Sentence examples for selection understanding from inspiring English sources

In an interesting inversion of Darwin's original analogous reasoning between natural and artificial selection, understanding how genetic architecture constrains and facilitates of natural selection can equally act as a guide for informing expectations about the effects of artificial selection on trait variation subject to pleiotropy, epistasis, and constraint.

Because we control for pre-test natural selection understanding in this regression, we are in effect, determining the relative influence of all other variables on student learning gains in natural selection understanding over the course.

We conducted a multiple linear regression to determine the relative influence of each variable, independently of the others, on the post-test natural selection understanding score.

Conversely, our measure of natural selection understanding focused entirely on student understanding of evolutionary facts and concepts, but did not address student attitudes toward evolution.

In summary, while cultural/attitudinal measures mainly predicted students' acceptance of evolution, cognitive measures were the main predictors of natural selection understanding.

Unlike other studies (e.g., Beardsley et al. 2011), we used the full CINS assessment of natural selection understanding (Anderson et al. 2002).

Only natural selection understanding-pre (β = .58; p < .001) and teleological reasoning-post (β = −.24; p < .001) contributed significant unique variance.

We further conducted similar analyses on the final semester grades to assess the influence of natural selection understanding-post on course grades.

Natural selection understanding-post, only, contributed significant unique variance (β = .38; p < .001) demonstrating a significant role for a principled understanding of natural selection in students' mastery of the course material.

Both significantly correlated variables, teleological reasoning-post (r = −.24; p = .004) and natural selection understanding-post (r = .42; p < .001) were simultaneously regressed on the final course/grade score; they explained 17% of the final semester grade: R 2  = .18 (R = .43); F (2, 146) = 15.87, p < .001 (Adjusted R 2  = .17).17

Thus, in modeling the effects of fishery and harvest selection and understanding ecosystem effects of such exploitation, scientists and managers cannot assume consistent, directional selection and the consequences of such.