The quadratic regression (γ) is the selection gradient, "ω" is the fitness of a trait value "z", and α is the y-intercept of the fitness function. Here, individuals with intermediate trait values may have the highest fitness (stabilizing selection) or those with extreme trait values may have the highest fitness (disruptive selection). When, β = 0 and γ is significantly positive, the selection gradient indicates disruptive selection. When, β= 0 and γ is significantly negative, the selection gradient indicates stabilizing selection. In both the cases γ measures the strength of selection. Evolutionary biologists use estimates of the selection gradient of traits to identify patterns in the evolutionary pressures on a population and predict changes in species traits. When traits are correlated with one another to some degree, for example beak length (z1) and body size (z2) in a bird, selection on one will affect the distribution of the other. For correlated traits, the effects of natural selection can be separated by estimating the selection gradient for one trait (beak length (z1)) while holding the other trait (body size (z2)) constant. The process enables researchers to determine how greatly variations in one trait (beak length) affect fitness among individuals with the same body size. In 1977 when the Galapagos Islands suffered from severe drought, Peter and Rosemary Grant estimated the selection gradient for Darwin’s finches to estimate the strength of the relationship between fitness and each trait while holding other traits constant. They estimated selection gradient for finches’ weight (0.23), bill length (-0.17) and bill depth (0.43). The result showed that selection strongly favored larger birds with deeper bills. Evolutionary biologists also use selection gradients to estimate strength and mode of natural selection. Selection gradients, for example, have provided an explanation for fitness differences among individuals in a population, among different species and strengths of selection. In a study of the fresh-water Eurasian perch, a change in fitness was reported with a change in their density. An estimate of the selection gradient by linear and quadratic regression indicated a shift of the selection regime between stabilizing and directional selection at low density to disruptive selection at higher density. Despite the conceptual simplicity of the selection gradient, there are ongoing debates about its usefulness as an estimator of causes and consequences of natural selection. In 2017, Franklin & Morrissey showed that when performance measures such as body size, biomass, or growth rate are used in place of fitness components in regression-based analysis, accurate estimation of selection gradient is limited, which may lead to under-estimates of selection.