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    Evolutionary clines, typically viewed as simple gradients, can exhibit complex, nonlinear paths through multidimensional trait space. This study reveals that phenotypic evolution across landscapes is often multivariate and nonlinear, challenging traditional univariate models.

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    Area of Science:

    • Evolutionary biology
    • Quantitative genetics
    • Ecomorphology

    Background:

    • Traditional evolutionary biology models clines as univariate spatial gradients.
    • This approach may obscure the complex, multivariate nature of phenotypic evolution.
    • An alternative is to represent clines as vectors in multidimensional morphospace.

    Purpose of the Study:

    • To model and empirically test for nonlinear, arcing clines in morphospace.
    • To investigate the multivariate complexity of phenotypic evolution along environmental gradients.
    • To challenge the limitations of traditional univariate cline representations.

    Main Methods:

    • Developed a theoretical model of clines in multidimensional trait space.
    • Collected morphological data from threespine stickleback populations across eight independent lake-stream transects.
    • Analyzed spatial patterns of phenotypic divergence using vector analysis in morphospace.

    Main Results:

    • Confirmed that clines can trace nonlinear, arcing paths through morphospace.
    • Observed that different traits diverge at varying rates and locations along geographic transects.
    • Found that successive vectors of divergence fluctuate in direction and magnitude, indicating complex evolutionary trajectories in threespine stickleback.

    Conclusions:

    • The study highlights the multivariate complexity of clines, often missed by univariate frameworks.
    • Arcing multivariate clines arise from differential divergence rates of various phenotypic characters.
    • Findings necessitate a re-evaluation of how phenotypic evolution across landscapes is visualized and modeled.