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Related Concept Videos

Variation01:19

Variation

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An important characteristic of any set of data is the variation in the data. In some data sets, the data values are concentrated closely near the mean; in other data sets, the data values are more widely spread out from the mean. The most common measure of variation, or spread, is the standard deviation, which is the square root of variance.
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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
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Multimachine Stability01:25

Multimachine Stability

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Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
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Variability: Analysis01:11

Variability: Analysis

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Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
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Genetic Drift03:33

Genetic Drift

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Natural selection—probably the most well-known evolutionary mechanism—increases the prevalence of traits that enhance survival and reproduction. However, evolution does not merely propagate favorable traits, nor does it always benefit populations.
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Natural Selection and Adaptation01:15

Natural Selection and Adaptation

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Natural selection, a fundamental concept in evolutionary biology, is the mechanism by which evolution is driven, favoring organisms that are best adapted to their environments. This process enhances their chances of survival and reproduction. Adaptation, a key outcome of this process, involves genetic modifications that optimize an organism's functionality under specific environmental challenges, such as extreme cold or thinner air at high altitudes.
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Related Experiment Video

Updated: Jun 24, 2025

Cross-Modal Multivariate Pattern Analysis
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Published on: November 9, 2011

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Predictable and Divergent Change in the Multivariate P Matrix during Parallel Adaptation.

Stephen P De Lisle, Daniel I Bolnick, Yoel E Stuart

    The American Naturalist
    |June 10, 2024
    PubMed
    Summary

    Phenotypic variation evolves during adaptation, with most changes in multivariate trait structure (P) occurring in freshwater stickleback populations. This divergence is linked to habitat differences and low trait integration.

    Keywords:
    Gasterosteus aculeatuscovariance tensorgenetic lines of least resistanceparallel evolutionquantitative genetics

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    Area of Science:

    • Evolutionary biology
    • Ecomorphology
    • Quantitative genetics

    Background:

    • Adaptive radiation often shows predictable evolutionary patterns, suggesting parallel evolution.
    • Understanding how phenotypic variation evolves during adaptation is crucial but remains an open question.

    Purpose of the Study:

    • Investigate the evolution of phenotypic variation, specifically phenotypic covariance (P), during marine-to-freshwater transitions and subsequent diversification in threespine stickleback.
    • Examine how P evolves across different environments (marine, lake, stream) and its relationship with population means and total variation.

    Main Methods:

    • Analysis of morphological measurements from 35 threespine stickleback populations (16 lake-stream pairs, 3 marine).
    • Statistical assessment of phenotypic covariance (P) divergence among populations.
    • Comparison of P divergence with microevolutionary predictions for lake-stream transitions.

    Main Results:

    • Significant divergence in phenotypic covariance (P) was detected across populations, with the most diversification observed in freshwater populations.
    • Divergence in P was largely independent of the total variation in population means.
    • Microevolutionary predictions explained over 30% of P matrix divergence across lake-stream habitat boundaries.

    Conclusions:

    • Divergence in the structure of multivariate phenotypic variation (P) is a key component of adaptive radiation.
    • Changes in P primarily occur in less integrated trait dimensions, correlating with distinct lake and stream environments.
    • Both conserved and divergent aspects of multivariate variation contribute to adaptive radiation processes.