Size Of Sampling Unit Research Articles

CURRENT VARIABILITY SHAPES MORPHOLOGICAL COMPLEXITY IN COLONIAL STREAM DIATOMS

Passy, S. I.1,2 & Freehafer, D.2 1Department of Biology, Rensselaer Polytechnic Institute, Troy, NY 12180; 2US Geological Survey, 425 Jordan Rd., Troy, NY 12180‐8349 USAOn August 27, 1999 diatoms were sampled, and current velocity was measured at 81 locations on a regular square sampling grid in an unshaded, cobble‐bottom reach of White Creek, NY. The grid had an extent of 16 m2, interval, the distance between neighboring sampling points, of 0.5 m, and grain size, the size of the elementary sampling unit, of 0.01 m2. Six of the seven dominant benthic diatoms were colonial forms, including Diatoma vulgaris, Fragilaria capucina, F. crotonensis, Gomphoneis minuta, Melosira varians, and Synedra ulna. Their morphology and distribution were investigated from the perspective of fractal geometry and stream ecology, respectively. Fractal dimension of diatom colonies, indicative of their shape complexity, ranged from 1.06 to 1.54, demonstrating vast morphological variation from simple geometric shapes to complex outlines. The relative abundance of the six diatoms was regressed against current regime, which ranged from 0.03 to 0.66 mδ s‐1. All regression models were significant at P < 0.05 and explained between 55% and 94% of the variation in diatom distribution. The diatom niche breadth, i.e. the amount of environmental variability a species can tolerate, was defined from these models and showed substantial variation, from 5 to 14. The regression model of fractal dimension against niche breadth was significant and explained 76% of colonial shape variation, revealing a strong relationship between diatom colonial complexity and habitat variability. This finding suggests that environmental variability could create highly complex colonial morphologies in benthic diatoms as an evo‐lutionary strategy for survival in unpredictable environments.

Spatial point pattern analysis of aerial survey data to assess clustering in wildlife distributions

Assessing clustering in wildlife populations is crucial for understanding their dynamics. This assessment is made difficult for data obtained through aerial surveys because the shape and size of sampling units (strip transects) result in poor data supports, which generally hampers spatial analysis of these data. The problem may be solved by having more detailed data where exact locations of observed animal groups are recorded. These data, obtainable through GPS technology, are amenable to spatial analysis, thereby allowing spatial point pattern analysis to be used to assess observed spatial patterns relative to environmental factors like vegetation. Distance measures like the G-statistic and K-function classify such patterns into clustered, regular or completely random patterns, while independence between species is assessed through a multivariate extension of the K-function. Quantification of clustering is carried out using spatial regression. The techniques are illustrated with field data on three ungulates observed in an ecosystem in Kenya. Results indicate a relation between species spatial distribution and their dietary requirements, thereby concluding the usefulness of spatial point pattern analysis in investigating species spatial distribution. It also provides a technique for explaining and differentiating the distribution of wildlife species.

Occupancy‐abundance relationships and sampling scales

The area of occupancy of a species and its abundance are dependent on the spatial scale at which they are measured. However, it is less obvious how the scale of sampling affects their correlation. This study investigated and modeled the effects of sampling unit size and a real extent on the interspecific occupancy‐abundance relationships for a tropical tree species assemblage at a local scale and a temperate bird species assemblage at a regional scale. The results showed that both sampling unit size and study extent had profound quantitative effects on the occupancy‐abundance relationship, although it remained positive. Several properties of the occupancy‐abundance relationship can result from the effects of scale: 1) the linearity of the relationship decreases with the increase of sampling unit size; 2) for a given abundance, the area of occupancy increases with sampling unit size; and 3) variation in the area of occupancy increases with the increase of both sampling unit size and extent, and if the extent is large enough may be sufficient that no occupancy‐abundance relationship is observed. Although the occupancy‐abundance relationship can be satisfactorily modeled, the parameters depend on the scale used. This suggests that a model derived from one scale cannot be applied to another. In other words, to estimate the rarity or commonness of species using such a model, the estimation must be strictly done using the same sampling scale for all the species.

Forest Volume Relative to Cartographic Boundaries and Sample Spacing, Unit Size and Type

A 100-m-by-100-m (1 ha) square study area was established in the boreal forest of Quebec, Canada. The area was selected because of the presence of roughly equal amounts of three distinct forest types as identified from aerial photographs through subjective human interpretation. Within the study area, the position of every tree having a diameter breast height (dbh) of 9.1 cm and larger was determined in x- and y-coordinates. Sample units of various sizes and types were then established at a variety of spacings within the study area and the forest volume determined for each. A variety of analyses was conducted on each spacing/sample unit size and type combination to assess the nature of forest volume across cartographic boundaries. It was determined that at closer spacings and/or in using larger sample units, forest volume behaves as a continuum. However, the nature of the change in volume across forest stand boundaries was not (as was expected a priori) a gradual decrease in volume from a high volume stand...

Evaluation of the bus-route creel survey method in a large Australian marine recreational fishery: I. Survey design

Computer simulations were used to test the efficacy of the bus-route method of creel surveys when applied to a large-scale, heterogeneous, Australian marine fishery and to determine an optimal survey design. The simulations were based on real-world data from the South Australian inshore boat fishery and estimates were directly compared with known values of fishing effort. Results indicate that accuracy was not significantly affected by the size of primary or secondary sampling units or by the allocation of waiting times to ramps either equally or proportional to effort. The optimal design proved to be a combination of a smaller PSU with a larger SSU and waiting times allocated equally at all ramps. Using this design, the method yielded estimates of mean daily fishing effort accurate to within 12.6–13.3% of the true value at a sampling frequency of 35% of days with relative precision of 21.8–35.4%. The sampling distribution of the daily estimator was also examined and found to be non-normal. In cases where the distribution of daily fishing effort is highly asymmetrical, the random selection of a SSU can lead to systematic under- or over-estimation of the true value. The contribution of within-day variability to the total variance of the estimator was found to be much smaller than in a previous study and was deemed to be of insignificant magnitude compared with the inherent day-to-day variability in fishing effort caused by factors such as weather and season.

Aggregation of Sampling Units: An Analytical Solution to Predict Variance

Geographical variables generally show spatially structured patterns corresponding to intrinsic characteristics of the environment. The size of the sampling unit has a critical effect on our perception of phenomena and is closely related to the variance and correlation structure of the data. Geostatistical theory uses analytical relationships for change of support (change of sampling unit size), allowing prediction of the variance and autocorrelation structure that would be observed if a survey was conducted using different sampling unit sizes.To check the geostatistical predictions, we use a test case about tree density in the tropical rain forest of the Pasoh Reserve, Malaysia. This data set contains exhaustive information about individual tree locations, so it allows us to simulate and compare various sampling designs. The original data set was reorganized to compute tree densities for 5 times 5‐, 10 times 10‐, and 20 times 20‐meter quadrat sizes. Based upon the 5 times 5‐meter data set, the spatial structure is modeled using a nugget effect (white noise) plus an exponential model. The change of support relationships, using within‐quadrat variances inferred from the variogram model, predict the spatial autocorrelation structure and new variances corresponding to 10 times 10‐meter and 20 times 20‐meter quadrats. The theoretical and empirical results agreed closely, whereas neglecting the autocorrelation structure would have led to largely underestimating the variance. As the quadrat size increases, the range of autocorrelation increases, while the variance and the proportion of noise in the data decrease.

Variance and spatial scales in a tropical rain forest: changing the size of sampling units

The size of a sampling unit has a critical effect on our perception of ecological phenomena; it influences the variance and correlation structure estimates of the data. Classical statistical theory works well to predict the changes in variance when there is no autocorrelation structure, but it is not applicable when the data are spatially autocorrelated. Geostatistical theory, on the other hand, uses analytical relationships to predict the variance and autocorrelation structure that would be observed if a survey was conducted using sampling units of a different size. To test the geostatistical predictions, we used information about individual tree locations in the tropical rain forest of the Pasoh Reserve, Malaysia. This allowed us to simulate and compare various sampling designs. The original data were reorganised into three artificial data sets, computing tree densities (number of trees per square meter in each quadrat) corresponding to three quadrat sizes (5×5, 10×10 and 20×20 m(2)). Based upon the 5×5 m(2) data set, the spatial structure was modelled using a random component (nugget effect) plus an exponential model for the spatially structured component. Using the within-quadrat variances inferred from the variogram model, the change of support relationships predicted the spatial autocorrelation structure and new variances corresponding to 10×10 m(2) and 20×20 m(2) quadrats. The theoretical and empirical results agreed closely, while the classical approach would have largely underestimated the variance. As quadrat size increases, the range of the autocorrelation model increases, while the variance and proportion of noise in the data decrease. Large quadrats filter out the spatial variation occurring at scales smaller than the size of their sampling units, thus increasing the proportion of spatially structured component with range larger than the size of the sampling units.

Use of Fish Communities to Assess Environmental Impacts in South Carolina Coastal Plain Streams

Abstract We developed an index of biotic integrity (IBI) and a biotic index based on fish species richness (FSBI) to assess the ecological health of streams on the Savannah River Site, a 780-km2 U.S. Department of Energy facility located in the Sand Hills ecoregion on the upper coastal plain of South Carolina. To maintain the responsiveness of the IBI to a variety of impacts yet incorporate sufficient ecoregion specificity to achieve acceptable accuracy, we included metrics from each of six metric categories proven useful in other ecoregions (species number, species composition, trophic composition, local indicator species, fish abundance, and fish condition) but selected specific metrics within each category based on their ability to discriminate between disturbed and undisturbed sites in the Sand Hills ecoregion. We also developed a procedure based on species–area curves to remove the potentially confounding effects of site-specific differences in sample unit size and sampling effort from species number...

An examination of observer bias as a source of error in surveys of seagrass shoots

Abstract Mensurative experiments investigated the effects of different observers on estimates of the density of shoots of two species of seagrass: Posidonia australis Hook and Zostera capricorni Aschers. Balanced programmes of sampling were used to examine variation in counts of seagrass shoots attributable to different observers, sizes of quadrats, depths and locations within large beds of each species of seagrass. A separate experiment examined differences between novice observers and a more experienced observer, when an ‘optimal’ size of sampling unit was used. Estimated densities of Zostera shoots varied inconsistently among observers, quadrats, depths and locations. Differences between observers were not affected by the size of quadrat used to count Posidonia shoots, but varied between locations in the seagrass bed. Experience had only a minor impact on biases. Only two of 12 novices produced counts that were different from the experienced observer. These results emphasize the importance of considering both accuracy and precision in the design of field studies of seagrasses.

Comparison of Colored Sticky Traps for Monitoring Thrips Populations (Thysanoptera: Thripidae) in Staked Tomato Fields

The response of flower thrips, Frankliniella spp., to various colors and sticky trap designs was evaluated in staked tomato fields in western North Carolina. Yellow sticky traps caught significantly more thrips compared with blue or white traps. There were no differences in the relative proportion of thrips species caught on different colored traps. The size of the flower sampling unit (i.e., 10-flowers versus all flowers per plant) did not influence the correlation between sticky trap catches and thrips abundance in flowers. Only F. occidentalis (Pergande) exhibited a significant correlation between percent abundance on all colors of sticky traps and percent abundance in flowers. Colored sticky traps caught high numbers of F. tritici (Fitch), despite the fact that few F. tritici were collected from flowers. In tests to evaluate different trap designs, cylindrical and cup traps caught more F. tritici than glass slide traps. Cylindrical sticky traps were more economical and enabled easier identification of thrips than glass slide or cup traps.

Assessing the Effect of Intra-Haul Correlation and Variable Density on Estimates of Population Characteristics from Marine Surveys

In a previous paper (Pennington and V0lstad, 1991, Biometrics 47, 717-723), it was suggested that reducing the size of the sampling unit currently used in marine surveys could increase the precision of the resulting density estimates. But if unit size is reduced, fewer animals will be caught during a survey. Concern has been expressed that this reduction in total catch would lower the precision of estimates of population characteristics, such as age and length frequency distributions, of importance for stock management. In this paper we examine the effect of sampling unit size, intra-cluster correlation, and variable density on the precision of estimates of population characteristics. An examination of some survey data indicates that reducing the size of the sampling unit employed and using the time saved to take samples at more locations could also yield more precise estimates of population parameters.

Scale Dependence of Species Richness Maps∗

Mapping patterns of species richness is a longstanding tradition in biogeography and more recently in conservation planning. This paper describes the effects of sampling unit size on patterns of vertebrate richness across landscapes in the Intermountain Sagebrush and Rocky Mountain Forest ecoregions in Idaho. Variability of richness decreased with increasing size of the spatial sampling units at both sites, with greater overall variability in the forested site. Richness in the sagebrush region was best explained by alpha diversity, whereas beta diversity accounted for more of the variability in richness in the forested site.

Mineral resources versus geologic diversity in small areas

There is a simple linear relationship between geologic diversity ( = the number of rock types — 1) and the mineral-resource diversity ( = the number of commodities produced — 1) which may be used to predict the number of commodities produced from a given number of geologic rock types in a region. In addition it is shown that the geologic and mineral-resource diversities are related positively to size (expressed as log A km 2) of sampling unit. It is of some interest to determine whether these relationships hold for sample units the size of counties. 182 counties from six states, Maine (16), New Hamshire (10), Vermont (14), Pennsylvania (67), Nevada (17), and California (58) are used to investigate these relationships. The regression of geologic diversity ( s g − 1) on size (log A) is positive, linear, and about r 2 = 41.0% determining. Similarly, the regression of mineral-resource diversity ( s m − 1) on size (log A) is positive, linear, and r 2 = 39.6% determining. The regression of mineral resource on geologic diversity also is similarly linear and positive with r 2 = 54% determining. The regression of geologic diversity on size for a larger global population ( n = 413; where sample units are countries and states) is similar to that for the 182 counties with r 2 = 48% determining. Evidently, the relationships hold for sample units the size of counties with a similar slope but a smaller intercept. It then is shown that for sample units the size of states and countries (i.e. log A from 3.0 to 6.5) the intercept is about 12. In other words, given a sample unit the size of states or countries with a geologic diversity of zero, the region is geologically homogeneous, one would expect it to produce some 12 commodities; on the other hand if the sample unit is the size of counties with the same value of zero for geologic diversity, then it would likely produce from 3 to 5 commodities.

Inventory of Indigenous Forest and Woodland in Southern Africa

SYNOPSIS An inventory is an important component of a series of activities to ensure the sustained use of natural forest and woodland resources. Three levels of inventories are considered: national level to determine general forest policy: regional level to provide a basis for integrated multiple-use management: and local level for detailed working plans. Different approaches to mapping resource areas are discussed, with particular emphasis of the use of satellite imagery. Sampling design requirements are discussed with particular emphasis on accuracy and precision, stratification of the resource area. shape and size of the sampling units, random and systematic distribution of sampling units, sampling intensity and the allocation of sampling units to stratified vegetation units. Requirements for sampling variables, and analyses and presentation of inventory results are briefly discussed. Several examples are used from inventories conducted in forest and woodland in southern Africa.

Optimum Size of Sampling Unit for Estimating the Density of Marine Populations

Data from several trawling experiments and from some scallop dredge surveys indicate that, within limits, a smaller sampling unit can be more efficient than a larger unit for marine abundance surveys. Taking into account survey costs and sampling variability, the unit size is found that produces the most precise density estimate given a fixed amount of survey resources or, if a certain level of precision is required, the size of sampling unit that minimizes the total cost of the survey. As an illustration, the optimum sampling unit sizes are derived for surveys of some fish populations and for a scallop stock on Georges Bank.

Power-Cost Efficiency of Eight Macrobenthic Sampling Schemes in Puget Sound, Washington, USA

Power-cost efficiency (PCEi = (n × c)min/(ni × ci), where i = sampling scheme, n = minimum number of replicate samples needed to detect a difference between locations with an acceptable probability of Type I (α) and Type II (β) error (e.g. α = β = 0.05), c = mean "cost," in time or money, per replicate sample, and (n × c)min = minimum value of (n × c) among the i sampling schemes) is the appropriate expression for comparing the cost efficiency of alternative sampling schemes having equivalent statistical rigor when the statistical model is a redistribution for comparisons of two means. PCEs were determined for eight macrobenthic sampling schemes (four sample unit sizes and two sieve mesh sizes) in a comparison of a reference site versus a putative polluted site in Puget Sound, Washington. Laboratory processing times were, on average, about 2.5 times greater for the [Formula: see text]- than the [Formula: see text] samples. The 0.06-m2, 0- to 8-cm-deep sample unit size and 1.0-mm sieve mesh size was the overall optimum sampling scheme in this study; it ranked first in PCE on 8 and second on 3 of 11 measures of community structure. Rank order by statistical power of the 11 measures for this scheme was Infaunal Index > log10 (mollusc biomass + 1) > number of species > log10 (numerical abundance) > log10 (polychaete biomass + 1) > log10 (total biomass + 1) > log10 (crustacean biomass + 1) > McIntosh's index > 1 – Simpson's Index > Shannon's Index > Dominance Index.

On sampling procedures in population and community ecology

In this paper we emphasize that sampling decisions in population and community ecology are context dependent. Thus, the selection of an appropriate sampling procedure should follow directly from considerations of the objectives of an investigation. We recognize eight sampling alternatives, which arise as a result of three basic dichotomies: parameter estimation versus pattern detection, univariate versus multivariate, and a discrete versus continuous sampling universe. These eight alternative sampling procedures are discussed as they relate to decisions regarding the required empirical sample size, the selection or arrangement of sampling units, and plot size and shape. Our results indicate that the decision-making process in sampling must be viewed as a flexible exercise, dictated not by generalized recommendations but by specific objectives: there is no panacea in ecological sampling. We also point to a number of unresolved sampling problems in ecology.

Effects of Heterogeneity in the Spatial and Temporal Pattern of Juvenile Alewife (Alosa pseudoharengus) and Blueback Herring (A. aestivalis) Density on Estimation of an Index of Abondance

Significant heterogeneity occurred in the spatial and temporal pattern of the nocturnal, near-surface (0 to 1.5 m depth) density of juvenile anadromous alewife (Alosa pseudoharengus) and blueback herring (A. aestivalis) in the Mactaquac Dam headpond, Saint John River, New Brunswick. Juvenile alewife were less available to a pushnet than were blueback herring. Patches of higher density persisted for up to 4 wk for both species. Estimates of patch size depended upon the sampling scale, with larger patches (16 to 58 km long) composed of smaller patches (0.5 to 2.2 km long). The index of aggregation of Taylor's power law was similar for both species pooled value 2.20) Lloyd's index of patchiness varied significantly, for both species, spatially but not temporally, and tended to be higher for blueback herring than for alewife. Density and patchiness differed for both species within and between an upriver, lotic zone and downriver, lentic zone. Mean densities were more precisely estimated at high than at low densities and for alewife than for blueback herring (n = 61). Stratification increased the precision, relative to simple random sampling, of estimates of mean density, particularly at high densities of the more patchily dispersed blueback herring. Autocorrelation occurred in the spatial density pattern of both species at coarse (1–100 km) and medium (0.1–4 km) sampling scales. Variations in the intensity and grain of patchiness may affect the choice of survey design, sample and sample unit size, and frequency of sampling required for a desired precision of density estimates.

Sample unit sizes and shapes for quantitative sampling of earthworm populations in crop lands

Quantitative study of earthworm (Oligochaeta) populations involves time consuming excavation and sorting of large volumes of soil. Although sampling efficiency has been shown to vary widely between sample unit sizes and shapes for other organisms (Clapham, The Journal of Ecology 20, 192–197, 1932), no statistical evaluation of sample units for earthworm sampling has been made. During April and May 1987, soil pits along 42 m transects at two unfilled sites under a corn ( Zea mays) and soya bean ( Glycine max) rotation were divided horizontally into four unequal parts (sub-pits) and excavated. Earthworms ( Aporrectodea tobercolata and Lumbricus rubellus) present in each sub-pit were sorted from the soil and counted. By adding the sub-pit counts together in various combinations, counts from a total of nine sample unit size-shapes were obtained. All count distributions appeared to be Poisson and spatial distribution was frequently non-random (patchy). After suitable transformation, row and interrow counts were compared and requisite sample sizes were calculated using the t distribution. There was no systematic difference in counts due to row position. Distribution symmetry and total requistie sampling area were the criteria used to compare the various sample unit size-shapes. As much as a 3-fold difference in the efficiency of sampling existed within the range of sample unit size-shapes examined. The pattern of relative efficiencies for sampling of adults and juveniles together was consistent between sites. The most efficient, acceptable sample unit size-shape was 10 cm along the row (the shortest increment in the experiment) by 45 cm across the row (slightly over one-half of the row spacing). The least efficient size-shape was also the largest, 30 cm along the row by 75 cm across the row (equivalent to the row spacing). Cocoon counts were made concurrently at one site. The most efficient size-shape for cocoon population estimation was square (30 cm along and across the row), and the least efficient was the most elongated (10 cm along the row and 75 cm across the row).

Surveying otter Lutra lutra populations: A discussion of problems with spraints

Abstract The faeces (‘spraints’) of otters Lutra lutra are often used to survey populations or to indicate habitat preferences. Problems with this method are reviewed and recent criticisms of a test of the method are discussed. It is suggested that further comparisons between known otter populations and their spraint distributions are needed to test the required size of sampling units and interpretation of surveys.