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Related Concept Videos

Range00:59

Range

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The range is one of the measures of variation. It can be defined as the difference between a dataset's highest and lowest values. For example, in the study of seven 16-ounce soda cans, the filled volume of soda was measured, thus producing the following amount (in ounces) of soda:
15.9; 16.1; 15.2; 14.8; 15.8; 15.9; 16.0; 15.5
Measurements of the amount of soda in a 16-ounce can vary since different subjects record these measurements or since the exact amount - 16 ounces of liquid, was not...
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Range Rule of Thumb to Interpret Standard Deviation01:13

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The range rule of thumb in statistics helps us calculate a dataset's minimum and maximum values with known standard deviation. This rule is based on the concept that 95% of all values in a dataset lie within two standard deviations from the mean.
For instance, the range rule of thumb can be used to find the tallest and the shortest student in a class, given the mean student height and standard deviation. If the mean student height is 1.6 m and the standard deviation, s is 0.05 m, the height...
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Prediction Intervals01:03

Prediction Intervals

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The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
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Organisms that are well-adapted to their environment are more likely to survive and reproduce. However, natural selection does not lead to perfectly adapted organisms. Several factors constrain natural selection.
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Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

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The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
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The Squeeze Theorem01:30

The Squeeze Theorem

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Certain mathematical functions exhibit unpredictable or highly variable behavior near specific input values, making direct evaluation of their limits challenging. This complexity may arise from rapid oscillations or irregular patterns that obscure the function’s trend. In such cases, the Squeeze Theorem offers a reliable method for determining limits.According to the Squeeze Theorem, if a function is confined between two other functions near a particular point, and both outer functions...
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Interactive range-limit theory (iRLT): An extension for predicting range shifts.

Alexej P K Sirén1,2, Toni Lyn Morelli1,2

  • 1Department of Interior Northeast Climate Adaptation Science Center, U.S. Geological Survey, Amherst, MA, USA.

The Journal of Animal Ecology
|November 24, 2019
PubMed
Summary

Range-limit theory (RLT) is expanded to interactive RLT (iRLT), proposing that abiotic and biotic factors interact to shape species

Keywords:
abiotic stressbiotic interactionclimate changecondition-specific competitionecological nichepredator-prey theoryrange limitsstress-gradient hypothesis

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Area of Science:

  • Ecology
  • Biogeography
  • Theoretical Ecology

Background:

  • Range-limit theory (RLT) traditionally posits abiotic factors define high-latitude/altitude limits and biotic interactions define low limits.
  • This Darwinian hypothesis is widely assumed but lacks empirical support, particularly for biotic factors at lower limits.
  • Abiotic factors can constrain both range limits, and correlations between abiotic and biotic factors, scale, or data limitations may explain deviations.

Purpose of the Study:

  • To propose an expansion of RLT, termed interactive RLT (iRLT), integrating niche and predator-prey theories.
  • To understand how biotic interactions influence range limits across trophic levels and predict range shifts.
  • To evaluate support for iRLT and assess trophic differences along species' range margins.

Main Methods:

  • Conducted an integrative review of existing literature on range limits.
  • Surveyed the mammal community along the boreal-temperate and forest-tundra ecotones in North America.
  • Synthesized findings to evaluate the predictions of iRLT and identify trophic differences.

Main Results:

  • Range-limit dynamics are more nuanced and interactive than predicted by classical RLT.
  • Biotic factors ameliorate climatic conditions at high-latitude/altitude limits in most studies (57/70).
  • Abiotic factors mediate biotic interactions at low-latitude/altitude limits (44/68), influencing range stability, expansion, or contraction.
  • Carnivores are limited by competition, while herbivores are primarily influenced by predation and parasitism at lower range limits.

Conclusions:

  • iRLT provides a more comprehensive framework for understanding range limits by incorporating interactions between abiotic and biotic factors.
  • Trophic differences exist in the biotic factors influencing lower range limits, with carnivores facing competition and herbivores facing predation/parasitism.
  • Future research should focus on further evaluating iRLT across diverse taxa and ecosystems to refine predictions of species' range shifts.