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Sampling and monitoring the environment Marian Scott Sept 2006

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Outline Variation General sampling principles Methods of sampling –Simple random sampling –Stratified sampling –Systematic sampling –How many samples (power calculations) Spatial sampling –Grid, transect and cluster sampling

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Variation Natural variation in the attribute of interest, might be due to –feeding habits if measuring sheep, rainfall patterns if measuring plants Also variation/ uncertainty due to analytical measurement techniques. Natural variation may well exceed the analytical uncertainty Expect therefore that if you measure a series of replicate samples, they will vary and if there is sufficient you may be able to define the distribution of the attribute of interest.

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The normal density Symmetrical Defined by mean and variance

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Log-normal Very skew, long tail Defined by up to three parameters –Mean, scale and threshold

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The loge transformation Taking logs of the values, generates a distribution which is normal (i.e. symmetrical)

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Quantifying variation The simple way- coefficient of variation Defined as sample standard deviation, s divided by sample mean, typically expressed as a % For log normal, alternative geometric average and geometric standard deviation

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What is statistical sampling? Statistical sampling is a process that allows inferences about properties of a large collection of things (commonly described as the population), to be made from observations made on a relatively small number of individuals belonging to the population (the sample). In conducting statistical sampling, one is attempting to make inferences to the population.

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Statistical sampling The use of valid statistical sampling techniques increases the chance that a set of specimens (the sample, in the collective sense) is collected in a manner that is representative of the population. Statistical sampling also allows a quantification of the precision with which inferences or conclusions can be drawn about the population.

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Statistical sampling the issue of representativeness is important because of the variability that is characteristic of environmental measurements. Because of variability within the population, its description from an individual sample is imprecise, but this precision can be described in quantitative terms and improved by the choice of sampling design and sampling intensity (Peterson and Calvin, 1986).

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Good books The general sampling textbooks by Cochran (1977) and Thompson (1992), the environmental statistics textbook by Gilbert (1987), and papers by Anderson-Sprecher et al. (1994), Crepin and Johnson (1993), Peterson and Calvin (1986), and Stehman and Overton (1994).

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Know what you are setting out to do before you start describing a characteristic of interest (usually the average), describing the magnitude in variability of a characteristic, describing spatial patterns of a characteristic,mapping the spatial distribution, quantifying contamination above a background or specified intervention level detecting temporal or spatial trends, assessing human health or environmental impacts of specific facilities, or of events such as accidental releases, assessing compliance with regulations

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Rules Rule 1: specify the objective

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Use your scientific knowledge the nature of the population such as the physical or biological material of interest, its spatial extent, its temporal stability, and other important characteristics, the expected behaviour and environmental properties of the compound of interest in the population members, the sampling unit (i.e., individual sample or specimen), the expected pattern and magnitude of variability in the observations.

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Rules Rule 1: specify the objective Rule 2: use your knowledge of the environmental context

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What is the population? The concept of the population is important. The population is the set of all items that could be sampled, such as all fish in a lake, all people living in the UK, all trees in a spatially defined forest, or all 20-g soil samples from a field. Appropriate specification of the population includes a description of its spatial extent and perhaps its temporal stability

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What is the sampling unit? The environmental context helps define the sampling unit. It is not practical to consider sampling units so small that their concentration cannot be easily measured. to consider extremely large sampling units, if they are too difficult to manipulate or process. A sampling unit is a unique element of the population that can be selected as an individual sample for collection and measurement.

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Sampling units In some cases, sampling units are discrete entities (i.e., animals, trees), but in others, the sampling unit might be investigator-defined, and arbitrarily sized. Statistical sampling leads to a description of the sampled members of the population and inference(s) and conclusion(s) about the population as a whole.

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example Cyanotoxins in shellfish The objective here is to provide a measure (the average) of cyanotoxins in shellfish (eg mussels for human consumption) for the west coast of Scotland. Population is Sampling unit is.

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example Cyanotoxins in shellfish the population would be all mussels on the west coast. One problem with this study scenario is that the population is mussels on the west coast, but the sampled population may be just those mussels large enough to be caught by the prevailing commercial fishing methods. Sampling unit is

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Dioxins and PCBs One third of UK popn may exceed the recommended tolerable daily intake Objective: to reduce human intake levels Dioxins are trans-boundary, advective inputs contributing 30-70% of total The Stockholm convention on POP (to minimise and eliminate unintentionally produced POPs) in effect from 2004

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RIA Risks –Adverse toxicological effects –They are persistent, and bio-accumulate –Dietary intake is main route Significant reductions have already occurred in releases of dioxin and PCBs What are the costs and benefits of further emission reduction policies?

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Options Do nothing, –reductions are likely to continue but at slower rate than already achieved Further reduction measures –Would require to identify further sources; quantify the emissions; identify technology which which might give further reduction and cost;

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Evidence base Dietary surveys Laboratory studies (dose-effect) Emissions (national and industry specific) Where are the uncertainties?

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But a case in point Hites et al, 2004 Science article on PCB and dioxin in farmed and wild salmon (found that levels were highest in farmed salmon, and Scottish salmon one of the highest group. Health advice (using EPA guidelines) was consume less than one half-meal of salmon (55g) per month applied to scottish farm salmon Krebs (FSA) comments that levels of dioxin and PCB are within internationally recognised safety limits Wadge (FSA) comments that the risk assessment method used by EPA is different compared with WHO and EU and UK committee on toxicity Benefit of eating one portion of farmed salmon per week far outweighs any possible risk

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example Estimation of the baseline level of PCBs in salmon In order to estimate the baseline level of PCBs in salmon, representative samples need to be selected. For this particular problem, definition of the target population should include identification of the species (farmed or wild) and information on where and when it was caught. For analysis, if we suppose 10g of flesh is needed, then this completes the first three steps, resulting in a clear definition of the target population, namely all salmon within the location/fish farm and of a sampling unit, 10g of salmon flesh.

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representativeness An essential concept is that the taking of a sufficient number of individual samples should provide a collective sample that is representative of all samples that could be taken and thus provides a true reflection of the population.

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representativity A representative collective sample should reflect the population not only in terms of the attribute of interest, but also in terms of any incidental factors that affect the attribute of interest. Representativeness of environmental samples is difficult to demonstrate. Usually, representativeness is considered justified by the procedure used to select the samples.

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5 step approach Define the objectives and questions to be answered Summarize the environmental context for the quantities being measured. Identify the population, including spatial and temporal extent. Select an appropriate sampling design. Document the sampling design and its rationale.

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Methods Judgemental sampling non-probability based, based only on judgment. problems include the facts that the sample may be biased, that precision cannot be quantified, and that representativeness is unknown. Thus ultimately, it is not possible to evaluate the accuracy or bias of the estimator based on such a sample. It is clear that expert knowledge, allied with probability sampling, is far superior to judgmental sampling.

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Methods Simple random sampling With simple random sampling, every sampling unit in the population has, in theory, an equal probability of being included in the sample. The resulting estimator based on such a sample will be unbiased, but it may not be efficient, in either the statistical or practical senses. Simple random sampling designs are easy to describe but may be difficult to achieve in practice.

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Methods Two-stage sampling This design involves definition of primary units, some fraction of which is selected randomly, then the selected primary units are sub-divided and a fraction of the sub-units are selected randomly. At each stage, the units in the design may be sub-divided and randomly selected. This design is useful for components of variation estimation, and it can be cost-effective..

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Methods Stratified sampling The population is divided into strata, each of which is likely to be more homogeneous than the entire population. In other words, the individual strata have characteristics that allow them to be distinguished from the other strata, and such characteristics are known to affect the measured attribute of interest. Some ordinary sampling method (e.g., a simple random sample or systematic sample) is used to estimate the properties of each stratum.

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Methods Stratified sampling Usually, the proportion of sample observations taken in each stratum is similar to the stratum proportion of the population, but this is not a requirement. If good estimates are wanted for rare strata that have a small occurrence frequency in the population, then the number of samples taken from the rare strata can be increased. Stratified sampling is more complex and requires more prior knowledge than simple random sampling, and estimates of the population quantities can be biased if the stratum proportions are incorrectly specified.

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Methods Systematic sampling Systematic sampling is probably the most commonly used method for field sampling. It is generally unbiased as long as the starting point is randomly selected and the systematic rules are followed with care. Line transects and two dimensional grids are specific types of systematic samples that are described in more detail in the spatial section.

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Methods Systematic sampling Systematic sampling is often more practical than random sampling because the procedures are relatively easy to implement in practice, but this approach may miss important features if the quantity being sampled varies with regular periodicity and the sampling scheme has similar periodicity.

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Methods Cluster sampling Cluster sampling is most frequently applied in situations where members of the population are found in clusters or colonies. Then, clusters of individuals are selected randomly and all individuals within each cluster are selected and measured. Another variant would involve random selection of a fraction of the individuals within a cluster. Cluster sampling is a convenient and practical design if individuals naturally group within the population. Adaptive sampling is a form of cluster sampling in which decisions are made during the survey, particularly when a cluster, such as a community or herd, are detected unexpectedly.

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Methods Double sampling A procedure known as double sampling can be useful when one characteristic may be difficult or expensive to measure but another related characteristic is simple or easy to measure. This might involve making a relatively large number of analyses using the more efficient technique, and selecting a few specimens from this sample on which to make the more expensive analysis. Then, if the two techniques yield a reasonably strong predictive relationship, one can use data from the efficient technique and the relationship to make an inference to the entire sample.

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How many samples do I need? How many samples to do what? Estimate the mean (with a specified precision) Estimate the difference between two treatment groups Commonly classed as power calculations

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Compositing and pooling Suppose the average concentration in a wheat crop grown in a specific region is to be estimated. The major sampling decision would be to determine how many fields should be sampled and how they should be chosen.

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Compositing and pooling scheme 1, a single large sample is taken from a randomly located position in the field. scheme 2, the field is divided into four quarters and one small sample is taken from a randomly located position in each quarter, and then each sample is analyzed separately. scheme 3, four small samples are taken, as in scheme 2, and these are combined into a single sample before analysis.

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Compositing and pooling Compositing is a general technique for reducing the variability among sample units, and scheme 3 above is an example of compositing or pooling. It is especially useful when the cost for sample processing or analysis is high. In composite sampling, a specified number of sub-samples (wheat) are collected from a single sampling unit (field) and combined into a single sample before analysis. The average concentration in the analyzed sample is expected to be the same (assuming very low analysis uncertainty) as the average activity among the sub-samples, but this average is obtained from a single analysis.

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How would we choose which of the 3 schemes to adopt?

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Compositing and pooling If the field is homogeneous, with little small-scale spatial variability, all three sampling schemes have equal precision. If the field is heterogeneous, samples from scheme 1 are less precise than those from schemes 2 and 3. Schemes 2 and 3 are equally precise and require similar amounts of field effort, but scheme 2 requires analyzing four times as many samples as does scheme 3.

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Compositing and pooling If the cost of analysis is markedly higher than the cost of field work, scheme 3 provides an equally precise answer for substantially less cost. However, scheme 2 provides an estimate of within-field variation, while scheme 3 does not, which therefore limits statistical comparisons, say between fields.

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Practical issues Qn 1: not being able to follow exactly the pre-determined statistical sampling design. Qn 2: Absence of suitable material is a common source of missing values in environmental sampling. What do we do?

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So we have sampled, what next? Two of the most common sampling objectives are: estimation of the mean, or estimation of a proportion (e.g., the unknown fraction of a population > a specified value), We consider how to achieve these under different sampling schemes

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Estimate the population mean Simple random sampling every sampling unit in the population is expected to have an equal probability of being included in the sample. The first step requires complete enumeration of the population members. In the simple random-sampling scheme, one generates a set of random digits that are used to objectively identify the individuals to be sampled and measured.

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Estimate the population mean The sampling frame In simple random sampling, one might assume a population of N units (N 100-cm 2 areas), and use simple random sampling to select n of these units. This typically involves generation of n random digits between 1 and N, which would identify the units to sample. If a number is repeated, then one would simply generate a replacement digit.

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Estimate the population mean In the salmon example, if we imagine a fish farm cage, this would require a conceptual view of the population, as N fish numbered consecutively from 1 to N. The random digits generated then identify the fish to be sampled from the cage. From the n units sampled, suppose that the PCB concentration is measured in each sample, any one of which is denoted as y i ; then the sample average, is an unbiased estimate of the population mean PCB concentration and the sample variance,s 2, would provide an unbiased estimate of the population variance:

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Sample mean and variance

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Sampling error the sampling fraction f is usually very small and given by n/N.

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Number of samples needs you to state the desired limits of precision for the population inference (how precisely does one want to know the average PCB concentration, or, what size of difference is needed to be detected and with what precision?), state the inherent population variability of the attribute of interest, and derive an equation which relates the number (n) of samples with the desired precision of the parameter estimator and the degree of significance (the chance of being wrong in the inference).

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Number of samples Testing mean = null (versus not = null) Calculating power for mean = null + difference Alpha = 0.05 Assumed standard deviation = 1 Sample Target Difference Size Power Actual Power

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Number of samples What is the power? Power is a probability, it is the probability that we correctly conclude that the null hypothesis should be rejected. The null would say there is no difference/no effect/no trend. We want a high power

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Number of samples Testing mean = null (versus not = null) Calculating power for mean = null + difference Alpha = 0.05 Assumed standard deviation = 1 Sample Target Difference Size Power Actual Power

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PCB estimate the mean concentration with an estimated standard error (e.s.e.) precision of 0.1 mg kg -1. The variation of PCB in salmon flesh is Therefore, how many samples would be required? Since the e.s.e. of the sample mean is s/ n, then one must solve for n, for example:

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Sample size-too big Thus this degree of improvement in precision, can only be achieved by increasing the number of samples taken to approximately This may well be impractical; therefore the only solution may be to accept a lower precision.

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Example: 137 Cs contained activity (inventory) in sediment of an estuary Suppose wanted to estimate the inventory of 137 Cs in the sediments of an estuary whose boundaries have been clearly defined. Assume a precise estimate of the area (m 2 ) involved is available, so it becomes necessary to measure 137 Cs areal activity densities (Bq m -2 ), which are multiplied by the area to estimate the contained activity in Bq.

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What is? What is the population What is the sampling unit What is the context

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Stratified random sampling In stratified sampling, the population is divided into two or more strata that individually are more homogeneous than the entire population, and a sampling method is used to estimate the properties of each stratum. Usually, the proportion of sample observations in each stratum is similar to the stratum proportion in the population.

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Stratified random sampling In stratified sampling, the population of N units is first divided into sub-populations of N 1, N 2,….N L units. These sub-populations are non-overlapping and together comprise the whole population. The sub-populations are called strata. They need not have the same number of units, but, to obtain the full benefit of stratification, the sub-population sizes or areas must be known. In stratified sampling, a sample is drawn from each of the strata, the size of each sample ideally in proportion to the population size or area of that stratum.

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Sample mean and variance

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Systematic sampling Systematic sampling differs from the methods of random sampling in terms of practical implementation and in terms of coverage. Again, assume there are N (= nk) units in the population. Then to sample n units, a unit is selected for sampling at random. Then, subsequent samples are taken at every k units. Systematic sampling has a number of advantages over simple random sampling, not least of which is convenience of collection.

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Systematic sampling A systematic sample is thus spread more evenly over the population. In a spatial context such as the sediment sampling problem, this would involve laying out a regular grid of points, which are fixed distances apart in both directions within a plane surface.

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Systematic sampling Data from systematic designs are more difficult to analyze, especially in the most common case of a single systematic sample. Consider first the simpler case of multiple systematic samples. For example, xxx in pond sediment could be sampled using transects across the pond from one shoreline to the other. Samples are collected every 5m along the transect. The locations of the transects are randomly chosen. Each transect is a single systematic sample.

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Systematic sampling Each sample is identified by the transect number and the location along the transect. Suppose there are i = 1,.., t systematic samples (i.e. transect in the pond example) and the y ij is the jth observation on the ith systematic sample for j = 1,…, n i. The average of the samples from the ith transect is calculated.

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Population mean and variance estimates

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Back to the beginning, estimating the proportion SRS As before, a target population of N units is assumed, from which n samples are taken for measurement. Then, to estimate the population proportion, P, the obvious estimate is the sample proportion, p, where from a sample of size n, m units are observed that fall into the defined class

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Sample size calculation N large

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Stratified sampling Again, assume that we have l strata, and that the proportion in the class of interest in the stratum is P l = M l /N l. The estimate is p l = m l /n l and the proportion in the whole population, p st, is the weighted average of the sample proportions

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Stratified sampling

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Spatial sampling In ecology, spatial data usually fall into one of two different general cases: Case 1: We assume that there is an attribute that is spatially continuous, where in principle it is possible to measure the attribute at any location defined by coordinates (x, y) over the domain or area of interest. Case 2: The attribute is not continuous through space; it exists and can be measured only at specific locations.

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Random and stratified random sampling In random sampling, a random sample of locations at which the attribute is to be measured is chosen from the target population of locations. If there is knowledge of different strata over the sampling domain (such as soil type), the use of a stratified sample would be recommended and a random sample of locations would be selected within each strata. The data set is then given by the spatial coordinates of each measurement location and the measured value of the attribute at that location

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systematic sampling Usually, for systematic sampling the region is considered as being overlaid by a grid (rectangular or otherwise), and sampling locations are at gridline intersections at fixed distance apart in each of the two directions. The starting location is expected to be randomly selected. Both the extent of the grid and the spacing between locations are important. The sampling grid should span the area of interest (the population). If the goal of the study is to describe spatial correlations, the spacing between locations should be shorter than the range of the correlation.

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Quadrats and transects A quadrat is a well-defined area within which one or more samples are taken; it is usually square or rectangular in shape, with fixed dimensions. The position and orientation of the quadrat will be chosen as part of the sampling scheme. A line transect is a straight line along which samples are taken, the starting point and orientation of which will be chosen as part of the sampling scheme. In addition, the number of samples to be collected along the transect, and their spacing requires definition.

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Cluster sampling Cluster sampling is particularly useful in patchy environments. The population is divided into primary units; each primary unit is divided into secondary units. In the spatial context, all the sub- units are physically close together. A sampling design (e.g., a simple random sample or a systematic design) is used to select a subset of the primary units. Then, all the sub-units in the chosen primary units are sampled.

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Cluster sampling As an example, consider sampling lichens growing on boulders scattered in a forest. It would be difficult and perhaps impossible to enumerate all the lichens, from which to draw a simple random sample. A systematic sample would be a problem because there may not be any lichen at many of the grid points. A cluster sample then becomes a reasonable sampling method. The boulders are the primary units. The secondary units are the lichens.

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Time and time and space Category A: Temporal sampling with sampling units selected independently from the population at each time point. Category B: Temporal sampling with repeated measurements on the same sampling units. Category C: Environmental impact assessment. Category D: Spatio-temporal sampling

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Before-after-control-impact (BACI) designs One of the most plausible alternative explanations of a change is that the system changed on its own. That is, the observed change (from the before-event samples to the after-event samples) would have happened even in the absence of the known impact. One simple impact-assessment design evaluates this alternative by estimating the change at a control site presumed to be unaffected by the known event. Data are collected at four combinations of sites and times: affected and unaffected sites, each sampled before the impact and after the impact.

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Before-after-control-impact (BACI) designs The impact of the known event is estimated by the interaction between sites and times, i.e., the difference between the change at the impacted site and the change at the control site. The BACI design controls for additive temporal change unrelated to the known event. Elaborations on the basic BACI design include using multiple control sites to estimate spatial variability and spatial trends, multiple samples from the impacted area to estimate variability within the impacted area, and very frequent sampling to better characterize the nature of the impact.

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summary Sampling and monitoring the environment is carried out for many purposes, including estimation of certain characteristics. Many experimental and monitoring programs have multiple objectives that must be clearly specified before the sampling program is designed, because different purposes require different sampling strategies and sampling intensities in order to be efficient, and to permit general inferences.

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summary Statistical sampling is pertinent and necessary in ecology because of the natural stochastic variation that occurs in all environmental media, and the fact that this variation is usually much larger than variations associated with measurement uncertainties.

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