Geology Reports⌕ Search
Home/Archive/USGS

USGS · 70208999

Reduction of taxonomic bias in diatom species data

Meredith Tyree·Ian W. Bishop·Charles P. Hawkins·Richard M. Mitchell·Sarah A. Spaulding
View original source ↗Export metadata ↓

Abstract

Inconsistency in taxonomic identification and analyst bias impede the effective use of diatom data in regional and national stream and lake surveys. In this study, we evaluated the effect of existing protocols and a revised protocol on the precision of diatom species counts. The revised protocol adjusts four elements of sample preparation, taxon identification and enumeration, and quality control (QC). We used six independent datasets to assess the effect of the adjustments on analytical outcomes. The first dataset was produced by three laboratories with a total of five analysts following established protocols (Charles et al. 2002), or their slight variations. The remaining datasets were produced by 1-3 laboratories with a total of 2-3 analysts following a revised protocol. The revised protocol included the following modifications: 1) development of coordinated pre-count voucher floras based on morphological operational taxonomic units (mOTUs), 2) random assignment of samples to analysts, 3) post-count identification and documentation of taxa (as opposed to an approach in which analysts assign names while they enumerate), and 4) increased use of QC samples. The revised protocol reduced taxonomic bias, as measured by reduction in analyst signal, and improved similarity among QC samples. Reduced taxonomic bias improves the performance of biological assessments, facilitates transparency across studies, and refines estimates of diatom species distributions.

Explore related subjects

Limnology and Oceanography: MethodsArticle

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Meredith Tyree, Ian W. Bishop, Charles P. Hawkins, Richard M. Mitchell, Sarah A. Spaulding. 2020-02-13. Reduction of taxonomic bias in diatom species data. https://doi.org/10.1002/lom3.10350

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related USGS reports

USGS2023

An assessment of HgII to preserve carbonate system parameters in organic-rich estuarine waters

This work assesses the effectiveness of sample preservation techniques for measurements of pH T (total scale), total dissolved inorganic carbon (C T ), and total alkalinity (A T ) in organic-rich estuarine waters as well as the internal consistency of measurements and calculations (e.g., A T , pH T , and C T ) in these waters. Using mercuric chloride (HgCl 2 )-treated and untreated water samples, measurements of these carbonate system parameters were examined over a period of 3 months. Respiration of dissolved organic matter in untreated samples created large discrepancies in C T concentrations (~37 μ mol kg −1 increase, p < 0.0001), while C T was effectively constant in treated samples (3095.0 ± 1.14 μ mol kg −1 ). A T changes were observed for both treated and untreated samples, with HgCl 2 -treated samples showing the greatest variation (~ 26 μ mol kg −1 decrease, p < 0.001). In response to changing A T /C T ratios, pH T changes occurred in both treated and untreated samples but were relatively small in treated samples. Results in organic-rich estuarine waters that reflect the in situ carbonate system characteristics of the samples at the time of collection can be improved when samples obtained for C T and A T analysis are collected and stored separately. Accurate analyses of C T can be obtained by filtration and preservation with HgCl 2 . Accuracy of A T analyses can be improved by filtration and storage without adding HgCl 2 . The quality of pH T measurements can be improved by prompt analysis in the field and, if this cannot be accomplished, then samples can be preserved with HgCl 2 and measured in the laboratory within 1 week.

Limnology and Oceanography: Methods↗
USGS2023

Reconstructing missing data by comparing interpolation techniques: Applications for long-term water quality data

Missing data are typical yet must be addressed for proper inferences or expanding datasets to guide our limnological understanding and management of aquatic systems. Interpolation methods (i.e., estimating missing values using known values within the dataset) can alleviate data gaps and common problems. We compared seven popular interpolation methods for predicting substantial missingness in a long-term water quality dataset from the Upper Mississippi River, U.S.A. The dataset included 80,000 sampling sites collected over 30 yr that had substantial missingness for total nitrogen (TN), total phosphorus (TP), and water velocity. For all three interpolated water quality variables, random forests had very high prediction accuracy and outperformed the methods of ordinary kriging, polynomial regressions, regression trees, and inverse distance weighting. TP had a mean absolute error (MAE) of 0.03 mg (L-TP) −1 , TN had a MAE of 0.39 mg (L-TN) −1 , and water velocity had a MAE of 0.10 m s −1 . The random forests' error rates were mapped and showed low spatiotemporal variability across the riverscape, indicating high model performance across many habitat types and large spatial scales. In the current era of “big data,” interpolation becomes an imperative step prior to ecological analyses yet remains unfamiliar and underutilized. Our research briefly describes the importance of addressing missingness and provides a roadmap to conduct model intercomparisons of other big datasets. We also share adaptable data analysis scripts, which allows others to readily conduct interpolation comparisons for many limnology applications and contexts.

Illinois, Iowa, Minnesota, Missouri, Wisconsin↗
USGS2021

Estimating pelagic primary production in lakes: Comparison of 14C incubation and free-water O2 approaches

Historically, estimates of pelagic primary production in lake ecosystems were made by measuring the uptake of carbon-14 ( 14 C)-labeled inorganic carbon in samples incubated under laboratory or in situ conditions. However, incubation approaches are increasingly being replaced by methods that analyze diel changes in high-frequency in situ data such as free-water dissolved oxygen (O 2 ). While there is a rich literature on the comparison of approaches for estimating primary production using incubations (e.g., 14 C and O 2 bottle experiments), as well for approaches using high-frequency data (e.g., diel O 2 and CO 2 metabolism models), there are few direct comparisons of 14 C incubations and free-water O 2 approaches for estimating primary production. We used 20 lake-years of concurrent measurements of primary production quantified from high-frequency free-water O 2 data and 14 C incubations in four different lakes (4–7 years per lake) to compare these different approaches. Across all lakes, 61% of the 14 C production estimates were within the 95% credible intervals of the free-water O 2 production estimates. Error-in-variable regressions support the assumption that 14 C methods estimate a production value between gross primary production and net primary production and the bottle effect is constant across the entire range of production values considered here. There was little evidence that daily pelagic, epilimnetic estimates of primary production differed substantially based on the selection of free-water O 2 or 14 C approaches in these lakes during summer stratified conditions.

Limnology and Oceanography: Methods↗