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Propagation of Uncertainty from Systematic Error01:10

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The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
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Propagation of Uncertainty from Random Error00:59

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An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
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Random Error01:04

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Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...
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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
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Data error propagation in stacked bioclimatic envelope models.

Xueyan Li1, Babak Naimi2, Peng Gong3

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Summary
This summary is machine-generated.

Different data sources and bioclimatic envelope models (BEMs) create varying species richness patterns. Citizen science data yields accurate models, while range maps are least accurate for biodiversity assessments.

Keywords:
richness patternsspecies distributionstacked bioclimatic envelope modelsuncertainty

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Area of Science:

  • Ecology
  • Biodiversity Research
  • Computational Biology

Background:

  • Species richness patterns are crucial for understanding ecological processes.
  • Bioclimatic Envelope Models (BEMs) are widely used to infer species distributions.
  • Data limitations in species distribution modeling can impact ecological inferences.

Purpose of the Study:

  • To investigate the influence of different data sources on estimated species richness gradients in China.
  • To compare the accuracy and robustness of various BEMs and data types for biodiversity assessment.

Main Methods:

  • Fitted BEMs for 334 bird species using global range maps, regional checklists, museum records, and citizen science data.
  • Employed presence-only, presence-background, and presence-absence BEMs (Mahalanobis distance, MAXENT, GAM, BRT).
  • Stacked individual species predictions to generate species richness gradients and performed sensitivity analyses.

Main Results:

  • Species richness gradients varied significantly based on data sources and BEMs used.
  • Citizen science data resulted in the highest model accuracy; global range maps had the lowest.
  • Environmental predictors explaining species distributions differed across data sources.
  • GAM and BRT models demonstrated robustness to data uncertainty.

Conclusions:

  • Explicitly addressing data uncertainty through sensitivity analyses is crucial when multiple datasets are available.
  • The choice of data source and BEM significantly influences biodiversity pattern inferences.
  • Citizen science data offers a valuable, high-accuracy resource for species distribution modeling.