoring program that combines aerial photointerpretation with field inventory to follow landscape-scale biophysical conditions and changes. A statistical power analysis was conducted before the NILS program began in 2003 with the aim to determine an appropriate sampling effort and compare some design alternatives. The chosen sampling effort was then evaluated in a second power analysis conducted just before the first 5-year re-inventory rotation started. The latter power analysis revealed which magnitude of actual change might be detected within the future for different central monitoring variables. This article reports results from these power analyses and discusses our experiences in using power analysis as a tool for designing large-scale monitoring programs. The results showed that even quite small changes in the more common variables, such as land cover types and more common plant species, can be detected on the national scale. However, on the regional scale, or for less common variables, changes will be more difficult to detect. The power analyses have revealed the size level of changes that will be possible to detect. The results have also generated incentives for further improvements of NILS, e.g., input to the modification and revision of the variable content, flow and hierarchy, and incentives for launching other complementary monitoring programs connected to NILS. They have also created a basis for a better and more user-oriented communication of results from NILS to different stakeholders.     

Design of on-line river water quality monitoring systems using the entropy theory: a case study

The design of a water quality monitoring network is considered as the main component of water quality management including selection of the water quality variables, location of sampling stations and determination of sampling frequencies. In this study, an entropy-based approach is presented for design of an on-line water quality monitoring network for the Karoon River, which is the largest and the most important river in Iran. In the proposed algorithm of design, the number and location of sampling sites and sampling frequencies are determined by minimizing the redundant information, which is quantified using the entropy theory. A water quality simulation model is also used to generate the time series of the concentration of water quality variables at some potential sites along the river. As several water quality variables are usually considered in the design of water quality monitoring networks, the pair-wise comparison is used to combine the spatial and temporal frequencies calculated for each water quality variable. After selecting the sampling frequencies, different components of a comprehensive monitoring system such as data acquisition, transmission and processing are designed for the study area, and technical characteristics of the on-line and off-line monitoring equipment are presented. Finally, the assessment for the human resources needs, as well as training and quality assurance programs are presented considering the existing resources in the study area. The results show that the proposed approach can be effectively used for the optimal design of the river monitoring systems.     

北方缓流水体水质在线自动监测系统采样口的设计思考

以DO作为水体的污染指标来判定水质类别存在一定的缺陷,北方缓流水体在不同季节常存在不同的DO分层现象,现设在河床底以上0.5米处的自动监测采样口设计缺少科学性。在叙述缓流水体DO特点的同时,介绍一种新的水质在线自动监测采样口的方案,便于类似水体水质自动监测设计中借鉴。     

Groundwater monitoring plans at small-scale sites--an innovative spatial and temporal methodology

An innovative methodology for improving existing groundwater monitoring plans at small-scale sites is presented. The methodology consists of three stand-alone methods: a spatial redundancy reduction method, a well-siting method for adding new sampling locations, and a sampling frequency determination method. The spatial redundancy reduction method eliminates redundant wells through an optimization process that minimizes the errors in plume delineation and the average plume concentration estimation. The well-siting method locates possible new sampling points for an inadequately delineated plume via regression analysis of plume centerline concentrations and estimation of plume dispersivity values. The sampling frequency determination method recommends the future frequency of sampling for each sampling location based on the direction, magnitude, and uncertainty of the concentration trend derived from representative historical concentration data. Although the methodology is designed for small-scale sites, it can be easily adopted for large-scale site applications. The proposed methodology is applied to a small petroleum hydrocarbon-contaminated site with a network of 12 monitoring wells to demonstrate its effectiveness and validity.     

A feasible method to assess inaccuracy caused by patchiness in water quality monitoring

Patchiness is a typical property of water quality in lakes. However, in conventional water quality monitoring, patchiness is usually too expensive to take into account, due to the high number of required samples. This study examines a feasible methodology developed for estimating the representativeness of discrete chlorophyll a measurements. Four spatially extensive data sets were collected from the Enonselkä basin of Lake Vesijärvi in Southern Finland, using a flow trough system with a fluorometer in a moving boat. Data sets were used to estimate (1) the spatial representativeness of discrete sampling; (2) the effect of varying sample size on the detected mean chlorophyll a concentration and on the observed proportion of variance. Spatial representativeness was assessed using semivariogram analysis. Results indicate that the spatial representativeness of discrete sampling can remain undesirably low. Furthermore, in monitoring programs involving just one or only a few samples, there is a significant risk of obtaining a false estimate for the mean value and variance of chlorophyll a concentration over the whole monitoring area.