The concepts of bias, precision and accuracy, and their use in testing the performance of species richness estimators, with a literature review of estimator performance

By B. Walther, Joslin L. Moore. 2005

830 citations recorded by the source corpus.

Subfields: Ecology, Nature and Landscape Conservation, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/45477bc87177ad7008438b89f4fb1d4da9e6b62c

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In Ecology

In Nature and Landscape Conservation

In Statistics and Probability

Citations in the corpus

Testing the performance of beta diversity measures based on incidence data: the robustness to undersampling (2009, neutral): “Angra do Heroı́smo, Portugal, 2Natural”
photoQuad: A dedicated seabed image processing software, and a comparative error analysis of four photoquadrat methods (2012, methodology): “Simulation approaches are feasible alternatives (Dethier et al., 1993; Nadon and Stirling, 2006), and their power is always maximized when their statistical characteristics are backed up by real datasets (Walther and Moore, 2005).”
Allometric models for non-destructive leaf area estimation in coffee (Coffea arabica and Coffea canephora) (2008, background): “34 Ann Appl Biol 153 (2008) 33–40 ª 2008 The Authors Journal compilation ª 2008 Association of Applied Biologists precision denotes the variation among individual measurements (variance) (Walther & Moore, 2005).”
Quantifying variable importance in a multimodel inference framework (2016, methodology): “The accuracy of a metric is related to its precision and bias: the more precise and less biased a metric, the more accurate it is (Walther & Moore 2005).”
Allometric models for non-destructive leaf area estimation of Jatropha curcas (2012, neutral): “These criteria allowed us to evaluate the occurrence of bias and the precision and accuracy of the models [32].”
Undersampling and the measurement of beta diversity (2013, methodology): “We assessed the estimates using averages from bootstrapping and indicated their precision (Walther & Moore 2005) by drawing standard deviations in figures.”
Nonparametric Estimation and Comparison of Species Richness (2016, background): “This is likely the reason why many studies (Palmer, 1991; Chiarucci et al., 2003; Walther and Moore, 2005; Xu et al., 2012) found the jackknife estimators to exhibit a relatively good performance.”
Using taxonomic revision data to estimate the geographic and taxonomic distribution of undescribed species richness in the Braconidae (Hymenoptera: Ichneumonoidea) (2009, background): “Although some researchers have found that such estimators often do not perform particularly well (e.g. Petersen & Meier, 2003; Petersen et al., 2003), others (e.g. Walther & Moore, 2005; Hortal et al., 2006) found that they perform aswell, if not better, than othermethods.”
Saproxylic beetle diversity in a managed boreal forest: importance of stand characteristics and forestry conservation measures (2007, methodology): “Two species richness estimators (generally considered to be well performing; Walther & Moore, 2005; Hortal et al ., 2006) were used to evaluate sampling representivity, i.e. the Incidence Coverage Estimator (ICE) and Chao1 (Colwell & Coddington, 1994).”
When can we ignore the problem of imperfect detection in comparative studies? (2012, neutral): “Plants Sightings 4 morphological types Species 72Æ7 50Æ5–86Æ1 Archaux et al. (2009) 4 cover classes 87 42Æ6–100 11 observers 81 67–90 Butterflies Sightings 150 species Species 50 17–81 Kéry & Plattner (2007) 40 observers 61 37–83 3 dates 50Æ3 42–65 Anurans Sightings ⁄ Songs 3 dates Species 19Æ1 3Æ4–”
Improving estimates of population status and trend with superensemble models (2017, background): “For continuous response variables such as the mean and slope of population status, performance metrics often measure some form of bias, precision, accuracy (a combination of bias and precision) or the ability to correctly rank or correlate across populations (e.g. Walther & Moore, 2005).”
Benchmarking DNA Metabarcoding for Biodiversity-Based Monitoring and Assessment (2016, methodology): “In order to evaluate the performance of pa-gAMBI for each condition, root-mean-square error (RMSE) and bias were calculated (Walther and Moore, 2005).”