Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
Results
Showing 1 to 17 of 17 for “"sampling biases"”.
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Evaluating Sampling Biases in Policy Analysis of Environmental Markets
not peer reviewed
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Habitat evaluation and production of rock bass in two Virginia streams
… included seasonal fish movements, fish sampling biases, habitat homogeneity, and the omission of potentially important variables, stitch as depth and cover, from HSI models. Fish production was essentially the same in Little Walker and Back Creeks and averaged 1.04 gm⁻²yr⁻¹. The factors …
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Baited remote underwater video survey of reef fish in the Stilbaai marine protected area, with an assessment of monitoring requirements
… are being reached. The costs, logistics and sampling biases inherent to traditional monitoring methods limit sustainable monitoring in all MPAs along the South African coastline. Baited remote underwater video (BRUV) technology offers non-extractive monitoring with lower labour and cost …
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Emotions in Sports
… may have been low statistical power and possible sampling biases.
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Palaeoecology and population demographics of the extinct New Zealand moa (Aves: Dinornithiformes).
… used in Chapters Six and Seven. I discuss how sampling biases common to most aDNA datasets affect the robustness of the Bayesian Skyline Plot (BSP), a commonly used analytical method for inferring the past population demographic history of species or populations. The analyses indicate that …
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Seasonal and Ontogenetic Changes in the Sex Ratio of a Population of Stinkpots (Kinosternidae: Sternotherus odoratus)
… with biased sex ratios are the result of sampling errors such as improper sex determination, trapping bias, and behavioral differences between the sexes. This study was undertaken to determine if the observed sex ratio of a population of stinkpots (Sternotherus odoratus) was an artifact of …
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Is the Indian Ocean MOC driven by internal wave breaking?
… result appears robust when considering potential sampling biases in the internal wave observations and leads to the main conclusion of this work: the Indian Ocean MOC cannot primarily be driven by internal wave breaking. A preliminary investigation into other processes capable of dissipating …
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Improved integration of information to reduce subsurface model bias
… with data-related issues like cognitive and sampling biases, and model-related challenges including statistical assumptions, misspecification, and algorithmic biases. These challenges introduce four critical implications during subsurface modeling. Firstly, subsurface sampling is subject to …
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Differences in Fish Abundance Among Habitat Types in a Warmwater Stream; The James River, South Dakota
… 1989 and 1990. A total of 57 sites were sampled. Sampling efficiency was estimated for each habitat using rotenone and mark-recapture procedures. Correction factors, proportional to sampling efficiency, were used to minimize inherent sampling biases among habitats. Density (no./hectare) and …
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Effects of Impoundments on the Community Assemblage and Gene Flow of Stream Crayfishes
… USA. First, I assessed the most effective sampling methods for collecting crayfishes in high gradient southern Appalachian streams. A combination of kick seining and electroshocking were most effective at collecting crayfishes, with higher species richness and decreased sampling biases when …
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Effects of burn season on bee and floral community in tallgrass prairies, and the use of museum collections data
… areas and species for conservation, but sampling biases and differences in specimen deposition into museum collections by various collectors over time, data entry errors, and misidentification of specimens can limit the accuracy of date collected from museums. This chapter describes the …
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Where the Spores at? Insights into the Residential Indoor Mycobiome of South Africa
… remain heterogeneous, with a variety of sampling, detection, enumeration and predication methods and a lack of dose/response metrics to aid in abundance thresholds. Moreover, the review highlighted a dearth of data from South Africa, where literature indicates that latitude, climatic …
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Seasonality in a changing climate: Insights from the modern ocean with application to the Eocene Epoch
… 2, I use analyses of modern SST data to identify sampling biases in the paleo record and propose a new framework within which to more meaningfully interpret annually- and seasonally-resolved SST proxy data. In Chapter 3, I develop a bivalve growth rate model, which accounts for variable …
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Tree-ring analysis and stem biomass estimation of limba (Terminalia superba Engl. & Diels) in the framework of sustainable forest management
… It would also enable a better detection of sampling biases, more reliable estimates of the culmination of growth variables and serve as a solid base for bootstrapping. Apart from the tree level and the inter-annual level, the obtained high-resolution wood density profiles offer also …
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Sampling scale sensitivities in surface ocean pCO2 reconstructions in the Southern Ocean
… this role is poorly understood. The seasonal sampling biases have hampered observation-based reconstructions of partial pressure of CO2 at the surface ocean (pCO2) using machine learning (ML) and contributed to the convergence of the root mean squared errors (RMSEs) of ML methods to a common …
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An examination of geographic patterns of soil climate and its classification in the U.S. system of soil taxonomy
… limitations in the NCSS data include unknown sampling biases, ambiguous geographical precision of observation, inconsistent sampling and analysis protocols, incomplete data records, etc. Limitations of the usefulness of the NSM include high levels of multicollinearity among model output …
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Upper Missouri River Basin Aquatic GAP Fish Distribution Model Accuracy Assessment and White Sucker, Catostomus commersonii, Population Characteristics in the Upper Missouri River Basin
… fish species data in the area. Differences in sampling and modeling scales, potential sampling biases, temporal variation in fish communities, variation in habitat conditions, and biases in model input data and classifications are reasons for discrepancies between model predictions and …