Sampling Benthic Macroinvertebrates in a Large Flood-Plain River: Considerations of Study Design, Sample Size, and Cost

Estimation of benthic macroinvertebrate populations over large spatial scales is difficult due to the high variability in abundance and the cost of sample processing and taxonomic analysis. To determine a cost-effective, statistically powerful sample design, we conducted an exploratory study of the spatial variation of benthic macroinvertebrates in a 37 km reach of the Upper Mississippi River. We sampled benthos at 36 sites within each of two strata, contiguous backwater and channel border. Three standard ponar (525 cm²) grab samples were obtained at each site ('Original Design'). Analysis of variance and sampling cost of strata-wide estimates for abundance of Oligochaeta, Chironomidae, and total invertebrates showed that only one ponar sample per site ('Reduced Design') yielded essentially the same abundance estimates as the Original Design, while reducing the overall cost by 63%. A posteriori statistical power analysis ( = 0.05, = 0.20) on the Reduced Design estimated that at least 18 sites per stratum were needed to detect differences in mean abundance between contiguous backwater and channel border areas for Oligochaeta, Chironomidae, and total invertebrates. Statistical power was nearly identical for the three taxonomic groups. The abundances of several taxa of concern (e.g., Hexagenia mayflies and Musculium fingernail clams) were too spatially variable to estimate power with our method. Resampling simulations indicated that to achieve adequate sampling precision for Oligochaeta, at least 36 sample sites per stratum would be required, whereas a sampling precision of 0.2 would not be attained with any sample size for Hexagenia in channel border areas, or Chironomidae and Musculium in both strata given the variance structure of the original samples. Community-wide diversity indices (Brillouin and 1-Simpsons) increased as sample area per site increased. The backwater area had higher diversity than the channel border area. The number of sampling sites required to sample benthic macroinvertebrates during our sampling period depended on the study objective and ranged from 18 to more than 40 sites per stratum. No single sampling regime would efficiently and adequately sample all components of the macroinvertebrate community.     

Serial Correlation and Inter-annual Variability in Relation to the Statistical Power of Monitoring Schemes to Detect Trends in Fish Populations

We studied the effects of inter-annual variability and serial correlation on the statistical power of monitoring schemes to detect trends in biomass of bream (Abramis brama) in Lake Veluwemeer (The Netherlands). In order to distinguish between 'true' system variability and sampling variability we simulated the development of the bream population, using estimates for population structure and growth, and compared the resulting inter-annual variabilities and serial correlations with those from field data. In all cases the inter-annual variability in the field data was larger than in simulated data (e.g. for total biomass of all assessed bream sigma = 0.45 in field data, and sigma = 0.03-0.14 in simulated data) indicating that sampling variability decreased statistical power for detecting trends. Moreover, sampling variability obscured the inter-annual dependency (and thus the serial correlation) of biomass, which was expected because in this long-lived population biomass changes are buffered by the many year classes present. We did find the expected serial correlation in our simulation results and concluded that good survey data of long-lived fish populations should show low sampling variability and considerable inter-annual serial correlation. Since serial correlation decreases the power for detecting trends, this means that even when sampling variability would be greatly reduced, the number of sampling years to detect a change of 15%.year(-1) in bream populations (corresponding to a halving or doubling in a six-year period) would in most cases be more than six. This would imply that the six-year reporting periods that are required by the Water Framework Directive of the European Union are too short for the existing fish monitoring schemes.     

Temporal scale-induced uncertainty in load duration curves for instream-dissolved oxygen

Load duration curves were developed using the Hydrological Simulation Program FORTRAN (HSPF) for dissolved oxygen (DO) for the Amite River in Louisiana, USA. The concept of ‘dissolved oxygen reserve’, defined as the total quantity of DO, is introduced. The effect of temporal resolution on duration curves of DO reserve was examined using duration curves developed based on daily, weekly, biweekly, and monthly average data. Duration curves for DO exhibited high variability in the load estimated using daily data as compared to those based on biweekly and monthly data. A seasonal analysis revealed the trend in the DO reserve. The daily DO reserve for the Amite River at Port Vincent was 44,049.31 kg when daily summer data were used and 74,255.15 kg for daily annual data. A surplus of 10,691 kg of DO reserve was shown in the monthly data during critical summer months. The coefficient of variation (CV), used to define the temporal scale-induced uncertainty, was found to be linearly and inversely correlated with the logarithm of the time scale. Regression equations were developed to extrapolate near real-time flow and water quality data, greatly simplifying flow and water quality monitoring and reducing the cost involved in flow and water quality monitoring.     

Determining storm sampling requirements for improving precision of annual load estimates of nutrients from a small forested watershed

This study sought to determine the lowest number of storm events required for adequate estimation of annual nutrient loads from a forested watershed using the regression equation between cumulative load (∑L) and cumulative stream discharge (∑Q). Hydrological surveys were conducted for 4 years, and stream water was sampled sequentially at 15-60-min intervals during 24 h in 20 events, as well as weekly in a small forested watershed. The bootstrap sampling technique was used to determine the regression (∑L-∑Q) equations of dissolved nitrogen (DN) and phosphorus (DP), particulate nitrogen (PN) and phosphorus (PP), dissolved inorganic nitrogen (DIN), and suspended solid (SS) for each dataset of ∑L and ∑Q. For dissolved nutrients (DN, DP, DIN), the coefficient of variance (CV) in 100 replicates of 4-year average annual load estimates was below 20% with datasets composed of five storm events. For particulate nutrients (PN, PP, SS), the CV exceeded 20%, even with datasets composed of more than ten storm events. The differences in the number of storm events required for precise load estimates between dissolved and particulate nutrients were attributed to the goodness of fit of the ∑L-∑Q equations. Bootstrap simulation based on flow-stratified sampling resulted in fewer storm events than the simulation based on random sampling and showed that only three storm events were required to give a CV below 20% for dissolved nutrients. These results indicate that a sampling design considering discharge levels reduces the frequency of laborious chemical analyses of water samples required throughout the year.     

Assessing temporal representativeness of water quality monitoring data

The effectiveness of different monitoring methods in detecting temporal changes in water quality depends on the achievable sampling intervals, and how these relate to the extent of temporal variation. However, water quality sampling frequencies are rarely adjusted to the actual variation of the monitoring area. Manual sampling, for example, is often limited by the level of funding and not by the optimal timing to take samples. Restrictions in monitoring methods therefore often determine their ability to estimate the true mean and variance values for a certain time period or season. Consequently, we estimated how different sampling intervals determine the mean and standard deviation in a specific monitoring area by using high frequency data from in situ automated monitoring stations. Raw fluorescence measurements of chlorophyll a for three automated monitoring stations were calibrated by using phycocyanin fluorescence measurements and chlorophyll a analyzed from manual water samples in a laboratory. A moving block bootstrap simulation was then used to estimate the standard errors of the mean and standard deviations for different sample sizes. Our results showed that in a temperate, meso-eutrophic lake, relatively high errors in seasonal statistics can be expected from monthly sampling. Moreover, weekly sampling yielded relatively small accuracy benefits compared to a fortnightly sampling. The presented method for temporal representation analysis can be used as a tool in sampling design by adjusting the sampling interval to suit the actual temporal variation in the monitoring area, in addition to being used for estimating the usefulness of previously collected data.     

Evaluating performance of stormwater sampling approaches using a dynamic watershed model

Accurate quantification of stormwater pollutant levels is essential for estimating overall contaminant discharge to receiving waters. Numerous sampling approaches exist that attempt to balance accuracy against the costs associated with the sampling method. This study employs a novel and practical approach of evaluating the accuracy of different stormwater monitoring methodologies using stormflows and constituent concentrations produced by a fully validated continuous simulation watershed model. A major advantage of using a watershed model to simulate pollutant concentrations is that a large number of storms representing a broad range of conditions can be applied in testing the various sampling approaches. Seventy-eight distinct methodologies were evaluated by "virtual samplings" of 166 simulated storms of varying size, intensity and duration, representing 14 years of storms in Ballona Creek near Los Angeles, California. The 78 methods can be grouped into four general strategies: volume-paced compositing, time-paced compositing, pollutograph sampling, and microsampling. The performances of each sampling strategy was evaluated by comparing the (1) median relative error between the virtually sampled and the true modeled event mean concentration (EMC) of each storm (accuracy), (2) median absolute deviation about the median or "MAD" of the relative error or (precision), and (3) the percentage of storms where sampling methods were within 10% of the true EMC (combined measures of accuracy and precision). Finally, costs associated with site setup, sampling, and laboratory analysis were estimated for each method. Pollutograph sampling consistently outperformed the other three methods both in terms of accuracy and precision, but was the most costly method evaluated. Time-paced sampling consistently underestimated while volume-paced sampling over estimated the storm EMCs. Microsampling performance approached that of pollutograph sampling at a substantial cost savings. The most efficient method for routine stormwater monitoring in terms of a balance between performance and cost was volume-paced microsampling, with variable sample pacing to ensure that the entirety of the storm was captured. Pollutograph sampling is recommended if the data are to be used for detailed analysis of runoff dynamics.     

Spatio-temporal variability of solid, total dissolved and labile metal: passive vs. discrete sampling evaluation in river metal monitoring

In order to obtain representative dissolved and solid samples from the aquatic environment, a spectrum of sampling methods are available, each one with different advantages and drawbacks. This article evaluates the use of discrete sampling and time-integrated sampling in illustrating medium-term spatial and temporal variation. Discrete concentration index (CI) calculated as the ratio between dissolved and solid metal concentrations in grab samples are compared with time-integrated concentration index (CI) calculated from suspended particulate matter (SPM) collected in sediment traps and labile metals measured by the diffusive gel in thin films (DGT) method, collected once a month during one year at the Seine River, upstream and downstream of the Greater Paris Region. Discrete CI at Bougival was found to be significantly higher than at Triel for Co, Cu, Mn, Ni and Zn, while discrete metal partitioning at Marnay was found to be similar to Bougival and Triel. However, when using time-integrated CI, not only was Bougival CI significantly higher than Triel CI, CI at Marnay was also found to be significantly higher than CI at Triel which was not observed for discrete CI values. Since values are time-averaged, dramatic fluctuations were smoothed out and significant medium-term trends were enhanced. As a result, time-integrated concentration index (CI) was able to better illustrate urbanization impact between sites when compared to discrete CI. The impact of significant seasonal phenomenon such as winter flood, low flow and redox cycles was also, to a certain extent, visible in time-integrated CI values at the upstream site. The use of time-integrated concentration index may be useful for medium- to long-term metal studies in the aquatic environment.     

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

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

Auditing the Accuracy of a Volunteer-Based Surveillance Program for an Aquatic Invader Bythotrephes

We tested the sampling methods of a volunteer-based monitoring program designed to detect the non-indigenous spiny water flea, Bythotrephes longimanus, and found that the program could detect the majority of Bythotrephes invasions. Volunteers take two vertical hauls with a 30 cm diameter net at each of three pelagic stations. To determine if the volunteers were using a large enough net at their three stations, we performed a 17-lake comparison of the volunteer's net with a 75 cm diameter, research-grade net. We found no difference in the number of stations at which Bythotrephes was detected (paired t-test, p = 0.155) with the two nets, because Bythotrephes densities were above the detection limits for both nets. To determine if three stations were sufficient to detect the invader with the volunteer's net, we deployed it at 30 stations in two lakes with average (Harp Lake, 4.17 Bythotrephes m(-3)) vs. low Bythotrephes densities (Sugar Lake, 0.92 m(-3)). In Harp Lake, repeated randomized sampling of the 30 sets of data indicated that only three stations were needed for 100% capture success. In Sugar Lake, seven stations were needed for 100% capture success, but three stations, the current program design, failed to detect the invasion only 14% of the time.     

Influence of different nitrate–N monitoring strategies on load estimation as a base for model calibration and evaluation

Model-based predictions of the impact of land management practices on nutrient loading require measured nutrient flux data for model calibration and evaluation. Consequently, uncertainties in the monitoring data resulting from sample collection and load estimation methods influence the calibration, and thus, the parameter settings that affect the modeling results. To investigate this influence, we compared three different time-based sampling strategies and four different load estimation methods for model calibration and compared the results. For our study, we used the river basin model Soil and Water Assessment Tool on the intensively managed loess-dominated Parthe watershed (315 km²) in Central Germany. The results show that nitrate–N load estimations differ considerably depending on sampling strategy, load estimation method, and period of interest. Within our study period, the annual nitrate–N load estimation values for the daily composite data set have the lowest ranges (between 9.8% and 15.7% maximum deviations related to the mean value of all applied methods). By contrast, annual estimation results for the submonthly and the monthly data set vary in greater ranges (between 24.9% and 67.7%). To show differences between the sampling strategies, we calculated the percentage deviation of mean load estimations of submonthly and monthly data sets as related to the mean estimation value of the composite data set. For nitrate–N, the maximum deviation is 64.5% for the submonthly data set in the year 2000. We used average monthly nitrate–N loads of the daily composite data set to calibrate the model to achieve satisfactory simulation results [Nash–Sutcliffe efficiency (NSE) 0.52]. Using the same parameter settings with submonthly and monthly data set, the NSE dropped to 0.42 and 0.31, respectively. Considering the different results from the monitoring strategy and the load estimation method, we recommend both the implementation of optimized monitoring programs and the use of multiple load estimation methods to improve water quality characterization and provide appropriate model calibration and evaluation data.     

Selection of appropriate sampling stations in a lake through mapping

Much valuable information is obtained from water quality measurements and monitoring of lakes around the world. A powerful tool is the use of mapping techniques, as it offers potential use in water quality research. Both remote sensing techniques and traditional water quality monitoring are required to collect data at sampling stations. This study suggests another approach to determine the most appropriate distribution of sampling stations in water reservoirs that will be mapped for water quality parameters. Tests were conducted for the proposed approach for Secchi disc depth (SDD), chlorophyll-a, turbidity and suspended solids parameters in Lake Beysehir, Turkey. Results of analysis are available for a total of 30 sampling stations in August 2006. Ten sampling stations were used to model Lake Beysehir while the others were used for validation of the model. Sampling stations that offered the best representation of the lake for each parameter were determined. Then, the best representative sampling stations for all parameters in the study were determined. Moreover, in order to confirm the accuracy of these re-determined sampling stations, modelling was performed on the results of the analysis of June 2006, and it was found that the values obtained from the re-determined sampling stations were acceptable.     

Effect of diel period and season on seining effort required to detect changes in Lake Erie beach fish assemblages

Using beach seining data from Lake Erie beaches, we examined the effect of diel period and season on two common study objectives considered sensitive to sampling effort: (1) has fish assemblage composition changed between two sampling time periods?, and (2) how many samples are necessary to provide a representative sample of the fish assemblage? Across a range of effect sizes and power levels, sample sizes were estimated for species richness, total fish abundance, round goby (Neogobius melanostomus) abundance and emerald shiner (Notropis atherinoides) abundance. For most sampling periods and effect sizes, several hundred to several thousand seine hauls were estimated to be required for detecting large-scale (e.g. basin-wide) changes in round goby and emerald shiner abundance. Depending on the indicator of interest, night seining (either spring or fall) was interpreted as the most efficient approach. For both seasons, sampling effort beyond 40 to 50 night seine hauls results in few additional species being detected. Both species accumulation curves and estimated sample sizes indicate that less effort is required to detect species richness changes than fish abundance. For most effect sizes and power levels, estimates of the number of seine hauls required to detect changes in fish abundance are unrealistically large for most monitoring programs. More practical monitoring strategies could be achieved by adopting alternative indicators (e.g. guilds), a more liberal significance level (p=0.1), a paired-site sampling design, or including reference sites.     

Dynamics of harmful dinoflagellates driven by temperature and salinity in a northeastern Mediterranean lagoon

To attempt to determine the effects of temperature and salinity on the dynamics of the dinoflagellate community, a monthly sampling was carried out from October 2008 to March 2009 at eight sampling stations in Ghar El Melh Lagoon (GML; Mediterranean Sea, Northern Tunisia). Dinoflagellates were dominant among plankton, accounting for 73.9 % of the lagoon’s overall plankton community, and were comprised of 25 different species among which 17 were reported in the literature as harmful. While no significant difference was found in the distribution of dinoflagellates among the stations, a strong monthly difference was observed. This temporal variability was due to an increase in the abundance of Prorocentrum micans from December to February, leading to a strong decrease in the Shannon diversity index from station to station. At the onset of P. micans development, dinoflagellate abundances reached 1.26.10⁵ cells l^?1. A redundance analysis indicates that both temperature and salinity have a significant effect on the dynamics of the dinoflagellate community. Using a generalized additive model, both temperature and salinity appear to have significant nonlinear relationships with P. micans abundances. Model predictions indicate that outbreaks of P. micans may occur at a temperature below 22.5 °C and with salinity above 32.5. We discuss our results against a backdrop of climate change which, by affecting temperature and salinity, is likely to have an antagonistic impact on P. micans development and subsequently on the dinoflagellate dynamics in GML.     

Towards a protocol for stream macroinvertebrate sampling in China

Standard protocols are critical for maximizing data comparability and aggregation in national monitoring programs, and taxa richness is a common indicator of site condition and biological diversity. There are two general approaches for sampling stream macroinvertebrate assemblages: targeted richest habitat and site wide. At seven sites, we compared three methods: Ontario Benthic Biomonitoring Network (OBBN), Environmental Monitoring and Assessment Program (EMAP), and Rapid Bioassessment Protocol (RBP). The OBBN method produced a biased sample at a site with a single small riffle, the RBP method produced the most total taxa, and the EMAP method produced the most taxa at four sites and the most individuals at six sites. The RBP method produced asymptotes for percent tolerant individuals, percent chironomid individuals, and Hilsenhoff Biotic Index score after five to ten stations. The EMAP method produced asymptotes for those metrics after 10 to 20 stations per site. The EMAP method typically required half the number of stations as the RBP method to obtain 70–90% of true taxa richness as estimated by the Jaccard coefficient. We conclude that the EMAP method is preferable because of its greater precision in taxa richness estimates.     

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.     

Siting analyses for water quality sampling in a catchment

Pollution loads discharged from upstream development or human activities significantly degrade the water quality of a reservoir. The design of an appropriate water quality sampling network is therefore important for detecting potential pollution events and monitoring pollution trends. However, under a limited budgetary constraint, how to site an appropriate number of sampling stations is a challenging task. A previous study proposed a method applying the simulated annealing algorithm to design the sampling network based on three cost factors including the number of reaches, bank length, and subcatchment area. However, these factors are not directly related to the distribution of possible pollution. Thus, this study modified the method by considering three additional factors, i.e. total phosphorus, nitrogen, and sediment loads. The larger the possible load, the higher the probability of a pollution event may occur. The study area was the Derchi reservoir catchment in Taiwan. Pollution loads were derived from the AGNPS model with rainfall intensity estimated using the Thiessen method. Analyses for a network with various numbers of sampling sites were implemented. The results obtained based on varied cost factors were compared and discussed. With the three additional factors, the chosen sampling network is expected to properly detect pollution events and monitor pollution distribution and temporal trends.     

The Use of Mytilus Galloprovincialis Acetylcholinesterase and Glutathione S-Transferases Activities as Biomarkers of Environmental Contamination Along the Northwest Portuguese Coast

With the aim of using Mytilus galloprovincialis acetylcholinesterase (AChE) and glutathione S-transferases (GST) activities as biomarkers of environmental contamination in risk assessment studies along the northwest Portuguese coast, the objective of this study was to provide background information related to: (1) baseline values of these enzymatic activities both in reference and contaminated areas and their responsiveness as indicators of exposure to environmental contaminants; (2) the importance of seasonal variations of such biomarkers in M. galloprovincialis along this area. For this, the activity of these enzymes was seasonally determined in mussels collected from four sites, including a reference and three stations with different contamination sources along the referred area. Statistically significant differences on both enzymatic activities were found among the four sampling stations, at the four sampling periods. In comparison to the reference station, lower AChE and higher GST activity values were found in mussels collected in stations potentially contaminated by pesticides and domestic/industrial effluents and in mussels collected in the vicinity of an oil refinery and an industrial/mercantile harbour, respectively. The results obtained in this work highlighted the potential suitability of these biomarkers to be used as components of environmental monitoring programs in risk assessment studies along the northwest Portuguese coast. Since a seasonal variation in both enzymatic activities was found, the possible implications of such variability in the use of these enzymes as environmental biomarkers are also discussed.     

A terrain-based paired-site sampling design to assess biodiversity losses from eastern hemlock decline

Biodiversity surveys are often hampered by the inability tocontrol extraneous sources of variability introduced intocomparisons of populations across a heterogenous landscape. If not specifically accounted for a priori, this noisecan weaken comparisons between sites, and can make itdifficult to draw inferences about specific ecologicalprocesses. We developed a terrain-based, paired-sitesampling design to analyze differences in aquaticbiodiversity between streams draining eastern hemlock (Tsuga canadensis) forests, and those draining mixedhardwood forests in Delaware Water Gap National RecreationArea (USA). The goal of this design was to minimize variancedue to terrain influences on stream communities, whilerepresenting the range of hemlock dominated streamenvironments present in the park. We used geographicinformation systems (GIS) and cluster analysis to define andpartition hemlock dominated streams into terrain types basedon topographic variables and stream order. We computedsimilarity of forest stands within terrain types and usedthis information to pair hemlock-dominated streams withhardwood counterparts prior to sampling. We evaluated theeffectiveness of the design through power analysis and foundthat power to detect differences in aquatic invertebratetaxa richness was highest when sites were paired and terraintype was included as a factor in the analysis. Precision ofthe estimated difference in mean richness was nearly doubledusing the terrain-based, paired site design in comparison toother evaluated designs. Use of this method allowed us tosample stream communities representative of park-wide forestconditions while effectively controlling for landscapevariability.     

Calculating of river water quality sampling frequency by the analytic hierarchy process (AHP)

River water quality sampling frequency is an important aspect of the river water quality monitoring network. A suitable sampling frequency for each station as well as for the whole network will provide a measure of the real water quality status for the water quality managers as well as the decision makers. The analytic hierarchy process (AHP) is an effective method for decision analysis and calculation of weighting factors based on multiple criteria to solve complicated problems. This study introduces a new procedure to design river water quality sampling frequency by applying the AHP. We introduce and combine weighting factors of variables with the relative weights of stations to select the sampling frequency for each station, monthly and yearly. The new procedure was applied for Jingmei and Xindian rivers, Taipei, Taiwan. The results showed that sampling frequency should be increased at high weighted stations while decreased at low weighted stations. In addition, a detailed monitoring plan for each station and each month could be scheduled from the output results. Finally, the study showed that the AHP is a suitable method to design a system for sampling frequency as it could combine multiple weights and multiple levels for stations and variables to calculate a final weight for stations, variables, and months.