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Measuring Selection on Physiology in the Wild and Manipulating Phenotypes (in Terrestrial Nonhuman Vertebrates)

Jerry F. Husak

10.1002/cphy.c140061

Source: Volume 6, Issue 1, January 2016

Published online: December 2015

Full Article on Wiley Online Library



ABSTRACT

To understand why organisms function the way that they do, we must understand how evolution shapes physiology. This requires knowledge of how selection acts on physiological traits in nature. Selection studies in the wild allow us to determine how variation in physiology causes variation in fitness, revealing how evolution molds physiology over evolutionary time. Manipulating phenotypes experimentally in a selection study shifts the distribution of trait variation in a population to better explore potential constraints and the adaptive value of physiological traits. There is a large database of selection studies in the wild on a variety of traits, but very few of those are physiological traits. Nevertheless, data available so far suggest that physiological traits, including metabolic rate, thermal physiology, whole‐organism performance, and hormone levels, are commonly subjected to directional selection in nature, with stabilizing and disruptive selection less common than predicted if physiological traits are optimized to an environment. Selection studies on manipulated phenotypes, including circulating testosterone and glucocorticoid levels, reinforce this notion, but reveal that trade‐offs between survival and reproduction or correlational selection can constrain the evolution of physiology. More studies of selection on physiological traits in nature that quantify multiple traits are necessary to better determine the manner in which physiological traits evolve and whether different types of traits (dynamic performance vs. regulatory) evolve differently. © 2016 American Physiological Society. Compr Physiol 6:63‐85, 2016.

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Figure 1. Figure 1. A graphical illustration of the forms of selection that may operate on a continuous trait. The upper panels show the relationship between fitness and values of the trait (the red line indicates stronger selection than the blue), and the lower panels show the frequency of trait values in a population before and after selection. (A) Directional selection results from a linear relationship between fitness and a trait, with the slope of the relationship indicating the strength of the selection. The population mean will shift either positively or negatively, depending on whether fitness is positively or negatively related to trait variation, respectively. (B) Stabilizing selection results from an inverted‐U shaped relationship with fitness and causes a decrease in trait variation in the population after selection. (C) Disruptive selection results from a U‐shaped relationship with fitness and causes an increase in trait variation in the population after selection.
Figure 2. Figure 2. Diagrammatic representation of how selection shapes physiology over evolutionary time, as outlined by Arnold (12), the morphology‐performance‐fitness paradigm. Variation in lower level traits, such as biochemistry, morphology, and physiology constrains variation in whole‐organism performance (the performance gradient), which in turn leads to variation in fitness (the fitness gradient). How individuals use their lower level traits to be manifested in performance (red arrows) may alter the performance gradient (see also Fig. 5), whereas how performance is used in nature (blue arrows) may alter the fitness gradient (67,120,118,184,185,225). Since behavior may act as a “filter” between in the performance gradient and fitness gradient, what selection acts upon, and what the evolutionary response will be, is demonstrable only by studying each facet of these hypothetical links. For example, if individuals do not use maximal performance capacity in nature, then selection cannot act on it, and lower level traits that determine it will respond in a straightforward manner.
Figure 3. Figure 3. Frequency distributions of selection gradients found in a review of selection studies on whole‐animal performance in nature (189). (A) Positive directional selection is common for performance traits, as most linear selection gradients are greater than 0. (B) Stabilizing and disruptive selection are equally common on performance traits, as the distribution of selection gradients is symmetrical around 0.
Figure 4. Figure 4. Graphical illustration of trait integration due to endocrine regulation of multiple systems in the vertebrate body. This figure is a modified recreation of Figure 1 in Sinervo and Calsbeek (313) used with permission. The hypothalamic‐pituitary‐gonadal (HPG) axis and HPA axis (depicted in the top triangle) mediates various physiological and life history traits associated with somatic and reproductive physiology, which contribute to the two major components of fitness, survival (in red) and reproductive success (in orange ovals). Phenotypic and genetic correlations result in trade‐offs among traits, such that selection on any one trait can have effects on numerous others. This is a simplified illustration of links known in side‐blotched lizards (313) and other vertebrates, but many other links could be added. Hormone abbreviations are as follows: ACTH, adrenocorticotropic hormone; B, corticosterone; CRH, corticotrpin‐releasing hormone; E, estradiol; FSH, follicle‐stimulating hormone, GnRH, gonadotropin‐releasing hormone; LH, luteinizing hormone, P, progesterone; T, testosterone, T3, triiodothyronine; TRH, thyrotropin‐releasing hormone; TSH, thyroid‐stimulating hormone; (‐), negative feedback within the HPA and HPG axes.
Figure 5. Figure 5. Diagrammatic representation of the environment may alter links in the morphology‐performance‐fitness paradigm (12). How an organism uses its physiology to perform in a situation may be due to assessment of a stimulus (e.g., run quadrupedally or bipedally) or constraints placed on performance by the physical substrate on which the organism performs (e.g., standing on sand vs. in grass). Both alter how lower level traits predict performance. Similarly, the assessment of how to respond to a stimulus (e.g., run quickly away or hide slowly) and the physical substrate can alter how performance affects fitness. Given these factors, selection studies of physiology in nature should consider traits important to detecting and assessing environmental stimuli (i.e., sensory systems, neural processing, and sensorimotor integration), as well as how performance is altered on relevant substrates (i.e., performance sensitivity, 186) and what lower level traits predict performance on those diverse substrates.
Figure 6. Figure 6. An example of the morphology‐performance‐fitness gradient quantified in nature in collared lizards (Crotaphytus collaris). Size‐corrected limb length predicts maximal sprint speed in hatchlings (upper figure) and adult males (lower figure). Survival was only predicted by maximal sprint speed in hatchlings (168), whereas reproductive success of adult males was predicted by maximal sprint speed (172). The evolutionary response to these bouts of selection in nature would not only include limb length, but also other traits that contribute to variation in maximal sprint speed.
Figure 7. Figure 7. A hypothetical thermal performance curve that shows aspects of thermal physiology that may be targets of selection. The highest level of some function (maximal performance, Pmax) occurs at the optimal temperature (Topt), with function constrained between some lower (CTmin) and upper (CTmax) temperature. The range of temperatures in which function is >80% represents the thermal performance breadth. Performance can be broadly defined here to include a variety of physiological traits.
Figure 8. Figure 8. Manipulating phenotypes experimentally can shift the distribution of a continuous trait to what is desired by the investigator to answer a question about the adaptive nature of a physiological trait. Supplements or chemical treatments can be used to shift variation toward the high end of natural variation (orange) or above it into supraphysiological ranges (red). Conversely, organ ablation or chemical blocking actions can be used to shift variation toward the low end of natural variation (blue) or below it into infraphysiological ranges (purple). Studies that use infra‐ and supraphysiological ranges of variation cannot reveal advantages of current physiology, but they can yield powerful insights into constraints and the adaptive value of current variation.


Figure 1. A graphical illustration of the forms of selection that may operate on a continuous trait. The upper panels show the relationship between fitness and values of the trait (the red line indicates stronger selection than the blue), and the lower panels show the frequency of trait values in a population before and after selection. (A) Directional selection results from a linear relationship between fitness and a trait, with the slope of the relationship indicating the strength of the selection. The population mean will shift either positively or negatively, depending on whether fitness is positively or negatively related to trait variation, respectively. (B) Stabilizing selection results from an inverted‐U shaped relationship with fitness and causes a decrease in trait variation in the population after selection. (C) Disruptive selection results from a U‐shaped relationship with fitness and causes an increase in trait variation in the population after selection.


Figure 2. Diagrammatic representation of how selection shapes physiology over evolutionary time, as outlined by Arnold (12), the morphology‐performance‐fitness paradigm. Variation in lower level traits, such as biochemistry, morphology, and physiology constrains variation in whole‐organism performance (the performance gradient), which in turn leads to variation in fitness (the fitness gradient). How individuals use their lower level traits to be manifested in performance (red arrows) may alter the performance gradient (see also Fig. 5), whereas how performance is used in nature (blue arrows) may alter the fitness gradient (67,120,118,184,185,225). Since behavior may act as a “filter” between in the performance gradient and fitness gradient, what selection acts upon, and what the evolutionary response will be, is demonstrable only by studying each facet of these hypothetical links. For example, if individuals do not use maximal performance capacity in nature, then selection cannot act on it, and lower level traits that determine it will respond in a straightforward manner.


Figure 3. Frequency distributions of selection gradients found in a review of selection studies on whole‐animal performance in nature (189). (A) Positive directional selection is common for performance traits, as most linear selection gradients are greater than 0. (B) Stabilizing and disruptive selection are equally common on performance traits, as the distribution of selection gradients is symmetrical around 0.


Figure 4. Graphical illustration of trait integration due to endocrine regulation of multiple systems in the vertebrate body. This figure is a modified recreation of Figure 1 in Sinervo and Calsbeek (313) used with permission. The hypothalamic‐pituitary‐gonadal (HPG) axis and HPA axis (depicted in the top triangle) mediates various physiological and life history traits associated with somatic and reproductive physiology, which contribute to the two major components of fitness, survival (in red) and reproductive success (in orange ovals). Phenotypic and genetic correlations result in trade‐offs among traits, such that selection on any one trait can have effects on numerous others. This is a simplified illustration of links known in side‐blotched lizards (313) and other vertebrates, but many other links could be added. Hormone abbreviations are as follows: ACTH, adrenocorticotropic hormone; B, corticosterone; CRH, corticotrpin‐releasing hormone; E, estradiol; FSH, follicle‐stimulating hormone, GnRH, gonadotropin‐releasing hormone; LH, luteinizing hormone, P, progesterone; T, testosterone, T3, triiodothyronine; TRH, thyrotropin‐releasing hormone; TSH, thyroid‐stimulating hormone; (‐), negative feedback within the HPA and HPG axes.


Figure 5. Diagrammatic representation of the environment may alter links in the morphology‐performance‐fitness paradigm (12). How an organism uses its physiology to perform in a situation may be due to assessment of a stimulus (e.g., run quadrupedally or bipedally) or constraints placed on performance by the physical substrate on which the organism performs (e.g., standing on sand vs. in grass). Both alter how lower level traits predict performance. Similarly, the assessment of how to respond to a stimulus (e.g., run quickly away or hide slowly) and the physical substrate can alter how performance affects fitness. Given these factors, selection studies of physiology in nature should consider traits important to detecting and assessing environmental stimuli (i.e., sensory systems, neural processing, and sensorimotor integration), as well as how performance is altered on relevant substrates (i.e., performance sensitivity, 186) and what lower level traits predict performance on those diverse substrates.


Figure 6. An example of the morphology‐performance‐fitness gradient quantified in nature in collared lizards (Crotaphytus collaris). Size‐corrected limb length predicts maximal sprint speed in hatchlings (upper figure) and adult males (lower figure). Survival was only predicted by maximal sprint speed in hatchlings (168), whereas reproductive success of adult males was predicted by maximal sprint speed (172). The evolutionary response to these bouts of selection in nature would not only include limb length, but also other traits that contribute to variation in maximal sprint speed.


Figure 7. A hypothetical thermal performance curve that shows aspects of thermal physiology that may be targets of selection. The highest level of some function (maximal performance, Pmax) occurs at the optimal temperature (Topt), with function constrained between some lower (CTmin) and upper (CTmax) temperature. The range of temperatures in which function is >80% represents the thermal performance breadth. Performance can be broadly defined here to include a variety of physiological traits.


Figure 8. Manipulating phenotypes experimentally can shift the distribution of a continuous trait to what is desired by the investigator to answer a question about the adaptive nature of a physiological trait. Supplements or chemical treatments can be used to shift variation toward the high end of natural variation (orange) or above it into supraphysiological ranges (red). Conversely, organ ablation or chemical blocking actions can be used to shift variation toward the low end of natural variation (blue) or below it into infraphysiological ranges (purple). Studies that use infra‐ and supraphysiological ranges of variation cannot reveal advantages of current physiology, but they can yield powerful insights into constraints and the adaptive value of current variation.
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Article Title: Measuring Selection on Physiology in the Wild and Manipulating Phenotypes (in Terrestrial Nonhuman Vertebrates)
 

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Jerry F. Husak. Measuring Selection on Physiology in the Wild and Manipulating Phenotypes (in Terrestrial Nonhuman Vertebrates). Compr Physiol 2015, 6: 63-85. doi: 10.1002/cphy.c140061