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Related Concept Videos

Range00:59

Range

11.0K
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

Range Rule of Thumb to Interpret Standard Deviation

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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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Midrange01:07

Midrange

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A somewhat easy to compute quantitative estimate of a data set’s central tendency is its midrange, which is defined as the mean of the minimum and maximum values of an ordered data set.
Simply put, the midrange is half of the data set’s range. Similar to the mean, the midrange is sensitive to the extreme values and hence the prospective outliers. However, unlike the mean, the midrange is not sensitive to all the values of the data set that lie in the middle. Thus, it is prone to...
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Interpreting R Charts01:22

Interpreting R Charts

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R chart, or range chart, is a fundamental tool in statistical process control used to monitor the variability within a process. It complements the X-bar (x̄) chart by focusing on the range of the data, rather than individual values, providing a clear picture of the process dispersion over time.
An R chart plots the range of subsets of measurements collected from a process. Each point on the chart represents the range—defined as the difference between the maximum and minimum...
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IR Frequency Region: X–H Stretching01:24

IR Frequency Region: X–H Stretching

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In IR spectroscopy, signals produced by the X−H bonds (such as C−H, O−H, or N−H) can be observed in the frequency range of  2700–4000 cm–1. The C−H stretching vibration forms sharp bands in the region 2850–3000 cm–1. The presence of the O−H stretching vibration leads to the forming of an absorption band in the frequency range 3650–3200 cm−1. At the same time, N−H stretching can be confirmed by absorption bands in...
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Common Leveling Mistakes and Errors01:17

Common Leveling Mistakes and Errors

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A survey team is tasked with determining the elevation difference between points Point A and Point B, separated by uneven terrain. They use a leveling instrument and a leveling rod.Common MistakesMisreading the Rod: During a backsight reading at Point A, the instrumentman observes the rod partially obscured by tall grass. Instead of reading 1.135 m, they mistakenly record 1.735 m due to the misalignment of the crosshair with the wrong graduation. This error adds 0.600 m to all subsequent...
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Kinematic History of a Salient-recess Junction Explored through a Combined Approach of Field Data and Analog Sandbox Modeling
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Can internal range structure predict range shifts?

Neil A Gilbert1,2, Stephen R Kolbe3, Harold N Eyster4,5

  • 1Department of Integrative Biology, Oklahoma State University, Stillwater, Oklahoma, USA.

The Journal of Animal Ecology
|September 2, 2024
PubMed
Summary
This summary is machine-generated.

Species range shifts due to climate change are variable. This study found weak evidence that range edge hardness predicts population trends, suggesting different mechanisms drive range expansions versus contractions.

Keywords:
biogeographyclimate changemacroecologyrange shiftsspecies distributions

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Area of Science:

  • Ecology
  • Climate Change Biology
  • Conservation Biology

Background:

  • Species are shifting their geographic ranges in response to climate change, but predicting these shifts remains challenging.
  • Functional traits have limited ability to predict range shifts, necessitating exploration of other range characteristics.

Purpose of the Study:

  • To test the hypothesis that range edge hardness, defined by relative abundance at range edges, can predict population trends at range limits.
  • To investigate the inertia and limitation hypotheses regarding range edge hardness and range shifts.

Main Methods:

  • Utilized a long-term avian monitoring dataset from northern Minnesota, USA.
  • Estimated population trends for 35 trailing-edge and 18 leading-edge species.
  • Modeled population trends as a function of range edge hardness derived from eBird data.

Main Results:

  • Found limited evidence for associations between range edge hardness and population trends at range limits.
  • Trailing-edge species with harder edges showed a slight tendency towards decline (weak support for limitation hypothesis).
  • Leading-edge species with harder edges showed a slight tendency towards increase (weak support for inertia hypothesis).

Conclusions:

  • Opposing results for leading and trailing edges suggest distinct mechanisms govern range expansions and contractions.
  • Range edge hardness shows weak predictive power for population trends, indicating complexity in range shift dynamics.
  • Future advancements in data and modeling may improve predictions of range shifts using within-range abundance patterns.