Note: I wrote this summary in March 2021, and I am only now sharing it in September 2023. Please keep in mind that the information presented here is a summary, not a review. All errors here are mine.
Organisms possess qualities that can enhance or constrain their performance in diverse ways. Often, these qualities emanate from genetics, physiology and environmental factors. Changes in one quality can positively or negatively affect another. A change in one quality that has a corresponding effect on another is known as a trade-off. Previous studies have assessed performance trade-offs in multiple species, but detecting trade-offs at the intra-specific level has been difficult. Although researchers know that enhancement of a specific body quality can enhance a specific performance whiles having a counter-effect on another, trade-offs are rarely detected at the intra-specific level. Surprisingly, researchers largely use ‘personal best’ values in addition to focusing on individual quality to explain why tradeoffs are rarely detected at the within-individual level without paying attention to trait (co)variances. However, Berberi and Careau, (2019) have shown that when trait (co)variances are partitioned at the among-vs within-individual levels, we can better understand trade-offs at the within-individual and among-individual levels. This study was conducted using wild white-footed mice, Peromyscu leucopus.
Materials and Methods
Grip strength and sprint speed were measured in 3 experimental sample groups of Peromyscus leucopus. Mice were captured using traps and assigned binary variables for sex (male or female), age (juvenile or adult) and reproductive status (active or non-active). However, the sex of three mice were unknown. 193 mice were captured and taken through performance tests. An average of two tests were conducted per individual, but the number of tests ranged from 1 and 8. Test window was affected by migration and mortality.
Maximum grip strength was measured with a gauge connected to an inclined metal grid held
by each individual. Maximum force was recorded on the gauge by pulling a mouse at the tail.
The more a mouse was pulled, the more strongly it holds to the inclined metal grid until the
maximum grip was recorded.
This was quantified by measuring the speed of mice running across a race track. Sensors were embedded at successive points on the track. Signals from the sensors were relayed across an Arduino microprocessor to a software. If any sensor was skipped over, values were interpolated.
Two univariate linear mixed models were performed on grip strength and sprint speed using ASReml-R. This analysis assessed the relationship between each of grip strength and sprint speed to age, sex, body mass, reproductive status and tests sequence. Independent variables like age, sex and reproductive status were static points and dependent variables revolved around those points. Moreover, measurements were made among tests within individuals, among trials within tests, and among-individuals. When analyzing running speed, additional static effects like motivation level and its interaction with trial sequence, as well as interpolated time values were included. Making the estimation of variance in both long-term and short-term possible. As a result, variance of performance factors with time were detected. Short-term and long-term repeatability were estimated to assess the changes in variation with test timeframe. Genetic and lasting environmental factors were the causal factors for long term repeatability, whiles daily environmental changes were attributed to short term repeatability. Maximum performance was recorded for each individual. And these values were used to compute phenotypic correlations between grip strength and sprint speed.
A bivariate model was used to examine the relationship between grip strength and sprint speed using maximum performance scores. To establish bias that could result from personal best scores, the number of tests conducted on each individual, was used as a fixed effect.
Results and Discussion
Grip strength had a positive correlation to body mass irrespective of the sex of test subject. However, juvenile mice had a weaker grip strength than adults. Interestingly, grip strength increased with test sequence but not trial sequence. That could mean that organisms fare well in summer, or they simply develop better grip as they mature.
Body mass and sex had no effects on sprint speed. This concurs with current literature, including Charters et al. 2018, where speed was not directly correlated with increasing mass. Since larger organisms have a larger mass, stride cadence proportionally requires greater energy and more muscle mass per unit area. Hence, giving both larger and smaller organisms fair competitive advantage. However, sprint speed was positively correlated with trial sequence, indicating that mice get better after each trial. Also, sprint speed was higher in reproductively active individuals than inactive ones. That could be due to higher physical activity associated with reproduction, especially as both sexes of P. leucopus possess multiple mating partners at reproductive seasons.
Repeatability was significant for both short term and long-term variance. For long-term, 19% of variance in grip strength and 17% variance in sprint speed was attributed to among-individual variance. Among-individual variance was higher for short term, at 29% and 19% for grip strength and sprint speed, respectively. Significant variance among trials were attributed to difference in motivation levels and errors in measurement. Using each individual’s ‘personal best’ across all tests gave a positive and significant correlation between performance factors. Individuals with higher number of trials had high
Individual maximum performances. In contrast, an inverse relationship was detected between grip strength and sprint speed at the among-individual level, indicating that good sprinters were bad at gripping. But no significant relationship was detected between grip strength and sprint
speed at the within-individual level.
To sum up, there were positive and significant correlations across all tests, negative and significant correlation at the among-individual level and positive but insignificant correlation at the within-individual level. Overall, this is an all-important study that questions the findings of previous research that failed to partition trait (co)variances. Consequently, current and future research have to rethink the statistical and analytical models they employ when studying trade-offs.