Effects of sampling error on bioassessments of stream ecosystems: application to RIVPACS-type models

By J. Ostermiller, Charles P. Hawkins. Journal of The North American Benthological Society, 2004

127 citations recorded by the source corpus.

Subfields: Ecology, Nature and Landscape Conservation, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/b3c12a387bc25922578f55ddd9b4ca63d6c1e4ab

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Ecology

1. Charles P. Hawkins
2. N. Bonada
3. A. Schmidt‐Kloiber
4. N. Prat
5. V. Resh
6. J. Sickle
7. D. Huff
8. Jodi G. McClory

In Nature and Landscape Conservation

1. Charles P. Hawkins
2. N. Bonada
3. A. Schmidt‐Kloiber
4. Donald A. Jackson
5. J. Sickle
6. D. Huff
7. B. Statzner
8. Jodi G. McClory

In Water Science and Technology

1. J. Sickle
2. D. Huff
3. N. Bonada
4. Jodi G. McClory
5. N. Prat
6. V. Resh
7. R. Knapp
8. Joshua Ladau

Citations in the corpus

• FAUNA OF YOSEMITE NATIONAL PARK LAKES HAS LOW RESISTANCE BUT HIGH RESILIENCE TO FISH INTRODUCTIONS (2005, methodology): “Using data from streams, Ostermiller and Hawkins (2004) reported that the accuracy of RIVPACS models was quite robust to subsampling effort (ranging from 50 to 450 individuals per subsample), and that the effects of sampling error on model precision could be minimized by constructing models from…”
• Effects of sampling and sub-sampling variation using the STAR-AQEM sampling protocol on the precision of macroinvertebrate metrics (2006, background): “In a well-designed study of several sources of variation in site assessments in western Oregon and Washington in the USA, Ostermiller and Hawkins (2004) assessed the effect of using subsamples fixed-size counts of 50, 100, ..., 450 individuals on variability of observed to expected ratios (O/E) of”
• Modeling natural environmental gradients improves the accuracy and precision of diatom-based indicators (2007, background): “0 (Hawkins et al. 2000b, Ostermiller and Hawkins 2004).”
• Assessing the impact of errors in sorting and identifying macroinvertebrate samples (2006, background): “It arises from every stage of data collection, from sampling (e.g. Carter & Resh, 2001; Clarke et al., 2002; Ostermiller & Hawkins, 2004) to sample analysis and data handling (e.g. Doberstein et al., 2000; Haase et al., 2004a, b).”
• Developments in aquatic insect biomonitoring: a comparative analysis of recent approaches. (2006, methodology): “Sampling is moderately standardized in all methods (VII) and includes either diverse microhabitats or only well-defined habitat types (102).”
• Selecting discriminant function models for predicting the expected richness of aquatic macroinvertebrates (2006, methodology): “If the mean value of O/E at reference sites is equal to 1.0 then a predictive model is unbiased, and a small standard deviation of O/E indicates a model with high precision ( Ostermiller & Hawkins, 2004; Van Sickle et al., 2005).”
• Reconstructing community relationships: the impact of sampling error, ordination approach, and gradient length (2007, background): “The effect of these various sampling problems is that it is frequently difficult to establish a reliable estimate of abundance, and that all sample data include various sources of error (Ostermiller & Hawkins, 2004).”