Sampling strategies for tropical forest nutrient cycling studies: a case study in São Paulo, Brazil

The precise sampling of soil, biological or micro climatic attributes in tropical forests, which are characterized by a high diversity of species and complex spatial variability, is a difficult task. We found few basic studies to guide sampling procedures. The objective of this study was to define a sampling strategy and data analysis for some parameters frequently used in nutrient cycling studies, i. e., litter amount, total nutrient amounts in litter and its composition (Ca, Mg, Κ, Ν and P), and soil attributes at three depths (organic matter, Ρ content, cation exchange capacity and base saturation). A natural remnant forest in the West of São Paulo State (Brazil) was selected as study area and samples were collected in July, 1989. The total amount of litter and its total nutrient amounts had a high spatial independent variance. Conversely, the variance of litter composition was lower and the spatial dependency was peculiar to each nutrient. The sampling strategy for the estimation of litter amounts and the amount of nutrient in litter should be different than the sampling strategy for nutrient composition. For the estimation of litter amounts and the amount of nutrients in litter (related to quantity) a large number of randomly distributed determinations are needed. Otherwise, for the estimation of litter nutrient composition (related to quality) a smaller amount of spatially located samples should be analyzed. The determination of sampling for soil attributes differed according to the depth. Overall, surface samples (0-5 cm) showed high short distance spatial dependent variance, whereas, subsurface samples exhibited spatial dependency in longer distances. Short transects with sampling interval of 5-10 m are recommended for surface sampling. Subsurface samples must also be spatially located, but with transects or grids with longer distances between sampling points over the entire area. Composite soil samples would not provide a complete understanding of the relation between soil properties and surface dynamic processes or landscape aspects. Precise distribution of Ρ was difficult to estimate.

Sampling in chemical analysis

The uncertainty of any analytical determination depends on analysis and sampling. Uncertainty arising from sampling is usually not controlled and methods for its evaluation are still little known. Pierre Gy’s sampling theory is currently the most complete theory about samplingwhich also takes the design of the sampling equipment into account. Guides dealing with the practical issues of sampling also exist, published by international organizations such as EURACHEM, IUPAC (International Union of Pure and Applied Chemistry) and ISO (International Organization for Standardization). In this work Gy’s sampling theory was applied to several cases, including the analysis of chromite concentration estimated on SEM (Scanning Electron Microscope) images and estimation of the total uncertainty of a drug dissolution procedure. The results clearly show that Gy’s sampling theory can be utilized in both of the above-mentioned cases and that the uncertainties achieved are reliable. Variographic experiments introduced in Gy’s sampling theory are beneficially applied in analyzing the uncertainty of auto-correlated data sets such as industrial process data and environmental discharges. The periodic behaviour of these kinds of processes can be observed by variographic analysis as well as with fast Fourier transformation and auto-correlation functions. With variographic analysis, the uncertainties are estimated as a function of the sampling interval. This is advantageous when environmental data or process data are analyzed as it can be easily estimated how the sampling interval is affecting the overall uncertainty. If the sampling frequency is too high, unnecessary resources will be used. On the other hand, if a frequency is too low, the uncertainty of the determination may be unacceptably high. Variographic methods can also be utilized to estimate the uncertainty of spectral data produced by modern instruments. Since spectral data are multivariate, methods such as Principal Component Analysis (PCA) are needed when the data are analyzed. Optimization of a sampling plan increases the reliability of the analytical process which might at the end have beneficial effects on the economics of chemical analysis,

Variograms of ancillary data to aid sampling for soil surveys

To provide reliable estimates for mapping soil properties for precision agriculture requires intensive sampling and costly laboratory analyses. If the spatial structure of ancillary data, such as yield, digital information from aerial photographs, and soil electrical conductivity (EC) measurements, relates to that of soil properties they could be used to guide the sampling intensity for soil surveys. Variograins of permanent soil properties at two study sites on different parent materials were compared with each other and with those for ancillary data. The ranges of spatial dependence identified by the variograms of both sets of properties are of similar orders of magnitude for each study site, Maps of the ancillary data appear to show similar patterns of variation and these seem to relate to those of the permanent properties of the soil. Correlation analysis has confirmed these relations. Maps of kriged estimates from sub-sampled data and the original variograrns showed that the main patterns of variation were preserved when a sampling interval of less than half the average variogram range of ancillary data was used. Digital data from aerial photographs for different years and EC appear to show a more consistent relation with the soil properties than does yield. Aerial photographs, in particular those of bare soil, seem to be the most useful ancillary data and they are often cheaper to obtain than yield and EC data.

Estimating the spatial scale of herbicide and soil interactions by nested sampling, hierarchical analysis of variance and residual maximum likelihood

An unbalanced nested sampling design was used to investigate the spatial scale of soil and herbicide interactions at the field scale. A hierarchical analysis of variance based on residual maximum likelihood (REML) was used to analyse the data and provide a first estimate of the variogram. Soil samples were taken at 108 locations at a range of separating distances in a 9 ha field to explore small and medium scale spatial variation. Soil organic matter content, pH, particle size distribution, microbial biomass and the degradation and sorption of the herbicide, isoproturon, were determined for each soil sample. A large proportion of the spatial variation in isoproturon degradation and sorption occurred at sampling intervals less than 60 m, however, the sampling design did not resolve the variation present at scales greater than this. A sampling interval of 20-25 m should ensure that the main spatial structures are identified for isoproturon degradation rate and sorption without too great a loss of information in this field.

Adaptive, Decentralized, And Real-Time Sampling Strategies For Resource Constrained Hydraulic And Hydrologic Sensor Networks

We discuss the development and performance of a low-power sensor node (hardware, software and algorithms) that autonomously controls the sampling interval of a suite of sensors based on local state estimates and future predictions of water flow. The problem is motivated by the need to accurately reconstruct abrupt state changes in urban watersheds and stormwater systems. Presently, the detection of these events is limited by the temporal resolution of sensor data. It is often infeasible, however, to increase measurement frequency due to energy and sampling constraints. This is particularly true for real-time water quality measurements, where sampling frequency is limited by reagent availability, sensor power consumption, and, in the case of automated samplers, the number of available sample containers. These constraints pose a significant barrier to the ubiquitous and cost effective instrumentation of large hydraulic and hydrologic systems. Each of our sensor nodes is equipped with a low-power microcontroller and a wireless module to take advantage of urban cellular coverage. The node persistently updates a local, embedded model of flow conditions while IP-connectivity permits each node to continually query public weather servers for hourly precipitation forecasts. The sampling frequency is then adjusted to increase the likelihood of capturing abrupt changes in a sensor signal, such as the rise in the hydrograph – an event that is often difficult to capture through traditional sampling techniques. Our architecture forms an embedded processing chain, leveraging local computational resources to assess uncertainty by analyzing data as it is collected. A network is presently being deployed in an urban watershed in Michigan and initial results indicate that the system accurately reconstructs signals of interest while significantly reducing energy consumption and the use of sampling resources. We also expand our analysis by discussing the role of this approach for the efficient real-time measurement of stormwater systems.

Effects of different sampling intervals on apparent protein and energy digestibility of common feed ingredients by juvenile oscar fish (Astronotus ocellatus)

O presente estudo avaliou a digestibilidade aparente da proteína e da energia de ingredientes (farelo de soja, farinha de peixe, farelo de trigo e milho) por juvenis de apaiari (Astronotus ocellatus) usando dois diferentes intervalos de coleta (30 min. e 12h). Os 160 juvenis de apaiari utilizados (22,37 ± 3,06 g de peso corporal) foram divididos em quatro tanques rede plásticos e cilíndricos, cada um colocado em um tanque de alimentação de 1.000 L. O experimento foi inteiramente casualizado em esquema fatorial 2 x 4 (2 intervalos de coleta de fezes e 4 ingredientes foram) com quatro repetições. Os testes estatísticos não detectaram efeito da interação entre o intervalo de coleta e tipo de ingrediente nos coeficientes de digestibilidade. O intervalo de coleta não afetou a digestibilidade da proteína e da energia. As características físicas das fezes dos juvenis de apaiari aparentemente as tornam menos sensíveis à perda de nutrientes por lixiviação, permitindo intervalos de coleta maiores. A digestibilidade da proteína dos ingredientes avaliados foi semelhante, mostrando que a digestibilidade aparente de ingredientes animais e vegetais por juvenis de apaiari é eficiente. Os coeficientes de digestibilidade da energia foram maiores para a farinha de peixe e o farelo de soja comparado a farelo de trigo e milho. Ingredientes ricos em carboidratos (farelo de trigo e milho) apresentaram os piores coeficientes de digestibilidade da energia e, portanto, não são usados eficientemente pelos juvenis de apaiari.

Gráficos adaptativos de controle por atributos e seu projeto na prática

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Economic design of (X)over-bar and R charts under Weibull shock models

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Constrained optimization model for the design of an adaptive (X)over-bar chart

An economic-statistical model is developed for variable parameters (VP) (X) over bar charts in which all design parameters vary adaptively, that is, each of the design parameters (sample size, sampling interval and control-limit width) vary as a function of the most recent process information. The cost function due to controlling the process quality through a VP (X) over bar chart is derived. During the optimization of the cost function, constraints are imposed on the expected times to signal when the process is in and out of control. In this way, required statistical properties can be assured. Through a numerical example, the proposed economic-statistical design approach for VP (X) over bar charts is compared to the economic design for VP (X) over bar charts and to the economic-statistical and economic designs for fixed parameters (FP) (X) over bar charts in terms of the operating cost and the expected times to signal. From this example, it is possible to assess the benefits provided by the proposed model. Varying some input parameters, their effect on the optimal cost and on the optimal values of the design parameters was analysed.

Joint conomic design of (x)over-bar and R charts under Weibull shock models

This paper deals with the joint economic design of (x) over bar and R charts when the occurrence times of assignable causes follow Weibull distributions with increasing failure rates. The variable quality characteristic is assumed to be normally distributed and the process is subject to two independent assignable causes (such as tool wear-out, overheating, or vibration). One cause changes the process mean and the other changes the process variance. However, the occurrence of one kind of assignable cause does not preclude the occurrence of the other. A cost model is developed and a non-uniform sampling interval scheme is adopted. A two-step search procedure is employed to determine the optimum design parameters. Finally, a sensitivity analysis of the model is conducted, and the cost savings associated with the use of non-uniform sampling intervals instead of constant sampling intervals are evaluated.

A hierarchy of adaptive (X)over-bar control charts

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Joint conomic design of x̄ and R charts under Weibull shock models

This paper deals with the joint economic design of x̄ and R charts when the occurrence times of assignable causes follow Weibull distributions with increasing failure rates. The variable quality characteristic is assumed to be normally distributed and the process is subject to two independent assignable causes (such as tool wear-out, overheating, or vibration). One cause changes the process mean and the other changes the process variance. However, the occurrence of one kind of assignable cause does not preclude the occurrence of the other. A cost model is developed and a non-uniform sampling interval scheme is adopted. A two-step search procedure is employed to determine the optimum design parameters. Finally, a sensitivity analysis of the model is conducted, and the cost savings associated with the use of non-uniform sampling intervals instead of constant sampling intervals are evaluated.

Smooth Hazard Function Estimation for Interval Censored Data with Time Varying Covariates

In this paper we propose methods for smooth hazard estimation of a time variable where that variable is interval censored. These methods allow one to model the transformed hazard in terms of either smooth (smoothing splines) or linear functions of time and other relevant time varying predictor variables. We illustrate the use of this method on a dataset of hemophiliacs where the outcome, time to seroconversion for HIV, is interval censored and left-truncated.

High resolution color reflectance and hematite and goethite record of ODP Hole 184-1143A in the South China Sea

Precipitation has a larger variability than temperature in tropical monsoon regions, thus it is an important climate variable. However, reconstructions of long-term rainfall histories are scarce because of the lack of reliable proxies. Here we document that iron oxide minerals, specifically the ratio of hematite to goethite (Hm/Gt), is a reasonable precipitation proxy. Using diffuse reflectance spectrophotometry, we measured samples from Ocean Drilling Program (ODP) 1143 drilling site (9°21.72'N, 113°17.11'E, 2777 m water depth) for hematite and goethite, whose formation processes are favored by opposing climate conditions. In order to determine the content of hematite and goethite we produced a set of calibration samples by removing the iron oxides to generate the natural matrix to which hematite and goethite in known percentages were added. From these calibration samples we developed a transfer function for determining hematite and goethite concentration from a sample's spectral reflectance. Applying this method to ODP 1143 sediments (top 34 m of a 510 m core with sampling interval of 10 cm) we were able to reconstruct a continuous precipitation history for SE Asia of the past 600 kyr using the Hm/Gt ratio as a proxy of the precipitation variability of Asian monsoon. The reliability of this Hm/Gt proxy is corroborated by its consistency with the stalagmite delta18O data from South China. Comparing long-term Hm/Gt records with the surface temperature gradient of equatorial Pacific Ocean, we found that monsoon precipitation and El Niño are correlated for the last 600 kyr. The development of El Niño-like conditions decreased SE Asia precipitation, whereas precipitation increases in response to La Niña intensification

Sinking rates of particles measured from deployments in the Atlantic Ocean based on seasonal data

The flux of materials to the deep sea is dominated by larger, organic-rich particles with sinking rates varying between a few meters and several hundred meters per day. Mineral ballast may regulate the transfer of organic matter and other components by determining the sinking rates, e.g. via particle density. We calculated particle sinking rates from mass flux patterns and alkenone measurements applying the results of sediment trap experiments from the Atlantic Ocean. We have indication for higher particle sinking rates in carbonate-dominated production systems when considering both regional and seasonal data. During a summer coccolithophorid bloom in the Cape Blanc coastal upwelling off Mauritania, particle sinking rates reached almost 570 m per day, most probably due the fast sedimentation of densely packed zooplankton fecal pellets, which transport high amounts of organic carbon associated with coccoliths to the deep ocean despite rather low production. During the recurring winter-spring blooms off NW Africa and in opal-rich production systems of the Southern Ocean, sinking rates of larger particles, most probably diatom aggregates, showed a tendency to lower values. However, there is no straightforward relationship between carbonate content and particle sinking rates. This could be due to the unknown composition of carbonate and/or the influence of particle size and shape on sinking rates. It also remains noticeable that the highest sinking rates occurred in dust-rich ocean regions off NW Africa, but this issue deserves further detailed field and laboratory investigations. We obtained increasing sinking rates with depth. By using a seven-compartment biogeochemical model, it was shown that the deep ocean organic carbon flux at a mesotrophic sediment trap site off Cape Blanc can be captured fairly well using seasonal variable particle sinking rates. Our model provides a total organic carbon flux of 0.29 Tg per year down to 3000 m off the NW African upwelling region between 5 and 35° N. Simple parameterisations of remineralisation and sinking rates in such models, however, limit their capability in reproducing the flux variation in the water column.