Reduction Of Sampling Frequency Research Articles

The dependence of forecasts on sampling frequency as a guide to optimizing monitoring in community ecology

AbstractFacing climate change and biodiversity loss, it is critical that ecology advances so that processes, such as species interactions and dynamics, can be correctly estimated and skillfully forecasted. As different processes occur on different time scales, the sampling frequency used to record them should intuitively match these scales. Yet, the effect of data sampling frequency on ecological forecasting accuracy is understudied. Using a simple simulated dataset as a baseline and a more complex high‐frequency plankton dataset, we tested how different sampling frequencies impacted abundance forecasts of different plankton classes and the estimation of their interactions. We then investigated whether plankton growth rates and body sizes could be used to select the most appropriate sampling frequency. The simple simulated dataset showed that the optimal sampling frequency scaled positively with growth rate. This finding was not repeated in the analyses of the plankton time series, however. There, we found that a reduction in sampling frequency worsened forecasts and led us to both over‐ and underestimate plankton interactions. This suggests that forecasting can be used to determine the ideal sampling frequency in scientific and monitoring programs. A better study design will improve theoretical understanding of ecology and advance policy measures dealing with current global challenges.

Optimising sampling frequency for monitoring heterotrophic protists in a marine ecosystem

Abstract Heterotrophic protists are essential components of the marine ecosystem, yet they are often excluded from monitoring programmes. With limited resources, monitoring strategies need to be optimised considering both scientific knowledge and available resources. In doing so, it is crucial to understand how sampling frequency affects the value of the data. We analysed 11 years of weekly heterotrophic protist time-series data from Station L4 in the Western English Channel to explore how different sampling intervals impact data quality. In the L4 dataset, comprising 55 protist taxa, the reduction of sampling frequency from weekly to four times a year at specific seasons decreased the number of taxa encountered by 38% for ciliates and 29% for heterotrophic dinoflagellates while the mean annual biomass or its mean variation were not affected. Furthermore, when samples were taken only four times a year, biomass peaks of the ten most important taxa were often missed. The primary motivator for this study was furthering the development of the heterotrophic protist monitoring in temperate and subarctic marine areas, e.g. the Baltic Sea. Based on our findings, we give recommendations on sampling frequency to optimise the value of heterotrophic protist monitoring.

An Investigation of Takagi-Sugeno Fuzzy Modeling for Spatial Prediction with Sparsely Distributed Geospatial Data

Fuzzy set theory has shown potential for reducing uncertainty as a result of data sparsity and also provides advantages for quantifying gradational changes like those of pollutant concentrations through fuzzy clustering based approaches. The ability to lower the sampling frequency and perform laboratory analyses on fewer samples, yet still produce an adequate pollutant distribution map, would reduce the initial cost of new remediation projects. To assess the ability of fuzzy modeling to make spatial predictions using fewer sample points, its predictive ability was compared with the ordinary kriging (OK) and inverse distance weighting (IDW) methods under increasingly sparse data conditions. This research used a Takagi–Sugeno (TS) fuzzy modelling approach with fuzzy c-means (FCM) clustering to make spatial predictions of the lead concentrations in soil. The performance of the TS model was very dependent on the number of outliers in the respective validation set. For modeling under sparse data conditions, the TS fuzzy modeling approach using FCM clustering and constant width Gaussian shaped membership functions did not show any advantages over IDW and OK for the type of data tested. Therefore, it was not possible to speculate on a possible reduction in sampling frequency for delineating the extent of contamination for new remediation projects.

Introducing sampling frequency estimation using variograms in water research with the example of nutrient loads in the Kis-Balaton Water Protection System (W Hungary)

In most of the world along with urbanization came the deterioration of surface waters. The case of Hungary's Lake Balaton (the largest shallow freshwater lake in Central Europe) and its catchment area was no exception. In the second part of the twentieth century it became clear that measures had to be taken to prevent further water quality deterioration. In order to do so, the Kis-Balaton Water Protection System (KBWPS) was established. To measure its effectiveness in filtering Lake Balaton's water input, total phosphorus and total nitrogen loads are regularly estimated. In this study the practice of temporal sampling frequency estimation is described using data series from the KBWPS in order to suggest a sampling frequency that is suitable for estimating annual nutrient loads and still produces a dataset from which environmental processes can be followed. In this case, temporal sampling frequency estimation was conducted using variogram analysis, before which trend removal was performed on four sampling sites and two daily sampled parameters for the years 1993–2007. Although the calculations were only carried out on two parameters they are applicable to other environmental parameters as well. Nutrient load estimation is used as a verification tool. The result of the variogram analysis pointed towards a three-day sampling frequency for the whole KBWPS in the case of total phosphorus and total nitrogen (if the purpose of the monitoring is nutrient estimation). This reduction in sampling frequency would make an approximately 50% cost reduction possible. The study gives an example of sampling frequency estimation which is a key question in every aspect of environmental science, so that hopefully it will be useful for scientists dealing with water protection issues.

Dynamic groundwater monitoring networks: a manageable method for reviewing sampling frequency

Optimization of a water quality network through a change in sampling frequency is the only way to increase cost-efficiency without any reduction in the robustness of the data. Existing techniques define optimal sampling frequency based on analysis of historical data from the monitoring network under investigation. Their application to a large network comprised of many sites and many monitored parameters is both technical and challenging. This paper presents a simple non-parametric method for reviewing sampling frequency that is consistent with highly censored environmental data and oriented towards reduction of sampling frequency as a cost-saving measure. Based on simple descriptive statistics, the method is applicable to large networks with long time series and many monitored parameters. The method also provides metrics for interpretation of newly collected data, which enables identification of sites for which a future change in sampling frequency may be necessary, ensuring that the monitoring network is both current and adaptive. Application of this method to the New Zealand National Groundwater Monitoring Programme indicates that reduction of sampling frequency at any site would result in a significant loss of information. This paper also discusses the potential for reducing analysis frequency as an alternative to reduction of sampling frequency.

Real-Time Dynamic Articulations in the 2-D Waveguide Mesh Vocal Tract Model

Time domain articulatory vocal tract modeling in one-dimensional (1-D) is well established. Previous studies into two-dimensional (2-D) simulation of wave propagation in the vocal tract have shown it to present accurate static vowel synthesis. However, little has been done to demonstrate how such a model might accommodate the dynamic tract shape changes necessary in modeling speech. Two methods of applying the area function to the 2-D digital waveguide mesh vocal tract model are presented here. First, a method based on mapping the cross-sectional area onto the number of waveguides across the mesh, termed a widthwise mapping approach is detailed. Discontinuity problems associated with the dynamic manipulation of the model are highlighted. Second, a new method is examined that uses a static-shaped rectangular mesh with the area function translated into an impedance map which is then applied to each waveguide. Two approaches for constructing such a map are demonstrated; one using a linear impedance increase to model a constriction to the tract and another using a raised cosine function. Recommendations are made towards the use of the cosine method as it allows for a wider central propagational channel. It is also shown that this impedance mapping approach allows for stable dynamic shape changes and also permits a reduction in sampling frequency leading to real-time interaction with the model

Prediction of the reproductive status of cattle on the basis of milk progesterone measures: model description

Reproductive management, in particular timely oestrus detection, is important for profitable dairy production. The aim of this study was to develop a biological model to predict reproductive state on the basis of milk progesterone measures. A number of additional inputs were incorporated to make use of other known effectors of reproductive performance that are not reflected in progesterone levels. These are: days from calving, breed, parity, signs of behavioural oestrus, insemination dates, pregnancy determinations, energy status, body fat status, milk urea content and reproductive disorders associated with calving. A dynamic, deterministic model was developed. It is designed to run each time a new trigger input (progesterone, behavioural oestrus, inseminations, pregnancy determinations) occurs using the current and previous values and can run in the absence of the additional inputs. The milk progesterone values are smoothed using an extended Kalman filter before being processed in the biological component of the model. The model predicts the reproductive status of the cow, which can be one of three mutually exclusive states: postpartum anoestrus, oestrus cycling, and potentially pregnant. The other model outputs are all reproductive status specific with the exception of days to next sample (DNS), which is calculated in each model run regardless of reproductive status. DNS is designed to feedback to the sampling system so that the frequency of milk sampling (i.e. progesterone measurement) can be varied according to the predicted likelihood of a future reproductive event, such as onset of oestrus cycling. The other model outputs are: risk of prolonged postpartum anoestrus, risk and type of ovarian cyst, onset of oestrus, likelihood of a potential insemination succeeding, and likelihood of being pregnant (following oestrus). The model was evaluated using three simulated datasets consisting of a timeseries of progesterone values centred on each of the three reproductive statuses and including relevant additional information. Test runs were carried out on the full datasets and then on reduced data. The data reductions were made by using only those values that would have been available if the model days to next sample function was used to control sampling frequency. The sensitivity of the model to noise in the raw progesterone data was examined by adding 1, 2, or 3 residual standard deviations (1.85 ng/ml) random variation to the original data and evaluating model performance. The model was found to be able to readily identify and distinguish reproductive states. A reduction in sampling frequency to 36% of original sample resulted in an average increase in days to detection of oestrus of 0.36. The addition of 1 S.D. noise did not cause additional oestruses to be detected and all oestruses were correctly identified. However, when 2 or 3 S.D. noise were added, the model found on average 1.4 and 3 extra oestruses. It was concluded that reproductive status can be predicted from milk progesterone values using a biological model and that such a model is robust to reductions in sampling frequency number and to a doubling in the random variation in the raw progesterone values. It therefore has the potential to provide the basis for a useful reproductive management tool.

Load estimation methodologies for British rivers and their relevance to the LOIS RACS(R) programme

Data collected from rivers within the LOIS study area have been analysed to test the reliability of commonly-used interpolation and extrapolation methods for estimating loads from the concentration and flow records that are typically available for rivers in Eastern England. Detailed time series of suspended sediment concentration and discharge, which were collected by means of turbidity monitoring and ultrasonic flow gauging on the River Wharfe at Tadcaster over a 6-month period, have been used to compute a reference load. These data were then artificially decimated to evaluate different procedures for calculating suspended sediment loads. None of the methods investigated produced very reliable load estimates when weekly suspended sediment concentration data were used, and replicate estimates ranged from 24.4 to 550.1% of the reference load. Extrapolation by means of a simple rating relationship was found to produce estimates of suspended sediment load with the highest level of accuracy and precision, but loads calculated by this procedure still varied from −57.2% to +29.1% of the true value at the 95% level of confidence. Reduction in sampling frequency from weekly to fortnightly or monthly intervals generally caused a greater underestimation and increased imprecision in the suspended sediment load estimates. The effect of applying different calculation procedures to estimate chemical fluxes was evaluated using information from the Harmonized Monitoring database for the River Derwent at Church Wilne. For 20 out of the 36 determinands investigated, the difference between the minimum and maximum load estimate, expressed as a percentage of the minimum value, exceeded 50%, and for five determinands, including arsenic in both dissolved and particulate form, the difference was 100% or more. The results of this preliminary analysis suggest that considerable caution should be exercised in calculating river fluxes to the LOIS study coastline, and underscore the need for new and improved procedures to be developed.

Selection of CSP-V continuous sampling plans

MIL-STD-1235C establishes standard procedures for the selection and implementation of single- and multi-level continuous sampling plans, such as CSP-1, CSP-F, CSP-2, CSP-T and CSP-V. CSP-V is a single-level continuous sampling procedure which provides for alternating sequences of 100%inspection (either at full or reduced clearance number) and sampling inspection. It requires a return to 100% inspection whenever a non-confirming unit is found during sampling inspection, but provides for a reduced clearance number upon demonstration of superior product quality. The CSP-V procedure serves as an alternative to the CSP-T procedure where a reduction in sampling frequency has no economic merit. In this paper, expressions for the average outgoing quality, the average fraction inspected and the operating characteristic function are derived using a Markov chain model. Four tables are given to enable the selection of CSP-V plans when the acceptable quality level or the limiting quality level and the average outgoing...