Power, precaution, Type II error and sampling design in assessment of environmental impacts

Increasingly, environmental managers attempt to incorporate precautionary principles into decision making. In any quantitative analysis of impacts, precaution is closely related to the power of the analysis to detect an impact. Designs of sampling to detect impacts are, however, complex because of natural spatial and temporal variability and the intrinsic nature of the statistical interactions which define impacts. Here, pulse and press responses and impacts that affect time courses (temporal variance) were modelled to determine the influences of increasing temporal replication—sampling more times in each of several longer periods before and again after an impact.Increasing the number of control or reference locations and number of replicate sample units at each time and place of sampling investigated the influence of spatial replication on power. From numerous scenarios of impacts, with or without natural spatial and temporal interactions (i.e. not caused by an impact), general recommendations are possible. Detecting press impacts requires maximal numbers of control locations. Shorter-term pulse impacts are best detected when the number of periods sampled is maximized. Impacts causing changes in temporal variance are most likely to be detected by sampling with the greatest possible number of periods or times within periods.To allow precautionary decision making, the type of predicted impact should be specified with its magnitude and duration. Only then can sampling be designed to be powerful, thereby allowing precautionary concepts to be invoked.     

Using Asymmetrical Designs for Environmental Impact Assessment of Unplanned Disturbances

Environmental impact assessment of unplanned disturbances is often difficult to accomplish due to the absence of ‘before’ data for the impacted sites. In an attempt to overcome this problem, a beyond BACI model is used in order to detect possible changes in the temporal patterns of variation when no previous data are available. The model attempted to detect changes in the abundance of macroinvertebrate species inhabiting the intertidal mussel matrix after an oil spill which occurred in northern Portugal. The detection of a significant impact failed, most probably due to low temporal replication. An extension of the analysis, including the hierarchical arrangement of temporal variability in periods, suggests that increasing the number of sampling times may result in a higher efficiency of the model.     

Does restoration work? It depends on how we measure success

The restoration of 4 partial stream barriers was evaluated in watersheds of Terra Nova National Park, Newfoundland, Canada from 2009 to 2011. Brook trout (n = 462) were tagged and tracked moving through our study sites using passive‐integrated transponder telemetry and the restoration actions were assessed using 3 different measures: passage success rates; the range of passable flows; and the availability of passable flows. We considered the observed results within a before‐after‐control‐impact (BACI) design that included reference reaches and pre‐restoration observations. The conclusions of BACI analyses were also contrasted with those that would have been obtained from commonly used before‐after (B‐A) or control‐impact (C‐I) study designs. While the restoration actions changed hydrological conditions in a way that should facilitate fish passage, our biological measures indicated that success was variable across culverts and within culverts depending on the measure evaluated. Furthermore, the natural temporal and spatial variability of fish movements often resulted in different conclusions between the more robust BACI design and the more commonly used B‐A and C‐I designs. Our results demonstrate that restoration of partial barriers may not always yield dramatic improvements. Furthermore, without suitable controls, the chances of drawing false conclusions regarding restorations in temporally and spatially dynamic systems are substantial.     

Detecting anthropogenic disturbance in an environment with multiple gradients of physical disturbance,Manukau Harbour,New Zealand

Demonstrating spatial or temporal gradients of effects on macrobenthic communities can be a useful way of providing strong empirical evidence of natural or anthropogenic disturbance. Gradient designs for environmental assessment are sensitive to change for point source data, enabling the scale of the effects of a disturbance to be readily identified. If the spatial scale that is sampled from the point source is adequate, problems of selecting control sites can be avoided. However, sources of spatial variation in macrobenthic communities, which are not related to the impact, can confound the use of gradient designs. This can occur if the natural spatial structure overlaps that of the gradient and cannot be identified either as a location or environmental covariable. The ability to detect point source impacts using a gradient design against natural spatial variability was tested using benthic macrofaunal data collected from Manukau Harbour, New Zealand. Treated sewage wastewater is discharged into the north-west area of the Manukau Harbour. Sandflats in the vicinity of the outfall are also subject to physical disturbance from wind-waves and strong tides. Ordination techniques and the testing of a priori predictions were used to try and separate the relative effects of organic and physical disturbance on the benthic communities. While the occurrence of other environmental disturbances along a gradient of anthropogenic disturbance makes interpretation of community pattern more difficult, the use of a gradient sampling layout, ordination analysis and the testing of a priori predictions enabled impacts of the anthropogenic and natural environmental disturbances to be interpreted. Gradient designs, therefore, provide a method of assessing complex impacts that operate over broad spatial and temporal scales.     

Spatial analyses of intertidal assemblages on sheltered rocky shores

Abstract Understanding processes in complex assemblages depends on good understanding of spatial and temporal patterns of structure at various spatial scales. There has been little quantitative information about spatial patterns and natural temporal changes in intertidal assemblages on sheltered rocky shores in temperate Australia. Natural changes and responses to anthropogenic disturbances in these habitats cannot be accurately measured and assessed without quantitative data on patterns of natural variability in space and through time. This paper describes some suitable quantitative methods for examining spatial and temporal patterns of diversity and abundances of highshore, midshore and lowshore intertidal assemblages and the important component species for a number of shores in a bay that has not been severely altered by human disturbance. Despite a diverse flora and fauna on these shores, the midshore and lowshore assemblages on sheltered shores were characterized by a few species which were also the most important in discriminating among assemblages on a shore and, for each assemblage, among different shores. The same set of species was also important for measuring small-scale patchiness within each assemblage (i.e. between replicate sites on a shore). Therefore, these data provide a rationale for selecting species that are useful for measuring differences and changes in abundance among places and times at different scales and, hence, can be used in the more complex sampling designs necessary to detect environmental impacts. There was considerable spatial variability in all assemblages and all species (or taxa) examined at scales of metres, tens of metres and kilometres. There were no clear seasonal trends for most measures, with as much or more variability at intervals of 3 months as from year to year. Most interactions between spatial and temporal measures were at the smallest scale, with different sites on the same shore generally showing different changes from time to time. The cause(s) of this apparently idiosyncratic variability1 were not examined, but some potential causes are discussed. These data are appropriate for testing hypotheses about the applicability of these findings to other relatively undisturbed sheltered shores, about effects of different anthropogenic disturbances on sheltered intertidal assemblages and to test hypotheses about differences in intertidal assemblages on sheltered versus wave-exposed shores.     

Spatial autocorrelation and sampling design in plant ecology

Using spatial analysis methods such as spatial autocorrelation coefficients (Moran's I and Geary's c) and kriging, we compare the capacity of different sampling designs and sample sizes to detect the spatial structure of a sugar-maple (Acer saccharum L.) tree density data set gathered from a secondary growth forest of southwestern Québec. Three different types of subsampling designs (random, systematic and systematic-cluster) with small sample sizes (50 and 64 points), obtained from this larger data set (200 points), are evaluated. The sensitivity of the spatial methods in the detection and the reconstruction of spatial patterns following the application of the various subsampling designs is discussed. We find that the type of sampling design plays an important role in the capacity of autocorrelation coefficients to detect significant spatial autocorrelation, and in the ability to accurately reconstruct spatial patterns by kriging. Sampling designs that contain varying sampling steps, like random and systematic-cluster designs, seem more capable of detecting spatial structures than a systematic design.Abbreviations UPGMA = Unweighted Pair-Group Method using Arithmetic Averages     

Randomized intervention analysis for detecting non-random change and management impact: Dragonfly examples

The quasi-experimental approach of before–after control–impact (BACI) sampling can help decide when changes are due to human activities rather than natural variability. Detailed arguments for and against BACI designs and analytic methods are widespread in the literature, but far less attention has been paid to the mechanics of analyzing a BACI experiment. This paper demonstrates randomized intervention analysis with user-friendly software, where observations are paired in time before and after intervention. We provide examples using dragonfly count data in vegetation removal experiments.     

Snorkelers impact on fish communities and algae in a temperate marine protected area

Multiple-use marine protected areas (MPAs) are used to manage marine resources, allocate space to different users and reduce conflicts while protecting marine biodiversity. In the Mediterranean, MPA managers are increasingly interested in containing the effects of coastal recreation within underwater trails, but snorkelers impacts on the surrounding ecosystem remain largely unknown. In a Mediterranean MPA, an underwater snorkeling trail was established to concentrate snorkelers and increase their awareness of marine habitats and species. The high level of summer frequentation may have negative impacts on the surrounding environment through trampling on the sessile flora or disturbance to the vagile fauna. We used a before-after-control-impact (BACI) design to analyse these potential human impacts. The structure of macroalgae and fish assemblages were used as indicators. Permutational multivariate analyses of variance (PERMANOVA) were carried out to assess potential temporal and spatial changes of the indicators between the trail and a control location within the adjacent no-take/no-use area. Fish communities and macroalgae were subjected to natural temporal trends but no significant impacts of snorkelers were found. Four reasons could explain the absence of snorkelers impact on the surrounding marine environment: (1) the absence of very fragile organisms within the trail (and the control no-take/no-use area) such as gorgonians or bryozoans; (2) the life cycle of the algae with a natural decreasing trend in summer, corresponding to the trail opening period; (3) only a few snorkelers are practicing apnoea; and (4) the information at the entrance and along the trail may influence the snorkelers’ behaviour.     

Sample design effects in landscape genetics

An important research gap in landscape genetics is the impact of different field sampling designs on the ability to detect the effects of landscape pattern on gene flow. We evaluated how five different sampling regimes (random, linear, systematic, cluster, and single study site) affected the probability of correctly identifying the generating landscape process of population structure. Sampling regimes were chosen to represent a suite of designs common in field studies. We used genetic data generated from a spatially-explicit, individual-based program and simulated gene flow in a continuous population across a landscape with gradual spatial changes in resistance to movement. Additionally, we evaluated the sampling regimes using realistic and obtainable number of loci (10 and 20), number of alleles per locus (5 and 10), number of individuals sampled (10–300), and generational time after the landscape was introduced (20 and 400). For a simulated continuously distributed species, we found that random, linear, and systematic sampling regimes performed well with high sample sizes (>200), levels of polymorphism (10 alleles per locus), and number of molecular markers (20). The cluster and single study site sampling regimes were not able to correctly identify the generating process under any conditions and thus, are not advisable strategies for scenarios similar to our simulations. Our research emphasizes the importance of sampling data at ecologically appropriate spatial and temporal scales and suggests careful consideration for sampling near landscape components that are likely to most influence the genetic structure of the species. In addition, simulating sampling designs a priori could help guide filed data collection efforts     

Patterns of spatial and temporal variation of macrofauna under boulders in a sheltered boulder field

Abstract Spatial and temporal patterns of abundance of animals and plants must be quantified before models to explain distributions can be developed. These patterns also provide essential data for measuring potential effects of environmental disturbances. Studies in many different habitats have shown that most organisms, particularly invertebrates, have highly variable and interactive patterns of abundance, with much variability at the smallest temporal and spatial scales. Intertidal boulder fields in New South Wales, Australia, support a diverse fauna, many species of which are relatively rare. These habitats are commonly found near rock‐platforms and in sheltered estuaries and are subjected to many human disturbances. Although there have been a few studies on the fauna in boulder fields, none has documented variability of the assemblage using multivariate and univariate techniques and most studies have not incorporated different spatial and temporal scales. This study quantifies spatial variation at three scales (metres, tens of metres alongshore and tens of metres upshore) and temporal variation at two scales (3 months and 2 years) of the assemblage of molluscs and echinoderms in a sheltered boulder field subjected to little natural or human disturbance. Multivariate analyses revealed that each site contained a distinct assemblage, mainly due to the relative abundances of a few species. Most species, those generally only found under boulders and common, widespread species, had considerable spatial variability in abundances, with more than 90% measured at the smallest scale, that is metre to metre within a site. Changes in abundances over 3 months or 2 years varied among species and sites in unpredictable ways. These data show that sampling designs to measure impacts on these fauna will need to be complex and must incorporate a number of spatial and temporal scales if they are to be able to detect impact against such a variable background.     

Optimizing the trade‐off between spatial and genetic sampling efforts in patchy populations: towards a better assessment of functional connectivity using an individual‐based sampling scheme

Genetic data are increasingly used in landscape ecology for the indirect assessment of functional connectivity, that is, the permeability of landscape to movements of organisms. Among available tools, matrix correlation analyses (e.g. Mantel tests or mixed models) are commonly used to test for the relationship between pairwise genetic distances and movement costs incurred by dispersing individuals. When organisms are spatially clustered, a population‐based sampling scheme (PSS) is usually performed, so that a large number of genotypes can be used to compute pairwise genetic distances on the basis of allelic frequencies. Because of financial constraints, this kind of sampling scheme implies a drastic reduction in the number of sampled aggregates, thereby reducing sampling coverage at the landscape level. We used matrix correlation analyses on simulated and empirical genetic data sets to investigate the efficiency of an individual‐based sampling scheme (ISS) in detecting isolation‐by‐distance and isolation‐by‐barrier patterns. Provided that pseudo‐replication issues are taken into account (e.g. through restricted permutations in Mantel tests), we showed that the use of interindividual measures of genotypic dissimilarity may efficiently replace interpopulation measures of genetic differentiation: the sampling of only three or four individuals per aggregate may be sufficient to efficiently detect specific genetic patterns in most situations. The ISS proved to be a promising methodological alternative to the more conventional PSS, offering much flexibility in the spatial design of sampling schemes and ensuring an optimal representativeness of landscape heterogeneity in data, with few aggregates left unsampled. Each strategy offering specific advantages, a combined use of both sampling schemes is discussed.     

Exploring the robustness of macrophyte-based classification methods to assess the ecological status of coastal and transitional ecosystems under the Water Framework Directive

Identifying and quantifying the factors that contribute to the potential misclassification of the ecological status of water bodies is a major challenge of the Water Framework Directive (WFD). The present study compiles extensive biomonitoring data from a range of macrophyte-based classification methods developed by several European countries. The data reflect spatial and temporal variation as well as inter-observer variation. Uncertainty analysis identified that factors related to the spatial scale of sampling generally contributed most to the uncertainty in classifying water bodies to their ecological status, reflecting the high horizontal and depth-related heterogeneity displayed by macrophyte communities. In contrast, the uncertainty associated with temporal variation was low. In addition, inter-observer variation, where assessed, did not contribute much to overall uncertainty, indicating that these methods are easily transferable and insensitive to observer error. The study, therefore, suggests that macrophyte-based sampling schemes should prioritize large spatial replication over temporal replication to maximize the effectiveness and reliability of water body classification within the WFD. We encourage conducting similar uncertainty analyses for new/additional ecological indicators to optimize sampling schemes and improve the reliability of classification of ecological status.     

Application of repeated measures designs in environmental impact and monitoring studies

Abstract Traditional environmental studies have employed sampling at different times, but based on re-randomized ‘replicate’ samples taken at each time. For example, in a 4 year monitoring study of near-shore marine benthic communities there might be three box cores collected annually at each of three depths along each of three transects. Repeated measures designs, long used in medicine and the social sciences, are based on resampling replicates (e.g. sites) at a series of times. In such designs spatial sampling variability is not used for tests of environmental impact. Error for such tests is based on variability of time trends among similar sites (similar with respect to impact). For example in a tropical oil spill study five oiled and five unoiled coral reefs were studied over 5 years. Error for tests of oil impact was based on variability among reefs (within degree-of-oiling category) in the year-to-year trends of biological response variables. It was not based on variability among field samples within reefs at given times. The two approaches (univariate and multivariate) to repeated measures analysis of variance are described. The pros and cons of each are discussed, as are the assumptions and consequences of their violations. Emphasis is especially placed on the adequacy of error degrees of freedom in the two approaches, and some exploration of power to detect impact is presented. Examples of application of repeated measures designs to various impact and monitoring studies are presented and discussed, including (i) interpretation of significant effects; (ii) decomposition of effects by contrasts (e.g. before vs after impact); and (iii) modelling time trends by polynomial and cosine functions.     

Use of classical bird census transects as spatial replicates for hierarchical modeling of an avian community

New monitoring programs are often designed with some form of temporal replication to deal with imperfect detection by means of occupancy models. However, classical bird census data from earlier times often lack temporal replication, precluding detection‐corrected inferences about occupancy. Historical data have a key role in many ecological studies intended to document range shifts, and so need to be made comparable with present‐day data by accounting for detection probability. We analyze a classical bird census conducted in the region of Murcia (SE Spain) in 1991 and 1992 and propose a solution to estimating detection probability for such historical data when used in a community occupancy model: the spatial replication of subplots nested within larger plots allows estimation of detection probability. In our study, the basic sample units were 1‐km transects, which were considered spatial replicates in two aggregation schemes. We fit two Bayesian multispecies occupancy models, one for each aggregation scheme, and evaluated the linear and quadratic effect of forest cover and temperature, and a linear effect of precipitation on species occupancy probabilities. Using spatial rather than temporal replicates allowed us to obtain individual species occupancy probabilities and species richness accounting for imperfect detection. Species‐specific occupancy and community size decreased with increasing annual mean temperature. Both aggregation schemes yielded estimates of occupancy and detectability that were highly correlated for each species, so in the design of future surveys ecological reasons and cost‐effective sampling designs should be considered to select the most suitable aggregation scheme. In conclusion, the use of spatial replication may often allow historical survey data to be applied formally hierarchical occupancy models and be compared with modern‐day data of the species community to analyze global change process.     

Improving sampling designs for measuring restoration in aquatic habitats

Restoration of degraded habitat is an increasingly important toll for management. Unfortunately, much of the emphasis has been on restoring large structural elements of habitat (e.g. planting vegetation,removing weeds), with little consideration about how well these activities restore ecologically functioning habitat. There has been considerable research in recent years into improving sampling designs and analytical techniques to measure the effects of environmental impacts taking into account the large spatial and temporal variability that occurs naturally in undisturbed habitats. In a similar manner to detection of impacts, restoration needs to be measured as an interaction between spatial and temporal components of variation against a variable background. Very few studies of restoration have explicitly addressed how best to do this. Neither have they attempted to assess the usefulness of some of these new techniques for measuring restoration. This review discusses some of the problems that need to be considered when measuring restoration and the potential value of some of these new methodologies. This revised version was published online in July 2006 with corrections to the Cover Date.     

Monitoring temporal trends in spatially structured populations: how should sampling effort be allocated between space and time?

Estimating temporal trends in spatially structured populations has a critical role to play in understanding regional changes in biological populations and developing management strategies. Designing effective monitoring programmes to estimate these trends requires important decisions to be made about how to allocate sampling effort among spatial replicates (i.e. number of sites) and temporal replicates (i.e. how often to survey) to minimise uncertainty in trend estimates. In particular, the optimal mix of spatial and temporal replicates is likely to depend upon the spatial and temporal correlations in population dynamics. Although there has been considerable interest in the ecological literature on understanding spatial and temporal correlations in species’ population dynamics, little attention has been paid to its consequences for monitoring design. We address this issue using model‐based survey design to identify the optimal allocation of sampling effort among spatial and temporal replicates for estimating population trends under different levels of spatial and temporal correlation. Based on linear trends, we show that how we should allocate sampling effort among spatial and temporal replicates depends crucially on the spatial and temporal correlations in population dynamics, environmental variation, observation error and the spatial variation in temporal trends. When spatial correlation is low and temporal correlation is high, the best option is likely to be to sample many sites infrequently, particularly when observation error and/or spatial variation in temporal trends are high. When spatial correlation is high and temporal correlation is low, the best option is likely to be to sample few sites frequently, particularly when observation error and/or spatial variation in temporal trends are low. When abundances are spatially independent, it is always preferable to maximise spatial replication. This provides important insights into how spatio‐temporal monitoring programmes should be designed to estimate temporal trends in spatially structured populations.     

Dealing with Trade-Offs in Destructive Sampling Designs for Occupancy Surveys

Occupancy surveys should be designed to minimise false absences. This is commonly achieved by increasing replication or increasing the efficiency of surveys. In the case of destructive sampling designs, in which searches of individual microhabitats represent the repeat surveys, minimising false absences leads to an inherent trade-off. Surveyors can sample more low quality microhabitats, bearing the resultant financial costs and producing wider-spread impacts, or they can target high quality microhabitats were the focal species is more likely to be found and risk more severe impacts on local habitat quality. We show how this trade-off can be solved with a decision-theoretic approach, using the Millewa Skink Hemiergis millewae from southern Australia as a case study. Hemiergis millewae is an endangered reptile that is best detected using destructive sampling of grass hummocks. Within sites that were known to be occupied by H. millewae, logistic regression modelling revealed that lizards were more frequently detected in large hummocks. If this model is an accurate representation of the detection process, searching large hummocks is more efficient and requires less replication, but this strategy also entails destruction of the best microhabitats for the species. We developed an optimisation tool to calculate the minimum combination of the number and size of hummocks to search to achieve a given cumulative probability of detecting the species at a site, incorporating weights to reflect the sensitivity of the results to a surveyor’s priorities. The optimisation showed that placing high weight on minimising volume necessitates impractical replication, whereas placing high weight on minimising replication requires searching very large hummocks which are less common and may be vital for H. millewae. While destructive sampling methods are sometimes necessary, surveyors must be conscious of the ecological impacts of these methods. This study provides a simple tool for identifying sampling strategies that minimise those impacts.     

Comparison of point counts and territory mapping for detecting effects of forest management on songbirds

Point counts are commonly used to assess changes in bird abundance, including analytical approaches such as distance sampling that estimate density. Point‐count methods have come under increasing scrutiny because effects of detection probability and field error are difficult to quantify. For seven forest songbirds, we compared fixed‐radii counts (50 m and 100 m) and density estimates obtained from distance sampling to known numbers of birds determined by territory mapping. We applied point‐count analytic approaches to a typical forest management question and compared results to those obtained by territory mapping. We used a before–after control impact (BACI) analysis with a data set collected across seven study areas in the central Appalachians from 2006 to 2010. Using a 50‐m fixed radius, variance in error was at least 1.5 times that of the other methods, whereas a 100‐m fixed radius underestimated actual density by >3 territories per 10 ha for the most abundant species. Distance sampling improved accuracy and precision compared to fixed‐radius counts, although estimates were affected by birds counted outside 10‐ha units. In the BACI analysis, territory mapping detected an overall treatment effect for five of the seven species, and effects were generally consistent each year. In contrast, all point‐count methods failed to detect two treatment effects due to variance and error in annual estimates. Overall, our results highlight the need for adequate sample sizes to reduce variance, and skilled observers to reduce the level of error in point‐count data. Ultimately, the advantages and disadvantages of different survey methods should be considered in the context of overall study design and objectives, allowing for trade‐offs among effort, accuracy, and power to detect treatment effects.     

Adaptive web sampling

A flexible class of adaptive sampling designs is introduced for sampling in network and spatial settings. In the designs, selections are made sequentially with a mixture distribution based on an active set that changes as the sampling progresses, using network or spatial relationships as well as sample values. The new designs have certain advantages compared with previously existing adaptive and link-tracing designs, including control over sample sizes and of the proportion of effort allocated to adaptive selections. Efficient inference involves averaging over sample paths consistent with the minimal sufficient statistic. A Markov chain resampling method makes the inference computationally feasible. The designs are evaluated in network and spatial settings using two empirical populations: a hidden human population at high risk for HIV/AIDS and an unevenly distributed bird population.     

Optimizing cost-efficiency of long term monitoring programs by using spatially balanced sampling designs: The case of manila clams in Arcachon bay

Lack of funds is one major issue in ecology, in particular at local scale. It is known that sustainable management of a natural population requires a good understanding of its functioning, itself dependent on a good long term monitoring program. Such programs are usually very difficult to implement, especially for resources characterized by high spatio-temporal variation in their distribution, resulting in a trade off between efficiency and costs. Today, thanks to rapidly evolving statistical theory, new survey designs are developed, some with the characteristic of well balancing samples in the study area. This paper aims at demonstrating that theses advanced sampling designs perform better than the usual ones for long term monitoring program of local resources, with the added benefices of saving money and also increasing results accuracy. To prove it, and for it high spatio-temporal variation in it distribution, we choose the example of Manila clam's stock monitoring in Arcachon bay. This stock is under high scrutiny and last campaigns could not be done because of lack of funding (at least 50,000€/survey). We use a simulation study based on real data to assess and compare performances of news and older sampling designs on this survey. Three sampling designs are tested in both of the 6 past monitoring campaigns data and we estimate the cost of their application in the field. Selected sampling designs are: 1 - simple random sampling (SRS - the one used in the past years of this monitoring program), 2 - generalized tessellation sampling (GRTS - a recent spatially balanced sampling design known for its high performance) and, 3 - balanced acceptance sampling design (BAS - a newly developed spatially balanced sampling design, never tested yet in a real population). We first confirm that the two spatially balanced sampling designs perform better than simple random sampling. Both of the advanced sampling designs perform equally and allow achieving same accuracy in the results with almost half sampling intensity than SRS. This makes them so cost-effective that 30% of each campaign price could be saved if they were used. Moreover, the three sampling designs need a constant sample size thought years to achieve a fixed accuracy in results. This will permit us to fix one sample size that could be done for all future campaigns; and this, despite the existence of spatial and temporal variations in clam's distribution.