Some individuals survive and reproduce better than others. Traits that help them do so may be passed on to the next generation, leading to evolutionary change. Because of this, evolutionary biologists are interested in what differentiates the winners from the losers – how do their traits differ, and by how much? These differences are known as natural selection.
Linear and Nonlinear Selection
Traditionally, natural selection is separated into linear selection (differences in average trait values) and nonlinear selection (any other differences in trait distributions between winners and the rest). For example, successful individuals might be unusually close to average: this is known as stabilizing selection. Alternatively, winners might split into two camps, some with unusually high trait values, and others with unusually low trait values. This is disruptive selection (famously thought to explain the ur-origin of sperm and eggs). Stabilizing and disruptive selection are important types of nonlinear selection. In general, though, the trait distribution of successful individuals can differ from the general population in arbitrarily complicated ways.
When individuals with larger trait values have higher fitness on average (left panel), the trait distribution of successful individuals is shifted towards the right (right panel, orange curve). The difference in mean trait values between the winners and the general population is called linear selection.
Damselflies marked in the field, which will hopefully be recaptured later. This small insect at our field site had only about 10% recapture probability.
The quantification of survival selection in the field has a long history in evolutionary biology. A considerable milestone in this field was the highly influential publication by Russel Lande and Steve Arnold in the early 1980s.
The practical implementation of Lande and Arnold’s method involved simply fitting a linear model with standardized response (survival) and explanatory (trait) variables values with quadratic terms (multiplied by two). This straightforward method allowed evolutionary biologists to measure selection coefficients using commonly available statistical software and these estimates could be used directly within a quantitative genetic framework. Continue reading →