A new method to measure the strength of trade-offs is presented and tested in the Early View paper “A standardized approach to estimate life history tradeoffs in evolutionary ecology” by Sandra Hamel and co-workers. Here’s Sandra’s summary of the paper:
A major goal of life-history studies is to understand how natural selection shapes individual fitness-related traits, such as growth, reproduction, and survival. So far, a large number of studies have demonstrated the occurrence of many trade-offs (e.g. number vs. size of offspring, age at first reproduction vs. longevity), but most researches have concentrated on detecting trade-offs – that is answering “yes” or “no” to the question “Is there a trade-off between trait A and trait B”. Although these studies are fundamental because they have provided substantial empirical evidence for the existence of trade-offs, they are somewhat limited. For instance, if we wish to understand how different life-history strategies evolve among different species, among populations of the same species, or among individuals of the same population, we need to be able to tell not only whether there is or not a trade-off, but most importantly what is the strength of this trade-off.
Measuring the strength of a trade-off would be highly valuable for determining its relative importance. For example, to determine whether the trade-off between current and future reproduction is stronger in shorter- vs. longer-lived species, we need to measure the strength of this trade-off in different species. Within a single species, we might also want to determine whether trade-offs among growth and survival traits are stronger than trade-offs among growth and reproductive traits, which could allow us to better understand where the strongest selection pressures occur.
Our paper therefore presents a method to measure the strength of trade-offs. Although some methods have been used previously to quantify trade-offs, these methods cannot be applied with respect to binary traits – that is traits usually described by “yes/no”. Indeed, analyses of binary data present many analytical issues and thereby are more complex and often more limited compared with other types of data. Nevertheless, binary traits are central in life histories (e.g. probability of reproduction, nesting success, offspring survival), and so we need a method that can be applied to any type of traits to be able to compare the importance of different life-history trade-offs. Our paper provides such a standardized approach, which also accounts for the confounding effects of both environmental variation in resource availability and individual heterogeneity.
We illustrate the large potential of our approach by applying our method to longitudinal data from roe deer and mountain goats. Out of seven trade-offs measured, the strongest was observed between current and future parturition in mountain goats, a capital breeder, whereas this trade-off did not occur and rather showed a weak positive effect in roe deer, an income breeder. Although the trade-offs presented are only a few examples in two species, they suggest that the between-species differences might result from different tactics of energy allocation to reproduction. Most importantly, these examples illustrate how our method can be used to compare the relative importance of different trade-offs, and how it opens the door to a deeper understanding of the evolution of life-history traits in free-ranging populations.
The pictures represent the two species used in the examples. On one picture we have a 14 year-old mountain goat female nursing her kid. On the other picture we have a roe deer female that is being checked for pregnancy with an ultrasonic scanner seen in the background.